Matthieu Bret

Great post enjoyed the read! I think Data Governance is very important to AI. The quality of the AI’s decisions are tied to the quality of the data presented to the tool. However, I think Data Governance can be taken a step further to even greater enhance AI capability. One of the great limitations around AI is the that it requires integrated data to be powerful, but integrating data is expensive and time consuming, mostly do the requirements of transforming the data. However, there is an approach that leverages data standards, around master data, which Data Governance can help create, that makes datasets directly interoperable at the source. These new standardized data sets can all share data through master data commonality, even though they are de-centralized. So, in this approach, Data Governance not only improves the quality of the data for AI but can be leveraged to increase the variety of enriched data the AI has access to. Enjoyed the post and look forward to more.

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🌍 The strength of AI lies in its ability to reflect real-world experiences, and Toloka has been at the forefront of bridging technology with human insight. From large-scale data labeling to the launch of our Mindrift platform, we've continuously evolved to meet the changing demands of AI development. Our latest step? Building custom datasets with input from domain experts across fields like medicine, finance, and software development. 🔗 Learn more about how we're pushing the boundaries of AI data from our CEO Olga Megorskaya #AI #DataLabeling #Crowdsourcing #Mindrift #Toloka

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How is Generative AI significant for Product Development ? Generative AI in product development is a game-changer, but it comes with its own set of challenges. Let's explore! Key use Cases : 1️⃣ Generate Entire Products - Input a good detailed prompt, Gen AI does its magic, and generates a fully designed and coded feature product . https://lnkd.in/gguZmmpx 2️⃣ Co-Pilot Development - While development cannot be done in entirety without human intervention yet (Devin presented a stunning case but we know how the claims were exaggerated) , assisted development is on the rise. GitHub’s Co-Pilot is reportedly being actively used by 1.5ml developers across 37,000 orgs, with high levels of satisfaction and retention. Amazing feat in such a short span. 3️⃣ Power Your Product - Tech company area adding Gen Ai capabilities to their products and services, mostly using off the shelf models like GPT after finetuning. Digital Marketing , presales and Customer Service operations are already witnessing good adoption . https://lnkd.in/gK2dY6kA The Risks Involved: 1️⃣ Missing Context: Beware of generic solutions- one key reason for this is improperly formulated prompts. Prompt is the new code. 2️⃣ Hallucinations: AI's tendency to produce nonsense - There are several techniques that allow a user to remove or reduce hallucinations. This is an evolving topic and proper education is necessary 3️⃣ Costs and Risks: Trial and error can be costly - Compute and storage requirements require investment, Third Party Gen AI APIs are expensive. In such scenarios, iteration can be very expensive. Imagine having an AI co-pilot for your product development journey, guiding you through data analysis, idea generation, and even coding. But beware, like any powerful tool, Generative AI has its pitfalls. It might lead to a culture of copy-pasting solutions without critical thinking, ultimately churning out junk features at lightning speed. What are your thoughts on Generative AI's role in product development? Share your insights in the comments below and let's navigate this technological frontier together! 💬🔍 Remember, with great AI comes great responsibility! #AI #Simplilearn #ProductDevelopment #Innovation #TechTrends #FutureOfWork

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At Toloka, we’re not just connecting data contributors with AI projects—we’re shaping the future of AI architecture. By designing pipelines that merge human expertise with synthetic data, we aim to enhance and solidify AI models. Read this article from our CEO, Olga Megorskaya, on this pioneering approach. #AI #DataArchitecture #SyntheticData #LLMs #Toloka #genAI #LLMs

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📃 [New blog article] Shaping #datastrategy through a comprehensive data maturity assessment, by Amine Eddaifi, Data Consulting Manager at Artefact. In today's fast-paced world, data is the engine of innovation—but merely collecting it isn’t enough. Organizations must progress along the #datamaturity journey, transforming raw #data into actionable insights and strategic #growthopportunities. 📊 👉 Read more to discover how a #datamaturityassessment can drive long-term growth and create a data-driven culture: https://lnkd.in/gHXNACqV 🔍 In this article, Amine breaks down Artefact’s Data Maturity Assessment Framework—a holistic, customized approach to assess and improve #datamanagement practices, align with strategic goals, and chart a course toward data-driven success. Here’s what you'll learn: 🔹What is Data Maturity? From fragmented data silos to advanced ML / #AIsolutions. 🔹Why assess Data Maturity? Build a data strategy aligned with business goals. 🔹Artefact's 6-Dimension Framework: Data Vision, Governance, Technology, and more. 🔹Best Practices for Success: Including gamifying the process and setting realistic targets. Partnering with Artefact’s data experts accelerates this transformation, providing an objective roadmap to stay ahead of the competition. 🏆

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#agentic #llms #future #ai If it's not obvious, it should be.. there are other ways to collect data and process it then the internet. Direct connections to the observable real world directly to train new software constructs will be an emerging field now that's now that the simulations have gone pretty well. Rhis is powerful computing of the future. While we turn our eyes to building applications with narrow AI today, this next round of robots will be created in parallel. The LeCunn Wave will have very different operations and results that few humans will understand but many humans will gain the benefit from. #futuresthinking

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𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 introduces 𝐕𝐀𝐒𝐀, a framework for 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐥𝐢𝐟𝐞𝐥𝐢𝐤𝐞 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐟𝐚𝐜𝐞𝐬 of virtual charactors with appealing visual affective skills (VAS), given 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐬𝐭𝐚𝐭𝐢𝐜 𝐢𝐦𝐚𝐠𝐞 𝐚𝐧𝐝 𝐚 𝐬𝐩𝐞𝐞𝐜𝐡 𝐚𝐮𝐝𝐢𝐨 𝐜𝐥𝐢𝐩! #msft #genai

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As we approach 2025, generative AI is revolutionizing business operations by enhancing efficiency and customer experiences. 💡 Key trends include hyper-personalized marketing, where AI uses data insights for more targeted strategies, and intelligent automation, which streamlines processes like supply chain management and customer service to boost productivity. Conversational AI, with advances in natural language processing, is improving customer interactions, especially in support services. Multi-modal AI integrates diverse data forms for richer insights, benefiting various sectors. In healthcare, generative AI is transforming personalized treatment and drug discovery, while in cybersecurity, it enhances threat detection and data protection. Ethical AI development focuses on guidelines addressing bias, privacy, and transparency. Decentralized AI using blockchain strengthens data security, crucial for healthcare and finance. In creative fields, AI is innovating content creation, and in gaming and entertainment, it's providing immersive experiences. How do you see generative AI transforming your specific industry by 2025? Share in the comments! #GenerativeAI #AITrends #BusinessEvolution #DigitalInnovation

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Want to build a free AI-Powered project based on Google DeepMind ? 🚀 For small-scale or experimental projects, now you can get for free your API Keys of the latest Google LLMs like Gemini Pro and Gemini Flash and start your project in no time through Google AI Studio Free Tier Limits: * 15 requests per minute * 1 million tokens per minute * 1,500 requests per day

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France plays a significant role in the field of artificial intelligence A lot of French firms are driving global advancements in AI and NLP. Here are some key players: ↳ Hugging Face: Transforming NLP with their open-source library and work closely with OpenAI. ↳ Mistral AI: Specializes in advanced language models and AI technologies. ↳ Kyutai (Moshi): Innovates in natural language processing and other AI applications. ↳ Dataiku: Their platform is widely used for data science and machine learning. We also see many French experts at major companies like OpenAI (Romain Huet Head of Developer Experience) and Meta (Yann LeCun Director of AI Research) How do you see French innovation shaping the future of AI ?

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#AgenticAI systems is a fascinating area of research ! Here: https://lnkd.in/eUnXwF-Q , an interesting paper where researchers introduced Automated Design of Agentic Systems (ADAS), a new approach to AI development. ADAS aims to automatically create AI agents by inventing novel building blocks and combining them in unique ways. The paper presents Meta Agent Search, an algorithm demonstrating ADAS by using a "meta" agent to iteratively program new agents in code creating a “self-referential algorithm where AI agents invent new AI agents.” Experiments across multiple domains, including coding, science, and math, show that agents discovered through Meta Agent Search consistently outperform state-of-the-art hand-designed agents. Notably, these agents demonstrate strong performance even when transferred across different domains and models, highlighting their robustness and generality. Sofiane Schaack, PhD Philippe Cordier Pierre Demeulemeester Matthieu CIRELLI Hao LI Florian Arthur Arnaud Gelas, Ph.D.

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The AI Data Mobility Challenge: Limitations and Opportunities - a blog by Tobias Hooton CEO Stelia As we enter the era of AI it is essential for #innovation leaders to understand the difference between #data movement and data mobility. You could say the chess board has been kicked over and new games with new rules are being created. 🎯 “We believe data represents the strongest long-term competitive moat in the #AI arms race," Fred Havemeyer, Macquarie Group, wrote in a July 20 client note. 🚨 Data movement refers to the simple act of transferring data from one location to another. This is how the Internet operates today. 💡 Data mobility, on the other hand, encompasses the ability to move, access, and utilize huge data flows across different platforms in realtime and on demand. 🏁 This is how private AI networks of the future will operate, led by forward-thinking ecosystems of builders and developers such as NVIDIA, Intel Corporation, VAST Data, WEKA, AMD and DDN Storage to name a few examples. #artificialintelligence, #machinelearning, Edge #Computing, #Healthcare, #Robotics are some industry sectors with data mobility needs. For example, real-time patient monitoring needs native data mobility across multiple environments, to offer life saving proactive care. (many more examples included in blog post) 🎯 As Krishna Subramanian, COO & Co-founder of Komprise, states, "Unstructured data is expensive to store, protect and manage due to its sheer volume and pace of growth." However, as AI applications become more sophisticated and data-intensive, companies now face 4 challenges: 1️⃣ Data Deluge: AI workloads require moving, processing and analysing massive volumes of data, increasingly in real-time. 2️⃣ Distributed Data: AI data is used across geographically dispersed locations, from edge devices to #cloud #datacentres and GPUaaS providers. 3️⃣ Integration Complexity: seamless integration with existing #IT systems and infrastructure. 4️⃣ Scalability and Flexibility: beyond simple AI hashtag #productivity, businesses need to scale their data mobility capabilities up and down for changing requirements. Aging #infrastructure is ill-equipped to address #digitaltransformation. As datasets trend towards petabytes, the demands of realtime processing for IoT and 6G, and endpoints and processing platforms proliferate, the need for an entirely new data mobility topology is coming rapidly into focus. To seize AI opportunities, organisations will demand advanced code-driven data orchestration platforms for the complexities of distributed #bigdata management. 👉 For the full list of data mobility use cases per industry and the complete blog post by Tobias Hooton https://lnkd.in/duBqMyPT A future article will discuss how Stelia addresses the data mobility challenge in the era of AI. In the meantime, follow us for more content and news like this.

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🤖 Is your data AI-ready? If the issues explained in this carousel sound familiar, join Laurent Dresse ☁ for our upcoming webinar to learn more about how a data catalog can help you optimize your AI data governance. Register here: https://lnkd.in/d8Dp_s4H #webinar #ai #datacatalog #datagovernance

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Thrilled to announce the launch of IBM watsonx.ai Text Extract ! Unlock content from your enterprise complex documents and make it amenable for RAG and other GenAI solutions 🚀 📚 Supports popular document formats .pdf (programmatic, scanned and hybrid), and images: .bmp, .gif, .jpg, .png, .tiff (single-page and multi-page) and exports to JSON and Markdown 📝 🌎 OCR for scanned documents and images with machine print in many languages and handwriting 🖋️ in English 📑 Advanced PDF document understanding, including reading order, page layout & table structures 🔍 Preserves positional information of extracted content, for downstream visual representation of the document with text highlighting 🔗 Available as REST API and Python SDK Try it out now 🚀 - Documentation: https://lnkd.in/gQmCMcZZ - Examples: https://lnkd.in/gf84v6ky #IBM #AI #GenAI

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"Join us at #Artefact Data and AI Summit in Riyadh on October 8th!" 🚀 Data and AI summits serve as vital platforms for professionals, researchers, and industry leaders to converge, share knowledge, and explore the latest advancements in these transformative fields. Looking forward to learning from thought leaders, networking with like-minded professionals, and discovering innovative solutions. Let's connect if you are at the summit. #Data #AI #Summit #Riyadh #Artefact #Technology #Innovation

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𝐈𝐬 OpenAI'𝐬 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐩𝐚𝐭𝐡 𝐭𝐨 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞? OpenAI has shared a five-level step path towards AGI. According to Bloomberg, there are the five levels defined on that path: 1: 𝐂𝐡𝐚𝐭𝐛𝐨𝐭𝐬, Al with conversational language 2: 𝐑𝐞𝐚𝐬𝐨𝐧𝐞𝐫𝐬, human-level problem solving 3: 𝐀𝐠𝐞𝐧𝐭𝐬, systems that can take actions 4: 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐨𝐫𝐬, Al that can aid in invention 5: 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬, Al that can do the work of an organization I found this approach to have several limitations at this level of detail, including the following ones: 1. It does not reflect on the 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬 we might need to achieve those different levels, as if incremental improvements would be enough to achieve all of them. 2. Potential 𝐨𝐯𝐞𝐫𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐨𝐧 achieving AGI through the combination of 𝐧𝐚𝐫𝐫𝐨𝐰 𝐀𝐈 solutions. 3. 𝐎𝐯𝐞𝐫𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 of the complexity of replicating the full spectrum 𝐨𝐟 𝐡𝐮𝐦𝐚𝐧 𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬. Specially on step 5, as the work of an organization is not just a set of human to machine or machine to machine interactions. 4. Oversight of 𝐞𝐭𝐡𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬. 5. 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬. What level of resources would be needed to achieve any of those steps at scale? Is it something we can afford (even from a pure business case perspective? Let me share a short video of Bloomberg's journalist Rachel Metz briefly discussing this topic. 🔗 Full article: https://lnkd.in/dHqhVsag #ai #agi #openai https://lnkd.in/dqSPQfCR

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Check out this Drumeo video of 66SAMUS for some unconventional and extraordinary examples of - #DataObservability #DataInterpretation #DataTranslation #DataInference #DataLiteracy I was amazed and captivated by the performance, which in summary: plays a well know song to an exceptional drummer who has not previously heard it, after removing the drum track, to let the drummer create their own in live play along. Then allowing them to hear the original to see how their interpretation differed. Drumeo have several videos available on their profile, all with amazing results. What other Data Parallels can you see in this unconventional example? https://lnkd.in/ehjJi5sk

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