Friday, 25 September 2020

As Cloud Computing Grows Rapidly, Companies Look to Manage Costs 

By John P. Desmond, AI Trends Editor  

As cloud computing has taken off and continues to grow rapidly, companies are motivated to get a handle on potentially runaway costs.  

Many companies use multiple public cloud provider services, in what is known as multicloud computing. Other companies use a mix of public and private cloud services in what is known as a hybrid cloud architecture.  

Public cloud refers to shared network use such as by Amazon AWS, Microsoft Azure and Google Cloud Platform; private clouds refer to private network connections between the user and provider of the cloud services. Rates differ, with public cloud rates generally less.  

The global cloud computing market is expected to exceed $330 billion in 2020, according to statistics from HostingTribunal, a site run by technical experts who attempt to improve services from all web hosting providers. 

A hybrid cloud architecture has been adopted by 58% of cloud users, according to RightScale, a company offering cloud computing management services acquired by Flexera in 2018. Of the organizations that use cloud computing, 84% have a multi-cloud strategy, using more than one public or private cloud. 

The 2020 State of the Cloud Report from Flexera is based on a survey of 750 IT administrators taken in February and March of this year. Among the highlights:  

  • 93% of enterprises have a multi-cloud strategy; 87% have a hybrid cloud strategy; 
  • Organizations are over budget for cloud spend by an average of 23% and expect cloud spend to increase by 47% next year; 
  • 73% of organizations plan to optimize existing use of cloud (cost savings), making it the top initiative for the fourth year in a row; 
  • The top three public cloud providers for enterprises remain AWS, Azure and Google; 
  • Azure is rapidly narrowing the gap with AWS in both the percentage of enterprises using it and the number of virtual machines (VMs) enterprises are running in it; 
  • Among the larger providers, Google experienced the fastest growth in enterprise; 
  • A subset of 187 survey respondents indicated how they expect COVID-19 to change their cloud plans. More than half said cloud usage will be higher than initially planned. Some of the increase resulted from the extra capacity needed for current cloud-based applications to meet increased demand as online usage grows. 

To help corral runaway hybrid cloud costs, one manager suggested implementing chargebacks, whereby IT bills cloud spending back to teams or departments based on their use.  

Gordon Haff, a technology evangelist at Red Hat

“Establish default policies and shut down services that aren’t being actively used,” suggested Gordon Haff, a technology evangelist at Red Hat, supplier of open source software products now owned by IBM, quoted in a recent account in The Enterprisers Project 

Another suggestion was to set alerts. “Use billing alert features,” suggested Haff. “In general, large cloud providers don’t offer hard cost caps but they do provide a variety of alert types – and often, the ability to create programmatic actions in response to alerts.”  

Another suggested carefully scrutinizing how the cloud computing services are being used.   

“Cost reduction must be a top priority and treated as a standard process, especially during COVID, when IT budgets are facing increased scrutiny,” stated Manish Srivastava, VP and General Manager of IT asset management at ServiceNow, a supplier of digital workflow tools. “It is important to monitor shifting usage patterns for various cloud applications as employees work from home and at some point in the future start coming back into offices. Companies can get started by rightsizing budgets, pre-buying compute, developing spending agreements, and terminating unused and unidentified spend.” 

Manish Srivastava, VP and General Manager of IT asset management, ServiceNow

He suggested first identifying the cloud spend by owner or use, cost center, application project and environment. “The more dimensions available in the analysis, the better breakdowns and tracking an organization will have across cloud spend,” Srivastava suggested.  

Finally, attempt to optimize the use of cloud services and make that part of the work routine, a standard operating procedure. “The faster businesses can understand their entire cloud landscape, clarify policies, and enact accountability, the better they will be at managing the wide variety of cloud spend sprawling throughout their business, particularly in this current work-from-home environment,” he stated. 

AI Can Be Helpful in Monitoring Hybrid Cloud Costs  

AI tools can be helpful in monitoring hybrid cloud costs, with machine learning able to identify behavioral patterns and propose spend optimization alternatives.  “Machine learning can be leveraged for suggesting cloud resource tagging and spend anomaly detection as well. AI for hybrid cloud optimization may include proactive provisioning based on anticipated workloads, change control recommendations, or cost-effective image configurations, to name a few,” Srivastava suggested. 

The hybrid cloud architecture has financial implications. Moving data in and out of the cloud costs money, and can be strategized, such as with a microservice architecture, that structures an application as a collection of services that are loosely coupled and owned by a small team.  

“The key is where you put what part of the applications,” stated Lenley Hensarling, chief strategy officer at Aerospike, which offers an in-memory open source database. One customer runs a microservices-based customer engagement app in a public cloud. “You get tremendous gains in elasticity as workloads change during the day or seasonally,” Hensarling stated. “This pays real dividends in being able to scale as needed, but not carry the cost of infrastructure for peak loads.”  

Total cost of ownership studies that compare the costs of alternative cloud architectures are not easy to come by. One offering is from Dell/EMC, a sponsored IDC White Paper on a TCO analysis of a Dell/EMC consistent hybrid cloud. 

A consistent hybrid cloud allows enterprise to operate both public and private platforms using one set of tools and processes.   

The results showed that the Dell Technologies Cloud achieved savings of up to 47% over a five-year period compared with a native public cloud. The report states, “While hybrid cloud offers great promises, including clear TCO advantages, it is also intrinsically complex. Therefore, you must consult with a trusted partner.” 

Read the source articles at HostingTribunal, the 2020 State of the Cloud Report from Flexera, in The Enterprisers Project and in the Dell/EMC sponsored IDC White Paper. 

The Trolley Problem Undeniably Applies to AI Autonomous Cars 

By Lance Eliot, the AI Trends Insider  

The famous or perhaps notably infamous Trolley Problem is considered one of the most controversial and outright fist-fighting topics in the field of AI autonomous self-driving cars. If you mention the Trolley Problem to any industry insider, you’ll likely get one of two reactions. One response is by those that consider themselves as in-the-know gurus and will immediately discount the Trolley Problem as being entirely hypothetical and obtuse, looking at you askance as though you have naively fallen for some kind of scam or trickery. Others might concede reluctantly that it is an interesting topic for discussion, perhaps even worth seriously pondering, but otherwise not especially relevant to any day-to-day practical matters involving self-driving cars.   

I’d like to see if we can give the matter its serious consideration and proper due. 

Avid readers will realize that I originally covered this topic in 2017, but it seems worthwhile to give the topic some added fresh air and revive it, doing so with additional insights and somewhat tuned-up with more vigorous commentary as a stolid response to various widespread downplaying of the relevancy of the Trolley Problem. For my earlier piece, see the link here: https://www.aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/ 

To get us all on the same page, the place to start entails clarifying what the Trolley Problem consists of.   

Turns out that it is an ethically-stimulating thought experiment that traces back to the early 1900s. As such, the topic has been around for quite a while and more recently has become generally associated with the advent of self-driving cars. In brief, imagine that a trolley is going down the tracks and there is a fork up ahead. If the trolley continues in its present course, alas there is someone stuck on the tracks further along, and they will get run down and killed. You are standing next to a switch that will allow you to redirect the trolley into the forking rail track and thus avoid killing the person. 

Presumably, obviously, you would invoke the switchover.   

But there is a hideous twist, namely that the forked track also has someone entangled on it, and by diverting the trolley you will kill that person instead. 

This is one of those no-win situations. 

Whichever choice you make, a person is going to be killed. 

You might be tempted to say that you do not have to make a choice and therefore you can readily sidestep the whole matter. Not really, since by doing nothing you are essentially “agreeing” to have the person killed that is on the straight-ahead path. You cannot seemingly avoid your culpability by shrugging your shoulders and opting to do nothing, instead, you are inextricably intertwined into the situation.   

Given this preliminary setup of the Trolley Problem as a lose-lose with one person at stake in your choice of either option, it does not especially spark an ethical dilemma since each outcome is woefully considered the same.   

The matter is usually altered in various ways to try and see how you might respond to a more ethically challenging circumstance. 

For example, suppose you can discern that the straight-ahead track has a child on it, while the forked track has an adult. 

What now? 

Well, you might attempt to justify using the switch to get the trolley to fork onto the track with the adult, doing so under the logic that the adult has already lived some substantive part of their life, while the child is only at the beginning of their life and perhaps ought to be given a chance for a longer existence. 

How does that seem to you? 

Some buy into it, some do not. 

Some might argue that every person has an equal “value” of living and it is untoward to prejudge that the child should live while the adult is to die.   

Some would argue that the adult should be the one that is kept alive since they have already shown that they can survive longer than the child.   

Here’s another variation.   

Both are adults, and the one on the forked path is Einstein. 

Does this change your viewpoint about which way to direct the trolley? 

Some would say that averting the trolley away from Einstein is the “right” choice, saving him and allowing him to live and inevitably offer the tremendous insights that he was destined to provide (we are assuming in this scenario that it is a younger adult moment-in-time, Einstein).   

Not so fast, some might say, and they wonder whether the other adult, the one on the straight-ahead, maybe they are someone that is destined to be equally great or perhaps make even more notable contributions to society (who’s to know?).   

Anyway, I think you can see how the ethical dilemmas can be readily postulated with the Trolley Problem template.   

Usually, the popular variants involve the number of people that are stuck on the tracks. For example, assume there are two people trapped on the straight-ahead path, while only one person is jammed on the forked path.   

Some would say this is an “easy” answered variant since the aspect of two people is presumed to be spared over just saving one person. In that sense, you are willing to consider that lives are somewhat additive, and the more there are, the more ethically favorable is that particular choice.   

Not everyone would concur with that logic. 

In any case, we now have placed on the table herein the crux of the Trolley Problem. 

I realize that your initial reaction likely is that it is a mildly interesting and thought-provoking notion but seems overly abstract and does not offer any practical utility. 

Some object and point out that they do not envision themselves ever coming upon a trolley and perchance finding themselves in this kind of obtuse pickle. 

Shift gears.   

A firefighter has rushed up to a burning building. There is a man in the building that is poking out of a window, acrid smoke billowing around him, and yelling to be saved. What should the firefighter do? 

Well, of course, we would hope that the firefighter would seek to rescue the man. But, wait, there is the sound of a child, screaming uncontrollably, stuck in a bedroom inside the burning building. The firefighter has to choose which to try and rescue, and for which the firefighter will not have time to save both of them. If the firefighter chooses to save the child, the man will perish in the fire. If the firefighter chooses to save the man, the child will succumb to the fire.  

Does this seem familiar? 

The point is that there are potentially real-life related scenarios that exhibit the underlying parameters and the overarching premise of the Trolley Problem.   

Remove the trolley from the problem as stated and look at the structure or elements that underpin the circumstances (we can still refer to the matter as the Trolley Problem for sake of reference, yet remove the trolley and still retain the core essentials).   

We have this: 

  • There are dire circumstances of a life-or-death nature (more like death-or-death) 
  • All outcomes are horrific (even the do-nothing option) and lead to fatality 
  • Time is short and there are urgency and immediacy involved 
  • Options are extremely limited, and a forced-choice is required 

You might try to argue that there is not a “forced choice” since there is the do-nothing option always available in these scenarios, but we are going to assume that the person faced with the predicament is aware of what is taking place and realizes they are making a choice even if they choose to do nothing.   

Obviously, if the person confronted with the choice is unaware of the ramifications of doing nothing, they perhaps could be said to have not been cognizant of the fact that they tacitly made a choice. Likewise, someone that miscomprehends the situation might falsely believe that they do not have to make a choice. 

Assume that the person involved is fully aware of the do-nothing and must choose to do nothing or to not do-nothing (I emphasize this due to the aspect that sometimes people mulling over the Trolley Problem will attempt to weasel out of the setup by saying that the do-nothing is the “right” choice since they then have averted making any decision; the selection of do-nothing is in fact considered a decision in this setup). 

As an aside, in the case of the burning building, if the firefighter does nothing, presumably both the man and the child will die, so this is somewhat kilter of the Trolley Problem as presented, thus, it is perhaps more evident that the firefighter will almost certainly make a choice. It differs from the classic Trolley Problem in that the firefighter has the opportunity to always, later on, point out that the do-nothing was certainly worse than making a choice, no matter which apparent choice was ultimately selected. 

One other point, this is not particularly a so-called Hobson’s choice scenario, which sometimes is misleadingly likened to the Trolley Problem. 

Hobson’s choice is based on an historic story of a horse owner that told those wanting a horse that they could choose either the horse closest to the barn door or take no horse at all. As such, the upside is taking the horse as proffered, while the downside is that you end-up without getting a horse. This is a decision-making scenario of a take-it-or-leave-it style, and decidedly not the same as the Trolley Problem. 

With all of the background setting the stage, we can next consider how this seems to be an issue related to self-driving cars.   

The focus will be on AI-based true self-driving cars, which deserves clarity as to what that phrasing means. 

For my framework about AI autonomous cars, see the link here: https://aitrends.com/ai-insider/framework-ai-self-driving-driverless-cars-big-picture/ 

Why this is a moonshot effort, see my explanation here: https://aitrends.com/ai-insider/self-driving-car-mother-ai-projects-moonshot/   

For more about the levels as a type of Richter scale, see my discussion here: https://aitrends.com/ai-insider/richter-scale-levels-self-driving-cars/ 

For the argument about bifurcating the levels, see my explanation here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self-driving-cars-bifurcation-of-autonomy/   

The Role of AI-Based Self-Driving Cars 

True self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task. 

These driverless vehicles are considered a Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at a Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there. 

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some point out).   

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different from driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable).   

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car. 

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/ 

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: http://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/ 

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Self-Driving Cars And The Trolley Problem   

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers. 

The AI is doing the driving.   

Here’s the vexing question: Will the AI of true self-driving cars have to make Trolley Problem decisions during the act of driving the self-driving vehicle? 

The reaction by some insiders is that this is a preposterous idea and utterly miscast, labeling the whole matter as falsehood and something that has no bearing on self-driving cars. 

Really? 

Start with the first premise that is usually given, which is that there is no such thing as a Trolley Problem in the act of driving a car.   

For anyone trying to use the “never happens” argument (for nearly anything), they find themselves on rather shaky and porous ground, since all it takes is the showing of existence to prove that the “never” is an incorrect statement.   

I can easily provide that existence proof.   

Peruse the news about car crashes, and by doing so, here’s an example of a recent news headline: “Driver who hit pedestrians on sidewalk was veering to avoid crash.” Here’s a link to the story: https://www.mlive.com/news/grand-rapids/2020/05/driver-who-hit-pedestrians-on-sidewalk-was-veering-to-avoid-crash.html   

The real-world reporting indicated that a driver was confronted with a pick-up truck that unexpectedly pulled in front of him, and he found himself having to choose whether to ram into the other vehicle or to try and veer away from the vehicle, though he also realized apparently that there were nearby pedestrians and his veering would take him into the pedestrians. 

 Which to choose? 

I trust that you can see that this is very much like the Trolley Problem. 

If he opted to do nothing, he was presumably going to ram into the other vehicle. If he veered away, he was presumably going to potentially hit the pedestrians. Either choice is certainly terrible, yet a choice had to be made.   

Some of you might bellow that this is not a life-or-death choice, and indeed fortunately the pedestrians though injured were not actually killed (at least as stated in the reporting), but I think you are fighting a bit hard to try and reject the Trolley Problem. 

It can be readily argued that death was on the line. 

Anyone of an open mind would agree that there was a horrific choice to be made, involving dire circumstances, and with limited choices, involving a time urgency factor, and otherwise conformed with the Trolley Problem overall (minus the trolley).   

As such, for those in the “never happens” camp, this is one example, of many, for which the word never is blatantly wrong. 

It does happen.   

It is an interesting matter to try and gauge how often this kind of decision-making does take place while driving a car. In the United States alone, there are 3.2 trillion miles driven each year, doing so by about 225 million licensed drivers, and the result is approximately 40,000 deaths and 2.3 million injuries due to car crashes annually.   

We do not know how many of those crashes involved a Trolley Problem scenario, but we do know that reportedly it does occur (as evidenced by news reporting). 

On that aspect of reporting, it is quite interesting that apparently, we should be cautious in interpreting any of the stories and coverage of car crashes, due to a suggested bias by such reporting.   

A study discussed in the Columbia Journalism Review points out that oftentimes the driver is quoted by news reporters, rather than quoting the victims that are harmed by the driving act (this is logically explainable, since the victims are either hard to reach as they are at a hospital and possibly incapacitated, or, sadly, they are dead and thus unable to explain what happened). Here’s a link to the study: https://www.cjr.org/analysis/when-covering-car-crashes-be-careful-not-to-blame-the-victim.php   

You might recognize this kind of selective attention as the survivability bias, a type of everyday bias in which we tend to focus on that which is more readily available and neglect or underplay that which is less so available or apparent. 

For the driving of a car and the reporting of car crashes, we need to be mindful of this facet. 

It could be that there are instances involving the Trolley Problem that the surviving participants might not realize had occurred, or are reluctant to state as so, and so on. In that sense, it could be that the Trolley Problem in car crashes is underreported.   

Being fair, we can also question the veracity of those that make a claim that amounts to a Trolley Problem and be cautious in assuming that just because someone says it was, it might not have been. In that sense, we could be mindful of potential overreporting. 

All in all, though, we can reasonably reject the claim that the Trolley Problem does not exist in the act of driving a car. Stated more affirmatively, we can reasonably accept and acknowledge that the Trolley Problem does exist in the act of driving a car. 

There, I said it, and I’m sure some pundits are boiling mad.   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/   

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/   

The ethical implications of AI driving systems are significant, see my indication here: http://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/ 

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Self-Driving Cars And Dealing With The Trolley Problem

Anyway, with that under our belt, we hopefully might agree that human drivers can and do face the Trolley Problem.   

But is it only human drivers that experience this?   

One can assert that an AI-based driving system, which is supposed to drive a car and do so to the same or better capability than human drivers, could very well encounter Trolley Problem situations. 

Let’s tackle this carefully.   

First, notice that this does not suggest that only AI driving systems will encounter a Trolley Problem, which is sometimes confusion that exists.   

Some claim the Trolley Problem will only happen to self-driving cars, but it hopefully is clear-cut that this is something that faces human drivers, and we are extending that known facet to what we assume self-driving cars will encounter too.   

Second, some argue that we will have only and exclusively AI-based true self-driving cars on our roadways, and as such, those vehicles will communicate and coordinate electronically via V2X, doing so in a fashion that will obviate any chance of a Trolley Problem arising. 

Maybe so, but that is a Utopian-like future that we do not know will happen, and meanwhile, there is inarguably going to be a mixture of both human-driven cars and AI-driven cars, for likely a long time to come, at least decades, and we also do not know if people will ever give up their perceived “right” (it’s a privilege). 

This is an important point that many never-Trolley proponents overlook.   

Here’s how they get themselves into a corner. 

The oft refrain is that an AI-based self-driving car has “obviously” been poorly engineered or essentially a lousy job done by the AI developers if the vehicle ever perchance finds itself amid a Trolley Problem. 

Usually, these same claims are associated too with the belief that we will have zero fatalities as a result of self-driving cars. 

As I have exhorted many times, zero fatalities is a zero chance. See my analysis at this link here: https://www.aitrends.com/ai-insider/self-driving-cars-zero-fatalities-zero-chance/ 

 It is a lofty goal, and a heartwarming aspiration, but nonetheless a misleading and outright false establishment of expectations.   

The rub is that if a pedestrian darts into the street, and there was no forewarning of the action, and meanwhile a self-driving car is coming down the street at perhaps 35 miles per hour, the physics of stopping in-time cannot be overcome simply because the AI is driving the car.   

The usual retort is that the AI would have always detected the pedestrian beforehand, but this is a falsehood that implies the sensors will always and perfectly be able to detect such matters, and that it will always be done sufficiently in advanced time that the self-driving car can avoid the pedestrian. 

I dare say that a child that runs out from between two parked cars is not going to offer such a chance.   

We are once again into the existence proof, meaning that there are going to be circumstances whereby no matter how good the AI is, and how good the sensors are, there will still be instances of the AI not being able to avoid a car crash.   

Likewise, one can argue in that same vein that the Trolley Problem will be indeed encountered by AI self-driving cars, ones that are on our public streets, and traveling amongst human drivers, and driving near to human pedestrians. 

The news report about the human driver that was cut-off by a pick-up truck could absolutely happen to a self-driving car.   

This seems undebatable.   

If you are now of the mind that the Trolley Problem can occur and can occur too in the case of AI self-driving cars, the next aspect is what will the AI do. 

Suppose the AI jams on the brakes, and slams head-on into that pick-up truck.   

Did the AI consider other options? 

Was the AI even considering veering to the side of the road and up onto the sidewalk (and, into the pedestrians)? 

If you are a self-driving carmaker or automaker, you need to be very, very, very careful about what your answer is going to be.   

I’ll tell you why.   

You might say that the AI was only programmed to do whatever was the obvious thing to do, which was to apply the brakes and attempt to slow down. 

We can likely assume that the AI was proficient enough to calculate that despite the braking, it was going to ram into the pick-up truck. 

So, it “knew” that a car crash was imminent.   

But if you are also saying that the AI did not consider other options, including going up onto the sidewalk, this certainly seems to showcase that the AI was doing an inadequate job of driving the car, and we would have expected a human driver to try and assess alternatives to avoid the car crash.   

In that sense, the AI is presumably deficient and perhaps should not be on our public roadways.   

You are also opening wide your legal liability, which I have repeatedly stated is something that will ultimately be a huge exposure for the automakers and self-driving carmakers. Once self-driving cars are prevalent, and once they get into car crashes, which they will, the lawsuits are going to come flying, and there are lawyers already priming to go after those deep-pocketed billion-dollar funded makers of self-driving tech and self-driving cars. 

Meanwhile, some of you might say that the AI did consider other alternatives, defending the robustness of your AI system, including that it considered going up on the sidewalk, but it then calculated that the pedestrians might be struck and so opted to stay the course and rammed instead into the pick-up truck.   

Whoa, you have just admitted that the AI was entangled into a Trolley Problem scenario.   

Welcome to the fold.   

Conclusion   

When a human driver confronts a Trolley Problem, they presumably take into account their potential death or injury, which thusly differs from the classic Trolley Problem since the person throwing the switch for the trolley tracks is not directly imperiled (they might suffer emotional consequences, or maybe even legal repercussions, but not bodily harm).   

We can reasonably assume that the AI of a self-driving car is not concerned about its well-being (I don’t want to detract from this herein discussion and take us onto a tangent, but some argue we might someday ascribe human rights to AI).   

In any case, the self-driving car might have passengers in it, which introduces a third element of consideration for the Trolley Problem.   

This is akin to adding a third track and another fork.   

The complications though somewhat extend beyond the traditional Trolley Problem since the AI must now take into account a potential joint probability or level of uncertainty, involving the facet that in the case of the pick-up truck involves the possible death or injury to the pick-up driver and the self-driving car passengers, versus the possible death or injury to the pedestrians and the self-driving car passengers. 

Maybe that is the Trolley Problem on steroids. 

Time for a wrap-up. 

For those flat earthers that deny the existence of the Trolley Problem in the case of AI-based true self-driving cars, your head-in-the-sand perspective is not only myopic but you are going to be the easiest of the legal targets for lawsuits. 

Why so?  

Because it was a well-known and oft-discussed matter that the Trolley Problem exists, yet you did nothing about it and hid behind the assertion that it does not exist. 

Good luck with that. 

For those of you that are the rare earthers, you acknowledge that the Trolley Problem exists for self-driving cars, but argue that it is a rarity, an edge problem, a corner case. 

Tell that to the people killed when your AI-based true self-driving car hits someone, doing so in that “rare” instance that will indisputably eventually arise.   

Again, it is not going to hold any legal water. 

Then there are the get-round-to-it earthers that acknowledge the Trolley Problem, and lament that you are so busy right now that it is low on the priority list, and pledge that one day, when time permits, you are going to deal with it. 

There is little difference between the rare earthers and the get-round-to-it earthers, and either way, they are going to have quite some explaining to do to a jury and a judge when the time comes.   

Here’s what the automakers and self-driving tech firms should be doing: 

  • Develop a sensible and explicit strategy about the Trolley Problem 
  • Craft a viable plan that entails the development of AI to cope with the Trolley Problem 
  • Undertake appropriate testing of the AI to ascertain the Trolley Problem handling 
  • Rollout when so readied the AI capabilities and monitor for usage 
  • Adjust and enhance the AI as feasible to increasingly improve Trolley Problem handling 

Hopefully, this discussion will awaken the flat earthers, and nudge forward the rare earthers and the get-round-to-it earthers, urging them to put proper and appropriate attention to the Trolley Problem and sufficiently preparing their AI driving systems to cope with these life-or-death matters.   

It is a real problem with real consequences.  

Copyright 2020 Dr. Lance Eliot  

This content is originally posted on AI Trends. 

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/] 

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AI and IoT Applied to Supply Chains Are Driving Digital Twins 

By AI Trends Staff 

The combination of IoT and machine learning growing at the same time is leading to a rise in the use of digital twins in the supply chain, as a digital replica that can be used for various purposes. The connection with the physical model and the corresponding virtual model is established by generating real time data using sensors.  

The Digital Twin Consortium, launched in August as a program of the Object Management Group, is working on defining a taxonomy and standards and enabling technology including AI and simulation. Engineers are being attracted to the work. Founding members include Ansys, Dell, GE, Lendlease, Microsoft and Northrop Grumman.   

Scott Lundstrom, analyst focused on the intersection of AI, IoT and Supply Chains

IoT and ML are the raw materials and the toolsthe insight is in the repository where we model processes and create context. While this might be a database or a data lake, the most interesting example of this for me is the digital twin,” wrote Scott Lundstrom, an analyst focused on the intersection of AI, IoT and Supply Chains, on his blog, Supply Chain Futures.  

The digital twin in the supply chain allows a comparison between current and historical data on performance, wherever a sensor is located. It could be a component such as a thermostat, an asset such as a truck or a machine, an employee such as a service technician, or a process, as in manufacturing. “Part of the capability of the digital twin is driven by this complexity of having models of models to describe complex assets, processes, and systems,” Lundstrom wrote.  

In the supply chain, the digital twin model can encompass items packed in containers, moving through the physical world to distributors and customers. The model could inherit data from the process that created the product at one end of the chain, and inform a customer model at the other end.  

Supply chains and manufacturing assets are just the beginning. As this technology becomes better understood, and deployments become easier, use will grow into increasing complex spaces. There is already development of digital twins in life sciences in support of systems biology modeling complex organs like the human heart,” Lundstrom wrote. (See “Virtual Twins: Their Roles in Healthcare, Drug Discovery and Pandemic Response,” in BioITWorld.) 

Ideally for the supply chain, characterized by many complex, multi-model use cases, the inclusive digital twin can have a view of the entire supply chain from the supplier’s supplier to the customer’s customer. An understanding of the status and history of assets and processes allows machine learning tools to be brought into the equation to execute simulations, optimizations, and predictive capabilities to the models, Lundstrom suggests.   

“To realize the benefits of this tremendous opportunity we need standards, agreed upon taxonomies, and commercial development tools and platforms for this market to flourish,” he stated. “The supplier community is reacting to this opportunity, and many practitioners from the PLM [product lifecycle management], IoT, and analytics/data science market are beginning to focus on resolving some of these foundational standards.”   

The large platform suppliers are moving forward with tools and platform as a service (PaaS) offerings to try to win share and develop “de facto” standards. Amazon Web Services (AWS), Google Cloud Platform (GCP), Predix Platform from GE, IBM and Microsoft are all building extensions to their existing IoT tools and platforms to add support for the creation of digital twins.   

Lundstrom pointed to Microsoft’s Azure Digital Twins as one of the more complete early offerings. Featured at the Microsoft Build 2020 event, held virtually in May, the preview release supports a new Digital Twin Definition Language (DTDL) based on an implementation of JSON-LD (JavaScript Object Notation for Linked Data). 

“By leveraging JSON-LD, a well-accepted and simple object framework, Microsoft is supporting an open standard from the beginning,” Lundstrom writes. “This is a key requirement as users begin to understand that digital twins require an open object-oriented approach to support the requirements for inheritance, and multiple instances in creating complex multitier models that are portable and support the use of widely available cloud platforms and AI frameworks.”  

Are Supply Chain Digital Twins Just Another Fad? 

Is the supply chain digital twin just another fad, asked a blog post on the site of River Logic, a supplier of prescriptive analytics technology for supply chain optimization using digital twins. In business since 2000 in Dallas, the company offers pre-built applications with knowledge of business planning and optimization.  

Simulation and modeling software allows organizations to create realistic and verifiable supply chain digital twins of their supply chains. Data mining techniques along with inputs from Internet of Things (IoT) sensors allow real-time data to be fed into models. The models can monitor and determine what’s happening in the real world and plan the appropriate corrective action. 

Gartner study on IoT implementation in July 2018 showed that 13% of companies working with IoT projects already had digital twins, while another 62% were working toward their implementation. “It seems that digital planning twins are more than just a fad,” the River Logic post stated. 

Engineers in the 1970s and ‘80s were using three-dimensional CAD models of complex engineering equipment to conduct virtual walkthroughs. As the CAD technology advanced, it became possible to represent physical stress, making it possible to conduct virtual stress testing. Today it is possible to construct “almost perfect” digital models of real equipment, such as aircraft, autonomous vehicles and drilling equipment, and by inputting real data, such as the static and dynamic loads experienced during aircraft takeoff, to measure performance.  

“In this way, it’s possible to simulate the real world and bridge the gap between our physical and digital environment,” River Logic states. Several company experiences with digital twins are highlighted on the River Logic website. 

Digital Twin of a Warehouse in Pacific-Asia Built by DHL Supply Chain 

DHL Supply Chain built its first digital twin of a warehouse in Pacific-Asia for Tetra Pak, a multinational food packaging and processing company based in Switzerland. The digital twin is supplied with real-time data on a consistent basis from a physical warehouse in Singapore, which DHL developed to be integrated into the supply chain, according to an account in Supply Chain magazine.  

Gillet Jerome, CEO, DHL Supply Chain Singapore, Malaysia, Philippines

“The joint implementation of such a digital solution to improve Tetra Pak’s warehousing and transport activities is an excellent example of the smart warehouses of the future,” stated Gillet Jerome, CEO, DHL Supply Chain Singapore, Malaysia, Philippines. “This enables agile, cost-effective and scalable supply chain operations.”  

At the warehouse, the DHL Control Tower tracks incoming and outgoing goods to ensure all goods are stored in the correct way within 30 minutes of receipt. Incoming trucks are outfitted with IoT technology. A smart storage solution developed by Tetra Pak tracks and simulates the physical condition and individual stock levels in real-time, allowing non-stop coordination of operations. .   

“We expect the partnership with DHL Supply Chain to further increase our productivity and maintain high standards in our supply chains,” commented Devraj Kumar, Director, Integrated Logistics, South Asia, East Asia & Oceania for Tetra Pak.  

Digital Twins in Paris Will Protect Wind Turbine from North Sea Gales  

GE engineers in Paris are partnering with Ansys, a global supplier of engineering simulation software, to build a digital twin of a wind turbine in the North Sea. One goal is to maximize output and minimize downtime by spotting problems before they lead to an unplanned outage. The predictive maintenance relies not only on physical sensors on the machines, but also virtual sensors put in places where physical sensors cannot be used, according to an account from GE News.  

The virtual sensor has the ability to guess with fair precision a value such as temperature of pressure, by using other data from sensors and smart algorithms based on historical data or models.  

For example, the GE engineers have developed a digital twin of the Haliade 150-6 wind turbine’s yaw motors, which enable the 6-megawatt turbine to rotate and position itself into the wind. Using virtual sensors, this digital twin simulates the temperature at various parts of the motors. 

HervĂ© Sabot, engineering director at GE’s Digital Foundry in Paris

The better you monitor the temperature, the better you know the impact of the way you are using it,” stated HervĂ© Sabot, engineering director at GE’s Digital Foundry in Paris. “The challenge here is to boost the capacity of our customer’s assets to avoid outages and have them perform as fast as possible.” 

Sabot and his team used the Ansys simulation tools to computer the motor’s internal temperature from a model. They accomplished this by tracking the electrical current feeding into the wind turbine motors.   

Using algorithms built on Predix, the GE software platform for the industrial internet, and a modeling approach developed by Ansys, the engineers can now estimate the motor temperature at any given moment. At the Foundry, they can also monitor how the motors perform under different strains over time. In the field, engineers are able to use an app with a dashboard connected to the twin, to monitor the motor’s temperature.  

“For the simulation, thanks to the digital twin we only need to know the current to understand the temperature and optimize the use of the motor,” Sabot stated.  

GE reports it has 1.2 million digital twins of jet engines, gas turbines and locomotives already working in the field.  

Read the source articles and accounts at  Supply Chain FuturesDigital Twin Consortium, the blog of River LogicSupply Chain magazine and from GE News.