Press Failure: The Guardian’s “Meet Erica”

Meet Erica, the world’s most human-like autonomous android. From its title alone, this documentary promises a sensational encounter. As the screen fades in from black, a marimba tinkles lightly in the background and a Japanese alleyway appears. Various narrators ask us:

“What does it mean to think?”

“What is human creativity?”

“What does it mean to have a personality?”

“What is an interaction?”

“What is a minimal definition of humans?”

The title, these questions, and nearly everything that follows mislead. This article is an installment in a series of posts addressing the various sources of misinformation feeding the present AI hype cycle.

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DeepMind Solves AGI, Summons Demon

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. As it turns out, we may not have to wait much longer.

This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asynchronous  bisecting convolutions. By the year’s end, Alphabet executives expect that these neural networks will exhibit fully autonomous self-improvement. What comes next may affect us all.

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Notes on Response to “The AI Misinformation Epidemic”

On Monday, I posted an article titled The AI Misinformation Epidemic. The article introduces a series of posts that will critically examine the various sources of misinformation underlying this AI hype cycle.

The post came about for the following reason: While I had contemplated the idea for weeks, I couldn’t choose which among the many factors to focus on and which to exclude. My solution was to break down the issue into several narrower posts. The AI Machine Learning Epidemic introduced the problem, sketched an outline for the series, and articulated some preliminary philosophical arguments.

To my surprise, it stirred up a frothy reaction. In a span of three days, the site received over 36,000 readers. To date, the article received 68 comments on the original post, 274 comments on hacker news, and 140 comments on machine learning subreddit.

To ensure that my post contributes as little novel misinformation as possible, I’d like to briefly address the response to the article and some common misconceptions shared by many comments. Continue reading “Notes on Response to “The AI Misinformation Epidemic””

The AI Misinformation Epidemic

Interest in machine learning may be at an all-time high. Per Google Trends, people are searching for machine learning nearly five times as often as five years ago. And at the University of California San Diego (UCSD), where I’m presently a PhD candidate, we had over 300 students enrolled in both our graduate-level recommender systems and neural networks courses.

Much of this attention is warranted. Breakthroughs in computer vision, speech recognition, and, more generally, pattern recognition in large data sets, have given machine learning substantial power to impact industry, society, and other academic disciplines.

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Fake News Challenge – Revised and Revisited

The organizers of the The Fake News Challenge have subjected it to a significant overhaul. In this light, many of my criticisms of the challenge no longer apply.

Some context:

Last month, I posted a critical piece addressing the fake news challenge. Organized by Dean Pomerleau and Delip Rao, the challenge aspires to leverage advances in machine learning to combat the epidemic viral spread of misinformation that plagues social media. The original version of the the challenge asked teams to take a claim, such as “Hillary Clinton eats babies”, and output a prediction of its veracity together with supporting documentation (links culled from the internet). Presumably, their hope was that an on-the-fly artificially-intelligent fact checker could be integrated into social media services to stop people from unwittingly sharing fake news.

My response criticized the challenge as both ill-specified (fake-ness not defined), circular (how do we know the supporting documents are legit?) and infeasible (are teams supposed to comb the entire web?)

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The Deception of Supervised Learning – V2

[This article is a revised version reposted with permission from KDnuggets]

Imagine you’re a doctor tasked with choosing a cancer therapy. Or a Netflix exec tasked with recommending movies. You have a choice. You could think hard about the problem and come up with some rules. But these rules would be overly simplistic, not personalized to the patient or customer. Alternatively, you could let the data decide what to do!

The ability to programmatically make intelligent decisions by learning complex decision rules from big data is a driving selling point of machine learning. Leaps forward in the predictive accuracy of supervised learning techniques, especially deep learning, now yield classifiers that outperform human predictive accuracy on many tasks. We can guess how an individual will rate a movie, classify images, or recognize speech with jaw-dropping accuracy. So why not make our services smart by letting the data tell us what to do?

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Is Fake News a Machine Learning Problem?

On Friday, Donald J. Trump was sworn in as the 45th president of the United States. The inauguration followed a bruising primary and general election, in which social media played an unprecedented role. In particular, the proliferation of fake news emerged as a dominant storyline. Throughout the campaign, explicitly false stories circulated through the internet’s echo chambers. Some fake stories originated as rumors, others were created for profit and monetized with click-based advertisements, and according to US Director of National Intelligence James Clapper, many fake news were orchestrated by the Russian government with the intention of influencing the results.  While it is not possible to observe the counterfactual, many believe that the election’s outcome hinged on the influence of these stories.

For context, consider one illustrative case as described by the New York Times. On November 9th, 35-year old marketer Erik Tucker tweeted a picture of several buses, claiming that they were transporting paid protesters to demonstrate against Trump. The post quickly went viral, receiving over 16,000 shares on Twitter and 350,000 shares on Facebook. Trump and his surrogates joined in, promoting the story through social media. Tucker’s claim turned out to be a fabrication. Nevertheless, it likely reached millions of people, more than many conventional news stories.

A number of critics cast blame on technology companies like Facebook, Twitter, and Google, suggesting that they have a responsibility to address the fake news epidemic because their algorithms influence who sees which stories. Some linked the fake news phenomenon to the idea that personalized search results and news feeds create a filter bubble, a dynamic in which readers only encounter stories that they are likely to click on, comment on, or like. As a consequence, readers might only encounter stories that confirm pre-existing beliefs.

Facebook, in particular, has been strongly criticized for their trending news widget, which operated (at the time) without human intervention, giving viral items a spotlight, however defamatory or false. In September, Facebook’s trending news box promoted a story titled ‘Michele Obama was born a man’. Some have wondered why Facebook, despite its massive investment in artificial intelligence (machine learning), hasn’t developed an automated solution to the problem.

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Policy Field Notes: NIPS Update

By Jack Clark and Tim Hwang. 

Conversations about the social impact of AI often are very abstract, focusing on broad generalizations about technology rather than talking about the specific state of the research field. That makes it challenging to have a full conversation about what good public policy regarding AI would be like. In the interest of helping to bridge that gap, Jack Clark and I have been playing around with doing recaps that’ll take a selection of papers from a recent conference and talk about the longer term policy implications of the work. This one covers papers that appeared at NIPS 2016.

If it’s helpful to the community, we’ll plan to roll out similar recaps throughout 2017 — with the next one being ICLR in April.

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AI Safety Highlights from NIPS 2016

[This article is cross-posted from my blog. Thanks to Jan Leike, Zachary Lipton, and Janos Kramar for providing feedback on this post.]

This year’s Neural Information Processing Systems conference was larger than ever, with almost 6000 people attending, hosted in a huge convention center in Barcelona, Spain. The conference started off with two exciting announcements on open-sourcing collections of environments for training and testing general AI capabilities – the DeepMind Lab and the OpenAI Universe. Among other things, this is promising for testing safety properties of ML algorithms. OpenAI has already used their Universe environment to give an entertaining and instructive demonstration of reward hacking that illustrates the challenge of designing robust reward functions.

I was happy to see a lot of AI-safety-related content at NIPS this year. The ML and the Law symposium and Interpretable ML for Complex Systems workshop focused on near-term AI safety issues, while the Reliable ML in the Wild workshop also covered long-term problems. Here are some papers relevant to long-term AI safety:

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Machine Learning Meets Policy: Reflections on HUML 2016

Last Friday, the University of Ca’ Foscari in Venice organized an IEEE workshop on the Human Use of Machine Learning (HUML 2016). The workshop, held at the European Centre for Living Technology, hosted roughly 30 participants and broadly addressed the social impacts and ethical problems stemming from the wide-spread use of machine learning.

HUML joins a growing number workshops for critical voices in the ML community. These include Fairness, Accountability and Transparency in Machine Learning (FAT-ML), the #Data4Good at ICML 2016, and Human Interpretability of Machine Learning (WHI), held this year at ICML and Interpretable ML for Complex Systems, held this year at NIPS. Among this company, HUML was notable especially notable for diversity of perspectives. While FAT-ML, DS4Good and WHI featured presentations primarily by members of the machine learning community, HUML brought together scholars from philosophy of science, law, predictive policing, and  machine learning.

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