The 4 Unsolved Problems in AI

FUTURE

The main goal of this newsletter is to show you AI just how it is, not how they usually portray it with all the bells and whistles.

Today, following that vision, I've gathered the four main unanswered questions that remain a mystery to everyone in areas such as technology, engineering, markets, and model intelligence.

Each one of them has to be solved. Otherwise, AI is DOA (Dead On Arrival), which is why you rarely, if ever, see incumbents mentioning these problems.

Therefore, in this article, you’ll learn about the ugly side of frontier AI, such as:

the underlying complexities of running AI models at scale,

the key intuitions of how they work,

engineering problems that are the ‘it question’ that keeps Silicon Valley CEOs awake at night,

A deep look at the leaked and extremely concerning financials of OpenAI,

And finally, we will look at recent data that suggests that Large Language Models (LLMs) might actually be in the utterly wrong direction toward AGI, and also about a bet by one company that calls bullshit on the entire industry (& now on the Nobel Prize, too)

And all this in simple-to-understand language. Let’s dive in.

Problem 1: Hallucinations & Non-Determinism

As the Nobel Prize’s recent rewardees suggest, AI is eating the world. More specifically deep learning (DL), the field of AI that works with neural networks (all five physics and chemistry laureates went to neural network researchers).

Awards aside, DL is also leading the mass arrival of AI products in society, democratizing the use of AI tools by the masses with examples like ChatGPT. However, while these products offer some value, they have extremely complex technological barriers to solve: hallucinations and non-determinism.

As the definition implies, a generative model generates an output based on an input. For that, it needs to be exposed to billions of examples to learn that mapping and, over time, generate sequences that closely match the ones it saw during training.

But there’s a problem. If they simply memorize the training sequences (of text, for instance), they always generate the exact sequences every time. That’s no different from a simple database and pointless.

Therefore, we add two design features to make LLMs useful:

Contrary to popular belief, LLMs do not just predict the most likely token (let’s assume every token in a word or subword) but also rank all other possible words by probability. This way, it’s modeling uncertainty, which is the same as saying that the model is also estimating the likelihood it’s wrong. This is crucial, as we’ll see in a minute.

When the time comes to output a word, we don’t always choose the most likely, but one of the top ones at random.

Both things imply that if the most likely next word is correct (let’s say we are predicting a fact), but the random sampling chooses the second one, it might be a good guess but wrong nonetheless.

Consequently, defining ‘hallucinations’ as when the model gets something wrong is a…

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