Winners & Losers in the age of Augmented LLMs

s🏝 TheTechOasis 🏝

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In AI, learning is winning.

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You will also find this deep-dive in TheWhiteBox ‘Key Future Trends in AI’, for future reference if necessary and to ask me any questions you consider.

Based on recent developments in academia, I finally have the conviction to draw a clear picture of how the AI industry will develop, including winners and losers.

You are reading this here before anyone else: We are entering the era of augmented AI.

In fact, based on this insight, I can confidently predict how the war on demand (who will eventually win the customers) will play out in every cohort, from enterprises to everyday users.

What’s more, I am also bringing to you the high-level Generative AI Demand Framework.

With this framework, you will be able to identify what models to use in each case with confidence that you are making the right choice, especially if there’s money on the line, based on what cutting-edge research indicates.

Finally, we will analyze which type of AI model (& company) will surf this wave and, importantly, those most likely to drown.

When Your Problem is a Feature, not a Bug

The biggest problem in Generative AI models is inaccuracies, sometimes referred to, although wrongly, as hallucinations.

In simple terms, the models eloquently and convincingly respond to many questions with inaccurate statements, which can lead to embarrassing moments for companies like Google.

But here’s the thing: it’s not like the model has decided to generate wrong answers or that the data they were trained for was saying those things… it’s just working as intended.

Understanding How GenAI Works

As you probably know, LLMs provide a reasonable next word to a given sequence.

But here’s the thing: normally, they do not provide the most statistically probable next work, but one of the ‘top-k’ most reasonable ones.

And how do they choose which one is outputted? Mostly random choice.

Looking at the image above, all five words are reasonable continuations, but only one of them (playground) is the actual ground truth. Still, by design, the model is trained to sample one of the five at random, which can lead to inaccuracies.

But, again, it is not because it’s trying to deceive you; it’s by design. Or let me put it more clearly: Every LLM response is a hallucination; only sometimes they get it right.

But why do this? This stochasticity (randomness) is added so that the model can generate similar ideas or statements differently, nurturing its linguistic prowess and creativity at the expense of accuracy.

Now, this isn’t a problem when you use ChatGPT as a fun toy or for occasional random conversations. But when money is on the line, these systems, in their current form, are useless, explaining why the Generative AI industry is massively underperforming and in a clear economic bubble.

And…

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