New Developments in Artificial Intelligence

AGI leading to the Dawn of AI Scientists The concept of “AI scientists” is poised to transform how we approach scientific research. Eric Schmidt envisions advanced AI systems conducting independent research, unlocking new levels of efficiency and scalability. With millions of AI systems collaborating globally, we could accelerate breakthroughs in medicine, energy, and climate solutions. Unlike human researchers, AI scientists can analyze vast datasets, conduct experiments, and refine hypotheses at unprecedented speed. Imagine AI systems generating and testing millions of hypotheses daily, driving discoveries at a scale never before possible. Key Innovations Driving AI Scientists Recent advancements are laying the groundwork for AI scientists: • OpenAI’s Strawberry Model: A reasoning powerhouse solving 83% of International Mathematics Olympiad problems using chain-of-thought reinforcement learning. • Harmonic’s Aristotle: A mathematical superintelligence, achieving 90% on the MiniF2F benchmark and tackling hallucinations. • Magic’s Active Reasoning: A novel approach focused on dynamic problem-solving, pushing boundaries in logical and contextual reasoning. • Nous Research’s Forge Engine: Excels in symbolic reasoning and solving complex tasks essential for scientific exploration. These breakthroughs, coupled with formal verification mechanisms and active reasoning, are setting the stage for reliable, autonomous systems to lead research. Leaders Shaping the Future 2024 has seen a surge in AGI-focused startups. Here are some notable players: • Safe Superintelligence Inc. (SSI): Backed by $1 billion, SSI is dedicated to safe and scalable AGI development. • SingularityNET: A decentralized marketplace for collective AGI innovation. • Magic: Positioned as a rising star, claiming breakthroughs in active reasoning critical for applied research. • DeepMind (Google): Continues to excel in reinforcement learning and practical applications like healthcare and protein folding. • Hippocratic AI: Focused on Health General Intelligence (HGI) to transform personalized medicine. The Road Ahead The rise of AI scientists raises profound questions: Will they complement or compete with human ingenuity? How do we ensure these systems are ethical and safe? As we approach this transformative era, the stakes couldn’t be higher. AI scientists have the potential to redefine discovery, but their power must be guided toward humanity’s collective good. The age of AGI-driven scientific discovery isn’t just a possibility—it’s here. Are we ready for the speed, scale, and ethical challenges of this new reality?

A lot has changed since my #LLM inference article last January—it’s hard to believe a year has passed! The AI industry has pivoted from focusing solely on scaling model sizes to enhancing reasoning abilities during inference. This shift is driven by the recognition that simply increasing model parameters yields diminishing returns and that improving inference capabilities can lead to more efficient and intelligent AI systems. OpenAI's o1 and Google's Gemini 2.0 are examples of models that employ #InferenceTimeCompute. Some techniques include best-of-N sampling, which generates multiple outputs and selects the best one; iterative refinement, which allows the model to improve its initial answers; and speculative decoding. Self-verification lets the model check its own output, while adaptive inference-time computation dynamically allocates extra #GPU resources for challenging prompts. These methods represent a significant step toward more reasoning-driven inference. Another exciting trend is #AgenticWorkflows, where an AI agent, a SW program running on an inference server, breaks the queried task into multiple small tasks without requiring complex user prompts (prompt engineering may see end of life this year!). It then autonomously plans, executes, and monitors these tasks. In this process, it may run inference multiple times on the model while maintaining context across the runs. #TestTimeTraining takes things further by adapting models on the fly. This technique fine-tunes the model for new inputs, enhancing its performance. These advancements can complement each other. For example, an AI system may use agentic workflow to break down a task, apply inference-time computing to generate high-quality outputs at each step and employ test-time training to learn unexpected challenges. The result? Systems that are faster, smarter, and more adaptable. What does this mean for inference hardware and networking gear? Previously, most open-source models barely needed one GPU server, and inference was often done in front-end networks or by reusing the training networks. However, as the computational complexity of inference increases, more focus will be on building scale-up systems with hundreds of tightly interconnected GPUs or accelerators for inference flows. While Nvidia GPUs continue to dominate, other accelerators, especially from hyperscalers, would likely gain traction. Networking remains a critical piece of the puzzle. Can #Ethernet, with enhancements like compressed headers, link retries, and reduced latencies, rise to meet the demands of these scale-up systems? Or will we see a fragmented ecosystem of switches for non-Nvdia scale-up systems? My bet is on Ethernet. Its ubiquity makes it a strong contender for the job... Reflecting on the past year, it’s clear that AI progress isn’t just about making things bigger but smarter. The future looks more exciting as we rethink models, hardware, and networking. Here’s to what the 2025 will bring!

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

Top 10 research trends from the State of AI 2024 report: ✨Convergence in Model Performance: The gap between leading frontier AI models, such as OpenAI's o1 and competitors like Claude 3.5 Sonnet, Gemini 1.5, and Grok 2, is closing. While models are becoming similarly capable, especially in coding and factual recall, subtle differences remain in reasoning and open-ended problem-solving. ✨Planning and Reasoning: LLMs are evolving to incorporate more advanced reasoning techniques, such as chain-of-thought reasoning. OpenAI's o1, for instance, uses RL to improve reasoning in complex tasks like multi-layered math, coding, and scientific problems, positioning it as a standout in logical tasks. ✨Multimodal Research: Foundation models are breaking out of the language-only realm to integrate with multimodal domains like biology, genomics, mathematics, and neuroscience. Models like Llama 3.2, equipped with multimodal capabilities, are able to handle increasingly complex tasks in various scientific fields. ✨Model Shrinking: Research shows that it's possible to prune large AI models (removing layers or neurons) without significant performance losses, enabling more efficient models for on-device deployment. This is crucial for edge AI applications on devices like smartphones. ✨Rise of Distilled Models: Distillation, a process where smaller models are trained to replicate the behavior of larger models, has become a key technique. Companies like Google have embraced this for their Gemini models, reducing computational requirements without sacrificing performance. ✨Synthetic Data Adoption: Synthetic data, previously met with skepticism, is now widely used for training large models, especially when real data is limited. It plays a crucial role in training smaller, on-device models and has proven effective in generating high-quality instruction datasets. ✨Benchmarking Challenges: A significant trend is the scrutiny and improvement of benchmarks used to evaluate AI models. Concerns about data contamination, particularly in well-used benchmarks like GSM8K, have led to re-evaluations and new, more robust testing methods. ✨RL and Open-Ended Learning: RL continues to gain traction, with applications in improving LLM-based agents. Models are increasingly being designed to exhibit open-ended learning, allowing them to evolve and adapt to new tasks and environments. ✨Chinese Competition: Despite US sanctions, Chinese AI labs are making significant strides in model development, showing strong results in areas like coding and math, gaining traction on international leaderboards. ✨Advances in Protein and Drug Design: AI models are being successfully applied to biological domains, particularly in protein folding and drug discovery. AlphaFold 3 and its competitors are pushing the boundaries of biological interaction modeling, helping researchers understand complex molecular structures and interactions. #StateofAIReport2024 #AITrends #AI

The 2025 AI Index Report is out, and it provides a comprehensive look at the state of artificial intelligence across various sectors. This report, published by Stanford Institute for Human-Centered Artificial Intelligence (HAI), is essential reading for anyone looking to understand the evolving landscape of AI.    Key trends from this year’s report include: ✔ The rise of smaller, more efficient models, which are becoming more capable while dramatically reducing costs.  ✔ A rapid increase in AI-related incidents, underscoring the growing importance of responsible AI practices.  ✔ A shift in AI regulation, with U.S. states taking the lead as federal policies move at a slower pace.  ✔ AI's growing presence in businesses, with 78% of organizations using AI, up from 55% in 2023.  ✔ Global AI investment is soaring, particularly in generative AI.    This report not only highlights impressive technological progress but also emphasizes the need for thoughtful governance as AI continues to permeate industries and daily life.    The future of AI is bright, with vast opportunities for innovation, growth, and meaningful impact across sectors: https://lnkd.in/geYjvs8z

🚀 Just released: The 2025 AI Index Report by Stanford HAI is packed with insights on where AI stands today—and where it’s headed tomorrow. If you're navigating AI’s growing impact on business, policy, or innovation, this is a must-read. Here are some of the standout highlights from the report: 💼 AI goes mainstream in business: 78% of organizations reported using AI in 2024—up from 55% in 2023. Gen AI is now part of daily operations. 📊 💰 Costs are crashing: Inference costs for models like GPT-3.5 have dropped 280x in just 18 months—AI is becoming dramatically more affordable. 💸 🧠 Agents on the rise: AI agents are getting smarter and faster. In short tasks, some are already outperforming humans. 🤖 🌏 China is catching up: U.S. still leads in model development, but China is quickly closing the quality gap—and leads in AI patents and publications. 🇨🇳 🔍 Small models, big performance: Compact AI models like Microsoft’s Phi-3-mini (3.8B parameters) now match the performance of giants like PaLM (540B). 📉 ⚖️ States take charge of regulation: With federal progress slow, U.S. states have stepped up—passing 131 AI-related laws in 2024 alone. 🏛️ 📌 Read the full report here: https://lnkd.in/guGUDcqW Let me know what caught your attention the most! 👇 #AI #ArtificialIntelligence #GenerativeAI #ProjectManagement #StanfordHAI #AITrends2025 #AIIndex2025 #DigitalTransformation #AIAgents #FutureOfWork