Difference between revisions of "Meta"

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* [[Python]]
 
* [[Python]]
 
** [[PyTorch]] authored by Facebook
 
** [[PyTorch]] authored by Facebook
−
* [[Recommendation]]
+
* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 
* [[Vision#Segment Anything Model (SAM)|Segment Anything Model (SAM)]]
 
* [[Vision#Segment Anything Model (SAM)|Segment Anything Model (SAM)]]
 
* [[Caffe / Caffe2]]
 
* [[Caffe / Caffe2]]
 
* [[AI Marketplace & Toolkit/Model Interoperability]]
 
* [[AI Marketplace & Toolkit/Model Interoperability]]
 
* [[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]]
 +
* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 +
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
 
* [[Sequence to Sequence (Seq2Seq)#Retrieval Augmented Generation (RAG)|Retrieval Augmented Generation (RAG)]]  ...an end-to-end differentiable model that combines an information retrieval component (Facebook AI’s dense-passage retrieval system ) with a seq2seq generator  
 
* [[Sequence to Sequence (Seq2Seq)#Retrieval Augmented Generation (RAG)|Retrieval Augmented Generation (RAG)]]  ...an end-to-end differentiable model that combines an information retrieval component (Facebook AI’s dense-passage retrieval system ) with a seq2seq generator  
 
* [http://pytext.readthedocs.io/en/master/overview.html PyText]  ...build end-to-end pipelines for training and inference
 
* [http://pytext.readthedocs.io/en/master/overview.html PyText]  ...build end-to-end pipelines for training and inference

Revision as of 21:11, 2 September 2023

YouTube search... ...Google search

Inside the Lab: Building for the metaverse with AI (2022) | Meta AI
Meta AI's Inside the Lab event streamed live on February 23rd, 2022.

Realizing the Potential of AI Today and Creating the Experiences of Tomorrow Our researchers and technologists work closely with open source communities, academia, and partners to realize the potential of AI today, and create a new class of experiences for tomorrow. Through the power of AI, we will enable a world where people can easily share, create, and connect physically and virtually, with anyone, anywhere.

Chapters:

  • 00:01:43 AI in the Metaverse (Mark Zuckerberg)
  • 00:17:18 Unlocking the Metaverse with AI and Open Science (Joelle Pineau & Jérôme Pesenti)
  • 00:36:37 Toward Self-Learning Vision Systems (Piotr Dollar)
  • 00:49:17 Delivering Inclusive Technologies Through Translation (Angela Fan)
  • 01:04:37 Building the Assistants of Tomorrow (Albert Geramifard)
  • 01:15:43 The Path to Human Level Intelligence (Yann LeCun, Lex Fridman & Yoshua Bengio)
  • 01:55:45 Building Responsible AI at Meta (Jacqueline Pan & Stevie Bergman)
  • 02:08:38 Supporting Innovation (Irina Kofman)
  • 02:22:25 Closing Remarks (Mark Zuckerberg)

Deep Learning at Facebook - Yann LeCunn | Lecture Series on AI #3
In this talk, Yann dives into the history of deep learning, and what deep learning looks like at Facebook. ann LeCun is a VP & Chief AI Scientist at Facebook, and Silver Professor of CS and Neural Science at NYU. Previously, Yann was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received a PhD in Computer Science from Université P&M Curie (Paris). After a postdoc at the University of Toronto, Yann joined AT&T Bell Labs, and became head of Image Processing Research at AT&T Labs in 1996. He joined NYU in 2003 and Facebook in 2013. Yann’s current interests include AI, machine learning, computer vision, mobile robotics, and computational neuroscience. He is a member of the National Academy of Engineering.

ML at Facebook: Understanding Inference at the Edge | AI & ML on the Edge | Brandon Reagen
Research Scientist, Facebook performance from the Applied Machine Learning Days.

Realistic Day in the Life of AI/ML Researcher at Facebook
Ever wondered what actually #AI/#ML Researcher/#Engineer 's do? Let me show you a sneak peek of one of the typical workdays. I bet it is very similar at Google, Microsoft, Amazon, DeepMind, Stanford, Berkeley, MIT, and other big research labs (that's how you feed youtube algos with tags, lol). Obviously, what Researchers/Engineers do most of the time is training deep learning models written in PyTorch/TensorFlow, 👉writing research papers👈 and sometimes even reading papers. This one sentence actually contains more info than the whole video.

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