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Key ML Concepts, How was AI Coined, Can NNs Think? and +!

Author(s): Towards AI Team

Artificial Intelligence (AI) Newsletter by Towards AI #16

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Hey everyone. We hope you are well. In this issue, we dive into an exciting cheat sheet outlining key machine learning concepts, the history of artificial intelligence (AI), can neural networks think?, the evolution of creating art from text, and data science job market trends.

This issue is brought to you thanks to Toloka AI:

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You have probably noticed that we have changed the format of the newsletter a bit. We would love to obtain your feedback on whether you like this new format or not and any feedback you have. So please feel free to drop us a note in any case. We’d love to hear from you.

All right, so let’s get to it.

Machine Learning Cheat Sheet: Google Software Engineer Frank Dai, and many other contributors have compiled a very technical, thorough, and significant machine learning cheat sheet that contains many classical machine learning equations and diagrams. It is instrumental, as it includes a lot of key concepts of ML, along with serving as an asset for machine learning job interviews.

Artificial Intelligence (AI) Origin: Back in 1955, a group of researchers launched the AI Project, led by John McCarthy, Marvin Minsky, Nathaniel Rochester, Claude Shannon, Allen Newell, Herbert Simon, William Kautz and many other prominent scientists. The project was originally called “A Proposal for the Darthmouth Summer Research Project on Artificial Intelligence,” and was published on August 31 of the same year — an exciting piece of technological history to remember.

Can Neural Networks Think?: Recently, a group of DeepMind scientists led by Andrea Banino, Jan Balaguer, and Charles Blundell published a scientific paper called “PonderNet: Learning to Ponder,” that talks about a new algorithm that lets neural networks “ponder” or “think for a while” before making decisions on complex tasks — analogous to how both humans and computers think.

Creating Art from Text: You may already be familiar with the remarkable project by Open AI, called DALL-E, which showcases how a neural network can generate images from text. But, how about art? Charlie Snell, an undergrad researcher at Berkeley AI, recently published a blog on “Alien Dreams: An Emerging Art Scene,” which talks in-depth about how art can be created with different types of models, such as CLIP, DALL-E, VQ-Gan, StyleGAN, etc. — giving light to the milestones in the evolution of CLIP-based generative art.

Data Science Job Market Trends: We have updated our work on data science job market trends, where we take a deep dive and showcase the most crucial skills needed nowadays to land a data role in 2021 based on over 3000 data-related jobs postings. If there’s anything that you’d like to see, we’d love your feedback.

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Thank you for reading! Until next time,

Saray and Towards AI

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Key ML Concepts, How was AI Coined, Can NNs Think? and +! was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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