Our bi-weekly quick take on a bunch of the most important recent media stories about AI for the period 10th September 2018 - 24th September 2018

Advances & Business

Beyond Deep Fakes

Byron Spice, CMU AI

Deep Fakes have certainly been causing a lot of concern recently, but they do have limitations - thus far it has been tricky to use the existing algorithms to generate high quality outputs with a lot of data. As summarized in this article, CMU’s new Recycle-GAN algorithm reduces some of those limitations, and once again increases the need for algorithms to be developed to detect algorithmically modified media.

These Robots Run, Dance and Flip. But Are They a Business?

Cade Metz, The New York Times

“Boston Dynamics’s robot only looks like it thinks for itself.”

A good summary of the state of the company Boston Dynamics - already famous for its impressive walking and trotting robots, but not yet a successful business. With its Spot Mini going on sale next year, it will be interesting to see if the technology is mature enough to have useful applications for generating revenue.

AI gliders learn to fly using air currents, just like birds

James Vincent, The Verge

Another day, another novel application of recent AI algorithms - this time, for controlling drones to make use of air currents similarly to birds. The methods are not ready for deployment yet, but mark yet another area in which AI could prove useful.

IBM launches cloud tool to detect AI bias and explain automated decisions

Natasha Lomas, Techcrunch

IBM has launched a new open source toolkit to detect and mitigate bias in datasets. The tool is said to increase interpretability of the results produced by AI algorithms as well as, in some cases, compliance with policies such as GDPR. How much the toolkit is welcomed by the AI research community remains to be seen.

Concerns & Hype

Machine Learning Confronts the Elephant in the Room

Kevin Hartnett, Quanta Magazine

Researchers from Toronto have developed a new adversarial attack for object detection systems - literally introducing an elephant into a picture of a room. The inability of current computer vision systems to go back and recheck images is a shortcoming that needs to be addressed to build robust systems. Effectively, computer vision systems will have to figure out how to do a double take.

Former Head of Google China Foresees an AI Crisis—and Proposes a Solution

Eliza Strickland, IEEE Spectrum

“the big AI question isn’t whether China or the United States will dominate. Instead it’s how we’ll deal with the “real AI crisis” of job losses, wealth inequality, and people’s sense of self-worth.”

Kai-Fu Lee recently wrote a new book titled AI Superpowers: China, Silicon Valley, and the New World Order, and discussed the ideas presented in it with IEEE Spectrum in this interview.

AI may not be bad news for workers

The Economist

”Previous technology shifts have not had as negative effects on employment as was first feared.”

A new report from University of California, Berkeley in collaboration with Tata Communications suggests that with the rise in AI, the job satisfaction of ordinary employees will be higher, contrary to the long held belief that AI will render these people without jobs.

“Job satisfaction will be enhanced by the elimination of mundane tasks, giving people time to be more creative.”

An AI Analyzed My Twitter Feed and Discovered I’m a Shithead

Derek Mead, Motherboard

A journalist describes his experiments with a tool that claims to analyze a person’s personality and employability through their twitter feed. He dissects the output of the tool for his feed and embellishes by providing psychological insights, possible shortcomings, and biases in using a tool such as this.

Analysis & Policy

China’s leaders are softening their stance on AI

Will Knight, MIT Technology Review

“China might be at loggerheads with the United States over trade, but it is calling for a friendlier approach to the development of artificial intelligence.”

After an initially aggressive stance towards competing with the US for domination in the space of AI, China is now communicating a more pro-collaboration stance.

Why Google’s Inclusive Images Challenge Is So Important

Kalev Leetaru, Forbes

“Deep learning systems learn from the training data available to them”

To move towards systems that are more robust to bias in image datasets, Google launched the Inclusive Images Challenge which promotes development of algorithms that are less prone to be biased even when skewed data is used. Such a challenge helps generalisation for underrepresented geographies and cultures and is a step towards solving an important problem in AI today.

UK cops run machine learning trials on live police operations. Unregulated. What could go wrong?

Rebecca Hill, The Register

“We need to be very careful that if these new technologies are put into day-to-day practices, they don’t create new gaming and target cultures,”

The Royal United Services Institute (RUSI) a defense and security think tank published a report on the use of machine learning in police decision making. The report says that it is hard to predict the impact of ML-driven tools and algorithmic bias. It seems, however, that police in the UK continue to use these tools.

Expert Opinions & Discussion within the field

The Human Promise of the AI Revolution

Kai-Fu Lee, The Wall Street Journal

This essay, an adapted excerpt from Kai-Fu Lee’s upcoming book, talks about the short-term impact that AI will have in terms of job losses. Issues relating to feelings of obsolescence, the need for a basic-income like fund and jobs that will likely not be replaced by AI are also discussed.

Safe artificial intelligence requires cultural intelligence

Gillian Hadfield, Techcrunch

Successful and safe AI that achieves our goals within the limits of socially accepted norms requires an understanding of not only how our physical systems behave, but also how human normative systems behave.

This essay argues that to ensure that AI systems are aligned with human goals they need to understand cultural norms.

Explainers

The Use Of Embeddings In OpenAI Five

Tambet Matiisen, Computational Neuroscience Lab, University of Tartu

This blog post explains the use of embeddings in OpenAI Five’s network architecture.

Variational Autoencoders Explained

Yoel Zeldes, Another Datum

Variational Autoencoders (VAE) are widely used to generate new examples similar to the dataset they are trained on. This post goes into the theory behind the inner workings of a VAE.

Awesome Videos

Computers can see. Now what?

Joseph Redmon, TEDxGateway

Joseph Redmon delivers a thought provoking talk address free and open source being AI technology being used with malicious intent.

Machine Learning: The Opportunity and the Opportunists

Zachary Lipton, EMTECH, MIT Review

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