“In a paper released Thursday, the OpenAI team demonstrates that we can get those results from an “unsupervised” AI — meaning the system learned from reading 8 million internet articles, not from being explicitly trained for the tasks. Their AI advances the state of the art — in some cases, by a lot. The OpenAI team says their system sets a record for performance on so-called Winograd schemas, a tough reading comprehension task; achieves near-human performance on the Children’s Book Test, another check of reading comprehension; and — most thrillingly to me — generates its own text, including highly convincing news articles and Amazon reviews.”
Found via Product Hunt: Computer generated people. Refresh to get a new one.
The Verge: “The site is the creation of Philip Wang, a software engineer at Uber, and uses research released last year by chip designer Nvidia to create an endless stream of fake portraits. The algorithm behind it is trained on a huge dataset of real images, then uses a type of neural network known as a generative adversarial network (or GAN) to fabricate new examples.”
Favorites & Follow Ups: Updated More Frequently
Uber releases Ludwig, an open source AI ‘toolbox’ built on top of TensorFlow • The ride-hailing giant today debuted Ludwig, an open source “toolbox” built on top of Google’s TensorFlow framework that allows users to train and test AI models without having to write code.
AI 100: The Artificial Intelligence Startups Redefining Industries • CB Insights’ third annual cohort of AI 100 startups is a list of 100 of the most promising private companies providing hardware and data infrastructure for AI applications, optimizing machine learning workflows, and applying AI across a variety of major industries.
How It Feels to Learn Data Science in 2019 • Do not take this article too seriously. This is satire so do not treat it as actual advice. Like all advice, some of it is good and some of it is terrible. This piece is just an opinion, much like people’s definition of data science.
Bullshit, Hype, and a Little Magic: How to Make Sense of It All When Buying A.I. Products • Rob May, knows what to look for when it comes to A.I. But many products on the market are being labeled as A.I. just to sound sexy. In this eBook, May explains how to sort through the BS, identify the over-hyped solutions, and critically evaluate what is most important when buying an A.I. product for your business.
Rachel Thomas: I’m an AI researcher, and here’s what scares me about AI
“In particular, these 5 things scare me about AI:
- Algorithms are often implemented without ways to address mistakes.
- AI makes it easier to not feel responsible.
- AI encodes & magnifies bias.
- Optimizing metrics above all else leads to negative outcomes.
- There is no accountability for big tech companies.”
The Verge: “While the phrase “artificial intelligence” is unquestionably, undoubtedly misused, the technology is doing more than ever — for both good and bad. It’s being deployed in health care and warfare; it’s helping people make music and books; it’s scrutinizing your resume, judging your creditworthiness, and tweaking the photos you take on your phone. In short, it’s making decisions that affect your life whether you like it or not.”
Additional Reference & Resource: The Real World AI Issue
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