Digimezzo

by Raphaël Godart

[Digimezzo @digimezzo]

good morning☕️

[Santiago @svpino]

The first AI Machine Learning Engineer is here!

• Name: Neo
• Composition: Multi-agent + multiple specialized models
• Skills: It can work on the entire machine learning lifecycle

I'm glad we now have something to help millions of data scientists, machine learning, and AI engineers.

Attached, you'll see the results on MLE-bench, a benchmark for measuring how well AI agents perform in machine learning engineering tasks.

Neo (@withneo) smokes everyone!

This is a very interesting system. Here is what I could gather from the information I have:

1. It's a multi-agent system. These agents will work in parallel while solving a problem.

2. It plans ahead of time every potential path to solve a problem and explores them all to choose the best one.

3. It uses several fine-tuned models, each specialized in a different portion of the machine learning lifecycle.

Neo doesn't replace data scientists or engineers, but it works with them and takes care of all of the boring, repetitive tasks.

Neo can turn a week of work in a few hours.

Really impressive to see how much this can help!

[Digimezzo @digimezzo]

I recommend the same things for beginners.

[Shaw (spirit/acc) @shawmakesmagic]

If you're interested in getting into how AI really works and you're not sure where to start, here are some resources that you can go really far with:

For engineering implementation, Andrej Karpathy's Neural Networks Zero to Hero playlist:
https://t.co/YJTuBO7vqT

On the math side, it's mostly linear algebra. Gilbert Strang's MIT lectures on Linear Algebra are a great place to start:
https://t.co/5L8ZtDDBty

I also recommend the 3Blue1Brown lectures on these topics. The visualizations really help elucidate some of the deeper concepts in a way that is far less dry.

Neural Networks:
https://t.co/tXQjd0WopG

Essence of linear algebra:
https://t.co/ZY3cvKBOzQ

Calculus is essential to backpropagation, but mostly you just need an intuitive understanding of a few concepts like derivates and chain rule. 3Blue1Brown has some good resources on this as well:
https://t.co/kUkbSqIg9r

You don't really need to know any of this to build agents, but it is really good to have a foundational understanding of how the tech you're using works from the ground up.

Learning all of this could change your life. Good luck!

[LangChain @LangChain]

🤖AI-Driven Research Assistant

This is an advanced AI-powered research assistant system that utilizes multiple specialized agents to assist in tasks such as data analysis, visualization, and report generation

https://t.co/s5ChhuMOtK

[AI @DeepLearn007]

8 Machine Learning Algorithms explained in Human language
#AI #MachineLearning #BigData #Fintech #Insurtech #Datascience #ML #HealthTech #martech #tech
https://t.co/sCzp5Fdlmb

[Google AI @GoogleAI]

Today we are announcing SLING, an experimental system for parsing natural language text directly into a representation of its meaning as a semantic frame graph. We hope the research community finds SLING useful! Check it out at https://t.co/tQc9PGhaHk

[Joao Kogler @joao_kogler]

Xavier Amatriain’s Machine Learning and Artificial Intelligence Year-end Roundup: https://t.co/PquMhbOsGh

[Oh-Hyun Kwon @OhHyunKwon]

Tired of blindly tweaking layout parameters to visualize your graph? Our #MachineLearning model builds a WYSIWYG interface for you to intuitively produce a layout you want! Check out our #IEEEVIS paper (https://t.co/f7UiRCED7I) and demo (https://t.co/MwBJmLaHi5). #NetworkScience

[WIRED @WIRED]

We're proud to announce that our documentary, 'Machine Learning: Living in the Age of AI,' is premiering at the Cannes Lions Festival on June 19. Go see it! https://t.co/SSp0qZVSBi

The project is sponsored by McCann Worldgroup.