Why Talking Models are not going to take your jobs
A lot of social media influencers are jumping onto this hype train
Lately, I’ve seen a lot of social media influencers talking about how AI will replace everyone.
If you’ve followed AI, you will recognize this trend. However, this time it has gained some serious traction. AI agents are able to do insane things like generate art, write some pretty impressive code, and even decipher jokes. This article goes over some of the more recent innovations that make these tasks possible.
However, from an AI and engineering perspective, this is a long way away from replacing people. And any decent person worth their salt can tell you that. However, these influencers don’t really care. They’re just here to stoke your fears, make some noise, and profit from the engagement.
In this article, I will go over the mathematical reason that these models won’t be replacing you, your artist friends, or the many other jobs that these influencers claim.
- How data is stored- To understand how these things work (and why they won’t replace people), let’s first understand how models process inputs. In the simplest case (and is most often), input is stored as a vector (which is a fancy math term for a list). Each element of the vector represents a particular feature. Think of as a feature as a characteristic of your sample. If we were creating a data vector of you, things like height, weight, numberOfGrandmasPunched, likesChocolateMilk, etc would all be features.
- Comparing Similarity between 2 data points- Remember, each data point is stored as a vector. To compare similarities, all we have to do is compare the distance between the vectors. That is where the formula above becomes helpful. It is called Cosine Similarity. There are other distance metrics you might want to use, based on your needs. As with every other decision, there is a tradeoff. I’ve covered Cosine Similarity in more detail here, for those interested. For our purposes, the important thing to note is that Cosine Similarity can give us the similarity between 2 vectors.
- What does this have to do with Big Models- Reasonable question. To oversimplify a behemoth, the models take your user input and try to generate an answer they think would match it well. The match is determined by the similarity (or the complement, which is called disagreement). They do this very well because of the enormous resources sunk into training them. They’re able to build very fine search spaces based on this, which is not the case for standard models.
- Why these models will not take over your jobs (utility)- Now for the bit that these influencers conveniently leave out (or haven’t considered). These models’ pattern match existing data. They can’t work with new inputs. For example, if I created a new library, the vaunted GT-CoPilot would struggle with it (it even struggles with existing libraries). If all you did was create art/scripts from older data, you would never create new characters that reflect the tastes of your contemporary audience.
- Why these models will not take over your jobs (engineering)- These models have also gotten some engineering problems that make them unusable at scale. For one these are extremely expensive to run. This makes them impractical at scale. Secondly, they are very weak to perturbations and can be broken very easily. Not good for anything that has to be deployed into the real world. I’ll be doing a more detailed into Github CoPilot soon, so make sure you stay tuned for that.
For those of you interested in AI, I created a video in June about why Google’s Lambda model was not alive. At the time of its creation, the internet was going crazy about the possibility of Google having created a model that was alive. It goes over most of these points in more detail. Check it out below-
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