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|title=PRIMO.ai
 
|title=PRIMO.ai
 
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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|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
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script>
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[https://www.youtube.com/results?search_query=Facebook+deep+machine+learning+ML+artificial+intelligence YouTube search...]
 
[https://www.youtube.com/results?search_query=Facebook+deep+machine+learning+ML+artificial+intelligence YouTube search...]
 
[http://www.google.com/search?q=Facebook+deep+machine+learning+ML+artificial+intelligence ...Google search]
 
[http://www.google.com/search?q=Facebook+deep+machine+learning+ML+artificial+intelligence ...Google search]
  
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* [https://research.fb.com/category/machine-learning/ Facebook Research]
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* [http://ai.facebook.com/ Facebook AI]
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** [http://research.fb.com/category/machine-learning/ Facebook Research]
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*** [http://ai.facebook.com/tools/ Tools for Advancing the World's AI]
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* [[Immersive Reality]] ... [[Metaverse]] ... [[Omniverse]] ... [[Transhumanism]] ... [[Religion]]
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** [https://www.the-sun.com/tech/9209911/mark-zuckerberg-lex-fridman-interview-metaverse/ Mark Zuckerberg shocks the world with eerie first interview INSIDE the metaverse – and people are divided | Millie Turner - The U.S. Sun] ... Lex Fridman, computer scientist and podcaster, have released a video of the pair having a conversation in the virtual world.
 +
* [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 +
* [[LLaMA]]
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* [[Toolformer]]
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* [[ImageBind]]
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* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 +
* [[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]]
 
* [[Python]]
 
* [[Python]]
 
** [[PyTorch]] authored by Facebook
 
** [[PyTorch]] authored by Facebook
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* [[Generative Adversarial Network (GAN)]]
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* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
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* [[Platforms: Machine Learning as a Service (MLaaS)]]
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* [[Vision#Segment Anything Model (SAM)|Segment Anything Model (SAM)]]
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* [[Caffe / Caffe2]]
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* [[AI Marketplace & Toolkit/Model Interoperability]]
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* [[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]]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
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* [[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
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* [http://pytext.readthedocs.io/en/master/overview.html PyText]  ...build end-to-end pipelines for training and inference
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* [[Bidirectional Encoder Representations from Transformers (BERT)]]
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** [[StructBERT]]
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** [[TaBERT]]
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* [[Bot Framework]]
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* [[Building Your Environment]]
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* [[Spell]]
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* [[DialogFlow]]
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* [[Decentralized: Federated & Distributed]]
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* [[Differentiable Programming]]
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* [https://makeavideo.studio/ Make-A-Video |] [[Meta]] ... generates videos from text, add motion to a single image or fill-in the in-between motion to two images.
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* [https://arstechnica.com/information-technology/2023/02/chatgpt-on-your-pc-meta-unveils-new-ai-model-that-can-run-on-a-single-gpu/ Meta unveils a new large language model that can run on a single GPU | Benj Edwards - Ars Technica]  ... LLaMA-13B reportedly outperforms [[ChatGPT]]-like tech despite being 10x smaller. 
  
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<youtube>puImGG5giPc</youtube>
  
 
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<youtube>2xA-at8IM90</youtube>
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<youtube>4P3DMMwvCfY</youtube>
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<b>How Artificial intelligence and Machine learning used by Facebook | AI and ML in 2020
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<b>Inside the Lab: Building for the metaverse with AI (2022) | Meta AI
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</b><br>in this video, we will discuss the innovative machine learning and artificial technology used by Facebook. Facebook started out as a platform for connecting people in different parts of the world using images, videos, and text. With more than 4 billion people using Facebook every month, Facebook has put in a lot of hard work getting these users engaged and connected and this is where machine learning Deep Learning and artificial intelligence comes in. Let's have a look at how Facebook is using machine learning and artificial intelligence tool practically 
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</b><br>Meta AI's Inside the Lab event streamed live on February 23rd, 2022.
 +
 
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Realizing the Potential of AI Today and Creating the Experiences of Tomorrow
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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)
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* 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)
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* 00:49:17 Delivering Inclusive Technologies Through Translation (Angela Fan)
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* 01:04:37 Building the Assistants of Tomorrow (Albert Geramifard)
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* 01:15:43 The Path to Human Level Intelligence (Yann LeCun, Lex Fridman & Yoshua Bengio)
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* 01:55:45 Building Responsible AI at Meta (Jacqueline Pan & Stevie Bergman)
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* 02:08:38 Supporting Innovation (Irina Kofman)
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* 02:22:25 Closing Remarks (Mark Zuckerberg)
 
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<youtube>DJ6J9SVQ9lA</youtube>
 
<youtube>DJ6J9SVQ9lA</youtube>
 
<b>Deep Learning at Facebook - Yann LeCunn | Lecture Series on AI #3
 
<b>Deep Learning at Facebook - Yann LeCunn | Lecture Series on AI #3
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</b><br>In this talk, Yann dives into the history of deep learning, and what deep learning looks like at Facebook.  
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</b><br>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.
 
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<youtube>1NIcxxV7XIo</youtube>
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<youtube>T13qcqpyyRA</youtube>
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<b>How to get into AI + Facebook AI reading list (the best resources)
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<b>ML at Facebook: Understanding Inference at the Edge | AI & ML on the Edge | Brandon Reagen
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</b><br>Hi, I'm Oleksii and I work at Facebook AI Research. The list of the best resources to learn ML and AI is right here. Yann 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.
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</b><br>Research Scientist, Facebook performance from the Applied Machine Learning Days.
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========== Stage 1 ==========
 
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-Theory:
 
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Linear Algebra, Statistics, Theory of Probability (any courses/books will work)
 
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-Practice:
 
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Basic Python skills, NumPy
 
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========== Stage 2 ==========
 
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-Theory:
 
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Online courses / books on ML and Deep Learning;
 
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Machine Learning course by Andrew Ng, Coursera;
 
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Deep Learning Book, Ian Goodfellow;
 
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Stanford CS231n course;
 
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Recent MIT course: http://introtodeeplearning.com/;
 
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https://www.fast.ai/
 
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-Practice:
 
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Pandas, PyTorch, Tensorflow and "official" tutorials (form their websites)
 
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========== Stage 3 ==========
 
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-Theory:
 
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"Classic" papers, e.g. AlexNet, ResNet, BERT;
 
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Reading lists:
 
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http://deeplearning.net/reading-list/
 
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https://github.com/ujjwalkarn/Machine...
 
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https://github.com/floodsung/Deep-Lea...
 
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MILA reading list: https://docs.google.com/document/d/1I...
 
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Andrej Karpathy's blog: http://karpathy.github.io/
 
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-Practice:
 
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Random tutorials on the internet, Advanced tutorials, replicating papers
 
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========== Stage 4 ==========
 
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-Theory:
 
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New paper on your topic, conferences, ArXiv, mailing lists and news channels;
 
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https://distill.pub/;
 
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Sebastian Rudder: http://newsletter.ruder.io/ ;
 
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THE BATCH: https://www.deeplearning.ai/thebatch/
 
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-Practice:
 
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Working on toy-projects;
 
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Contributing to open-source projects;
 
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Getting an internship;
 
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Trying to get hands-on experience in any way possible
 
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========= Never Stop =========
 
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These all will be usefull for anyone, engineer or researcher. Also try to concentrate on one particular topic and get an expert in it first, it will be much easier to accomplish and will open you opportunities right away. Don't try to become an expert in everything and also do not neglect "not prestigious" experience. Any experience is very valuable.  
 
 
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Latest revision as of 21:58, 26 April 2024

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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