This Week in Turing Post:

How much do we know about AI

Well, I would assume that my readers know some. You are builders, you are engineers, you are vivid learners. But there are literally billions of other people who are conflicted about what AI is and how deeply machine learning is integrated into our lives.

This week, I’m moderating a few sessions at the HumanX conference, and yesterday, at the opening, that was the main cry from AI leaders I talked to and from the stage, which featured a few notable politicians – the lack of knowledge. How often do I give the stage to a politician here? Never before, but here are a few quotes that made me hopeful (though I’m still very skeptical that the government can properly regulate AI, being mostly so unknowledgable about what AI is).

Meet Congressman Jay Obernolte, Chairman of the Task Force on AI at the U.S. House of Representatives:

Because that was exactly the motivation behind starting TheSequence and Turing Post: to bust that persistent Terminator myth and build knowledge about AI and ML.

When we say AI, does it mean computer vision? Does it mean data labeling? Or is it robotics? The thing is – and that’s exactly where it gets tricky – it’s all of these things. And now, in the age of GenAI, we are forced to combine so many technologies to stay ahead of the curve. You can’t just stick to your cozy data labeling anymore – you need to upgrade to synthetic data. You can’t just fine-tune models – you need to leverage retrieval-augmented generation (RAG).You can’t just focus on single-modal AI – you need to work with multimodal architectures that integrate text, image, and audio. You can’t just build classifiers – you need to create AI systems that understand context and nuance, etc etc.

And the politicians working on AI bills? They know so little. Yesterday, when Congressman Jay Obernolte used the word distillation, the room applauded: “He knows knowledge distillation!” But he is a computer scientist, after all.

It’s really a shame that we still lack so much knowledge about machine learning and AI. As I always try to demonstrate through Turing Post, ML has a rich history spanning more than a hundred years. And now, all the important stakeholders – government officials who regulate, teachers who educate our kids, doctors who diagnose and treat us and many many others – need to understand what they are working with.

You know why? Because there’s no sign of slowing down. They are going to work with AI. Here’s an interesting observation from my colleague Alyona:

“The emergence of Chain of Draft gave me a new insight: models with higher intelligence (probably) won’t need to rely on detailed, step-by-step reasoning, explaining every step. Instead, short but meaningful steps will be enough to find the right answer. Let’s look at this from a human perspective to see the parallels. Chain-of-Thought is similar to how humans learn and explain their thinking during childhood and school. When people learn something new, they need detailed reasoning to check themselves and explore all aspects. But when they take exams, they don’t have time for such lengthy thinking – they must demonstrate their knowledge using only the most important points. Similarly, when solving tasks they’ve encountered many times before, people naturally skip over unnecessary details to save time. This is also an indicator of professionalism (just imagine how much time we would waste if everyone explained every small step at work).

Chain-of-Thought illustrates how models process knowledge in detail, requiring them to go over the same tasks repeatedly with full explanations. In contrast, Chain of Draft represents the next step in intelligence – where models are effectively ‘taking an exam,’ demonstrating their knowledge concisely. Chain of Draft is more user-oriented, it’s more “mature”, while Chain-of-Thought remains a crucial technique for developers to assess models’ capabilities, much like teachers evaluating students in school.”

I don’t believe humans are at the Chain of Draft stage with AI. We lack tremendously in AI literacy. So if even the models are passing an exam and graduating to a different level, we humans most certainly have to do the same. And that’s not even touching on the topic of educating our kids about AI. (I want to start working on a course for AI for kids – let me know if you want to collaborate on this topic.)

What can we do? Please educate those around you. Share resources like Turing Post, Interconnects, AI Made Simple, Latent Space, and blogs on Hugging Face with those who need this knowledge. It’s no longer just a nice thing to do – obtaining knowledge about our own creation is of utmost importance.

Curated Collections