Turing Layer

GenAI | NLP | LLM Agents | AI Ethics & Alignment

[Turing Layer @TuringLayer]

1/ Diffusion Models can generate high-quality images given a prompt. How do their algorithms work? Let's dive in! 👇
@DeepLearningAI @realSharonZhou @AndrewYNg

[Turing Layer @TuringLayer]

@bohaweedy @Sanpabloenfure cuando Samuels juega concentrado nos aporta muchísimo en rebote, inteligencia, y entrando a canasta q no le para nadie. y creo q tiene todavía bastante margen de crecimiento

[Turing Layer @TuringLayer]

🤖 Introducing MINERVA: A multi-agent AI system harnessing 7 specialized LLM agents to detect digital scams. Built with #AutoGen as part of the
@BerkeleyRDI #LLM #Agents course:

🎓LLM-Agents Course:
https://t.co/sVyKAMVW6L

📄Full article:
https://t.co/cYDlE4uLjs

[Turing Layer @TuringLayer]

8/ Quantization

Quantization reduces model parameter precision to shrink size and computational needs. This notebook demonstrates dynamic quantization on BERT, benchmarking size reduction, quantization effects, inference latency, and accuracy preservation:

[Turing Layer @TuringLayer]

1/ Just released generative-ai-101, a new repo comprising annotated NBs and visuals to dive into the best practices for building w/ LLMs. It covers: ⬇️

https://t.co/FVV712uul1

@huggingface @langchain @OpenAI @pydantic @GroqInc @wandb @neo4j

[Turing Layer @TuringLayer]

7/ Fine-Tuning

While Transformer models provide robust understanding of language, they require fine-tuning to adjust their parameters for specific tasks such as question answering. In this notebook we fine-tune BERT for paraphrase identification:

[Turing Layer @TuringLayer]

🧠 Curious about emerging NLP research topics? Dive into our step-by-step guide! We analyzed 25k+ #arXiv papers using #embeddingquantization and #LLMs. Built w/ @huggingface, @pydantic, @langchain, @MistralAI, and @AnthropicAI #AI

https://t.co/XLE6cgjoeC

[Turing Layer @TuringLayer]

@karpathy Amazing progress in just hours!

[Turing Layer @TuringLayer]

A gentle guide to build, train, and deploy a #neuralnetwork from scratch for image classification! Dive into the approximation theorem, parameter initialization, regularization, gradients, and backpropagation. @huggingface #DeepLearning #Python

https://t.co/07wLea7AcM

[Konstantine Buhler @Konstantine]

The sixth annual Forbes AI 50 list is now live.

There were many impressive companies that didn't make this year's top 50. We had over 1,900 applicants for just 50 slots.

The process from 1,900 to top 100 is fully algorithmic based on metrics like revenue scale, revenue growth, Glassdoor reviews, and product reviews. For the first time, nearly all winners had impressive financial metrics, a great sign for the AI industry. From the top 100 to final 50 we then have a panel of judges from the AI and VC industry vote.

When we started the list in 2019, there were maybe 200 applicants (the industry was much smaller). Now, with 1,900 and I think we should make it the AI 100 next year in 2025! If there is a company you think should make the list next year, please tag them below so we can reach out when we put the next list together!

Here are some rising trends we noticed in this year's list:

General productivity
Five productivity apps, @OpenAI’s ChatGPT, @AnthropicAI’s Claude, @DeepLcom, @NotionHQ and @magicaltome are now catering to customers at the consumer, prosumer and enterprise levels. Productivity has always been a focus of AI, but we saw huge gains revenue growth and user engagement this year.

Applications for creators
Image editor @photoroom_app, video generation app @pika_labs and game-builder @Rosebud_AI show that the lines are blurring between consumer and prosumer for creative software. Products like OpenAI's Sora are mind-blowing, and just the beginning of the next generation of applications for creators.

AI in the physical world
@Figure_robot in robotics, @tractian in industrial maintenance and @Waabi_ai in self-driving are beginning to show how the integration of AI software with hardware will transform work in the physical world. Our guess is next year's list will have many more companies in the AI robotics trend. It seems right around the corner.

Infrastructure
@MistralAI is an amazing new entrant in foundation models. In many ways Mistral has taken leadership in the Open Source community alongside list veteran @huggingface. @langchain established itself in a category of its own as an all-purpose application development framework for working with LLMs.

Thanks for reading. Tag any companies we should include for next year's list, as well as any other trends or ideas you noticed this year!

https://t.co/QT05fZ40sp

[Turing Layer @TuringLayer]

@aidangomez Looks promising Aidan, really glad that you focused also on low latency. Will test it soon!

[Turing Layer @TuringLayer]

@JayAlammar @OReillyMedia @MaartenGr 1) gorilla 🦍
2) orangutan 🦧

[Turing Layer @TuringLayer]

2/ Also available as a template at the official Cohere Repo! Check it out to discover integrations for vector stores, data lakes, and software tools.
https://t.co/UPtKw1DXxz

[Turing Layer @TuringLayer]

1/ Build your own @cohere connector to ground #LLM responses on dynamic, up-to-date proprietary data. We just released a Python package to help you bootstrap:
- A @FastAPI search endpoint w/ Auth
- @pydantic models for your data
- A deployment script
https://t.co/3QBqDrCahz

[Turing Layer @TuringLayer]

Trained also with specific objects:

[Turing Layer @TuringLayer]

#DeepLearning project to classify images under an astronomy class:
- galaxies
- nebulae
- comets
- asteroids
- quasars
- star clusters

Built for the @fastdotai DL course w/ #ResNet50

@jeremyphoward @math_rachel @ESA_Webb @NASAHubble

https://t.co/bz8QsxHpe9