Do You Really Want a Robotic Car?

Robot Driver
Sixteen percent of Canadians and twenty-six percent of Americans say they “would not use a driverless car.” Mopic/Shutterstock

15 August 2018 – Many times in my blogging career I’ve gone on a rant about the three Ds of robotics. These are “dull, dirty, and dangerous.” They relate to the question, which is not asked often enough, “Do you want to build an automated system to do that task?”

The reason the question is not asked enough is that it needs to be asked whenever anyone looks into designing a system (whether manual or automated) to do anything. The possibility that anyone ever sets up a system to do anything without first asking that question means that it’s not asked enough.

When asking the question, getting a hit on any one of the three Ds tells you to at least think about automating the task. Getting a hit on two of them should make you think that your task is very likely to be ripe for automation. If you hit on all three, it’s a slam dunk!

When we look into developing automated vehicles (AVs), we get hits on “dull” and “dangerous.”

Driving can be excruciatingly dull, especially if you’re going any significant distance. That’s why people fall asleep at the wheel. I daresay everyone has fallen asleep at the wheel at least once, although we almost always wake up before hitting the bridge abutment. It’s also why so many people drive around with cellphones pressed to their ears. The temptation to multitask while driving is almost irresistable.

Driving is also brutally dangerous. Tens of thousands of people die every year in car crashes. Its pretty safe to say that nearly all those fatalities involve cars driven by humans. The number of people who have been killed in accidents involving driverless cars you can (as of this writing) count on one hand.

I’m not prepared to go into statistics comparing safety of automated vehicles vs. manually driven ones. Suffice it to say that eventually we can expect AVs to be much safer than manually driven vehicles. We’ll keep developing the technology until they are. It’s not a matter of if, but when.

This is the analysis most observers (if they analyze it at all) come up with to prove that vehicle driving should be automated.

Yet, opinions that AVs are acceptable, let alone inevitable, are far from universal. In a survey of 3,000 people in the U.S. and Canada, Ipsos Strategy 3 found that sixteen percent of Canadians and twenty six percent of Americans say they “would not use a driverless car,” and a whopping 39% of Americans (and 30% of Canadians) would rarely or never let driverless technology do the parking!

Why would so many people be unwilling to give up their driving priviledges? I submit that it has to do with a parallel consideration that is every bit as important as the three Ds when deciding whether to automate a task:

Don’t Automate Something Humans Like to Do!

Predatory animals, especially humans, like to drive. It’s fun. Just ask any dog who has a chance to go for a ride in a car! Almost universally they’ll jump into the front seat as soon as you open the door.

In fact, if you leave them (dogs) in the car unattended for a few minutes, they’ll be behind the wheel when you come back!

Humans are the same way. Leave ’em unattended in a car for any length of time, and they’ll think of some excuse to get behind the wheel.

The Ipsos survey fourd that some 61% of both Americans and Canadians identify themselves as “car people.” When asked “What would have to change about transportation in your area for you to consider not owning a car at all, or owning fewer cars?” 39% of Americans and 38% of Canadians responded “There is nothing that would make me consider owning fewer cars!”

That’s pretty definative!

Their excuse for getting behind the wheel is largely an economic one: Seventy-eight pecent of Americans claim they “definitely need to have a vehicle to get to work.” In more urbanized Canada (you did know that Canadians cluster more into cities, didn’t you.) that drops to 53%.

Whether those folks claiming they “have” to have a car to get to work is based on historical precedent, urban planning, wishful thinking, or flat out just what they want to believe, it’s a good, cogent reason why folks, especially Americans, hang onto their steering wheels for dear life.

The moral of this story is that driving is something humans like to do, and getting them to give it up will be a serious uphill battle for anyone wanting to promote driverless cars.

Yet, development of AV technology is going full steam ahead.

Is that just another example of Dr. John Bridges’  version of Solomon’s proverb “A fool and his money are soon parted?”

Possibly, but I think not.

Certainly, the idea of spending tons of money to have bragging rights for the latest technology, and to take selfies showing you reading a newspaper while your car drives itself through traffic has some appeal. I submit, however, that the appeal is short lived.

For one thing, reading in a moving vehicle is the fastest route I know of to motion sickness. It’s right up there with cueing up the latest Disney cartoon feature for your kids on the overhead DVD player in your SUV, then cleaning up their vomit.

I, for one, don’t want to go there!

Sounds like another example of “More money than brains.”

There are, however, a wide range of applications where driving a vehicle turns out to be no fun at all. For example, the first use of drone aircraft was as targets for anti-aircraft gunnery practice. They just couldn’t find enough pilots who wanted to be sitting ducks to be blown out of the sky! Go figure.

Most commercial driving jobs could also stand to be automated. For example, almost nobody actually steers ships at sea anymore. They generally stand around watching an autopilot follow a pre-programmed course. Why? As a veteran boat pilot, I can tell you that the captain has a lot more fun than the helmsman. Piloting a ship from, say, Calcutta to San Francisco has got to be mind-numbingly dull. There’s nothing going on out there on the ocean.

Boat passengers generally spend most of their time staring into the wake, but the helmsman doesn’t get to look at the wake. He (or she) has to spend their time scanning a featureless horizontal line separating a light-blue dome (the sky) from a dark-blue plane (the sea) in the vain hope that something interesting will pop up and relieve the tedium.

Hence the autopilot.

Flying a commercial airliner is similar. It has been described (as have so many other tasks) as “hours of boredom punctuated by moments of sheer terror!” While such activity is very Zen (I’m convinced that humans’ ability to meditate was developed by cave guys having to sit for hours, or even days, watching game trails for their next meal to wander along), it’s not top-of-the-line fun.

So, sometimes driving is fun, and sometimes it’s not. We need AV technology to cover those times when it’s not.

The Future Role of AI in Fact Checking

Reality Check
Advanced computing may someday help sort fact from fiction. Gustavo Frazao/Shutterstock

8 August 2018 – This guest post is contributed under the auspices of Trive, a global, decentralized truth-discovery engine. Trive seeks to provide news aggregators and outlets a superior means of fact checking and news-story verification.

by Barry Cousins, Guest Blogger, Info-Tech Research Group

In a recent project, we looked at blockchain startup Trive and their pursuit of a fact-checking truth database. It seems obvious and likely that competition will spring up. After all, who wouldn’t want to guarantee the preservation of facts? Or, perhaps it’s more that lots of people would like to control what is perceived as truth.

With the recent coming out party of IBM’s Debater, this next step in our journey brings Artificial Intelligence into the conversation … quite literally.

As an analyst, I’d like to have a universal fact checker. Something like the carbon monoxide detectors on each level of my home. Something that would sound an alarm when there’s danger of intellectual asphyxiation from choking on the baloney put forward by certain sales people, news organizations, governments, and educators, for example.

For most of my life, we would simply have turned to academic literature for credible truth. There is now enough legitimate doubt to make us seek out a new model or, at a minimum, to augment that academic model.

I don’t want to be misunderstood: I’m not suggesting that all news and education is phony baloney. And, I’m not suggesting that the people speaking untruths are always doing so intentionally.

The fact is, we don’t have anything close to a recognizable database of facts from which we can base such analysis. For most of us, this was supposed to be the school system, but sadly, that has increasingly become politicized.

But even if we had the universal truth database, could we actually use it? For instance, how would we tap into the right facts at the right time? The relevant facts?

If I’m looking into the sinking of the Titanic, is it relevant to study the facts behind the ship’s manifest? It might be interesting, but would it prove to be relevant? Does it have anything to do with the iceberg? Would that focus on the manifest impede my path to insight on the sinking?

It would be great to have Artificial Intelligence advising me on these matters. I’d make the ultimate decision, but it would be awesome to have something like the Star Trek computer sifting through the sea of facts for that which is relevant.

Is AI ready? IBM recently showed that it’s certainly coming along.

Is the sea of facts ready? That’s a lot less certain.

Debater holds its own

In June 2018, IBM unveiled the latest in Artificial Intelligence with Project Debater in a small event with two debates: “we should subsidize space exploration”, and “we should increase the use of telemedicine”. The opponents were credentialed experts, and Debater was arguing from a position established by “reading” a large volume of academic papers.

The result? From what we can tell, the humans were more persuasive while the computer was more thorough. Hardly surprising, perhaps. I’d like to watch the full debates but haven’t located them yet.

Debater is intended to help humans enhance their ability to persuade. According to IBM researcher Ranit Aharanov, “We are actually trying to show that a computer system can add to our conversation or decision making by bringing facts and doing a different kind of argumentation.”

So this is an example of AI. I’ve been trying to distinguish between automation and AI, machine learning, deep learning, etc. I don’t need to nail that down today, but I’m pretty sure that my definition of AI includes genuine cognition: the ability to identify facts, comb out the opinions and misdirection, incorporate the right amount of intention bias, and form decisions and opinions with confidence while remaining watchful for one’s own errors. I’ll set aside any obligation to admit and react to one’s own errors, choosing to assume that intelligence includes the interest in, and awareness of, one’s ability to err.

Mark Klein, Principal Research Scientist at M.I.T., helped with that distinction between computing and AI. “There needs to be some additional ability to observe and modify the process by which you make decisions. Some call that consciousness, the ability to observe your own thinking process.”

Project Debater represents an incredible leap forward in AI. It was given access to a large volume of academic publications, and it developed its debating chops through machine learning. The capability of the computer in those debates resembled the results that humans would get from reading all those papers, assuming you can conceive of a way that a human could consume and retain that much knowledge.

Beyond spinning away on publications, are computers ready to interact intelligently?

Artificial? Yes. But, Intelligent?

According to Dr. Klein, we’re still far away from that outcome. “Computers still seem to be very rudimentary in terms of being able to ‘understand’ what people say. They (people) don’t follow grammatical rules very rigorously. They leave a lot of stuff out and rely on shared context. They’re ambiguous or they make mistakes that other people can figure out. There’s a whole list of things like irony that are completely flummoxing computers now.”

Dr. Klein’s PhD in Artificial Intelligence from the University of Illinois leaves him particularly well-positioned for this area of study. He’s primarily focused on using computers to enable better knowledge sharing and decision making among groups of humans. Thus, the potentially debilitating question of what constitutes knowledge, what separates fact from opinion from conjecture.

His field of study focuses on the intersection of AI, social computing, and data science. A central theme involves responsibly working together in a structured collective intelligence life cycle: Collective Sensemaking, Collective Innovation, Collective Decision Making, and Collective Action.

One of the key outcomes of Klein’s research is “The Deliberatorium”, a collaboration engine that adds structure to mass participation via social media. The system ensures that contributors create a self-organized, non-redundant summary of the full breadth of the crowd’s insights and ideas. This model avoids the risks of ambiguity and misunderstanding that impede the success of AI interacting with humans.

Klein provided a deeper explanation of the massive gap between AI and genuine intellectual interaction. “It’s a much bigger problem than being able to parse the words, make a syntax tree, and use the standard Natural Language Processing approaches.”

Natural Language Processing breaks up the problem into several layers. One of them is syntax processing, which is to figure out the nouns and the verbs and figure out how they’re related to each other. The second level is semantics, which is having a model of what the words mean. That ‘eat’ means ‘ingesting some nutritious substance in order to get energy to live’. For syntax, we’re doing OK. For semantics, we’re doing kind of OK. But the part where it seems like Natural Language Processing still has light years to go is in the area of what they call ‘pragmatics’, which is understanding the meaning of something that’s said by taking into account the cultural and personal contexts of the people who are communicating. That’s a huge topic. Imagine that you’re talking to a North Korean. Even if you had a good translator there would be lots of possibility of huge misunderstandings because your contexts would be so different, the way you try to get across things, especially if you’re trying to be polite, it’s just going to fly right over each other’s head.”

To make matters much worse, our communications are filled with cases where we ought not be taken quite literally. Sarcasm, irony, idioms, etc. make it difficult enough for humans to understand, given the incredible reliance on context. I could just imagine the computer trying to validate something that starts with, “John just started World War 3…”, or “Bonnie has an advanced degree in…”, or “That’ll help…”

A few weeks ago, I wrote that I’d won $60 million in the lottery. I was being sarcastic, and (if you ask me) humorous in talking about how people decide what’s true. Would that research interview be labeled as fake news? Technically, I suppose it was. Now that would be ironic.

Klein summed it up with, “That’s the kind of stuff that computers are really terrible at and it seems like that would be incredibly important if you’re trying to do something as deep and fraught as fact checking.”

Centralized vs. Decentralized Fact Model

It’s self-evident that we have to be judicious in our management of the knowledge base behind an AI fact-checking model and it’s reasonable to assume that AI will retain and project any subjective bias embedded in the underlying body of ‘facts’.

We’re facing competing models for the future of truth, based on the question of centralization. Do you trust yourself to deduce the best answer to challenging questions, or do you prefer to simply trust the authoritative position? Well, consider that there are centralized models with obvious bias behind most of our sources. The tech giants are all filtering our news and likely having more impact than powerful media editors. Are they unbiased? The government is dictating most of the educational curriculum in our model. Are they unbiased?

That centralized truth model should be raising alarm bells for anyone paying attention. Instead, consider a truly decentralized model where no corporate or government interest is influencing the ultimate decision on what’s true. And consider that the truth is potentially unstable. Establishing the initial position on facts is one thing, but the ability to change that view in the face of more information is likely the bigger benefit.

A decentralized fact model without commercial or political interest would openly seek out corrections. It would critically evaluate new knowledge and objectively re-frame the previous position whenever warranted. It would communicate those changes without concern for timing, or for the social or economic impact. It quite simply wouldn’t consider or care whether or not you liked the truth.

The model proposed by Trive appears to meet those objectivity criteria and is getting noticed as more people tire of left-vs-right and corporatocracy preservation.

IBM Debater seems like it would be able to engage in critical thinking that would shift influence towards a decentralized model. Hopefully, Debater would view the denial of truth as subjective and illogical. With any luck, the computer would confront that conduct directly.

IBM’s AI machine already can examine tactics and style. In a recent debate, it coldly scolded the opponent with: “You are speaking at the extremely fast rate of 218 words per minute. There is no need to hurry.”

Debater can obviously play the debate game while managing massive amounts of information and determining relevance. As it evolves, it will need to rely on the veracity of that information.

Trive and Debater seem to be a complement to each other, so far.

Author BioBarry Cousins

Barry Cousins, Research Lead, Info-Tech Research Group specializing in Project Portfolio Management, Help/Service Desk, and Telephony/Unified Communications. He brings an extensive background in technology, IT management, and business leadership.

About Info-Tech Research Group

Info-Tech Research Group is a fast growing IT research and advisory firm. Founded in 1997, Info-Tech produces unbiased and highly relevant IT research solutions. Since 2010 McLean & Company, a division of Info-Tech, has provided the same, unmatched expertise to HR professionals worldwide.

Who’s NOT a Creative?

 

Compensting sales
Close-up Of A Business Woman Giving Cheque To Her Colleague At Workplace In Office. Andrey Popov/Shutterstock

25 July 2018 – Last week I made a big deal about the things that motivate creative people, such as magazine editors, and how the most effective rewards were non-monetary. I also said that monetary rewards, such as commissions based on sales results, were exactly the right rewards to use for salespeople. That would imply that salespeople were somehow different from others, and maybe even not creative.

That is not the impression I want to leave you with. I’m devoting this blog posting to setting that record straight.

My remarks last week were based on Maslow‘s and Herzberg‘s work on motivation of employees. I suggested that these theories were valid in other spheres of human endeavor. Let’s be clear about this: yes, Maslow’s and Herzberg’s theories are valid and useful in general, whenever you want to think about motivating normal, healthy human beings. It’s incidental that those researchers were focused on employer/employee relations as an impetus to their work. If they’d been focused on anything else, their conclusions would probably have been pretty much the same.

That said, there are a whole class of people for whom monetary compensation is the holy grail of motivators. They are generally very high functioning individuals who are in no way pathological. On the surface, however, their preferred rewards appear to be monetary.

Traditionally, observers who don’t share this reward system have indicted these individuals as “greedy.”

I, however, dispute that conclusion. Let me explain why.

When pointing out the rewards that can be called “motivators for editors,” I wrote:

“We did that by pointing out that they belonged to the staff of a highly esteemed publication. We talked about how their writings helped their readers excel at their jobs. We entered their articles in professional competitions with awards for things like ‘Best Technical Article.’ Above all, we talked up the fact that ours was ‘the premier publication in the market.'”

Notice that these rewards, though non-monetary. were more or less measurable. They could be (and indeed for the individuals they motivated) seen as scorecards. The individuals involved had a very clear idea of value attached to such rewards. A Nobel Prize in Physics is of greater value than, say, a similar award given by, say, Harvard University.

For example, in 1987 I was awarded the “Cahners Editorial Medal of Excellence, Best How-To Article.” That wasn’t half bad. The competition was articles written for a few dozen magazines that were part of the Cahners Publishing Company, which at the time was a big deal in the business-to-business magazine field.

What I considered to be of higher value, however, was the “First Place Award For Editorial Excellence for a Technical Article in a Magazine with Over 80,000 Circulation” I got in 1997 from the American Society of Business Press Editors, where I was competing with a much wider pool of journalists.

Economists have a way of attempting to quantify such non-monetary awards called utility. They arrive at values by presenting various options and asking the question: “Which would you rather have?”

Of course, measures of utility generally vary widely depending on who’s doing the choosing.

For example, an article in the 19 July The Wall Street Journal described a phenomenon the author seemed to think was surprising: Saudi-Arabian women drivers (new drivers all) showed a preference for muscle cars over more pedestrian models. The author, Margherita Stancati, related an incident where a Porche salesperson in Riyadh offered a recently minted woman driver an “easy to drive crossover designed to primarily attract women.” The customer demurred. She wanted something “with an engine that roars.”

So, the utility of anything is not an absolute in any sense. It all depends on answering the question: “Utility to whom?”

Everyone is motivated by rewards in the upper half of the Needs Pyramid. If you’re a salesperson, growth in your annual (or other period) sales revenue is in the green Self Esteem block. It’s well and truly in the “motivator” category, and has nothing to do with the Safety and Security “hygiene factor” where others might put it. Successful salespeople have those hygiene factors well-and-truly covered. They’re looking for a reward that tells them they’ve hit a home run. That is likely having a bigger annual bonus than the next guy.

The most obvious money-driven motivators accrue to the folks in the CEO ranks. Jeff Bezos, Elon Musk, and Warren Buffett would have a hard time measuring their success (i.e., hitting the Pavlovian lever to get Self Actualization rewards) without looking at their monetary compensation!

The Pyramid of Needs

Needs Pyramid
The Pyramid of Needs combines Maslow’s and Herzberg’s motivational theories.

18 July 2018 – Long, long ago, in a [place] far, far away. …

When I was Chief Editor at business-to-business magazine Test & Measurement World, I had a long, friendly though heated, discussion with one of our advertising-sales managers. He suggested making the compensation we paid our editorial staff contingent on total advertising sales. He pointed out that what everyone came to work for was to get paid, and that tying their pay to how well the magazine was doing financially would give them an incentive to make decisions that would help advertising sales, and advance the magazine’s financial success.

He thought it was a great idea, but I disagreed completely. I pointed out that, though revenue sharing was exactly the right way to compensate the salespeople he worked with, it was exactly the wrong way to compensate creative people, like writers and journalists.

Why it was a good idea for his salespeople I’ll leave for another column. Today, I’m interested in why it was not a good idea for my editors.

In the heat of the discussion I didn’t do a deep dive into the reasons for taking my position. Decades later, from the standpoint of a semi-retired whatever-you-call-my-patchwork-career, I can now sit back and analyze in some detail the considerations that led me to my conclusion, which I still think was correct.

We’ll start out with Maslow’s Hierarchy of Needs.

In 1943, Abraham Maslow proposed that healthy human beings have a certain number of needs, and that these needs are arranged in a hierarchy. At the top is “self actualization,” which boils down to a need for creativity. It’s the need to do something that’s never been done before in one’s own individual way. At the bottom is the simple need for physical survival. In between are three more identified needs people also seek to satisfy.

Maslow pointed out that people seek to satisfy these needs from the bottom to the top. For example, nobody worries about security arrangements at their gated community (second level) while having a heart attack that threatens their survival (bottom level).

Overlaid on Maslow’s hierarchy is Frederick Herzberg’s Two-Factor Theory, which he published in his 1959 book The Motivation to Work. Herzberg’s theory divides Maslow’s hierarchy into two sections. The lower section is best described as “hygiene factors.” They are also known as “dissatisfiers” or “demotivators” because if they’re not met folks get cranky.

Basically, a person needs to have their hygiene factors covered in order have a level of basic satisfaction in life. Not having any of these needs satisfied makes them miserable. Having them satisfied doesn’t motivate them at all. It makes ’em fat, dumb and happy.

The upper-level needs are called “motivators.” Not having motivators met drives an individual to work harder, smarter, etc. It energizes them.

My position in the argument with my ad-sales friend was that providing revenue sharing worked at the “Safety and Security” level. Editors were (at least in my organization) paid enough that they didn’t have to worry about feeding their kids and covering their bills. They were talented people with a choice of whom they worked for. If they weren’t already being paid enough, they’d have been forced to go work for somebody else.

Creative people, my argument went, are motivated by non-monetary rewards. They work at the upper “motivator” levels. They’ve already got their physical needs covered, so to motivate them we have to offer rewards in the “motivator” realm.

We did that by pointing out that they belonged to the staff of a highly esteemed publication. We talked about how their writings helped their readers excel at their jobs. We entered their articles in professional competitions with awards for things like “Best Technical Article.” Above all, we talked up the fact that ours was “the premier publication in the market.”

These were all non-monetary rewards to motivate people who already had their basic needs (the hygiene factors) covered.

I summarized my compensation theory thusly: “We pay creative people enough so that they don’t have to go do something else.”

That gives them the freedom to do what they would want to do, anyway. The implication is that creative people want to do stuff because it’s something they can do that’s worth doing.

In other words, we don’t pay creative people to work. We pay them to free them up so they can work. Then, we suggest really fun stuff for them to work at.

What does this all mean for society in general?

First of all, if you want there to be a general level of satisfaction within your society, you’d better take care of those hygiene factors for everybody!

That doesn’t mean the top 1%. It doesn’t mean the top 80%, either. Or, the top 90%. It means everybody!

If you’ve got 99% of everybody covered, that still leaves a whole lot of people who think they’re getting a raw deal. Remember that in the U.S.A. there are roughly 300 million people. If you’ve left 1% feeling ripped off, that’s 3 million potential revolutionaries. Three million people can cause a lot of havoc if motivated.

Remember, at the height of the 1960s Hippy movement, there were, according to the most generous estimates, only about 100,000 hipsters wandering around. Those hundred-thousand activists made a huge change in society in a very short period of time.

Okay. If you want people invested in the status quo of society, make sure everyone has all their hygiene factors covered. If you want to know how to do that, ask Bernie Sanders.

Assuming you’ve got everybody’s hygiene factors covered, does that mean they’re all fat, dumb, and happy? Do you end up with a nation of goofballs with no motivation to do anything?

Nope!

Remember those needs Herzberg identified as “motivators” in the upper part of Maslow’s pyramid?

The hygiene factors come into play only when they’re not met. The day they’re met, people stop thinking about who’ll be first against the wall when the revolution comes. Folks become fat, dumb and happy, and stay that way for about an afternoon. Maybe an afternoon and an evening if there’s a good ballgame on.

The next morning they start thinking: “So, what can we screw with next?”

What they’re going to screw with next is anything and everything they damn well please. Some will want to fly to the Moon. Some will want to outdo Michaelangelo‘s frescos for the ceiling of the Sistine Chapel. They’re all going to look at what they think was the greatest stuff from the past, and try to think of ways to do better, and to do it in their own way.

That’s the whole point of “self actualization.”

The Renaissance didn’t happen because everybody was broke. It happened because they were already fat, dumb and happy, and looking for something to screw with next.