AI topics’ Post

Chat GPT-5 will Change Everything

Generative AI is about to take another leap, but the following quote towards the end of this short interview resonates most with me: “One thing I generally believe is that it’s sometimes useful to zoom out and look at the entire map, and I think this is true for like a technical problem, I think this is true for like innovating in business, but things come together in surprising ways and having and understanding of that whole picture even if most of the time you are operating in the weeds in one area pays off with surprising insights” This perspective is a powerful reminder of the importance of holistic understanding in the face of relentless specialization. It encourages us not only to delve deep into our specific fields of interest but also to occasionally step back and appreciate the broader landscape. This approach not only enhances our appreciation of how interconnected and interdependent the facets of innovation are but also reveals unexpected avenues for breakthroughs and advancements. As we stand on the brink of another monumental leap in generative AI, let us carry this wisdom forward. Let us strive to maintain a balance between the depth of our expertise and the breadth of our curiosity. For it is in this balance that the most profound insights and the most transformative innovations are born.

My latest AI Research Roundup covers LLM Reasoning: Rest-MCTS for LLM self-training, LLM monkeys scale inference compute, REAP for LLM problem solving, enhanced LLM agent decision-making with Q-value models, diagram of thought. https://lnkd.in/gs3bayyG

Ainsider Ai Newsletter vol.36 is live ⚡️ Inside: ✔️ OpenAI released the ‘o1’ models ✔️ Adobe released own Video Model ✔️ Notebook LM from Google is insane for research and learning ✔️ Last AI Tools added to Library Explore: https://lnkd.in/dhA_NrGH #ai #technology #artificialintelligence

This series of articles by Leopold Aschenbrenner represents a deep view of the next decade and the AI megashift. https://lnkd.in/g3bT9yHn

Was reminded today of an article in a fascinating talk about the future by Prokar Dasgupta OBE at #EAU24 We have work to do with AI tools in terms of standards, and ensuring we are developing tools that truly benefit the community https://lnkd.in/e-gKwavb

TWAI - This Weeks AI Insights LoRA: The Efficient Fine-Tuning for LLMs You Should Know LoRA uses a technique called matrix rank decomposition to achieve similar performance with far fewer parameters. This makes it possible to store checkpoints in RAM and switch between tasks more quickly. In the future, LoRA could be used to create domain-specific models or even models for individual users.

https://lnkd.in/eB2a4zSP new scalable methods to decompose GPT-4’s internal representations into 16 million oft-interpretable patterns using sparse auto-encoders. 1st Steps towards interpretable LLM's? #openai, #inclusiveai #ai4good

Little-known fact, JPEG images are created out of mathematical patterns. Try it here: https://lnkd.in/eGYWPhiR ↓ Are you technical? Check out https://AlphaSignal.ai to get a daily summary of breakthrough models, repos and papers in AI. Read by 200,000+ devs.

🤫 LLMs: The Secret Weapon for Text Embeddings? 🤫 Move over, encoder-only models! Thanks to a new approach called LLM2Vec, decoder-only LLMs are stepping into the spotlight for text embedding tasks. This research reveals that LLMs can be transformed into powerful text encoders with simple tweaks, achieving state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). 🏆 The secret sauce of LLM2Vec: 1)Enabling bidirectional attention allows the LLM to consider the entire input sequence, not just the preceding tokens. 2)Masked next token prediction helps the LLM adapt to bidirectional attention and learn richer contextual representations. 3)Unsupervised contrastive learning further improves sequence representations, making them ideal for various tasks. The results are impressive: →LLM2Vec outperforms encoder-only models on word-level tasks. →It achieves state-of-the-art performance among unsupervised models on MTEB. →Combined with supervised contrastive learning, it sets a new standard for models trained on publicly available data. Want to unlock the hidden potential of LLMs for text embeddings? Read the full paper: https://lnkd.in/eG469d_w _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.