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PaperclipPerfector  1yr ago (text post)   33014 thread views

Culture War Roundup for the week of April 17, 2023

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Is the rapid advancement in Machine Learning good or bad for society?

For the purposes of this comment, I will try to define good as "improving the quality of life for many people without decreasing the quality of life for another similarly sized group" an vice versa.

I enjoy trying to answer this question because the political discourse around it is too new to have widely accepted answers disseminated by the two American political parties being used to signify affiliation like many questions. However, any discussion of whether something is good or bad for society belongs in a Culture War threat because, even here on The Motte, most people will try to reduce every discussion to one along clear conservative/liberal lines because most people here are salty conservatives who were kicked out of reddit by liberals one way or another.

Now on to the question: Maybe the best way to discover if Machine learning is good or bad for society is to say what makes it essentially different from previous computing? The key difference in Machine Learning is that it changes computing from a process where you tell the computer what to do with data, and turns it into a process where you just tell the computer what you want it to be able to do. before machine learning, you would tell the computer specifically how to scan an image and decide if it is a picture of a dog. Whether the computer was good at identifying pictures of dogs relied on how good your instructions were. With machine learning, you give the computer millions of pictures of dogs and tell it to figure out how to determine if there's a dog in a picture.

So what can be essentialized from that difference? Well before Machine Learning, the owners of the biggest computers still had to be clever enough to use them to manipulate data properly, but with Machine Learning, the owners of the biggest computers can now simply specify a goal and get what they want. It seems therefore that Machine Learning will work as a tool for those with more capital to find ways to gain more capital. It will allow people with the money to create companies that can enhance the ability to make decisions purely based on profit potential, and remove the human element even more from the equation.

How about a few examples:

Recently a machine learning model was approved by the FDA to be used to identify cavities on X-rays. Eventually your dental insurance company will require a machine learning model to read your X-rays and report that you need a procedure in order for them to cover treatment from your dentist. The justification will be that the Machine Learning model is more accurate. It probably will be more accurate. Dentists will require subscriptions to a Machine Learning model to accept insurance, and perhaps dental treatment will become more expensive, but maybe not. It's hard to say for sure if this will be a bad or a good thing.

Machine learning models are getting very good at writing human text. This is currently reducing the value of human writers at a quick pace. Presumably with more advanced models, it will replace commercial human writing all together. Every current limitation of the leading natural language models will be removed in time, and they will become objectively superior to human writers. This also might be a good thing, or a bad thing. It's hard to say.

I think it's actually very hard to predict if Machine Learning will be good or bad for society. Certain industries might be disrupted, but the long term effects are hard to predict.

The key difference in Machine Learning is that it changes computing from a process where you tell the computer what to do with data, and turns it into a process where you just tell the computer what you want it to be able to do.

I think there is yet another point to make here. With current Large Language Models, we have systems that treat Natural Language as a code, that is where revolution comes from. Even before LLMs, there were multiple "revolutions" where instead of working directly with machine code you could work with higher level languages utilizing concepts more suitable for humans as opposed to "data" in its raw form. This made programming incrementally more accessible to wider population. Even things like invention of graphical user interface for operating systems enabled people to tell computers what to do with data in more natural way without some arcane knowledge.

Also on the level of let's say algorithms creating novel things on some simple inputs, there was procedural generation around for a long time. Giving the computer system some simple parameters and computer running the simulation to confirm/falsify end result was a standard thing in the past. Again, the key difference is that we now have a very powerful system that can treat natural language as a code.

You might be interested in this post on LessWrong which discusses Scaffolded LLM's as natural language computers.. I'd be curious on @DaseindustriesLtd's take on this as well.

Key points:

What we have essentially done here is reinvented the von-Neumann architecture and, what is more, we have reinvented the general purpose computer. This convergent evolution is not surprising -- the von-Neumann architecture is a very natural abstraction for designing computers. However, if what we have built is a computer, it is a very special sort of computer. Like a digital computer, it is fully general, but what it operates on is not bits, but text. We have a natural language computer which operates on units of natural language text to produce other, more processed, natural language texts. Like a digital computer, our natural language (NL) computer is theoretically fully general -- the operations of a Turing machine can be written as natural language -- and extremely useful: many systems in the real world, including humans, prefer to operate in natural language. Many tasks cannot be specified easily and precisely in computer code but can be described in a sentence or two of natural language.

The LLM itself is clearly equivalent to the CPU. It is where the fundamental 'computation' in the system occurs. However, unlike the CPU, the units upon which it operates are tokens in the context window, not bits in registers. If the natural type signature of a CPU is bits -> bits, the natural type of the natural language processing unit (NLPU) is strings -> strings.

The RAM is just the context length. GPT4 currently has an 8K context or an 8kbit RAM (theoretically expanding to 32kbit soon). This gets us to the Commodore 64 in digital computer terms, and places us in the early 80s.

The obvious thing to think about when programming a digital computer is the programming language. Can there be programming languages for NL computers? What would they look like? Clearly there can be. We are already beginning to build up the first primitives. Chain of thought. Selection-inference. Self-correction loops. Reflection. These sit at a higher level of abstraction than a single NLOP. We have reached the assembly languages. CoT, SI, reflection, are the mov, leq, and goto, which we know and love from assembly. Perhaps with libraries like langchains and complex prompt templates, we are beginning to build our first compilers, although they are currently extremely primitive.

I find this framing extremely persuasive, and awesome in the true sense. If transformers can actually act as a new type of general purpose computer using natural language, the world will become strange indeed very quickly.

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