Competing against robots

Some scholars are trying to discern what kinds of learning have survived technological replacement better than others. Richard J. Murnane and Frank Levy in their book “The New Division of Labor” (Princeton, 2004) studied occupations that expanded during the information revolution of the recent past. They included jobs like service manager at an auto dealership, as opposed to jobs that have declined, like telephone operator.
 

The successful occupations, by this measure, shared certain characteristics: People who practiced them needed complex communication skills and expert knowledge. Such skills included an ability to convey “not just information but a particular interpretation of information.” They said that expert knowledge was broad, deep and practical, allowing the solution of “uncharted problems.”
 

These attributes may not be as beneficial in the future. But the study certainly suggests that a college education needs to be broad and general, and not defined primarily by the traditional structure of separate departments staffed by professors who want, most of all, to be at the forefront of their own narrow disciplines. But this old departmental structure is still fundamental at universities, and it is hard to change.
 

Full article here from Robert Shiller.

A few random thoughts. Disciplines which are purely about knowledge accumulation are risky if the type of knowledge acquired is that which computers can accumulate in a fraction of the time. Lots of Ph.D's seem unlikely to be economically worthwhile considering the cost of higher education.

Watch the virtual assistant on your phone. Siri or Google Now are good benchmarks for what skills are becoming obsolete, and which are still of great value.

Most humans still prefer a bit of entropy and warmth from those they interact with, especially in the service sector, and indexing high on that still commands a premium.

A cookbook from IBM's Watson

Robots taking all the jobs, cooking edition:

Steve Abrams, the director of IBM’s Watson Life research program, told Quartz that Watson scanned publicly available data sources to build up a vast library of information on recipes, the chemical compounds in food, and common pairings. (For any budding gastronomers out there, Abrams said Wikia was a surprisingly useful source.) Knowledge that might’ve taken a lifetime for a Michelin-starred chef to attain can now be accessed instantly from your tablet.
 

What separates Watson from the average computer (or chef) is its ability to find patterns in vast amounts of data. It’s essentially able figure out, through sheer repetition, what combinations of compounds and cuisines work together. This leads to unusual pairings, like Waton’s apple kebab dish, which has some odd ingredients: “Strawberries and mushrooms share a lot of flavor compounds,” Abrams said. “It turns out they go quite well together.”
 

The researchers are publishing a cookbook with recipe ideas from Watson, and it releases this Tuesday: Cognitive Cooking with Chef Watson: Recipes for Innovation from IBM & the Institute of Culinary Education. I have not read the book, but some of the recipes sound intriguing (“Belgian bacon pudding, a desert containing dried porcini mushrooms”) while others sound, at best, like clever wordplay (“the shrimp cocktail, which is a beverage with actual shrimp in it”). Regardless, I'm purchasing a copy just out of sheer curiosity. Let's hope they turn this resource into an app or service instead of a book, I blame Watson's vanity for wanting this in the outdated format of a book.

To the extent that standout recipes and flavor pairings are a matter of pattern recognition, there's no reason a computer, with its infinitely more scalable hardware and software for that purpose, couldn't match or exceed a human. And, so, a variant of the infinite monkey theorem: given enough time, a computer will write the French Laundry cookbook (and win a third Michelin star).

To be clear, I'm okay with this. I just want to eat tasty food, I'm fine with employing computers to come up with more amazing things to feed me.

For now, however, the computer still requires a human to actually prepare the recipe. In a true demonstration of how far artificial intelligence has progressed, no sufficiently advanced computer wants the drudgery of life as a line chef. Better profits in cookbooks than restaurants anyway.

A new cooking show concept already comes to mind: Top Freestyle Chef. Like freestyle chess, in freestyle cooking competitors would consist of a human or a human consulting with a computer. I am ready to program this into my DVR already, as long as they don't replace Padma Lakshmi with a robot host. I'm as big a fan of artificial intelligence and robots as the next guy, but I think we're a long way from replacing this.

Robots taking all the jobs, cont.

By studying the brains of drivers when they were negotiating a race-track, the scientists were intrigued to find that during the most complex tasks, the experts used less brain power. They appeared to be acting on instinct and muscle memory rather than using judgement as a computer programme would. 

“It looks as if the skilled race car drivers are able to control their cars with very little cognitive load,” said Prof Gerdes. 

Mr Vodden agreed saying in difficult manouvres experience kicked in. "If you're thinking you're going too slow."

You'd think from that excerpt that the human driver remains superior, but it turns out the driverless car beat the track champion by 0.4 seconds on a track in Northern California.

One race track, the worlds' greatest driver (whoever is the Michael Schumacher of the moment) versus the best computer driver. I don't enjoy watching auto racing on TV, but I'd watch one that pits man and machine against machine and machine.

One more wrinkle for AI to learn: how and when to cheat.

In the race between Shelley and Mr Vodden, the racing driver left the track at a sharp corner, rejoining the race ahead of the robot car. 

“What we’re doing as humans we’re weighting a number of different things,” added Prof Gerdes. 

“We’re not driving within the lines, we’re balancing our desire to follow the law with other things such as our desire for mobility and safety. 

“If we really want to get to the point where we can have a car that will drive as well as the very best drivers with the car control skills and also the judgment it seems to me that we really need to have a societal discussion about what are the different priorities we place on mobility and safety on judgement and following the law.”

Robot handwriting

When humans write by hand, our margins are — you guessed it — irregular. On the left-hand side of a paragraph, we try to keep a nice straight margin, beginning each line in the same location. But we usually can’t keep it very precise. We drift inwards, producing a left margin that slowly slopes towards the center of the page. Here’s an example of a human-written letter Maillift composed to send to customers:

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See how the left-hand side drifts inwards? The lines beginning “choose”, “color” and “logo” all move to the right.

But the activity on the right-hand margin is even weirder.

Curliss and Jurek have noticed that their human handwriters often produce a “rounded” right margin: It bulges outwards and then tucks back inwards. In the example above, “showcase” sticks out further than the line above, “option”. But then the lines begin rounding inwards, with “size” and “own” moving further and further into the center of the page.

Why would humans do this? Probably because we aren’t terribly good at judging how many words fit on each line. If we accidentally overpack a line — trying to cram too many words into it — we immediately overcompensate by becoming too cautious, and putting too few words on the next couple of lines. You can see that dynamic at work in the example above. The composer wrote a nicely-kerned first line, but in the next line crammed in too many words, such that “showcase” comes uncomfortably close to the edge of the page. So he or she pulled back sharply, and the next three lines shrink in length.

A robot, in contrast, knows precisely how much space each word takes up, and doesn’t make these mistakes. You won’t see that rounded margin on the right.

Until, of course, robots are programmed to mimic that, too.

From a fascinating Clive Thompson post on the recent adoption of robots to generate hand-written letters to try to improve response rates on marketing offers. As Thompson notes, it's actually the exception, not the rule, to receive hand-written communications nowadays. A hand-written note actually may be the clearest sign that a letter is just marketing collateral.

I don't have kids; do they even teach handwriting in school anymore?

Optimal robot personality

They gave them distinct personalities. It was an experiment: would humans react to the robots differently based on how they carried themselves? They tried out two different personalities on each robot. One version was extraverted; the robot would speak loudly and quickly, use more animated hand gestures, and start conversations instead of waiting to be spoken to. The other personality was more reserved, speaking much more slowly and quietly, moving around less, and letting the user initiate communication.

What the researchers found, as they described in a recently published paper, was a striking difference between the two. When it came to the nurse robot, people preferred and trusted it more when its personality was outgoing and assertive. What people wanted in a security guard was exactly the opposite: the livelier, extraverted version clearly rubbed people the wrong way. Not only were they less confident in its abilities, and dubious that it would keep them away from danger, they simply liked it less overall.

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What researchers are finding is that it’s not enough for a machine to have an agreeable personality—it needs the right personality. A robot designed to serve as a motivational exercise coach, for instance, might benefit from being more intense than a teacher-robot that plays chess with kids. A museum tour guide robot might need to be less indulgent than a personal assistant robot that’s supposed to help out around the house.

A growing body of research is starting to reveal what works and what doesn’t. And although building truly human-like robots will probably remain technologically impossible for a long time to come, researchers say that imbuing machines with personalities we can understand doesn’t require them to be “human-like” at all. To hear them describe the future is to imagine a world—one coming soon—in which we interact and even form long-term relationships with socially gifted devices that are designed to communicate with us on our terms. And what the ideal machine personalities turn out to be may expose needs and prejudices that we’re not even aware we have.
 

More here. How many of our personality preferences for robots will we inherit from the human analogues we're most familiar with? We may wish for a robot personal trainer to be tough, forceful, while we may prefer a calm, almost flat affect from our robot therapist.

Regardless, I'm excited to see the first generation of robots or AI's with personality roll out to the world. It feels like one of the most likely vectors of delight in user experience design when it comes to AI.