摘要： Analog deep learning, a new branch of artificial intelligence, promises quicker processing with less energy use. The amount of time, effort, and money needed to train ever more complex neural network models is soaring as researchers push the limits of machine learning.
摘要： Numerous examples of machine learning show that machine learning (ML) can be extremely useful in a variety of crucial applications, including data mining, natural language processing, picture recognition, and expert systems. In all of these areas and more, ML offers viable solutions, and it is destined to be a cornerstone of our post-apocalyptic civilization.
摘要： Last year, San Francisco-based research lab OpenAI released Codex, an AI model for translating natural language commands into app code. The model, which powers GitHub’s Copilot feature, was heralded at the time as one of the most powerful examples of machine programming, the category of tools that automates the development and maintenance of software. Not to be outdone, DeepMind — the AI lab backed by Google parent company Alphabet — claims to have improved upon Codex in key areas with AlphaCode, a system that can write “competition-level” code.