Rage Inside the Machine: an insightful, brilliant critique of AI's computer science, sociology, philosophy and economics

[I ran a review of this in June when the UK edition came out -- this review coincides with the US edition's publication]

Rob Smith is an eminent computer scientist and machine learning pioneer whose work on genetic algorithms has been influential in both industry and the academy; now, in his first book for a general audience, Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All, Smith expertly draws connections between AI, neoliberalism, human bias, eugenics and far-right populism, and shows how the biases of computer science and the corporate paymasters have distorted our whole society. Read the rest

AI Drama: Google DeepMind's Mustafa Suleyman abruptly placed on leave, no reason given for departure

Google DeepMind co-founder Mustafa Suleyman has been placed on leave from the applied artificial intelligence lab he ran. Read the rest

Uganda installs Huawei's AI-powered facial recognition surveillance system 'nationwide'

“The cameras are already transforming modern day policing in Uganda, with facial recognition and artificial intelligence as part of policing and security.” — Ugandan Police.

OpenAI releases larger GPT-2 dataset. Can it write fake news better than a human?

OpenAI has released a more extensive version of its generative language model.

We’re releasing the 774 million parameter GPT-2 language model after the release of our small 124M model in February ...

2. Humans can be convinced by synthetic text. Research from our research partners Sarah Kreps and Miles McCain at Cornell published in Foreign Affairs says people find GPT-2 synthetic text samples almost as convincing (72% in one cohort judged the articles to be credible) as real articles from the New York Times (83%). Additionally, research from AI2/UW has shown that news written by a system called “GROVER” can be more plausible than human-written propaganda. These research results make us generally more cautious about releasing language models

Blockquoted below is something I just had it make (using Talk to Transformer, which has been updated with the new dataset.)

I wrote the first (bolded) paragraph. GPT-2 wrote the rest.

Former Democratic presidential candidate and United States Senator Hillary Clinton was arrested today and charged on four counts of conspiracy, one count of fraud, and one count of lying to Federal investigators.

The details of the case are detailed below.

A Brief Overview of the Case

On June 2, 2014, Clinton (pictured) admitted to FBI agents that, on June 23, 2013, she, and others, had conspired with other political figures to take "official action" in response to a series of negative articles which she wrote in the Washington Times and other outlets.

The following is a summary of Clinton's admission:

Secretary Clinton used the Washington Post as her de facto personal email account and for the official State Department email account.

Read the rest

Training bias in AI "hate speech detector" means that tweets by Black people are far more likely to be censored

More bad news for Google's beleaguered spinoff Jigsaw, whose flagship project is "Perspective," a machine-learning system designed to catch and interdict harassment, hate-speech and other undesirable online speech. Read the rest

"Intellectual Debt": It's bad enough when AI gets its predictions wrong, but it's potentially WORSE when AI gets it right

Jonathan Zittrain (previously) is consistently a source of interesting insights that often arrive years ahead of their wider acceptance in tech, law, ethics and culture (2008's The Future of the Internet (and how to stop it) is surprisingly relevant 11 years later); in a new long essay on Medium (shorter version in the New Yorker), Zittrain examines the perils of the "intellectual debt" that we incur when we allow machine learning systems that make predictions whose rationale we don't understand, because without an underlying theory of those predictions, we can't know their limitations. Read the rest

Scite: a tool to find out if a scientific paper has been supported or contradicted since its publication

The Scite project has a corpus of millions of scientific articles that it has analyzed with deep learning tools to determine whether any given paper has been supported or contradicted by subsequent publications; you can check Scite via the website, or install a browser plugin version (Firefox, Chrome). (Thanks, Josh!) Read the rest

A generalized method for re-identifying people in "anonymized" data-sets

"Anonymized data" is one of those holy grails, like "healthy ice-cream" or "selectively breakable crypto" -- if "anonymized data" is a thing, then companies can monetize their surveillance dossiers on us by selling them to all comers, without putting us at risk or putting themselves in legal jeopardy (to say nothing of the benefits to science and research of being able to do large-scale data analyses and then publish them along with the underlying data for peer review without posing a risk to the people in the data-set, AKA "release and forget"). Read the rest

Interactive map of public facial recognition systems in America

Evan Greer from Fight for the Future writes, "Facial recognition might be the most invasive and dangerous form of surveillance tech ever invented. While it's been in the headlines lately, most of us still don't know whether it's happening in our area. My organization Fight for the Future has compiled an interactive map that shows everywhere in the US (that we know of) facial recognition being used -- but also where there are local efforts to ban it, like has already happened in San Francisco, Oakland, and Somerville, MA. We've also got a tool kit for local residents who want to get an ordinance or state legislation passed in their area." Read the rest

Many of the key Googler Uprising organizers have quit, citing retaliation from senior management

The Googler Uprising was a string of employee actions within Google over a series of issues related to ethics and business practices, starting with the company's AI project for US military drones, then its secretive work on a censored/surveilling search tool for use in China; then the $80m payout to Android founder Andy Rubin after he was accused of multiple sexual assaults. Read the rest

China's AI industry is tanking

In Q2 2018, Chinese investors sank $2.87b into AI startups; in Q2 2019, it was $140.7m. Read the rest

AI is like a magic trick: amazing until it goes wrong, then revealed as a cheap and brittle effect

I used to be on the program committee for the O'Reilly Emerging Technology conferences; one year we decided to make the theme "magic" -- all the ways that new technologies were doing things that baffled us and blew us away. Read the rest

Instagram's new solution against bullying: Artificial Intelligence, and 'Restrict'

Instagram launched a new feature today, Restrict, intended to help vulnerable users avoid abuse. Facebook's Head of Instagram Adam Mosseri says the company will also be focusing on new uses for AI to crack down on bullying. Read the rest

Self-driving car jargon

Bruce Sterling republishes the acronyms in a recent Daimler white-paper on self-driving cars: Read the rest

Make: a machine-learning toy on open-source hardware

In the latest Adafruit video (previously) the proprietors, Limor "ladyada" Friend and Phil Torrone, explain the basics of machine learning, with particular emphasis on the difference between computing a model (hard) and implementing the model (easy and simple enough to run on relatively low-powered hardware), and then they install and run Tensorflow Light on a small, open-source handheld and teach it to distinguish between someone saying "No" and someone saying "Yes," in just a few minutes. It's an interesting demonstration of the theory that machine learning may be most useful in tiny, embedded, offline processors. (via Beyond the Beyond) Read the rest

Using machine learning to pull Krazy Kat comics out of giant public domain newspaper archives

Joël Franusic became obsessed with Krazy Kat, but was frustrated by the limited availability and high cost of the books anthologizing the strip (some of which were going for $600 or more on Amazon); so he wrote a scraper that would pull down thumbnails from massive archives of pre-1923 newspapers and then identified 100 pages containing Krazy Kat strips to use as training data for a machine-learning model. Read the rest

Rage Inside the Machine: an insightful, brilliant critique of AI's computer science, sociology, philosophy and economics

Rob Smith is an eminent computer scientist and machine learning pioneer whose work on genetic algorithms has been influential in both industry and the academy; now, in his first book for a general audience, Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All, Smith expertly draws connections between AI, neoliberalism, human bias, eugenics and far-right populism, and shows how the biases of computer science and the corporate paymasters have distorted our whole society. Read the rest

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