Blog Article from Deepmind about the paper: [https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor](https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor)

commonly used algorithms on the same hardware, which showcases AlphaTensor’s flexibility in optimising arbitrary objectives. [https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor](https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor)

commonly used algorithms on the same hardware, which showcases AlphaTensor’s flexibility in optimising arbitrary objectives.” https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor

enough IT sectors that we will start seeing some really big changes soon. [Advancements like this ](https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor) are going to lead to more powerful LLM's too. Highly suggest reading this article from

that and answer as if it was up to date. Using this trick a [25x smaller](https://www.deepmind.com/publications/improving-language-models-by-retrieving-from-trillions-of-tokens) model could have similar results with a big model, they had 1 trillion tokens of text

that is not narrow, it already exists. Models like [Deepmind's GATO](https://www.deepmind.com/publications/a-generalist-agent), are capable of a myriad of tasks with the same weights. They are the very definition of AGI, but nobody

predictions or running a test (such as in testing code) Minerva - https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html AlphaCode - https://www.deepmind.com/publications/competition-level-code-generation-using-deep-language-models (above average competitive programmers) FLAN-PaLM - https://paperswithcode.com/paper/scaling-instruction-finetuned-language-models (top score on MMLU math problems) DiVeRSe

www.deepmind.com/blog](https://www.deepmind.com/blog) read all these. The most notable ones : [https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html) [https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html](https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html) For an example of a third party scientist venturing an opinion on their work: see here : [https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/](https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/)

talking about AI , but AGI , The first proto AGI was demonstrated a few months ago. [https://www.deepmind.com/publications/a-generalist-agent](https://www.deepmind.com/publications/a-generalist-agent) Scale it up to 300k tasks and that's an AGI. I am saying if industry doesn

learning to ask where the bathroom is so I don't know the methods they used. [https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor](https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor) [https://www.nature.com/articles/s41586-022-05172-4](https://www.nature.com/articles/s41586-022-05172-4)

that yet but it can solve problems that combine text and images. https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model If I remember Correctly, Open Ai also has the goal of combining vision and LLM systems on their path

really close to passing a Turing test, the papers for robotics are like this : [https://www.deepmind.com/blog/building-interactive-agents-in-video-game-worlds](https://www.deepmind.com/blog/building-interactive-agents-in-video-game-worlds) And not able, yet, to actually control this: [https://www.youtube.com/watch?v=XPVC4IyRTG8](https://www.youtube.com/watch?v=XPVC4IyRTG8) . (that boston dynamics machine is kinda hard

pricing is decent and they have a visual language model as well. Something like flamingo https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model

claim is. Right now, Gato demonstrated expert performance or better on a set of tasks. [https://www.deepmind.com/blog/a-generalist-agent](https://www.deepmind.com/blog/a-generalist-agent) . So Gato is an *AI.* You might call it a 'narrow general AI' because it's only

www.deepmind.com/publications/a-generalist-agent](https://www.deepmind.com/publications/a-generalist-agent) **Published** November 10, 2022 **Abstract** Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which

these models are under-trained https://www.deepmind.com/publications/an-empirical-analysis-of-compute-optimal-large-language-model-training and seem to be forming a type of "lossy" text compression, where their ability to memorize data is both poorly understood, and accomplished using only a fraction