Can Bots Replace Human Teachers?

“You want to order another pizza?” I suggested.

Eric just shook his head. Something was obviously bothering him, and not even Flatbread Company’s pizza (quite possibly the best pizza in the known universe, or in Rhose Island) could provide him with some peace of mind.

“It’s the bot.” He finally erupted at me. “That damned bot. It’s going to take over my job.”

“You’re a teaching assistant.” I reminded him. “It’s not a real job. You barely have enough money to eat.”

“Well, it’s some kind of a job, at least.” He said bitterly. “And soon it’ll be gone too. I just heard that in Georgia’s Technological Institute they actually managed to have a bot – an artificial intelligence – perform as a teaching assistant, and no one noticed anything strange!”

“Yeah, I remember.” I remembered. “It happened in the last semester. What was the bot’s name again?”

“It’s Jill.” He said. “Jill Watson. It’s based on the same Watson AI engine that IBM developed a few years ago. That Watson can already have debates about current issues, conduct scientific literature reviews, and even provide legal consultation. And now it can even assist students just like a human teaching assistant, and they don’t even note the difference!”

“How can that be?” I tried to understand.

“It all happened in a course about AI, that Prof. Ashok Goel gave in Georgia Tech.” He explained. “Goel realized that the teaching assistants in the course were swamped with questions from students, so he decided to train an artificial intelligence that would help the teaching assistants. The AI went over forty thousand questions, answers and comments written by students and teaching assistants in the course’s forum, and was trained to similarly answer new questions.”

“So how well did it go?” I asked.

“Wonderful. Just wonderful.” He sighed. “The AI, masquerading as Jill Watson, answered students’ questions throughout the semester, and nobody realized that there’s not a human being behind the username. Some students even wanted to nominate ‘her’ as an outstanding teaching assistant.”

“Well, where’s the harm in that?” I asked. “After all, she did lower the work volume for all the human teaching assistants, and the students obviously feel fine about that. So who cares?”

He sent a dirty look my way. “I care – the one who needs a job, even a horrible one like this, to live.” He said. “Just think about it: in a few years, when every course is managed by a bunch of AIs, there won’t be as many jobs open for human teaching assistants. Or maybe not even for teachers!”

“You need to think about this differently.” I advised him. “The positive side is that there’s still place for human teaching assistants, as long as they know how to work with the automated ones. After all, even the best AI in the world, at the moment, doesn’t know how to answer all the questions. There’s still a place for human common sense. So there’s definitely going to be a place for the human teaching assistant, but he’ll just have to be the best as what he does: he’ll need to operate several automated assistants at the same time that will handle the routine questions, and will pass to him only the most bizarre and complex questions; He’ll need to know how to work with computers and AI, but also to have good social skills to solve difficult situations for students; And he’ll need to be reliable enough to do all of the above proficiently over time. So yes, lots of people are going to compete for this one job, but I’m sure you can succeed at it!”

Eric didn’t look convinced. Quite honestly, I wasn’t either.

“Well,” I tried, “you can always switch occupations. For example, you can become a psychologist…”

“There are already companies that provide psychological services on the internet, using text messages.” He said. “Turns out it’s really going well for the patients. You want to bet bots can do this too in a few years? So get ready to wave bye-bye at many of the human psychologists out there.”

“Or maybe you could become an author and write novels…” I tried to continue.

“An AI managed to write a novel this year, and it passed the first round in a Japanese literary competition.” He stated.

“Or write political speeches…”

“Computers do that too.”

“Ok, fine!” I said. “So just sell flowers or something!”

“Facebook is now opening a new bot service, so that people can open an online conversation with them, and order food, flowers and other products.” He said with frustration. “So you see? Nothing left for humans like us.”

“Well,” I thought hard. “There must be some things left for us to do. Like, you see that girl over there at the end of the bar? Cute, isn’t she? Did you notice she was looking at your for the last hour?”

He followed my eyes. “Yes.” He said, and I could hear the gears start turning in his head.

“Think about it.” I continued. “She’s probably interested in you, but doesn’t know how to approach.”

He thought about it. “I bet she doesn’t know what to say to me.”

I nodded.

“She doesn’t know how best to attract my attention.” He went on.

“That’s right!” I said.

“She needs help!” He decided. “And I’m just the guy who can help her. Help everyone!”

He stood up resolutely and went for the exit.

“Where are you going?” I called after him. “She’s right here!”

He turned back to me, and I winced at the sight of his glowing eyes – the sure sign of an engineer at work.

“This problem can definitely be solved using a bot.” He said, and went outside. I could barely hear his muffled voice carrying on behind the door. “And I’m about to do just that!”

I went back to my seat, and raised my glass in what I hoped was a comforting salute to the girl on the other side of the bar. She may not realize it quite yet, but soon bots will be able to replace human beings in yet another role.


Do You Want to Keep Your Job? Then You Have to be Nice

The Uber driver was being exceptionally nice to me this morning.

“Nice to meet you, sir!” He greeted me cheerily. “I see you want to get to the university. Please, come on in! Can I offer you a bottle of mineral water? Or maybe some pretzels?”

“Thanks.” I said. I looked at the ceiling. No hidden cameras there. “You’re very nice. Very, very nice.”

“Yes, I know.” His face shone in understanding. “But it pays big time. I get good grades from the customers, so Uber’s algorithm is providing me with even more passengers all the time. It just pays to be nice.”

“Oh, so you’re just like those lawyers, physicians and accountants?”

“I don’t know.” He said. “Am I?”

“Absolutely.” I said. “Or rather, soon they’re going to be a lot like you: just plain nice. The thing is, the knowledge industries – and by that I mean professions which require that human beings go over data and develop insights – are undergoing automation. That means artificial intelligence is going to perform a major part of the work in those professions, and then the human workers – the successful ones, at least – will become nice and more polite to their customers.”

“Take Uber for example.” I gestured at the smartphone at the dashboard. “Taxi drivers partly deal with knowledge generation: they receive information from the passenger about the desired destination, and they have to come up with the knowledge of how to get there, based on their memory of the roads. In the past, a mere decade ago, taxi drivers needed to know the streets of the city like the back of their hand.”

“But today we have GPS.” Said my driver.


“Exactly.” I said. “Today, modern taxi drivers rely on a virtual assistant. It’s not just a GPS that tells you where you are. More advanced apps like Waze and Google Maps also show you how best to reach your destination, with vocal instructions at each step of the way. These virtual assistants allow anyone to be a taxi driver. Even if you never drove in a certain city in the past, you can still do a satisfactory job. In effect, the AI has equalized the playing ground in the field of taxi driving, since it lowered to a minimum the needed skill level. So how can a cabby still distinguish himself and gain an advantage over other drivers?”

“He has to be nice.” Smiled the guy at the wheel. I wondered to myself if he ever stops smiling.

“That’s what we see today.” I agreed. “The passengers are rating every driver according to the experience they had in his cab, since that is the main criteria left when all the others are equal. And Uber is helping the process of selecting for niceness, since they stop working with drivers who aren’t nice enough.”

“But what does it have to do with lawyers, accountants and physicians?” Asked the driver.

“We’re beginning to see a similar process in other knowledge-based professions.” I explained. “For example, just last week a new AI engine made the news: it’s starting to work in a big law firm, as a consultant to lawyers. And no wonder: this AI can read and understand plain English. When asked legal questions, the AI conducts research by going over hundreds of thousands of legal papers and precedents in seconds, and produces a final answers report with detailed explanations about how it has reached each answer. It even learns from experience, so that the more you work with it – the better it becomes.”

“So we won’t even need lawyers in the future?” Finally, the guy’s smile became genuine.

“Well, we may reach that point in the end, but it’ll take quite some time for us to get there.” I said. “And until that time, we’ll see AI engines that will provide free legal consultation online. This kind of a free consultation will suffice for some simple cases, but in the more sophisticated cases people will still want a living lawyer in the flesh, who’ll explain to them how they should act and will represent them in court. But how will people select their lawyers out of the nearly-infinite number of law school graduates out there?”

“According to their skill level.” Suggested the driver.’

“Well, that’s the thing. Everyone’s skills will be near equal. It won’t even matter if the lawyers have a big firm behind them. The size of the firm used to matter because it meant the top lawyers could employ tens of interns to browse through precedents for them. But pretty soon, AI will be able to do that as well. So when all lawyers – or at least most – are equal in skills and performance, the most employed lawyers will be the nice ones. They will be those who treat the customer in the best way possible: they will greet their clients with a smile, offer them a cup of tea when they set for in the office, and will have great conversational skills with which to explain to the client what’s going on in court.”

“And the same will happen with accountants and physicians?” He asked.


“It’s happening right now.” I said. “The work of accountants is becoming easier than ever before because of automation, and so accountants must be nicer than ever before. Soon, we’ll see the same phenomenon in the medical professions as well. When AI can equalize the knowledge level of most physicians, they will be selected according to the way they treat their patients. The patients will flock to the nicer physicians. In fact, the professionals treating the patients won’t even have to have a deep understanding in the field of medicine, just as today’s cabbies don’t need to fully remember the roads in the city. Instead, the medical professionals will have to understand people. They will need to relate to their patients, to figure them out, to find out what’s really bothering them, and to consult with the AI in order to come up with the insights they need in order to solve the patients’ issues.”

“So we gotta keep the niceness on.” Summarized my driver, as he parked the car in front of the entrance to the mall. “And provide the best customer service possible.”

“That’s my best advice right now about work in the future.” I agreed. I opened the door and started getting out of the car, and then hesitated. I turned on my smartphone. “I’m giving you five stars for the ride. Can you give me five too?”

His gaze lingered on me for a long time.

“Sorry.” He finally said. “You talk too much, and really – that’s not very nice.”


Futuronymity: Keeping Our Privacy from Our Grandchildren

History is a story that will never be told fully. So much of the information is lost to the past. So much – almost all – the information is gone, or has never been recorded. We can barely make sense of the present, in which information about the events and the people behind them keeps being released every day. What chance do we have, then, at fully deciphering the complex stories underlying history – the betrayals, the upheavals, the personal stories of the individuals who shaped events?

The answer has to be that we have no way of reaching any certainty about the stories we tell ourselves about our past.

But we do make some efforts.

Medical doctors and historians are trying to make sense of biographies and ancient skeletons, in order to retro-diagnose ancient kings and queens. Occasionally they identify diseases and disorders that were unknown and misunderstood at the time those individuals actually lived. Mummies of ancient pharaohs are x-rayed, and we suddenly have a better understanding of a story that unfolded more than two thousand years ago and realize that the pharaoh Ramesses II suffered from a degenerative spinal condition.

Similarly, geneticists and microbiologists use DNA evidence to end mysteries and find conclusive endings to some historical stories. DNA evidence from bones has allowed us to put to rest the rumors, for example, that the two children of Czar Nicholas II survived the 1918 revolution in Russia.

The Russian czar Nicholas II with his family. DNA evidence now shows conclusively that Anastasia, the youngest daughter, did not survive the mass execution of the family in 1918. Source: Wikipedia

The above examples have something in common: they all require hard work by human experts. The experts need to pore over ancient histories, analyze the data and the evidence, and at the same time have good understanding of the science and medicine of the present.

What happens, though, when we let a computer perform similar analyses in an automatic fashion? How many stories about the past could we resolve then?

We are rapidly making progress towards such achievements. Recently, three authors from Waseda University in Japan have published a new paper showing they can use a computer to colorize old black & white photos. They rely on convolutional neural networks, which are in effect a simulation of certain structures of a biological brain. Convolutional neural networks have a strong capacity for learning, and can thus be trained to perform certain cognitive tasks – like adding color to old photos. While computerized coloring has been developed and used before, the authors’ methodology seems to achieve better results than others before them, with 92.6 percent of the colored images looking natural to users.

Colorized pictures from the past
Colorized black & white pictures from the past. AI engine was used to add color – essentially new information – to these hints from our past. Source: paper by Iizuka, Simo-Serra and Ishikawa

This is essentially an expert system, an AI engine operating in a way similar to that of the human brain. It studies thousands of thousands of pictures, and then applies its insights to new pictures. Moreover, the system can now go autonomously over every picture ever taken, and add a new layer of information to it.

There are boundaries to the method, of course. Even the best AI engine can miss its mark in cases where the existing information is not sufficient to produce a reliable insight. In the examples below you can see that the AI colored the tent orange rather than blue, since it had no way of knowing what color it was originally.

But will that stay the case forever?

Colorized black & white picture - with wrong color
Colorized black & white picture that was colored incorrectly since no information existed about the tent from other sources. Source: paper by Iizuka, Simo-Serra and Ishikawa

As I previously discussed in the Failures of Foresight series of posts on this blog, the Failure of Segregation is making it difficult for us to forecast the future because we’re trying to look at each trend and each piece of evidence on its own. Let’s try to work past that failure, and instead consider what happens when an AI expert coloring system is combined with an AI system that recognizes items like tents and associates them with certain brands, and can even analyze how many tents of each color of that brand were sold on every year – or at least what was the most favorite tent color for people at that time.

When you combine all of those AI engines together, you get a machine that can tell you a highly nuanced story about the past. Much of it is guesswork, obviously, but those are quite educated guesses.


The Artificial Exploration of the Past

In the near future, we’ll use many different kinds of AI expert systems to explore the stories of the past. Some artificial historians will discover cycles in history – princes assassinating their kingly fathers, for example – that have a higher probability to occur, and will analyze ancient stories accordingly. Other artificial historians will compare genealogies, while yet others will analyze ancient scriptures and identify different patterns of writing. In fact, such an algorithm had already been applied to the Bible, revealing that the Torah has been written by several different authors and distinguishing between them.

The artificial exploration of the past is going to add many fascinating details to stories which we’ve long thought were settled and concluded. But it also raises an important question: when our children and children’s children look back at our present and try to derive meaning from it – what will they find out? How complete will their stories of their past and our present be?

I suspect the stories – the actual knowledge and understanding of the order between events – will be even more complete than what we who dwell in the present know about.



In the not-so-far-away future, machines will be used to analyze all of the world’s data from the early 21st century. This is a massive amount of data: 2.5 quintillion bytes of data are created daily, which would fill ten million blu-ray discs altogether. It is astounding to realize that 90 percent of the world’s data today has been created just in the last two years. Human researchers would not be able to make much sense of it, but advanced AI algorithms – a super-intelligence, in some ways – could actually have the tools to crosslink many different pieces of information together to obtain the story of the present: to find out what movies families had watched on a specific day, in which hotel the President of the United States stayed during a recent visit to France and what snacks he ordered on room service, and many other paraphernalia.

Are those details useless? They may seem so to our limited human comprehension, but they will form the basis for the AI engines to better understand the past, and produce better stories of it. When the people of the future will try to understand how World War 3 broke out, their AI historians may actually conclude that it all began with a presidential case of indigestion which happened at a certain French hotel, and which annoyed the American president so much that it had prevented him from making the most rational choices in the next couple of days. An hypothetical scenario, obviously.


Futuronymity – Maintaining Our Privacy from the Future

We are gaining improved tools to explore the past with, and to derive insights and new knowledge even where information is missing. These tools will be improved further in the future, and will be used to analyze our current times – the early 21st century – as well.

What does it mean for you and me?

Most importantly, we should realize that almost every action you take in the virtual world will be scrutinized by your children’s children, probably after your death. Your actions in the virtual world are recorded all the time, and if the documentation survives into the future, then the next generations are going to know all about your browsing habits in the middle of the night. Yes, even though you turned incognito mode on.

This means we need to develop a new concept for privacy: futuronymity (derived from Future and Anonymity) which will obscure our lives from the eyes of future generations. Politicians are always concerned about this kind of privacy, since they know their critical decisions will be considered and analyzed by historians. In the future, common people will find themselves under similar scrutiny by their progenies. If our current hobby is going to psychologists to understand just how our parents ruined us, then the hobby of our grandchildren will be to go to the computer to find out the same.

Do we even have the right to futuronymity? Should we hide from next generations the truth about how their future was formed, and who was responsible?

That question is no longer in the hands of individuals. In the past, private people could’ve just incinerated their hard drives with all the information on them. Today, most of the information is in the hands of corporations and governments. If we want them to dispose of it – if we want any say in which parts they’ll preserve and which will be deleted – we should speak up now.