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19/07/2024 09:55 – Inventors who came to regret their inventions - MSN
#ArtificialIntelligence #Science #Technology #GeoffreyHinton

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It's safe to say that most inventions come about with pure intentions. Through the scientific method, we as a species have been able to create technology, culture, architecture, and so much more to help us solve the seemingly infinite problems that arise, be it a problem of convenience or one of a more dire essence. Still, there are always opportunities for good ideas to turn bad, and unfortunately we have also seen numerous examples of such inventions that to this day plague us.
19/07/2024 09:41 – A short history of AI, and what it is (and isn’t)
#ArtificialIntelligence #Technology #OpenAI #DeepLearning #LargeLanguageModel #ComputationalNeuroscience #GPT4 #Creativity #GenerativeArtificialIntelligence #Learning #JohnMcCarthyComputerScientist #TimnitGebru

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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

It’s the simplest questions that are often the hardest to answer. That applies to AI, too. Even though it’s a technology being sold as a solution to the world’s problems, nobody seems to know what it really is. It’s a label that’s been slapped on technologies ranging from self-driving cars to facial recognition, chatbots to fancy Excel. But in general, when we talk about AI, we talk about technologies that make computers do things we think need intelligence when done by people.

For months, my colleague Will Douglas Heaven has been on a quest to go deeper to understand why everybody seems to disagree on exactly what AI is, why nobody even knows, and why you’re right to care about it. He’s been talking to some of the biggest thinkers in the field, asking them, simply: What is AI? It’s a great piece that looks at the past and present of AI to see where it is going next. You can read it here.

Here’s a taste of what to expect:

Artificial intelligence almost wasn’t called “artificial intelligence” at all. The computer scientist John McCarthy is credited with coming up with the term in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire. But more than one of McCarthy’s colleagues hated it. “The word ‘artificial’ makes you think there’s something kind of phony about this,” said one. Others preferred the terms “automata studies,” “complex information processing,” “engineering psychology,” “applied epistemology,” “neural cybernetics,” “non-numerical computing,” “neuraldynamics,” “advanced automatic programming,” and “hypothetical automata.” Not quite as cool and sexy as AI.

AI has several zealous fandoms. AI has acolytes, with a faith-like belief in the technology’s current power and inevitable future improvement. The buzzy popular narrative is shaped by a pantheon of big-name players, from Big Tech marketers in chief like Sundar Pichai and Satya Nadella to edgelords of industry like Elon Musk and Sam Altman to celebrity computer scientists like Geoffrey Hinton. As AI hype has ballooned, a vocal anti-hype lobby has risen in opposition, ready to smack down its ambitious, often wild claims. As a result, it can feel as if different camps are talking past one another, not always in good faith.

This sometimes seemingly ridiculous debate has huge consequences that affect us all. AI has a lot of big egos and vast sums of money at stake. But more than that, these disputes matter when industry leaders and opinionated scientists are summoned by heads of state and lawmakers to explain what this technology is and what it can do (and how scared we should be). They matter when this technology is being built into software we use every day, from search engines to word-processing apps to assistants on your phone. AI is not going away. But if we don’t know what we’re being sold, who’s the dupe?

For example, meet the TESCREALists. A clunky acronym (pronounced “tes-cree-all”) replaces an even clunkier list of labels: transhumanism, extropianism, singularitarianism, cosmism, rationalism, effective altruism, and longtermism. It was coined by Timnit Gebru, who founded the Distributed AI Research Institute and was Google’s former ethical AI co-lead, and Émile Torres, a philosopher and historian at Case Western Reserve University. Some anticipate human immortality; others predict humanity’s colonization of the stars. The common tenet is that an all-powerful technology is not only within reach but inevitable. TESCREALists believe that artificial general intelligence, or AGI, could not only fix the world’s problems but level up humanity. Gebru and Torres link several of these worldviews—with their common focus on “improving” humanity—to the racist eugenics movements of the 20th century.

Is AI math or magic? Either way, people have strong, almost religious beliefs in one or the other. “It’s offensive to some people to suggest that human intelligence could be re-created through these kinds of mechanisms,” Ellie Pavlick, who studies neural networks at Brown University, told Will. “People have strong-held beliefs about this issue—it almost feels religious. On the other hand, there’s people who have a little bit of a God complex. So it’s also offensive to them to suggest that they just can’t do it.”

Will’s piece really is the definitive look at this whole debate. No spoilers—there are no simple answers, but lots of fascinating characters and viewpoints. I’d recommend you read the whole thing here—and see if you can make your mind up about what AI really is.

***NOW READ THE REST OF THE ALGORITHM***

Deeper Learning

AI can make you more creative—but it has limitsGenerative AI models have made it simpler and quicker to produce everything from text passages and images to video clips and audio tracks. But while AI’s output can certainly seem creative, do these models actually boost human creativity? A new study looked at how people used OpenAI’s large language model GPT-4 to write short stories. The model was helpful—but only to an extent. The researchers found that while AI improved the output of less creative writers, it made little difference to the quality of the stories produced by writers who were already creative. The stories in which AI had played a part were also more similar to each other than those dreamed up entirely by humans. Read more from Rhiannon Williams.

Bits and Bytes

Robot-packed meals are coming to the frozen-food aisleFound everywhere from airplanes to grocery stores, prepared meals are usually packed by hand. AI-powered robotics is changing that. (MIT Technology Review)

AI is poised to automate today’s most mundane manual warehouse taskPallets are everywhere, but training robots to stack them with goods takes forever. Fixing that could be a tangible win for commercial AI-powered robots. (MIT Technology Review)

The Chinese government is going all-in on autonomous vehiclesThe government is finally allowing Tesla to bring its Full Self-Driving feature to China. New government permits let companies test driverless cars on the road and allow cities to build smart road infrastructure that will tell these cars where to go. (MIT Technology Review)

The US and its allies took down a Russian AI bot farm on XThe US seized control of a sophisticated Russian operation that used AI to push propaganda through nearly a thousand covert accounts on the social network X. Western intelligence agencies traced the propaganda mill to an officer of the Russian FSB intelligence force and to a former senior editor at state-controlled publication RT, formerly called Russia Today. (The Washington Post)

AI investors are starting to wonder: Is this just a bubble?After a massive investment in the language-model boom, the biggest beneficiary is Nvidia, which designs and sells the best chips for training and running modern AI models. Investors are now starting to ask what LLMs are actually going to be used for, and when they will start making them money. (New York magazine)

Goldman Sachs thinks AI is overhyped, wildly expensive, and unreliableMeanwhile, the major investment bank published a research paper about the economic viability of generative AI. It notes that there is “little to show for” the huge amount of spending on generative AI infrastructure and questions “whether this large spend will ever pay off in terms of AI benefits and returns.” (404 Media)

The UK politician accused of being AI is actually a real personA hilarious story about how Mark Matlock, a candidate for the far-right Reform UK party, was accused of being a fake candidate created with AI after he didn’t show up to campaign events. Matlock has assured the press he is a real person, and he wasn’t around because he had pneumonia. (The Verge)
19/07/2024 09:21 – Building supply chain resilience with AI
#SupplyChain #ArtificialIntelligence #CloudComputing #Business #BusinessProcess #BusinessManagement #Manufacturing #Marketing #DisruptiveInnovation #ValueChain

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Sponsored In partnership with Amazon Web Services

If the last five years have taught businesses with complex supply chains anything, it is that resilience is crucial. In the first three months of the covid-19 pandemic, for example, supply-chain leader Amazon grew its business 44%. Its investments in supply chain resilience allowed it to deliver when its competitors could not, says Sanjeev Maddila, worldwide head of supply chain solutions at Amazon Web Services (AWS), increasing its market share and driving profits up 220%. A resilient supply chain ensures that a company can meet its customers’ needs despite inevitable disruption.

Today, businesses of all sizes must deliver to their customers against a backdrop of supply chain disruptions, with technological changes, shifting labor pools, geopolitics, and climate change adding new complexity and risk at a global scale. To succeed, they need to build resilient supply chains: fully digital operations that prioritize customers and their needs while establishing a fast, reliable, and sustainable delivery network.

The Canadian fertilizer company Nutrien, for example, operates two dozen manufacturing and processing facilities spread across the globe and nearly 2,000 retail stores in the Americas and Australia. To collect underutilized data from its industrial operations, and gain greater visibility into its supply chain, the company relies on a combination of cloud technology and artificial intelligence/machine learning (AI/ML) capabilities.

“A digital supply chain connects us from grower to manufacturer, providing visibility throughout the value chain,” says Adam Lorenz, senior director for strategic fleet and indirect procurement at Nutrien. This visibility is critical when it comes to navigating the company’s supply chain challenges, which include seasonal demands, weather dependencies, manufacturing capabilities, and product availability. The company requires real-time visibility into its fleets, for example, to identify the location of assets, see where products are moving, and determine inventory requirements.

Currently, Nutrien can locate a fertilizer or nutrient tank in a grower’s field and determine what Nutrien products are in it. By achieving that “real-time visibility” into a tank’s location and a customer’s immediate needs, Lorenz says the company “can forecast where assets are from a fill-level perspective and plan accordingly.” In turn, Nutrien can respond immediately to emerging customer needs, increasing company revenue while enhancing customer satisfaction, improving inventory management, and optimizing supply chain operations.

“For us, it’s about starting with data creation and then adding a layer of AI on top to really drive recommendations,” says Lorenz. In addition to improving product visibility and asset utilization, Lorenz says that Nutrien plans to add AI capabilities to its collaboration platforms that will make it easier for less-tech-savvy customers to take advantage of self-service capabilities and automation that accelerates processes and improves compliance with complex policies.

To meet and exceed customer expectations with differentiated service, speed, and reliability, all companies need to similarly modernize their supply chain operations. The key to doing so—and to increasing organizational resilience and sustainability—will be applying AI/ML to their extensive operational data in the cloud.

Resilience as a business differentiator

Like Nutrien, a wide variety of organizations from across industries are discovering the competitive advantages of modernizing their supply chains. A pharmaceutical company that aggregates its supply chain data for greater end-to-end visibility, for example, can provide better product tracking for critically ill customers. A retail startup undergoing meteoric growth can host its workloads in the cloud to support sudden upticks in demand while minimizing operating costs. And a transportation company can achieve inbound supply chain savings by evaluating the total distance its fleet travels to reduce mileage costs and CO2 emissions.
19/07/2024 09:15 – The Role of AI and Predictive Analysis in Modern Perimeter Security
#ComputerSecurity #MachineLearning #ArtificialIntelligence #PredictiveAnalytics #AnomalyDetection #Technology #Surveillance #BigData #AppliedMathematics #Automation #PhysicalSecurity #Learning #Management #Analysis

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***THE ROLE OF AI AND PREDICTIVE ANALYSIS IN MODERN PERIMETER SECURITY***

Analytics, Artificial Intelligence, Cyber Security, Machine Learning, Predictive Analytics

Tags: AI Tools, Analytics, Artificial intelligence, chatbots, Cyber security, machine learning, marketing analytics, Predictive Analytics, Real-Time Analytics, Web Analytics

Most of us know that AI is making waves in every conceivable commercial niche, and attention here tends to focus on things like marketing and cybersecurity. However, there’s also a lot to be said about the potential that this tech represents in terms of physical security.

If you have yet to consider this, keep reading as we unravel the ins and outs of why the predictive power of AI is so relevant to protecting the perimeters of premises from a wide array of common concerns.

**Leveraging Machine Learning For Anomaly Detection**

Machine learning (ML) shines when it comes to anomaly detection in perimeter security. It does this by sifting through mountains of data, picking up unusual patterns that human eyes might miss. This is relevant given that 75% of businesses are putting more emphasis on security at the moment, with average spending up by 15.3%.

Advantages include:

Real-Time Monitoring: ML models monitor live feeds from cameras and sensors. They spot irregular movements or behaviors immediately.Behavioral Analysis: These models learn what’s normal by analyzing data from past surveillance efforts. For example, they know if a vehicle is moving erratically or someone is loitering suspiciously. This same tech can be applied in lots of other contexts, from recruitment to finance, showing the extent of the flexibility at play.Adaptive Algorithms: As new threats emerge, these algorithms adapt without manual intervention, enhancing security measures over time.False Positive Reduction: By understanding the context better than traditional systems, ML reduces false alarms. This saves valuable response time.

**Predicting Security Breaches With Historical Data Patterns**

Predictive analytics is part and parcel of the perimeter security overhaul taking place thanks to intelligent tools. Mining historical data means potential breaches can be forecast before they happen. With losses from property crime topping $15.8 billion in the most recent data from the FBI, this type of foresight is valuable.

Features include:

Pattern Recognition: These tools analyze past incidents to identify trends. If a breach occurred at night during specific weather conditions, the system flags similar situations.Risk Assessment: It evaluates which areas are most vulnerable based on historical data. This helps in allocating resources more efficiently.Proactive Measures: Instead of reacting to threats, predictive analytics enables proactive defense strategies by forecasting possible security events.Enhanced Surveillance Planning: Using predictions from historical patterns, you can adjust surveillance focus dynamically where and when needed.

**Optimizing Gate Opening Timings With AI**

AI takes operational efficiency up a notch by optimizing gate opening timings. It works seamlessly with automatic gate operators to streamline access without compromising security. There are several upsides, such as:

Traffic Flow Management: AI predicts peak times for vehicle and foot traffic, adjusting gate schedules accordingly.Minimized Wait Times: By anticipating demand, it reduces bottlenecks, ensuring smooth passage while maintaining strict checks.Energy Savings: Optimized timings mean gates open only when necessary, saving energy and reducing wear on mechanical parts.Incident Response: In case of a security alert, AI can lock down gates instantly or redirect traffic away from danger zones.

**Wrapping Up**

From dealing with anomalies in real time and determining future risks based on historical data, to ensuring that access points are managed more efficiently and conveniently, AI and predictive analytics clearly have a lot to offer in a perimeter security context.

Moreover, if your business is already turning this tech to tasks in other departments, then you are ideally positioned to adopt adjacent tools. Being smart about automation and data use across the board will amp up your competitiveness, keep costs low, and ensure employees have what they need to succeed as well, so there are no downsides.
19/07/2024 08:57 – The Role Of AI And Big Data In Modern Call Centers Enhancing Customer Experiences
#CallCentre #ArtificialIntelligence #BigData #SentimentAnalysis #Analytics #CustomerExperience #SpeechRecognition #MachineLearning #Data #Learning #CustomerService #PredictiveAnalytics #DeepLearning #BusinessIntelligence #Customer #ServiceQuality

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***THE ROLE OF AI AND BIG DATA IN MODERN CALL CENTERS ENHANCING CUSTOMER EXPERIENCES***

Analytics, Artificial Intelligence, Big Data, ChatGPT, Machine Learning, Predictive Analytics

Tags: AI Tools, Analytics, Big Data, Big Data Analytics, big data services, business intelligence, marketing analytics, Predictive Analytics, Real-Time Analytics, Web Analytics

In today’s fast-paced world, customer experience is paramount. Call centers are at the forefront of this endeavor, serving as the primary point of contact for customer interactions.

With the advent of AI and Big Data, call centers are undergoing a significant transformation. These technologies enable call centers to operate more efficiently, provide personalized service, and enhance customer satisfaction.

The integration of AI and Big Data isn’t merely about improving response times; it’s about revolutionizing the entire customer experience, making it more seamless and tailored to individual needs.

Read on to discover how AI and Big Data enhance customer experiences in modern call centers.

**Intelligent Routing And Self-Service Options**

AI technology has revolutionized call centers by optimizing call routing and enhancing self-service options. Integrating these advancements ensures a seamless and efficient customer experience.

Below are vital ways AI optimizes call center operations:

Streamlined call routing

Imagine a system that intelligently routes calls to the most qualified agent based on the caller’s issue, past interactions, and sentiment analysis. AI can achieve this by learning from historical data stored in the cloud and identifying patterns using sophisticated algorithms. This reduces wait times and ensures customers get connected with the right agent for their specific needs.

Empowering self-service

AI-powered chatbots handle simple inquiries, freeing up human agents for more complex issues. These virtual assistants can answer FAQs, provide basic troubleshooting steps, and even schedule appointments. Customers appreciate the 24/7 availability and convenience of self-service options.

Enhanced efficiency

Outsourced inbound call center services leverage AI for efficient call management. AI-driven call routing and self-service solutions streamline operations, reducing costs and improving service quality.

By providing multiple channels for issue resolution and intelligent call routing powered by algorithms, AI streamlines the call center experience, saving both customers and agents valuable time.

**Personalized Interactions And Proactive Support**

Big Data and AI together create an environment where personalized interactions and proactive support become the norm. These technologies enable call centers to offer tailored service and anticipate customer needs.

Below are the ways AI and Big Data enhance customer interactions:

Tailored service

Big data analytics unlocks customer information, including purchase history, past interactions, and social media sentiment. This data, stored securely in the cloud focusing on data privacy, empowers agents to personalize interactions by understanding individual needs and preferences. Imagine an agent who can greet you by name, recall your previous inquiries, and suggest relevant solutions.

Predictive analytics

AI analyzes vast amounts of call data using machine learning algorithms to identify potential issues before they become problems. Proactive outreach can then be initiated, offering solutions or preventing frustrations before they arise. This proactive approach demonstrates customer care and fosters loyalty.

Customized experiences

AI and Big Data allow for the customization of services, enhancing the overall customer experience by making it more relevant and engaging.

Personalized interactions and proactive support go a long way in building positive customer relationships. AI and Big Data empower call centers to deliver a more human touch, even in a digital environment.

**Speech Recognition And Sentiment Analysis**

Speech recognition and sentiment analysis technologies are transforming call centers’ handling of customer interactions. These tools provide real-time insights that improve service quality and customer satisfaction.

Below are the benefits of speech recognition and sentiment analysis:

Enhanced call handling

Speech recognition technology allows AI to transcribe calls in real-time, providing agents with valuable insights into customer sentiment and intent. This can significantly improve call resolution times by enabling agents to anticipate needs and tailor responses.

Real-time feedback

Sentiment analysis, powered by deep learning algorithms, can detect emotions in a caller’s voice, allowing agents to adapt their communication style. With real-time feedback, de-escalating a frustrated caller or offering additional support to a concerned one becomes easier.

Improved communication

AI takes the guesswork out of communication, creating a more efficient and positive call center experience by ensuring that agents respond appropriately to customer emotions.

Improved call handling and real-time feedback empower agents to provide exceptional customer service. AI takes the guesswork out of communication, creating a more efficient and positive call center experience.

**Quality Assurance And Continuous Improvement**

AI and Big Data play crucial roles in quality assurance and continuous improvement of call center operations. These technologies help identify areas for improvement and track performance metrics.

Below are the ways AI and Big Data contribute to quality assurance:

Automated evaluation

AI can analyze call recordings stored in the cloud to identify areas for agent improvement. Speech analytics can pinpoint common customer pain points and identify opportunities for training.

Data-driven insights

Big Data provides valuable business intelligence insights into overall call center performance. Metrics like call resolution times, customer satisfaction scores, and agent performance can be tracked and analyzed, enabling data-driven decisions to optimize call center operations.

Continuous feedback loop

AI helps identify areas for improvement, while Big Data provides the metrics to track progress. This ensures call centers are constantly evolving to meet the ever-changing needs of their customers.

Automated evaluation and data-driven insights create a continuous feedback loop. AI helps identify areas for improvement, while Big Data provides the metrics to track progress. This ensures call centers are constantly evolving to meet the ever-changing needs of their customers.

**Final Thoughts**

AI and Big Data have transformed modern call centers, offering numerous benefits that enhance customer experiences. These technologies are essential for delivering superior customer satisfaction, from optimizing operations and gaining customer insights to improving agent performance and streamlining outsourced services. AI and Big Data will further revolutionize the customer service landscape as they continue to evolve.
19/07/2024 08:51 – Mastering the Digital Frontier: Unleashing the Potential of Deployment Software
#DevOps #BigData #ContinuousDelivery #Automation #ArtificialIntelligence #Software #Scalability #Analytics #SoftwareEngineering #SystemsEngineering #SoftwareDevelopment #ComputerEngineering #ProvisioningTechnology #MachineLearning #ConfigurationManagement #CloudComputing #ReliabilityEngineering #Infrastructure #ChaosEngineering #Management #Innovation #SoftwareAsAService #Data #SystemsArchitecture

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***MASTERING THE DIGITAL FRONTIER: UNLEASHING THE POTENTIAL OF DEPLOYMENT SOFTWARE***

Analytics, Artificial Intelligence, Cloud Computing, Predictive Analytics

Tags: AI Tools, Analytics, Augmented Reality, Big Data Analytics, Cloud Computing, cloud databases, Devops, machine learning, MLOps, software, Virtual Reality

In the ever-evolving digital ecosystem, the ability to swiftly and reliably deliver software innovations can make or break a company's competitive edge. Enter the unsung hero of modern software development: deployment software. Far more than just a tool for pushing code to production, deployment software has become the backbone of agile development practices and the cornerstone of digital transformation strategies.

**Redefining Software Delivery In The Digital Age**

Application release automation isn't merely about moving code from point A to point B. It's a sophisticated orchestrator of complex processes, a guardian of software quality, and a catalyst for organizational efficiency. By automating and streamlining the journey from development to production, software provisioning tools empowers organizations to innovate at breakneck speeds while maintaining rock-solid reliability.

The Multifaceted Nature of Modern Deployment Software

Today's software provisioning tools solutions are Swiss Army knives of functionality, offering:

Intelligent release orchestrationDynamic environment provisioningAdaptive configuration managementPredictive rollback mechanismsReal-time performance analyticsCollaborative workflow integration

**The Metamorphosis Of Continuous Delivery Platforms**

The evolution of deployment orchestration solutions mirrors the broader transformation of the tech industry:

Manual Mayhem: The prehistoric era of software deployment, characterized by error-prone manual processes and late-night server room adventures.Script Sorcery: The dawn of automation, where clever scripts began to tame the chaos of deployment, albeit with limitations.Configuration Crusade: The rise of tools like Puppet and Chef, bringing order to the wild west of server management.Container Revolution: Docker and its ilk ushered in a new paradigm of software packaging and portability.Cloud-Native Nirvana: The current golden age of continuous delivery platforms, where cloud-native solutions offer unprecedented flexibility and power.

**Unlocking The Treasure Trove: Benefits Of Advanced Configuration Management Tools**

Leveraging state-of-the-art automation technologies can lead to transformative outcomes:

1. Velocity Unleashed

By eliminating bottlenecks and automating repetitive tasks, configuration management tools acts as a turbocharger for your development pipeline, enabling teams to iterate and innovate at lightning speed.

2. Quality Assurance Reinvented Modern incorporates sophisticated testing and validation mechanisms, serving as a vigilant gatekeeper against bugs and regressions.

3. Collaboration Reimagined By breaking down silos between development, operations, and QA teams, fosters a culture of shared responsibility and continuous improvement.

4. Scalability Without Limits As your digital footprint expands, provides the elasticity needed to manage ever-growing complexity across diverse environments.

5. Time-to-Market Turbocharged In the race to capture market share, gives organizations the agility to rapidly respond to customer needs and outmaneuver competitors.

**The Anatomy Of Cutting-Edge Deployment Software**

To truly appreciate the sophistication of modern deployment software, let's dissect its core components:

Intelligent Release Orchestration: The maestro of the deployment process, coordinating complex sequences of events with precision.Artifact Ecosystem: A robust repository system that manages and version-controls all deployment assets.Dynamic Environment Provisioning: On-demand creation and management of deployment targets, from dev sandboxes to production clusters.Adaptive Configuration Management: Smart handling of application and infrastructure configurations across diverse environments.Predictive Deployment Engine: The workhorse that executes deployment tasks while anticipating and mitigating potential issues.Real-time Observability Suite: Comprehensive monitoring and logging capabilities that provide deep insights into the deployment process.Intelligent Rollback Mechanisms: Sophisticated failsafe systems that can automatically revert changes at the first sign of trouble.

**Pioneering Solutions **

**The Landscape Is Rich With Innovative Solutions:**

CircleCI: A cloud-native CI/CD platform known for its scalability and extensive integrations.Harness: An AI-powered continuous delivery platform that leverages machine learning to optimize deployments.Armory: An enterprise-grade solution built on top of Spinnaker, offering advanced compliance and governance features.Drone: A lightweight, container-native tool popular in the open-source community.Argo CD: A declarative, GitOps continuous delivery tool for Kubernetes.Azure DevOps: Microsoft's end-to-end DevOps solution with robust capabilities.Buildkite: A flexible, scalable platform that allows teams to run their pipelines on their own infrastructure.

**Forging Ahead: Best Practices For Excellence**

To truly harness the power of deployment, organizations should embrace these forward-thinking practices:

Adopt a GitOps Mindset: Use Git repositories as the single source of truth for both application code and infrastructure definitions.Embrace Immutable Infrastructure: Treat your infrastructure as disposable and recreate it from scratch for each deployment, enhancing consistency and security.Implement Canary Deployments: Use advanced features to gradually roll out changes to a subset of users, minimizing risk.Leverage Feature Flags: Integrate feature flag management to enable fine-grained control over feature releases.Automate Compliance Checks: Incorporate compliance and security scans directly into your deployment pipeline to catch issues early.Implement Chaos Engineering: Orchestrate controlled failures, building more resilient systems.Embrace AIOps: Leverage artificial intelligence and machine learning capabilities to predict and prevent deployment issues.

**The Horizon: Emerging Trends In Deployment**

As we peer into the future, several exciting trends are shaping the evolution of deployment:

Quantum-Ready Deployments: As quantum computing inches closer to reality, systems will need to adapt to manage the complexities of quantum algorithms and infrastructure.Edge-to-Cloud Continuum: Future solutions will seamlessly manage deployments across a spectrum of computing environments, from edge devices to hybrid cloud setups.Self-Healing Systems: Advanced AI capabilities will enable systems to automatically detect and remediate issues without human intervention.Augmented Reality DevOps: Imagine debugging a deployment issue using AR glasses that overlay real-time metrics and logs onto your physical environment.Blockchain-Powered Integrity: Distributed ledger technologies could be integrated into systems to provide tamper-proof audit trails of all deployment activities.

**Conclusion: Charting Your Course In The Software Revolution**

In the digital age, mastering efficient implementation and management of software solutions has become a critical differentiator for organizations seeking to thrive amidst constant change. By embracing cutting-edge technologies and practices in this field, businesses can unlock unprecedented levels of agility, reliability, and innovation.

As you navigate the complex landscape of modern deployment technologies, remember that the journey is just as important as the destination. Cultivate a culture of continuous learning and experimentation, and don't be afraid to push the boundaries of what's possible with your implementation processes.

The future of software delivery is bright, and innovative deployment methods stand at its vanguard. By staying abreast of emerging trends and continuously refining your approach, you'll be well-positioned to turn the challenges of tomorrow into opportunities for growth and success.

Embrace the power of advanced deployment technologies, and let them be the wind in your sails as you chart a course towards digital excellence. The next frontier of software innovation awaits – are you ready to launch?
19/07/2024 08:42 – Why Data Science Matters and How It Powers Business in 2024
#DataScience #Analytics #Data #BigData #Cognition #Technology #Business #Computing #Management #InformationTechnology #Science #DataManagement #MachineLearning #DecisionMaking #PredictiveAnalytics #DataAnalysis #Strategy #BusinessIntelligence #Learning #Analysis #StrategicManagement #BusinessEconomics #BusinessManagement #Economy #DashboardBusiness #Simulation #DataScience #Analytics #Data #BigData #Cognition #Technology #Business #Computing #Management #InformationTechnology #Science #DataManagement #MachineLearning #DecisionMaking #PredictiveAnalytics #DataAnalysis #Strategy #BusinessIntelligence #Learning #Analysis #StrategicManagement #BusinessEconomics #BusinessManagement #Economy #DashboardBusiness #Simulation #DataScience #Analytics #Data #BigData #Cognition #Technology #Business #Computing #Management #InformationTechnology #Science #DataManagement #MachineLearning #DecisionMaking #PredictiveAnalytics #DataAnalysis #Strategy #BusinessIntelligence #Learning #Analysis #StrategicManagement #BusinessEconomics #BusinessManagement #Economy #DashboardBusiness #Simulation

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***WHY DATA SCIENCE MATTERS AND HOW IT POWERS BUSINESS IN 2024***

Analytics, Artificial Intelligence, Big Data, Data Science, Predictive Analytics

Data is at the core of business success in 2024. How you generate, manage, and utilize your business data can make or break your organization. From customer relations to budget allocation, from creating a marketing strategy to launching a new product, information controls everything. Therefore, knowing how to leverage this data is crucial.

Around 95% of organizations say managing unstructured data is challenging for their industry. So, how do you navigate this gap? This is where data science for business comes into play.

**What Is Data Science?**

Data science is the study of data or information to derive understandable, meaningful, and actionable insights. Through scientific methods and advanced tools, data scientists extract valuable insights from data to inform decision-making across various fields.

Data science for business analytics can empower businesses to make proactive decisions and enhance their overall performance.

**How Do Organizations Benefit From Data Science Business Analytics?**

Data science for business has multifold benefits. Here are some of them:

1. Empowering teams to make better decisions

It is undeniable that informed, data-driven outcomes are more likely to be productive than those based on intrusions and assumptions. Data science for business combined with decision science helps organizations make accurate decisions backed by evidence, contributing to business growth, strong customer relationships, employee retention, and profitability.

An experienced data scientist is equivalent to a trusted advisor and strategic companion to the upper management of a company. They can ensure that the team maximizes their analytical capabilities.

2. Analyzing data for more actionable insights

Data science for business analytics is all about data interpretation. It utilizes statistical methods, machine learning algorithms, and data visualization techniques to transform raw data into meaningful insights. This information helps decision-makers feel more confident about their strategies and allows them to make modifications.

Advanced data visualization tools make it seamless for companies to transform complex business data into comprehensive and intuitive dashboards. This enables stakeholders to grasp insights and take timely, informed actions.

3. Encouraging the staff to follow best practices

Data science for business can aid in tracking employee performance and adhering to protocols. With the ability to understand data, it can recognize areas of improvement and identify exemplary performance. This fosters a data-centric culture that is clear of any unconscious biases.

Additionally, firms can use data science to make sure their employees are familiar with the company’s analytical products. Data scientists can demonstrate how to use the technology and extract insights, actionable outcomes, etc. As a result, teams can solve critical business problems and develop their products and services accordingly.

4. Identifying new opportunities & prospects

When managed by a skilled data scientist, data science models can yield hidden patterns and trends from large data sets. These patterns and trends reflect effectively on business strategies and their effectiveness and help identify new opportunities and prospects.

By utilizing predictive analytics and machine learning algorithms, organizations can gauge market demands, optimize pricing strategies, tailor offerings to suit current customer needs and tap into the future potential of respective industries. Data science for businesses can also segment customers into smaller groups for a more targeted approach.

5. Incorporating data-driven decision-making

Decisions based on data are always more accurate than those based on intuition. As the business grows, the volume of data becomes impossible to manage manually. Data science can seamlessly manage, study, and leverage data with minimal human intervention.

Data science for business analytics helps gather and analyze data from various sources, ruling out the requirement to take risks. A data scientist can develop models and frameworks that process the existing data to simulate several potential outcomes. Based on these, businesses can make informed decisions.

6. Measuring the key metrics for business growth

Half of business is about making the right choices at the right time, and the other is about evaluating those decisions. It is pivotal to know whether the decisions made have affected the organization. This is where a data scientist comes to play.

A data scientist measures the key metrics related to strategies and decides whether the ROI meets the expectation. These KPIs could be conversion rates, customer acquisition costs, social media engagement rates, etc. Tracking these metrics over time, data scientists can determine the effectiveness of growth initiatives and identify areas for improvement.

7. Identifying and refining the target audiences

Whether it is Google Analytics, social listening tools, CMS, or other tools, businesses today must have at least one source of customer data. However, how they use that data makes all the difference. Data science for businesses takes the unusable raw data, combines it with other data points, and generates customer insights.

This helps businesses identify customer preferences, pain points, habits, behaviors, etc. Once you know where your audience spends most of their time, you can run marketing campaigns to reach them. Businesses can also study demographics and refine their target audience.

8. Enhances recruitment process with precision

Going through countless resumes was a struggle for recruiters, but thanks to big data, that practice has become obsolete. The amount of information online – from social media profiles to professional databases and job board listings – presents a goldmine for talent acquisition. Data scientists can leverage these resources to identify candidates with the skills and experiences that perfectly align with an organization's needs.

By leveraging the power of data science for business, HR teams can streamline the shortlisting process. In-house data processing tools can automate resume screening and application sorting, saving valuable time and resources. Additionally, data-driven aptitude tests and assessments can be implemented to provide a more objective evaluation of candidate skills.

**Conclusion**

According to reports, the big data analytics market will grow to $103 billion, which means more and more businesses are moving towards data visualization and data science for business. Companies like Mu Sigma have helped many organizations across various industries make this transition. Whether you are – in the manufacturing industry, finance, or healthcare – adapting data science for business analytics can manifold your business progress. So, when are you making the shift?
19/07/2024 05:45 – To Move or Not to Move: Optimizing Agile Teams 'sStagesOfGroupDevelopment
#Team #Tuckman'sStagesOfGroupDevelopment #Business #Knowledge #Technology #Productivity #Organization #Cognition #AgileSoftwareDevelopment #Risk #Management #BehaviouralSciences #ProgramOptimization

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To Move or Not to Move: Optimizing Agile Teams

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By Derk-Jan de Grood - July 15, 2024

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Summary: Agile teams often benefit from stability, but this isn't always the best approach. While stable teams enhance collaboration and productivity, they can also lead to isolated work and knowledge hoarding. Restructuring teams can offer advantages like employee growth, improved knowledge sharing, and better alignment with organizational goals. Ultimately, the decision to keep teams stable or shuffle members depends on specific project needs and desired outcomes.Organizations that embrace agile principles often apply the ground rule that teams should be stable. When you see teams as autonomous units of multidisciplinary professionals who are trained to do their job as effectively as possible, it makes sense not to disturb the team composition. It takes time to grow an effective team that has the right culture and where the members complement each other. A team where members feel safe and at home will excel. Psychologist Bruce Tuckman described the stages of group development that results in effective teams:Forming – getting to know each otherStorming – figuring out how to work together effectivelyNorming – codifying team processes and practicesPerforming – accelerating productivity as the team hits its strideAdjourning – ramp down when a team is finished with a project or productThese phases are considered necessary and inevitable for a team to grow, overcome challenges, solve problems, plan effectively, and deliver consistent results. When team members are regrouped, they need to go through these phases again. This results in delay and reduction of output. So keeping teams stable often makes logical sense. But there are also compelling reasons to switch teams up in certain circumstances, as described below.Support Staff Growth and Organizational ImprovementsTeams are actually never stable. Employers get sick, team members leave the company, and new employees join. These life events are part of reality. When a team's composition is already changing, it's an opportunity to consider additional changes that can benefit the organization.Team members benefit from changing environments and seeing other parts of the organization. Staff members gain fresh career perspectives when they have the opportunity to expand their horizons and collaborate with different teams. Spreading people around the organization will also boost collaboration between teams. It’s usually more productive to work with someone you know and who knows your side of the story.Another nice side-effect of moving people around is that knowledge and experience get distributed throughout the organization. So moving people might not only help to retain and grow employees but also contribute to better collaboration and a more unified way of working.Realignment for Autonomy and OwnershipIn many organizations, team structures will evolve. When scaling agile for larger interconnected efforts, it becomes clear that initial team structures are no longer sufficient. Teams that must interoperate experience a lot of obstacles and blockades that will keep them from being effective. When teams are organized around a specific system or component, they often discover that they represent only a part of the customer journey and cannot deliver end customer value. Some teams also have a lot of dependencies on other teams which makes planning and coordination complicated.While maintaining team stability is desirable, organizations seeking to optimize value flow often need to restructure teams to enhance coordination. Ideally create ‘teams of teams’ (or team clusters) that have autonomy within the product or product portfolio they work. Autonomy allows teams to take ownership of their code and own the end-to-end delivery process.The Risk of Local OptimizationTeams that manage to become effective can deliver value and are predictable in their output. Although that may seem fantastic from a distance, upon a second glance it may reveal a risk: local optimizations by the teams.Teams often work only on items that match their purpose, for example a mobile team selects features to support mobile app development, while the web team focuses on features for the website. When teams are unbalanced or in different stages of group development, one team may be idle waiting for results from another team. For instance, the web team front-end developers may be waiting for database changes or testing to finish. Or perhaps one team is working on items that are less of a priority than other items sitting in another team’s backlog.With one of the organizations I work with, we recently became aware of this issue. After we improved the way we determine the value of the backlog items within our cluster, it became apparent that high-value items that the one team couldn’t commit to, were not automatically picked up by the other teams. We tried to optimize the value flow and asked teams if they could drop one of their items and replace it with an item that had more business value. But that didn’t work. Lack of experience and knowledge made it difficult to exchange items between the teams. The lack of specific functions within certain teams created bottlenecks that made it ineffective to move the work items to the other team.Moving People to Other DepartmentsWhen we look across departmental boundaries, we might find additional localization risks. Does a deadline for a major release justify a transfer of development capacity to that project? When a lot of money is being made within one division, should development capacity not be aligned?Rather than moving people, we can of course also move entire teams around. Again, to do this seems only logical when the new work is structural and there is enough work for the whole team. It might be wise to assess whether the new team disrupts the department and introduces inter-team tension or unwanted competition. But when the new team can pick up the new work without too many problems, this might be an option to consider.Destabilize to Eliminate the BottlenecksThe above bottlenecks can be reduced when we change the team composition by moving people around. Yes, it may be preferable to keep teams intact and stimulate that team-members invest in their knowledge. But when bottlenecks appear, consider restructuring your teams and move a few people around.Although this might seem controversial, it might be productive when the following conditions are met:People embrace a change and see it as a chance to learn and grow.Team structure is about to change anyway either because the team is growing too big, newly hired people are added to teams or team members are leaving.Original teams and targeted teams are not yet effective as a team and benefit from losing old or welcoming new members.The receiving team does not resist, and the new members are welcome.The remaining team can compensate for the loss of their teammates. There is enough knowledge and skills with the remaining team to do the job. The originating team or department can do without (or train people for) the lack of unique skills.Working methods in the new team do not differ much from the original team and onboarding the new members can be done effectively.Development efforts will be concentrated on those products where the profit is highest, and this situation seems stable.Expected value of the newly completed work covers the time the teams will spend on learning and switching time/cost are covered.The organization needs to spread best practices and knowledge across teams for long-term success.Tags: agileleadershipteams

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About the author

Derk-Jan de GroodDerk-Jan de Grood (from The Netherlands) works for Squerist. He guides organizations on their agile journey, improves their way of working, and helps them maximize their value delivery. He is an experienced trainer, workshop host, and a regular keynote speaker at conferences like Agile Summit Ibiza, Agile Amsterdam, Testing Uruguay, the Agile Testing Days, and STAR conferences in Europe and America. Among the books he wrote is The Waves of Agile – Value Delivery in Medium and Large Organizations. The book examines agile transformations and value delivery in complex organizations. Last year he co-authored The Virtual Coffee Experience. In his blog he shares his knowledge and experience for everyone to benefit.

Connect with Mehttps://www.linkedin.com/in/derkjandegrood/

https://twitter.com/DerkJanDeGrood

https://djdegrood.wordpress.com

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18/07/2024 18:21 – The Risks of Botshit
#BardChatbot #Business #BusinessEconomics #Economy #Risk #Management #ChatGPT #OrganizationalBehavior #Organization #Chatbot #Cognition #FinancialEconomics #PrivateSector #Economics #Technology #SocialPsychology #Innovation #HumanCommunication #OpenAI #Entities

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AI and machine learning

***THE RISKS OF BOTSHIT***

Don’t let inaccurate chatbot content harm your business.

by

Ian P. McCarthy,

Timothy R. Hannigan,

and

André Spicer

by

Ian P. McCarthy,

Timothy R. Hannigan,

and

André Spicer

July 17, 2024

Illustration by HBR Staff

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Summary.

Botshit — made-up, inaccurate, and untruthful chatbot content that humans uncritically use for tasks — can pose major risks to your business in the form of reputational damage, incorrect decisions, legal liability, economic losses, and even human safety. Yet, it's unlikely that chatbots are going away. How can you manage these risks while taking advantage of the benefits of promising new tools? The authors suggest asking two key questions based on their research: How important is chatbot response veracity for a task? And how difficult is it to verify the veracity of the chatbot response? Based on your responses to these questions, you can better identify the risks associated with a given task — and successfully mitigate them.

Don't let inaccurate chatbot content harm your business.

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Hot off the heels of OpenAI releasing their GenAI chatbot ChatGPT to the public in November 2022, Google released their own chatbot called Bard (now Gemini). During Bard’s first public demonstration, it generated a major factual error in response to a question about the discoveries made by the James Webb Space Telescope. This wrong answer by the chatbot led to a 9% drop in the stock price of Alphabet, Google’s parent company — at the time, $100 billion in market value.

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Ian P. McCarthy is the W.J. VanDusen Professor of Innovation and Operations Management at Simon Fraser University, and a Professor at the Center in Leadership, Innovation and Organisation (CLIO) at Luiss.

Timothy R. Hannigan is an Associate Professor of Organization Theory and Entrepreneurship at the Alberta School of Business, University of Alberta. From September, he will be an Associate Professor of Strategy and Organization and the Thivierge Chair in Canadian Business at the Telfer School of Management, University of Ottawa.

André Spicer is Dean and Professor of Organizational Behavior at Bayes Business School, City, University of London.

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18/07/2024 18:21 – The Risks of Botshit Summary. Botshit — made-up, inaccurate, and untruthful chatbot content that humans uncritically use for tasks — can pose major risks to your business in the form of reputational damage, incorrect decisions, legal liability, economic losses, and even human safety. Yet, it’s unlikely that chatbots are going away. How can you manage these risks while taking advantage of the benefits of promising new tools? The authors suggest asking two key questions based on their research: How important is chatbot response veracity for a task? And how difficult is it to verify the veracity of the chatbot response? Based on your responses to these questions, you can better identify the risks associated with a given task — and successfully mitigate them
#BardChatbot #Business #BusinessEconomics #Economy #Risk #Management #ChatGPT #OrganizationalBehavior #Organization #Chatbot #Cognition #FinancialEconomics #PrivateSector #Economics #Technology #SocialPsychology #Innovation #HumanCommunication #OpenAI #Entities
18/07/2024 18:17 – How to Integrate Cloud, Data, and AI Technologies — and Make Your Company More Adaptable
#ArtificialIntelligence #Innovation #Technology #StrategicManagement #Economy #Business #Management #BranchesOfScience #Strategy #EconomicGrowth #EmergingTechnologies #Change #MachineLearning #BusinessEconomics #Cognition #CognitiveScience #ArtificialIntelligence #Innovation #Technology #StrategicManagement #Economy #Business #Management #BranchesOfScience #Strategy #EconomicGrowth #EmergingTechnologies #Change #MachineLearning #BusinessEconomics #Cognition #CognitiveScience

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Growth strategy

***HOW TO INTEGRATE CLOUD, DATA, AND AI TECHNOLOGIES — AND MAKE YOUR COMPANY MORE ADAPTABLE***

Companies that invested in this “digital core” saw more growth and higher returns.

by

Paul R. Daugherty,

H. James Wilson,

Karthik Narain,

and

Prashant Shukla

by

Paul R. Daugherty,

H. James Wilson,

Karthik Narain,

and

Prashant Shukla

July 17, 2024

Hector Roqueta Rivero/Getty Images

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Summary.

Building a strong, flexible “digital core” that integrates cloud, data, and AI technologies to serve as an interconnected foundation for your company is the key to future growth. It is your means of supporting the current business drive toward efficiency and effectiveness, while remaining flexible enough to respond to the new needs of the organization and quickly adopt and scale the latest technology innovations. According to new research from Accenture on more than 1,500 large, global companies across industries, those that built a reinvention-ready digital core achieved what the researchers call the “60:40 effect.” Compared to a sample baseline, these leaders experienced a 60% acceleration in their revenue growth rate (from 7.1% to 11.1% on average) while increasing profitability by 40% (from 14.2 to 19.4 percentage points on average). The researchers found that the digital core has vastly expanded in importance, driven by the pandemic and explosion of gen AI. No matter your industry, the strategic shift toward continuous reinvention is fueling an urgent need for a digital core that amplifies machines, humans, and the interaction of the two in significant new ways.

Companies that invested in this “digital core” saw more growth and higher returns.

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We’ve entered an era of radical disruption, where waves of new technologies make continuous reinvention the default business strategy. Generative AI is only the latest innovation of many more to come that organizations will need to accommodate at pace and scale, or risk falling behind their competitors. That means companies must create a “digital core” that integrates cloud, data, and AI technologies to build an interconnected foundation for the enterprise that balances growth and reinvention, instead of focusing merely on cost.

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Paul R. Daugherty is Accenture's chief technology and innovation officer. He is coauthor, with H. James Wilson, of Human + Machine: Reimagining Work in the Age of AI (HBR Press, New and Expanded Edition, 2024).

pauldaugh

H. James Wilson is the global managing director of technology research and thought leadership at Accenture Research. He is coauthor, with Paul R. Daugherty, of Human + Machine: Reimagining Work in the Age of AI (HBR Press, New and Expanded Edition, 2024).

hjameswilson

Karthik Narain is group chief executive of Technology at Accenture. He oversees cloud, data & AI, enterprise & industry technologies, security and ecosystem partnerships.

KarthikSNarain

Prashant Shukla is principal director at Accenture Research.

PrashShukla

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18/07/2024 17:38 – When Cyberattacks Are Inevitable, Focus on Cyber Resilience
#ComputerSecurity #Cybercrime #CyberpunkThemes #CrimePrevention #SecurityEngineering #ComputerLaw #InformationTechnology #BattlesAndConflictsWithoutFatalities #Technology #InformationAge #Cyberspace #Cyberwarfare #InformationTechnologyManagement #SecurityTechnology #NationalSecurity #SecureCommunication #Security #Computing #SocialInfluence #DataLaws #Management #Cyberattack #InformationGovernance #Telecommunications #SocialIssues #Cyberattacks #RegulationOfTechnologies #Politics #ControlSocialAndPolitical #Business #Crimes #DataProtection #Secrecy #DigitalMedia #InformationScience #Internet #DataSecurity #WeaponsCountermeasures #Hazards

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Cybersecurity and digital privacy

***WHEN CYBERATTACKS ARE INEVITABLE, FOCUS ON CYBER RESILIENCE***

Preparing for an attack that breaches your defenses is the best way to minimize the damage it can do.

by

Keri Pearlson

by

Keri Pearlson

July 18, 2024

Eugene Mymrin/Getty Images

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Cybersecurity experts and the companies that employ need to let go of their prevention mindset and adopt one focused on resilience. A prevention mindset means doing all you can to keep the bad guys out. A resilience mindset adds a layer: while you do all you can to prevent an attack, you also work with the expectation that they still might break through your defenses and invest heavily preparing to respond and recover when the worst happens. Companies that have successfully built resilience have done a few things differently: built a culture of cybersecurity, prepared and practiced their responses to cyber attacks, embraced “secure by design” principles, and put in place communication processes so they can respond no matter what happens.

Preparing for an attack that breaches your defenses is the best way to minimize the damage it can do.

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There’s a common — but serious — mistake cybersecurity experts make: they focus all their resources on keeping malicious actors out of our system. It’s an understandable misstep. Protecting ourselves from potential cyber breaches is a critical part of the job. Even so, it’s impossible to be completely protected from every vulnerability. That’s because the good guys must protect against every possible vulnerability, while the bad guys only need one small crack in a company’s armor to get in.

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Keri Pearlson is the executive director of the research consortium Cybersecurity at MIT Sloan (CAMS). Her research investigates organizational, strategic, management, and leadership issues in cybersecurity. Her current focus is on the board's role in cybersecurity.

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New!

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Digital Intelligence Course

Accelerate your career with Harvard ManageMentor®. HBR Learning's online leadership training helps you hone your skills with courses like Digital Intelligence . Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.

Excel in a world that's being continually transformed by technology.

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18/07/2024 17:37 – Research: People Still Want to Work. They Just Want Control Over Their Time.
#RemoteWork #Employment #Economy #Business #SocialPsychology #Labor #Work #GreatResignation #EmployeeRelations #LaborRelations #OrganizationalBehavior #Workplace #Policy #BranchesOfScience #IndustrialAndOrganizationalPsychology #Politics #SocialIssues #IndustrialRelations #BehaviouralSciences #OrganizationalTheory #PublicPolicy #PrimateBehavior #Psychology #Organization

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Remote work

***RESEARCH: PEOPLE STILL WANT TO WORK. THEY JUST WANT CONTROL OVER THEIR TIME.***

It’s a strong predictor for how satisfied they are with their work — and their lives.

by

Stephanie Tepper

and

Neil Lewis, Jr.

by

Stephanie Tepper

and

Neil Lewis, Jr.

July 18, 2024

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Summary.

To better understand the role that control over one's time plays in job and life satisfaction, the authors analyzed survey data from a nationally representative sample. They found: 1) People who had greater control over their time had the highest job satisfaction and overall satisfaction with their lives, 2) Those who felt a sense of time scarcity had less satisfaction with their jobs and were less satisfied with their lives, 3) The number of hours people worked was not related to how satisfied people were with their jobs, and 4) For those who had more control over their time, feeling time scarcity did not undermine their job satisfaction as much as it did for those who had less control over their time. Employers should therefore create and tailor flexible work policies to meet diverse employee needs, fostering satisfaction and retention.

It's a strong predictor for how satisfied they are with their work — and their lives.

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Workers — particularly those considered “knowledge workers” who are able to do most if not all of their work with a laptop and an internet connection — have been fighting for the right to maintain control over their time for years. While working from home in 2020 and 2021, they demonstrated to their bosses that they are able to maintain, or in some cases even increase, their productivity while working flexibly. Their bosses, on the other hand, have been pulling them in the opposite direction; executives and managers have been fighting to get workers back into the offices that companies are paying a lot of money to lease. This struggle has affected workers and companies alike. Workers quit en masse during a period that became known as “the Great Resignation,” and employers who instituted return-to-office mandates have struggled to hire and retain top talent. Now, especially with Gen Z making up an increasing share of the working population and the conversations around hybrid work and returning to the office stagnating, demands for increased flexibility in work arrangements are still top of mind for many employees and job seekers.

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ST

Stephanie Tepper is a behavioral scientist who studies behavioral and policy interventions to reduce economic inequality and promote economic opportunity. She is an Associate Fellow at the U.S. Office of Evaluation Sciences and a Postdoctoral Scholar at Jeb E. Brooks School of Public Policy at Cornell University.

NL

Dr. Neil Lewis Jr is a behavioral scientist who studies the motivational, behavioral, and equity implications of social interventions and policies. He is a Nancy and Peter Meinig Family Investigator in the Life Sciences at Cornell University and Weill Cornell Medicine, where he is also associate professor of communication, medicine, and public policy.

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18/07/2024 17:10 – Artificial Intelligence Tools Can Help Foster Leadership in Pharmacy
#ArtificialIntelligence #Health #DecisionMaking #EmotionalIntelligence #CognitiveScience #PredictiveAnalytics #HealthCare

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NewsAll NewsPress ReleasesMediaAll VideosDigital DetailIndependent CornerInsightsInterviewsMedical World NewsMicrositesPeer ExchangePerfect ConsultPodcastsPractice PearlsSponsored WebcastStudent VoicesWebinars/WebcastsConferencesConference CoverageConference ListingPublicationsPharmacy TimesPharmacy Practice in Focus: OncologyPharmacy Practice in Focus: Health SystemsPharmacy CareersAll PublicationsAbout the PublicationsAuthor SubmissionSubmit a ManuscriptBecome a Peer ReviewerClinical503B Compounding PharmacyADHDAllergyAlzheimer DiseaseAnxietyAsthmaAtopic DermatitisBiosimilarsBipolar DisorderBrain HealthBreast CancerC. DifficileCOPDCardiovascular HealthCervical CancerCholangiocarcinomaChronic Kidney DiseaseChronic Lymphocytic LeukemiaColorectal CancerCoronavirusCough and ColdDepressionDermatologyDiabetesDigestive HealthDry Eye DiseaseEpilepsyEye CareFDA UpdatesFluFuture of PharmacyGastrointestinal CancerGastrointestinal HealthGoutHIVHeart FailureHematologyHepatitisIVIGImmunizationImmuno-oncologyInfectious DiseaseLung CancerLymphomaMacular DegenerationMental HealthMigraineMultiple MyelomaMultiple SclerosisNeurologyNeutropeniaOsteoporosisOvarian CancerPain ManagementParkinson DiseasePediatricsPharmacy HeroesPharmacy ManagementPneumococcalPrEPProstate CancerPsoriasisPsoriatic ArthritisPsychedelicsReimbursementReproductive HealthRespiratory Syncytial VirusRheumatoid ArthritisSchizophreniaSkin CancerSleepVeterinary PharmacyVitamins and SupplementsWeight ManagementWomen's HealthZosterEventsUpcoming EventsUpcoming WebinarsCME/CEResourcesClinical ForumCou-CoEducated PatientInformation for the PharmacistInteractive ToolsPartnersSponsoredWhiteboardsSubscribeSpotlightCommunity/RetailHospitalOncologyPharmacy TechnicianStudentClinical RoleCommunity/RetailHospitalOncologyPharmacy TechnicianSpecialty PharmacyStudentClinicalCoronavirusBrain HealthCardiovascular HealthDermatologyDiabetesFluHeart FailureHepatitisImmunizationMigrainePneumococcalVitamins and SupplementsSupplement SpotlightOctober 2023 Pharmacy Technician EditionNewsAll NewsPress ReleasesMediaAll VideosDigital DetailIndependent CornerInsightsInterviewsMedical World NewsMicrositesPeer ExchangePerfect ConsultPodcastsPractice PearlsSponsored WebcastStudent VoicesWebinars/WebcastsConferencesConference CoverageConference ListingPublicationsPharmacy TimesPharmacy Practice in Focus: OncologyPharmacy Practice in Focus: Health SystemsPharmacy CareersAll PublicationsAbout the PublicationsAuthor SubmissionSubmit a ManuscriptBecome a Peer ReviewerClinical503B Compounding PharmacyADHDAllergyAlzheimer DiseaseAnxietyAsthmaAtopic DermatitisBiosimilarsBipolar DisorderBrain HealthBreast CancerC. DifficileCOPDCardiovascular HealthCervical CancerCholangiocarcinomaChronic Kidney DiseaseChronic Lymphocytic LeukemiaColorectal CancerCoronavirusCough and ColdDepressionDermatologyDiabetesDigestive HealthDry Eye DiseaseEpilepsyEye CareFDA UpdatesFluFuture of PharmacyGastrointestinal CancerGastrointestinal HealthGoutHIVHeart FailureHematologyHepatitisIVIGImmunizationImmuno-oncologyInfectious DiseaseLung CancerLymphomaMacular DegenerationMental HealthMigraineMultiple MyelomaMultiple SclerosisNeurologyNeutropeniaOsteoporosisOvarian CancerPain ManagementParkinson DiseasePediatricsPharmacy HeroesPharmacy ManagementPneumococcalPrEPProstate CancerPsoriasisPsoriatic ArthritisPsychedelicsReimbursementReproductive HealthRespiratory Syncytial VirusRheumatoid ArthritisSchizophreniaSkin CancerSleepVeterinary PharmacyVitamins and SupplementsWeight ManagementWomen's HealthZosterEventsUpcoming EventsUpcoming WebinarsCME/CEResourcesClinical ForumCou-CoEducated PatientInformation for the PharmacistInteractive ToolsPartnersSponsoredWhiteboardsSubscribeAdvertisement***ARTIFICIAL INTELLIGENCE TOOLS CAN HELP FOSTER LEADERSHIP IN PHARMACY***

July 17, 2024By Taylor Mackinnon, PharmD, MS, BCPSPublicationArticlePharmacy TimesJuly 2024 Volume 90Issue 7 Leaders Must Continue to Prioritize Empathy and Human Connection.

The intersection of artificial intelligence (AI) and leadership has become increasingly prominent in today’s rapidly evolving digital landscape.1 In 2022, the health care sector saw significant AI investments of $6.1 billion. This was followed by significant investments in data management, processing, and cloud services ($5.9 billion); financial technology ($5.5 billion); cybersecurity and data protection ($5.4 billion); and retail ($4.2 billion).2

Image Credit: Jacob Lund - stock.adobe.com

The growing attention on AI in pharmacy highlights the potential to identify both beneficial and potentially detrimental patterns within health care, particularly with the use of machine learning.3 Pharmacy leaders must pay close attention to this dynamic intersection as the health care industry’s growing reliance is unavoidable, especially within medication use processes.4 As health care organizations embrace tools to streamline operations, enhance decision-making, and drive innovation, pharmacy leaders face the unique challenge of integrating these powerful tools while maintaining authenticity in their leadership roles.

Moreover, it is essential to acknowledge that technologies do not stand alone; they require careful consideration of human and procedural factors.5 Although technology can offer efficiency and objectivity, pharmacy leaders must ensure their leadership approach preserves essential human qualities, including common sense, situational awareness, context, and compassion. Striking a balance between leveraging AI for its benefits and upholding authenticity is pivotal for effective leadership in the pharmacy sector. Authenticity is a critical aspect of effective leadership, involving being true to oneself, showcasing genuine emotions, and aligning actions with values and beliefs. In today’s fast-paced world, in which technology is becoming increasingly prevalent, leaders must navigate the challenges of maintaining authenticity while leveraging these advancements for the benefit of their strategies and decision-making processes.

THE IMPACT OF AI

According to one study, AI adoption has the potential to create an additional $13 trillion in global economic activity by 2030, showcasing the immense prospects presented by these tools.6 However, this integration also poses challenges for leaders. Reliance on technology may cause leaders to become detached from the human elements of leadership. They must balance leveraging tools and maintaining their authentic leadership style.7

AI fundamentally alters traditional leadership practices, offering valuable insights, automating tasks, and enhancing productivity.8 In health care, AI has played a pivotal role in improving diagnostic accuracy and speeding up decision-making processes.9 Integrating AI technologies significantly shifts how leaders approach strategic planning, problem solving, and decision-making. AI tools save leaders time in the collection, filtering, and interpretation of data. This allows them to focus on data analysis and developing subsequent action plans.10

AI’s influence on health care leadership and decision-making will only grow. It will reshape how organizations operate and make critical choices while prioritizing patient care. This transformation underscores the urgent need for leaders to adapt and effectively utilize AI tools while preserving the essential human aspects of leadership.

BENEFITS

Embracing AI tools offers leaders myriad specific advantages, revolutionizing the way they operate and make decisions. Some examples of these advantages include the following:

Harnessing big data: AI empowers leaders to make data-driven decisions with unprecedented accuracy and speed by leveraging system-level data from electronic health records, point of sale, and inventory management software.Streamlining operations: AI tools automate repetitive tasks, freeing leaders to focus on high-level strategic thinking and innovation.Predictive analytics: AI allows leaders to anticipate market trends, customer behavior, and potential risks, enabling proactive decision-making.Enhanced time management: AI tools automate data collection, analysis, and interpretation, saving leaders time that they can put toward strategic planning and decision-making.Improved writing: AI-powered writing assistance tools can enhance the quality, efficiency, and consistency of leaders’ written communication, improving productivity and collaboration.

To effectively navigate AI integration, leaders must actively engage in discussions, share insights, and stay informed about the evolving AI-leadership relationship. This fosters a culture of curiosity and collaboration, paving the way for a future marked by trust, innovation, and shared success. Additionally, to maximize AI benefits, leaders must prioritize data availability. Access to accurate and reliable data is crucial for informed decision-making. However, they must also be mindful of authenticity challenges that AI integration poses, such as the potential loss of emotional intelligence.

CHALLENGES

One critical challenge leaders face when embracing AI is the potential loss of human connection. AI excels at data analysis and automation but cannot replace the empathy, emotional intelligence, and interpersonal skills vital in leadership.1 Leaders must remain mindful of this and prioritize authentic relationships with their teams and stakeholders.

Integrating AI into leadership roles raises concerns about authenticity. Although AI streamlines decision-making and provides valuable insights, there is a risk of overshadowing the human touch that authentic leadership emphasizes. Authentic leadership is rooted in genuine relationships, self-awareness, and ethical decision-making. Overreliance on AI for crucial leadership tasks may lead to a perception of detachment and lack of empathy, potentially undermining the authenticity and trust that leaders strive to cultivate.11,12

Another challenge lies in the potential perception of AI tools as a threat to job security. Leaders must proactively address concerns or fears among team members, emphasizing that AI tools are designed to augment human capabilities, not replace them. This requires effective communication and transparency to build trust and foster a positive work environment.

Although AI offers numerous benefits, its use in leadership can inadvertently diminish the interpersonal interactions vital for building trust and empathy. Using AI for tasks such as communication, feedback analysis, and performance evaluations may reduce opportunities for the personal connections so important to authentic leadership. Additionally, reliance on AI for tasks that require emotional intelligence can hinder leaders’ development of this essential skill. To address this, health care leaders should pay special attention to preserving human connection in patient care and team collaboration, striking a balance between leveraging AI’s capabilities and preserving authentic personal connections and emotional intelligence.11,12

ACTIONABLE STRATEGIES

In this rapidly evolving landscape, there are some important, actionable strategies leaders can adopt to maintain their authenticity amid the rise of AI, as follows:

Embrace emotional intelligence: Encourage leaders to hone their emotional intelligence because AI cannot replicate genuine human emotions and empathy. In health care leadership, this means emphasizing qualities that are crucial to patient care, such as empathy and patient-centeredness. Developing strong emotional intelligence helps leaders connect with their teams on a deeper level, fostering trust and loyalty.Communicate openly and transparently: Prioritize open and transparent communication to demonstrate trustworthiness and authenticity. Honestly address the role of AI within the organization and its potential impact on the workforce. This transparency can alleviate concerns and build credibility.Lead by example: Authentic leaders demonstrate integrity, humility, and a genuine concern for their team members. Encourage leaders to embody these qualities, which are especially important as technology advances.Cultivate diversity: Promote a diverse work culture that values individual strengths, creativity, and innovation. In pharmacy settings, this involves recognizing and celebrating the unique contributions of various health care professionals. Diversity helps counterbalance the potential dehumanizing effects of AI and reinforces the importance of authentic human connections.Stay true to core values: Encourage leaders to stay true to the organization’s core values and principles, emphasizing patient-centric aspects that define the company’s identity. Remaining committed to values that prioritize staff well-being and compassionate care helps leaders preserve their authenticity amid rapid technological change.Recognize contributions: Acknowledge and celebrate the unique talents and contributions of team members. Implement recognition programs or awards specific for employees who exemplify authenticity, empathy, and human connection in their interactions with patients and colleagues. This reinforces the value of authenticity and inspires others to follow suit.

By implementing these strategies, health care leaders can navigate technological advancements while maintaining their authenticity and preserving the human touch in patient care and organizational culture.

PROACTIVE EMBRACE

To effectively embrace the potential of AI, leaders must proactively adapt their practices, seamlessly integrating AI tools into existing workflows. This strategic approach ensures that AI becomes integral to leadership practices rather than a separate entity. As AI evolves, leaders must adopt a future-oriented mindset, identifying areas where AI offers the most significant value and proactively preparing for future advancements. Some strategies for this include the following:

Embrace a continuous learning mindset: Commit to constant learning and adaptation. Attend conferences, workshops, and webinars focused on leadership in the age of AI. Subscribe to industry publications and newsletters to stay current with the latest research and insights.Develop AI-specific skills: Although not every leader needs to be a technical expert, having a foundational understanding of AI principles, capabilities, and limitations is crucial. Seek out training opportunities that demystify AI and provide practical knowledge for effective implementation within the health care setting.Foster a culture of experimentation: Encourage a safe space for testing and piloting AI tools before full-scale implementation. This controlled experimentation helps identify potential benefits and potential shortcomings, such as risks to patient data privacy, allowing for informed adjustments and better outcomes.Build collaborative partnerships: Seek collaboration with AI experts, tech companies, and other organizations at the forefront of AI development. These partnerships can provide valuable guidance, accelerate learning, and spark innovative solutions tailored to your organization’s needs.Prioritize ethical considerations: Proactive leadership requires careful consideration of the ethical implications of AI. Establish clear guidelines for responsible AI use, upholding transparency, fairness, and accountability, particularly within the health care context where patient well-being is paramount.Focus on human-AI collaboration: Emphasize the complementary relationship between AI and human leaders. AI tools should augment leadership abilities, not replace them. Train leaders on how to work effectively in tandem with AI, leveraging its strengths while maintaining essential human qualities for optimal outcomes.Seek out industry-leading AI education: Some technology companies are pioneering AI boot camps designed specifically for organizational leaders. These programs offer a deep dive into AI concepts, practical applications, and ethical considerations, empowering leaders to make informed decisions about AI integration within their organizations.13-15

By embracing these strategies, leaders can position themselves and their organizations at the forefront of AI adoption. Taking a proactive approach ensures leaders are not simply reacting to technological change but actively shaping its integration in a way that maximizes benefits, safeguards authenticity, and builds a resilient organization ready to thrive in the AI-driven future.

About The Author

Taylor MacKinnon, PharmD, MS, BCPS, is pharmacy manager of clinical and operational services at University of Michigan Health and is an adjunct clinical instructor at the University of Michigan College of Pharmacy.

CONCLUSION

As AI tools advance, the future of leadership authenticity hinges on striking a delicate balance between leveraging technology and nurturing human connections. Leaders who successfully navigate this landscape will find themselves well prepared to inspire and guide their teams in the era of AI.

Although leadership authenticity remains paramount, the benefits brought by AI tools are substantial. Leaders must acknowledge and address the challenges posed to authenticity. By implementing strategies such as self-reflection, transparent communication, and seamless integration of AI into workflows, leaders can embrace technology while remaining genuine. They must ensure that technological advancements enhance patient care rather than detract from the compassionate and personalized approach that defines the health care profession, and pharmacy in particular. Organizations play a pivotal role by providing robust training and development programs that equip leaders with the necessary skills to navigate AI tools effectively.

Looking ahead, leaders and organizations must stay abreast of ongoing developments in AI and leadership. This dynamic landscape calls for continuous learning and adaptation. By actively engaging in discussions, sharing insights, and staying informed about the evolving relationship between AI and leadership, we can shape a future marked by trust, innovation, and shared success.

References1. Execs in the Know. How work and leadership will change as AI advances. CX Insight. October 2023. Accessed June 6, 2024. https://execsintheknow.com/magazines/october-2023-issue/how-work-and-leadership-will-change-as-ai-advances/2. Maslej N, Fattorini L, Brynjolfsson E, et al. The AI Index 2023 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. April 2023. Accessed June 6, 2024. https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf3. Flynn A. Using artificial intelligence in health-system pharmacy practice: finding new patterns that matter. Am J Health Syst Pharm. 2019;76(9):622-627. doi:10.1093/ajhp/zxz0184. Cobaugh DJ, Thompson KK. Embracing the role of artificial intelligence in the medication-use process. Am J Health Syst Pharm. 2020;77(23):1915-1916. doi:10.1093/ajhp/zxaa3045. Nelson SD. Artificial intelligence and the future of pharmacy. Am J Health Syst Pharm. 2024;81(4):83-84. doi:10.1093/ajhp/zxad3166. Bughin J, Seong J, Manyika J, Chui M, Joshi R. Notes from the AI frontier: modeling the impact of AI on the world economy. McKinsey Global Institute. September 4, 2018. Accessed June 6, 2024. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy7. Hougaard R, Carter J, Stembridge R. The best leaders can’t be replaced by AI. Harvard Business Review. January 12, 2024. Accessed June 6, 2024. https://hbr.org/2024/01/the-best-leaders-cant-be-replaced-by-ai?ab=HP-hero-latest-text-18. Mantsch PE. The future of leadership: AI leadership vs. traditional leadership. LinkedIn. January 25, 2024. Accessed June 6, 2024. https://www.linkedin.com/pulse/future-leadership-ai-vs-traditional-peter-e-mantsch-dspue/9. Artificial intelligence (AI) in cardiovascular medicine. Mayo Clinic. March 16, 2024. Accessed June 6, 2024. https://www.mayoclinic.org/departments-centers/ai-cardiology/overview/ovc-2048664810. McKendrick J. How generative AI could give us back at least 40% of our time. Forbes. May 24, 2023. Accessed June 6, 2024. https://www.forbes.com/sites/joemckendrick/2023/05/24/how-generative-ai-could-give-us-back-at-least-40-of-our-time/?sh=2544cf7f2efc11. Tarasenko I. Rethinking authenticity in the AI revolution. Medium. December 15, 2023. Accessed June 6, 2024. https://medium.com/@saik0s/dissecting-authenticity-in-the-ai-era-a-disruptive-take-6c54f2f8a35a12. IBRAHIM & Co. Valuing the human touch: AI, authenticity, and the future of original content. Medium. May 29, 2023. Accessed June 6, 2024. https://medium.com/@ibrahimandcompany/valuing-the-human-touch-ai-authenticity-and-the-future-of-original-content-7b44e9b5d66d13. Spatacco A. This artificial intelligence (AI) company just took a page out of Palantir’s playbook. Yahoo! Finance. December 13, 2023. Accessed June 6, 2024. https://finance.yahoo.com/news/artificial-intelligence-ai-company-just-140000210.html?14. Palantir. Deploying full spectrum AI in days: how AIP bootcamps work. Medium. October 12, 2023. Accessed June 6, 2024. https://blog.palantir.com/deploying-full-spectrum-ai-in-days-how-aip-bootcamps-work-21829ec8d56015. Spatacco A. Palantir’s artificial intelligence (AI) strategy is genius: here’s why. The Motley Fool. October 22, 2023. Accessed June 6, 2024. https://www.fool.com/investing/2023/10/22/palantirs-artificial-intelligence-ai-strategy-is/Download Issue PDFDownload RISArticles in this issueRecap: Pharmacy Times Covers the Pharmacy Quality Alliance Annual MeetingPharmacy Policy, July 2024Artificial Intelligence Tools Can Help Foster Leadership in PharmacyProvider Status Is Finally Emerging. Now What?Pharmacist Spotlight: Cassandra Esperant, PharmD, AAHIVPThe AI Revolution Comes to PharmacyCounsel Patients Who Have Questions About Maternal RSV ImmunizationsRecent VideosRelated Content Advertisement

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18/07/2024 06:23 – L’Iran propose de connecter les systèmes de paiement des BRICS – Ministère des Affaires étrangères
#Bank #Iran #Money #Banking #FinancialServices #Russia #CentralBank #CentralBankOfRussia #Economy #BRICS #Finance #PaymentSystem #BankingTechnology

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devises, fiance, Iran, MIR, Russie, Shetab

Source : tass.com – 8 juillet 2024

https://tass.com/economy/1813955

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L'Iran propose de connecter les systÃ¨mes de paiement nationaux des pays BRICS, en suivant l'exemple de l'intÃ©gration des systÃ¨mes Mir de la Russie et Shetab de l'Iran, selon le porte-parole du ministÃ¨re des Affaires Ã©trangÃ¨res de la RÃ©publique islamique, Nasser Kanani.

â«â Outre le fait qu'au niveau bilatÃ©ral, l'Iran et la Russie ont achevÃ© la connexion des systÃ¨mes de paiement nationaux par l'intermÃ©diaire des banques centrales des deux Ã‰tats, les conditions nÃ©cessaires ont Ã©tÃ© crÃ©Ã©es pour transfÃ©rer les paiements commerciaux vers les monnaies nationales, nous poursuivons les pourparlers, au cours desquels l'Iran a fait part de sa proposition à la partie russe – d'étendre ce mécanisme aux pays BRICS. La partie russe considère cette proposition de manière positiveâ â», a déclaré le diplomate lors d'une conférence de presse diffusÃ©e par la chaîne de télévision IRIB.

Le 4 juillet, le gouverneur de la Banque centrale d'Iran, Mohammadreza Farzin, a tenu des négociations avec le gouverneur de la Banque de Russie, Elvira Nabiullina, Ã Saint-PÃ©tersbourg. Le 6 juillet, il a dÃ©clarÃ© que lors de la rÃ©union, les parties ont noté l'achèvement des travaux d'intégration du système de paiement russe Mir et du systéme de paiement iranien Shetab et ont également signé un accord sur la fourniture de liquidités aux monnaies nationales pour les transactions commerciales.

Selon le directeur de la Banque centrale d'Iran, à partir du 22 août, les Iraniens pourront retirer des roubles avec des cartes Ã puce Shetab dans tous les distributeurs automatiques de billets russes. Il est prévu qu'au cours des prochaines étapes du projet, les Russes puissent utiliser les cartes Mir en Iran et que les propriétaires de Shetab puissent régler leurs achats dans les magasins russes avec des cartes bancaires iraniennes.

La carte de paiement russe Mir est acceptée dans 12 pays. Outre la Russie, elle fonctionne sans restriction en Abkhazie, au Belarus, à Cuba et en Ossétie du Sud. L'Égypte, l'Iran, l'Ã®le Maurice et le Myanmar prévoient de mettre Mir en service. Six autres pays sont prêts à engager un dialogue sur son utilisation.
17/07/2024 18:28 – LightOn développe une offre d’IA générative avec HPE pour faire baisser les coûts d'infrastructure
#CloudComputing #ArtificialIntelligence #LargeLanguageModel #Computing #Technology #Business #GenerativeArtificialIntelligence #DigitalTransformation #LanguageModel #SoftwareAsAService #OpenSource #DigitalTechnology #Chatbot

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HPE

***LIGHTON DéVELOPPE UNE OFFRE D’IA GéNéRATIVE AVEC HPE POUR FAIRE BAISSER LES COûTS D'INFRASTRUCTURE***

En s'alliant à HPE, LightOn espère toucher une clientèle plus large avec sa plateforme d'IA générative Paradigm. Reposant sur des serveurs ProLiant accélérés par des GPU H100, l'offre conjointe s'adresse principalement aux grands comptes en quête d'intégration de cette technologie sans pour autant avoir à investir des sommes conséquentes dans le hardware nécessaire.

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Aperçu de la plateforme Paradigm développée par LightOn.

LightOn accélère. Lancée en 2016 avec pour ambition de concevoir un coprocesseur d'IA photonique, présenté en 2020, la start-up a opéré un virage à 180 degrés lorsqu'OpenAI a dévoilé GPT-3 en 2020. La société a alors réorienté ses efforts sur le développement d’un grand modèle de langage du même genre. "Il a fallu opérer une transformation totale, passant d’une start-up de hardware à celle d’éditeur de solution d’IA purement logicielle", avait alors commenté Laurent Daudet, directeur général de LightOn. Aujourd'hui, l'entreprise semble avoir passé un cap avec sa plateforme d'IA générative Paradigm commercialisée depuis mars 2023. Elle annonce en effet se rapprocher de HPE pour lancer une offre conjointe.

"L'idée, c'est de croiser nos clients et ceux de HPE qui voudraient utiliser nos solutions mais n'auraient pas encore de serveurs adaptés. Ainsi, nos utilisateurs peuvent acquérir les serveurs HPE avec l'offre Paradigm préinstallée. Il y a aussi la réciproque qui est tout à fait possible", nous confie Laurent Daudet. Pour mémoire, Paradigm permet de gérer tout le cycle de vie des LLM en entreprise et déployer ces derniers de manière industrielle et robuste, "de façon à avoir plusieurs dizaines, voire plusieurs centaines d'utilisateurs qui font des requêtes en simultané", précise le co-fondateur de LightOn.

**Des Serveurs Dopés Aux GPU Nvidia**

L'offre développée avec HPE repose sur les serveurs ProLiant DL380a Gen11 accélérés par des GPU H100 NVL 94GB avec NVLink. Avec une telle installation, les deux entreprises peuvent adresser plusieurs cas d'usage, qu'il s'agisse d'un simple chatbot, de systèmes RAG (Retrieval Augmented Generation) pour pouvoir converser avec une base de documents ou de développer des agents qui sauront réaliser des tâches plus complexes sur des données hétérogènes (tableaux de chiffres, données textuelles, etc.). L'offre reste centrée sur les grands comptes en quête de solutions on premise, précise Laurent Daudet.

"Nous nous adressons principalement aux entreprises qui sont très sensibles à ce que leurs données restent privées, soit des secteurs comme la banque, l'assurance, les données de santé et le secteur public". Une offre au coût moins élevé pour ces clients : auparavant, ces derniers devaient opter pour une licence Paradigm annuelle couplée à un investissement conséquent dans des serveurs. Désormais, l'offre commercialisée élimine ces coûts : "Vous ne faites que du leasing de ces serveurs avec l'offre Paradigm dessus. Le prix est une licence par serveur et ne dépend pas de l'utilisation". Une stratégie optimale selon Laurent Daudet pour "certaines entreprises qui seraient rebutées par l'investissement nécessaire à débourser en entrée".

**Pousser à L'utilisation De Modèles Open Source Plus Petits**

La plateforme est aujourd'hui capable de gérer tout type de LLM, même si la société propose sa propre famille de grands modèles de langage baptisée Alfred. "Nous avons nos LLM qui sont parfaitement adaptés à nos usages, notamment pour faire du RAG. Toutefois, la plateforme Paradigm est susceptible d'accueillir n'importe quel autre LLM open source. Si les clients veulent utiliser un modèle Mistral ou un modèle Llama (Meta) ou peu importe, c'est également possible", assure Laurent Daudet.

Et à la question de savoir s'il existe une limite de taille de modèle supporté, ce dernier répond par la négative, mais n'hésite pas à prêcher pour sa paroisse : "Nous avons tendance à penser que pour les cas d'usage d'entreprise, souvent, il n'y a pas besoin d'avoir les modèles les plus gros du monde. Souvent, avec 8 milliards ou 40 milliards de paramètres, ce sont déjà des modèles qui savent répondre à une grande majorité de cas d'usage". Un point que les clients entendent facilement, d'autant plus que la question du coût de l'infrastructure est intimement liée. "Mettre en place de l'infrastructure pour faire tourner des modèles à 100 milliards de paramètres coûte très cher et cela devient finalement un facteur limitant", argumente le directeur général de LightOn.

**Une Offre Lancée En Parallèle Avec Orange Business**

Dans le même temps, le Français accélère sur la mise en place d'une autre offre d'IA générative annoncée en mars dernier avec Orange Business. Ce service SaaS est entièrement intégré et "prêt à l'emploi", comme le précisait Aliette Mousnier-Lompré, directrice de la filiale du groupe Orange lors de l'annonce. Cette solution s'adapte à de nombreux cas d'usage, incluant la gestion documentaire, le reporting ainsi que la génération de contenus. Le rôle de la start-up consiste à fournir le moteur de type LLM et une interface métier facile à prendre en main, aussi bien par des profils techniques que métiers.

Les trois grandes forces de cette offre : un hébergement en interne sur le cloud privé virtuel baptisé "Cloud Avenue" d'Orange Business, une interface utilisateur simplifiée reposant sur du RAG (Retrieval Augmented Generation) et une maîtrise des coûts garantie. A noter que l'offre de cloud privé est hébergée dans les datacenters d'Orange à Chartres et à Val-de-Reuil, en France. Laurent Daudet ajoute que l'offre devrait être "mise en place au cours de l'été".

** Anthropic S'associe à Son Bailleur Menlo Ventures Pour Investir Dans Les Start-up D'IA **

** Manutan Dope Son Chatbot à L'IA Générative Pour Mieux Guider Ses Clients En Ligne **

** IA Générative : Anthropic Dévoile Une App Android De Son LLM Claude **

**SUR LE MÊME SUJET**

LightOn lance Paradigm, une plateforme d'IA générative française pour les grandes entreprises

Orange dévoile ses premières offres d'IA générative à destination des entreprises

Docaposte lance une offre d'IA générative souveraine avec trois entreprises françaises

LightOn développe une offre d’IA générative avec HPE pour faire baisser les coûts d'infrastructure

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17/07/2024 18:27 – Manutan dope son chatbot à l'IA générative pour mieux guider ses clients en ligne
#Chatbot #ArtificialIntelligence #DigitalTransformation #Computing #Technology #GenerativeArtificialIntelligence #ProjectManager #BusinessToBusiness #BigData #Communication #Business #MediaCommunication #Innovation #InternetOfThings

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Distribution

***MANUTAN DOPE SON CHATBOT à L'IA GéNéRATIVE POUR MIEUX GUIDER SES CLIENTS EN LIGNE***

Depuis mars dernier, Manutan expérimente l'intégration de l'IA générative dans son chatbot présent sur son site e-commerce. Cette initiative se concentre principalement sur les renseignements produits qui représentent environ 20% de ses demandes sur le chat.

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Manutan est spécialisé dans la distribution BtoB d’équipements et de fournitures.

L'ultra personnalisation du service client. Le graal que nombre d'entreprises tentent d'atteindre en intégrant l'IA générative dans leurs canaux de communication destinés aux clients. En mars dernier, Manutan a lancé une expérimentation de l’IA générative, greffant cette technologie à son chatbot présent sur son site e-commerce. La technologie prend le relais dès lors que l'utilisateur choisit "Renseignement techniques produits" dans les propositions faites par le chatbot. Le groupe français de distribution en ligne d'équipements pour les entreprises et les collectivités explique s'être concentré sur ces renseignements qui représentent environ 20% de ses demandes sur le chat.

Le fonctionnement est simple : lorsque le client demande une information sur un produit via la fenêtre de discussion, le chatbot dopé à l'IA générative cherche dans la base de données produits Manutan et formule une réponse complète en langage naturel. Si l’IA ne trouve pas de réponse à la question du client, elle lui propose alors de le mettre en contact avec un conseiller. Ce dernier reste épaulé par la technologie grâce à la fonctionnalité "Copilot Agent" : l’IA reformule les réponses, corrige les éventuelles fautes d’orthographe et de syntaxe, etc.

**Une Expérimentation Durant Plusieurs Mois**

Fabrice Gicquere, directeur de la relation client chez Manutan, précise que derrière le chatbot se trouve "un LLM sécurisé pour entreprises développé par notre fournisseur chat iAdvize. Le modèle a été affiné et adapté en une quinzaine de jours en partenariat avec notre prestataire et une chef de projet interne Manutan", précise-t-il. "Nous avons mis à disposition des données source sous un format spécifique".

Côté sécurité, Manutan affirme évaluer systématiquement la satisfaction des clients à la fin de chaque interaction, afin "de maintenir un niveau de service élevé et d'ajuster continuellement les processus". L'expérimentation doit durer jusqu’à fin septembre. Les premiers résultats semblent prometteurs comme en atteste le directeur de la relation client : "Aujourd’hui, l’IA est autonome dans 25% des conversations portant sur les renseignements produits, et vient en support des conseillers clientèles dans 20% des cas. Sa mise en place nous a permis de gagner en disponibilité pour nos clients – avec +10 points de taux de réponse – tout en maintenant le délai moyen de réponse en dessous de 30 secondes, conformément à notre objectif".

** Anthropic S'associe à Son Bailleur Menlo Ventures Pour Investir Dans Les Start-up D'IA **

** LightOn Développe Une Offre D’IA Générative Avec HPE Pour Faire Baisser Les Coûts D'infrastructure **

** IA Générative : Anthropic Dévoile Une App Android De Son LLM Claude **

**SUR LE MÊME SUJET**

Manutan déploie une solution d'analyse automatique du langage pour améliorer son service client

"Les clients valorisent une offre contrôlée, dans le respect des normes, avec un engagement clair", Julie Dang Tran (Manutan)

Comment la réalité virtuelle dope les performances commerciales de Manutan

Manutan dope son chatbot à l'IA générative pour mieux guider ses clients en ligne

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Green IT : les métiers de demain pour un avenir durable

Le Green IT, ou numérique durable, émerge comme une priorité pour les entreprises soucieuses de leur empreinte écologique. Mais quelles sont les implications pour le marché de l'emploi ? Quels postes sont les plus recherchés et quelles entreprises recrutent dans ce domaine ? Plongeons dans les opportunités d'emploi offertes par le secteur du Green IT.

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17/07/2024 18:26 – La pénurie de talents en cybersécurité, une épine dans le pied des entreprises françaises
#ComputerSecurity #DigitalTransformation #Fortinet #Technology #InformationTechnology #Computing #Cyberspace #France #Cyberattack #InformationAge #Safety #BigData #MediaCommunication #DigitalData

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Étude

Emploi

Future of work

***LA PéNURIE DE TALENTS EN CYBERSéCURITé, UNE éPINE DANS LE PIED DES ENTREPRISES FRANçAISES***

Les chercheurs de la société américaine Fortinet ont publié un rapport sur l'impact des difficultés de recrutement dans le domaine de la cybersécurité. 76% des entreprises françaises considèrent que ce manque de personnel qualifié peut conduire à davantage d'incidents de sécurité dans leurs systèmes.

Yoann Bourgin

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2

min. de lecture

**Mon Actualité personnalisable**

Yoann Bourgin

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© Dylan Gillis / Unsplash

Plus de 70% des entreprises du globe peinent à recruter des profils qualifiés en cybersécurité, selon l'étude de Fortinet.

72% : c’est la proportion d’entreprises, à l’échelle internationale, qui estiment que la pénurie de talents dans le domaine de la cybersécurité crée plus de risques pour leur entreprise (70% en France). Un chiffre qui ressort du rapport “Global Security Skills Gap Report”, publié par la société américaine Fortinet, qui édite des logiciels, services et équipements de cybersécurité.

**Des Entreprises Plus Vulnérables Aux Cyberattaques**

L’étude a été menée en janvier 2024 auprès de 1850 responsables en cybersécurité et en IT de 29 pays différents, sur des sociétés allant de 100 à plus de 5 000 salariés. Parmi ces 1850 responsables, 87% considèrent qu’un incident de sécurité survenu dans leurs systèmes peut être attribué pour partie à un manque de compétences en cybersécurité (76% en France). C’est trois points de plus qu’en 2023, et sept points de plus qu’en 2022.

Ce constat est d’autant plus important que plus de la moitié des responsables évaluent les dépenses et pertes financières liées à une intrusion dans leurs systèmes à 1 million de dollars ou plus (43% dans l’Hexagone). En outre, 51% des professionnels interrogés affirment que des directeurs ou cadres dirigeants ont subi des sanctions financières, pénales, ou un licenciement à la suite d’une cyberattaque. Les responsables français (près de 200) s’estiment deux fois moins concernés (26%).

**Sept Entreprises Sur Dix Prêtes à Financer La Certification D’un Collaborateur**

Pour recruter des professionnels en cybersécurité, les entreprises interrogées privilégient la formation continue et la certification, soit une validation des connaissances dans le domaine. Les profils certifiés sont ainsi priorisés pour 90% des recruteurs, un chiffre qui monte à 98% pour les entreprises françaises. Face au manque de candidats, quatre responsables sur cinq se disent prêts à financer la certification en cybersécurité d’un collaborateur. 72% d’entre eux éprouvent également des difficultés à recruter des profils disposant de certifications.

À l’occasion du Forum InCyber, qui s’est tenu en mars à Lille, Vincent Strubel, directeur général de l’Agence nationale de sécurité des systèmes d’information (Anssi) déplorait le manque d’attractivité de la filière. Un problème intimement lié à la perception peu valorisante des différentes professions dans le secteur.

**Un Déficit Criant De Main-d’œuvre**

Afin de combler ces besoins en recrutement, certaines entreprises n’hésitent pas à se tourner vers de nouveaux profils, moins conventionnels et traditionnels. La moitié des entreprises françaises continuent néanmoins à privilégier des candidats ayant suivi un cursus spécialisé de plusieurs années, et deux tiers d’entre elles ne recrutent que des candidats passés par le cursus traditionnel.

Suivant un autre rapport publié l’année dernière par l’association de professionnels en cybersécurité ICS2, 4 millions de nouveaux professionnels seraient nécessaires pour combler l’écart entre l’offre et la demande dans le domaine. Un écart 12,6% plus important que l’année précédente.

** Cybersécurité : Après L’interdiction De Ses Logiciels, Kaspersky Quitte Les États-Unis **

** Atos échappe Au Naufrage, Sauvé Par Un Accord Définitif Avec Banques Et Créanciers **

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**SUR LE MÊME SUJET**

[FIC 2024] JO, NIS2, emploi… En cette “année particulière”, l’Anssi évoque ses défis de cybersécurité

Ransomwares : les versements de rançons en baisse de 40% en 2022

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17/07/2024 16:04 – Impact of Data Quality on Big Data Management
#BigData #Data #Analytics #DataAnalysis #ArtificialIntelligence #Computing #Technology #InformationTechnology #InformationScience #InformationTechnologyManagement #DataManagement #InformationRetrieval #DataQuality #Database #Business #Management #Communication #Information #ComputerScience #Cognition #Research #Cyberspace #KnowledgeManagement #CognitiveScience #DataWarehouse #Sales #InformationAndCommunicationsTechnology

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In today's big data era, businesses generate and collect data at unprecedented rates. More data should imply more knowledge but it also comes with more challenges. Maintaining data quality becomes harder as the amount of data being handled increases.

It's not just the difference in volumes, data may be inaccurate and incomplete or it may be structured differently. This limits the power of big data and business analytics.

According to recent research, the average financial impact of poor quality data can be as high as $15 million annually. Hence the need to emphasize data quality for big data management.

**Understanding The Big Data Movement**

Big data can seem synonymous with analytics. However, while the two are related, it would be unfair to consider them synonymous.

Like data analytics, big data focuses on deriving intelligent insights from data and using it to create opportunities for growth. It can predict customer expectations, study shopping patterns to aid product design and improve services being offered, analyze competitor intelligence to determine USPs and influence decision-making.

The difference lies with data volume, velocity and variety.

Big data allows businesses to work with extremely high data volumes. Instead of megabytes and gigabytes, big data talks of data volumes in terms of petabytes and exabytes. 1 petabyte is the same as 1000000 gigabytes – that's data that would fill millions of filing cabinets!

Then there's the speed or velocity of big data generation. Businesses can process and analyze real-time data with their big data models. This allows them to be more agile as compared to competitors.

For example, before a retail outlet can record sales, location data from mobile phones in the parking lot can be used to infer the number of people coming to shop and estimated sales.

The variety of data sources is one of the biggest differentiators for big data. Big data can collect data from social media posts, sensor readings, GPS data, messages and updates, etc. Digitization and the steadily decreasing costs of computing have made data collection easier but this data may be unstructured.

**Data Quality And Big Data**

Big data can be leveraged to derive business insights for various operations and campaigns. It makes it easier to spot hidden trends and patterns in consumer behavior, product sales, etc. Businesses can use big data to determine where to open new stores, how to price a new product, who to include in a marketing campaign, etc.

However, the relevance of these decisions depends largely on the quality of data used for the analysis. Bad quality data can be quite expensive. Recently, bad data disrupted air traffic between the UK and Ireland. Not only were thousands of travelers stranded, airlines faced a loss of about $126.5 million!

Interested in what the future will bring? Download our 2024 Technology Trends eBook for free. **Common Data Quality Challenges For Big Data Management**

Data flows through multiple pipelines. This magnifies the impact of data quality on big data analytics. The key challenges to be addressed are:

High volume of data

Businesses using big data analytics deal with a few terabytes of data every day. Data flows from traditional data warehouses as well as real-time data streams and modern data lakes. This makes it next to impossible to inspect each new data element entering the system. The import-and-inspect design that works for smaller data sets and conventional spreadsheets may no longer be adequate.

Complex data dimensions

Big data comes from customer onboarding forms, emails, social networks, processing systems, IoT devices and more. As the sources expand, so do data dimensions. Incoming data may be structured, unstructured, or semi-structured.

New attributes get added while old ones gradually disappear. This can make it harder to standardize data formats and make information comparable. This also makes it easier for corrupt data to enter the database.

Inconsistent formatting

Duplication is a big challenge when merging records from multiple databases. When the data is present in inconsistent formats, the processing systems may read the same information as unique. For example, an address may be entered as 123, Main Street in one database and 123, Main St. This lack of consistency can skew big data analytics.

Varied data preparation techniques

Raw data often flows from collection points in to individual silos before it is consolidated. Before it gets there, it needs to be cleaned and processed. Issues can arise when data preparation teams use different techniques to process similar data elements.

For example, some data preparation teams may calculate revenue as their total sales. Others may calculate revenue by subtracting returns from the total sales. This results in inconsistent metrics that make big data analysis unreliable.

Prioritizing quantity

Big data management teams may be tempted to collect all the data available to them. However, it may not all be relevant. As the amount of data collected increases, so does the risk of having data that does not meet your quality standards. It also increases the pressure on data processing teams without offering commensurate value.

**Optimizing Data Quality For Big Data**

Inferences drawn from big data can give businesses an edge over the competition but only if the algorithms use good quality data. To be categorized as good quality, data must be accurate, complete, timely, relevant and structured according to a common format.

To achieve this, businesses need to have well defined quality metrics and strong data governance policies. Data quality cannot be seen as a single department's responsibility. This must be shared by business leaders, analysts, the IT team and all other data users.

Verification processes must be integrated at all data sources to keep bad data out of the database. That said, verification isn't a one-time exercise. Regular verification can address issues related to data decay and help maintain a high quality database.

The good news – this isn't something you need to do manually. Irrespective of the amount of data, number of sources and data types, quality checks like verification can be automated. This is more efficient and delivers unbiased results to maximize the efficacy of big data analysis.

About Muhammad Akheel

Responsible for developing, executing and delivering the company's digital/online marketing strategy, planning and budget to include online, new media, and web to drive the business forwards through key marketing channels. Works at www.Melissa.com. Passionate blogger and enjoys writing about data quality, KYC, AML, BLOCK Chain, crypto, Big Data, and AI.
17/07/2024 16:03 – Can Modern Data Centers Keep up With the Exponential Growth of AI?
#ArtificialIntelligence #CentralProcessingUnit #Computing #Technology #ComputerScience #Watt #ServerComputing #Engineering #InformationTechnology #Computers #Infrastructure #ComputerEngineering #ComputerHardware #ComputerCooling #InformationScience #Data

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Artificial intelligence's exponential growth has stirred controversy and concern among data center professionals. How will facilities accommodate the fast-approaching high-density kilowatt requirements AI requires? As conventional solutions become less feasible, they must find a viable – and affordable – alternative.

**Data Centers Are Facing The Consequences Of AI Demand **

AI's adoption rate is steadily climbing across numerous industries. It increased to about 72% in 2024, up from 55% the previous year. Most metrics suggest widespread implementation isn't a fleeting trend, indicating modern data centers will soon need to retrofit to keep up with its exponential growth.

The recent surge in AI demand has long-term implications for the longevity of data center information technology (IT) infrastructure. Since a typical facility can last 15-20 years, depending on its design and modularization, many operators are ill-prepared for the sudden, drastic change they now face.

For decades, operators have updated hardware in phases to minimize downtime, so many older data centers are crowded with legacy technology. Despite several massive technological leaps, fundamental IT infrastructure has changed very little. Realistically, while 10-15 kW per rack may be enough for now, 100 kW per rack may soon be the new standard.

**What Challenges Are Data Centers Facing Because Of AI? **

Current data center capacity standards may become inadequate within a few years. The resource drain will be significant whether operators augment equipment to perform AI functions or integrate model-focused into existing hardware. Already, these algorithms are driving the average rack density higher.

Currently, a standard facility's typical power density ranges from 4 kW to 6 kW per rack, with some more resource-intensive situations requiring roughly 15 kW. AI processing workloads operate consistently from 20 kW to 40 kW per rack, meaning the previous upper limit has become the bare minimum for algorithm applications.

Thanks to AI, data center demand is set to more than double in the United States. One estimate states it will increase to 35 gigawatts (GW) by 2030, up from 17 GW in 2022. Such a significant increase would require extensive reengineering and retrofitting, a commitment many operators may be unprepared to make.

Many operators are concerned about power consumption because they need up-to-date equipment or an increased server count to train an algorithm or run an AI application. To accommodate the increased demand for computing resources, replacing central processing unit (CPU) servers with high-density racks of graphics processing units (GPUs) is unavoidable.

However, GPUs are very energy intensive – they consume 10-15 times more power per processing cycle than standard CPUs. Naturally, a facility's existing systems likely won't be prepared to handle the inevitable hot spots or uneven power loads, impacting the power and cooling mechanisms' efficiency substantially.

Interested in what the future will bring? Download our 2024 Technology Trends eBook for free.

While conventional air cooling works well enough when racks consume 20 kW or less, IT hardware won't be able to maintain stability or efficiency when racks begin exceeding 30 kW. Since some estimates suggest higher power densities of 100 kW are possible – and may become more likely as AI advances – this issue's implications are becoming more pronounced.

**Why Data Centers Must Revisit Their Infrastructure For AI**

The pressure on data centers to reengineer their facilities isn't a fear tactic. Increased hardware computing performance and processing workloads require higher rack densities, making equipment weight an unforeseen issue. If servers must rest on solid concrete slabs, simply retrofitting the space becomes challenging.

While building up is much easier than building out, it may not be an option. Operators must consider alternatives to optimize their infrastructure and save space if constructing a second floor or housing AI-specific racks on an existing upper level isn't feasible.

Although data centers worldwide have steadily increased their IT budgets for years, reports claim AI will prompt a surge in spending. While operators' spending increased by approximately 4% from 2022 to 2023, estimates forecast AI demand will drive a 10% growth rate in 2024. Smaller facilities may be unprepared to commit to such a significant jump.

**Revitalizing Existing Infrastructure Is The Only Solution**

The necessity of revitalizing existing infrastructure to meet AI demands isn't lost on operators. For many, modularization is the answer to the rising retrofitting urgency. A modular solution like data center cages can not only protect critical systems and servers, they can support air circulation to keep systems cool and provide an ease to scale as more servers are needed.

Accommodating training or running an AI application – while managing its accompanying big data – requires an alternative cooling method. Augmented air may work for high-density racks. However, open-tub immersion in dielectric fluid or direct-to-chip liquid cooling is ideal for delivering coolant directly to hot spots without contributing to uneven power loads.

Operators should consider increasing their cooling efficiency by raising the aisle's temperature by a few degrees. After all, most IT equipment could tolerate a slight elevation from 68-72 F to 78-80 F as long as it remains consistent. Minor improvements matter because they contribute to collective optimization.

Alternative power sources and strategies are among the most important infrastructure considerations. Optimizing distribution to minimize electricity losses and improve energy efficiency is critical when AI requires anywhere from 20 kW to 100 kW per rack. Eliminating redundancies and opting for high-efficiency alternatives is necessary.

**Can Data Centers Adapt To AI Or Will They Be Left Behind?**

Data center operators may be willing to consider AI's surging demand as a sign to overhaul most of their existing systems as soon as possible. Many will likely shift from conventional infrastructure to modern alternatives. However, tech giants running hyperscale facilities will have a much easier time modernizing than most. For others, retrofitting may take years, although the effort will be necessary to maintain relevance in the industry.

About Emily Newton

Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine that explores innovations in science and technology. She loves seeing the impact technology can have on every industry.
17/07/2024 16:01 – Revolutionize Your Business Dashboards with Large Language Models
#Data #ArtificialIntelligence #LargeLanguageModel #BusinessIntelligence #CognitiveScience #NaturalLanguageProcessing #Intelligence

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***REVOLUTIONIZE YOUR BUSINESS DASHBOARDS WITH LARGE LANGUAGE MODELS***

New AI assistant within Cloudera Data Visualization gets users talking to their data

Data VisualizationEnterprise AI

by Varun Jaitly, and Juno Schaser

Posted in Technical |

June 26, 2024 2 min read

In today's data-driven world, businesses rely heavily on their dashboards to make informed decisions. However, traditional dashboards often lack the intuitive interface needed to truly harness the power of data. But what if you could simply talk to your data and get instant insights?

In the latest version of Cloudera Data Visualization, we’re introducing a new AI visual that helps users leverage the power of Large Language Models (LLMs) to “talk” to their data. Cloudera Data Visualization now leverages the latest advancements in natural language processing to transform your business dashboards into intelligent platforms.

Gone are the days of tedious filtering schemes and dropdown menus. With Cloudera Data Visualization, users can now have interactive conversations with their data, thanks to its seamless integration with LLMs of their choosing.. This means users can ask questions in plain language and receive accurate, contextually relevant responses. Say goodbye to static dashboards and hello to a whole new level of engagement.

One of the most remarkable features of the AI visual is its ability to understand context. For example, if a user asks their office supply data sets about “binders,” Cloudera Data Visualization automatically recognizes that the query might be referring to both durable and economy types that are sold by your organization. This level of intelligence streamlines the analysis process and saves valuable time. In the example below, the assistant is answering a question about sales performance of a particular product in a particular region.

AI Visual in Cloudera Data Visualization

And don't worry about losing track of the data behind the insights. Cloudera Data Visualization allows users to easily delve deeper into the underlying data, providing transparency and fostering trust in the results. This means users can build powerful visual dashboards and reports, and also have an additional layer of contextual intelligence through the AI visual for a comprehensive business intelligence workflow. So, whether you're a data scientist, business analyst, or executive, Cloudera Data Visualization revolutionizes the way you interact with data. It empowers users to make faster, more informed decisions by putting the power of natural language processing at their fingertips.

Dashboards, Visuals and Apps in Cloudera Data Visualization

Ready to experience the future of business intelligence? Cloudera customers can now access the Technical Preview of this new AI visual within any Data Visualization dashboard or application, and see firsthand how LLMs can transform dashboards to make it easier to surface insights. For more information on these features and our AI capabilities, visit our Enterprise AI page. When you’re ready, you can request a demo at the bottom of the page to see how these capabilities can work in the context of your business. The future of data insight from visualization is here – and it's smarter than ever before.

Varun Jaitly
17/07/2024 15:37 – Craignant pour sa sécurité, l’Allemagne bannit les chinois Huawei et ZTE de son réseau 5G
#Huawei #5G #ServiceIndustries #InformationTechnology #Telecommunications #Computing #InformationAndCommunicationsTechnology #Technology #Communication #Electronics #China #Cyberspace #MassMediaTechnology #Germany #Business #DigitalTechnology #ComputerSecurity #DigitalTransformation

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***CRAIGNANT POUR SA SéCURITé, L’ALLEMAGNE BANNIT LES CHINOIS HUAWEI ET ZTE DE SON RéSEAU 5G***

Après plusieurs mois de négociations avec les opérateurs télécoms, le gouvernement allemand a annoncé l'interdiction des produits chinois fournis par Huawei et ZTE dans son réseau 5G. Avec ce bannissement, qui sera progressif, l'Allemagne emboîte le pas aux états-Unis et à plusieurs pays européens.

Les produits fournis par les équipementiers chinois Huawei et ZTE vont être progressivement écartés des réseaux 5G allemands.

Le gouvernement allemand a annoncé le 11 juillet qu’il allait progressivement interdire l’utilisation de composants et de technologies fabriqués par les groupes chinois Huawei et ZTE dans ses réseaux 5G. “Nous devons réduire les risques pour la sécurité et, contrairement au passé, éviter les dépendances, a justifié dans un communiqué Nancy Faeser, ministre allemande de l’Intérieur. Nous protégeons ainsi le système nerveux de l’Allemagne en tant que place économique, ainsi que la communication des citoyens, de l’entreprise et de l’État”.

**Cinq Ans Pour Abandonner Les Composants Des Deux Sociétés**

Cette interdiction devrait s’appliquer avant la fin 2026 pour les technologies alimentant le cœur du réseau 5G du pays. Il s’agit ici des composants critiques, qui permettent de router et acheminer les appels, mais aussi de contrôler la qualité de la communication en cas de déplacement des interlocuteurs.

Les opérateurs télécoms auront jusqu’à fin 2029 pour abandonner les technologies des deux fabricants qui équipent les systèmes d’accès et de transport des réseaux 5G. Ceci comprend les équipements physiques, comme les tours et lignes de transmission. Pour mettre en œuvre ce calendrier, un accord a été conclu entre le gouvernement allemand et les opérateurs du pays Deutsche Telekom, Telefónica et Vodafone.

**Des “dangers De Sabotage Et D’espionnage”, Selon Le Gouvernement Allemand**

Selon un représentant de Huawei, “il n’y a pas de preuve ou de scénario spécifique indiquant que la technologie de Huawei présente des risques en matière de cybersécurité”. Un porte-parole du ministère chinois des Affaires étrangères a de son côté critiqué une décision restrictive pour les marchés, dénonçant une “politisation” qui “ne peut que nuire aux échanges et à la coopération”. D’après une étude publiée en 2022 par le cabinet Strand Consult, Huawei et ZTE disposent d’une part de marché sur la 5G de l’ordre de 59% en Allemagne, bien loin devant la France (17%).

Cette décision marque un revirement dans la position de l’Allemagne vis-à-vis des composants chinois, l’ex-chancelière Angela Merkel refusant tout boycott qui dégraderait les relations économiques entre les deux pays. La première puissance économique européenne souhaite désormais passer à une “infrastructure de télécommunications sûre et résiliente”, appuie la ministre social-démocrate Nancy Faeser, avec des “fabricants dignes de confiance”, compte tenu des “dangers de sabotage et d’espionnage”.

**L’Allemagne Prise En étau Entre Bruxelles Et Washington**

Avec cette interdiction, Berlin répond à la pression des États-Unis, qui durcissent petit à petit les sanctions contre l’empire du Milieu et ont déjà interdit la vente d’équipements de cinq fournisseurs chinois, dont ZTE et Huawei en 2019. En mai, Washington est allé encore plus loin vis-à-vis de Huawei, en mettant un terme aux licences d’exportation liant les fabricants de semi-conducteurs Qualcomm et Intel au groupe chinois.

En juin 2023, la Commission européenne avait appelé les 27 États membres de l’UE à exclure les équipements télécoms de Huawei et ZTE de leurs réseaux mobiles, considérant qu’ils représentaient “des risques matériellement plus élevés que d’autres fournisseurs de 5G”. Au Royaume-Uni, le gouvernement de Boris Johnson avait décidé en 2020 d’exclure Huawei de son réseau 5G. Il avait alors interdit l’achat de nouveaux produits fin 2020 et se donnait jusqu’en 2027 pour retirer les équipements actuels.

**Huawei Devrait être écarté Des Réseaux 5G Français En 2031**

En France, une loi de 2019 a imposé de fortes restrictions à Huawei. En cas d’installation par un opérateur d’une antenne du fabricant chinois, une autorisation doit désormais être accordée par l’Agence nationale de la sécurité des systèmes d’information (Anssi). Aucune antenne Huawei ne peut alors être installée à proximité de sites stratégiques militaires, gouvernementaux ou industriels. Les opérateurs français étaient censés abandonner toute antenne 5G Huawei d’ici 2028, mais un sursis a été accordé l’année dernière à SFR et Bouygues Telecom, qui pourront toujours utiliser des équipements chinois jusqu’en 2031.

Yoann Bourgin
17/07/2024 15:34 – OpenAI visé par une plainte pour avoir imposé des restrictions illégales à ses employés –FrankWallStreetReformAndConsumerProtectionAct
#Whistleblowing #OpenAI #ArtificialIntelligence #USSecuritiesAndExchangeCommission #ComputerSecurity #DigitalTransformation #IssuesInEthics #InformationTechnology #Computing #Technology #Safety #VulnerabilityComputing #ChatGPT #Business #Dodd–FrankWallStreetReformAndConsumerProtectionAct #Encryption #ApplicationSoftware #MediaCommunication #ComputerScience #StartupCompany #BigData #Lawyer #CompetitionEconomics #Cyberspace #DigitalMedia #NationalSecurity #MacOS

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***OPENAI VISé PAR UNE PLAINTE POUR AVOIR IMPOSé DES RESTRICTIONS ILLéGALES à SES EMPLOYéS***

Coup de chaud pour la société d'intelligence artificielle la plus en vue du globe. D'après une plainte déposée auprès de la SEC par des lanceurs d'alerte, OpenAI a forcé ses employés à signer des accords les obligeant à renoncer à leurs droits fédéraux et à une indemnisation en tant que lanceur d'alerte. Manque de pot pour l'entreprise, certains d'entre eux ont décidé de passer outre et de dénoncer des pratiques et politiques considérées comme illégales. Une enquête est ouverte.

La sécurité reste source de critiques au sein d'OpenAI, un manque d'engagement étant pointé du doigt.

La sécurité est un mot qui s'accorde mal avec OpenAI. Début juillet, des lanceurs d'alerte d'OpenAI ont déposé une plainte auprès de la Securities and Exchange Commission des États-Unis, demandant une enquête sur les accords de non-divulgation prétendument restrictifs de la start-up d'IA. Une lettre des avocats des lanceurs d'alerte destinée à la SEC rend compte des différents points évoqués précédemment dans la plainte et revient sur l'urgence d'ouvrir une enquête.

"Compte tenu des risques potentiels bien documentés posés par le déploiement irresponsable de l'IA, nous exhortons les commissaires à approuver immédiatement une enquête sur les accords de confidentialité antérieurs d'OpenAI et à examiner les efforts actuels apparemment entrepris par la société pour garantir une conformité totale avec les règles de la SEC", peut-on lire dans la lettre publiée par The Washington Post.

**Les Employés Forcés De Signer Des Accords Illégaux**

La lettre rappelle que la société aurait contraint ses employés à signer des accords les obligeant à renoncer à leurs droits fédéraux et à une indemnisation en tant que lanceur d'alerte. Les lanceurs d'alerte ont allégué que des accords d'emploi, de licenciement et de non-divulgation trop restrictifs ont été émis aux employés de la start-up, ce qui aurait pu conduire à des sanctions contre les salariés ayant fait part de leurs inquiétudes concernant OpenAI aux autorités fédérales.

OpenAI exigeait également que les employés obtiennent son consentement préalable s'ils souhaitaient divulguer des informations aux régulateurs fédéraux. Les lanceurs d'alerte ont ainsi demandé à la SEC d'infliger une amende à OpenAI pour chaque accord inapproprié conclu dans la mesure où l'organisme fédéral le juge approprié. "La SEC doit prendre des mesures rapides et énergiques pour faire appliquer la règle 21F-17(a) de la SEC dans le secteur de l'IA et pour s'assurer qu'il n'y a pas eu de violation de l'article 1513(e) du 18 U.S.C.", affirment les avocats.

Ces derniers se rapportent également à un décret qui met en garde contre le fait que "l'intelligence artificielle (IA) recèle un potentiel extraordinaire de péril" et que "l'utilisation irresponsable" de cette technologie émergente "pourrait exacerber les préjudices sociétaux tels que la fraude, la discrimination, les préjugés et la désinformation ; déplacer et priver d'autonomie les travailleurs ; étouffer la concurrence ; et poser des risques pour la sécurité nationale".

**Le Développement De L'IA Exige Des Efforts Mutuels**

Le décret conclut que la sécurité du développement de la technologie de l'IA "exige un effort de toute la société, y compris du gouvernement, du secteur privé, du monde universitaire et de la société civile". Dans ce contexte, OpenAI a sa part de responsabilité et ne peut échapper aux lois. Le sénateur Chuck Grassley, dont le bureau a indiqué que la lettre avait été fournie par les lanceurs d’alerte, a déclaré à Reuters :"Les politiques et pratiques d’OpenAI semblent avoir un effet dissuasif sur le droit des lanceurs d’alerte à s’exprimer et à recevoir une compensation appropriée pour leurs révélations protégées".

Les avocats représentant les lanceurs d'alerte rappellent que les employés sont protégés par la loi Dodd-Frank qui leur confère le droit de déposer ces rapports de manière confidentielle et anonyme directement auprès de la SEC. "Ils doivent également savoir qu'ils ne peuvent faire l'objet de représailles pour avoir effectué ces signalements et qu'ils sont potentiellement éligibles à une indemnisation si leurs signalements aboutissent à des mesures d'exécution destinées à protéger le public et les investisseurs".

**La Sécurité Manque à L'appel ?**

Rappelons par ailleurs qu'OpenAI s'est fait épinglé à plusieurs reprises ces derniers mois pour manque de transparence et de sérieux vis-à-vis des problématiques de sécurité inhérentes à la société et aux modèles d'IA développés en interne. Après le départ de plusieurs membres clés en charge de la sécurité menant à la dissolution de l'équipe Superalignment, la start-up d'IA a, fin mai, tenté de sauver les apparences. Elle a fait part de la création d'un comité de sûreté et de sécurité. Cependant, ce dernier ne brille pas par son éthique et son indépendance puisqu'il est constitué des membres du conseil d'administration de la société.

Un comité qui n'a apparemment pas réussi à prévenir plusieurs incidents relatés dans la presse. Il y a quelques jours, le New York Times a ainsi révélé qu'en début d'année dernière, OpenAI a été victime d'une intrusion au sein de ses systèmes de messagerie interne. Le hacker a dérobé aux employés des informations sur le fonctionnement des technologies de la start-up, mais n'a pas eu accès aux systèmes hébergeant les technologies d'IA.

Il ne s'agit pas du seul incident de sécurité relaté récemment. Plus tôt ce mois-ci, l'entreprise a également corrigé une faille majeure dans son l'application ChatGPT pour macOS. Un développeur avait découvert une vulnérabilité permettant à quiconque d'accéder aux conversations de tous les utilisateurs stockées sur l'ordinateur et non chiffrées. Depuis, OpenAI affirme avoir remédié à cette vulnérabilité par le biais d'une mise à jour.

Célia Séramour
17/07/2024 14:27 – “Cloud de confiance” : S3NS entre dans le processus de qualification SecNumCloud écuritéDesSystèmesD'information
#CloudComputing #ThalesGroup #DigitalTransformation #Safety #AgenceNationaleDeLaSécuritéDesSystèmesD'information #Technology #Computing #InformationTechnology #Microsoft #OrangeSA #JointVenture #ComputerSecurity #Cyberspace #Capgemini #BigData #DigitalMedia #InformationPrivacy #DataCenter #Encryption #MediaCommunication #Innovation #InternetOfThings

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***“CLOUD DE CONFIANCE” : S3NS ENTRE DANS LE PROCESSUS DE QUALIFICATION SECNUMCLOUD***

La coentreprise créée par Thales et Google Cloud avance dans son processus de qualification SecNumCloud. L'Anssi vient de lui accorder un premier jalon pour son offre “cloud de confiance”. Elle affirme que cette solution sera disponible pour les premiers clients dès décembre.

La joint-venture entre Thales et Google, S3NS, a franchi une première étape dans l'obtention de la qualification SecNumCloud de l'Anssi dans le cadre de son "cloud de confiance".

S3NS, société créée conjointement par Thales et Google en juin 2022, a annoncé que l’Agence nationale de la sécurité des systèmes d’information (Anssi) avait accepté l’entrée de son offre “cloud de confiance” dans le processus de qualification SecNumCloud. Plus précisément, la joint-venture s’est vue octroyer un jalon dit “J0” et se donne pour objectif de finaliser la qualification à l’été 2025.

Ceci permettra à S3NS de justifier d’un haut niveau d’exigence en ce qui concerne la sécurité numérique et la protection des données sensibles des entreprises et administrations publiques. Le visa de sécurité comprend des dispositions pour éviter que le traitement de ces données ne soit soumis à des lois extraterritoriales, américaines notamment, et à des tentatives de prédation étrangères.

**Une Première étape**

Ce jalon n’est que la première marche vers l’obtention de la qualification SecNumCloud pour l’offre “cloud de confiance”. Il passe par un formulaire de demande sur le site de l’Anssi, afin de valider la candidature. L’équipe d’audit devra ensuite établir une stratégie d’évaluation (jalon 1), avant que l’évaluation ne soit réalisée (jalon 2). Le rapport devra enfin être validé par l’Anssi lors d’une réunion formelle qui donnera lieu à l’octroi de la qualification, autorisant le fournisseur à afficher le visa de sécurité.

La coentreprise propose déjà depuis février 2023 une solution transitoire intitulée “Contrôles locaux avec S3NS”, qui s’appuie sur la région France de Google Cloud et rajoute un contrôle des accès par clés de chiffrement dans les data centers de Thales. Cette offre ne dispose toutefois pas de la qualification SecNumCloud. S3NS a par ailleurs confirmé que dès décembre 2024, de premiers clients dit “early adopters”, pourraient déployer des applications existantes sur le cloud de confiance, créer nativement des applications, exploiter de gros volumes de données et déployer des cas d’usage artificielle. S3NS précise qu’une trentaine de clients sont déjà concernés, comme le groupe AGPM, Club Med, la Matmut ou Thales.

**Se Démarquer De Bleu**

Avec le passage de ce premier jalon, S3NS compte prendre de l’avance sur Bleu, lancé en mai 2021 par Orange et Capgemini pour proposer les solutions de Microsoft au sein d’un “cloud de confiance”. Le mois dernier, son CEO Jean Coumaros annonçait ne pas avoir encore déposé de dossier auprès de l’Anssi, visant une qualification au début du deuxième semestre 2025.

Reste à savoir si le dossier déposé par S3NS, tout comme celui de Bleu, trouveront une place sur le marché français. L’année, dernière, l’Autorité de la concurrence avait émis un “avis de vigilance” sur ces coentreprises, en raison de la force de frappe de Google et de Microsoft sur le marché du cloud d’infrastructure.

Yoann Bourgin
17/07/2024 12:35 – Atos échappe au naufrage, sauvé par un accord définitif avec banques et créanciers
#Atos #DigitalTransformation #Bank #Business #BigData #Technology #Restructuring #Euro #Innovation #Finance #BlackRock #BondFinance #PrivateSector #Economy #Debt #InternetOfThings #BusinessModel #Computing #DigitalData #MediaCommunication

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***ATOS éCHAPPE AU NAUFRAGE, SAUVé PAR UN ACCORD DéFINITIF AVEC BANQUES ET CRéANCIERS***

L'ex-fleuron tricolore Atos sort la tête de l'eau. Le groupe est parvenu à un accord définitif avec un groupe de banques et ses créanciers pour trouver une issue à sa restructuration financière. Une opération qui lui permet de souffler avant les Jeux Olympiques, avec une procédure de sauvegarde accélérée débutant la semaine prochaine.

Le groupe informatique Atos a annoncé le 15 juillet dans un communiqué avoir trouvé un accord avec un groupe de banques et de créanciers obligataires, afin de sécuriser le financement de son plan de restructuration. Il décroche ainsi un nouveau financement de 1,675 milliard d’euros, accordé pour moitié par les banques et pour moitié par les porteurs d’obligations. L’accord comprend aussi une augmentation de capital de 233 millions d’euros et une réduction de la dette d’environ 3 milliards d’euros. Un financement intérimaire de 800 millions d’euros a également été accordé afin de financer l’activité jusqu’à la clôture du plan de restructuration. Le groupe dispose déjà de 450 millions d’euros sur ces 800 millions.

Atos précise qu’il ouvrira une procédure de sauvegarde accélérée au cours de la semaine du 22 juillet, “afin d’obtenir l’approbation du tribunal et de mettre en œuvre le plan de restructuration financière”. Jusqu’à cette date, les créanciers n’étant pas partie à l’accord peuvent se manifester. Un soulagement pour le groupe informatique, à moins de deux semaines de la cérémonie d’ouverture des Jeux Olympiques, le 26 juillet. Lors de l’événement, Atos devra assurer, entre autres, la cybersécurité des Jeux, la gestion des 30 000 accrédités et la diffusion instantanée des résultats.

L’opération intervient deux semaines après l’annonce par le groupe d’un accord “de principe” avec les créanciers obligataires, menés par AG2R, Blackrock, Boussard & Gavaudan, DE Shaw, Fidera, SPG, Syquant et Tresidor, et un groupement de banques. Le mois dernier, les négociations avaient pris fin entre le groupe et le consortium Onepoint, qui avait été sélectionné pour la reprise. David Layani, fondateur d’Onepoint et premier actionnaire d’Atos, avait été abandonné par ses principaux soutiens financiers. Pour rappel, l’accord prévoit la préservation de l’ensemble du périmètre du groupe, sous réserve de la “finalisation des discussions” avec l’État, ayant proposé 700 millions d’euros pour racheter ses activités sensibles et stratégiques, et avec Alten, qui souhaite reprendre sa filiale de système de contrôle nucléaire.

Yoann Bourgin

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