Is job loss due to AI exaggerated?

Three reports were released earlier this year, each focused on the potential of AI to take over jobs done by humans.

“Pew Research Center conducted this study to understand how American workers may be exposed to artificial intelligence (AI) at their jobs. The study emphasizes the impact of AI on different groups of workers, such as men and women and racial and ethnic groups …”

These researchers considered whether particular jobs are more or less “exposed” to AI. “In our analysis, jobs are considered more exposed to artificial intelligence if AI can either perform their most important activities entirely or help with them.” The study found that white-collar jobs dealing in information gathering or data analysis are “more exposed,” while jobs requiring manual labor and hands-on caregiving as “less exposed.”

As far as job losses, or jobs disappearing, the researchers concluded they just don’t know. Rather than being replaced (say, customer-service workers being replaced by AI chatbots), workers might use AI to make themselves more productive.

Goldman Sachs’s report focused on productivity and generative AI. They estimated that “roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI.”

The McKinsey Global Institute released a 76-page PDF that said, in part, “we see generative AI enhancing the way STEM, creative, and business and legal professionals work rather than eliminating a significant number of jobs outright.” Looking at the near term (up to 2030), the analysts predicted changes in worker training and continued mobility of workers (“occupational shifts”), following pandemic-era job attrition in food service, customer service and sales, office support, and production work such as manufacturing.”

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The AI teaching assistant

Back in 2016, a professor teaching an online course about artificial intelligence developed a program that he called an AI teaching assistant. The program was given a name (“Jill Watson”) and referred to as “she.” A TEDx Talk video was published that same year.

A 2016 video features Professor Ashok Goel, who developed the “Jill Watson” teaching assistant.

In my recent reading about AI, I’ve found this case mentioned quite often. Sometimes it is generalized to imply that AI teaching assistants are in common use. Another implication is that AI teaching assistants (or even full-fledged AI teachers) are the solution to many challenges in K–12 education.

I wanted to get a better idea of what’s really going on, so I did a search at Google Scholar for “AI teaching assistant” (on March 16, 2022). I got “about 194 results,” which was more than I wanted to look at as search-result pages, so I downloaded 200 results using SerpApi and organized them in a spreadsheet. After eliminating duplicates, I read the titles and the snippets (brief text provided in the search results). I marked all items that appeared relevant — including many that are broadly about AI in education, but eliminating all those focused on how to teach about AI. I ended with 84 articles to examine more closely.

Quite a lot of these refer to the “Jill Watson” program. Many of the articles are speculative, describing potential uses of AI in education (including but not limited to virtual TAs), and contain no empirical research. Few of them could be considered useful for learning about AI teaching assistants — most of the authors have indicated no experience with using any AI teaching assistant themselves, let alone training one or programming one. Thus in most of the articles, the performance of an actual AI teaching assistant was not evaluated and was not even observed.

Kabudi, Pappas and Olsen (2021) conducted a much more rigorous search than mine. They analyzed 147 journal articles and conference presentations (from a total of 1,864 retrieved) about AI-enabled adaptive learning systems, including but not limited to intelligent tutoring systems. The papers were published from 2014 through 2020.

“There are few studies of AI-enabled learning systems implemented in educational settings,” they wrote (p. 2). The authors saw “a discrepancy between what an AI-enabled learning intervention can do and how it is actually utilised in practice. Arguably, users do not understand how to extensively use such systems, or such systems do not actually overcome complex challenges in practice, as the literature claims” (p. 7).

My interest in AI teaching assistants centers on whether I should devote attention to them in a survey course about artificial intelligence as it is used today. My conclusion is that much has been written about the possibilities of using “robot teachers,” intelligent tutoring systems, “teacherbots,” or virtual learning companions — but in fact the appearances of such systems in real classrooms (physical or online) with real students have been very few.

If classrooms are using commercial versions of AI teaching assistants, there is a lack of published research that evaluates the results or the students’ attitudes toward the experience.

Further reading

For an overview of recent research about AI in education, see: AI-enabled adaptive learning systems: A systematic mapping of the literature, an open-access article. This is the study referred to above as Kabudi, Pappas and Olsen (2021).

Another good resource is AI and education: Guidance for policy makers (2021), a 50-page white paper from UNESCO; free download.


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Beyond drones: Assassination by robot?

A Page One story in Sunday’s New York Times detailed the assassination of a nuclear scientist in Iran in November: The Scientist and the A.I.-Assisted, Remote-Control Killing Machine (published online Sept. 18, 2021). I was taken aback by the phrase “AI-assisted remote-control killing machine” — going for the shock value!

Here’s a sample of the writing about the technology:

“The straight-out-of-science-fiction story of what really happened that afternoon and the events leading up to it, published here for the first time, is based on interviews with American, Israeli and Iranian officials …”

The assassination was “the debut test of a high-tech, computerized sharpshooter kitted out with artificial intelligence and multiple-camera eyes, operated via satellite and capable of firing 600 rounds a minute.”

Unlike a drone, the robotic machine gun draws no attention in the sky, where a drone could be shot down, and can be situated anywhere, qualities likely to reshape the worlds of security and espionage” (boldface mine).

Most of the (lengthy) article is about Iran’s nuclear program and the role of the scientist who was assassinated.

The remote assassination system was built into the bed of a pickup truck, studded with “cameras pointing in multiple directions.” The whole rig was blown up after achieving its objective (although the gun robot was not destroyed as intended).

A crucial point about this setup is to understand the role of humans in the loop. People had to assemble the rig in Iran and drive the truck to its waiting place. A human operator “more than 1,000 miles away” was the actual sniper. The operation depended on satellites transmitting data “at the speed of light” between the truck and the distant humans.

So where does the AI enter into it?

There was an estimated 1.6-second lag between what the cameras saw and what the sniper saw, and a similar lag between the sniper’s actions and the firing of the gun positioned on the rig. There was the physical effect of the recoil of the gun (which affects the bullets’ trajectory). There was the speed of the car in which the nuclear scientist was traveling past the parked rig. “The A.I. was programmed to compensate for the delay, the shake and the car’s speed,” according to the article.

A chilling coda to this story: “Iranian investigators noted that not one of [the bullets] hit [the scientist’s wife], seated inches away, accuracy that they attributed to the use of facial recognition software.”

If you’re familiar with the work of Norbert Wiener (1894–1964), particularly on automated anti-aircraft artillery in World War II, you might be experiencing déjà vu. The very idea of a feedback loop came originally from Wiener’s observations of adjustments that are made as the gun moves in response to the target’s movements. To track and target an aircraft, the aim of the targeting weapon is constantly changing. Its new position continually feeds back into the calculations for when to fire.

The November assassination in Iran is not so much a “straight-out-of-science-fiction story” as it is one more incremental step in computer-assisted surveillance and warfare. An AI system using multiple cameras and calculating satellite lag times, the shaking of the truck and the weapon, and the movement of the target will be using faster computer hardware and more sophisticated algorithms than anything buildable in the 1940s — but its ancestors are real and solid, not imaginary.


Algorithmic warfare and the reinvention of accuracy (Suchman, 2020)

Killer robots already exist, and they’ve been here a very long time (Ryder, 2019)


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How weird is AI?

For Friday AI Fun, I’m sharing one of the first videos I ever watched about artificial intelligence. It’s a 10-minute TED Talk by Janelle Shane, and it’s actually pretty funny. I do mean “funny ha-ha.”

I’m not wild about the ice-cream-flavors example she starts out with, because what does an AI know about ice cream, anyway? It’s got no tongue. It’s got no taste buds.

But starting at 2:07, she shows illustrations and animations of what an AI does in a simulation when it is instructed to go from point A to point B. For a robot with legs, you can imagine it walking, yes? Well, watch the video to see what really happens.

This brings up something I’ve only recently begun to appreciate: The results of an AI doing something may be entirely satisfactory — but the manner in which it produces those results is very unlike the way a human would do it. With both machine vision and game playing, I’ve seen how utterly un-human the hidden processes are. This doesn’t scare me, but it does make me wonder about how our human future will change as we rely more on these un-human ways of solving problems.

“When you’re working with AI, it’s less like working with another human and a lot more like working with some kind of weird force of nature.”

—Janelle Shane

At 6:23 in the video, Shane shows another example that I really love. It shows the attributes (in a photo) that an image recognition system decided to use when identifying a particular species of fish. You or I would look at the tail, the fins, the head — yes? Check out what the AI looks for.

Shane has a new book, You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place. I haven’t read it yet. Have you?

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Robots, and what’s not AI

Think of a robot. Do you picture a human-looking construct? Does it have a human-like face? Does it have two legs and two arms? Does it have a head? Does it walk?

It’s easy to assume that a robot that walks across a room and picks something up has AI operating inside it. What’s often obscured in viral videos is how much a human controller is directing the actions of the robot.

I am a gigantic fan of the Spot videos from Boston Dynamics. Spot is not the only robot the company makes, but for me it is the most interesting. The video above is only 2 minutes long, and if you’ve never seen Spot in action, it will blow your mind.

But how much “intelligence” is built into Spot?

The answer lies in between “very little” and “Spot is fully autonomous.” To be clear, Spot is not autonomous. You can’t just take him out of the box, turn him on, and say, “Spot, fetch that red object over there.” (I’m not sure Spot can be trained to respond to voice commands at all. But maybe?) Voice commands aside, though, Spot can be programmed to perform certain tasks in certain ways and to walk from one given location to another.

This need for additional programming doesn’t mean that Spot lacks AI, and I think Spot provides a nice opportunity to think about rule-based programming and the more flexible reinforcement-learning type of AI.

This 20-minute video from Adam Savage (of MythBusters fame) gives us a look behind the scenes that clarifies how much of what we see in a video about a robot is caused by a human operator with a joystick in hand. If you pay attention, though, you’ll hear Savage point out what Spot can do that is outside the human’s commands.

Two points in particular stand out for me. The first is that when Spot falls over, or is upside-down, he “knows” how to make himself get right-side-up again. The human doesn’t need to tell Spot he’s upside-down. Spot’s programming recognizes his inoperable position and corrects it. Watching him move his four slender legs to do so, I feel slightly creeped out. I’m also awed by it.

Given the many incorrect positions in which Spot might land, there’s no way to program this get-right-side-up procedure using set, spelled-out rules. Spot must be able to use estimations in this process — just like AlphaGo did when playing a human Go master.

The second point, which Savage demonstrates explicitly, is accounting for non-standard terrain. One of the practical uses for a robot would be to send it somewhere a human cannot safely go, such as inside a bombed-out building — which would require the robot to walk over heaps of rubble and avoid craters. The human operator doesn’t need to tell Spot anything about craters or obstacles. The instruction is “Go to this location,” and Spot’s AI figures out how to go up or down stairs or place its feet between or on uneven surfaces.

The final idea to think about here is how the training of a robot’s AI takes place. Reinforcement learning requires many, many iterations, or attempts. Possibly millions. Possibly more than that. It would take lifetimes to run through all those training episodes with an actual, physical robot.

So, simulations. Here again we see how super-fast computer hardware, with multiple processes running in parallel, must exist for this work to be done. Before Spot — the actual robot — could be tested, he existed as a virtual system inside a machine, learning over nearly endless iterations how not to fall down — and when he did fall, how to stand back up.

See more robot videos on Boston Dynamics’ YouTube channel.

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