AI

2018 in Review: 10 AI Failures

Last December Synced compiled its first “Artificial Intelligence Failures” recap of AI gaffes from the previous year. AI has achieved remarkable progress, and many scientists dream of creating the Master Algorithm proposed by Pedro Domingos — which can solve all problems envisioned by humans.

Last December Synced compiled its first “Artificial Intelligence Failures” recap of AI gaffes from the previous year. AI has achieved remarkable progress, and many scientists dream of creating the Master Algorithm proposed by Pedro Domingos — which can solve all problems envisioned by humans. It’s unavoidable however that researchers, fledgling technologies and biased data will also produce blunders not envisioned by humans.

That’s why a review of AI failures is necessary and meaningful: The aim of the article is not to downplay or mock research and development results, but to take a look at what went wrong with the hope we can do better next time.

Synced 10 AI failures of 2018.

Chinese billionaire’s face identified as jaywalker

Traffic police in major Chinese cities are using AI to address jaywalking. They deploy smart cameras using facial recognition techniques at intersections to detect and identify jaywalkers, whose partially obscured names and faces then show up on a public display screen.

The AI system in the southern port city of Ningbo however recently embarrassed itself when it falsely “recognized” a photo of Chinese billionaire Mingzhu Dong on an ad on a passing bus as a jaywalker. The mistake went viral on Chinese social media and Ningbo police apologized. Dong was unfazed, posting on Weibo: “This is a trivial matter. Safe travel is more important.”

CloudWalk Deep Learning Researcher Xiang Zhou told Synced the algorithm’s lack of live detection was the likely problem. “Live detection at this distance is challenging, recognizing an image as a real person is pretty common now.”

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Chinese billionaire Mingzhu Dong’s face on a public display screen.

Uber self-driving car kills a pedestrian

In the first known autonomous vehicle-related pedestrian death on a public road, an Uber self-driving SUV struck and killed a female pedestrian on March 28 in Tempe, Arizona. The Uber vehicle was in autonomous mode, with a human safety driver at the wheel.

So what happened? Uber discovered that its self-driving software decided not to take any actions after the car’s sensors detected the pedestrian. Uber’s autonomous mode disables Volvo’s factory-installed automatic emergency braking system, according to US National Transportation Safety Board preliminary report on the accident.

In the wake of the tragedy Uber suspended self-driving testing in North American cities, and Nvidia and Toyota also stopped their self-driving road tests in the US. Eight months after the accident Uber announced plans to resume self-driving road tests in Pittsburgh, although the company’s self-driving future remains uncertain.

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ABC 15 screenshot of deadly Uber accident.

IBM Watson comes up short in healthcare

“This product is a piece of shit” wrote a doctor at Florida’s Jupiter Hospital regarding IBM’s flagship AI program Watson, according to internal documents obtained by Stat. Originally a question-answering machine, IBM has been exploring Watson’s AI capabilities across a broad range of applications and processes, including healthcare. In 2013 IBM developed Watson’s first commercial application for cancer treatment recommendation, and the company has secured a number of key partnerships with hospitals and research centers over the past five years. But Watson AI Health has not impressed doctors. Some complained it gave wrong recommendations on cancer treatments that could cause severe and even fatal consequences.

After spending years on the project without significant advancements, IBM is reportedly downsizing Watson Health and laying off more than half the division’s staff.

Amazon AI recruiting tool is gender biased

Amazon HR reportedly used an AI-enabled recruiting software between 2014 and 2017 to help review resumes and make recommendations. The software was however found to be more favorable to male applicants because its model was trained on resumes submitted to Amazon over the past decade, when many more male candidates were hired.

The software reportedly downgraded resumes that contain the word “women” or implied the applicant was female, for example because they had attended a women’s college. Amazon has since abandoned the software. The company did not deny using the tool to produce recommendations, but said it was never used to evaluate candidates.

DeepFakes reveals AI’s unseemly side

Last December several porn videos appeared on Reddit “featuring” top international female celebrities. User “DeepFakes” employed generative adversarial networks to swap celebrities’ faces with those of the porn stars. While face-swapping technology has been under development for years, DeepFakes’ method showed that anyone with enough facial images could now produce their own highly convincing fake videos.

Realistic-looking fake videos of well-known people flooded the Internet through 2018. While the method is not technically a “failure,” its potential dangers are serious and far-reaching: if video evidence is no longer credible, this could further encourage the circulation of fake news.

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Star Wars star Daisy Ridley’s face swapped with a porn actress in a DeepFake video.

Google Photo confuses skier and mountain

Google Photos includes a relatively unknown AI feature that can automatically detect images with the same backgrounds/scenes and offer to merge them into a single panoramic picture. In January Reddit User “MalletsDarker” posted three photos taken at a ski resort: two were landscapes, the other shot of his friend. When Google Photos merged the three a weird thing happened, as his friend’s head was rendered as a peak-like giant peering out from the forest.

The photo made the r/funny subreddit top ten and has received 202k upvotes. Social media hailed the Google algorithm’s smart blending of the images while mocking its stupidity for missing compositional basics.

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Blended image by Google Photos.

LG robot Cloi gets stagefright at its unveiling

January 8 was supposed to be the day when LG’s IoT AI assistant Cloi made its stunning debut at CES 2018 in Las Vegas. Cloi was presented as a simple and pleasant interface able to recognize voice commands to control home appliances. However when the cute robot took to the stage for its live demo, with the audience watching and waiting, and waiting… it failed to respond to commands from LG’s marketing chief, producing only awkward silence.

Boston Dynamics robot blooper

SoftBank-owned robot-maker Boston Dynamics has wowed the Internet more than once this year: Its robodog SpotMini can deftly open doors with its head-mounted gripper arm; and its humanoid robot Atlas can now do parkour — smoothly jumping over a log and leaping up a series of 40cm steps without breaking pace.

But even Boston Dynamics has its “oops” moments: In its debut at the Congress of Future Scientists and Technologists, Atlas lifted boxes etc. in a flawless demo. But just as Atlas had wrapped its demo and was attempting to leave, the poor robot tripped over a curtain and awkwardly tumbled off the stage. On the bright side, it fell much like a human might.

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Boston Dynamics robot Atlas jumps over a log.

AI World Cup 2018 predictions almost all wrong

The World Cup 2018 was the top sporting event of year, and AI researchers at Goldman Sachs, German Technische University of Dortmund, Electronic Arts, Perm State National Research University and other institutions ran machine learning models to predict outcomes for the multi-stage competition. Most however were totally wrong, with only EA — which ran its simulations using new ratings for its video game FIFA 18 — correctly favouring winner France. The EA game engine is backed by numerous machine learning techniques designed to make player performance as realistic as possible.

SQL Services Data Scientist Nick Burns offered an explanation: “No matter how good your models are, they are only as good as your data… recent football data just isn’t enough to predict the performance in the World Cup. There’s too much missing information and undefined influences.”

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Startup claims to predict IQ from faces

Israeli machine learning startup Faception made the controversial claim that its AI tech could analyse facial images and bone structure to reveal people’s IQ, personality, and even violent tendencies. Data scientist Ben Snyder rebuked the company’s tech on Twitter: “That’s phrenology. You just made the ML equivalent of a racist uncle.” The tweet has received over 6,500 retweets and almost 17,000 likes.

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Faception website screenshot.

Journalist: Tony Peng | Editor: Michael Sarazen

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