AI/ML

:fire: A series of code examples for all sorts of machine learning tasks and applications. - GitHub - dair-ai/ML-Notebooks: A series of code examples for all sorts of machine learning tasks and app...

Benjamin Alarie (Toronto; Google Scholar) & Arthur J. Cockfield (Queen's; Google Scholar), Will Machines Replace Us? Machine-Authored Texts and the Future of Scholarship, 3 L., Tech & Humans 5 (2021): We present here the first machine-generated law review article. Our self-interest motivates us to believe that knowledge workers who write...

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Discover amazing ML apps made by the community

It's put up or shut up time for Data Science teams. When the business depends on our work for revenue growth, success is expected. Failure is fatal.

Did you ever get ANGRY on the internet?

I've been meaning to write some more about artificial intelligence, machine learning, and drug discovery, and this paper (open access) by Andreas Bender is an excellent starting point. I'm going to be talking in fairly general terms here, but for practitioners in the field, I can recommend this revi

OpenAI disclosed the problem on GitHub — but released GPT-3 anyway

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

Feature stores solve the full set of data management problems encountered when building and operating operational ML applications.

Ryan Mac / BuzzFeed: In interview Kogan admits he broke Facebook's ToS but his app was within norms, says he worked on 10+ papers with Pete Fleming, now Instagram's head of research

A drug designed entirely by artificial intelligence is about to enter clinical human trials for the first time. The drug, which is intended to treat obsessive-compulsive disorder, was discovered using AI systems from Oxford-based biotech company Exscientia. While it would usually take around four and a half years to get a drug to this stage of development, Exscientia says that by using the AI tools it's taken less than 12 months.

Gregory Barber / Wired: AI researchers are beginning to acknowledge and confront the “reproducibility” crisis, which makes it hard for others to replicate the results of AI systems

Dave Lee / BBC: Amazon CTO says AI tools like Lex are the next big thing after AWS, and it's not Amazon's responsibility to ensure Rekognition is used accurately or ethically

Corinne Purtill / Quartz: A look at how the Enron Corpus, a public database of 600K+ emails that helped bring down Enron, has enabled AI researchers to train natural language models

Is this the best place to start?