AI Product Leader | GenAI Agent Platforms | Evaluation Frameworks | Responsible AI Adoption | Ex-Microsoft, Nokia
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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