Some day in the distant future, a droid with an AI brain will ride a motorcycle around Mugello faster than the best human MotoGP riders. But that will be long after every other motorsport has been conquered by robots.

Clever applications of existing technology will not suffice to get us there. Fundamental breakthroughs must happen in AI algorithms, neuromorphic chip design, and robotics. Those breakthroughs must then all converge together.

The undertaking is so tremendously challenging that it would serve as a highly meaningful technology milestone.

As Kennedy once said, “We choose to go to the Moon in this decade and do the other things, not because they are easy, but because they are hard”.

In other words, accomplishments become meaningful milestones precisely because they are challenging.

After all, motorcycle racing is the most challenging of all motorsports. On top of that, it coincidentally plays to the inherent strengths of humans and exacerbates the inherent weaknesses of AI/robots.

What about Yamaha’s Motobot project?

Before we continue any further, let’s address the obvious. What about Motobot?

Motobot is a great project with a great team of passionate engineers. They are breaking ground on many meaningful autonomous and robotics technologies.

But Motobot is not a true artificial intelligence in the modern sense.

What do I mean by that? Motobot makes its decisions based on conditional logic. The heart of Motobot’s brain is a “speed-control-logic”. Go to the 4:13 mark in this in-depth tech talk:

“We developed a speed-control-logic by referring to the riding data and other bytes from the professional riders, as well as simulation and actual learning tests.”-Toshifumi Uchiyama, Motobot control engineer

True “AI” in the modern sense means feeding a bunch of training data to neural nets and letting it come to its own conclusions. Which much more closely resembles “intuition”, hence all the catchy headline stories along the lines of “nobody can explain why AI does what it does…. how terrifying!”.

To quote pages 6–7 of AI Superpowers by Kai Fu Lee:

“By the time I began my Ph.D., the field of artificial intelligence had forked into two camps: the “rule-based” approach and the “neural networks” approach. Researchers in the rule-based camp attempted to teach computers to think by encoding a series of logical rules: If X, then Y. This approach worked well for simple and well-defined games but fell apart when the universe of possible choices or moves expanded. To make the software more applicable to real-world problems, the rule-based camp tried interviewing experts in the problems being tackled and coding their wisdom into the program’s decision-making. The “neural networks” camp, however, took a different approach. Instead of trying to teach the computer the rules that had been mastered by a human brain, these practitioners tried to reconstruct the human brain itself…Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon-pictures, chess games, sounds-into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better.”

The rule-based approach is especially problematic for motorcycle racing because it requires far more intuition than any other motorsport. Hence why Motobot was a staggering 31.764 seconds slower in its most recent race against Valentino Rossi.

Any serious racing fan would know that even a half-second is an eternity of difference on the track. They would also immediately recognize that The Doctor was having a pretty underwhelming day. His time around the Thunderhill is something plenty of semi-pro, club, and amateur racers could probably post too (if they were having a relatively good day).

Conditional logic might be good enough for cars, but definitely not for motorcycles. Which segues right into our next topic…

Why is motorcycle racing the most challenging motorsport in the world?

Racing a motorcycle requires a tremendous amount of racing intuition. It’s an art as much as it is a cold, hard science.

To quote Ali Rowland-Rouse; an amateur motorcycle racer and professional aerodynamics engineer for Le Mans and Formula-1:

“Motorcycles are infinitely more complex than F1 cars. A lot of calculations are based on the centre of gravity position, which stays in the same place on an F1 car, but not on a bike. Motorcycles are really difficult because the centre of gravity is always moving. Plus, there’s also a lot more feel involved in riding bikes. It’s about getting the set-up right for each rider — it’s not all cold, hard physics. If you fed Marc Márquez’s riding technique into a computer, the computer would say ‘does not compute!’”

Motorcycles are inherently unstable vehicles that flirt with the edge of traction and have a constantly changing center of gravity. This makes them too computationally complex to be susceptible to brute force approaches. If car racing is chess, then motorcycle racing is go. The AI must do things the human way; riding the superbike by “feel”. Taking a Deep Blue approach won’t be enough.

Even the Motobot team has repeatedly acknowledged that not being able to “feel” tires gripping the track puts the droid at a disadvantage to the human.

Finally, the magnitude of this “feel factor” is further compounded by how bodily-kinesthetic motorcycle racing is. In fact, the motorcycle is only half the picture. The body position of the human is the other half. This is why bikers often refer to each other as “centaurs”.

Being an effective motorcycle racer requires mastering a whole host of techniques; from elbow dragging to the “Valentino Rossi leg dangle”. Racers are really just ballerinas that happen to dance on 220mph machines.

As it turns out, humans who choose to dedicate many years of their lives to motorcycle racing develop staggering levels of intuition and feel that can only be described as superhuman. The Motobot voiceover at the 1:21 mark of this video remarks:

“With every millisecond that ticks, the number of decisions to make is infinite. So…how do human minds find the right ones so quickly?”

Consider this, there have been numerous bike → car success stories in racing and pretty much none the other way around. The implication is obvious:

It is much harder for car racers to learn the “intuition” and “feel” it takes to become a great motorcycle racer than it is for motorcycle racers to learn the cold, hard physics that go into lapping a car quickly.

It’s no wonder that the only man to ever win world championships in MotoGP and Formula-1, John Surtees, started his career on a motorcycle.

Valentino Rossi, who has very little car experience, has been able to casually post incredible lap times in both F1 and NASCAR practice. In 2013, he posted NASCAR lap times that would have landed him in the top 15 for Nationwide Series practice. Back in 2004, he posted lap times in F1 practice that “astonished” the great Michael Schumacher.

It’s no accident that the undisputed greatest off-road racer in history, Stéphane Peterhansel, won 6 Dakar Rallies on a motorcycle before winning 7 more in a car.

Why does motorcycle racing play to the inherent strengths of humans and exacerbate the inherent weaknesses of AI/robots?

The first reason is Moravec’s paradox. The demand for graceful dance moves that we just discussed shifts the tide drastically to the human’s favor. To quote Kai-Fu Lee on pages 166–167 of AI Superpowers:

“Hans Moravec was a professor of mine at Carnegie Mellon University, and his work on artificial intelligence and robotics led him to a fundamental truth about combining the two: contrary to popular assumptions, it is relatively easy for AI to mimic the high-level intellectual or computational abilities of an adult, but it’s far harder to give a robot the perception and sensorimotor skills of a toddler. Algorithms can blow humans out of the water when it comes to making predictions based on data, but robots still can’t perform the cleaning duties of a hotel maid. In essence, AI is great at thinking, but robots are bad at moving their fingers. Moravec’s Paradox was articulated in the 1980s and some things have changed since then. The arrival of deep learning has provided machines with superhuman perceptual abilities when it comes to voice or visual recognition. Those same machine-learning breakthroughs have also turbocharged the intellectual abilities of machines, namely, the power of spotting patterns in data and making decisions. But the fine motor skills of robots — the ability to grasp and manipulate objects — still lag far behind humans.”

It is for this reason that professional athletes are generally considered to have fairly automation-proof jobs (at least in the near future). All in all, it will take a lot of fundamental breakthroughs in robotic joints and actuators for a droid to ride a motorcycle gracefully enough to beat a top human.

Second, motorcycles experience relatively low G-forces. In the case of a F1 car or fighter jet, a less skillful droid might beat a more skillful human simply because the human is unconscious after a certain point.

But motorcycles have pretty tame G-forces. The lateral G-force can always be calculated by simply taking the tangent of the lean angle.

Modern tire technology has such incredible grip that the motorcycle and rider can be supported by a contact patch the size of a credit card (both wheels combined), which corresponds to a lean angle of 64°. By accepting some “controlled chaos”, riders have been able to push it even more. Marc Marquez does 68° slides on a regular basis and a few riders lean even more.

But that’s still only 2 to 2.5 Gs. No more than an average roller coaster ride and nowhere near blacking out.

Third, motorcycles are the smallest and lightest racing vehicles. Which means that while all fast vehicles (F1 cars and fighter jets included) are already very weight-sensitive, motorcycles are disproportionately so.

This is a problem given how energy-inefficient today’s AI chips are in comparison to human brains. You’d have to add a bunch of batteries to power a chip with enough “intuition” to race a motorcycle, resulting in a massive weight disadvantage.

Today, this is a problem even for attempts to automate Formula-1 since the amount of energy required to power AI engines at top racing speeds is enormous:

“If you want to run an AI engine on an F1 car, you probably have to double your engine size just to have enough electrical power to fire the processor. So, we need a paradigm change in our electronics before AI can have the ability to do this.”-Peter Ho, CEO of Singapore-based AI firm HOPE Technik

But aren’t F1 cars way faster than MotoGP bikes? Won’t that help reduce the computational demands?

Not really. F1 may have much faster lap times than MotoGP bikes, but this is mainly because F1 cars can corner much faster (aerodynamic downforce) and brake much later (2-wheeled vehicles are inherently unstable and can tumble over themselves when braking too hard). According to Brembo (who supplies brakes for MotoGP, F1, and Le Mans), the bikes actually have higher top speeds.

In short, there must be fundamental breakthroughs in neuromorphic chip design. Chips must mimic human brains much more closely and thereby consume orders of magnitude less energy if droids are to beat humans in F1, let alone MotoGP.

How would one go about training an AI that can finally beat a top human MotoGP racer?

Let’s assume for a moment that all the ingredients and foundation blocks for success are in place:

1. Fundamental breakthroughs in neuromorphic chip design

2. Fundamental breakthroughs in robotics that overcome Moravec’s paradox; joints and actuators move as gracefully as human limbs.

3. Data scientists achieve algorithm breakthroughs enabling the machine to be a “highly intuitive” motorcycle racer the way AlphaZero is “highly intuitive” at strategic board games.

How would you go about collecting enough training data to beat the humans? There are essentially 4 ways to train the AI:

1. Let the AI race against itself in a simulation environment, aka the classic generational adversarial network (GAN) approach. Experiment with different racing techniques and strategies, compare the results, and gradually learn with experience.

2. Let the AI race against itself on a physical racetrack, a sort of “real-life GAN” if you will.

3. Feed the AI data from professional human racers, both telemetry from the bike itself and body position data (from motion capture sensors embedded in the racing suit). Recall that the motorcycling community is a herd of “centaurs”. Unless the droid has data on what the human body is doing, it won’t be a serious contender.

4. Feed the AI data from amateur, club, and semi-pro human racers, both telemetry from the bike itself and body position data (from motion capture sensors embedded in the racing suit).

Approach #1 is not adequate simply because there are significant differences between a simulation environment and a real race-track. To quote the Motobot team:

“Ultimately, nothing perfectly replicates the real world, so we still needed a lot of track time and had to manage the risks that come with that”-Brian Foster, Stanford Research Institute robotics engineer (partner to Yamaha in the Motobot project)

Approach #2 is not viable because it will involve a lot of crashing. To quote the Motobot team once more:

“The most significant one is the cost — not only money but time and resources — to learn. AI for a board game, such as AlphaGo, can learn how to play and how to win pretty quickly since there is no risk of it getting damaged. I believe that there were millions of failures before it eventually won over a human champion. For Motobot, the learning cost is way more expensive and repairs take a long time.”-Hiroshi Saijou

Given that a Yamaha R1M is priced at $23,000, it’s probably not financially sustainable to crash millions of them for the sake of acquiring enough data to beat Valentino Rossi. Not to mention the cost of crashing that many droids.

Approach #3 is problematic because top racing teams will charge exorbitant fees for their data. And rightfully so. That data is what gives them a leg up on competition. The equivalent of proprietary information in the corporate world.

Teams will likely charge millions for data from a single racer. AI needs a large pool of data to train itself. Data from a single elite rider just won’t cut it, even if it is Marc Márquez himself. Data will be needed from at least hundreds of riders, which inevitably drives acquisition costs into the billions.

By process of elimination, only approach #4 is viable. Fortunately, there is plenty of good news.

In recent years, the line between production motorcycles and actual racing prototypes has been significantly blurred. Plenty of models that you can buy in a dealership, such as the Yamaha R1M and Ducati Panigale V4R, now come with factory-standard data loggers.

Industry trends are also favorable in the world of motion capture sensors. The cost of entry has been significantly reduced by Noitom, a Beijing-based company founded in 2011 by Haoyang Liu and Tristan Ruoli Dai that began life as a Kickstarter project. A professional version of their Perception Neuron product line comes in at a price point of $4,500. Offers a professional-scale, full-body, inertial sensor-based motion capture system that is fully wireless with high tolerance to magnetic fields. Perfect for motorcycle racing!

How much will you have to pay semi-pro racers to turn over their logged data and go through the extra hassle of wearing motion capture sensors? Not all that much.

There isn’t a lot of money on the local and regional circuits. Racers are there for the love of the sport, not to become millionaires. The money earned usually goes back into tire replacements, brake pad replacements, and various mechanical upgrades that make the bike more controllable (such as Scotts steering dampers). For example:

Even if you did a clean sweep of all races in WA state for an entire year (1st place finishes for an entire WMRRA series), that OEM contingency would only be a combined $25,950 if you rode a BMW (or $35,000 if you rode a Yamaha).

For the average semi-pro racer, a $10,000 sponsorship would probably mean a doubling of their earnings. For that price, they would be more than happy to hand over their data and put up with wearing motion capture sensors.

But wait a minute, isn’t this much lower level racing than MotoGP? How can the AI learn to beat MotoGP racers if most of its data is coming from inferior riders?

A highly intuitive AI doesn’t need all its data to come from top-tier competition. As noted by Jason Roell’s excellent article, most of AlphaGo’s 150,000 sample games came from good, but non-elite, players:

“With Deep Blue, the computer scientists heavily relied on Chess experts, professionals, and masters to help the program have as many chess games programmed into it as possible…With AlphaGo, however, the computer scientists simply used lots and lots of games from a myriad of players, who were all at different levels of Go knowledge and experience.

What matters is having lots of data and perhaps just a little bit of top-tier data to fine-tune things. So how much is enough?

Scenario #3 above would have likely required hundreds of riders’ data. Since this is lower tier racing, we will need data from thousands of riders.

My educated guess is that we will need to feed the AI around three seasons’ worth of data from 1,000 semi-pro racers around the country (to get conditions from a variety of different racetracks). Along with 1 season’s worth of data from 3 elite MotoGP riders. Which turns things into a hybrid of approaches #3 and #4, but with the overwhelming majority of data still coming from the latter.

The cost of the Perception Neurons is amortized over three years, so the annual cost per semi-pro racer is only $10,000+$1,500=$11,500. So it all works out to:

1,000 x 3 x $11,500 = $34.5 million

Telemetry and motion capture from Dani Pedrosa to anchor and fine-tune the dataset= $5 million

Telemetry and motion capture from Andrea Dovizioso to anchor and fine-tune the dataset= $5 million

Telemetry and motion capture from Takaaki Nakagami to anchor and fine-tune the dataset= $5 million

$50 million all told. Not cheap, but much more feasible than any of the other approaches.

Concluding thoughts:

A droid triumphing over the top humans on the MotoGP track would be a highly meaningful milestone for artificial intelligence. It would represent fundamental breakthroughs in neuromorphic chip design, robots that overcome Moravec’s Paradox, and highly intuitive algorithms all converging together.

When that day finally arrives, we should celebrate it. But only as an experiment and technology milestone. We should not interpret it to mean the beginning of the end for human motorsports athletes.

After all, why do we even watch sports in the first place? To be inspired by the life stories of the athletes and the obstacles they overcome. Shaqiem Griffin becoming an NFL linebacker despite only having one hand. Tra Telligman becoming a UFC heavyweight despite only having one lung and one pectoral muscle. Jared Goff becoming the QB of the Rams despite an enzyme deficiency that prevents him from processing red meat. Jim Ryun becoming a world recorder holder in the mile despite suffering from exercise-induced asthma. Wilma Rudolph becoming an Olympic gold medalist despite being partially paralyzed by polio in her childhood. Etc.

Motorsports is no different. Viewers looked to be inspired by stories such as Nicolas Hamilton, who has managed to become a F1 driver despite having cerebral palsy.

In conclusion, sports exist as a cultural institution because they help us explore the human experience. Our favorite athletes are the ones that we can identify with.

A droid cannot have feelings, emotions, or a life story with obstacles to overcome. Thus, we would never find them to be entertaining athletes and there would be nothing for us to identify with.

MotoGP will always be a human story.