By Lance Eliot, the AI Trends Insider

They’ll cost too much. They will only be for the elite. Having one will be a sign of prestige. It’s a rich person’s toy. The “have nots” will not be able to get one. People are going to rise-up in resentment that the general population can’t get one. Maybe the government should step in and control the pricing. Refuse to get into one as a form of protest. Ban them because if the rest of us cannot have one, nobody should.

What’s this all about?

It’s some of the comments that are already being voiced about the potential affordability (or lack thereof) of AI self-driving cars.

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars, and we get asked quite frequently about whether AI self-driving cars will be affordable or not. I thought you might find of interest my answer (read on).

When people clamor about the potential sky reaching cost of AI self-driving cars, you might at first wonder if people are maybe talking about flying cars, rather than AI self-driving cars. I mention this because there are some that say that flying cars will be very pricy and I think we all pretty much accept that notion. We know that jet planes are pricey, so why shouldn’t a flying car be pricey. But, an earth-based car that rolls on the ground and cannot fly in the air, nor can it submerge like a submarine, we openly question how much such a seemingly “ordinary” car should cost.

It is said that a Rolls-Royce Sweptail is priced upwards of $13 million dollars. Have there been mass protests about this? Are we upset that only a few that are wealthy can afford such a car? Not really. It is pretty much taken for granted that there are cars that are indeed very expensive. Of course, we might all consider it rather foolish of those that are willing to pump hard-earned millions of dollars into such a car. We might think them pretentious for doing so. Or, we might envy them that they have the means to buy such a car. Either way, the Rolls-Royce and other such top to-end cars are over-the-top pricey and most people not especially complain or argue about it.

Part of the reason that people seem to object to the possible high price tag on an AI self-driving car is that the AI self-driving car is being touted as a means to benefit society. AI self-driving cars are ultimately hopefully going to cut down on the number of annual driving related deaths. AI self-driving cars will provide mobility to those that need it, and that cannot otherwise achieve it, such as the poor and the elderly. If an AI self-driving car has such tremendous societal benefits, then we want to as a society ensure that society as a whole gets those benefits and that those benefits will presumably apply across the board. It’s a car of the people, for the people.

What kind of pricing then, for an AI self-driving car, are people apparently thinking of? Some that don’t have any clue of what the price might be are leaving the price tag unknown and thus it makes things easier to get into a lather about how expensive it is. It could be a zillion dollars. Or more. This though seems like a rather vacuous way to discuss the topic. It would seem that we might be better off if we start tossing around some actual numbers and then see if that’s prohibitive or not to buy an AI self-driving car.

The average transaction price (ATP) for a traditional passenger car in the United States for this year is so far around $36,000 according to various published statistics. That’s the national average.

When AI self-driving cars first get started a few years ago, the cost of the added sensors and other specialized gear for achieving self-driving capabilities was estimated at somewhere around $100,000. Meanwhile, since then, the price on those self-driving car specialized components aspects has steadily come down. As with most high-tech, the cost starts “high” and then as it is perfected and the costs to make it wringed out of the process, the price heads downward. In any case, some at the time were saying that an AI self-driving car might be around $150,000 to $200,000, though that’s a wild guess and we don’t yet know what the real pricing will be. Will it be a million dollars for an AI self-driving car? That doesn’t seem to be in anyone’s estimates at this time.

Of course, any time a new car comes out, particularly one that has new innovations, there is usually a premium price placed on the car. It’s a novelty item at first. The number of such cars is usually scarce initially, and so the usual laws of supply and demand help to punch up the price. If the car is able to be eventually mass produced, gradually the price starts to come down as more of those cars enter into the marketplace. If there are competitors that provide equivalent alternatives, the competition of the marketplace tends to drive down the price. You can refer to the Tesla models as prime examples of this kind of marketplace phenomena.

Will True AI Self-Driving Cas Be Within Financial Reach?

Suppose indeed that the first true AI self-driving cars in the low hundreds of thousands of dollars. Does that mean that those cars are out of the reach of the everyday person?

Before we jump into the answer for that question, let’s clarify what I mean by true AI self-driving cars. There are levels of self-driving cars. The topmost level is Level 5. A Level 5 AI self-driving car is able to be driven by the AI without any human intervention. In fact, there is not a human driver needed in a Level 5 car. So much so that there is unlikely to be any driving controls in a Level 5 self-driving car for a human to operate even if the human wanted to try and drive it. In theory, the AI of the Level 5 self-driving car is supposed to be able to drive the car as a human could.

Let’s therefore not consider in this affordability discussion the AI self-driving cars that are less than a Level 5. A less than level 5 self-driving car is a lot like a conventional car, though augmented in a manner that allows for co-sharing of the driving task. This means that there must be a human driver in a car that is classified as a less than Level 5 self-driving car. In spite of having whatever kind of AI in such a self-driving car, the driving task is still considered the responsibility of the human driver. No matter whether the human driver opts to take their eyes off the road, which can be an easy trap to fall into when in a less than level 5 self-driving car, and if the AI were to suddenly toss the control aspects to that human driver, it is nonetheless the human driver considered to be responsible for the driving. I’ve warned many times about the dangers this creates in the driving task.

For my article about the levels of AI self-driving cars, see: https://aitrends.com/selfdrivingcars/richter-scale-levels-self-driving-cars/

For my framework about AI self-driving cars, see: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

For the dangers of co-shared driving and AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/human-back-up-drivers-for-ai-self-driving-cars/

We’ll focus herein on the true Level 5 self-driving car. This is the self-driving car that has the full bells and whistles and really is a self-driving car. No human driver needed. This is the one that those referring to a driving utopia are actually meaning to bring up. The less than level 5’s aren’t quite so exciting, though they might well be important and perhaps stepping stones to the level 5.

Now, let’s get back to the question at hand – will a true Level 5 AI self-driving car be affordable?

We can first quibble about the word “affordable” in this context. If by affordability we mean that it should be around the same price tag as the ATP $36,000 of today’s average passenger car in the United States, I’d say that we aren’t going to see Level 5 Ai self-driving cars at that price for likely a long time until after they are quite prevalent. In other words, out the gate, it isn’t going to be that kind of price (it will be much higher). After years of growth of more and more AI self-driving cars coming into the marketplace, sure, it could possibly eventually come down to that range. Keep in mind that today there are around 200 million conventional cars in the United States, and presumably over time those cars will get replaced by AI self-driving cars. It won’t happen overnight. It will be a gradual wind down of the old ways, and a gradual wind-up of the new ways.

Imagine that the first sets of AI self-driving cars will cost in the neighborhood of several hundreds of thousands of dollars. Obviously, that price is outside the range of the average person. No argument there.

But, that’s if you only look at the problem or question in just one simple way, namely purchasing the car for purely personal use. That’s the mental trap that most fall into. They perceive of the AI self-driving car as a personal car and nothing more. I’d suggest you reconsider that notion.

It is generally predicted and accepted that AI self-driving cars are likely to be running 24×7. You can have your self-driving car going all the time, pretty much. Today’s conventional cars are only used around 5% of their available time. This makes sense because you drive your personal car to work, you park it, you work all day, you drive home. Over ninety percent of the day it is sitting and not doing anything other than being a paperweight, if you will.

For AI self-driving cars, you have an electronic chauffeur that will drive the car whenever you want. But, are you actually going to want to be going in your AI self-driving car all day long? I doubt it. So, you will have extra available driving capacity that is unused. You could just chock it up and say that’s the way the ball bounces. More than likely, you would realize that you could turn that idle time into personal revenue.

See my article about the non-stop use of AI self-driving cars: https://aitrends.com/selfdrivingcars/non-stop-ai-self-driving-cars-truths-and-consequences/

Here’s what is most likely to actually happen.

We all generally agree that the advent of the AI self-driving car will spur the ridesharing industry. In fact, some say that the AI self-driving car will shift our society into a ridesharing-as-an-economy model. This is why the Uber and Lyft and other existing ridesharing firms are so frantic about AI self-driving cars. Right now, ridesharing firms are able to justify what they do because they are able to connect together human drivers with cars to those that need a lift. If you eliminate the human driver out of the equation, what then if the ridesharing firm doing? That’s the scary proposition for the ridesharing firms.

This all implies that ridesharing-as-a-service will now be possible by the masses. It doesn’t matter if you have a full-time job and cannot spare the time to be a ridesharing driver, because instead you just let your AI self-driving car be your ridesharing service. You mainly need to get connected up with people that need a ridesharing lift. How will that occur? Uber and Lyft are hopeful it will occur via their platform, but it could instead be say a Facebook wherein the people are already there in the billions. This is all going to be a big shakeout coming.

Meanwhile, you buy yourself an AI self-driving car, and you use it for some portion of the time, and the rest of the time you have it earning some extra dough as a ridesharing vehicle. Nice!

This then ties into the affordability question posed earlier.

If you are going to have revenue generated by your AI self-driving car, you can then look at it as a small business of sorts. You then should consider your AI self-driving car as an investment. You are making an investment in an asset that you can put to work and earn revenue. As such, you should then consider what the revenue might be and what the cost might be to achieve that revenue.

Self-Driving Car Revenue Potential Opens Door to Affordability

This opens the door towards being able to afford an otherwise seemingly unaffordable car. Even if the AI self-driving car costs you say several hundreds of thousands of dollars, which seems doubtful as a price tag, but let’s use it as an example, you can weigh against that the revenue you can earn from that car.

For tax purposes (depending on how taxes will be regulated in the era of AI self-driving cars), you can usually deduct a car loan interest when using a car for business purposes (the deduction is only with respect to the portion of it used for business purposes). So, suppose you use your AI self-driving car for 15% of the time, and the other 85% of the time you use it for your ridesharing business, you can deduct the car loan interest normally for the 85% portion.

You can also do deductions for tax purposes, sometimes using the federal standard mileage rate, or also with actual vehicle expenses including:

•         Depreciation
•         Licenses
•         Gas and oil
•         Tolls
•         Lease payments
•         Insurance
•         Garage rent
•         Parking fees
•         Registration fees
•         Repairs
•         Tires

Therefore, you need to rethink the cost of an AI self-driving car. It becomes a potential money maker and you need to consider the cost to purchase the car, the cost of ongoing maintenance and support, the cost of special taxes, the cost of undertaking the ridesharing services, and other such associated costs.

These costs are weighed in comparison to the potential revenue. You might at first only be thinking of the revenue derived from the riders that use your AI self-driving car. You might also consider that there is the opportunity for in-car entertainment that you could possibly charge a fee for (access to streaming movies, etc.), perhaps in-car provided food (you might stock the self-driving car with a small refrigerator and have other food in it), etc. You can also possibly use your AI self-driving car for doing advertising and get money from advertisers based on how many eyeballs see their ads while people are going around in your AI self-driving car.

And, this all then becomes part of your budding small business. You get various tax breaks. You might also then expand your business into other areas of related operations or even beyond AI self-driving cars entirely.

One related tie-in might be with the companies that are providing ridesharing scooters and bicycles. Suppose someone gets into your AI self-driving car and they indicate that when they reach their destination, they’d like to have a bicycle to rent. Your ridesharing service might have an arrangement with a firm that does those kinds of ridesharing services, and you get a piece of the action accordingly.

Will the average person be ready to be their own AI self-driving car mogul?

Likely not. But, fear not, a cottage industry will quickly arise that will support the emergence of small businesses that are doing ridesharing with AI self-driving cars. I’ll bet there will be seminars on how to setup your own corporation for these purposes. How to keep your ridesharing AI self-driving car always on the go. Accountants will promote their tax services to the ridesharing start-ups. There will be auto maintenance and repair shops that will seek to be your primary go-to for keeping your ridesharing money maker going. And so on.

In that sense, there will be a ridesharing-as-a-business business that booms to help new entrepreneurs on how to tap into the ridesharing-as-a-service economy. Make millions off your AI self-driving car, will be the late night TV infomercials. You’ll see ads on YouTube of a smiling person that says until they got their AI self-driving car they were stuck in a dead-end job, but now, with their money producing AI self-driving car, they are so wealthy they don’t know where to put all the money they are making. The big bonanza is on its way.

This approach of being a solo entrepreneur to afford an AI self-driving car is only one of several possible approaches. I’d guess it will be perhaps the most popular.

I’ll caution though that it is not a guaranteed path to riches. There will be some that manage to get themselves an AI self-driving car and then discover that it is not being put to ridesharing use as much as they thought. It could be that they live in an area swamped with other AI self-driving cars and so they get just leftover crumbs of ridesharing requests. Or, they are in an area that has other mass transit and no one needs ridesharing. Or, maybe few will trust using an AI self-driving car and so there won’t be many that are willing to use it for ridesharing. Another angle is that you get such a car and do so under the assumption it will be ridesharing for 85% of the time, but you instead use it for personal purposes 70% of the time and this leaves only 30% of the time for the ridesharing (cutting down on the revenue potential).

Meanwhile, there are some other alternatives, let’s briefly consider them:

•         Solo ridesharing business as a money maker (discussed so far) of an AI self-driving car
•         Pooling an AI self-driving car
•         Timeshare an AI self-driving car
•         Personal use exclusively of an AI self-driving car
•         Other

In the case of pooling an AI self-driving car, imagine that your next door neighbor would like an AI self-driving car and so would you. The two of you realize that since the neighbor starts work at 7 a.m., while you start work at 8 a.m., and the kids of both families start school at 9 a.m., here’s what you could do. You and the neighbor split the cost of an AI self-driving car. It takes your neighbor to work at 7 a.m., comes back and takes you to work at 8 a.m., comes back and takes the kids to school by 9 a.m. In essence, you all pool the use of the AI self-driving car. There’s no revenue aspects, it’s all just being used for personal use, on a group basis. This could be done with more than just one neighbor.

The pooling would then allow you to split the cost of the AI self-driving car, making it more affordable per person. Suppose you have 3 people and they decided to evenly split the cost, this would make it so that you’d only need to afford one-third of whatever the prevailing cost would be of an AI self-driving car at that time. Voila, the cost is less, seemingly so. But, you’d need to figure out the sharing aspects and I realize it could get heated as to who gets to use the AI self-driving car when needed. It’s like having only one TV and it might be difficult at times to balance the aspect that someone wants to watch one show and someone else wants another one – say you need the AI self-driving car to take you to the store, while the kids need it to get to the ballpark.

In the case of the timeshare approach, you buy into an AI self-driving car like you would if buying into a condo in San Carlo. You purchase a time-based portion of the AI self-driving car. You can use it for whatever is the agreed amount of time. Potentially, you can opt to “invest” in more than one at a time, perhaps getting a timeshare in a passenger car that’s an AI self-driving car, and also investing in an RV that’s an AI self-driving vehicle. You would use them each at different times for their suitable purposes. With any kind of timesharing arrangement, watch out for the details and whether you can get out of it or it might have other such limitations.

There’s the purely personal use of an AI self-driving car option too, which we started this discussion by saying it might be too much for the average person to afford. Even that is somewhat malleable in that there are likely to be car loans that take into account that you are buying an AI self-driving car. The loans might be very affordable in the sense that there’s the collateral of the car, plus the AI self-driving car if needed can be repossessed and then turned into a potential money maker. The auto makers and the banks and others might be willing to cut some pretty good loans to get you into your very own AI self-driving car. As always, watch out for the interest and any onerous loan terms!

Well, before we get too far ahead of ourselves, the main point to be made is that even if AI self-driving cars are priced “high” in comparison to today’s conventional cars, it does not necessarily mean that those AI self-driving cars are only going to be only for the very rich. Instead, those AI self-driving cars are actually going to be a means to help augment the wealth of those that see this as an opportunity. Not everyone will be ready or willing to go the small business route. For many, it will be a means to not only enjoy the benefits of AI self-driving cars, but also spark them towards becoming entrepreneurs. Let’s see how this all plays out and maybe it adds another potential benefit to the emergence of AI self-driving cars.

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.

 

By Lance Eliot, the AI Trends Insider

Have you ever taken a road trip across the United States with your family? It’s considered a core part of Americana to make such a trip. Somewhat immortalized by the now classic movie National Lampoon’s Vacation, the film showcased the doting scatter brained father Clark Griswold with his caring wife, Ellen, and their vacation-with-your-parents trapped children, Rusty and Audrey, as they all at times either enjoyed or managed to endure a cross-country expedition of a lifetime.

As is typically portrayed in such situations, the father drives the car for most of the trip and serves as the taskmaster to keep the trip moving forward, the mother provides soothing care for the family and tries to keep things on an even keel, and the children must contend with parents that are out-of-touch with reality and that are jointly determined that come heck-or-high-water their kids will presumably have a good time (at least by the definition of the parents). The move was released in 1983 and became a blockbuster that spawned other variants. Today, we can find fault with how the nuclear family is portrayed and the stereotypes used throughout the movie, but nonetheless it put on film what generally is known as the family road trip.

What does this have to do with AI self-driving cars?

At the Cybernetic AI Self-Driving Car Institute, we are developing AI systems for self-driving cars and doing so with an eye towards how people will want to use AI self-driving cars. It is important to consider the behavior of how human occupants will be while inside an AI self-driving car and therefore astutely design and build AI self-driving cars accordingly.

In a conventional car, for a family road trip, it is pretty much the case that the parents sit in the front seats of the car. This makes sense since either the father or the mother will be the drivers of the car, often times switching off the driving task from one to the other. In prior times the driving task was considered to be “manly” and so usually the husband was shown driving the car. In contemporary times, whatever the nature of and gender of the parents, the point is that the licensed driving adults are most likely to be seated in the front of the car.

If there are two parents, why have both in the front seat, you might ask? Couldn’t you put one of the children up in the front passenger seat, next to the parent or adult that is driving the car? You can certainly arrange things that way, but the usual notion about having the front passenger be another adult or parent is that they can be watching the roadway, serving as an extra pair of eyes for the driver. The driver might be preoccupied with the traffic in front of the car, and meanwhile the front passenger notices that further up ahead there is a bridge-out sign warning that approaching cars need to be cautious. The front passenger is a kind of co-pilot, though they don’t have ready access to the car controls and must instead verbally provide advice to the driver.

The front passenger is not always shown though in movies as a dispassionate observer that thoughtfully aids the driver. Humorous anecdotes are often shown as the front passenger suddenly points at a cow and screams out load for everyone to look. The driver could be distracted by such an exclamation and inadvertently drive off the road at the sudden yelling and pointing. Another commonly portrayed scenario is the front passenger that insists the driver take the next right turn ahead, but offering such a verbal instruction once the car is nearly past the available turn. The driver is then torn between making a radical and dangerous turn, or passing the turn entirely and then likely getting berated by the front seat passenger.

Does this seem familiar to you?

If so, you are likely a veteran of family road trips. Congratulations.

What about the children that are seated in the back seat of the car? One portrayal would be of young children with impressionable minds that are carefully studying their parents and learning the wise ways of life, doing so during the vacation and they will become more learned young adults because of the experience. Of course, this is not the stuff of reality.

Kids Converse with Out-of-Touch Parents

Instead, the movies show something that pertains more closely to reality. The kids often feel trapped. Their parents are forcing them along on a trip. It’s a trip the parents want, but not necessarily what the kids want. At times feeling like prisoners, they need to occupy themselves for hours at time on long stretches of highway. Though at first it might be keen to see an open highway and the mountains and blue skies, it is something that won’t last your attention for hours upon hours, days upon days. Boredom sets in. Conversation with the parents also can only last so long. The parents are out-of-touch with the interests, musical tastes, and other facets of the younger generation.

The classic indication is that ultimately the kids will get into a fight. Not a fisticuffs fight per se, more like an arms waving and hands slapping kind of fight. And the parents then need to turn their heads and look at the kids with laser like eyes, and tell the kids in overtly stern terms, stop that fighting back there or it will be heck to pay. No more ice cream, no more allowance, or whatever other levers the parents can use to threaten the kids to behave. Don’t make me come back there, is the usual refrain.

Sometimes one or more of the kids will start crying. Could be for just about any reason. They are tired of the trip and want it to end. They got hit by their brother or sister and want the parents to know so. Etc. The parents will often retort that the kids need to stop crying. Or, as they are want to say, they’ll give them a true reason to cry (a veiled threat). If the kids are complaining incessantly about the trip, this will likely produce the other classic veiled threat of “I’d better not hear another peep out of you!”

Does the above suggest that the togetherness of the family road trip is perhaps hollow and we should abandon the pretense of having a family trip? I don’t think so. It’s more like showing how family trips really happen. In that sense, the movie National Lampoon’s Vacation was a more apt portrayal than a Leave It To Beaver kind of portrayal, at least in more modern times.

Indeed, today’s family road trips are replete with gadgets and electronics in the car. The kids are likely to be focusing on their smartphones and tablets. The car probably has WiFi, though at times only getting intermittent reception as the trip across some of the more barren parts of the United States takes place. There might be TV’s built into the headrests so the kids can watch movies that way. One of the more popular and cynical portrayals of today’s family road trips is that there is no actual human-to-human interaction inside the car, since everyone is tuned into their own electronic device.

Given the above description of how the family road trip seems to occur, what can we anticipate for the future?

First, it is important to point out that there are varying levels of self-driving cars. The topmost level, a level 5 self-driving car, consists of having AI that can drive the car without any human intervention. This means there is no need for a human driver. The AI should be able to do all of the driving, in the same manner that a human could drive the car. At the levels less than 5, there is and must be a human driver in the car. The self-driving car is not expected to be able to drive entirely on its own and relies upon having a human driver that is at-the-ready to take over the car controls.

See my article about the levels of AI self-driving cars: https://aitrends.com/selfdrivingcars/richter-scale-levels-self-driving-cars/

See my article that indicates my framework for AI self-driving cars: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

For the levels less than 5, the AI self-driving car is essentially going to be a lot like a conventional car in terms of what happens during the family road trip. Admittedly, the human driver will be able to have a direct “co-pilot” of sorts to co-share in the driving task via the AI, but otherwise the car design is pretty much the same as a conventional car. This is because you need to have the human driver seated at the front of the car, and the human driver has to have access to car controls to then drive the car. With that essential premise, you can’t otherwise change too much of the interior design of the car.

As an aside, there are some that have suggested maybe we don’t need the human driver to be looking out the windshield and that we can change the car design accordingly. We could put the human driver in the back seat and have them wear a Virtual Reality headset and be connected to the controls of the car via some kind of handheld devices or foot-operated nearby devices. Cameras on the hood and top of the car would beam the visual images to the VR headset. Yes, I suppose this is all possible, but I really doubt we are going to see cars go in that direction. I would say it is a likelier bet that cars less than a level 5 will be designed to look like a conventional car, and only will the level 5 self-driving cars have a new design. We’ll see.

For a level 5 self-driving car, since there is no need for a human driver, we can completely remake the interior of the car. No need to put a fixed place at the front of the car for the human driver to sit. No need for the human driver to look out the windshield. Some of the new designs suggest that one approach would be to have swivel seats for let’s say four passengers in the normal sized self-driving car. The four swivel seats can be turned to face each other, allowing a togetherness of discussion and interaction. At other times, you can rotate the seats so that you have let’s say two facing forward as though the front seats of the car, and the two behind those that are also facing forward.

Other ideas include allowing the seats to become beds. It could be that two seats can connect together and their backs be lowered, thus allowing for a bed, one that is essentially at the front of the car and another at the back of the car. Part of the reason that some are considering designing beds into an AI self-driving car is the belief that AI self-driving cars might be used 24×7, and people might sleep in their cars while on their way to work or while on their vacations.

See my article about the non-stop 24×7 nature of AI self-driving cars: https://aitrends.com/selfdrivingcars/non-stop-ai-self-driving-cars-truths-and-consequences/

Another design aspect involves lining the interior of the self-driving car with some kind of TV or LEDs that would allow for the interior to be a kind of movie theatre. This would allow for watching of movies, shows, live streaming, and even for doing online education. This also raises the question as to whether any kind of glass windows are needed at all. Some assert that we don’t need windows anymore for a Level 5 self-driving car. Instead, the cameras on the outside of the car can show what would otherwise be seen if you looked out a window. The interior screens would show what the cameras show, unless you then wanted to watch a movie and thus the interior screens would switch to displaying that instead.

Are we really destined to have people sitting in self-driving car shells that have no actual windows? It seems somewhat farfetched. You would think that people will still want to look out a real window. You would think that people would want to be able roll down their window when they wish to do so. Now, you could of course have true windows and make the glass out of material that can become transparent at times, and then become blocked at other times, thus potentially have the best of both worlds. We’ll see.

Interior Seat Configuration to be Determined

For a family road trip, you could configure the seats so that all four are facing each other, and have family discussions or play games or otherwise directly interact. This might not seem attractive to some people, or might be something that they sparingly do when trying to have a family chat. As mentioned, the seats could swivel to allow more of a conventional sense of privacy while sitting in your seat. I’d suggest though that the days of the parents saying don’t make us come back there are probably numbered. The “there” will be the same place that the parents are sitting. Maybe too much togetherness? Or, maybe it will spark a renewal of togetherness?

Another factor to consider is that none of the human occupants needs to be a driver. In theory, a family road trip has always consisted of one or more drivers, and the rest were occupants. Now, everyone is going to be an occupant. Will parents feel less “useful” since they are no longer undertaking the driving task directly? Or, will parents find this a relief since they can use the time to interact with their children or catch-up on their reading or whatever?

This has another potentially profound impact on the family road trip, namely that no one needs to know how to drive a car. Thus, in theory, you could even have just and only the children in the self-driving car and have no parents or adults at all. I’d agree that this doesn’t feel like a “family” trip at that point, but it could be that the parents are at the hotel and the kids want to go see the nearby theme park, and so the parents tell the kids they can take the self-driving car there.

How should the interior of the self-driving car be reshaped or re-designed if you have only children inside the car for lengths of time? Would there be interior aspects that you’d want to be able close off from use or slide away to be hidden from use? Perhaps you would not want the children to swivel the swivel seats and be able to lock in place the swivel seats during their journey. Via a Skype-like communication capability, you would likely want to interact with the kids, they seeing you and you seeing them via cameras pointed inward into the self-driving car.

Without a human driver, the AI is expected to do all of the driving. When you go on a cross-country road trip, you often discover “hidden” places to visit that are remote and not on the normal beaten path. The question will be how good is the AI when confronted with driving in an area that perhaps no GPS exists per se. Driving on city roads that have been well mapped is one thing. Driving on dirt roads that are not mapped or for which no map is available, this can be a trickier aspect. Suppose too that you want to have the self-driving car purposely go off-road. The AI has to be able to do that kind of driving, assuming that there is no provision for a human driver and only the AI is able to drive the car.

An AI self-driving car at a Level 5 will normally have some form of Over-The-Air (OTA) capability. This allows the AI to get updated by the auto maker or tech firm, and also for the AI to report what is has discovered into the auto maker or tech firm cloud for collective learning purposes. On a cross country road trip, the odds are that there will be places that have no immediate electronic communication available. Suppose there’s an urgent patch that the OTA needs to provide to the AI self-driving car? This can be dicey when doing a family road trip to off-road locations.

See my article about OTA: https://aitrends.com/selfdrivingcars/air-ota-updating-ai-self-driving-cars/

Suppose the family car, an AI self-driving car, suffers some kind of mechanical breakdown during the trip? What then? Keep in mind that a self-driving car is still a car. This means that parts can break or wear out. This means that you’ll need to get the car to a repair shop. And, with the sophisticated sensors on an AI self-driving car, it will likely have more frequent breakdowns and will require more sophisticated repair specialists and cost more to be repaired. The road trip could be marred by not being able to find someone in a small town that can deal with your broken down AI self-driving car.

See my article about automotive recalls and AI self-driving cars: https://aitrends.com/ai-insider/auto-recalls/

The AI of the self-driving car will become crucial as your driving “pilot” and companion, as it were. Take us to the next town, might be a command that the human occupants utter. One of the children might suddenly blurt out “I need to go to the bathroom” – in the olden days the parents would say hold it until you reach the next suitable place. What will the AI say? Presumably, if its good at what it does, it would have looked up where the next bathroom might be, and offer to stop there. This though is trickier than it seems. We cannot assume that the entire United States will be so well mapped that every bathroom can be looked up. The AI might need to be using its sensors to identify places that might appear to have a bathroom, in the same manner that a parent would furtively look at the window at a gas station or a rest stop.

See my article about NLP and voice commands for AI self-driving cars: https://aitrends.com/selfdrivingcars/car-voice-commands-nlp-self-driving-cars/

There is also the possibility of using V2V (vehicle to vehicle communications) to augment the family road trip. With V2V, an AI self-driving car can potentially electronically communicate with another AI self-driving car. Maybe up ahead there is an AI self-driving car that has discovered that the paved road has large ruts and it is dangerous to drive there. This might be relayed to AI self-driving cars a mile back, so those AI self-driving cars can avoid the area or at least be prepared for what is coming. The AI of those self-driving cars could even warn the family (the human occupants) to be ready for a bumpy ride for the mile up ahead.

There is too the possibility of V2I (vehicle to infrastructure communications). This involves having the roadway infrastructure electronically communicate with the AI self-driving car. It could be that a bridge is being repaired, but you wouldn’t know this from simply looking at a map. The bridge itself might be beaming out a signal that would forewarn cars within a few miles that the bridge is inoperable. Once again the AI self-driving car could thus re-plan the journey, and also warn the occupants about what’s going on.

One aspect that the AI can provide that might or might not have been done by a parent would be to explain the historical significance and other useful facets about where you are. Have you been on a family road trip and researched the upcoming farm that was once run by a U.S. president, or maybe there’s a museum where the first scoop of ice cream was ever dished out? A family road trip is often done to see and understand our heritage. What came before us? How did the country get formed? The AI can be a tour guide, in addition to driving the car.

See my article about AI as tour guide for a self-driving car: https://aitrends.com/selfdrivingcars/extra-scenery-perception-esp2-self-driving-cars-beyond-norm/

As perhaps is evident, the interior of the self-driving car has numerous possibilities in terms of how it might be reshaped for the advent of true Level 5 AI self-driving cars. For a family road trip, the interior can hopefully foster togetherness, while also allowing for privacy. It might accommodate sleeping while driving from place to place. The AI will be the driver, and be guided by where the human occupants want to go. In addition to driving, the AI can be a tour guide and perform various other handy tasks too. This is not all rosy though, and the potential for lack of electronic communications could hamper the ride, along with the potential for mechanical breakdowns that might be hard to get repaired.

No more veiled threats from the front seats to the back seats. I suppose some other veiled threats will culturally develop to replace those. Maybe you tell the children, behave yourselves or I won’t let you use the self-driving car to go to the theme park. Will we have AI self-driving cars possibly zipping along our byways with no adults present and only children, as they do a “family” road trip? That’s a tough one to ponder for now. In any case, enjoy the family road trips of today, using a conventional car or even a self-driving car up to the level 5. Once we have level 5 AI self-driving cars, it will be a whole new kind of family road trip experience.

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.

 

By Lance Eliot, the AI Trends Insider

Julius Caesar is famously known for his radical act in 49 BC of defying authority by marching his army across the Rubicon river. Unless you happen to be a historian, you might not be aware that the Roman Senate had explicitly ordered Caesar to disband his army, return to Rome, and not to bring his troops across the Rubicon. His doing so was an outright act of defiance.

Not only was Caesar defiant, he was risking everything by taking such a bold and unimaginable act. The government of Rome and its laws were very clear cut that that any imperium (a person appointed with the right to command) that dared to cross the Rubicon would forfeit their imperium, meaning they would no longer hold the right to command troops. Furthermore, it was considered a capital offense that would cause the commander to become an outlaw. The commander would be condemned to death, and — just to give the commander some pause for thought, all of the troops that followed the commander across the Rubicon would also be condemned to death. Presumably, the troops would not be willing to risk their own lives, even if the commander was willing to risk his life.

As we now know, Caesar made the crossing. When he did so, he reportedly exclaimed “alea iacta est” which loosely translated means that the die has been cast. We use today the idiom “crossing the Rubicon” to suggest a circumstance where you’ve opted to go beyond a point of no return. There is no crossing back. You can’t undo what you’ve done. In the case of Caesar, his gamble ultimately kind of paid-off, as he was never punished per se for his act of rebellion, and he led the Roman Empire, doing so until his assassination in 44 BC.

I’m sure that most of us have had situations where we felt like we were crossing the Rubicon.

One time I was out in the wilderness as a scout master and decided to take the scouts over to a mountain area that was readily hiked over to. While doing the hike, I began to realize that we were going across a dry streambed. Sure enough, when we reached the base of the mountain, rain began to fall, and the streambed began to fill with water. Getting back across it would not have been easy. The more the rain fell, the faster the stream became. Eventually, the stream was so active that we were now stuck on the other side of it. We had crossed our own Rubicon.

At work, you’ve probably had projects that involved making some difficult go or no-go decisions. At one company, I had a team of developers and we were going to create a new system to keep track of VHS video tapes, but we also knew that DVD was emerging. Should we make the system for VHS or for DVD? We only had enough resources to do one. After considering the matter, we opted to hope that DVD was going to catch-on and so we proceeded to focus on DVD’s. We got lucky and it turned out to be one of the first such systems and even earned an award for its innovation. Crossed the Rubicon and luckily landed on the right side.

Of course, crossing the Rubicon can lead to bad results. Caesar was fortunate that he was not right away killed for his insubordination. Maybe his own troops might have even tried to kill him, since there were bound to be some that didn’t want to get caught up in the whole you-are-condemned to death thing. The recent news story about the teenage soccer team in Thailand that went into the caves and became lost, and then the rain closed off their exit, it’s something that they all easily could have died in those caves, were it not for the tremendous and lucky effort that ultimately saved them.

What does this all have to do with AI self-driving cars?

At the Cybernetic AI Self-Driving Car Institute, we are developing AI for self-driving cars. As we do so, there are often very serious and crucial “crossing the Rubicon” kinds of decisions to be made. These same decisions are being made right now by auto makers and tech firms also developing AI self-driving cars.

Let’s take a look at some of those kinds of difficult and nearly undoable decisions that need to be made.

•         LIDAR

LIDAR is a type of sensor that can be used for an AI self-driving car. It makes use of Light and Radar to help ascertain the world around the self-driving car. Beams of light are sent out from the sensor, the light bounces back like a radar wave, and the sensor is able to gauge the shapes of nearby objects by the length of time involved in the returns of the light waves. This can be a handy means to have the AI determine if there is a pedestrian that is standing ahead of the self-driving car and at a distance of say 15 feet. Or that there is a fire hydrant over to the right of the self-driving car at a distance of 20 feet. And so on.

For my assessment of LIDAR for AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/lidar-secret-sauce-self-driving-cars/

AI self-driving cars tend to use conventional radar to try and identify the surroundings, they use sonic sensors to do likewise, and they use cameras to capture visual images and try to analyze what’s around via vision related processing. They can also use LIDAR. There is no stated requirement that an AI self-driving car has to use any of those kinds of sensors. It is up to whatever the designers of the self-driving car decide to do.

That being said, it is hard to imagine that a self-driving car could properly operate in the real-world if you didn’t have cameras on it and weren’t doing vision processing of the images. You could maybe decide you’ll only use cameras, but that’s a potential drawback since there are going to be situations where vision alone won’t provide a sufficient ability to sense the real-world around the self-driving car. Thus, you’d likely want to add at least radar. Now, with the cameras and radar, you have a fighting chance of being able to have a self-driving car that can operate in the real-world. Adding sonar would help further.

What about LIDAR? Well, if you only had LIDAR, you’d probably not have much of an operational self-driving car, so you’d likely want to add cameras too. Now, with LIDAR and cameras, you have a fighting chance. If you also add radar, you’ve further increased the abilities. Add sonic sensors and you’ve got even more going for you.

Indeed, you might say to yourself, hey, I want my self-driving car to have as many kinds of sensors that will increase the capabilities of the self-driving car to the maximum possible. Therefore, if you already had cameras, radar, and sonar, you’d likely be inclined to add LIDAR. That being said, you also need to be aware that nothing in life is free. If you add LIDAR, you are adding the costs associated with the LIDAR sensor. You are also increasing the nature of the AI programming required to be able to collect the LIDAR data and analyze it.

There are these major stages of processing for self-driving cars:

o   Sensor data collection and interpretation

o   Sensor fusion

o   Virtual model updating

o   AI action plan updating

o   Car controls commands issuance

See my framework about AI self-driving cars: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

If you add LIDAR to the set of sensors for your self-driving car, you also presumably need to add the software needed to do the sensor data collection and interpretation of the LIDAR. You also presumably need to boost the sensor fusion to be able to handle trying to figure out how to reconcile the LIDAR results, the radar results, the camera vision processing results, and the sonar results. Some would say that makes sense because it’s like reconciling your sense of smell, sense of sight, sense of touch, sense of hearing, and that if you lacked one of those senses you’d have a lesser ability to sense the world. You would likely argue that the overhead of doing the sensor fusion is worth what you’d gain.

Nearly all of the auto makers and tech firms would agree that LIDAR is essential to achieving a true AI self-driving car. A true AI self-driving car is considered by industry standards to be a self-driving car of a Level 5. There are levels less than 5 that are self-driving cars requiring a human driver. These involve co-sharing of the driving task with a human driver. For a Level 5 self-driving car, the idea is that the self-driving car is driven only by the AI, and there is no need for a human driver. The Level 5 self-driving car even is likely to omit entirely any driving controls for humans, and the Level 5 is expected to be able to drive the car as a human would (in terms of being able to handle any driving task to the same degree a human could do so).

For my article about the levels of AI self-driving cars, see: https://aitrends.com/selfdrivingcars/richter-scale-levels-self-driving-cars/

Tesla Foregoes LIDAR

It might then seem obvious that of course all self-driving cars would use LIDAR. Not so for Tesla. Tesla and Elon Musk have opted to go without LIDAR. One of Elon Musk’s most famous quotes for those in the self-driving car field is this one:

“In my view, it’s a crutch that will drive companies to a local maximum that they will find very hard to get out of. Perhaps I am wrong, and I will look like a fool. But I am quite certain that I am not.”

https://www.theverge.com/2018/2/7/16988628/elon-musk-lidar-self-driving-car-tesla

This is the crossing of the Rubicon for Tesla.

Right now and for the foreseeable future, they are not making use of LIDAR. It could be that they’ve made a good bet and everyone else will later on realize they’ve needlessly deployed LIDAR. Or, maybe there’s more than one way to skin a cat, and it will turn out that Tesla was right about being able to forego LIDAR, while the other firms were right to not forego it. Perhaps both such approaches will achieve the same ends of getting us to a Level 5 self-driving car.

For Tesla, if they are betting wrong, it would imply that they will be unable to achieve a Level 5 self-driving car. And if that’s the case, and the only way to get there is to add LIDAR, they would then need to add it to their self-driving cars. This would be a likely costly endeavor to retrofit and might or might not be viable. They might then opt to redesign future designs and write-off the prior models as unalterable, but at that point will be behind other auto makers, and will need to after-the-fact figure out how to integrate it into everything else. Either way, it’s going to be costly and could cause significant delays and a falling behind of the rest of the marketplace.

It would also cause Tesla to have to eat crow, as it were, since they’ve all along advertised that your Tesla has “Full Self-Driving Hardware on All Cars” – which might even get them caught in lawsuits by Tesla owners that argue they were ripped-off and did not actually get all the hardware truly needed for a self-driving car. This could lead to class action lawsuits. It could drain the company of money and focus. It would likely cause the stock to drop like a rock.

For my article about product liability for AI self-driving cars, see: https://aitrends.com/selfdrivingcars/product-liability-self-driving-cars-looming-cloud-ahead/

For my article about class action lawsuits against AI self-driving car makers see: https://aitrends.com/selfdrivingcars/first-salvo-class-action-lawsuits-defective-self-driving-cars/

This does not mean that Tesla couldn’t re-cross the Rubicon and opt to add LIDAR, but it just shows that when you’ve made the decision to cross the Rubicon, going back is often somewhat infeasible or going to be darned hard to do.

Perhaps Elon Musk had uttered “alea iacta est” when he made this rather monumental decision.

•         Straight to Level 5

Another potential crossing of the Rubicon involves deciding whether to get to Level 5 by going straight to it, or instead to get there by progressing via Level 3 and Level 4 first.

Some believe that you need to crawl before you walk, and walk before your run, in order to progress in this world. For self-driving cars, this translates into achieving Level 3 self-driving cars first. Then, after maturing with Level 3, move into Level 4. After maturing with Level 4, move into Level 5. This is the proverbial “baby steps” at a time kind of approach.

Others assert that there’s no need to do this progressively. You can skip past the intermediary levels. Just aim directly to get to Level 5. Some would say it is a waste of time to do the intermediary levels. Others would claim you’ll not get to Level 5 if you don’t cut your teeth first on the lower levels. No one knows for sure.

Meanwhile, Waymo has pretty much made a bet that you can get straight to Level 5 and there’s no need to do the intermediaries. They rather blatantly eschew the intermediary steps approach. They have taken the bold route of get to the moon or bust. No need to land elsewhere beforehand. Will they be right? Suppose their approach falls flat and it turns out those that got to Level 4 are able to make the leap to Level 5, meanwhile maybe the efforts underway on Level 5 aren’t able to be finalized.

For more about the notion that Level 5 is like a moonshot, see my article: https://aitrends.com/selfdrivingcars/self-driving-car-mother-ai-projects-moonshot/

Does this mean that Waymo cannot re-cross the Rubicon and opt to first settle for a Level 4. As with all of these crossings, they could certainly back-down, though it would likely involve added effort, costs, and so on.

•         Machine Learning Models

When developing the AI for self-driving cars, by-and-large it involves making use of various machine learning models. Tough choices are made about which kinds of neural networks to craft and what forms of learning algorithms to employ. Decisions, decisions, decisions.

Trying to later on change these decisions can be difficult and costly. It’s another crossing of the Rubicon.

For my article about machine learning and AI self-driving cars, see: https://aitrends.com/ai-insider/machine-learning-benchmarks-and-ai-self-driving-cars/

•         Virtual World Model

At the crux of most AI self-driving car systems there is a virtual world model. It is used to bring together all of the information and interpretations about the world surrounding the self-driving car. It embodies the latest status gleaned from the sensors and the sensor fusion. It is used for the creation of AI action plans. It is crucial for doing what-if scenarios in real-time for the AI to try and anticipate what might happen next.

In that sense, it’s like having to decide whether to use a Rubik’s cube or use a Rubik’s snake or a Rubik’s domino. Each has its own merits. Whichever one you pick, everything else gets shaped around it. Thus, if you put at the core a virtual world model structure that is of shape Q, you are going to base the rest of the AI on that structure. It’s no easy thing to then undo and suddenly shift to shape Z. It would be costly and involve gutting much of the AI system you’d already built.

It’s once again a crossing of the Rubicon.

•         Particular Brand/Model of Car

Another tough choice in some cases is which brand/model of car to use as the core car underlying your AI self-driving car. For the auto makers, they are of course going to choose their own brand/model. For the tech firms that are trying to make the AI of the self-driving car, the question arises as to whom do you get into bed with. The AI you craft will be to a certain extent particular to that particular car.

I know that some of you will object and say that the AI, if properly written, should be readily ported over to some other self-driving car. This is much harder than it seems. I assure you it’s not just like re-compiling your code and voila it works on a different kind of car.

Furthermore, many of these tech firms are painting themselves into a corner. They are writing their AI code with magic numbers and other facets that will make porting the AI system nearly impossible. Without good commenting and thinking ahead about generalizing your system, it’s going to be stuck on whatever brand/model you started with. The rush right now to get the stuff to work is more important than making it portable. There are many that will be shocked down the road that they suddenly realize they cannot overnight shift onto some other model car.

See my article about kits for AI self-driving cars: https://aitrends.com/selfdrivingcars/kits-and-ai-self-driving-cars/

See my article about idealism and AI self-driving cars: https://aitrends.com/selfdrivingcars/idealism-and-ai-self-driving-cars/

•         Premature Roadway Release

This last example of crossing the Rubicon has to do with putting AI self-driving cars onto public roadways, perhaps doing so prematurely.

The auto makers and tech firms are eager to put their self-driving cars onto public roadways. It is a sign to the world that there is progress being made. It helps boost stock prices. It helps for the AI itself to gain “experience” from being driven miles upon miles. It helps the AI developers as they tune and fix the AI systems and do so based on real-world encounters by the self-driving car.

That’s all well and good, except for the fact that it is a grand experiment upon the public. If the self-driving cars have problems and get into accidents, it’s not going to be good times for self-driving cars. Indeed, it’s the bad apple in the barrel in that even if only one specific brand of self-driving car gets into trouble, the public will perceive this as the entire barrel is bad.

If the public becomes disenchanted with AI self-driving cars, you can bet that regulators will change their tune and no longer be so supportive of self-driving cars. A backlash will most certainly occur. This could slow down AI self-driving car progress. It could somewhat curtail it, but it seems unlikely to stop it entirely. Right now, we’re playing a game of dice and just hoping that few enough of the AI self-driving cars on the roadways have incidents that it won’t become a nightmare for the whole industry.

For more about this rolling of the dice, see my article about responsibility and AI self-driving cars: https://aitrends.com/ai-insider/responsibility-and-ai-self-driving-cars/

This then is another example of crossing the Rubicon.

Putting AI self-driving cars onto the roadways, which if it turns out premature, might make it difficult to continue forward with self-driving cars, at least not at the pace that it is today.

For the AI self-driving car field, there are a plethora of crossings of the Rubicon. Some decision makers are crossing the Rubicon and doing so like Caesar, fully aware of the chances they are taking, and betting that in the end they’ve made the right choice. There are some decision makers that are blissfully unaware that they have crossed the Rubicon, and only once something untoward happens will they realize that oops, they made decisions earlier that now haunt them. Each of these decisions are not necessarily immutable and undoable per se, it’s more like there is a cost and adverse impact if you’ve made the wrong choice and need to backtrack or redo what you’ve done.

I’d ask that all of you involved in AI self-driving cars make sure to be cognizant of the Rubicon’s you’ve already crossed, and which ones are still up ahead. I’m hoping that by my raising your awareness, in the end you’ll be able to recite the immortal words of Caesar: Veni, vidi, vici (which translates loosely into I came, and I saw, and I conquered).

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.

 

By Lance Eliot, the AI Trends Insider

Did you realize that apparently more than half of all United States medical doctors are suffering from burnout?

You might at first glance not be overly surprised, since you’ve likely seen how harried most medical doctors are. Often, their patient load is at the max and they barely have time to say hello before they move onto the next patient. Many medical doctors bitterly complain that the nature of the healthcare system prevents them from spending quality time with their patients as they are under strict time guidelines and have little choice in the matter of how they use their time with patients.

You might also be thinking that it doesn’t matter that medical doctors might be suffering burnout. The perception is that they are well-paid anyway, and so if they have to work long and hard hours, so be it. Some might think of them as whiners that don’t realize how good they really have it. This though would be a misunderstanding about the impacts of burnout.

The medical doctors reported that nearly one in ten of them had committed a significant medical mistake, one or more such mistakes or errors, in the three-month period prior to the poll being taken. It is generally well proven already that burnout leads to medical doctors making mistakes or errors, and we now know the alarming frequency in which it can occur.

There can be errors in ascertaining the ailment that a patient has, or maybe a mistake in a prescription issued for a patient, and so on. The burnout therefore can directly and adversely impact the nature and quality of the healthcare provided to patients. In addition, medical doctors can become depressed, have high fatigue, and otherwise be less effective and efficient in performing their medical tasks.

Presumably, burnout is a reversible work-related matter.

If you can detect early enough that someone is suffering from burnout at work, you can potentially provide them guidance on how to alleviate the burnout. Some try stress management techniques to reduce their burnout. Some use the latest in so-called mindfulness training. Some try to seek a balance between the demands of work and their other life pursuits, carving out more time and attention to efforts outside work that enable them to better contend with the work situation.

It is usually unlikely that changes by the individual alone that has exhibited the burnout is going to be sufficient to curtail the burnout. The work situation often needs to also adjust. An organization has to realize what factors are leading to the burnout, and potentially readjust work schedules, or adjust the nature of the work being performed, and so on. Someone that is otherwise well prepared to contend with burnout is still going to have a tough time not getting burned out if the work environment that presumably is causing the burnout does not make adjustments too. It takes two to tango, as they say.

When I mention that burnout is potentially reversible, I’d like to clarify that for some people in some companies it is not reversible. When someone reaches a certain threshold, they can be so far gone that they cannot find within themselves the desire and nor the need to re-commit themselves to work, even if the company offers to try and find a means to do so. I’ve seen some workers that got burned out at a firm and they left in disgust and with no intention of ever returning. That being said, I’ve seen some firms that claimed they wanted to save someone that was burned out, but the firm did nothing more than token attempts to keep the person, which for that person made them even more determined to leave the firm.

When considering medical doctors that get burned out, you need to consider not just the impact on them and their patients, but also take a wider view and consider the larger ramifications. The odds are that if the patient gets less capable medical care due to the burn out, it might also indirectly impact their family and friends. Those family and friends might need to provide other outside care or additional care to make-up for whatever medical errors or omissions occur. The odds are that fellow staff at the medical facility will also suffer, having to either deal day-to-day with a medical doctor that might be difficult to deal with, or need to deal with patients that become irate when they realize they are not getting their desired care. Overall, you could make the claim that medical doctor burnout will raise costs overall for the medical delivery system and all of society accordingly, and also reduce the available medical care for others by needlessly consuming limited available medical resources by the burnout effects.

There are some workers that drive their own burnout. You’ve likely dealt with a workaholic that seems to work all of the time. They say that it makes them stronger and they enjoy it. This can sometimes be true, but more often than not it is the path towards burnout. A workaholic can work themselves to the bone. For some junior managers they think that having a workaholic under them is great, since the person gets so much work done. But, in the end, the person might be a prime candidate for burnout and thus the junior manager has likely done a disservice by not having done something about the matter earlier.

Besides the workaholic, there are other types of workers that can be especially susceptible to burnout. There’s the lone ranger that tries to take on all the work themselves and doesn’t appropriately make use of their fellow team mates. There’s the perfectionist that wants to do everything to the nth degree and often goes overboard in terms of their work. There’s the superhero type that relishes coming to the rescue on efforts and will become overwhelmed with work. There’s the martyr that likes to do tons of work to be able to let others know that they are doing so. Etc.

Besides medical doctors, there are other professions that involve substantial amounts of burnout.

One such occupation are the AI developers that are working on AI self-driving cars.

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars and besides our own AI developers we also keep in touch with other self-driving car AI developers.

Generally, burn out is pervasive among such AI developers.

Why?

Why Burnout is Pervasive Among Self-Driving Car Developers

You might at first think that it would be an exciting and enjoyable job to be an AI developer for AI self-driving cars. It’s like trying to achieve a moonshot and having been there in the early days of developing the Apollo spacecraft’s that got us to the moon. There’s a thrill about doing something that could change society. It has the potential for great benefits to all of us. That kind of a job should be joyous!

For my article about why AI self-driving cars are like a moonshot, see: https://aitrends.com/selfdrivingcars/self-driving-car-mother-ai-projects-moonshot/

For my article about idealism in AI self-driving car developers: https://aitrends.com/selfdrivingcars/idealism-and-ai-self-driving-cars/

Though it is true that you could perceive the job as striving to achieve new ends and changing our lives, getting there is not all fun and games.

Let’s consider why there is a high chance for burnout for AI developers that are developing AI self-driving cars:

•         New Ground

AI self-driving cars are pushing the boundaries of what we can do with AI. This is not run-of-the-mill stuff. We are using the latest AI techniques, the latest Machine Learning (ML) capabilities, etc. Many everyday developers often just reuse what they have done before. In this case, it’s new ground with every step you take.

•         Life-or-Death System

It’s one thing if you are pushing the boundaries of AI for let’s say a financial system, but in the case of a self-driving car, it’s a life-or-death matter. If your software hiccups at the wrong time, it could mean that the car will hurtle itself into a wall and kill the human occupants. Or, it could swerve unintentionally and kill pedestrians. And so on. This is serious stuff.

•         Real-time System

Whenever you are developing software for a real-time system, it tends to increase the difficulty factor. One of the first real-time systems I was involved in, years ago, involved a real-time controller for a roller coaster. I can tell you that we sweated quite a bit about how to get the timing just right and make sure the software was always able to handle whatever happened in real-time.

•         Intense Pressure

The pressure by the auto makers and tech firms to get their self-driving cars on the roadways is intense. Every day you see new announcements about one self-driving car maker is going to get to the market sooner than the other. This kind of gamesmanship is often taking place without regard to what the actual AI developers can do – it’s about what they leaders are telling the marketplace. Deadlines aplenty. Irrational deadlines aplenty.

•         Lack of Specs

Many of the auto makers and tech firms are developing their self-driving cars on-the-fly in an agile method and doing so without a definitive set of specs. To some degree, it’s make it up as you go. Some of the ideas that are being delved into these projects are dreams rather than something that can be actually achieved. AI developers are often told, rather than asked, what can be done.

•         Spotty Peer Expertise

There aren’t many that have industrial style expertise in developing software for cars, let alone for AI self-driving cars. Thus, it is somewhat unlikely that an AI developer can depend upon their fellow AI developer on their team to lend a hand. The odds are that they are all mainly in-the-dark and trying to figure out things as they go along.

•         Highly Secretive

The self-driving car efforts by each auto maker and tech firm are typically being done in a skunk works operation that’s considered for secure eyes only. This secretive manner makes sense because everyone is trying to do their own thing and they don’t want others to steal it. But, this also makes things harder for the AI developers since it narrows whom else they can turn to for assistance. In many cases, they aren’t supposed to talk about their work with family and friends – it’s like being in the CIA.

•         Shift From R&D

For many of the AI developers in the self-driving cars field, they most recently were working at an AI research lab at a university. That’s a whole different kind of work environment than in industry. For example, at a university, there is often the view that failing on something is OK since you are doing experimentation and not everything will work out. No matter what you hear about Silicon Valley saying to fail first and fail fast, I assure you that with the pressures to get self-driving cars going, the “let’s try failing” model is verboten.

•         Long Hours

With the vast amount of work to be done for the AI of a self-driving car, there are long hours involved. It can be frustrating too because as mentioned it is punctuated with trying new things and hoping they will work. And, you can’t readily explain to family and friends why you are working late and on the weekends, other than they know vaguely you are working on something important and hush-hush.

•         Other

There are a myriad of other factors involved too. For example, even the tools used to develop the AI systems are at times brittle and still untried. It would be like trying to make a house and you have hammers and screwdrivers that no one knows for sure will work properly.

Now, I realize that many of these AI developers are getting paid big bucks. As such, similar to the perception about medical doctors, you might have little sympathy about these AI developers possibly getting burned out. You might say they should relish their moment in the sun. Now’s the time to make enough bucks to then retire.

Well, maybe, but let’s also consider the impacts of burnout, similar to the concerns when medical doctors experience burnout.

In the case of the AI self-driving cars, it can lead to the AI developers making errors or mistakes, more likely than they might have otherwise. Perhaps mistakes are made in the machine learning and so the AI system is unable to properly interpret a road sign. Or, perhaps there’s a bug in the code that when the self-driving car reaches a particular speed that the code burps and gets stuck in a loop that it can’t get out of.

Here’s the major actions that an AI self-driving car undertakes:

•         Sensor data collection and interpretation
•         Sensor fusion
•         Virtual World model updating
•         AI Action Plan updating
•         Cars control commands issuance

See my framework about AI self-driving cars:

https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

Impacts of Burned-Out AI Self-Driving Car Developers

A burned out AI developer can be “lazy” when it comes to testing and decide that they’ve done too much testing already. Or, they might have an attitude of “why test it” since they don’t believe the whole thing will work anyway. Or, they might fix something that they find as broken, and in the effort of rendering the fix they inadvertently and unknowingly introduce another problem into the code.

Of course, any of these aspects can happen to any developer. And they do. But, as mentioned earlier, it is magnified in the case of AI developers for self-driving cars due to the pressures involved, and the pushing of new boundaries, and so on. Plus, this is a real-time system that involves life-and-death aspects. Thus, this happening for AI developers of self-driving cars has especially important and significant ramifications.

Imagine the problems of AI code that is half-hearted and does the sensor data collection interpretation. Or that does the sensor fusion. Or that does the virtual world modeling. Or that does the AI action plan updating. Or that does the cars control commands issuance. What also can hamper things is that an error in one of those crucial components can compound itself by then misleading the other components. It can have an adverse cascading impact. This includes the potential for the Freezing Robot Problem.

See my article about the dangers of the Freezing Robot Problem in AI self-driving cars: https://aitrends.com/ai-insider/freezing-robot-problem-and-ai-self-driving-cars/

What can be done about the burnout of AI developers that are creating the next generation of self-driving cars?

First, it’s vital to acknowledge that the burnout can and does exist. There are some firms that are blind to burnout and don’t know it happens. They often just say that Joe or Samantha need to take a day off, and when they get back they’ll be fine again. This kind of Band-Aid approach fails to recognize the depth and seriousness of true burnout, and the lengthy and complex process to typically undo it.

Next, watch out for the burnout culture that some firms seem to foster. I say this because there are many Silicon Valley firms that actually tout their burnout rates. They like to chew-up people. They make it into a macho kind of atmosphere and try to project an image that only the strong survive. In their viewpoint, if you aren’t already on the verge of burnout, get there or get out. I’ve been waiting to see what happens when those employees so treated decide to finally lawyer-up and go after those firms for the cruddy work environment. We’ll see.

In some firms, they buy a ping pong table or a foosball table and that’s their way of telling the employees to not burnout. Somehow, you are supposed to take time off from your non-stop high-pressure AI development work, and by playing ping pong a few times a day you’ll not get burned out. Doubtful.

Firms that are serious about detecting, mitigating, and preventing burnout will go out of their way to try and arrange the nature of the work and the work situations to deal with burnout. They need to hire the right people, put in place the right managers, provide the right kind of leadership, and otherwise aim to gauge how much work can be reasonably done and by whom. There are many key decisions being made about self-driving car designs, and the coding, which will either aid the fulfillment of self-driving cars, or will have the opposite impact and completely undermine the advent of self-driving cars.

See my article forewarning the groupthink occurring about AI self-driving car development: https://aitrends.com/selfdrivingcars/groupthink-dilemmas-for-developing-ai-self-driving-cars/

Some say that burnout in the workplace leads to the erosion of the soul. I know one promising AI developer that nearly collapsed at the pace that he was going, and decided that it was just too unhealthy to continue on this kind of work. He’s since switched to another occupation entirely. That’s a shame as we already have too few well-qualified AI developers to start with. And we need a lot more of them to ramp up for achieving a true Level 5 self-driving car.

I’ve seen some AI developers that have emotional exhaustion from work burnout. One that went home and took it out on his wife and kids. Another one that became so cynical that he pretty much was approving any kind of code going into production. He had lost the belief in caring for his work. Now, you might say that there should be double-checks to catch these kinds of things in terms of faulty designs and faulty code, but with the go-go atmosphere and high pressures to produce, there are developers that look the other way and figure that it’s up to the other person to make sure their stuff works properly.

I had mentioned earlier that if a medical doctor makes mistakes due to burnout, the patient suffers and also so do a lot of other stakeholders. The same can be said of the AI developers for self-driving cars. They can each in their own way lead to self-driving cars that just aren’t ready for prime time. Unfortunately, those at the upper levels of an auto maker or tech firm might not know or care to know, and just insist that the self-driving car be put onto our roadways. If those self-driving cars harm humans, it’s bad and it will also produce a backlash against self-driving cars overall.

And if that happens, if we have haywire AI that stop or stunts the advent of self-driving cars, it undermines or delays the potential benefits to society that we’re all hoping that self-driving cars will derive. Thus, in that manner, even just one burnout can be like the butterfly that flaps its wings on one side of the earth and it ultimately leads to being felt on the other side of the globe.

I implore the auto makers and tech firms to carefully do an assessment of how they are treating their AI developers, and if it’s “burnout city” then they would be wise to step-back, take another look, and see what can be done to overcome it. All of us need to watch out for that last straw on the camel’s back that will break the spirit of our most prized workers, those AI developers, tasked with creating the future of society via the advent of self-driving cars.

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.

 

By Lance Eliot, the AI Trends Insider

API’s have become the darling of the high-tech software world. There are conferences devoted to the topic of API’s. Non-tech business-oriented magazines and journals gush about the importance of API’s. Anyone that makes a software package nowadays is nearly forced into providing API’s.

It’s the rise of the Application Programming Interface (API).

Rather than being something magical, it’s actually just the providing of a portal into a software system that otherwise might be difficult to communicate with. One major advantage of a portal is that it can allow various extensions that can add-on to the software system and go beyond what the original software system itself can accomplish. You can also interface to the original software system and allow it to become interconnected with other software. And, you can avoid potentially having to redevelop the wheel, so to speak, by leveraging whatever capabilities the original software system has. Some would say it also allows the software to be at a higher level of abstraction.

I’ve written extensively about API’s for AI systems, which you can read about in Chapter 3 and Chapter 4 of my book “AI Guardian Angel Bots for Deep AI Trustworthiness: Practical Advances in Artificial Intelligence (AI) and Machine Learning”  (available on Amazon at https://www.amazon.com/Guardian-Angel-Bots-Deep-Trustworthiness/dp/0692800611)

By providing a healthy set of API’s, the developers of the original software system are able to encourage the emergence of a third-party add-on ecosystem. This in turn will help make the original software system more popular as others connect to it and rely upon it. Eventually, with some luck and skill, the original software system becomes immersed in so many other areas of life that it becomes undeniably necessary. What might have begun as a small effort can snowball into a widespread and highly known cornerstone for an entire marketplace radiating outward from the original software core.

With great promise often comes great peril. In the case of API’s, there is a chance that the use of the API’s can boomerang on a company that made the original software system. These portals can be used as intended and yet cause undesirable results, along with them being used for unintended nefarious reasons and also causing undesirable results.

Let’s consider the case of an API used as intended, but has caused what some perceive as an undesirable result. This particular example involving Gmail has been in the news recently and led to some untoward attention and concerns.

Google allows for API’s to connect to Gmail. This would seem to be handy since it would allow other software developers to connect their software with Gmail. This can provide handy new capabilities for Gmail that otherwise would have never existed. Meanwhile, those software developers that might have written something that would have never seen the light of day, might be able to piggyback onto the popularity of Gmail and hit themselves a homerun.

When an app that is able to connect to Gmail via the API is first run, it usually asks the user whether they are OK with the app connecting into their Gmail. For many users, they often don’t read the fine print on these kinds of messages and are so eager to get the app that they just say yes to anything that the app displays when being installed. Or, the user might be tempted to read what the conditions are, but it is so lengthy and written in arcane legalese that they don’t do so, and often wonder whether they maybe have given up their first born child by agreeing to the app’s conditions. It’s a combination of the app at times being tricky about explaining what’s up, and the end-user not diligently making sure that they know what they signing up for.

Typically, once the user agrees to the app request at first install, Google then grants to the app that it can access the Gmail of that user. This includes being able to access their emails. The app can potentially read the contents of those emails. It can potentially delete emails. It can potentially send emails on behalf of the user.

In recent widespread news reports, the media caused a stir by finding some companies that read the user’s Gmail emails via AI, doing so to try and figure out what interests the person has and possibly then hit them with ads. In some cases, the emails are even read by humans at the software company, presumably for purposes of being able to gauge how well the AI is doing the reading of the emails. There are also some firms that provide the emails or snapshots of the emails to other third-parties that they have deals with. All in all, it was a bit of a shock to many people that they had provided such access to their “private” email.

I realize that many software developers would blame the user on this – how dumb can you be to go ahead and agree to have your emails accessed and then later on complain that it is taking place? As I mentioned earlier, many users aren’t aware they are doing so, or might be vaguely aware but not really put together two-plus-two and fully understand the implications of what they have allowed to happen. There are some software developers that insist their app is doing a service for the user, and by reading their emails it is helping to target them with things that the person is interested in. That’s a bit of a stretch and for many users the logic doesn’t ring true to them.

You might remember the case of McDonald’s in India and the API that allowed personal information of the McDelivery mobile app to be leaked out. The API connection, normally intended for useful and proper uses, also allowed access to the name, phone numbers, home address, email addresses, and other private info. This was unintended and was an undesirable result.

Hackers Love API’s

As you might guess, hackers love it when there are API’s. It gives them hope that there might be a means to sneakily “break into” a system. I’ve likened this to a fortress that has all sorts of fortified locked doors, which also provides a window that someone with a bit of extra effort can use to get into the fort. Software companies often spend a tremendous amount of effort to try and make their software impervious to security breaches and attacks, and yet then provide an API that exposes aspects that undermine all the rest of their security.

How could that happen? Wouldn’t the API’s get as much scrutiny as the rest of the system in terms of becoming secure? The answer is that no, the API’s often don’t get as much scrutiny. The perception of the company making the software is that the API’s are some kind of techie detail and there’s no need to make sure those are tight. In my experience, most software firms happily provide the API’s in hopes that someone will want to use them, and aren’t nearly as concerned that those that might use them would do so for nefarious reasons.

The API’s are often classified into these three groupings:

•         Private
•         Partner
•         Public

API’s that are considered private are usually intended to be used solely by the firm making the software. They setup the API’s for their own convenience. This also though often means that the API’s have a lot of power and can access all sorts of aspects of the software. The firm figures that’s Okay since only the firm itself will presumably be using the API’s. These are often either undocumented and just known amongst those that developed the software, or there is written documentation but it is kept inside the firm and written for those that are insiders.

API’s that are oriented toward partners are intended to be used by allied firms that the firm making the software decides to cut some kind of deal with. Maybe I make a software package that does sales and marketing kinds of functions, while a firm I cut a deal with has a software package for accounting and wants to connect with my package. Once again, the assumption is that only authorized developers with firms that are properly engaged with will use these API’s. The power of the access by these API’s is once again relatively high, but usually less than the private API’s since the original developers often don’t want the third-party to mess-up and do great harm. The documentation often is a bit more elaborated than the private API’s since the partner firm and its developers need to know what the API’s do.

API’s of a public nature are intended to be used by anyone that wants to access the software. These are often very limited in their access capabilities and are considered potential threats to the system. Thus, only the need-to-know aspects are usually made available. The documentation can sometimes be very elaborate and extensive, while in other cases the documentation is slim and the assumption is that people will figure it out on their own or they might share amongst each other as they figure out what the API’s do.

What sometimes happens is that a firm provides say public API’s, and secretly has partner API’s and private API’s. Those developers that opt to use the public API’s become curious about the partner API’s, and either figure them out on their own, or convince a partner to leak details about what they are. If the partner API’s can be used, the next step is to go after the private API’s. It can become a stepwise progression to figuring out the whole set of API’s.

The API’s are often classified into whether they do this:

•         Perform an action
•         Provide object access

Let’s consider first the performing an action type of API. This allows an app invoking the API to request that the original software perform an action that has been made available via the API. For example, suppose there’s a car that has an electronic on-board system and there’s an API associated with the system. You develop a mobile app that connects to the on-board electronic system and you opt to use the API to invoke an action that the electronic system is capable of performing. Suppose the action consists of honking the horn. Your mobile app then connects to the on-board electronic system and via the API requests the electronic system to honk the horn, which it then dutifully does. Honk, honk.

Or, an app might seek to get access to an object and do so via the API. Suppose the electronic on-board system of the car has data in it that includes the name of the car owner and vehicle info such as the make, model, and number of miles driven. The developers of the electronic on-board system might make available an API that allows for access to the “car owner object” that has that data. You then create an app that connects to the electronic on-board car system and asks via the API to access the car owner object. Once the object is provided, your app then reads the data and now can display it on the screen of the mobile app.

How does this apply to AI self-driving cars?

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. This includes providing API’s, and also involves making use of API’s provided by other allied software systems and components.

If you’ve ever played with the API for the Tesla, you likely know that you can get access to vehicle information, vehicle settings, and the like. You can also invoke actions such as honking the horn, waking up the car, starting the charging of the car, setting the car climate controls, open the trunk, and so on. It’s fun and exciting to create your own mobile app to do these things. That being said, there is already a mobile app provided by Tesla that does these things, so it really doesn’t payoff to create them yourself, other than the personal satisfaction involved and also to explore the nature of API’s on cars, or if you are trying to develop your own third-party app and want to avoid or circumvent the official one.

One of the crucial aspects about API’s for cars is that a car is a life-or-death matter. It’s one thing to provide API’s to an on-board entertainment center, allowing you to write an app that can connect to it and play your favorite songs. Not much of a life or death matter there. On the other hand, if the car provides API’s that allow for actual car controls aspects, it could be something much more dangerous and of concern.

Now that I’ve dragged you through the fundamentals of API’s, it gets us to some important points:

•         What kind of API’s, if any, should an AI self-driving car provide?
•         If the API’s are provided for an AI self-driving car, how will they be protected from misuse?
•         If the API’s are provided for an AI self-driving car, how will they be tested to ensure their veracity?
•         Etc.

Some auto makers and tech firms are indicating they will not provide any API’s regarding their AI self-driving cars. That’s their prerogative and we’ll have to see if that’s a good strategy.

Some are making private API’s and trying to be secretive about it. The question always arises, how can you keep it secret and what happens if the secret gets discovered.

Some are making partner API’s and letting their various business partners know about it. This can be handy, though as mentioned earlier it might start other third-parties down the path of figuring out the partner API’s and then next aiming at the private API’s.

Overall, it’s a mixed bag as to the various AI self-driving car firms are opting to deal with API’s.

There’s also another twist to the API topic for AI self-driving cars, namely:

•         API’s for Self-Driving Car On-Board System

o   API for the AI portion of self-driving car on-board system

o   API for non-AI portions of the self-driving on-board system

•         API’s for Self-Driving Car Cloud-Based System

o   API for AI portion of self-driving car cloud-based system

o   API for non-AI portions of the self-driving car cloud-based system

There can be API’s for the on-board systems of the self-driving car, and there can be other API’s for the cloud-based system of the self-driving car. Most AI self-driving cars are going to have OTA (Over The Air) capabilities to interact with a cloud-based system established by the auto maker or tech firm. From a third-party perspective, it would be handy to be able to communicate with the software that’s in the cloud over OTA, in addition to the software that’s on-board the self-driving car.

See my article about the OTA in AI self-driving cars: https://aitrends.com/selfdrivingcars/air-ota-updating-ai-self-driving-cars/

Some AI developers think it is crazy talk to allow API’s for the self-driving car on-board systems. They believe that the on-board systems are sacrosanct and that nobody but nobody should be poking around in them. Likewise, there are AI developers that believe fervently that there should not be API’s allowed for the cloud-based systems associated with self-driving cars. They perceive that this could lead to incredible troubles, since it might somehow allow someone to do something untoward that could then get spread to all of the self-driving cars that connect to the cloud-based system.

See my article about kits for AI self-driving cars: https://aitrends.com/selfdrivingcars/kits-and-ai-self-driving-cars/

See my article about security and AI self-driving cars: https://aitrends.com/selfdrivingcars/ai-deep-learning-backdoor-security-holes-self-driving-cars-detection-prevention/

There are some auto makers and tech firms that want to provide API’s, which they do so in hopes that their AI self-driving car will become more popular than their competition. As mentioned earlier, if you can get a thriving third-party ecosystem going, it can greatly help boost your core system and get it to become more enmeshed into the marketplace. Also, if you have only one hundred developers in your company, they can only do so much, but if you can have thousands upon thousands of “developers” that write more software to connect to your system, you have magnified greatly your programming reach.

Innocent API’s Promise Not to Endanger

It is believed by some that the API’s can be provided for aspects that don’t endanger the self-driving car and its occupants, these are so-called innocent API’s.

Suppose for example that the API’s only allow for retrieval of information from the AI and the self-driving car. This presumably would prevent someone from getting the AI self-driving car to perform an undue action. Just make available API’s for object access, but none that allow for performing an action. You can still criticize this and suggest there might be a privacy of information loss due to object access the API’s, but at least it isn’t going to directly commandeer the AI self-driving car.

See my article on privacy of AI self-driving cars: https://aitrends.com/selfdrivingcars/privacy-ai-self-driving-cars/

Another viewpoint is that it is Okay to allow for action performing API’s, but those API’s would be constrained to only narrow and presumably safe actions. Suppose you have an API that allows for honking the horn or for flashing the lights of the car? Those seem innocuous. That being said, I suppose if you honk the horn at the wrong time it can confuse pedestrians and maybe also scare people. Similarly, flashing the lights of the car at the wrong time might be alarming to another human driver of a human driven car. Generally, those don’t seem overly unsafe per se.

There are five core stages of an AI self-driving car while in action:

•         Sensors data collection and interpretation
•         Sensor fusion
•         Virtual world model updating
•         AI action planning
•         Car controls commands issuance

See my article about my framework for AI self-driving cars: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

If there was an API for the retrieval of information from the sensors data collection and interpretation, this would seem to be innocuous. Indeed, it might allow a clever third-party to develop add-on’s that could do some impressive augmentation to the sensor analysis. You could potentially also grab the data and push it through other machine learning models to try and find better ways to interpret the data. As mentioned before, this could though have privacy and other complications.

For the sensor fusion, suppose you provided an API that would allow for invoking of some subroutines that combine together the radar data and the LIDAR data. This raises all sorts of potential issues. Will this undermine the validity of the system? Will this consume on-board computer resources and possibly starve other mission critical elements? And so on.

The same concerns can be raised about API’s that might invoke actions of the virtual world model, or actions involving the AI action plan updating. The same is the case for toying with the car controls commands issuance. Indeed, any kind of taxing of those components, even if only for data retrieval, would have to be done in such a manner that it does not simultaneously slow down or distract those aspects while they are working.

We must also consider that there can be a difference between what an API was intended to do, and what it actually does. If the auto maker or tech firm was not careful, they could have provided an API that is only supposed to honk the horn, but that if used in some other manner it can suddenly (let’s pretend) change the steering direction of the self-driving car. This shouldn’t happen, of course, and could produce deadly consequences. It wasn’t intended to happen. But inadvertently, while creating the API, the developers made a hole that allowed for this to occur. Some determined hackers might discover that the API has this other purpose.

Now, I am sure that some of you will say that even if there is something untoward in an API capability, all the auto maker or tech firm needs to do is send out an update via the OTA and close off that back-door. Yes, kind of. First, the auto maker or tech firm has to even find out that the back-door exists. Then, they need to create the plug or fix, and test it to make sure it doesn’t produce some other untoward result. They then need to push it out to the self-driving cars via the OTA. The self-driving cars have to have their OTA enabled and download the plug or fix, and install it. All of this can take time, and meanwhile the self-driving cars are “exposed” in terms of someone taking a nefarious advantage of the hole.

The API’s are often setup with authentication that requires any connecting system to have proper authority to access the API. This is a handy and important security feature. That being said, it is not necessarily an impenetrable barrier to still use the API. Remember the story of the app that gains access to your Gmail when you first install the app by getting your permission to do so. Suppose you are installing an app on your smartphone, which you’ve already connected to your AI self-driving car, and you are asked by the app to allow it to access the API’s in your self-driving car. You indicate yes, not knowing that ramifications this could have.

Will AI self-driving car makers provide API’s? Will they provide SDK’s (Software Development Kits)? Will they discourage or encourage so-called “hot wiring” of AI self-driving cars? Perhaps the path will be to limit any such capabilities to only on-board entertainment systems and not at all to any kind of car control or driving task elements.

Without such API’s, presumably the AI self-driving car might be safer, but will it also lose out on the possible bonanza of all sorts of third-party add-ons that will make your AI self-driving car superior to others and become the defacto standard AI self-driving car that everyone wants. We’ll have to wait and see how the API wars plays out.

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.

 

By Lance Eliot, the AI Trends Insider

You might find of interest the social psychology aspect known as the actor-observer effect. Before I explain what it is, allow me to provide you with a small story of something that happened the other day.

I was chatting with an AI developer that is creating software for an auto maker and he had approached me after I had finished talking at an industry conference. During my speech, I had mentioned several so-called “edge” cases involving AI self-driving cars. These edge cases involved aspects such as an AI self-driving car being able to navigate safely and properly a roundabout or traffic circle, and being able to navigate safely an accident scene, and so on.

See my article about AI self-driving cars and roundabouts: https://aitrends.com/selfdrivingcars/solving-roundabouts-traffic-circle-traversal-problem-self-driving-cars/

See my article about accident scene traversal and AI self-driving cars: https://aitrends.com/selfdrivingcars/accident-scene-traversal-self-driving-cars/

See my article about edge problems for AI self-driving cars:  https://aitrends.com/selfdrivingcars/edge-problems-core-true-self-driving-cars-achieving-last-mile/

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars and also advising other firms about the matter too. Thus, we’re working on quite a number of edge problems.

Well, the AI developer was curious why I cared about the “edge” problems in AI self-driving cars.

An edge problem is one that is not considered at the core of a system. It is considered less vital and an aspect that you can presumably come around and solve at a later time, after you’ve finished up the core. This is not a hard-and-fast rule in the sense that something that one person thinks is an edge might truly be part of the core. Or, something that is an edge might not be at the core but that otherwise without the edge you are going to have a very limited and potentially brittle core.

Edges are often in the eyes of the beholder. Thus, be careful when someone tosses out there that some feature or capability or issue is an “edge” problem. It’s an easy means to deflect attention and distract you from realizing that maybe the edge is truly needed, or that the core you are going to be getting will fall apart because it fails to solve an edge aspect. I’ve seen many people be dismissive of something important by trying to label it as an edge problem. This is a sneaky way at times to avoid having to cover an aspect, and instead pretend that it is inconsequential. Oh, that’s just an edge, someone will assert, and then walk away from the conversation or drop the microphone, as it were.

Anyway, back to my chat with the AI developer. I asked him why he was curious that I was so serious about the various edge problems of AI self-driving cars. It seemed relatively self-evident that these are aspects that can occur in real-world driving situations and that if we are going to have AI self-driving cars that are on our roadways we ought to be able to expect that those self-driving cars can handle everyday driving circumstances. Self-driving cars are a life-and-death matter. If an AI self-driving car cannot handle the driving tasks at-hand, things can get mighty dangerous.

His reply was that there was no particular need to deal with these various “edge” problems. As an example, I asked him what would his AI do when it encountered a driving situation involving a roundabout or traffic circle (I’m sure you know what these are — they are areas where cars go around a circle to then get to an exit radiating from the circle)?

He replied that it wouldn’t have to deal with it. The GPS would have alerted his AI that a roundabout was upcoming, and his AI would simply route itself another way. By avoiding the “edge” problem, he said that it no longer mattered.

Really, I asked?

I pointed out that suppose the GPS did not have it marked and thus the self-driving car went into the roundabout anyway? Or, suppose the GPS wasn’t working properly and so the AI self-driving car blindly went to the roundabout. Even if the GPS did indicate it was there, suppose that there was no viable alternative route and that the self-driving car would have to proceed through the roundabout? Was it supposed to always take the long way, assuming that such a path was even available? This reminded me of a teenage driver that I knew that avoided roundabouts because he was scared of them.

He insisted that none of these aspects would occur. He stood steadfast that there was no need to worry about it. He said I might as well say that suppose aliens from Mars came down to earth. Should he need to have his AI cope with that too?

The Pogo Stick Problem

This brings up another example that’s been making the hallways of AI developers for auto makers and tech firms doing self-driving cars systems. It’s the pogo stick problem. The AI self-driving car is going down a street, minding its own business (so to speak), and all of a sudden a human on a pogo stick bounces into the road and directly in front of the self-driving car. What does the AI do?

One answer that some have asserted is that this will never happen. They retort that the odds of a person being on a pogo stick is extremely remote. If there was such a circumstance, the odds that the person on the pogo stick would go out into the street is even further remote. And, the odds that they would do this just as a car was approaching was even more so remote, since why would someone be stupid enough to pogo stick into traffic and endanger getting hit?

In this viewpoint, we are at some kind of odds that are like getting hit by lightning. In fact, they would say it’s even lesser odds and more like getting hit by lightning twice in a row.

I am not so sure that the probability of this happening is quite as low as they would claim. They are also suggesting or implying that the probability is zero. This seems like a false suggestion since I think we can all agree there is a chance it could happen. No matter how small a chance, it is definitely more than zero.

Those that buy into the zero probability belief will then refuse to discuss the matter any further. They say it is like discussing the tooth fairy, so why waste time on something that will never happen. There are some that I can at least get them to consider that suppose it did happen, even if really remote odds. What then?

They then seem to divide into one of two camps. There’s the camp that says if the human was stupid enough to pogo stick into the road and directly in front of the self-driving car, whatever happens next is their fault. If the AI detects them and screeches the car to a halt and still hits them, because there wasn’t enough distance between them and the self-driving car, that’s the fault of the stupid human on the pogo stick. Case closed.

The other camp says that we shouldn’t allow humans on pogo sticks to go out onto the road. They believe that the matter should be a legal one, outlawing people from using pogo sticks on streets. When I point out that even if there was such a law, it is conceivable that a “law breaker” (like say a child on the pogo stick, which I guess might be facing a life of crime by using it in the streets), might wander unknowingly into the street. What then? The reply to that is that we need to put up barriers to prevent pogo stick riding humans from going out into the streets. All I can say is imagine a world in which we have tall barriers on all streets across all of the United States so that we won’t have pogo stick wandering kids. Imagine that!

If you think these kinds of arguments seem somewhat foolish in that why not just make the AI of the self-driving car so it can deal with a pogo stick riding human, you are perhaps starting to see what I call egocentric design of AI self-driving cars.

There are some firms and some AI developers that look at the world through the eyes of the self-driving car. What’s best for the self-driving car is the way that the world should be, in their view. If pogo riding humans are a pest for self-driving cars, get rid of the pests, so to speak, by outlawing those humans or do something like erecting a barrier to keep them from becoming a problem. Why should the AI need to shoulder the hassle of those pogo stick riding humans? Solve the problem by instead controlling the environment.

For those of you that are standing outside of this kind of viewpoint, you likely find it to be somewhat a bizarre perspective. It likely seems to you that it is real-world impractical to consider controlling the environment. The environment is what it is. Take it as a given. Make your darned AI good enough to deal with it. Expect that humans on pogo sticks are going to happen. Live with it.

What’s even more damming is that there are lots of variants beyond just a pogo stick riding human that could fall into the same classification of sorts. Suppose a human on a scooter suddenly went into the street in front of the self-driving car? Isn’t that the same class of problem? And, isn’t it pretty good odds that with the recent advent of ridesharing scooters that we’ll see this happening more and more?

If you are perplexed that anybody of their right mind could somehow believe that the AI of a self-driving car does not need to deal with the pogo stick riding human, and worse still the scooter riding human that is more likely prevalent, you might be interested in the actor-observer effect.

Here’s the background about the actor-observer effect.

Suppose we put someone into a room to do some work, let’s make it office type of work. We’ll have a one-way mirror that allows you to stand outside the room and watch what the person is doing. Let’s pretend that the person in the room is unaware that they are being observed. We’ll refer to the person in the room as an “actor” and we’ll refer to you standing outside the room as the “observer.”

At first, there will be work brought into the room, some kind of paperwork to be done, and it will be given to the actor. They are supposed to work on this paperwork task. You are watching them and so far all seems relatively normal and benign. They do the work. You can see that they are doing the work. The work is getting accomplished.

Next, the amount of work brought into the room starts to increase. The actor begins to sweat as they are genuinely trying to keep up with the volume of paperwork to be processed. Even more paperwork is brought into the room. Now the actor starts to get frantic. It’s way too much work. It is beginning to pile up. The actor is getting strained and you can see that they are obviously unable to get the work completed.

We stop the experiment.

If we were to ask you what happened, as an observer you would likely say that the person doing the work was incapable to keep up with the work required. The actor was low performing. Had the actor done a better job, they presumably could have kept up. They didn’t seem to know or find a means to be efficient enough to get the work done.

If we were to ask the actor what happened, they would likely say that they were doing good at the start, but then the environment went wacky. They were inundated with an unfair amount of paperwork. Nobody could have coped with it. They did the best they could do.

Which of these is right – the actor or the observer?

Perspective Determines What is Seen

It’s not so much about right or wrong, as it is the perspective of the matter. Usually, an actor or the person in the middle or midst of an activity tends to look at themselves as the stable part and the environment as the uncontrollable part. Meanwhile, the observer tends to see the environment as the part that is given, and it is the actor that becomes the focus of attention.

If you are a manager, you might have encountered this same kind of phenomena when you first started managing other people. You have someone working for you that seems to not be keeping up. They argue that it is because they are being given an unfair amount of work to do. You meanwhile believe they are being given a fair amount of work and it is their performance that’s at fault. You, and the person you are managing, can end-up endlessly going round and round about this, caught in a nearly hopeless deadlock. Each of you likely becoming increasingly insistent that the other one is not seeing things the right way.

It is likely due to the actor-observer effect, namely:

•         When you are in an observer position, you tend to see the environment as a given. The thing that needs to change is the actor.
•         When you are in the actor position, you tend to see the environment as something that needs to be changed, and you are the given.

Until both parties realize the impact of this effect, it becomes very hard to carry on a balanced discussion. Otherwise, it’s like looking at a painting that one of you insists is red, and the other insists is blue. Neither of you will be able to discuss the painting in other more useful terms until you realize that you each are seeing a particular color that maybe makes sense depending upon the nature of your eyes and your cornea.

Let’s then revisit the AI developer that I spoke with at the conference. Recall that he was insistent that the edge problems were not important. For the pogo stick riding human example, the “problem” at hand was the stupid human. I was saying that the problem was that the AI was insufficient to cope with the pogo stick riding human.  Why did we not see eye to eye?

His focus was on the self-driving car. In a sense, he’s like the actor in the actor-observer effect. His view was that the environment was the problem and so all you need to do is change the wacky environment. My view was that of the “observer” in that I assert the environment is a given, and you need to make the “actor” up to snuff to deal with that environment.

This then brings us to the egocentric design of AI self-driving cars. There are many auto makers and tech firms that are filled with AI developers and teams that view the world from the perspective of the AI self-driving car. They want the world to fit to what their AI self-driving car can do. This could be considered “egocentric” because it elevates the AI of the self-driving car in terms of being the focus. It does what it does. What it can’t do, that’s tough for the rest of us. Live with it.

For the rest of us, we tend to say wait a second, they need to make the AI self-driving car do whatever the environment requires. Putting an AI self-driving car onto our roadways is something that is a privilege and they need to consider it as such. It is on the shoulders of the AI developers and the auto makers and tech firms to make that AI self-driving car deal with whatever comes its way.

Believe it or not, I’ve had some of these auto makers and tech firms that have said we ought to have special roads just for AI self-driving cars. The reason for this is that whenever I point out that self-driving cars will need to mix with human driven cars, and so the AI needs to know how to deal with cars around it that are being driven by “unpredictable” humans, the answer I get is that we should devote special roads for AI self-driving cars. Divide the AI self-driving cars from those pesky human drivers.

There are some AI developers that dream wishfully of the day that there are only AI self-driving cars on our roadways. I point out that’s not going to happen for a very long time. In the United States alone we have 200 million conventional cars. Those are not going away overnight. If we are going to be introducing true Level 5 self-driving cars onto our roadways, it is going to be done in a mixture with human driven cars. As such, the AI has to assume there will be human driven cars and needs to be able to cope with those human driven cars.

The solution voiced by some AI developers is to separate the AI self-driving cars from the human driven cars. For example, convert the HOV lanes into AI self-driving car only lanes. I then ask them what happens when a human driven car decides to swerve into the HOV lane that has AI self-driving cars? Their answer is that the HOV lanes need to have barriers to prevent this from happening. And so on, with the reply always dealing with changing the environment to make this feasible. What about motorcycles? Answer, make sure the barriers will prevent motorcycles from going into the HOV lane. What about animals that wander onto the highway? Answer, the barriers should prevent animals or put up other additional barriers on the sides of the highway to prevent animals from wandering in.

After seeing how far they’ll go on this, I eventually get them to a point that I ask if maybe we ought to consider the AI self-driving car to be similar to a train. Right now, we usually cordon off train tracks. We put barriers to prevent anything from wandering into the path of the train. We put up signs warning about the train is coming. Isn’t that what they are arguing for? Namely, AI self-driving cars are to be treated like trains?

But, if that’s the case, I don’t quite then see where the AI part of the self-driving cars enters into things. Why not just make some kind of simpleton software that treats each car like it is part of a train. You then have these semi-automated cars that come together and collect into a series of cars like a train does. They then proceed along as a train. There are some that have even proposed this, though I’ll grant them that at least they view this as something like a “smart” colony of self-driving cars that come together when needed, but then still are individual “intelligent” self-driving once they leave the hive.

See my article about swarm intelligence and AI: https://aitrends.com/selfdrivingcars/swarm-intelligence-ai-self-driving-cars-stigmergy-boids/

See my framework about AI self-driving cars: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

Those that are making AI self-driving cars need to look past an egocentric view. We are not going to have true AI self-driving cars if we continue to try and limit the environment. A true level 5 self-driving car is supposed to be able to drive a car like a human would. If that’s the case, we then ought to not have to change anything per se about the existing driving environment. If humans can drive it, the AI should be able to do the same. I tried to explain this to the AI developer. I’m not sure that my words made much sense, since I think he was still seeing the painting as entirely in the color of red, while I was talking about the color blue. Maybe my words herein about the actor-observer effect might aid him in seeing the situation from both sides. I certainly hope so.

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.