Recent Developments in Applied AI and Machine Learning

Last February, a Stanford paper unveiled the potential of ChatGPT-driven human-like agents, sparking excitement about the concept of autonomous agents - AI agents that can plan and execute tasks with minimal human intervention. However, despite the excitement, today's agents are more hype than substance. OpenAI's latest GPTs, compact versions of ChatGPT, might bridge the gap between hype and reality. While not yet achieving full autonomy, GPTs impress with their code creation and execution skills, along with seamless integration with diverse services. Together with GPTs, recent developments like expanding context windows and decreasing LLM costs could be the catalysts needed for significant change in the field. Obstacles previously daunting for agents are now being addressed: * Small context windows - transitioning from a 4k token window to 128k with GPT-4 Turbo, we're seeing a significant expansion in the information an agent can access to complete a task. * Expensive API costs - agents heavily rely on LLMs like GPT-4 to plan and execute tasks. The recent trend of proprietary language models decreasing costs such as with Claude and GPT makes agent-driven tasks more financially viable. * Imature agentic frameworks and tooling - since the release of AutoGPT last March, a host of breakthrough papers and GitHub repositories emerged (AI Town, AgentBench, Voyager), simplifying the evaluation and construction of more capable agents significantly. * Sending sensitive data to external APIs such as OpenAI was a barrier for many individuals and companies. Now, open-source models like Mistral and Yi address privacy issues by allowing local or private cloud execution. * Agents lacked the multimodal understanding humans have and use to make decisions - progress in models such as GPT-4 Vision and Whisper empowers agents with the ability to interpret more than just text, including visuals and audio. With OpenAI’s development of GPTs and recent improvements, we might be on the cusp of a breakthrough in autonomous agents. These agents could transform how we approach both professional and personal tasks. Read the blog post for a deep dive into why now could be the moment for autonomous agents to shine https://lnkd.in/gNsKaeA4 Bonus: the post lists the most useful GitHub repositories for builders in the autonomous agents space.

Quantum-inspired machine learning will continue to play a significant role improving Artificial Intelligence performance. This is primarily due to myriad available applications utilizing efficient tensor network approximations that can be applied to complex or quantum systems. [01] Tensor networks, alongside active areas of dequantized algorithms and quantum variational algorithms represent effective 'QiML 2.0' software run on traditional computers. [02-04] In addition, quantum-inspired analogues will likely be further extended akin to Physics-Informed Machine Learning to 'assist machine learning tasks, representation of physical prior, and methods for incorporating physical prior.' [05] Recent ground breaking literature shown below has elevated tensor networks from efficient research tools to now increasing the pace of AI across disciplines. A) Tensor network/neural network hybrid performed better than standalone tensor networks or neural networks by NSF, MIT, and Harvard researchers. [06] B) More explainable and controllable compression of a Generative AI LLM to a fraction of its size by Multiverse Computing. [07] C) Researchers outperformed a leading quantum computer experiment in speed, precision, and accuracy - with scaling now corresponding to an infinite number of quantum bits on traditional hardware by Flatiron Institute, NYU. [08] Leading software platforms ITensor on C++ and Julia, and TeNPy on Python have been featured in a number of 2024 papers, and both maintain discussion forums to assist with tensor network developments. [09-12] In summary, High dimensional data in AI can now be distributed across tensor networks in more informed ways due to recent literature advancements and software library improvements. References [01] Tensor networks: https://lnkd.in/gipfeK_q [02] Ewin Tang: https://lnkd.in/gfgNSfKY [03] VQA: https://lnkd.in/gitb6TSq [04] QiML survey: https://lnkd.in/g97vr3_r [05] Physics-Informed ML: https://lnkd.in/gffmUFSx [06] NSF, MIT, Harvard: https://lnkd.in/gNkXEUtW [07] Multiverse: https://lnkd.in/gjNsqWJu [08] Flatiron, NYU: https://lnkd.in/gZmyJckE   [09] ITensor: https://lnkd.in/gXJWFNCU  [10] TeNPy: https://lnkd.in/g3Ciyyxs [11] ITensor: https://lnkd.in/gwhAp4BE [12] TeNPy: https://lnkd.in/gU5ceMFd

It's December! And I was just reminiscing about all the things that happened in / defined AI in 2023, putting together a short list of keywords that were top of my mind (in no particular order). 1) LLM efficiency & adapter methods: One of the biggest research threads has been to make LLMs more efficient through various method optimizations (e.g., FlashAttention) and adapter methods (e.g., LoRA, QLoRA) and so on (probably motivated by budget, compute, and time constraints). It's one of the most exciting and refreshing developments for us practitioners. 2) A push for open source: After ChatGPT made a big impact about 1 year ago, and some of the bigger companies are making their research and models (increasingly) private, we've seen much revitalizing activity around open source. To name a few examples:  - Llama 2 (still the best base model, in my opinion) - GPT4All (a nice UI to run LLMs locally) - Lit-GPT (a repo to finetuning and use various LLMs; disclaimer: I'm involved as a contributor - LlamaIndex (a toolkit for retrieval augmented generation with LLMs) - LangChain (the popular LLM API) 3) Big tech companies roll their own LLMs: Kickstarted by ChatGPT's success, every major company seems to be developing their own in-house LLM now, including Google's Bard, xAI's Grok, and Amazon's Q. 4) RLHF & DPO Finetuning: I mentioned efficiency methods for finetuning (like LoRA) above. Another trend is towards better instruction-following. We are slowly moving from supervised finetuning to reinforcement learning with human feedback (RLHF), or rather a simpler alternative: direct preference optimization (DPO). 5) Retrieval augmented generation (RAG): Many businesses are still wary of implementing pure LLM solutions. RAG solutions let them connect LLMs to existing data or knowledge bases, which may be the better option to feed LLMs new data (for now) due to reduced error, scalability, cost etc. 6) AI regulation & copyright: These are still hot, important, and largely unresolved topics. Japan had a statement this summer saying Japan's copyright laws cannot be enforced on materials and works used in datasets to train AI systems. In the US, there is no similar statement as far as I know. However, US President Biden recently issued an executive order on AI regarding the safety and security of large AI systems. 7) From text-to-image to text-to-video: 2022 was the year of text-to-image diffusion models like DALL-E 2 and Stable Diffusion. 2023 was the year of LLMs. Text-to-image models never truly went away but continued to improve. It was more likely that everyone's attention (no pun intended) was largely on LLMs. Diffusion models recently had quite the comeback, though, with the latest releases of text-to-video tools like Stable Video Diffusion or Pika 1.0. Also, so much happened on the research front! I'm excited about sitting down and compiling a list & recommendations of my favorite research papers in 2023 in a few weeks! #llms #AI #deeplearning

Your AI Will See You Now: Unveiling the Visual Capabilities of Large Language Models The frontier of AI is expanding with major advancements in vision capabilities across Large Language Models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude. These developments are transforming how AI interacts with the world, combining the power of language with the nuance of vision. Key Highlights: • #ChatGPTVision: OpenAI’s GPT-4V introduces image processing, expanding AI’s utility from textual to visual understanding. • #GeminiAI: Google’s Gemini leverages multimodal integration, enhancing conversational abilities with visual data. • #ClaudeAI: Anthropic’s Claude incorporates advanced visual processing to deliver context-rich interactions. Why It Matters: Integrating visual capabilities allows #AI to perform more complex tasks, revolutionizing interactions across various sectors: • #Robots and Automation: Robots will utilize the vision part of multimodality to navigate and interact more effectively in environments from manufacturing floors to household settings. • #Security and Identification: At airports, AI-enhanced systems can scan your face as an ID, matching your image against government databases for enhanced security and streamlined processing. • #Healthcare Applications: In healthcare, visual AI can analyze medical imagery more accurately, aiding in early diagnosis and tailored treatment plans. These advancements signify a monumental leap towards more intuitive, secure, and efficient AI applications, making everyday tasks easier and safer. Engage with Us: As we continue to push AI boundaries, your insights and contributions are invaluable. Join us in shaping the future of multimodal AI. #AIRevolution #VisualAI #TechInnovation #FutureOfAI #DrGPT 🔗 Connect with me for more insights and updates on the latest trends in AI and healthcare. 🔄 Feel free to share this post and help spread the word about the transformative power of visual AI!

Since the debut of OpenAI’s GPT-4 over a year ago, AI and machine learning have become a source of both anxiety and excitement over their potential to transform our lives. In the life sciences, these technologies have proven applications across the innovation cycle, from discovery through to post-market monitoring. In fact, recently, Insilico Medicine announced the development of an experimental drug for idiopathic pulmonary fibrosis, an as yet incurable disease. The drug is a first-of-its-kind pre-trial candidate based entirely on AI. Terence Flynn, Head of Biopharmaceutical Research for the US from Morgan Stanley, discussed the use of AI, noting: “Each 2.5% improvement in the success rate for pre-clinical development could lead to more than 30 new drug approvals in the next 10 years and doubling this figure would result in 60 new treatments being approved, adding another US$ 70 billion in value to the biopharmaceutical industry.” AI is more than a competitive advantage. It is a catalyst for the development of solutions that can address the most challenging health issues affecting patients around the globe and is poised to play a critical role in enabling the healthcare industry to fulfill its promise of improving patient lives. A lot more to be done and to be figured out, but I chose to be optimistic and am excited to work on and see what others can do to accelerate treatments to patients #Clarivate #Pharma #Healthcare #Innovation #Technology #AI #ClinicalTrials