AI/ML

AI/ML

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Michael Green on Twitter
Michael Green on Twitter
Experiments I conducted with DALL·E 2 from @OpenAI replicating styles of well known portrait photographers using photo-realistic AI. 🧵1. Dorothea Lange pic.twitter.com/845AzE51xu— Michael Green (@triplux) June 30, 2022
·twitter.com·
Michael Green on Twitter
bigscience/bloom · Hugging Face
bigscience/bloom · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
·huggingface.co·
bigscience/bloom · Hugging Face
Weights & Biases – Developer tools for ML
Weights & Biases – Developer tools for ML
WandB is a central dashboard to keep track of your hyperparameters, system metrics, and predictions so you can compare models live, and share your findings.
·wandb.ai·
Weights & Biases – Developer tools for ML
Reliance on metrics is a fundamental challenge for AI
Reliance on metrics is a fundamental challenge for AI
Optimizing metrics is a central aspect of most current artificial intelligence (AI) approaches, yet overemphasizing metrics leads to manipulation, short-termism, and other negative consequences. This poses a fundamental challenge within AI. We review how metrics can go wrong in practice, and we put forward an evidence-based framework toward mitigating these harms.
·cell.com·
Reliance on metrics is a fundamental challenge for AI
Enterprise Data Analytics for a Connected Multi-Cloud World
Enterprise Data Analytics for a Connected Multi-Cloud World
Teradata Vantage is the connected multi-cloud data platform for enterprise analytics that delivers actionable answers and predictive intelligence. Learn more.
·teradata.com·
Enterprise Data Analytics for a Connected Multi-Cloud World
Real World Recommendation System - Part 1
Real World Recommendation System - Part 1
Training a collaborative filtering based recommendation system on a toy dataset is a sophomore year project in colleges these days. But where the rubber meets the road is building such a system at scale, deploying in production, and serving live requests within a few hundred milliseconds while the user is waiting for the page to load. To build a system like this, engineers have to make decisions spanning multiple moving layers like:
·blog.fennel.ai·
Real World Recommendation System - Part 1
TaxProf Blog: Will Machines Replace Us? Machine-Authored Texts And The Future Of Scholarship
TaxProf Blog: Will Machines Replace Us? Machine-Authored Texts And The Future Of Scholarship
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...
·taxprof.typepad.com·
TaxProf Blog: Will Machines Replace Us? Machine-Authored Texts And The Future Of Scholarship
AI and Drug Discovery: Attacking the Right Problems
AI and Drug Discovery: Attacking the Right Problems
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
·blogs.sciencemag.org·
AI and Drug Discovery: Attacking the Right Problems
What is a Feature Store?
What is a Feature Store?
Feature stores solve the full set of data management problems encountered when building and operating operational ML applications.
·tecton.ai·
What is a Feature Store?
BenevolentAI: Worth Two Billion?
BenevolentAI: Worth Two Billion?
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
·techmeme.com·
BenevolentAI: Worth Two Billion?
This wood-fired hot tub is $20,000
This wood-fired hot tub is $20,000
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.
·engadget.com·
This wood-fired hot tub is $20,000