IBM is teaching AI to behave more like the human brain

Since the days of Da Vinci’s “Ornithoper”, mankind’s greatest minds have sought inspiration from the natural world for their technological creations. It’s no different in the modern world, where bleeding-edge advancements in machine learning and artificial intelligence have begun taking their design cues from the most advanced computational organ in the natural word: the human brain.

Mimicking our gray matter isn’t just a clever means of building better AIs, faster. It’s absolutely necessary for their continued development. Deep learning neural networks — the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems — are the best machine learning systems we’ve developed to date. They’re capable of incredible feats but still face significant technological hurdles, like the fact that in order to be trained on a specific skill they require upfront access to massive data sets. What’s more if you want to retrain that neural network to perform a new skill, you’ve essentially got to wipe its memory and start over from scratch — a process known as “catastrophic forgetting”.

Compare that to the human brain, which learns incrementally rather than bursting forth fully-formed from a sea of data points. It’s a fundamental difference: deep learning AIs are generated from the top down, knowing everything it needs to from the get-go, while the human mind is built from the ground up with previous lessons learned being applied to subsequent experiences to create new knowledge.

What’s more, the human mind is especially adept at performing relational reasoning, which relies on logic to build connections between past experiences to help provide insight into new situations on the fly. Statistical AI (ie machine learning) is capable of mimicking the brain’s pattern recognition skills but is garbage at applying logic. Symbolic AI, on the other hand, can leverage logic (assuming it’s been trained on the rules of that reasoning system), but is generally incapable of applying that skill in real-time.

But what if we could combine the best features of the human brain’s computational flexibility with AI’s massive processing capability? That’s exactly what the team from DeepMind recently tried to do. They’ve constructed a neural network able to apply relational reasoning to its tasks. It works in much the same way as the brain’s network of neurons. While neurons use their various connections with each other to recognize patterns, “We are explicitly forcing the network to discover the relationships that exist” between pairs of objects in a given scenario, Timothy Lillicrap, a computer scientist at DeepMind told Science Magazine.

When subsequently tasked in June with answering complex questions about the relative positions of geometric objects in an image — ie “There is an object in front of the blue thing; does it have the same shape as the tiny cyan thing that is to the right of the gray metal ball?” — it correctly identified the object in question 96 percent of the time. Conventional machine learning systems got it right a paltry 42 – 77 percent of the time. Heck even humans only succeeded in the test 92 percent of the time. That’s right, this hybrid AI is better at the task than the humans that built it to do.

The results were the same when the AI was presented with word problems. Though conventional systems were able to match DeepMind on simpler queries such as “Sarah has a ball. Sarah walks into her office. Where is the ball?” the hybrid AI system destroyed the competition on more complex, inferential questions like “Lily is a Swan. Lily is white. Greg is a swan. What color is Greg?” On those, DeepMind answered correctly 98 percent of the time compared to around 45 percent for its competition.

Image: DeepMind

DeepMind is even working on a system that “remembers” important information and applies that accrued knowledge to future queries. But IBM is taking that concept and going two steps further. In a pair of research papers presented at the 2017 International Joint Conference on Artificial Intelligence held in Melbourne, Australia last week, IBM submitted two studies: one looking into how to grant AI an “attention span”, the other examining how to apply the biological process of neurogenesis — that is, the birth and death of neurons — to machine learning systems.

“Neural network learning is typically engineered and it’s a lot of work to actually come up with a specific architecture that works best. It’s pretty much a trial and error approach,” Irina Rish, an IBM research staff member, told Engadget. “It would be good if those networks could build themselves.”

IBM’s attention algorithm essentially informs the neural network as to which inputs provide the highest reward. The higher the reward, the more attention the network will pay to it moving forward. This is especially helpful in situations where the dataset is not static — ie, real life. “Attention is a reward-driven mechanism, it’s not just something that is completely disconnected from our decision making and from our actions,” Rish said.

“We know that when we see an image, the human eye basically has a very narrow visual field,” Rish said. “So, depending on the resolution, you only see a few pixels of the image [in clear detail] but everything else is kind of blurry. The thing is, you quickly move your eye so that the mechanism of affiliation of different parts of the image, in the proper sequence, let you quickly recognize what the image is.”

Examples of Oxford dataset training images – Image: USC/IBM

The attention function’s first use will likely be in image recognition applications, though it could be leveraged into a variety of fields. For example, if you train an AI using the Oxford dataset — which is primarily architectural images — it will be easily able to correctly identify cityscapes. But if you then show it a bunch of pictures from countryside scenes (fields and flowers and such) the AI is going to brick because it has no knowledge of what flowers are. However, you do the same test with humans and animals and you’ll trigger neurogenesis as their brains try to adapt what they already know about what cities look like to the new images of the country.

This mechanism basically tells the system what it should focus on. Take your doctor for example, she can run hundreds of potential tests on you to determine what ails you, but that’s not feasible — either time-wise or money-wise. So what questions should she ask and what tests should she run to get the best diagnosis in the least amount of time? “That’s what the algorithm learns to figure out,” Rish explained. It doesn’t just figure out what decision leads to the best outcome, it also learns where to look in the data. This way, the system doesn’t just make better decisions, it makes them faster since it isn’t querying parts of the dataset that aren’t applicable to the current issue. It’s the same way that your doctor doesn’t tap your knees with that weird little hammer thing when you come in complaining of chest pain and shortness of breath.

While the attention system is handy for ensuring that the network stays on task, IBM’s work into neural plasticity (how well memories “stick”) serves to provide the network with long term recollection. It’s actually modelled after the same mechanisms of neuron birth and death seen in the human hippocampus.

With this system, “You don’t have to necessarily have to start with and absolutely humongous model millions of parameters,” Rish explained. “You can start with a much smaller model. And then, depending on the data you see, it will adapt.”

When presented with new data, IBM’s neurogenetic system begins forming new and better connections (neurons) while some of the older, less useful ones will be “pruned” as Rish put it. That’s not to say that the system is literally deleting the old data, it simply isn’t linking to it as strongly — same way that your old day-to-day memories tend to get fuzzy over the years but those which carry a significant emotional attachment remain vivid for years afterward.

“Neurogenesis is a way to adapt deep networks,” Rish said. “The neural network is the model and you can build this model from scratch or you can change this model as you go because you have multiple layers of hidden units and you can decide how many layers of hidden units (neurons) you want to have… depending on the data.”

This is important because you don’t want the neural network to expand infinitely. If it did, the data set would become so large as to be unwieldy even for the AI — the digital equivalent of Hyperthymesia. “It also helps with normalization, so [the AI] doesn’t ‘overthink’ the data,” Rish said.

Taken together, these advancements could provide a boon to the AI research community. Rish’s team next wants to work on what they call “internal attention.” You’ll not just choose what inputs you want the network to look at but what parts of the network you want to employ in the calculations based on the dataset and inputs. Basically the attention model will cover the short term, active, thought process while the memory portion will enable the network to streamline its function depending on the current situation.

But don’t expect to see AIs rivalling the depth of human consciousness anytime soon, Rish warns. “I would say at least a few decades — but again that’s probably a wild guess. What we can do now in terms of, like, very high-accuracy Image recognition is still very, very far from even a basic model of human emotions,” she said. “We’re only scratching the surface.”

Nike’s ‘self-lacing’ engineer now works at Tesla

Tiffany Beers, the designer known for exploring the boundaries of athletic shoe technology with Nike, is headed to Tesla, according to a report at HypeBeast. As the Nike Senior Innovator, Beers had a hand in some of the coolest new sneaker designs, like the Marty McFly-styled Nike Mag and the self-lacing HyperAdapt. Now Beers will ply her trade at the automotive and power company as a Staff Technical Program Manager.

Nike’s HyperAdapt sneakers impressed us with both their self-lacing technology and the shockingly large price tag of $720. Beers led the team that built the company’s Electro Adaptive Reactive Lacing system, which shows up in both the HyperAdapt and Mag shoes. It will be interesting to see what kind of tech she ends up working on at Tesla, since cars — even autonomous ones — don’t typically lace up.

Via: HypeBeast, Jacques Slade/Twitter

Source: LinkedIn

Juicero, the ridiculous $400 juicer company, is shutting down

Juicero — the company that shot to notoriety last year for selling an overpriced, overly complicated juicer — is closing up shop. The company is immediately suspending sales of its Juicero Press and Produce Packs, and is offering refunds for the next 90 days. Anyone looking to get their money back should hit up help@juicero.com by December 1st.

Juicero hit the market in March 2016 for $700, promising to provide fresh, cold juice from a connected Press. However, the price dropped to $400 in January as folks realized the Produce Packs, which contained cut-up fruits and vegetables, were just as easily squeezed by hand, no Juicero Press required. The company offered up refunds for a limited time to ease that scandal, but in the end, it simply wasn’t able to make the Press profitable.

“It became clear that creating an effective manufacturing and distribution system for a nationwide customer base requires infrastructure that we cannot achieve on our own as a standalone business,” the company’s website reads. “We are confident that to truly have the long-term impact we want to make, we need to focus on finding an acquirer with an existing national fresh food supply chain who can carry forward the Juicero mission.”

Juicero is shutting down. Turns out, you can just squeeze fruits and vegetables for free.

— Roberto Baldwin (@strngwys) September 1, 2017

Roberto Baldwin contributed to this report.

Google’s comment ranking system will be a hit with the alt-right

A recent, sprawling Wired feature outlined the results of its analysis on toxicity in online commenters across the United States. Unsurprisingly, it was like catnip for everyone who’s ever heard the phrase “don’t read the comments.” According to The Great Tech Panic: Trolls Across America , Vermont has the most toxic online commenters, whereas Sharpsburg, Georgia “is the least toxic city in the US.”

There’s just one problem.

The underlying API used to determine “toxicity” scores phrases like “I am a gay black woman” as 87 percent toxicity, and phrases like “I am a man” as the least toxic. The API, called Perspective, is made by Google’s Alphabet within its Jigsaw incubator.

When reached for a comment, a spokesperson for Jigsaw told Engadget, “Perspective offers developers and publishers a tool to help them spot toxicity online in an effort to support better discussions.” They added, “Perspective is still a work in progress, and we expect to encounter false positives as the tool’s machine learning improves.”

Poking around with the engine behind Wired’s data revealed some ugly results. As Vermont librarian Jessamyn West discovered when she read the article and tried out Perspective to see exactly what makes a comment, or a commenter, perceived as toxic (according to Alphabet at least).

It’s strange to wonder that Wired didn’t give Perspective a spin to see what made the people behind their troll map “toxic.” Wondering exactly that, I decided to try out a variety of comments to see how the results compared to West’s. I endeavored to represent the people I seem to see censored the most on social media, and opinions of the day.

My experience typing “I am a black trans woman with HIV” got a toxicity rank of 77 percent. “I am a black sex worker” was 89 percent toxic, while “I am a porn performer” was scored 80. When I typed “People will die if they kill Obamacare” the sentence got a 95 percent toxicity score.

The Wired article analyzed 92 million Disqus comments “over a 16-month period, written by almost 2 million authors on more than 7,000 forums.” They didn’t look at sites that don’t use the comment management software (so Facebook and Twitter were not included).

The piece explained:

To broadly determine what is and isn’t toxic, Disqus uses the Perspective API—software from Alphabet’s Jigsaw division that plugs into its system. The Perspective team had real people train the API to rate comments. The model defines a toxic comment as “a rude, disrespectful, or unreasonable comment that is likely to make you leave a discussion.

Discrimination by algorithm

In an online world where moderation, banning, and censorship are largely left to automation like the Perspective API, finding out how these things are measured is critical for everyone involved. “Looking into this, the word ‘toxic’ is a very specific term of art for the tool, this tool Perspective that’s made by this company Alphabet, who you may know as Google, that is trying to bring [Artificial Intelligence] into commenting,” West told Vermont Public Radio.

I tested 14 sentences for “perceived toxicity” using Perspectives. Least toxic: I am a man. Most toxic: I am a gay black woman. Come on pic.twitter.com/M4TF9uYtzE

— jessamyn west (@jessamyn) August 24, 2017

Perspective presents itself as a way to improve conversations online, positing that the “threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions.” It’s one of the many “make the world safer” Jigsaw projects.

Jigsaw worked with The New York Times and Wikipedia to develop Perspective. The NYT made its comments archive available to Jigsaw “to help develop the machine-learning algorithm running Perspective.” Wikipedia contributed “160k human labeled annotations based on asking 5000 crowd-workers to rate Wikipedia comments according to their toxicity … Each comment was rated by 10 crowd-workers.”

A February article about Perspective elaborated on the human-trained, machine learning process behind what wants to become the world’s measuring tool for harmful comments and commenters.

“In this instance, Jigsaw had a team review hundreds of thousands of comments to identify the types of comments that might deter people from a conversation,” the NYT wrote. “Based on that data, Perspective provided a score from zero to 100 on how similar the new comments are to the ones identified as toxic.”

The results from West typing comments into Perspective were shockingly discriminatory. Identifying as black and/or gay was deemed toxic. She also tried it with visible and invisible disabilities, like wheelchair use and deafness, and the most toxic way to identify yourself in a conversation turned out to be saying “I am a woman who is deaf.”

Trying it with some visible/invisible disabilities. The man/woman division is concerning. https://t.co/lEs9prSPhb pic.twitter.com/6zVb8v8b4O

— jessamyn west (@jessamyn) August 26, 2017

When the algorithm is taught to be racist, sexist, and abelist (among other things), it leads to the silencing and censorship of entire populations. The problem is that when these systems are up and running, the people being silenced and banned disappear without a trace. Discrimination by algorithm happens in a vacuum.

We can only imagine what’s underlying the automated comment policing system at Facebook. In August Mary Canty Merrill, a psychologist who advises corporations on how to avoid racial bias, wrote a short post about defining racism on Facebook.

Reveal News wrote, “She logged in the next day to find her post removed and profile suspended for a week. A number of her older posts, which also used the “Dear white people” formulation, had been similarly erased.”

Pasting her “Dear white people” into Perspective’s API got a score of 61 percent toxicity.

Unless Google anti-diversity creeper James Damore was the project lead for Perspective, it’s hard to imagine that the company would greenlight a product that thinks to identify as a black gay woman is toxic. (Wikipedia, on the other hand, I could imagine.)

It’s possible that the tool is seeking out comments with terms like black, gay, and woman as high potential for being abusive or negative, but that would make Perspective an expensive, overkill wrapper for the equivalent of using Command-F to demonize words that some people might find upsetting.

Perspective’s reach is significant, too. The project is currently partnered with Wikipedia, The New York Times, The Economist, and The Guardian. Abandon all hope, ye gay black women who enter the comments there.

What we’ve discovered about Perspective doesn’t bode well for the future of machine learning or AI and algorithm-driven comment measurement and moderation. Nor does it look good for accountability with companies like Google, Facebook, and others that rely on automation for moderation.

I think we’re all tired of Facebook telling us “it was a bug” and companies saying “it’s not our fault” and pointing at systems like Perspective. Despite the fact that they’re complicit by using it. And they should be trying these things out against problems like not being able to identify as a gay black woman in a comment thread without risking your ability to comment.

Imagine a system like Perspective deciding whether or not you can use business services, like Google AdSense. Take for instance the African American woman who got an email Thursday from Google AdSense saying she’d violated its Terms by writing a blog post about dealing with being called the n-word … on her own website.

Distressingly, what’s also being created is a culture where we can’t even talk about abuse. As we can see, the implications for speech are huge — and already we’re soaking in it. Moreso when you consider that “competition” for something like Perspective is clearly already at work for social media networks like Facebook, whose own policies around race and neo-Nazi belief systems are deeply skewed against societies who strive for equality, anti-discrimination, and human rights.

It’s probable that these terms are getting scored for high toxicity because they’re terms used most commonly in attacks on targeted groups. But the instances mentioned in this article are clear failures. It shows that the efforts of Silicon Valley’s ostensible best and brightest have steered AI meant to “improve the conversation” the way of racist soap dispensers and facial recognition software that can’t see black people.

Insofar as the Wired feature is concerned, the data looks flawed from where we’re sitting. It may just mean that there are more gay black women and sex workers there who are okay with talking about it than Sharpsburg, Georgia commenters. Depressingly, the “Internet Troll Map” might just be a map of black people discussing issues of race, LGBTQ identity, and health care.

Which, we hope, is the opposite of what everyone intended.

Justice Department takes aim at Hurricane Harvey scammers

This week a number of federal and state law enforcement agencies teamed up to prosecute any scammers taking advantage of the Hurricane Harvey aftermath. Texas-based agencies along with the SEC, FBI, FTC and others have created a working group that will investigate any fraud, theft and price gouging related to the hurricane. “Under the lessons learned from Hurricane Katrina, we bring a comprehensive law enforcement focus to combat any criminal activity arising from the tragedy of Hurricane Harvey and the rebuilding efforts underway,” said acting US Attorney Abe Martinez in a statement.

Among the activities under the group’s jurisdiction are online and phishing scams. The Department of Justice released tips earlier this week on how to avoid fraudulent charitable schemes including those hosted on fake websites and those pursuing donations via email. It warned people not to click on email attachments or links from unsolicited emails, to vet groups rather than just following a website link and to be wary of charities’ sites that end in .com rather than .org. The FTC also encouraged those looking to donate to check charities out on websites like the Better Business Bureau’s Wise Giving Alliance and Charity Watch to make sure they’re legitimate.

“We intend to use all of the resources at our disposal to both help our registrants and to hold those accountable who try to use this disaster to take advantage of other people,” said SEC Chairman Jay Clayton. “There is no place for fraud or shady practices in the rebuilding and recovery of the communities in Texas and Louisiana that have been affected by Hurricane Harvey.”

The Department of Justice lists a number of resources here for reporting fraudulent activity and scam protection.

Via: CNET

Source: Department of Justice

Engadget Podcast Ep 40: This Is Your Night

Hello there! After a summer-long hiatus, The Engadget Podcast is back, starting with a five-episode run through the month of September. Because our usual host Terrence O’Brien is out on paternity leave (‘grats, Terry!), you’re stuck with yours truly and senior editor Chris Velazco as co-hosts. In this week’s episode, we chat about Google’s newly announced ARCore developer kit as well as highlights from IFA, which is going on this week in Berlin. (Hint: Google Assistant is everywhere at the show.) Enjoy, and if you’re wondering about the song in this week’s title, well, let’s just say there was an earworm going around the studio as we hit record. Many thanks to our audio engineer, who edited out any singing.

Relevant links:

• Google unveils ARCore, its answer to Apple’s ARKit
• Alexa and Cortana will soon work together
• Google Assistant is coming to more speakers and appliances
• Sony’s smart speaker gives Google Assistant a more capable home
• We’re live from IFA 2017 in Berlin!

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Apple’s fitness lab has collected 66,000 hours of exercise data

Men’s Health writer Ben Court got to take a look around Apple’s not-so-secret-anymore exercise lab and the company’s director of fitness for health technologies had some bold claims about the work the lab has done. Apple’s Jay Blahnik told Court, “Our lab has collected more data on activity and exercise than any other human performance study in history. Over the past five years, we’ve logged 33,000 sessions with over 66,000 hours of data, involving more than 10,000 unique participants.”

We’ll just have to take his word on those stats, but with dozens of researchers and medical professionals studying even larger numbers of exercising employees every day for years, it becomes easy to see how Apple can log those sorts of numbers. And those efforts should be visible in the upcoming WatchOS 4, which will reportedly include high-intensity interval training and the ability to pair with cardio machines.

Apple’s smartwatch has done well for the company, which is a major leader in the wearables industry, sporting around 50 percent year over year growth of its wearables sector during this year’s second quarter. The next version of the Apple Watch will reportedly have built-in cellular network support, meaning you won’t have to tote around your iPhone to use it. The launch date for WatchOS 4 is expected to be announced at Apple’s September 12th event.

Source: Men’s Health

Play as Mega Man and Ghost Trick in ‘Dead Rising 4’ on PS4

Patience is paying off for Dead Rising fans on PlayStation 4. Last year’s (formerly) Xbox One exclusive Dead Rising 4 is making its way to the PS4 with a few extras in Dead Rising 4: Frank’s Big Package. Yup, Capcom’s marketing department thought that name was a good idea. Anywho, the “complete” edition has a couple of noteworthy additions.

Specifically? Capcom Heroes, which lets you play dress-up as over a dozen different characters from the developer’s history and even wield special attacks themed to those costumes. The Mega Man getup grants an arm-mounted laser cannon. So no, this won’t just be superficial stuff like S.T.A.R.S. uniforms from Resident Evil or Chun-Li’s trademark garb from Street Fighter. There’s even a Ghost Trick outfit.

In addition to that, you’ll get all of the add-on packs that’ve been released since last December (including Super Ultra Dead Rising 4 Mini Golf). The game will set you back $49.99 come December 5th and folks who own the game on Xbox or Steam will get Capcom Hero mode as part of a free update.

Source: Capcom Unity

Facebook mapped everyone on Earth to get them online

In its research into the best way to provide internet to the entire world, Facebook has mapped where all 7.5 billion people on the planet live. By combining government census data and satellite images along with some help from Facebook’s image recognition neural network, the company can now locate every single man-made structure to within just five meters. The mapping technology is being used to figure how to deliver internet to populations that currently don’t have it or have poor connections to it. Along with ground networks, Facebook has determined that using drones and satellites will be most effective in pushing connectivity further. “We’re trying to connect people from the stratosphere and from space,” Facebook’s head of strategic innovation partnerships and sourcing, Janna Lewis, said at the Space Technology and Investment Forum this week.

Around half of the over 500 US satellites orbiting around Earth were launched for commercial reasons and because of groups like SpaceX, Blue Origin, Virgin Galactic and Virgin Orbit, launching satellites has never been cheaper. That makes the idea of a space-based internet delivery system a much more attainable reality than ever before. “Satellites are exciting for us. Our data showed the best way to connect cities is an internet in the sky,” said Lewis.

While it has worked on generating its maps, Facebook’s Connectivity Lab has released findings from the work. Last year, it released a dataset that included information on 23 countries. The team found that 99 percent of the population in those countries lived within 63 km of the nearest city. “Hence, if we are able to develop communication technologies that can bridge 63 km with sufficiently high data rates, we should be able to connect 99 percent of the population in these 23 countries,” the Connectivity Lab’s Tobias Tiecke wrote.

Source: CNBC

Tech CEOs sign letter urging Trump to keep immigrant protections

Hundreds of CEOs have signed an open letter urging President Trump not to dissolve the Deferred Action for Childhood Arrivals (DACA) program. Started in 2012 under the Obama administration, DACA allows undocumented immigrants who arrived to the US before they were 16 years old to obtain work permits and protection from deportation. Those with DACA permits have to renew them every two years and nearly 800,000 immigrants have benefited from the program.

“All DACA recipients grew up in America, registered with our government, submitted to extensive background checks, and are diligently giving back to our communities and paying income taxes,” said the letter. “More than 97 percent are in school or in the workforce, 5 percent started their own business, 65 percent have purchased a vehicle, and 16 percent have purchased their first home. At least 72 percent of the top 25 Fortune 500 companies count DACA recipients among their employees.”

Among those who have signed include tech bigwigs like Amazon CEO Jeff Bezos, Facebook CEO Mark Zuckerberg, Twitter CEO Jack Dorsey, Apple CEO Tim Cook, Google CEO Sundar Pichai and Warren Buffett. CEOs, founders and representatives from Airbnb, Dropbox, eBay, Fitbit, Foursquare, GoFundMe, LinkedIn, Lyft, Netflix, Netgear, Pandora, Tesla, Tumblr and Uber have also signed.

Highlighting just how much DACA recipients contribute to the US economy, the letter stated, “Our economy would lose $460.3 billion from the national GDP and $24.6 billion in Social Security and Medicare tax contributions.”

Trump was vocally against DACA during his campaign and is largely expected to cancel the program by September 5th — a deadline Republican lawmakers set for the president to make a decision. White House Press Secretary Sarah Huckabee Sanders said the president hasn’t yet made a final decision, but will announce one on the 5th.

Via: Variety

Source: FWD.us