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In most companies it is unacceptable that internal data leaves the private cloud/on-premise environment for generative AI use cases and harness public model APIs due to a range of risks: Leakage of IP, Risk of PII data loss, copyright violations, legal risks via malicious use of the AI service by bad actors, …   Simultaneously, tuning and custom deploying GenAI services in a cost-effective manner, meeting use case latency requirements, and scaling to handle a large number of requests is a significant challenge. And that's only by achieving this that companies can unlock lasting business with a substantial ROI.   In our webinar “Building GenAI Products with Sensitive Data: A Production-ready Approach”, together with my colleagues Michael Brunzel and Arnaud Cartuyvels from CBTW, we will comprehensively outline our practically proven approach to bringing generative AI products from ideas to life. In case you are interested, do not miss out on registering for the Webinar on June 27th, 2024 – at 9:30 AM (CEST). https://hubs.la/Q02zk3q-0 #GenAI #LLM #NLP #AI

AI21 Labs alongside Pinecone, Vercel, Anyscale, and LangChain build technology that together makes up the new AI stack. As a collective, we have worked with thousands of companies that have transformed their businesses with AI and we know what they need to succeed. Read on to learn what infrastructure elements will enable your business to take advantage of AI innovation and how you can master the new AI stack. 

OpenShift AI helps customers solve some of their challenges by operationalizing both traditional and generative AI projects

As business leaders race to integrate AI, it's tempting to chase the latest shiny models. But real ROI comes from building a sustainable AI advantage. How? Invest in a robust data and MLOps foundations. Clean, structured data is the basis of performant AI. Well-architected ML pipelines allow rapid experimentation and deployment. Without these, you're building castles on sand. #AIinBusiness #MLFoundations

In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. 

There are concerns that AI scaling has hit a wall. I don’t think there is a reason to panic. Here’s the backdrop: Ilya Sutskever, OpenAI’s co-founder, recently suggested that AI scaling may have plateaued. Rumors are also circulating that OpenAI’s new model - codename Orion - offers only minor improvements over GPT-4. Should we really be worried? I’d say there’s little reason to panic. To understand where we stand, it helps to look at the concept of scale. Scaling laws suggest that larger models, with more parameters and more data, generally become more capable. Training these models requires immense computing power, measured in FLOPs. Higher FLOPs usually mean that models handle complex tasks better, score higher on benchmarks, and appear smarter. However, headlines about a plateau are a distraction. For now, it’s all speculation, and there’s little concrete evidence to suggest a true ceiling has been reached. The real question isn’t whether AI scaling has reached its limit but whether a potential slowing might actually be an unexpected advantage. Here’s why: First, progress in AI isn’t just about releasing the next big model, it’s about making the most of what we have today. If today’s models did nothing new, they’d still be transformative for countless industries. Today’s AI models are already multimodal. They can structure unstructured data, summarize information, write code, operate computers, browse the internet, translate documents, recognize and describe images, generate art, transcribe audio, compose music, answer complex questions, assist in research, and simulate real-world scenarios. Second, the benefits of AI applications largely depend on how well you can implement and adapt them to your specific needs. If we’d take time to properly integrate today’s capabilities, AI would perform a diverse range of tasks across your operations seamlessly. But more importantly, a slower pace of scaling gives decision-makers and founders a chance to build strategically, rather than racing to keep up with every upgrade. A slower approach would allow everyone to thoughtfully integrate AI and focus more on impact rather than novelty. The conversation shouldn’t be about pushing AI to scale endlessly but rather about using what’s already available. #AIscale #GenAI #frontiermodels

I recently spoke at the SagePath Artificial Intelligence Summit. I covered enterprise adoption, use-cases and more. 3 predictions coming out of some great conversation: ▪️You HAVE to start with the customer and work your way backwards. Too many companies are trying to adopt the technology without asking the question “Does this use-case necessitate AI?” ▪️Every enterprise whose core business is not AI will lean into off-the-shelf solutions. The resource cost from managing massive datasets and model architecture is too high with no signs of declining. The big players like OpenAI and Anthropic are offering great commercial API partnerships that reduce the incentive to build in-house. ▪️The space is saturated with large language models and generative adversarial networks. Each chatbot and creative generator that enters the market has diminishing value because there’s little differentiation. *** Big thanks to the folks at Sagepath Reply for having me. What direction do you see AI heading in? ⤵️

Explore the parallels between the internet boom and the AI revolution, featuring stories of technological evolution and the future of generative AI.

It is possible to significantly reduce the energy consumption of AI workloads without compromising from the performance and accuracy of these models. In this blog post, I lay out the best practices to achieve this. To read:

LLM , SLMs, LFMs, what's next ? The pace of AI progress is so fast :-) . It's like trying to watch a race where new runners keep joining halfway through ! Every week there's a new breakthrough, model, Apps, product ..and it feels like the moment you blink, something else has been announced. It's an exciting time for anyone in technology or business on how transformative Gen AI is becoming when moving from cloud to cloud&AI era ! In my perspective the speed of advancements and announcements in AI is unprecedented. This rapid pace is reshaping industries across all sectors.. It feels like every week there's something groundbreaking, reflecting not only the innovation from tech companies but also the growing demand for AI solutions across various sectors despite we are still in early stage.. The pace of announcements around Generative AI technology has been incredibly fast. I believe this rapid speed comes from a few key reasons: 1. Hype and Public Interest: There's also a lot of excitement around AI from both consumers and the business sector. Companies often announce early to generate buzz and attract interest, even if the tech is still evolving... 2. Fierce Competition: Major tech companies like OpenAI, Google, Microsoft, Amazon, Anthropic and Meta are racing to be the leaders in AI. Because of this, they are constantly releasing new updates, features improvements and partnershipsto gain a competitive edge in Cloud&AI Era 3. Advancements in AI Models: AI research is evolving quickly, and breakthroughs like new architectures (e.g., transformers, LLMs, LFMs,..) and improved training methods are happening often. Each time there's a new discovery, companies rush to implement and announce it. 4. Market Demand: As more businesses realize the potential of AI for productivity and cost savings, there's an increasing demand for GenAI solutions. AI companies and hyperscaler's are trying to release tools faster to meet this demand and ensure a competitive advantage. 5. Significant Market opportunity size arround AI value chain from infrastructure all the way up to application (Millions - Trillions) .. Recent big announcements ( 1) Meta's AI integration in WhatsApp/messenger/Instagram 2) Microsoft Low-code/no-code copilot/Agent development 3) Anthropic computer use with Claude 3.5 Sonnet, and Claude 3.5 Haiku 4) Meta's Llama 3.1 model Our most capable models ...) highlight how companies are not only improving their models but also focusing on practical applications that can bring solutions to real-world business problems and removing technology barriers for faster and simpler adoption and effective AI (ROI focus).. AI is very complex with multi layers technologies. it requires holistic view despite technology uncertainty.. Do the right things is important but more importantly is to do things right to deliver AI promises.. it’s exciting to see how this will continue to evolve and what new capabilities will emerge for On-device model ..