Dr. Juan Bernabe Moreno

Emmet Dowling, Technology Consulting Partner and Head of Data and Artificial Intelligence, EY Ireland, discusses the many ways that AI can be used to power new discoveries through research. Find out more from Emmet in a special AI report in The Irish Times. Read more here - https://lnkd.in/eUqtX-42 #AI

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I recently had the pleasure to speak with Leonardo Camiciotti, Executive Director at TOP-IX Consortium, and a dear friend from a distant and glorious past at Philips Research. We discussed the exciting potential of Edge AI and the future of AI deployment. Key Takeaways: 💡 Distributed inference at the edge: Processing AI models locally reduces data transfer costs and latency, making real-time decisions more efficient. 💡 Commoditization of AI compute: As AI compute becomes more accessible, balancing central processing with edge deployments will be key for scalability. 💡 The GenAI token stream economy: Leonardo and I discussed a future where synthetic generated speech, text, and data could become valuable, enabling a decentralized digital content economy as utility streams. These themes are especially valuable in many sovereign contexts, including Neom and Saudi Arabia, where AI model locality and efficient serving of AI applications are key aspects for cognitive cities like Neom. The future of AI at the edge is promising, with concrete opportunities on the horizon. #EdgeAI #GenAI #AIInnovation #DigitalEconomy #CognitiveCities

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🚀🏥 We are at the cusp of a transformative era in healthcare! At the #LondonBiotechnology Show 2024, Microsoft's #ThomasBalkizas unveiled how AI is poised to revolutionise the sector. From accelerating clinical trials to devising personalised medicine, AI is transforming from a helpful tool to a game-changer in healthcare. Balkizas emphasised the profound impact of Generative AI, enhancing everything from routine administrative tasks to complex clinical processes. This "tipping point" marks the dawn of a new era where AI becomes an integral part of healthcare innovation.🔗 #AI #HealthcareInnovation #Microsoft #Biotechnology #GenerativeAI #PersonalisedMedicine

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AI is better than humans when the rules are defined; which is why they are not that great in the Real World. It can play chess but can it borrow intelligence from other contexts (that we are supposedly better at) #AI #GenAI https://lnkd.in/gDXwzAZ9

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I took Meta’s brilliant paper, and asked Anthropic’s Claude 3.5 Sonnet to write a provocative article summarising it from the perspective of what this many mean for closed foundation models. Read below: Although, safety and alignment wise … it’s pretty easy to finetune these things if you know what you’re doing to turn them into subversive comedic monsters 😉🤔😂 Not that I would do such a thing. No sir! (full paper @ https://lnkd.in/d2qs6DPn ) —— Your Move, Sam Altman: Meta's Llama 3.1 Throws Down the Gauntlet In a bold move that has sent shockwaves through the AI community, Meta has unveiled Llama 3.1, a family of LLMs that not only rival the performance of OpenAI's vaunted GPT-4 but do so with a twist: they're giving it away for free. That's right Sam, the game has changed. While OpenAI has been guarding its golden goose behind an API paywall, Meta has decided to release not 1, not 2, but 3 powerhouse models into the wild. We're talking 8B, 70B, and a whopping 405B parameters of open-source AI goodness. It's like they're handing out Lamborghinis at a go-kart race. But it's not just about size (though let's face it, 405 billion parameters is nothing to sneeze at). Llama 3.1 is flexing on multiple fronts: Performance: The 405B model is going toe-to-toe with GPT-4 and Claude 3.5 Sonnet across a smorgasbord of tasks. It's like watching David take on Goliath, except David is also a giant and he's livestreaming the whole thing. Efficiency: While OpenAI's models are busy sipping mai tais in their cushy cloud servers, Llama 3.1’s 8B model can run on a toaster. Okay, maybe not a toaster, but certainly on your average laptop. Democratizing AI? More like starting an AI revolution. Transparency: Meta's not just showing their cards; they're explaining the whole trick. Training process? Here you go. Data curation? Have at it. Evaluation methods? It's all yours. It's so open, it makes OpenAI look like Fort Knox. Safety: They've got Llama Guard watching the gates. It's like they've hired a bouncer for their AI, but instead of keeping people out, it's keeping the AI in check. Multimodal Potential: Vision and speech capabilities are waiting in the wings. It's like Meta's saying, "Oh, you thought language was impressive? Hold my beer." So, what's the catch? There isn't one. Meta's basically dropped a nuke of openness on the AI landscape, and the fallout is going to reshape everything. To Sam Altman and the rest of the closed-source crew, the message is clear: the era of AI gatekeeping is over. Meta's not just inviting everyone to the party; they're giving away the DJ booth, the dance floor, and the whole damn club. In conclusion, Llama 3.1 isn't just a model; it's a manifesto. It's Meta saying, "We're not just in the game; we're changing it." So, Sam, the ball's in your court. Are you going to open up, or are you going to be left behind in a world where state-of-the-art AI is as open as a 24-hour diner? Your move, Sam. Your move.

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Recently, I had the pleasure of sitting down with Dr. Antigoni Chrysostomou to discuss the Rescale partnership with Microsoft and the evolution of R&D in industry. It was a really interesting and exciting discussion, covering many great areas of focus, including the confluence of traditional simulation and AI to create greater innovation velocity and efficiency. Looking at Microsoft's approach to its Industrial Metaverse, we examined where Digital Twins, Simulations and AI all merge to create a vastly different future for innovation. 🔗 https://lnkd.in/eAS8JmJJ

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Our episode on the The Data Exchange Podcast with our fantastic host Ben Lorica 罗瑞卡 , and featuring my great colleague Hima Patel , on data preparation for LLM applications (RAG, fine-tuning, instruct-tuning, and pre-training) using the Data Prep Kit is out! Data Prep Kit is the open-source (Apache 2.0 license) framework and transforms for language and code, which we used in IBM Research to prepare the data for the training of #IBM 's #Granite models ( https://lnkd.in/eUvZVK2t ). Running at any-scale, from a laptop to a datacenter and handling from a few pages of text to hundreds of billions of documents, it is available in pure-#Python , #Ray , #Spark and with #KubeFlow pipelines. Feel free to check it out here: https://lnkd.in/ehGjpbzG #IBMResearch #AI #GenAI #LLM

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Can We Trust Our Genetic Data with Giant Genomics Companies? After 23andMe’s data breach last year and consequent collapse (its value dropping by more than 99%), more and more questions are being asked about the levels of security in the genetics industry. It’s clear that trust and security are critical for biotech’s future in data protection. Interesting read on this topic, I'd check this article out!

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⚡️ Humbled about my Podcast interview by Transatlantic AI eXchange, hosted at Stanford Institute for Human-Centered Artificial Intelligence (HAI) 🎙 We discussed how spatial AI or Spatial intelligence guides machines to understand, perceive, reason, and act in 3D space and time. Some key points about our spatial AI chat: 1. How does AI transform UGC into immersive experiences? 2. What role does collaboration play across the Physical-Digital world and how does AI facilitate this? 3. How do Large Experience Model (LEM) work? 🎧 Listen now on Spotify, YouTube, or TAIx's website: Spotify: https://lnkd.in/e_Rw7uV4 YouTube: https://lnkd.in/e2C7vjJg Podcast Page: https://lnkd.in/es_Vuy2c Special thanks to Thomas Neubert 🙏🏼 #spatialai #spatialintelligence #phygital Phygtl, Inc

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🧠 The myth: More data makes an #AI model smarter and therefore more accurate 💪 The truth: It’s not just about data—it’s about #parameters With the around OpenAI’s #GPT4o it might seem that feeding more data into these models makes them smarter, the real game-changer is the number of parameters they have like Meta #Llama 3.1 with 450b parameters 💡 More parameters mean these models can process and understand data in more sophisticated ways, leading to increased accuracy and the appearance of being “smarter.” More parameters = higher trustworthiness, even with more focused data like the proprietary #data in your organization In the world of conversational user interfaces becoming the norm, more parameters will be crucial for a seamless experience. While yet too slow and too expensive for real fast interactions between humans and machines, with #quantumcomputing we will move in a new area of responsible #UI #design #AI #MachineLearning #LLM #conversationaldesign #hmi #Innovation

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When thinking about scaling AI, how many challenges come to mind? We’ve written about five key issues. QuantumBlack Labs have developed solutions for each challenge to ensure AI/ML models scale into production for long-term impact. Discover the five challenges of scaling AI and the solutions we’ve developed for each, as part of our QuantumBlack Horizon suite of development tools! 👇 https://lnkd.in/gszpnc7J #AIbyMcKinsey #ArtificialIntelligence #QuantumBlack

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Thanks Ashish Patel 🇮🇳 for the below ❓ What Is the Technical Debt of Large Language Models (hashtag #LLMs) and How Does It Affect Us❓ As Large Language Models (LLMs) become central to AI, understanding their technical debt is crucial. This debt impacts maintainability, scalability, and effectiveness, posing significant challenges. Efficient management is vital for sustainable AI development and deployment. Key areas of technical debt associated with LLMs: 1. Model Complexity: 🔸 Large Parameter Count: Billions of parameters make training and deployment complex and costly. 🔸 Inference Costs: High computational requirements for inference lead to increased operational costs and latency issues. 2. Data Management: 🔸 Data Quality: Poor quality or biased training data affect performance and introduce biases. 🔸 Data Versioning: Managing different dataset versions is challenging, impacting reproducibility. 3. Training and Fine-Tuning: 🔸 Resource Intensity: Training LLMs consumes substantial computational resources, leading to high costs and environmental impact. 🔸 Hyperparameter Tuning: This process is resource-intensive and time-consuming. 4. Deployment and Scalability: 🔸 Deployment Complexity: Requires robust, scalable infrastructure for efficient production deployment. 🔸 Serving Infrastructure: Ensuring low-latency, high-availability serving of LLMs needs advanced setups like distributed systems. 5. Model Maintenance: 🔸 Continuous Monitoring: Detecting issues like model drift and performance degradation requires constant vigilance. 🔸 Updating Models: Regular updates or retraining with new data are complex and resource-intensive. 6. Security and Compliance: 🔸 Data Privacy: Compliance with data privacy regulations and preventing data leaks is crucial. 🔸 Adversarial Attacks: Ensuring robustness against attacks is an ongoing challenge. 7. Interpretability and Explainability: 🔸 Opaque Decision-Making: LLMs are often "black boxes," making their predictions hard to explain. 🔸 Regulatory Requirements: Providing explanations for model decisions can be difficult but necessary for some applications. 8. Ethical Considerations: 🔸 Bias and Fairness: Addressing biases and ensuring fairness for all user groups is critical. 🔸 Responsible AI: Preventing misuse or harmful applications of LLMs adds to the technical debt. 9. Legacy Code and Dependencies: 🔸 Outdated Libraries: Reliance on outdated libraries can lead to technical debt. 🔸 Compatibility Issues: Ensuring compatibility with evolving systems and tools is challenging. 10. in the comment 👇 Addressing these areas of technical debt requires a proactive approach: regular reviews, testing, monitoring, and training. This ensures the long-term sustainability and success of LLM projects.

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If you want to preprocess data to train llm’s, consider using the data-prep-kit!

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AI Reading for Saturday June 15! https://lnkd.in/e_aTbDaS In EU, Meta halts plans to train AI on all your Facebook and Instagram data - Ars Technica The Big AI Question: Are You Ready to Pay for It? - WSJ Apple execs mock AI PCs, Microsoft Recall. - PCMAG Software was eating the world. Could AI now eat software? - Markets Insider OpenAI considers restructuring as a for-profit benefit corporation. - The Information Mustafa Suleyman is reviewing OpenAI code, which is a bit awkward. - Semafor Nvidia released a big open-source model for synthetic data generation - VentureBeat Scale.ai gig workers allege non-payment. - Inc. In Turing test, GPT-4 judged human 54% of the time vs. 67% for actual live humans. - BGR A simple CAPTCHA that eludes AI. To be fair, a trained or fine-tuned algo that detects objects and passes them to a counter would usually get it. - Mind Matters The one where Elon Musk asked Geoffrey Hinton to join an xAI advisory board - Observer Martin Luther King rises to endorse MAGA congressional hopeful. - The New Republic The AI, crypto, and Trump scams converge in a singularity of stupid. - Finbold Call centers get 'emotion-canceling' AI filters to translate angry callers. - NewsBytes Musk's secret plan to make Tesla worth $30T - The Onion Memes brought to life by Luma - Reddit

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