Good morning. Hope you all had a nice weekend.

If you were Elon Musk — are you, by the way? That’d be something — your weekend was pretty mixed. The whole robotaxi thing didn’t go over very well, but, on Sunday, Musk’s SpaceX launched its Starship rocket for the fifth time.

The launch tower caught — literally — the rocket’s booster upon its return to Earth.

You win some, you lose some.

— Ian Krietzberg, Editor-in-Chief, The Deep View

In today’s newsletter:

AI for Good: Search and rescue

As we’re about to get into in quite a bit of detail, generative artificial intelligence is really good at pattern recognition. This capability makes current generative algorithms — when combined with drones — a natural tool in the arsenal of wilderness search and rescue teams. 

The details: Earlier this year, a team of researchers at Scotland’s University of Glasgow designed such an algorith. Then, they pitted it against existing search algorithms and search patterns. 

The system isn’t yet ready for mass deployment; it needs quite a bit more testing, plus there are ethical considerations regarding surveillance without consent that such a system poses. But, as the lead researcher wrote: “This result is a clear indication that algorithms could improve the time to find a missing person and thus save lives. Whilst the experimental results are not concrete proof that humans are redundant, it does highlight the potential in fully autonomous drones used in real search missions.”

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Study: LLMs do not reason

A core element of the AI sector — and one that we revisit often — has to do with reasoning and intelligence. The question at hand is simple, though full of impactful implications: do Large Language Models (LLMs) understand their output? Can they reason? Or are they simple statistical pattern-matchers operating at scale? 

Thus far, there has been plenty of evidence — many of which we’ve covered here — that LLMs entirely lack understanding. They are probabilistic machines, operable only because of the immensity of their training data. 

What happened: A (preprint) of a new study conducted by a team of Apple researchers adds more weight to the existing pile of evidence that LLMs cannot actually reason. 

The team also found that as the questions got harder, not only did performance drop, but this variance in performance also increased. When the researchers added a sentence to the word problem that was irrelevant to the solution, performance dipped further, indicating that LLMs don’t understand mathematical concepts. 

“We found no evidence of formal reasoning in language models including open-source models like Llama, Phi, Gemma and Mistral, and leading closed models, including the recent OpenAI GPT-4o and o1-series,” lead researcher Mehrdad Farajtabar said. “Their behavior is better explained by sophisticated pattern matching.” 

He added that scaling models and improving training data will likely result in better pattern-matchers, not better reasoners. 

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Poll: US voters concerned about OpenAI’s o1 release

Last month, following months of increasingly dramatic rumors, OpenAI released o1, a powerful new Large Language Model (LLM) that, in a marked difference from its other models, was designed to ‘think’ before it generates a response. The technical idea here is called “chain of thought,” and the concept is to break down a query into a series of steps, an approach that improves the quality of output. 

o1, contrary to the hyped-up marketing language written into the interface itself, is not actually thinking. But it is a far more powerful model than any OpenAI had released before; OpenAI’s own system card found that the model posed a ‘medium’ risk of persuasion and biological threat creation. The company therefore classified the model as a ‘medium’ risk model, the highest level a model can reach while still being safe, in OpenAI’s eyes, to deploy. 

What happened: A recent poll of U.S. voters by the Artificial Intelligence Policy Institute (AIPI) found that the majority of voters are concerned about the model. 

"Contrary to some elite discourse about AI being overhyped, this new survey data makes it abundantly clear that the general public is feeling scared, not underwhelmed, about AI development,” Daniel Colson, the executive director of the AIPI, said in a statement.

Tesla stock plummets following robotaxi unveil 

In the case of Elon Musk’s highly-anticipated robotaxi unveil, it seems that investors were not sold on theatrics. Shares of Tesla dipped hard following the event, closing Friday down nearly 9%. Funnily enough, shares of Uber spiked nearly 11% at the same time, an indication that investors are pretty confident Tesla’s robotaxis won’t put Uber out of business. 

Let’s get into what happened (or didn’t happen) that left investors so unhappy. 

Prototypes of the vehicles — alongside remote-operated Optimus robots — roamed around during the event.

But Tesla’s robotaxi fleet will start — if you take Musk at his word — with its Model 3 and Model Y vehicles, which Musk said will go fully-autonomous in California and Texas next year. Of course, this would require pretty massive regulatory approval, something Tesla does not yet have. 

The problem is that, once again, Musk delivered plenty of theatrics without any details. Stages of regulatory approval remain unclear and technical advancements regarding Tesla’s autonomous tech remain lacking. 

Tesla’s current system, which it misleadingly calls full-self driving (FSD), is a Level 2 autonomous system, meaning it requires the hands-on, eyes-on attention of the driver. It is not at all clear how Tesla plans to jump from a Level 2 to a Level 4 or Level 5 fully-autonomous system, especially without adjusting the vehicle’s sensor array. 

Waymo, which operates as a Level 4 (ish) system, comes loaded with radar and lidar in addition to computer vision tech (and a team of humans standing by for remote assistance); the lidar is essential as a redundancy layer since computer vision and artificial intelligence have a number of dangerous vulnerabilities. But Musk doesn’t like lidar. Tesla’s self-driving tech uses only computer vision, something that some experts have told me will prevent Tesla from ever jumping beyond its current autonomy level. 

None of these predictions bore any fruit. The reality is that the self-driving problem is far more complex than it seems, requiring a ton of computing power and highly advanced algorithms to operate with even a semblance of autonomy. But full autonomy — especially at any kind of scale — is far away, due to that confounding combination of enormous technical and regulatory hurdles. 

"We found Tesla's Robotaxi event to be underwhelming and stunningly absent on detail," Bernstein analyst Toni Sacconaghi said in a note Friday.

Self-driving cars sound good. But they’re still years away. It’s a really hard technical challenge that requires concerted, cautious efforts. It doesn’t just happen. And neural networks alone aren’t enough. 

I don’t think Tesla will have it solved anytime soon. And I don’t think regulators will be eager to let unsupervised Teslas roam their streets until it is solved.

I don’t think we’ll see the Cybercab for a while.

🤔 Your thought process:

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💭 A poll before you go

Thanks for reading today’s edition of The Deep View!

We’ll see you in the next one.

Here’s your view on persuasive memes:

A third of you haven’t really encountered persuasive memes online, but an equal number of you encounter them all the time. Interesting. 20% of you said that, now that you think about, you do run into persuasive memes on social media.

All the time:

Something else: