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/)

ai.googleblog.com/2022/11/mixture-of-experts-with-expert-choice.html?m=1 https://ai.googleblog.com/2022/01/scaling-vision-with-sparse-mixture-of.html?m=1

Sooner than later. [Check this out.](https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html)

There's a good article from Google somewhere. Edited: here's a link to read https://ai.googleblog.com/2019/11/astrophotography-with-night-sight-on.html?m=1 GCam app from Google Pixel has been ported to a lot of different phone models

Acttual link: https://ai.googleblog.com/2022/11/beyond-tabula-rasa-reincarnating.html?m=1

agents need not be trained from scratch, except for initial forays into new problems. More at https://ai.googleblog.com/2022/11/beyond-tabula-rasa-reincarnating.html?m=1

pile.eleuther.ai/) and exhibit signs of generality in their abilities. Today NLP can solve [difficult problems](https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html), in code, math and natural language

really have a set of rules. They essentially learn probabilities of different word combinations. (e.g. [https://ai.googleblog.com/2019/10/exploring-massively-multilingual.html](https://ai.googleblog.com/2019/10/exploring-massively-multilingual.html)), which we could argue should count as "understanding" (since Searle didn't define it clearly

might find this interesting: https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html?utm\_source=feedburner

cluster sentences, take a look in LABSE. This model was specially designed for embedding extraction. https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html?m=1

filter/rank the candidates - ensemble of 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

Here's the blog post from google ai [https://ai.googleblog.com/2022/11/multi-layered-mapping-of-brain-tissue.html](https://ai.googleblog.com/2022/11/multi-layered-mapping-of-brain-tissue.html)

Maybe federated learning could be an option. See https://ai.googleblog.com/2017/04/federated-learning-collaborative.html?m=1

author is most likely wrong. Google itself claims https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html?m=1 (Language Models perform reasoning via chain of thought prompting). There is no lack of skeptics in this field

combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html https://techxplore.com/news/2022-06-fake-robots-ropes-faster.html And that was only up to the end of June. Do not take

right. There were some optimized work by Google called [ScanNN](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html), which is much faster on large scale vector similarity search. However, it's much more complicated to port this model

chips & minerals). in terms of timeframe, robotics intelligence is accelerating rapidly. look at [RT-1](https://ai.googleblog.com/2022/12/rt-1-robotics-transformer-for-real.html), for example. it's clear that the public domain has already adopted the means to operate

recommend the article above as well as [Language Models Perform Reasoning via Chain of Thought](https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html) for some examples of how incredibly effective "prompt programming" can be. In fact, i think

ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html Here is the model I keep seeing as the next step past ChatGPT

ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html This is another paper worth looking

Maybe you can take a look at [ScanNN](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html)

Google did the innovative work but OpenAI did a way better job of productizing the work. [https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html)

ai.googleblog.com/2022/11/making-traversable-wormhole-with.html?m=1

Google's blog post on this paper](https://ai.googleblog.com/2023/02/suppressing-quantum-errors-by-scaling.html), which gets into more technical details and also explains their development timeline. It's probably required reading and should be stickied. With that background, from

understand that. but LLMs and extensions of LLMs (like [PALM-E](https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html)) are a heck of a lot more than an abacus. I wonder what would happen if Google just said, "screw

never did anything with it for years. Just last week Google published their [PaLM-E paper](https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html) in which they re-trained their PaLM LLM to be multimodal including the ability to control

already in progress: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html Edit: and just saw this too: https://mobile.twitter.com/AiBreakfast/status/1639269771148509184

ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html Edit: just now saw this: https://mobile.twitter.com/AiBreakfast/status/1639269771148509184

effect from scratch. Edit: This is a pretty clear overview of CoT. Very compelling as well. https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html?m=1 I guess I'll start AB testing some prompts to breakdown problems and tool selections

additional step after learning P(X | context). But both approaches perform this fundamental autoregressive training. \[1\] - [https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html](https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html) \[2\] - [https://cdn.openai.com/research-covers/language-unsupervised/language\_understanding\_paper.pdf](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) \[3\] - [https://arxiv.org/pdf/1609.08144.pdf](https://arxiv.org/pdf/1609.08144.pdf)

nvidia’s megatron parameter count: https://developer.nvidia.com/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/ It’s also the same as PaLM: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html?m=1 This approach (chain of thought) has been discussed for a few months at least, so I think

Google's Spotlight paper https://ai.googleblog.com/2023/02/a-vision-language-approach-for.html

Google open sourced this https://arxiv.org/abs/1810.04719 https://ai.googleblog.com/2018/11/accurate-online-speaker-diarization.html?m=1