Neuralink and Tesla have an AI problem that Elon’s money can’t solve
All the money in the world + deep learning = more expensive deep learning
AI’s “mapping” problem
When we talk about amapping problemwe don’t mean Google Maps. We’re referring to the idea that maps themselves can’t possible by one-to-one representations of a given area.
Any “map” automatically suffers from severe data loss. In a “real” territory, you can count every blade of grass, every pebble, and every mud puddle. On a map, you just see a tiny representation of the immense reality. Maps are useful for directions, but if you’re trying to count the number of trees on your property or determine exactly how many wolverines are hiding in a nearby thicket, they’re pretty useless.
When we train a deep learning system to “understand” something, we have to feed it data. And when it comes to massively complex tasks such as driving a car or interpreting brain waves, it’s simply impossible to haveall of the data.We just sort of map out a tiny-scale approximation of the problem and hope we can scale the algorithms to task.
This is the biggest problem in AI. It’s why Tesla can useDojoto train its algorithms in millions, billions, or trillions of iterations – giving its vehicles more driving experience than that of every human who has ever existed combined — and, yet, it still makesinexplicable mistakes.
We can all pointto the statisticsand shriek “Autopilot is safer than unaugmented human driving!” just like Musk does, but the fact of the matter is that humans are far safer drivers without Autopilot than Tesla’s Full Self Driving features are without a human.
Making the safest, fastest, most efficient production car in history is an incredible feat for which Musk and Tesla should be lauded. But that doesn’t mean the company is anywhere near solving driverless cars or any of the AI problems that plague the entire industry.
No amount of money is going to brute-force human-level algorithms, and Elon Musk may be the only AI “expert” who still believesdeep learning-based computer vision aloneis the key to self-driving vehicles.
And the exact same problem applies to Neuralink, but at a much larger scale.
Experts believe there are more than100 billion neurons in the human brain. Despite what Elon Musk may have recently tweeted, we don’t even have a basic map of those neurons.
In fact, neuroscientists are still challenging the idea of regionalized brain activity.Recent studiesindicate that different neurons light up in changing patterns even when brains access the same memories or thoughts more than once. In other words: if you perfectly map out what happens when a person thinks about ice cream, the next time they think about ice cream the old map could be completely useless.
We don’t know how to map the brain, which means we have no way of building a dataset to train AI how to interpret it.
So how do you train an AI to model brain activity? You fake it. You teach a monkey to push a button to summon food and then you teach them how to use a brain computer interface to push the button – as Fetz did back in 1969.
Then you teach an AI to interpret the whole of the monkey’s brain activity in such a way that it can tell whether the monkey was trying to push the button or not.
Keep in mind, the AI doesnotinterpret what the monkey wants to do, it just interprets whether the button should be pushed or not.
So, you’d need a button for everything. You’d need enough test subjects wearing BCIs to generate enough generalized brainwave data to train the AI to perform every single function you desired.
The equivalent of this would be if Spotify had to build robots and teach them to play the actual instruments used to make every song on the platform.
Every time you wanted to listen to “Beat It” by Michael Jackson, you’d have to put a training request in with the robots. They’d pick up the instruments and start making absolutely random noises for thousands or millions of training hours until they “hallucinated” something similar to “Beat It.”
As the AI changed its version of the song, its human developers would give it feedback to indicate if it was getting closer to the original tune or further away.
Meanwhile, a semi-talented human musician could play the entire composition for just about any Michael Jackson song afteronly a couple of listens.
Elon’s money is no good here
Robots don’t care how rich you are. In fact, AI doesn’t care about anything because it’s just a bunch of algorithms getting smashed together with data to produce bespoke output.
People tend to assume Tesla and Neuralink are going to solve the AI problem because they have, essentially, unlimited backing.
But, as Ian Goodfellow at Apple, Yann LeCun at Facebook, and Jeff Dean at Google can all tell you: if you could solve self-driving cars, the human brain, or AGI with money, it would have already been solved.
Musk may be the richest man alive, but even his wealth doesn’t eclipsethe combined worth of the biggest companies in tech.
And, what the general public doesn’t quite seem to grasp is this: Facebook, Google, and Tesla, and all the other AI companies are all working on the exact same AI problems.
When DeepMind was founded its purpose was not to win chess or Go games. It’s purpose wasto create an AGI. It’sthe same with GPT-3and just about any other multimodal AI system being developed today.
WhenIan Goodfellow re-invigorated the field of deep learningwith his take on neural networking in 2014, he and others working on similar challenges lit a fire under the technology world.
In the time since, we’ve seen the development ofmulti billion-dollar neural networksthat push the very limits of compute and hardware. And, even with all of that, we could still be decades or more away from self-driving cars or algorithms that can interpret human neuronal activity.
Money can’t buy a technological breakthrough (it doesn’t hurt, of course, but scientific miracles take more than funding). And, unfortunately for Tesla and Neuralink, many of the smartest, most talented AI researchers in the world know thatmaking good on Musk’s enormous promises may be a losing endeavor.
Perhaps that’s why Musk has expanded his recruitment efforts beyond researchers with a background in AI and is now trying to lure any computer science talent he can find.
The good news is that absolutely no amount of sober evaluation can dampen the spirits of Musk’s indefatigable fans. Whether he candeliver on the goodsor nothas no impact on the amount of worship he receives.
And that’s as likely to change as a Tesla’s ability to produce a self-driving car or Neuralink’s ability to interpret neuron activity in human brains.
Story byTristan Greene
Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns:(show all)Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: He/him
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