AlphaDogfight wins 5-0 in F-16 battle vs human

Will Knight, writing for Wired:

Last week, a technique popularized by DeepMind was adapted to control an autonomous F-16 fighter plane in a Pentagon-funded contest to show off the capabilities of AI systems. In the final stage of the event, a similar algorithm went head-to-head with a real F-16 pilot using a VR headset and simulator controls. The AI pilot won, 5-0.

A Dogfight Renews Concerns About AI’s Lethal Potential

This is an under-discussed issue, but is inevitable. DeepMind is convinced that its AlphaZero DNN can master any two-player, turn-based game that shows perfect information. And its AlphaStar DNN shows what it can do in real-time games as well. It is a natural, and inevitable, extension to war capabilities.

Is this ok? Does that question even matter? How long before human-in-the-loop is the unacceptable bottleneck?

Freedom vs. Security continued…

Kashmir Hill for the NYT:

Floyd Abrams, one of the most prominent First Amendment lawyers in the country, has a new client: the facial recognition company Clearview AI.

Litigation against the start-up “has the potential of leading to a major decision about the interrelationship between privacy claims and First Amendment defenses in the 21st century,” Mr. Abrams said in a phone interview. He said the underlying legal questions could one day reach the Supreme Court.

Facial Recognition Start-Up Mounts a First Amendment Defense

Is everything known to the public truly available for any use whatsoever? We are trending away from that view, and this will be a battle to watch closely.

Facial recognition software countermeasures

Software that tweaks photos to hide them from facial recognition:

A start-up called Clearview AI, for example, scraped billions of online photos to build a tool for the police that could lead them from a face to a Facebook account, revealing a person’s identity.

Now researchers are trying to foil those systems. A team of computer engineers at the University of Chicago has developed a tool that disguises photos with pixel-level changes that confuse facial recognition systems.

This Tool Could Protect Your Photos From Facial Recognition

This is of course just an arms race. The facial recognition will improve, the hiding software will get tweaked.

Rite Aid has been using facial recognition for 8 years

Jeffrey Dastin writing for Reuters:

The cameras matched facial images of customers entering a store to those of people Rite Aid previously observed engaging in potential criminal activity, causing an alert to be sent to security agents’ smartphones. Agents then reviewed the match for accuracy and could tell the customer to leave.

Rite Aid deployed facial recognition systems in hundreds of U.S. stores

The DeepCam systems were primarily deployed in “lower-income, non-white neighborhoods,” and, according to current and former Rite Aid employees, a previous system called FaceFirst regularly made mistakes:

“It doesn’t pick up Black people well,” one loss prevention staffer said last year while using FaceFirst at a Rite Aid in an African-American neighborhood of Detroit. “If your eyes are the same way, or if you’re wearing your headband like another person is wearing a headband, you’re going to get a hit.”

Shortcuts in Learing

So starts a great article on DNN’s and learning shortcuts:

Recently, researchers trained a deep neural network to classify breast cancer, achieving a performance of 85%. When used in combination with three other neural network models, the resulting ensemble method reached an outstanding 99% classification accuracy, rivaling expert radiologists with years of training.

The result described above is true, with one little twist: instead of using state-of-the-art artificial deep neural networks, researchers trained “natural” neural networks – more precisely, a flock of four pigeons – to diagnose breast cancer.

Shortcuts: How Neural Networks Love to Cheat

Sometimes the learning is clever; sometimes the learning is a problem. Mostly though, it’s hard to make sure an AI learns what you want it to learn.

Crime fighting tools of social media posts

James Vincent, reporting for The Verge:

As reported by The Philadelphia Inquirer, at the start of their investigation, FBI agents only had access to helicopter footage from a local news station. This showed a woman wearing a bandana throwing flaming debris into the smashed window of a police sedan.

By searching for videos of the protests uploaded to Instagram and Vimeo, the agents were able to find additional footage of the incident, and spotted a peace sign tattoo on the woman’s right forearm. After finding a set of 500 pictures of the protests shared by an amateur photographer, they were able to clearly see what the woman was wearing, including a T-shirt with the slogan: “Keep the Immigrants. Deport the Racists.”

The only place to buy this exact T-shirt was an Etsy store, where a user calling themselves “alleycatlore” had left a five-star review for the seller just few days before the protest. Using Google to search for this username, agents then found a matching profile at the online fashion marketplace Poshmark which listed the user’s name as “Lore-elisabeth.” 

A search for “Lore-elisabeth” led to a LinkedIn profile for one Lore Elisabeth Blumenthal, employed as a massage therapist at a Philadelphia massage studio. Videos hosted by the studio showed an individual with the same distinctive peace tattoo on their arm. A phone number listed for Blumenthal led to an address. As reported by NBC Philadelphia, a subpoena served to the Etsy seller showed a “Keep the Immigrants. Deport the Racists.” T-shirt had recently been delivered to that same address.

FBI used Instagram, an Etsy review, and LinkedIn to identify a protestor accused of arson

Zoom and enhance!

Using computer systems to zoom and enhance is a tv trope.

But we’re getting better.

Researchers at Duke University have released a paper on PULSE, an AI algorithm that constructs a high resolution face from a low resolution image. And the results look pretty good:

6/23/2020 Update: The PULSE algorithm exhibits a notable bias towards Caucasian features:

It’s a startling image that illustrates the deep-rooted biases of AI research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man.

What a machine learning tool that turns Obama white can (and can’t) tell us about AI bias

Automated systems are often wrong

And automated background checks may be terrible!

The reports can be created in a few seconds, using searches based on partial names or incomplete dates of birth. Tenants generally have no choice but to submit to the screenings and typically pay an application fee for the privilege. Automated reports are usually delivered to landlords without a human ever glancing at the results to see if they contain obvious mistakes, according to court records and interviews.

How Automated Background Checks Freeze Out Renters

So much of ethical AI comes down to requiring a human-in-the-loop for any system that has a non-trivial impact on other humans.