EmTech Next, MIT Technology Review’s signature digital transformation conference, is June 13-15, 2023. This year’s event looks at the game-changing power of generative AI, the technology, and the legal implications of generated content. Leaders from OpenAI, Google, Meta, NVIDIA, and more are expected to discuss the future of AI.
Similar Posts
Latest from MIT Tech Review – Chinese ChatGPT-alternatives receive government approval for widespread public access
On Wednesday, Baidu, one of China’s leading artificial intelligence companies, announced it would open up access to its ChatGPT-like large language model, Ernie Bot, to the general public. It’s been a long time coming. Launched in mid-March, Ernie Bot was the first Chinese ChatGPT rival. Since then, many Chinese tech companies have followed suit and…
Latest from MIT : Precision home robots learn with real-to-sim-to-real
At the top of many automation wish lists is a particularly time-consuming task: chores. The moonshot of many roboticists is cooking up the proper hardware and software combination so that a machine can learn “generalist” policies (the rules and strategies that guide robot behavior) that work everywhere, under all conditions. Realistically, though, if you have a…
Latest from MIT : A fast and flexible approach to help doctors annotate medical scans
To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins. When trained to understand the boundaries of biological structures, AI systems can segment (or delineate) regions of…
UC Berkeley – Sequence Modeling Solutions
for Reinforcement Learning Problems
Sequence Modeling Solutions for Reinforcement Learning Problems Long-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model. Modern machine learning success stories often have one thing in common: they use methods that scale gracefully with ever-increasing amounts of data. This is particularly clear from recent advances in sequence modeling,…
Latest from MIT : MIT researchers make language models scalable self-learners
Socrates once said: “It is not the size of a thing, but the quality that truly matters. For it is in the nature of substance, not its volume, that true value is found.” Does size always matter for large language models (LLMs)? In a technological landscape bedazzled by LLMs taking center stage, a team of…
Latest from MIT Tech Review – Why does AI being good at math matter?
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week the AI world was buzzing over a new paper in Nature from Google DeepMind, in which the lab managed to create an AI system that can solve complex geometry problems….