State of ML-AI 2024

In an era where technology continues to evolve at breakneck speed, Machine Learning (ML) and Artificial Intelligence (AI) stand at the forefront of this digital revolution. This blog post aims to clarify what ML and AI entail, alongside highlighting the latest trends shaping these fields. What is Machine Learning?

Machine Learning is a subset of AI focused on the development of algorithms that can learn from and make decisions on data. ML allows computers to improve their performance without explicitly being programmed for specific tasks. The primary types of machine learning include:

• Supervised Learning: Involves learning from labeled data to make predictions or decisions.
• Unsupervised Learning: Seeks to identify patterns in data without pre-defined labels.
• Reinforcement Learning: A system learns to make sequences of decisions by trial and error, receiving rewards or penalties for actions taken. ## What is Artificial Intelligence? Artificial Intelligence refers to the broader concept of machines being able to carry out tasks in a way that we would consider "smart." AI includes:
• Machine Learning: As previously described.
• Natural Language Processing (NLP): The interaction between computers and humans in natural language.
• Robotics: AI in physical systems designed to perform tasks autonomously.
• Computer Vision: Enabling machines to interpret and understand visual information from the world. ## Latest Trends in Machine Learning and AI Edge AI: There's a significant move towards processing AI algorithms on the device itself rather than relying on cloud computing. This trend reduces latency, enhances privacy, and decreases reliance on internet connectivity. Explainable AI (XAI): With AI becoming more integrated into critical decision-making processes, there's a push for AI systems to be more transparent about how they make decisions. This trend is crucial for trust, ethical considerations, and regulatory compliance. AI for Sustainability: AI technologies are being harnessed to address environmental challenges, from optimizing energy use to predicting natural disasters and managing resources more sustainably. Hybrid AI Models: Combining different AI techniques, like symbolic AI with neural networks, to create systems that can leverage the strengths of multiple approaches for more robust solutions. Few-Shot and Zero-Shot Learning: Advances in ML are making it possible for models to learn from very little data or even learn to perform tasks they were not explicitly trained for, enhancing model adaptability and reducing data dependency. Automated Machine Learning (AutoML): This trend democratizes AI by automating the process of applying machine learning, making it accessible to those without deep technical expertise, thus broadening the application of AI across various industries. Ethics and Bias in AI: As AI systems become more prevalent, addressing ethical considerations, including preventing bias in decision-making algorithms, has become a priority to ensure fairness and equity in AI applications.

Latest research

Chain of Continuous Thought - Meta’s recent exploration into how large language models (LLMs) can reason more efficiently by thinking in neural patterns rather than traditional text-based reasoning. This could lead to more sophisticated AI systems capable of complex reasoning tasks.

Graph Reasoning with Transformers - Google AI’s comprehensive evaluation of transformer models on graph reasoning tasks shows advancements in how neural networks can handle complex relationships within data, potentially revolutionizing areas like drug discovery and network analysis.

Project Astra by Google DeepMind - A research initiative focusing on creating a universal AI assistant, showcasing advancements in multimodal AI where models can handle text, image, and video inputs simultaneously for more comprehensive AI applications.

Self-Correcting Retrieval-Augmented Generation (RAG) - Research into making AI systems that can self-correct errors in real-time, enhancing the reliability of AI in information retrieval and generation.

Sparsified Large Vision-Language Models - Developments in creating more efficient vision-language models that require fewer computational resources but maintain high performance, which could democratize AI usage in visual tasks. LLMs for Mathematical Reasoning - Research into how language models can be leveraged to solve mathematical problems, pushing the boundaries of what AI can understand and compute in logical reasoning.

Training Stability in Large Language Models - Google Gemini’s approach to optimizing the training process for stability and efficiency, which is crucial for developing more powerful and scalable AI models.

Veo 2 and Imagen 3 - Google DeepMind’s work on video and image generation models, showing significant improvements in creating high-quality, realistic media from text or image prompts, which has implications for creative industries and media production.

Conclusion

The domains of Machine Learning and Artificial Intelligence are not only growing but also evolving in ways that are making technology more accessible, ethical, environmentally conscious, and integrated into everyday life. The trends discussed highlight a move towards more responsible, efficient, and inclusive AI solutions. As we continue to push the boundaries of what’s possible with AI and ML, the focus on sustainability, transparency, and ethical use will be crucial in shaping a future where AI benefits all sectors of society.

Bonus

Open-source Large Language Models (LLMs) are rapidly closing the performance gap with their closed-source counterparts, with models like DeepSeek exemplifying this trend. DeepSeek has made significant strides, demonstrating that open-source models can not only compete but in some cases outperform proprietary models. For instance, DeepSeek-V3 has shown superior performance in coding and mathematical tasks compared to several top-tier closed-source models, including beating the likes of GPT-4 Turbo in specific benchmarks. This model’s success is attributed to its innovative use of a Mixture-of-Experts (MoE) architecture, which allows for more efficient and effective processing by engaging specialized “expert” sub-models for different tasks. Additionally, being open-source, DeepSeek provides transparency and flexibility, enabling broader research and application development at a significantly reduced cost, which is not always possible with closed-source models. This performance, alongside its accessibility, underscores the growing viability and appeal of open-source LLMs in the AI community

Reference:

1. Scaling Open-Source Language Models with Longtermism