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Context and why it matters that AI doesn’t have a clue what it is

(TECHNOLOGY NEWS) AI is learning and growing faster than ever. However, one flaw that AI cannot seem to get around is context.

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Contextual oops

Let’s start with a story. This might be my favorite story in all the annals of geekdom, which is saying something for someone whose literal job is “purveyor of geeky stories.” On March 1, 1990, in AG’s beloved hometown of Austin, Texas, the United States Secret Service raided a “suspected ring of hackers.”

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It was a full-on, TV level bust: armed agents broke locks, tore up files, carted off computers, even did that “simultaneous raid so the masterminds can’t get word to their button men” thing at the home of one of the people involved. Hooray! The cops beat the bad guys! Not so much.

Right reason, wrong time

Three years and a court decision later, the Secret Service had to fess up: they’d raided a game company. A tabletop game company. As in paper and dice, neither noted for being connected to the Internet. They weren’t hackers. At all. They’d written a game about hackers, and in the grim darkness of 1990, the Secret Service was fuzzy on the difference. That poor guy who got his very own private raid? He wrote their cyberpunk setting, and had dared to do research on the subject.

That’s as close as anyone there got to l33t h4x0r doings, and it turned out to be close enough for armed cops in a private citizen’s living room without an invitation.

There’s a halfway happy ending to that story, involving money paid to the company, an epic tonguelashing from a circuit court judge, and the founding of the leading advocacy organization for digital privacy rights, but the point is the Secret Service. Their actions weren’t malicious. Stupid, yes. Hilarious in hindsight, absolutely. Catastrophic to a small business innocent of any wrongdoing, big time. But they thought they were doing the right thing. They just Did It Wrong.

Doing It Wrong

As AI saturates our lives, I reflect, as I often do, on Doing It Wrong. Fundamentally, that ridiculous case came down to a misunderstanding of context. The Secret Service didn’t have the background or expertise to differentiate between hacking and a game about hacking. That’s absurd, that’s their job, but they didn’t.

Hacking, at least most hacking, is still a bad thing.

As simultaneously hilarious and horrible as it is to pull the equivalent of yanking a guy off his couch and charging him with murder for shooting someone in “Call of Duty,” shooting people is generally undesirable outside a fictional context.

Does AI know that?

Can AI make the distinction between “die, [expletive here]” in your favorite combat simulator and “die, [expletive here]” when an unpleasant person attempts to end the pizza guy with a fork? Because the Secret Service couldn’t, and they were human. Humans are pre-built for context. Computers have to be made that way, and it’s usually really hard. That’s a shade worrisome, what with AI growing like kudzu and the data it collects being used for everything from market analysis to, yes, murder investigations.

So consider this a gentle reminder that even the smartest computer is still fundamentally a box of switches.

Zero and one, off and on, puts certain limitations on a binary system’s ability to comprehend the complex, subjective, frankly weird human condition. Getting AI to understand context is a top priority for some of the best minds in computer science, but while they’re working we h. sapiens will have to double down on patience and nuance, because one of our most pervasive tools won’t be very good at either. They may never be as good at it as we are, though three years ago I’d have said that about go.

Mind your audience

For at least the next few years, everyone from multinational corporations and national governments down to the data junkies and media consumers reading this article will need to exercise some extra caution when it comes to AI and its assessment of people and their doings.AI doesn’t understand us quite yet.

It is, if not blind, at least a little nearsighted when it comes to context, which is basically the most important human thing.Click To Tweet

We’re going to have to keep doing that part ourselves. Fail in this, and you risk becoming your own hilarious Doing It Wrong cautionary tale. Nobody wants that.

This story originally ran on July 26, 2017.

Matt Salter is a writer and former fundraising and communications officer for nonprofit organizations, including Volunteers of America and PICO National Network. He’s excited to put his knowledge of fundraising, marketing, and all things digital to work for your reading enjoyment. When not writing about himself in the third person, Matt enjoys horror movies and tabletop gaming, and can usually be found somewhere in the DFW Metroplex with WiFi and a good all-day breakfast.

Tech News

AI technology is using facial recognition to hire the “right” people

(TECH NEWS) Artificial intelligence (AI) technology has made its way into the hiring process and while the intentions are good, I vote we proceed with extreme caution.

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Artificial intelligence technology has made its way into the hiring process and while the intentions are good, I vote we proceed with extreme caution.

A UK based consumer goods giant, Unilever, is just one of several UK companies who have begun using AI technology to sort through initial job candidates. The goal of this technology is to increase the number of candidates whom a company can interview at the initial stages of the hiring process and to improve response time for those candidates.

The AI, developed by American company Hirevue, analyzes a candidate’s language, tone, and facial expression during a video interview. Hirevue insists that their product is different from traditional facial recognition technologies because it analyzes far more data points.

Hirevue’s chief technology officer, Loren Larsen, says, “We get about 25,000 data points from 15 minutes of video per candidate. The text, the audio and the video come together to give us a very clear analysis and rich data set of how someone is responding, the emotions and cognitions they go through.”
This data is then used to rank candidates on a scale of 1 to 100 against a database of traits identified in previously successful candidates.

There are two main flaws to this system. First, unless this AI technology is pulling from a huge diverse data pool it could be unintentionally discriminating against people without even being aware of it. Human bias is not as easy to remove from the equation as AI proponents would have you believe.

As an example, how does this AI handle people who are disabled or whose facial expressions that read differently than the general population, such as people with Down Syndrome or those who have survived traumatic facial injuries?

Second, seeking to hire someone who possess the same qualities as the person who was previously successful at a role is shortsighted. There are many ways to accomplish the same task with above average results. Companies who adopt this low-risk mentality could be missing out on great opportunities long-term. You will never know what actually works best if you don’t try.

The big question here is whether or not AI technology is ready to influence the job market on this scale.

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Tech News

The ‘move fast and break things’ trend is finally over

(TECH NEWS) Time is running out for this decade — and for a popular Big Tech phrase responsible for a lot of collateral damage. What’s next?

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Time is running out for the decade. With less than 20 days left, it’s got us reflecting on the journeys of different economic sectors in the United States. And no industry has had a more tumultuous time of it than Big Tech.

A lot has changed in ten years. For starters, Americans have become increasingly disillusioned with Silicon Valley. The Pew Research Center found that only 50 percent of Americans believe technology firms have a positive effect on the country. That statistic is not too bad on its own, but that’s down 21 percent from only four years ago. Gallup found in 2019 that 48 percent of Americans also want more regulations on Big Tech. And The New York Times called the 2010s as “the decade Big Tech lost its way”.

Maybe that’s why big wigs at these tech firms have been quietly ditching a concept that was their Golden Rule in the early part of the decade: Move Fast and Break Things.

This concept is a modern take on the adage “you can’t make an omelet without breaking a few eggs.” For most of these firms, any innovation justified some of the collateral damage within its wake. And this scrappy “build it now and worry about it later” philosophy was a favorite of not just Facebook and Twitter, but also of many venture capital firms too.

But not anymore. Outlets from Forbes to HBR are saying this doesn’t work for Big Tech in the 2020s. Here are some reasons why it’s over.

Stability

The Move Fast and Break Things manta encouraged devs to push their coding changes to go live and let the chips fall where they may. But bugs pile up. Enter technical debt.

“Technical debt happens every time you do things that might get you closer to your goal now but create problems that you’ll have to fix later,” said The Quantified VC in an article on Medium. “As you move fast and break things, you will certainly accumulate technical debt.”

If enough technical debt comes into play, any new line of code could be the thing that topples a firm like a house of cards. And now that the consumer is used to tech in their daily routines, interruptions in service are extremely bad news for everyone.

As Mark Zuckerburg himself said it: “When you build something that you don’t have to fix 10 times, you can move forward on top of what you’ve built.”

Trust

To get back some of the trust that has ebbed from Big Tech over the years, firms can’t just keep with the Move Fast and Break Things status quo.

“The public will continue to grow weary of perceived abuses by tech companies, and will favor businesses that address economic, social, and environmental problems,” said Hemant Taneja in his article for Harvard Business Review. “Minimum viable products must be replaced by minimum virtuous products that … build in guards against potential harms.”

It’s not about chasing the bottom dollar at the cost of the consumer. Losing trust will hurt any company if left unchecked for long.

Innovation

There’s a cap on advancement in our current technological state. It’s called Moore’s Law. And we’re rapidly approaching the theoretical limits of it.

“When you understand the fundamental technology that underlies a product or service, you can move quickly, trying out nearly endless permutations until you arrive at an optimized solution. That’s often far more effective than a more planned, deliberate approach,” said Greg Satell in his article for HBR.

Soon enough, Big Tech will be in relatively new waters with quantum computing, biofeedback and AI. There’s no way to move as fast as these technology firms have in the past. And even if they could, should they?

Big Tech has experienced major growing pains since the dawn of our new Millenium. And now that some firms are entering their 20s, there’s a choice to be made. Continue to grow up or keep using an idea that’s worn out it’s welcome with the consumer and that has no guarantee will work with future technologies.

Maybe that’s why Facebook’s motto is now “Move Fast with Stable Infrastructure.”

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Tech News

Computer vision helps AI create a recipe from just a photo

(TECH NEWS) It’s so hard to find the right recipe for that beautiful meal you saw on tv or online. Well computer vision helps AI recreate it from a picture!

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Ever seen at a photo of a delicious looking meal on Instagram and wondered how the heck to make that? Now there’s an AI for that, kind of.

Facebook’s AI research lab has been developing a system that can analyze a photo of food and then create a recipe. So, is Facebook trying to take on all the food bloggers of the world now too?

Well, not exactly, the AI is part of an ongoing effort to teach AI how to see and then understand the visual world. Food is just a fun and challenging training exercise. They have been referring to it as “inverse cooking.”

According to Facebook, “The “inverse cooking” system uses computer vision, technology that extracts information from digital images and videos to give computers a high level of understanding of the visual world,”

The concept of computer vision isn’t new. Computer vision is the guiding force behind mobile apps that can identify something just by snapping a picture. If you’ve ever taken a photo of your credit card on an app instead of typing out all the numbers, then you’ve seen computer vision in action.

Facebook researchers insist that this is no ordinary computer vision because their system uses two networks to arrive at the solution, therefore increasing accuracy. According to Facebook research scientist Michal Drozdzal, the system works by dividing the problem into two parts. A neutral network works to identify ingredients that are visible in the image, while the second network pulls a recipe from a kind of database.

These two networks have been the key to researcher’s success with more complicated dishes where you can’t necessarily see every ingredient. Of course, the tech team hasn’t stepped foot in the kitchen yet, so the jury is still out.

This sounds neat and all, but why should you care if the computer is learning how to cook?

Research projects like this one carry AI technology a long way. As the AI gets smarter and expands its limits, researchers are able to conceptualize new ways to put the technology to use in our everyday lives. For now, AI like this is saving you the trouble of typing out your entire credit card number, but someday it could analyze images on a much grander scale.

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