Difference between revisions of "PaLM"

From
Jump to: navigation, search
m
m
 
(22 intermediate revisions by the same user not shown)
Line 2: Line 2:
 
|title=PRIMO.ai
 
|title=PRIMO.ai
 
|titlemode=append
 
|titlemode=append
−
|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Facebook, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
+
|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
−
|description=Helpful resources for your journey with artificial intelligence; Attention, GPT, chat, videos, articles, techniques, courses, profiles, and tools  
+
 
 +
<!-- Google tag (gtag.js) -->
 +
<script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script>
 +
<script>
 +
  window.dataLayer = window.dataLayer || [];
 +
  function gtag(){dataLayer.push(arguments);}
 +
  gtag('js', new Date());
 +
 
 +
  gtag('config', 'G-4GCWLBVJ7T');
 +
</script>
 
}}
 
}}
 
[https://www.youtube.com/results?search_query=PaLM+Language+Multimodal+Model YouTube]
 
[https://www.youtube.com/results?search_query=PaLM+Language+Multimodal+Model YouTube]
Line 11: Line 20:
 
[https://www.bing.com/news/search?q=PaLM+Language+Multimodal+Model&qft=interval%3d%228%22 ...Bing News]
 
[https://www.bing.com/news/search?q=PaLM+Language+Multimodal+Model&qft=interval%3d%228%22 ...Bing News]
  
 +
* [https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html PaLM-E] | [[Google]]
 
* [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s
 
* [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s
−
* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
+
* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
−
* [[Assistants]] ... [[Agents]] ... [[Negotiation]] ... [[Hugging_Face#HuggingGPT|HuggingGPT]] ... [[LangChain]]
+
** [[Bard#PaLM|PaLM]]
−
* [[Attention]] Mechanism  ...[[Transformer]] Model  ...[[Generative Pre-trained Transformer (GPT)]]
+
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
−
* [[Generative AI]] ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]] ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+
* [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
−
* [[Capabilities]]  
+
* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
−
** [[Video/Image]] ... [[Vision]] ... [[Colorize]] ... [[Image/Video Transfer Learning]]
+
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
−
** [[End-to-End Speech]] ... [[Synthesize Speech]] ... [[Speech Recognition]]  
+
* [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]]
−
* [[Development]] ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]] ... [[Visualization]] ... [[Diagrams for Business Analysis]]
+
* [[End-to-End Speech]] ... [[Synthesize Speech]] ... [[Speech Recognition]] ... [[Music]]
−
* [[Prompt Engineering (PE)]]
+
* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 +
* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 +
* [[Prompt Engineering (PE)]] ... [[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]]  
 
* [[Foundation Models (FM)]]
 
* [[Foundation Models (FM)]]
−
* [[Singularity]] ... [[Moonshots]] ... [[Emergence]] ... [[Explainable / Interpretable AI]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning]] ... [[Algorithm Administration#Automated Learning|Automated Learning]]
+
* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]]
−
* [https://www.marktechpost.com/2023/04/06/8-potentially-surprising-things-to-know-about-large-language-models-llms/ 8 Potentially Surprising Things To Know About Large Language Models LLMs | Dhanshree Shripad Shenwai - Marketechpost]
+
* [https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html PaLM-E: An embodied multimodal language model]
−
* [https://www.marktechpost.com/2023/04/07/this-ai-paper-introduce-self-refine-a-framework-for-improving-initial-outputs-from-llms-through-iterative-feedback-and-refinement/ This AI Paper Introduces SELF-REFINE: A Framework For Improving Initial Outputs From LLMs Through Iterative Feedback And Refinement | Aneesh Tickoo - MarkTechPost]
+
* [https://www.boteatbrain.com/p/google-palm-e PaLM-E, Google's smartest new bot | Anthony Castrio - Bot Eat Brain]
−
* [https://www.marktechpost.com/2023/04/11/meet-lmql-an-open-source-programming-language-and-platform-for-large-language-model-llm-interaction/ Meet LMQL: An Open Source Programming Language and Platform for Large Language Model (LLM) Interaction | Tanya Malhotra - MarkTechPost]
+
* [https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html Pathways Language Model (PaLM)] | [[Google]]
 +
* [https://arxiv.org/abs/2204.02311 PaLM: Scaling Language Modeling with Pathways] | A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghemawat, S. Dev, H. Michalewski, X. Garcia, V. Misra, K. Robinson, L. Fedus, D. Zhou, D. Ippolito, D. Luan, H. Lim, B. Zoph, A. Spiridonov, R. Sepassi, D. Dohan, S. Agrawal, M. Omernick, A. Dai, T. Pillai, M. Pellat, A. Lewkowycz, E. Moreira, R. Child, O. Polozov, K. Lee, Z. Zhou, X. Wang, B. Saeta, M. Diaz, O. Firat, M. Catasta, J. Wei, K. Meier-Hellstern, D. Eck, J. Dean, S. Petrov, & N. Fiedel]
 +
* [https://www.seroundtable.com/google-bard-better-palm-35163.html Google Bard Now Better At Math & Logic By Using PaLM, Google Says | Barry Schwartz - Search Engine Roundtable]
 +
  
 +
An Embodied Multimodal Language Model that directly incorporates real-world continuous sensor modalities into language models and thereby establishes the link between words and percepts. It was developed by Google to be a model for robotics and can solve a variety of tasks on multiple types of robots and for multiple modalities (images, robot states, and neural scene representations). PaLM-E is also a generally-capable vision-and-language model. It can perform visual tasks, such as describing images, detecting objects, or classifying scenes, and is also proficient at language tasks, like quoting poetry, solving math equations or generating code. 562B
  
−
<hr>
+
PaLM has been trained using a training system developed by Google for Pathways, which was used to train PaLM on 6144 chips in parallel on two Cloud TPU v4 pods. PaLM has demonstrated "breakthrough capabilities" in numerous particularly challenging language tasks such as language comprehension and generation, reasoning, and code-related tasks. PaLM can even generate explicit explanations for scenarios that require a complex combination of multi-step logical inference, world knowledge, and deep language understanding, such as providing high-quality explanations for novel jokes not found on the web. PaLM's ability to understand humor and make logical inferences is helping Google solve novel challenges that before would have taken someone with specific expertise. The ability to understand the nuances of human language will lead to better and more natural interactions with machines.
  
−
One of the more interesting, but seemingly academic, concerns of the new era of AI sucking up everything on the web was that AIs will eventually start to absorb other AI-generated content and regurgitate it in a self-reinforcing loop. Not so academic after all, it appears, because [[Bing]] just did it! When asked, it produced verbatim a [[COVID-19]] conspiracy coaxed out of [[ChatGPT]] by disinformation researchers just last month [https://techcrunch.com/2023/02/08/ai-is-eating-itself-bings-ai-quotes-covid-disinfo-sourced-from-chatgpt/ AI is eating itself: Bing’s AI quotes COVID disinfo sourced from ChatGPT | Devin Coldewey, Frederic Lardinois - TechCrunch]
+
<img src="https://media.beehiiv.com/cdn-cgi/image/fit=scale-down,format=auto,onerror=redirect,quality=80/uploads/asset/file/4f4f782c-a179-40cf-9785-923e8be0cfc2/palm_e_demo.gif" width="800">
  
−
<hr>
+
Achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks.
  
−
[[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]
 
−
= <span id="Multimodal"></span>Multimodal =
 
−
Multimodal Language Models; Multimodal Language Model (MLM)/Multimodal Large Language Model (MLLM) are is a type of Large Language Model (LLM) that combines text with other kinds of information, such as images, videos, audio, and other sensory data1. This allows MLLMs to solve some of the problems of the current generation of LLMs and unlock new applications that were impossible with text-only models [https://bdtechtalks.com/2023/03/13/multimodal-large-language-models/ What you need to know about multimodal language models | Ben Dickson - TechTalks]
 
  
−
* [[GPT-4]][https://openai.com/product/gpt-4 GPT-4 |] [[OpenAI]]  ... can accept prompts of both text and images1. This means that it can take images as well as text as input, giving it the ability to describe the humor in unusual images, summarize text from screenshots, and answer exam questions that contain diagrams. rumored to be more than 1 trillion parameters.
+
<youtube>2BYC4_MMs8I</youtube>
−
* [[Kosmos-1]][https://arxiv.org/abs/2302.14045 Kosmos-1] | [[Microsoft]]  ... can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). It can analyze images for content, solve visual puzzles, perform visual text recognition, and pass visual IQ tests. 1.6B
+
<youtube>spc-UGr0aq8</youtube>
−
* [[PaLM-E]][https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html PaLM-E] | [[Google]] ... an Embodied Multimodal Language Model that directly incorporates real-world continuous sensor modalities into language models and thereby establishes the link between words and percepts. It was developed by Google to be a model for robotics and can solve a variety of tasks on multiple types of robots and for multiple modalities (images, robot states, and neural scene representations). PaLM-E is also a generally-capable vision-and-language model. It can perform visual tasks, such as describing images, detecting objects, or classifying scenes, and is also proficient at language tasks, like quoting poetry, solving math equations or generating code. 562B
+
<youtube>dUk8h4-6Kf8</youtube>
−
* [https://arxiv.org/abs/2302.00923 Multimodal-CoT (Multimodal Chain-of-Thought Reasoning)] [https://github.com/amazon-science/mm-cot GitHub] ... incorporates language (text) and vision (images) modalities into a two-stage framework that separates rationale generation and answer inference. Under 1B
+
<youtube>yi-A0kWXEO4</youtube>
 +
<youtube>fiLFF4RyyKQ</youtube>
 +
<youtube>gD5rz8e9EQ8</youtube>

Latest revision as of 21:13, 26 April 2024

YouTube ... Quora ...Google search ...Google News ...Bing News


An Embodied Multimodal Language Model that directly incorporates real-world continuous sensor modalities into language models and thereby establishes the link between words and percepts. It was developed by Google to be a model for robotics and can solve a variety of tasks on multiple types of robots and for multiple modalities (images, robot states, and neural scene representations). PaLM-E is also a generally-capable vision-and-language model. It can perform visual tasks, such as describing images, detecting objects, or classifying scenes, and is also proficient at language tasks, like quoting poetry, solving math equations or generating code. 562B

PaLM has been trained using a training system developed by Google for Pathways, which was used to train PaLM on 6144 chips in parallel on two Cloud TPU v4 pods. PaLM has demonstrated "breakthrough capabilities" in numerous particularly challenging language tasks such as language comprehension and generation, reasoning, and code-related tasks. PaLM can even generate explicit explanations for scenarios that require a complex combination of multi-step logical inference, world knowledge, and deep language understanding, such as providing high-quality explanations for novel jokes not found on the web. PaLM's ability to understand humor and make logical inferences is helping Google solve novel challenges that before would have taken someone with specific expertise. The ability to understand the nuances of human language will lead to better and more natural interactions with machines.

Achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks.


Retrieved from "https://primo.ai/index.php?title=PaLM&oldid=36833"