Semanlink - News

Sergey Karayev sur Twitter : "I want to chat with AI about long-form content I'm reading. (It's a paper on Arxiv, but the solution would ideally support any website or PDF.)...

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2023-03-29 About

Physics of AI - YouTube

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- Intelligence has emerged: why? how?
- Let's study this with *controlled experiments* and *toy models*
- Clean and clear insights that peer slightly behind the magic curtain

emergence; rise of scale. Phase transition; fct(compute time for training: nb of params, dataset size)

(12:25) Transformers: the jump when compared to traditional NN: instead on oparating on single input X, operates on a set of inputs. (cf. training to say whether an image contains 2 elements of the same kind). **The aptitude to compare elements in the input sequence enables analogies** - the essence of reasoning.(Classical NN treat one high dimensional vector, tranformers hanlde sets of vectors. Attention layer replaces learned filters W of classical NN by the other elements in the sequence. Attention module compares part of input against other part of input. "Relative machines rather than absolute". Sets are a very powerful level of abstraction)

emergence
> The truth is that nobody has a clue what is going on"
>
> "Something unknown is doing we don't know what" (A. Eddington)

being inspired by physics' methodology: controled experiments and toy mathematical models

2023-03-28 About

Thérapies géniques : le ciseau moléculaire Crispr livre ses premiers traitements

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CRISPR

2023-03-28 About

Chatbase | Train ChatGPT on your data and add it to your website

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ChatGPT Over Your Data

2023-03-28 About

Using ChatGPT Plugins with LLaMA

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2023-03-27 About

[2303.14177] Scaling Expert Language Models with Unsupervised Domain Discovery

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2023-03-27 About

Niels Rogge sur Twitter : "@GoogleAI's Pix2Structnow available in 🤗 Transformers!"

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2023-03-27 About

Full interview: "Godfather of artificial intelligence" talks impact and potential of AI - YouTube

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```
2:45 – why mainstream AI in the 1980s shunned neural networks
3:21 – Hinton believed in neural networks because the brain worked this way 
4:31 – two different paths to intelligence
6:15 – lack of data and compute that impeded deep learning’s progress
8:57 – the start of deep learning in 2006
10:04 – two big deep learning developments: speech recognition at Google and object recognition at University of Toronto
10:49 – how object recognition works in layman’s terms
15:47 – breakthroughs in object recognition influencing the AI community
18:28 – why Hinton likes the company Cohere
19:20 – biological brains vs digital brains
21:39 – ChatGPT as an “idiot savant” with a lack of understanding of truth
24:56 – how society should handle this new AI
30:52 – self-healing minefield proposed by the government
31:43 – how to create an effective autonomous solder and the alignment problem
33:12 – are large language models “just autocomplete”? Are humans “just autocomplete”?
33:52 – translating “The trophy would not fit in the suitcase because it was too big” into French. English-French translation and the insights it provides into LLMs’ understanding of the world
35:50 – computers coming up with new ideas for itself
37:00 – AI displacing jobs
38:15 – how big of a revolution is AI?
40:37 – is AI sentient?
```

2023-03-27 About

Alpaca Finetuning of Llama on a 24G Consumer GPU

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2023-03-27 About

Fly.io

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2023-03-27 About

Introduction: how to build a plugin - OpenAI API

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2023-03-27 About

Mégabassines : « La débauche de moyens dépêchés par l’Etat contre les opposants contraste avec la tranquillité dont jouissent les tenants de l’agro-industrie »

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2023-03-27 About

ChatGPT + Code Interpreter = Magic – @AndrewMayne

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GPT Code Interpreter

2023-03-26 About

Niklas Muennighoff sur Twitter : "Benchmarked @OpenAIs latest embedding model on 56 datasets from MTEB & it comes in 4th place, sets a SoTA on Clustering"

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2023-03-26 About

Greg Brockman sur Twitter : "Retrieval is probably going to be the most ubiquitous language model plugin for the near future"

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2023-03-26 About

Enias Cailliau sur Twitter : "... I turned [some] Stanford's course into a chatbot that can answer the questions you need answers for..."

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2023-03-26 About

scrapeghost

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2023-03-26 About

Ilya Shabanov sur Twitter : "This is how I use AI (ChatGPT) to improve my academic research..."

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2023-03-25 About

Markprompt | Open Source GPT-4 platform for Markdown

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2023-03-25 About

Langchain - Tutorial: ChatGPT Over Your Data (Feb 2023)

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2023-03-25 About

Getting Started with Hybrid Search | Pinecone

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2023-03-25 About

LangChain: Retrieval

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LangChain

> Do you want to use external retrievers (like ChatGPT Retrieval Plugin) in 
@LangChainAI
? Do you want to experiment with alternative retrieval methods (like hybrid search)?
> We just updated LangChain abstractions to allow for this ([src](https://twitter.com/hwchase17/status/1639291011942989824?s=20)):

> We are adjusting our abstractions to make it easy
for other retrieval methods besides the LangChain
VectorDB object to be used in LangChain. This is done
with the goals of (1) allowing retrievers constructed
elsewhere to be used more easily in LangChain, (2)
encouraging more experimentation with alternative
retrieval methods (like [hybrid search](tag:hybrid_search)).

> Ever since ChatGPT came out, people have been building a personalized ChatGPT for their data. [Langchain - Tutorial: ChatGPT Over Your Data (Feb 2023)](doc:2023/03/langchain_tutorial_chatgpt_o)

> How do you go about building a
chatbot that knows about your data?
The main way of doing this is through a process
commonly referred to as "**Retrieval Augmented
Generation**". In this process, rather than just passing a
user question directly to a language model, the system
"retrieves" any documents that could be relevant in
answering the question, and then passes those
documents (along with the original question) to the
language model for a "generation" step. (This tag refers to paper/original implementation: [RAG (Retrieval-Augmented Generation)])(tag:retrieval_augmented_generation)

2023-03-25 About

Multilingual Semantic Search with Cohere and Langchain

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2023-03-24 About

DataChazGPT sur Twitter : "Just. Wow. @OpenAI's just showcased a #ChatGPT plugin for summarizing anything from the web!"

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2023-03-24 About

Harrison Chase sur Twitter : "LangChain AIPlugins A first open source attempt at using AIPlugins (the same ones ChatGPT is using)

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2023-03-24 About

Matei Zaharia sur Twitter : "Building a ChatGPT-like LLM might be easier than anyone thought. At @Databricks, we tuned a 2-year-old open source model to ..."

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2023-03-24 About

Radek Osmulski 🇺🇦 sur Twitter : "How to use ChatGPT in Jupyter Notebook"

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2023-03-24 About

Aran Komatsuzaki sur Twitter : "Capabilities of GPT-4 on Medical Challenge Problems ..."

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2023-03-24 About

GitHub Copilot X: The AI-powered developer experience | The GitHub Blog

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Copilot

2023-03-24 About

Jim Fan sur Twitter : "...NVIDIA AI Foundations "

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2023-03-24 About

Catalyzing next-generation Artificial Intelligence through NeuroAI | Nature Communications

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2023-03-24 About

Lior⚡ sur Twitter : "OpenAI just announced a new experimental model that knows when and how to browse the internet within ChatGPT! "

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2023-03-24 About

openai/chatgpt-retrieval-plugin

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> a flexible solution for semantic search and retrieval of personal or organizational documents using natural language queries

> It allows users to obtain the most relevant document snippets from their data sources, such as files, notes, or emails, by asking questions or expressing needs in natural language. Enterprises can make their internal documents available to their employees through ChatGPT using this plugin.

> uses OpenAI's text-embedding-ada-002 embeddings model to generate **embeddings of document chunks**, and then stores and queries them using a vector database on the backend (several databases supported)
>
> Developers can deploy their own Retrieval Plugin and register it with ChatGPT

(to be compared with [LangChain: Retrieval](doc:2023/03/langchain_retrieval))

Uses [FastAPI](tag:fastapi)

> The plugin can be hosted on **any cloud platform that supports Docker** containers, such as Fly.io, Heroku or Azure Container Apps

"Memory feature":

> capacity to provide ChatGPT with memory. (By utilizing the plugin's upsert endpoint)

Security

> main risk consideration is data authorization and privacy (no external effects). Developers should only add content into their Retrieval Plugin that they have authorization for and that they are fine with appearing in users’ ChatGPT sessions.

2023-03-24 About

Jim Fan sur Twitter : OpenAI just announced ChatGPT Plugins. If ChatGPT's debut was the "iPhone event", today is the "iOS App Store" event.

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2023-03-24 About

[2303.12712] Sparks of Artificial General Intelligence: Early experiments with GPT-4

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2023-03-24 About

ChatGPT plugins

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2023-03-24 About

Welcome to LangChain

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2023-03-24 About

PyTorch on Google Cloud: How to deploy PyTorch models on Vertex AI | Google Cloud Blog

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2023-03-23 About

Remote to a VM over an IAP tunnel with VSCode | by Albert Brand | Medium

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2023-03-23 About

PyTorch on Google Cloud: How To train and tune PyTorch models on Vertex AI | Google Cloud Blog (Sept. 2021)

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2023-03-23 About

anton sur Twitter : "Since ChatGPT has recently lost the ability to maintain conversations I moved over to self-hosted chatbot-ui... Everything is saved locally."

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2023-03-22 About

tloen/alpaca-lora: Instruct-tune LLaMA on consumer hardware

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2023-03-22 About

[2106.09685] LoRA: Low-Rank Adaptation of Large Language Models

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2023-03-21 About

antimatter15/alpaca.cpp: Locally run an Instruction-Tuned Chat-Style LLM

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2023-03-21 About

John H. Meyer 🚀 sur Twitter : "@emerywells That's actually what I built it for👀 Context: I unfortunately lost my dad unexpectedly at the young age of 50, back in 2017. There was a lot left un-said, and a lot I wish I could've spoken to him about in my adult life.…"

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2023-03-21 About

Jimmy Lin sur Twitter : "GPT-4 and its ilk are awesome for rapid prototyping and one-offs, but at the end of the day, enterprises will deploy far smaller distilled models in production. Here's my contrarian take -" / Twitter

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2023-03-21 About

Tutorial: VS Code with Google Cloud AI Platform as a backend | by Kyle Ziegler | Medium

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2023-03-21 About

« La préservation de l’unique lettre de Robespierre à Danton est une cause nationale »

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2023-03-21 About

Analyzing Document Layout with LayoutParser | by Ruben Winastwan | Towards Data Science

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Layout-Parser

2023-03-21 About

Brian Roemmele sur Twitter : "I am very excited to announce I have been successful in installing and operating a full ChatGPT knowledge set and interface fully trained on my local computer and it needs no Internet once installed."

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2023-03-21 About

LLM Zoo at Home: LLaMA & Alpaca | bergis universe of software, hardware and ideas

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2023-03-20 About

JB Rubinovitz sur Twitter : "The "Will GPT automate all the jobs?" paper is out With participation from @OpenAI, OpenResearch and @penn..."

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2023-03-20 About

[2303.09752] CoLT5: Faster Long-Range Transformers with Conditional Computation

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2023-03-20 About

Lior⚡ sur Twitter : "Quick tip, you can use pip-chill instead of pip freeze to get the packages you are actually using."

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2023-03-19 About

FP Servant sur Twitter : "while #LanguageModels are optimized to predict nearby words, the human brain [...] continuously predict a hierarchy of representations that spans multiple timescales"

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fps' tweet

2023-03-19 About

L’Europe enfin mûre pour une politique industrielle

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2023-03-19 About

Bizarre Buildings sur Twitter : "This building in Guizhou, China." / Twitter

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2023-03-19 About

[1904.10509] Generating Long Sequences with Sparse Transformers

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2023-03-19 About

Andrej Karpathy sur Twitter : "Base LLMs (non-finetuned) make very strong few-shot classifiers. Describe task in English, give few examples, read off the label probabilities on test example. No gradient-based optimization necessary. It brings a cannon to a knife fight but is fast, convenient, strong baseline." / Twitter

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2023-03-19 About

Clustering sentence embeddings to identify intents in short text | by David Borrelli | Towards Data Science

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2023-03-18 About

On the Surprising Behavior of Distance Metrics in High Dimensional Space

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2023-03-18 About

Nine ChatGPT Tricks for Knowledge Graph Workers - The Cagle Report

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2023-03-18 About

Using AI to make teaching easier & more impactful

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2023-03-18 About

BioBootloader sur Twitter : "Today I used GPT-4 to make "Wolverine" - it gives your python scripts regenerative healing abilities! Run your scripts with it and when they crash, GPT-4 edits them and explains what went wrong"

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2023-03-18 About

Jordan Jacobs sur Twitter : "AI is eating software. Why? Traditional software never improves. AI enables ‘smart’ software to learn & improve constantly. The world runs on software and AI is changing everything. Yet few people see or understand the massive AI wave on the horizon." / Twitter

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2023-03-16 About

OpenAI co-founder on company’s past approach to openly sharing research: ‘We were wrong’ - The Verge

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2023-03-16 About

Jim Fan sur Twitter : "I don't give a damn about what is or isn't AGI. It doesn't matter. Below is GPT-4's performance on many standardized exams: BAR, LSAT, GRE, AP, etc. The truth is, GPT-4 can apply to Stanford as a student now. AI's reasoning ability is OFF THE CHARTS. Exponential growth is the… https://t.co/on8XKqOazg" / Twitter

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2023-03-16 About

David Chalmers sur Twitter : "what are some new and interesting results about the relative capacities of multimodal models and pure language models... (thinking about "do language models need sensory grounding for meaning and understanding?".)"

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2023-03-15 About

Brigadoon (film) - Wikipedia

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2023-03-15 About

GPT-4 Technical Report

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2023-03-15 About

GPT-4 (OpenAI blog post)

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GPT-4

2023-03-15 About

Jim Fan sur Twitter : "GPT-4 is HERE. Most important bits you need to know..."

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2023-03-15 About

raphaelsty.github.io/knowledge demo

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2023-03-15 About

GPT-4 Developer Livestream - YouTube

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2023-03-15 About

raphaelsty/knowledge: Open-source personal bookmarks search engine

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2023-03-15 About

raphaelsrty sur Twitter : "I made Knowledge, an open-source tool the automatically index content I interact with on Github, Twitter, HackerNews and Zotero. It will make my life easier to retrieve and share documents I like..."

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2023-03-15 About

Alpaca: A Strong Open-Source Instruction-Following Model

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2023-03-14 About

A full training - Hugging Face Course

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2023-03-14 About

Improve OCR quality for receipt processing with Tesseract and Label Studio

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2023-03-13 About

Inria Paris NLP (ALMAnaCH team) sur Twitter : “Writing in two languages: Neural machine translation as an assistive bilingual writing tool”

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2023-03-13 About

LCP sur Twitter : "Le billet de @CamilleEtienne_ : "Alors que le vivant s'effondre, que le plancher se dérobe sous nos pieds, il faudrait rester assis, derrière nos écrans, produire pour la mégamachine. Il est urgent de prendre du temps, pour ne plus en perdre."

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2023-03-13 About

Six Nations 2023 : Le résumé d'Angleterre vs France - YouTube

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2023-03-12 About

De Londres à Tahiti, le mystère de la statue du dieu A’a

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2023-03-12 About

Overview - OpenAI API

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2023-03-12 About

Big data? 🤗 Datasets to the rescue! - Hugging Face Course

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2023-03-12 About

Support of very large dataset? - 🤗Datasets - Hugging Face Forums

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2023-03-12 About

« Pour la première fois peut-être, des avocats et des adversaires du nucléaire tentent de préserver ce qui les rassemble »

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2023-03-12 About

Cinq lieux à voir en Normandie

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2023-03-12 About

Contre le tout-plastique, le combat de la chercheuse Nathalie Gontard

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Plastic

2023-03-12 About

Judas de Amos Oz

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2023-03-12 About

Défenestration de Prague (1618) — Wikipédia

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2023-03-12 About

La fermeture de la Silicon Valley Bank marque la plus grosse faillite bancaire depuis la crise de 2008

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2023-03-11 About

Les Cavaliers (Kessel)

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2023-03-10 About

« Le Cavalier de la nuit » : Robert Penn Warren, l’œil perçant du Sud

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Livre à lire

2023-03-09 About

Muhammad Ali - "I'm as pretty as a girl!"

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2023-03-09 About

Leo Boytsov sur Twitter : "🧵A lot of multi-modal models were announced recently. A little summary..."

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2023-03-08 About

Epsilons, no. 1: The geometric series - by Tivadar Danka

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Mathématiques

2023-03-08 About

xenova/transformers.js: Run 🤗 Transformers in your browser!

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2023-03-08 About

[2104.07186] COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

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2023-03-08 About

Enabling Python VirtualEnv in JupyterLab | My Shitty Code

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2023-03-08 About

Riddle solved: Why was Roman concrete so durable? | MIT News

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2023-03-08 About

Sergey Levine sur Twitter : "What if we train a language model on images & robot data? That's the idea behind PaLM-E: a huge LLM (562B params) that is trained on language and "multimodal sentences" that include images and language"

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2023-03-08 About

What is OpenAI's CLIP and how to use it?

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2023-03-07 About

ChatGPT : la philosophie du baratin - YouTube

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2023-03-06 About

Bibliothèque de MariaGambina

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2023-03-05 About

La Fin de l'homme rouge

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2023-03-05 About

La Supplication : Tchernobyl, chronique du monde après l'apocalypse

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2023-03-05 About

La Vieille Fille (Balzac)

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2023-03-05 About

1177 B.C.: The Year Civilization Collapsed

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2023-03-05 About

ShareGPT: Share your wildest ChatGPT conversations with one click.

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2023-03-04 About

Hannah Fry sur Twitter : "Every single person who confuses correlation and causation ends up dying."

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2023-03-03 About

Evidence of a predictive coding hierarchy in the human brain listening to speech | Nature Human Behaviour

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> Language models still fail to match the language abilities of humans. **[Predictive coding theory](tag:predictive_coding.html)** offers a tentative explanation to this discrepancy: **while language models are optimized to predict nearby words, **the human brain would continuously predict a hierarchy of representations that spans multiple timescales**. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, **these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.**

> our
results support the idea that, unlike current language algorithms, the
brain is not limited to predict word-level representations but rather
predicts multiple levels of representations.

> our study thus calls for systematically training algorithms to predict
multiple timescales and levels of representations

2023-03-03 About

Meta AI sur Twitter : "New in Nature Human Behavior, Meta AI researchers show how current language models differ from the human brain & highlight the role of long-range & hierarchical predictions..."

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