Absolutely something!
So yeah, AI is pretty over-hyped right now. There are people talking about how it’s going to solve all of the world’s problems or create some post-labor utopia, which just seems like fantasy-land absurdity… but what is AI actually going to be good at?
I’m a big fan of the Gartner Hype Cycle model for assessing if new technologies will be able to meet the current hype. Gartner themselves assessed that AI had already passed the Peak of Inflated Expectations and was already well on its way to crashing down into the Trough of Disillusionment back in July. I suppose if you measure by the general sentiment in someplace like Reddit, that may be true. There is already a lot of negativity towards AI out there, but I don’t get the impression that the tech CEOs of the world see that way. If you read Microsoft’s vision for the future of Windows, they certainly aren’t laying off the hype.
So what’s the truth? Is AI going to revolutionize the world and make labor a thing of the past? Is it going to fail miserably and top out as an over-glorified chat bot? Obviously nobody knows for sure, but here’s what my crystal ball says…
The 2 Percent
I took an NVIDIA Getting Started with Deep Learning course last summer, and it felt incredibly enlightening to get a glimpse at what AI really is under the hood.
The course takes you through the basics of how AI modeling and training work. It demonstrates several strategies for improving accuracy and tailoring models to specific workloads, and lets the student “make” their own models (it actually gives you 95% of the answers pre-filled in, and the other 5% are a paragraph away if you get stuck, but it’s great for demo purposes). By the end, AI no longer felt like a black box. I wouldn’t claim I’m ready to start making my own LLM, I know there is a ton more to learn, but the basics are no longer mystifying.
At one point, the course has the student create a model to determine what number is written when given a handwritten digit between 0 and 9 (using a well-known public dataset). It walks you through the steps to create a model that has a success rate somewhere around 98%. A 2% error rate isn’t bad for my first AI model! Out in the real world, there are some models that can achieve 99.9% accuracy on the same data set. It’s actually quite impressive!
But it’s still not 100% accurate, and it can’t be 100% accurate. There is always going to be some error rate because, at some point, it’s less about our ability improve the quality of the model and more about our inability to ensure the quality in the input. There’s just too much overlap between some people’s 5s and other people’s 6s (or 4s and 9s, or 2s and 7s).
Now perhaps you are saying, “But wait! Humans are going to be fooled in those cases too!”, and to that I say, “Yes! You are completely correct!” I’m not claiming AI is or isn’t better than humans. I’m just saying that we are applying AI to problems that will always have an error rate.
There may also be some cases where AI can reliably be 100% accurate, but I would argue that, in the large majority of those cases, it’s likely that humans can be just as effective and/or classical computation methods will be better suited for the task by virtue of being able to accomplish the work more efficiently.
I’ve come to think of this error rate as “the 2%”. The actual rate may change depending on the problem. There may be times that it’s .2%, or 20%, or .0002%, but some degree of error is almost certain, given the nature of the problems we are throwing at AI.
Breaking the Contract
OK, so AI is going to have some error rate on fuzzy things. So what? If we can still train it to be better than humans, what’s the big deal?
The big deal is that AI breaks an implied contract between humans and computers that has existed since Alan Turing first laid down the foundations of modern computing 80 years ago.
Before AI, there was an implied understanding that anytime a computer is wrong, it’s our fault. Our algorithm was wrong! Our software had a bug! Our hardware had a bug! Perhaps we didn’t sufficiently shield it from gamma rays or implement enough error correction to fix the bits that got corrupted by the gamma rays that got through! There is is an infinite number of ways that we can mess up, but the math behind computers is absolute. If the computer gives us a wrong answer, somehow, it’s our fault.
With AI, we now need to learn to account for the 2%. We’ve taken our theoretically infallible deterministic Turing machines and made them as inherently fallible as us.
Now, we did it for good reason. Deterministic Turing machines are only good for problems that can be well defined and are relatively simple. In contrast, AI can sidestep finding exact solutions to complex problems the same way humans do, with lots and lots of pattern recognition. The downside is that we’ve now made our machines subject to human-style fallibility.
It’s Not All Bad
At this point, it’s probably coming across like I’m an AI hater. I don’t believe that’s true at all. Despite everything I’ve said above, I foresee a lot of good coming from AI.
Even though AI has adopted human fallibility, there are absolutely going to be times when AI is able to out-assess the best humans. Even a model that can out-assess 95% of humans would be incredibly valuable in many use cases. Medical imaging evaluations are a classic example of where this could provide great benefit. There is still a lot of room to improve AI model accuracy, so this is an area where AI can already shine today and can shine much more in the future.
AI will also be able to shine in cases where massive throughput would be useful. For example, you could feed terabytes of satellite imagery data through an AI to look for signs of drought or illegal deforestation. Even a relatively mediocre model could be immensely valuable as a triage system to point human analysts in the right direction. This is also useful for things like facial recognition, which brings up ethical concerns, but this post isn’t about what uses of AI are ethical, just which it is likely to be good at.
AI is also great for sparking inspiration when you’re struggling for ideas. Perhaps it’s writer’s block, or you’re not sure what to get somebody for a birthday present. Either way, AI can help generate ideas. Those ideas are going to tend towards “best practice” answers rather than “outside of the box answers”, but if you’re struggling to come up with anything at all, that sounds pretty amazing!
Even Better When Specializing
I foresee AI being even more valuable in cases where it’s specialized. The more an AI has a narrow focus, the more it can learn the patterns, trends, and best practices that will minimize the 2%.
One of my best experiences with AI was interacting with an AI leasing agent when I recently needed to rent an apartment (I’m honestly a bit shocked that I just typed that sentence and meant it). The AI was tailored specifically for that purpose, and it was actually pretty good at answering questions. I could tell it exactly what I wanted to know using plain language (e.g. “do you have any of this floor plan available starting somewhere between 15 and 35 days from now?”), and it was able to answer a surprisingly large number of questions quickly and accurately. In the cases where it didn’t know the answer, it was able to quickly recognize that it wouldn’t be able to help and forward me to apartment complex’s staff. It was basically everything a phone tree is supposed to be. May every phone tree in the world be similarly replaced!
I could see similar improvement from specializing in other areas. How much better could an AI model be at programming if it was trained on one specific language? What if it was also one specific type of application? If we hyper-specialize too much, we run into an issue with finding sufficient training data, but I’m guessing that producing good results using less training data is an area that we will be able to significantly improve over time.
I foresee a future where we are interacting with general large language models that offload most of the work to specialized AI models behind the scenes. For example, perhaps if you ask ChatGPT for the answers to your math homework, ChatGPT will hand your question off to it’s math specialized model and then tell you what it said. I’m sure this is already happening to some extent, but I expect that it will be the norm in the future for general purpose language model to be back by hundreds of specialized models.
It’s Still Dumb, but That’s OK
Still, the 2% looms large, and it unfortunately looms quite chaotically.
AI is still prone to making mistakes that moderately qualified humans would never do. Perhaps it will think that Toronto is a city in the U.S. There are also several reported cases of AI agents deleting data they weren’t given instructions to delete. It’s often stated that the AI agents didn’t have “permission” to delete the data, but clearly the AI did have permission to delete the files in the technical sense. The mixed use of the word permission is a perfect example of how “breaking the contract” has changed the rules.
The lesson is that, at least right now, you can’t truly trust AI. However, this isn’t a new problem. Junior human devs (and occasionally even senior human devs) have been known to delete data they aren’t supposed to, and we’ve learned how to work with those fallible humans. We use good technical permission restraints and implement good backup strategies that can only be deleted by our most trusted agents (or even better, not by any single agent). AI agents should be treated the same.
We’ve also learned how to work with fallible humans when it comes to looking for answers to questions. We generally understand when we should and shouldn’t trust Wikipedia articles, and with time, I expect we’ll similarly learn when we should and shouldn’t trust AI responses.
What Comes Next?
So just how good will AI get? I expect that AI has a lot of room to grow when it comes to training methods and procedures. I expect that we’ll find new processes that can improve AI accuracy using less training data, and I believe we’ll find new algorithms and approaches that can do the same work using less computational resources. However, it won’t match the massive growth we’ve seen in capability over the past couple decades.
I expect that we are already reaching significant diminishing returns on the physical side. Hardware is improving, but that improvement is being far surpassed by growth in demand. There is still some room for improved efficiency through the use of specialized hardware designs, but even that can only get so far. Power and water infrastructure are going to limit our ability to just build more data centers, so without significant improvements in efficiency, there will be a soft limit on how powerful the underlying hardware can become.
I expect that we’ll need to also deal with the fact that AI inherits the ethics and biases from the people that train it. Elon Musk has been very open about his politics shaping Grok’s design. It’s a given that at least one of “Grok” or “the other AI models” are shaped by the designer’s politics. I’d argue that the correct answer is “all of them” are. I don’t see how it could be possible for any AI to exist without being shaped by the designer’s politics, for better or worse. Decisions need to be made about what information to train on and what behaviors will be encouraged. There is no way to avoid making AI agents at least somewhat a reflection of the AI builders.
I expect that hackers (both in the classical meaning of the term and the more nefarious modern definition of the term) will make it their mission to figure out how to manipulate AI. Even if you somehow make your model perfect under normal conditions, people are going to do their best to trick your AI into doing things you don’t want it to do. The 2% will be their playground, and the tendency for AI to chaotically do things no moderately qualified human would do is going to be especially problematic in this area. This has already started.
Finally, I expect that any predictions about AI reducing the amount of labor humans are expected to do will fall completely flat. There have been a lot of revolutions in productivity over the past few hundred years, and none of them reduced the need for labor. They’ve only changed the nature of labor. AI will be no different here.
The End
So, in the short term, I believe we’ll find that we just can’t trust AI. We’ll see regular, but somewhat slowed, progress in AI capability, but it will become increasingly clear that AI will always have a somewhat unpredictable error rate.
In the medium term, I believe we will struggle with providing significant improvements in AI model capability, but we will counter that with more reliance on specialized models. At the same time, we’ll go through the process of figuring when we actually can trust AI.
In the long term, I believe we’ll have a solid understanding of our relationship with AI models, what they are and aren’t capable of, and then proceed to figure out how to best make use of them (for both good and bad).
As far as truly intelligent AI goes, I think we’re a couple major shifts in mindset away from making that happen. Us fallible humans can only be creative and come up with truly novel solutions when we have the freedom to be at least a little bit wrong, and right now, our traditional contract with computers won’t let us give AI the same freedom. Perhaps once we’ve fully dealt with, and become comfortable with, computers being wrong, we can then make that next step.
Truly intelligent AI isn’t going to be about creating models that are so smart that they don’t mistakes. Instead, it’s going to be about creating models that are able to make their own mistakes and learn from them at lighting speed. We would then set them free to iterate on a lot of mistakes… but that just brings us back to the big ethics questions, and that’s a topic for another day.
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