Hiring and Managing AI Rockstars: The Case of AI Self-Driving Cars


By Lance Eliot, the AI Trends Insider

Wanted, rockstar AI developer. You see this in numerous job ads these days. It doesn’t say superstar. It doesn’t say megastar. Instead, we seemed to have landed on using the now-so-popular rockstar moniker (though, some claim that it should be stated as two separate words, namely as “rock star” and that if you use “rockstar” you need to capitalize it to be “Rockstar” since this refers to a particular brand or trademark; confusing!).

Anyway, when you see such job ads, the question arises as to what the hiring firm and the hiring manager really are seeking to find.

It could be that the firm has no idea what a rockstar AI developer is or does. Instead, the company is hopeful of making the position itself seem more impressive, and a handy way to do so is to pump-up the volume surrounding the job ad. This might also make the firm appear loftier because they only hire the best of the best, and anything less than a “best best” (that’s best squared) won’t do.

You’ve got to wonder whether the firm has only and always hired these astounding AI rockstars, such that the entire AI team is, well, a bunch of rockstars. If so, whomever they hire will actually become just one of the motley crew, as they will be one among many of the vaunted AI rockstars. Might be disappointing for that new hire to realize they are now actually a run-of-the-mill rockstar when in the midst of a sizzling pool of all AI rockstars.

Or, it could be that the firm has never hired an AI rockstar and in this case they are hoping, no say begging, or make it beseech that they must have in their midst an AI rockstar developer. If they could just get one of these, akin to finding a unicorn, it would make all the difference in their AI efforts. The expectation perhaps is that the hired AI rockstar will leap tall buildings with a single bound and turn AI lackluster projects into golden nuggets of grandiose AI.

In talking with various firms that have been looking for an AI rockstar developer, which I often get asked for recommendations, I often find that the hiring manager didn’t actually put into the job requisition that they were seeking a rockstar. They indicated in flatter and less flashy terms the nature of the position requirements. The HR (Human Resources) team, sometimes nowadays referred to as the Talent Management group, opted to add the rockstar indication when they posted the position for the world to see.

Why? As earlier suggested, they did so partially to make the position standout amongst the clutter of AI wanted job ads. Secondly, they also did so because they figured they would otherwise get inundated with applicants that barely know how to spell AI.

See, what’s happening now is that everyone and their brother or sister wants into these AI jobs, which makes career sense due to the hefty pay premium and the belief that you are changing the world, but this also causes job seekers to perhaps stretch their AI savviness a tad.

By planting the AI rockstar proclamation as a flag of announcement in a job ad, HR hopes that it will discourage the AI unwashed, particularly those that decide they want to apply for the job and yet aren’t up-to-par by the perceived AI stupendous standards of the HR team. It also makes things easier to turn down many that arise in the flood of applicants, due to merely being able to say that you aren’t an AI rockstar. You might be an AI newbie, or you might be an AI apprentice, or possibly even a seasoned AI specialist, and yet that’s not necessarily what these firms think they want to hire.

The funny thing is that some of these firms want someone with on-the-job AI developer experience of 10 to 20 years, meanwhile they have a tendency to screen out someone that they believe has over-the-hill AI experience. It can be a Catch-22 for some of those AI developers that were busy and had their hands full during the earlier heyday of AI. Those AI developers had to go into hibernation or hiding during the AI winter that came along. With the advent of the AI spring, they polished up their resumes and aimed to get back into the AI game. Turns out, it’s not so easy to do.

Admittedly, getting a true AI rockstar can be quite advantageous.

First, it allows the firm to tout the kind of talent they attract. Look at us, it says, given the fierce competition for the top 1% of AI developers, we got one. Obviously, our firm is on the cutting edge, they would then tout. We are the place to be and the proof is in the pudding, we got a big fish.

This is especially the gambit of the smaller firms or startups. They know that the bigger tech firms can readily attract these top enders. When the name of the firm is one that everyone knows, and one that already has an outstanding reputation, it is tough to compete when you are an unknown firm with an unknown future and with an unknown product.

Secondly, in my experience as a manager and leader, I’ve found that there is absolutely a difference in capability, effectiveness, and productivity between an average AI developer and an outstanding AI developer. There is no question that the outstanding AI developer is going to outperform the average AI developer. And not just by some minor incremental amount, instead more like an exponential amount.

In fact, at some points during my tenure as a corporate top executive, I’ve had to go to battle with HR over getting higher pay to acquire an outstanding AI developer. In the minds of some HR folks, they think that the outstanding AI developer is worth maybe a handful of percentage points more in pay than the average one. I usually need to go around this logic and try to explain that giving a sizable boost in pay is readily going to payoff many times over. I say this because of the gigantic return for value that you get with the outstanding AI developers.

I am not knocking the average AI developers. They are good to have. They get their jobs done. They grind out the work. Yet, if I could have at least one or more of the outstanding AI developers mixed into the pack, it would be huge game changer for what the AI team can accomplish.

Don’t be fooled though into thinking that if you toss one outstanding AI developer into a group of everyday AI developers that magic will somehow materialize. Not so.

There is a solid chance that some of the everyday AI developers will resent the outstanding AI developer. What makes the AI rockstar so revered, will be a common refrain. Let’s see what magic touch this person has. Some of the other AI developers will take potshots at the outstanding AI developer. The existing AI team members might refuse to share their code or might make it hard for the new-hire to come up-to-speed by acting mum, justifying this by saying that if the person is that good, they should be able to figure out things on their own.

This in my book comes down to the lot of the AI manager or leader. If they aren’t doing their job properly, they are going to make a mess of the AI talent that is amassed, likely whether there are any AI rockstars or not (and, worse so when there is an AI rockstar, since the odds are the rockstar will not be leveraged appropriately).

Indeed, I’ve seen some really topnotch AI teams that got completely kabobbed because the head of the AI team had no clue about how to actually manage or lead such a team. In some cases, they just tossed everyone together and figured it would coalesce, and in other cases they did so many Dilbert-like senseless management acts that the AI team was utterly counterproductive, in spite of the great talent that had been put together.

The nature of the AI rockstar also obviously makes a big difference too. If the top ender decides to act like they are there to save the day, it’s going to likely rub others of the AI team the wrong way. By the snooty nose and the walking on water attitude, there are some AI rockstars that limit what you can do with them. Yes, they might have great raw AI talent as a skillset, but from a working-with-people perspective, they are a loser.

I remember one AI team that landed a top AI rockstar and the first thing the person did was opt to takeover the entire AI project that was floundering. Sure, it was handy that they had someone that might know how to turnaround the effort, but this rockstar decided that nobody but themselves was capable to get the ship righted. Instant alienation with the rest of the team.

Once again, I put this squarely on the AI manager. When I spoke with the AI manager, it was obvious that they were in a panic and could not figure out how to get themselves out of a pickle. They assumed that this AI rockstar would do it all. By putting his own head into the sand, the AI manager was praying that a miracle would occur by giving over the AI project to the rockstar.

AI Rockstar Often Not an Effective People Manager

The problem often times is that the AI rockstar is not a manager. This kind of makes sense because they are usually being hired as an individual contributor, a heads-down developer. They are not being hired to be a manager. Thus, the actual AI manager was handing over the AI project keys to someone that wasn’t able to manage (of course, apparently neither was the anointed manager!).

I wrestle with some companies that want an AI rockstar and haven’t yet figured out what they really need, in the sense of do they need someone that has the highly technical AI skills and want to apply those to the effort, or do they want someone that will manage a team of AI developers? Those are two different things.

I’m not saying that you cannot be a highly technical AI developer and also be a manager. What makes the issue confounding is when a firm that is doing the hiring hasn’t figured out what they want or need. The firm might think it needs just the topnotch AI developer with pure technical skills, and meanwhile are blind to the aspect that they actually need someone either also with managerial skills or perhaps need another position made available of AI manger (or, they need to do something about an existing AI manager that maybe isn’t right for the role).

Ideally, if you are looking for an AI rockstar developer that is purely a developer, the position actually matches to that need, and furthermore there is an AI team manager that knows how to properly make use of that talent. This usage includes how to marry the AI rockstar into the team and ensure that the AI team works collaboratively and as a well-managed team is supposed to do.

Let’s suppose you do hire an AI rockstar and you well-integrate them into the AI team. That’s a good start.

There are some aspects that can occur to regrettably undermine things.

One frequent pattern is that the other members of the AI team will be looking to the rockstar as someone that can be a kind of unstated mentor. You could say that’s a positive sign that the rest of the AI team has taken to accepting and hopefully embracing the added AI rockstar.

What can happen though is that the AI rockstar, wanting to be part of the AI team and desirous of being collaborative (there are some that want this!), they will begin to allow these semi-mentoring relationships to build. Gradually, the AI rockstar is spending a sizable chunk of their time helping out the other AI team members.

I recall one AI rockstar that offered to do lunch-time brown bag sessions on the latest techniques for Machine Learning and Deep Learning, doing so because they had kept getting asked questions by individual members of the AI team. Rather than continuing to answer the questions one at a time, the rockstar realized it would be more efficient to do a voluntary lunch-time workshop series and get things done as a group rather than on an individual at a time basis.

This expanded into an after-work series too, taking place in the evenings. Gradually, the AI rockstar was becoming mired in trying to do the “right thing,” namely helping the fellow AI team members, and yet was becoming overloaded in doing so. The AI manager was at first unaware of the extra effort being undertaken by the AI rockstar (that’s one strike against the AI manager, since the manager ought to know when this is taking place).

When I entered into the picture, the AI manager told me that the AI rockstar was working out really well. The rest of the team loved the rockstar. The rockstar was excited to be able to share what they knew with the rest of the AI team.

Here’s though the rub. When I spoke with the AI rockstar, the person was exhausted, and they told me privately that this added part of the job was beyond what they had expected. Was it really their job duty to do this kind of bend-over-backwards non-stop assistance for the rest of the AI team?

The rockstar said they weren’t complaining, but that they were now working 40 to 50 hours on their regular work and putting in an additional 15+ hours per week on these added efforts. It was unrecognized by anyone else officially. Unofficially, the rest of the AI team was thanking the rockstar and bringing in free pizza and other goodies as a show of appreciation. The AI manager was clueless that the rockstar was suffering under the weight of trying to help the AI team members and simultaneously do they job they were presumably hired to do (let’s make that strike two against the AI manager).

In this case, the AI rockstar was so flummoxed by the situation that they were secretly seeking to reawaken another job opportunity elsewhere that had fallen by the wayside when they took this job.

You might be puzzled as to why the rockstar did not just go talk with the AI manager and explain the extracurricular efforts? The rockstar didn’t want to be a “tattletale” about the other AI team members, worrying that it might reflect poorly on them. The easiest “solution” seemed to be to find a job elsewhere and say that they had loved the job here but got a better offer and wanted to take it.

I’d say that this particular AI rockstar developer was a rarity. More often, the rockstar is one that has no bones about saying whatever they feel like saying. I knew one that would gladly tell a fellow AI team member that they were as dumb as a rock. This was done repeatedly and in front of other members of the AI team. Such a rockstar might either be unaware of the brashness and insulting nature of their demeanor, or they sometimes relish it and enjoy brandishing it.

Jerk-Rockstar Causality Confusion

This brings up one aspect that I admit gets my goat whenever it arises. I call it the jerk-rockstar causality confusion.

That’s a mouthful.

Allow me to give you an example. One firm was trying to hire an AI rockstar and they brought me into the hiring process to help vet candidates.

They already had two finalists. One of the finalists was a real jerk. You could discern this the moment you spoke to the person. They were full of themselves, they acted like they could part the sea, and when I asked some fundamental AI questions as means to gauge what they really knew, the person immediately rejected them as beneath them and refused to answer the questions. The other candidate was more moderate and nearly reasonable, especially in contrast to the complete jerk candidate.

After I spoke with the two finalists, I went over and chatted with HR. The recruiter on the HR team said that the candidate that was “at times difficult” was clearly the better candidate and wanted to know what I thought. I asked how the recruiter assessed that the “at times difficult” candidate (i.e., the jerk) was the better of the two candidates?

Answer: Because AI rockstars are jerks and since the one candidate was a jerk, it meant the candidate was better than the other candidate (the one that was not so much of a jerk).

Say what? Apparently, there is a causality that if you are a jerk, ergo you are an AI rockstar. Likewise, presumably, if you are not a jerk, ergo you are not an AI rockstar.

Some AI rockstars have actually figured out this mindset exists and therefore they ramp-up the jerk factor when they meet people or are trying to get hired. They realize that as a society, we seem to have bought into the notion that the brilliant people are jerks (consider various TV shows and movies that depict this stereotype). I guess the assumption is that they are so brilliant that they didn’t have the time or inclination to try and not be a jerk. Or, maybe being a jerk is a human default and the brilliant ones let the natural jerk shine through.

In any case, I mention this gets my goat because there are AI rockstar developers that are not jerks. I vehemently disagree with the thinking that being a jerk means you are a rockstar, and also, I vehemently disagree that not being a jerk means you are not a rockstar. I suppose if you want to go by a statistical factor, more often than not the rockstar is potentially going to be a jerk, but I wouldn’t therefore set my sights on thinking that the jerk level is a sign that the rockstar is a rockstar.

As a quick recap on this diatribe about AI rockstars: they do exist, they are worth their weight in silver, they are not necessarily jerks, they can enhance an AI team, they can bring a glow to a firm that lands them, the “title” can be an attractor for a rockstar that wants to be recognized for what they can bring to the table, it can be a handy means of weening out the non-rockstars, and it can be harder than it might seem to discern whether someone is a true AI rockstar.

Even if you get yourself an AI rockstar developer, you can’t just plop them into an AI project and an AI team and assume that the world will be wonderful. There needs to be an AI manager savvy enough to know how to leverage the capabilities of the AI rockstar. In some cases, the AI manager might need to rein in the jerk-aspects of a rockstar and harness it. In some cases, the AI manager might need to deal with others on the AI team that either wrongfully or rightfully might resent having the AI rockstar.

Furthermore, the AI manager has to be watching for signs that the AI rockstar is being pulled into too many directions at once. This can be exhausting for the rockstar, producing burnout, potentially undermining their rockstar skills, and might end-up with them leaving. They are a resource for the AI projects and usually aren’t accompanied by their own self-managing skills.

If you want an AI rockstar that is both high technical and going to be doing development, plus you want them to be an AI manager, you need to realize these are two different roles combined into one.

Did you make it clear cut in the job description what you expect in the role in terms of the potential mixture of developer and manager in the AI rockstar? Does the hiring manager realize this? Does HR understand this? Are you making sure the candidate’s matchup to both sides of that coin?

Realistically, too, how much time of the AI job consists of doing the development versus doing the managing? Often, the managing part is given short shrift and enormously underestimated. If so, it’s a problem that will rear its ugly head once the AI rockstar comes on-board and gets underway.

You might believe that the managing side of things is say at most 3-5 hours per week of the rockstar’s time, but the reality could be that it chews up half or more of their time. It is a common mistake to assume that managing is a trivial task and one that requires either no real skills or not much time.

I’d say the opposite is true, namely it requires some real topnotch skills and it will definitely consume a lot of time. In spite of the many jokes often made about management and managers, done properly it is a sight to see, and AI projects will arrive on-target and tend to avoid the disastrous results that most AI projects usually befall. Good AI managers are worth their weight in gold.

For my article about AI developer burnout, see: https://www.aitrends.com/selfdrivingcars/developer-burnout-and-ai-self-driving-cars/

For my article about egocentric AI developers, see: https://www.aitrends.com/selfdrivingcars/egocentric-design-and-ai-self-driving-cars/

For the dangers of AI team groupthink, see my article: https://www.aitrends.com/selfdrivingcars/groupthink-dilemmas-for-developing-ai-self-driving-cars/

For AI developers that are considered naysayers, see my article: https://www.aitrends.com/selfdrivingcars/internal-naysayers-and-ai-self-driving-cars/

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

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. We and everyone else involved in AI self-driving cars such as the auto makers and various tech firms are all on the hunt for AI rockstar developers, plus we are often asked to aid in the identification, selection, hiring, and onboarding process.

Allow me to elaborate how this pertains to AI self-driving car efforts.

I’d like to first clarify and introduce the notion that there are varying levels of AI self-driving cars. The topmost level is considered Level 5. A Level 5 self-driving car is one that is being driven by the AI and there is no human driver involved. For the design of Level 5 self-driving cars, the auto makers are even removing the gas pedal, brake pedal, and steering wheel, since those are contraptions used by human drivers. The Level 5 self-driving car is not being driven by a human and nor is there an expectation that a human driver will be present in the self-driving car. It’s all on the shoulders of the AI to drive the car.

For self-driving cars less than a Level 5, there must be a human driver present in the car. The human driver is currently considered the responsible party for the acts of the car. The AI and the human driver are co-sharing the driving task. In spite of this co-sharing, the human is supposed to remain fully immersed into the driving task and be ready at all times to perform the driving task. I’ve repeatedly warned about the dangers of this co-sharing arrangement and predicted it will produce many untoward results.

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

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

For why AI Level 5 self-driving cars are like a moonshot, see my article: https://aitrends.com/selfdrivingcars/self-driving-car-mother-ai-projects-moonshot/

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

Let’s focus herein on the true Level 5 self-driving car. Much of the comments apply to the less than Level 5 self-driving cars too, but the fully autonomous AI self-driving car will receive the most attention in this discussion.

Here’s the usual steps involved in the AI driving task:

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

Another key aspect of AI self-driving cars is that they will be driving on our roadways in the midst of human driven cars too. There are some pundits of AI self-driving cars that continually refer to a utopian world in which there are only AI self-driving cars on the public roads. Currently there are about 250+ million conventional cars in the United States alone, and those cars are not going to magically disappear or become true Level 5 AI self-driving cars overnight.

Indeed, the use of human driven cars will last for many years, likely many decades, and the advent of AI self-driving cars will occur while there are still human driven cars on the roads. This is a crucial point since this means that the AI of self-driving cars needs to be able to contend with not just other AI self-driving cars, but also contend with human driven cars. It is easy to envision a simplistic and rather unrealistic world in which all AI self-driving cars are politely interacting with each other and being civil about roadway interactions. That’s not what is going to be happening for the foreseeable future. AI self-driving cars and human driven cars will need to be able to cope with each other.

For my article about the grand convergence that has led us to this moment in time, see: https://aitrends.com/selfdrivingcars/grand-convergence-explains-rise-self-driving-cars/

See my article about the ethical dilemmas facing AI self-driving cars: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/

For potential regulations about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/assessing-federal-regulations-self-driving-cars-house-bill-passed/

For my predictions about AI self-driving cars for the 2020s, 2030s, and 2040s, see my article: https://aitrends.com/selfdrivingcars/gen-z-and-the-fate-of-ai-self-driving-cars/

Returning to the topic of AI rockstar developers, let’s consider how these rockstars are manifested in the context of the AI self-driving car industry.

Where to Find an AI Rockstar?

If you were looking for an AI rockstar developer that has AI self-driving car expertise, where would you look? By-and-large, many of these AI developers have been sourced out of various university research programs that have had a focus on autonomous vehicles.

Indeed, many of the key AI self-driving car specialists and leaders of today that are at the major auto makers and tech firms developing AI self-driving cars came out of the DARPA (Defense Advanced Research Projects Agency) Grand Challenges that took place in the early 2000s.

In 2004, the first of the DARPA Grand Challenges took place, involving a race in the Mojave Desert that generally parallels Interstate 15 in California and required trying to make a 150-mile journey with an autonomous vehicle in the desert. Though none of the vehicles were able to successfully complete the race, and did not win the coveted $1 million cash prize, this effort approved by the U.S. Congress was able to kick-up further interest in creating AI self-driving cars.

Then in 2005, there were five winning autonomous vehicles, arriving at the finish line in this order, and for which I indicate the name of the vehicle and who provided it: (1) Stanley of the Stanford Racing Team, (2) Sandstorm of the Red Team from CMU, (3) H1hglander of the Red Team from CMU, (4) Kat-5 of the Team Gray from The Gray Insurance Company, and (5) TerraMax of the Team TerraMax of the Oshkosh Truck Corporation.

At the time, there was some criticism that these accomplishments were impressive but not overwhelming due to the nature of the driving environment. Some pointed out that driving in a desert is not the same as driving in a city or suburb setting. Though you might have a few desert tortoises or scary sagebrush, attempting to drive in a relatively barren scene is unlike being faced with other nearby cars, roadway aspects, pedestrians, and the like in an urban setting.

For 2007, the third DARPA Grand Challenge took place in a closed-track urban-like setup setting and became famously known as the “Urban Challenge.” This took place at an Air Force Base in Victorville, California. Six of the submitted autonomous vehicles were able to complete the course, which included having to abide by various traffic related rules that has been stated.

That was over a dozen years ago. When the rush toward AI self-driving cars began just a few years ago commercially, many of those that had been directly or indirectly involved in the DARPA Grand Challenges either flocked to private industry or were lured out of universities into commercial enterprises. Some in the university settings opted to start their own firms or chose to try and remain in the academic world while splitting their time with being involved in the commercial efforts outside the gates of the university.

In any case, the point being that there has been an initial influx of AI rockstar developers from numerous university related AI self-driving car efforts.

I’m one of those.

But there are only so many of those such AI developers and it is insufficient as a pool for the number needed to fully resource the numerous and varied AI self-driving car efforts underway. As a result, there are now some being lured out of university research programs that were not around during the days of the DARPA Grand Challenge. These are researchers that came along after those days.

Another variant is that many universities are now doing research in autonomous vehicles of all kinds, including autonomous flying drones, autonomous flying planes, autonomous flying cars, autonomous submersible submarines, autonomous surface going boats, etc. This gives a wider choice of the kinds of AI developers that one might tap into when seeking AI specialists related to AI self-driving cars.

Those that have been in the automotive industry for a long time and have worked on ADAS (Advanced Driver Assistance Systems) are a potential pool of AI developers. They tend to have some strong skills on the automotive side and know well the inner elements of car-related systems and system standards. What they often lack is a strength in AI. Some of them have attempted to bolster their AI awareness by taking AI courses and otherwise supplementing their capabilities accordingly.

We are also on the verge of seeing poaching or the musical chairs game of AI developers hopping from one AI self-driving car effort to another. This hasn’t happened too much just yet, partially due to the aspect that many of these efforts are still relatively new. There is also the sometimes golden handcuffs that firms use to try and keep their AI developers from jumping ship to another firm. I’ve predicted that we’ll soon see more and more of movement between the AI self-driving car efforts.

For my article about reverse engineering of AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/reverse-engineering-and-ai-self-driving-cars/

For my article about startups in AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/how-to-best-pitch-your-startup/

For stealing secrets of AI self-driving cars, see my article: https://www.aitrends.com/selfdrivingcars/stealing-secrets-about-ai-self-driving-cars/

For open source about AI self-driving cars code, see my article: https://www.aitrends.com/selfdrivingcars/caveats-open-source-self-driving-cars/

Real-Time Systems Experience Needed To Develop for AI Self-Driving Cars

We’ve been helping to train those that are somewhat versed overall in AI about the aspects of AI self-driving cars, though it is a steep hill to climb if the person doesn’t have already some kind of relatively in-depth real-time systems experience.

AI self-driving cars and their systems all work in a very tight time constrained setting and involve the core aspects of real-time systems, plus these are real-time systems involving multi-ton cars that can bring about life-or-death.

In aiding the interviewing and selection process for some of the AI self-driving car searches for AI rockstars, one aspect that repeatedly comes across is the often-seen dogmatic perspective. It is somewhat common that a person versed in AI and self-driving cars or autonomous vehicles might have a particular bend or strongly wedded approach or technology that they adhere to.

This is akin to a computer programmer that insists on using a particular programming language and refuses to consider any other coding languages. Or, one that insists on using a particular software package or has opted to place all their eggs into one basket, and knows only that particular package, therefore they claim that it is the only and best way to go.

I recall one potential AI rockstar candidate that insisted cameras were the best way to collect sensory data for an AI self-driving car and eschewed the use of LIDAR. This candidate was absolutely convinced that LIDAR was overly expensive and not worth the effort to include in an AI self-driving car. Sidenote, I refer to this as the “myopic” or cyclops view of AI self-driving cars, wherein a person believes there is only one way that things are to be done.

What was especially interesting, and revealing was that the person had spent their entire prior efforts on traditional vision processing involving cameras. They had put at most a token effort towards learning about LIDAR. In that case, this “expert” had really had little basis for offering such a strong opinion of the tradeoffs between the two.

I also tried to point out that there is a rapid pace at which LIDAR costs are coming down, simultaneously the accuracy and features are rapidly increasing.

I also pointed out that this seemed to be a potentially false “mutually exclusive” type of debate. Does one necessarily need to choose between using cameras versus using LIDAR? Most of the AI self-driving car efforts to-date are using both (though, Tesla is a notable exception, and I’ve warned many times that I believe Elon Musk’s claim that LIDAR won’t be needed is sadly misguided and he’ll eventually regret the choice, which he has even stated might turnout to be the case).

This potential AI rockstar was surprised to be challenged on this point about the cameras versus LIDAR matter. To-date, he had been able to browbeat most interviewers into submission by using arcane jargon and attempting to bolster his argument about cameras, which really was an argument about why he should be hired. I’m not saying that he lied, and I do believe he sincerely believed in the cameras approach, but I am saying that his lack of awareness and coupled with his personal bias is what led to his insistence.

I would also say this was another example of the jerk-rockstar causality confusion. Those that had interviewed the candidate liked the sense of confidence and spirit of the person and though there was an underlying know-it-all and jerk factor, this merely added to the person’s glow that they must be an AI rockstar.

When I spoke with some of the executives and the hiring manager, I emphasized that since the firm was already using LIDAR, they were going to be bringing into their midst someone that was adamantly opposed to this part of their strategy and efforts. I was told that they would just keep the person in the cameras and vision processing team. No need to have them deal with the LIDAR team.

Sigh. I tried to point out that if they wanted to ensure a war between their teams, they certainly could so proceed. At every turn, this person would likely try to undermine the other team. The other team would likely become antagonistic toward the cameras team and if there wasn’t bad blood yet, the company would soon be bathed in bad blood. This didn’t seem to be a smart way to try and seamlessly craft an AI systems for which all of the parts need to work coherently and cohesively.

This does bring up another facet about AI rockstar developers in the AI self-driving car niche.

Typically, these AI rockstars have a specific and narrow area of skills and technology attention. That’s fine, as long as this is realized and leveraged. Someone that is highly skilled at the sensors part of self-driving cars might not be familiar with the car controls aspects. Thus, you cannot just slam dunk someone into the car controls side if you’ve hired them based on their sensor-focused skills.

I also forewarn to be on the watch for a kind of technological bigotry, such as the candidate that was so convinced that cameras rule the world. It’s handy for someone to be passionate about their area of expertise, but it is another thing when they bash another area of technology and want to fight against it. That’s where you are bound to have problems arise and it will likely shakeup any AI team.

For my article about AI developers that are myopic and take a cyclops approach, see: https://www.aitrends.com/selfdrivingcars/cyclops-approach-ai-self-driving-cars-myopic/

For idealism and AI developers, see my article: https://www.aitrends.com/selfdrivingcars/idealism-and-ai-self-driving-cars/

For the need for ethics review boards, see my article: https://www.aitrends.com/selfdrivingcars/ethics-review-boards-and-ai-self-driving-cars/

For the notion of starting over with AI for self-driving cars, see my article: https://www.aitrends.com/selfdrivingcars/starting-over-on-ai-and-self-driving-cars/

I’ll cover a few other salient points about potential AI rockstars and self-driving cars.

One point is that they sometimes are so strongly opinionated that when they get onboarded and have a chance to look under-the-hood of the AI systems being developed, they can suddenly decide that everything is wrong and that there should be a do-over.

That can be quite a shock to the firm.

If the AI rockstar is actually right and they have discovered that there are serious and severe flaws, well, okay, thankfully they have found this, preferably before the firm has gotten too far along on their AI self-driving car efforts. On the other hand, if they person is being a jerk and merely spouting out false failings, maybe to boost their own sense of importance, or perhaps based on a misjudging of what they’ve found, it is going to likely cause chaos.

Imagine a firm that has invested perhaps millions upon millions of dollars into their AI self-driving car development, along with multitudes of expensive AI developer time and effort, and have someone that walks in the door and proclaims that it is a waste. Yikes! The newly hired AI rockstar will likely get heard because it is assumed that they are an AI rockstar, since they got hired under those auspices, and so it will be difficult to quiet down such a charge.

This could take the firm in a path that will last for weeks or months of internal handwringing and debate. All of which might be warranted, or might be a false “the sky is falling” and that drains the attention and monies of the firm. With the frenetic pace of AI self-driving car efforts, and the desire to keep ahead of the other AI self-driving car firms, getting bogged down in an acrimonious internal debate that perhaps has no basis will be draining and likely cause the firm to fall behind.

I don’t want to suggest that the AI rockstar is necessarily wrong if they do find problems. In fact, it could be that others within the AI team have had qualms about the AI system, but they were either unsure of how to voice those qualms or felt they would not be heard if they did. Internal naysayers are often cast aside and gain little by coming forth, other than a personal sense of doing what they believe to be right. When a newly hired AI rockstar opts to rock-the-boat, it can be an opportunity for suppressed members of the AI team to voice their concerns.

For my article about the Frankenstein aspects of AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/frankenstein-and-ai-self-driving-cars/

For the super-intelligence AI issue, see my article: https://www.aitrends.com/selfdrivingcars/super-intelligent-ai-paperclip-maximizer-conundrum-and-ai-self-driving-cars/

For my article about the apparent grand singularity heading our say, see: https://www.aitrends.com/selfdrivingcars/singularity-and-ai-self-driving-cars/

For safety aspects of AI self-driving cars, see my article: https://www.aitrends.com/selfdrivingcars/safety-and-ai-self-driving-cars-world-safety-summit-on-autonomous-tech/


Everyone wants to hire a rockstar. And, why not? The implication is that if you are hiring someone less than a rockstar, you are presumably settling for the mediocre. What firm wants to run job ads saying they want to hire mediocre AI developers? Imagine the reaction. It would hurt the firm’s reputation in the industry and it would likely have the internal AI teams feel like they have also been slapped in the face.

What exactly constitutes being an AI rockstar? Is it the number of years’ experience in developing AI systems? Is it the kind of AI systems developed? Is it the prominence among your AI peers and within the AI community? In one sense, an AI rockstar status is in the eye of the beholder.

Some believe that AI rockstar labeling might be gradually running thin. Critics would say that it is akin to how school children are all told they are winners when playing a sport, even though they were participants and did not necessarily place in the top three or top five positions. Maybe everyone that does AI wants to believe they are an all-out super-duper AI rockstar. In that case, the search for AI rockstars is going to be easy, pick anyone that knows AI.

In any case, I advise firms to be mindful that when hiring an AI rockstar they ought to be carefully considering how the person will fit into their existing AI team. Furthermore, there needs to be a solid AI manager overseeing and managing the AI team, otherwise adding the AI rockstar could inadvertently cause the AI team to get waylaid or go kilter. It takes a village to make an AI self-driving car and hiring one AI rockstar as a solo act is not going to get you there.

Copyright 2018 Dr. Lance Eliot

This content is originally posted on AI Trends.