Hey, I wonder what’s your policy on linking blog posts? I have some texts that might be interesting to this community, but I don’t really feel like copying everything from HTML here and duplicating the content. At the same time I know that some communities don’t like people promoting their content. What are the best practices here?
mocny-chlapik
Karma: 47
In general, LM-generated text is still easily distinguishable by other LMs. Even though we humans can not tell the difference, the way they generate text is not really human-like. They are much more predictable, simply because they are not trying to convey information as humans do, they are guessing the most probable sequence of tokens.
Humans are less predictable, because they have always something new to say, LMs on the other hand are like the most cliche person ever.
No indication in this context means that:
Our current paradigm is almost depleted. We are hitting the wall with both data (PaLM uses 780B tokens, there are 3T tokens publicly available, additional Ts can be found in closed systems, but that’s it) and compute (We will soon hit Landauer’s limit so no more exponentially cheaper computation. Current technology is only three orders of magnitude above this limit).
What we currently have is very similar to what we will ultimately be able to achieve with current paradigm. And it is nowhere near AGI. We need to solve either the data problem or the compute problem.
There is no practical possibility of solving the data problem ⇒ We need a new AI paradigm that does not depend on existing big data.
I assume that we are using existing resource nearly optimally and no significantly more powerful AI paradigm will be created until we have significantly more powerful computers. To have more significantly more powerful computers, we need to sidestep Landauer’s limit, e.g. by using reversible computing or other completely different hardware architecture.
There is no indication that such architecture is currently in development and ready to use. It will probably take decades for such architecture to materialize and it is not even clear whether we are able to build such computer with our current technologies.
We will need several technological revolutions before we will be able to increase our compute significantly. This will hamper the development of AI, perhaps indefinitely. We might need significant advances in material science, quantum science etc to be theoretically able to build computers that are significantly better than what we have today. Then, we will need to develop the AI algorithms to run on them and hope that it is finally enough to reach AGI-levels of compute. Even then, it might take additional decades to actually develop the algorithms.
There is no indication for many catastrophic scenarios and truthfully I don’t worry about any of them.
I don’t see any indication of AGI so it does not really worry me at all. The recent scaling research shows that we need non-trivial number of magnitudes more data and compute to match human-level performance on some benchmarks (with a huge caveat that matching a performance on some benchmark might still not produce intelligence). On the other hand, we are all out of data (especially high quality data with some information value, no random product reviews or NSFW subreddit discussions) and our compute options are also not looking that great (Moore’s law is dead, the fact that we are now relying on HW accelerators is not a good thing, it’s a proof that CPU performance scaling is after 70 years no longer a viable option. There are also some physical limitations that we might not be able to break anytime soon.)
I believe that fixating on benchmark such as chess etc is ignoring the G part of AGI. Truly intelligent agent should be general at least in the environment he resides in, considering the limitation of its form. E.g. if a robot is physically able to work with everyday object, we might apply Wozniak test and expect that intelligent robot is able to cook a dinner in arbitrary house or do any other task that its form permits.
If we assume that right now we develop purely textual intelligence (without agency, persistent sense of self etc) we might still expect this intelligence to be general. I.e. it is able to solve arbitrary task if it seems reasonable considering its form. In this context for me, an intelligent agent is able to understand common language and act accordingly, e.g. if a question is posed it can provide a truthful answer.
BIG Bench has recently showed us that our current LMs are able to solve some problems, but they are nowhere near general intelligence. They are not able to solve even very simple problems if it actually requires some sort of logical thinking and not only using associative memory, e.g. this is a nice case:
You can see in the Model performance plots section that scaling did not help at all with tasks like these. This is a very simple task, but it was not seen in the training data so the model struggles to solve it and it produces random results. If the LMs start to solve general linguistic problems, then we are actually having intelligent agents at our hand.
It’s not goapost moving, it’s the hype that’s moving. People reduce intelligence to arbitrary skills or problems that are currently being solved and then they are let down when they find out that the skill was actually not a good proxy.
I agree that LMs are concetually more similar to ELIZA than to AGI.
I believe that over time we will understand that producing human-like text is not a sign of intelligence. In the past people believed that only intelligent agents are able to solve math equations (naturally, since only people can do it and animals can). Then came computer and they were able to do all kinds of calculations much faster and without errors. However, from our current point of view we now understand that doing math calculations is not really that intelligent and even really simple machines can do that. Chess playing is similar story, we thought that you have to be intelligent, but we found a heuristic to do that really well. People were afraid that chess-algorithm-like machines can be programmed to conquer the world, but from our perspective, that’s a ridiculous proposition.
I believe that text generation will be a similar case. We think that you have to be really intelligent to produce human-like outputs, but in the end with enough data, you can produce something that looks nice and it can even be useful sometimes, but there is no intelligence in there. We will slowly develop an intuition about what are the capabilities of large-scale ML models. I believe that in the future we will think about them as basically a kinda fuzzy databases that we can query with natural language. I don’t think that we will think about them as intelligent agents capable of autonomous actions.
in order for this to occupy any significant probability mass, I need to hear an argument for how our current dumb architectures do as much as they do, and why that does not imply near-term weirdness. Like, “large transformers are performing {this type of computation} and using {this kind of information}, which we can show has {these bounds} which happens to include all the tasks it has been tested on, but which will not include more worrisome capabilities because {something something something}.”
What about: State-of-the-art models with 500+B parameters still can’t do 2-digit addition with 100% reliability. For me, this shows that the models are perhaps learning some associative rules from the data, but there is no sign of intelligence. An intelligent agent should notice how addition works after learning from TBs of data. Associative memory can still be useful, but it’s not really an AGI.
The post starts with the realization that we are actually bottlenecked by data and then proceeds to talk about HW acceleration. Deep learning is in a sense a general paradigm, but so is random search. It is actually quite important to have the necessary scale of both compute and data and right now we are not sure about either of them. Not to mention that it is still not clear whether DL actually leads to anything truly intelligent in a practical sense or whether we will simply have very good token predictors with very limited use.
I believe I have read a paper about superhuman performance of LSTM LMs maybe 4 years ago. The fact that LMs are better than humans is not that surprising. With the amount of data they have seen, even relatively simple models are able to precisely calculate the probabilities for individual words. But the comparison to humans does not make much sense here. People are not really doing language modeling in their day-to-day communication. When we speak, we are not predicting what will be our next word, we are communicating ideas and selecting words that will represent these ideas. When we hear someone speaking, we are using context clues to understand their language, we are not making the predictions based solely on the words that are being said in that moment. When thinking about language, we are not sorting all the words from the vocabulary in our heads, we are usually selecting the one word that fits our needs the best. Language modeling as used in computer science today is completely unnatural to human thinking and useless in communication.
So many S-curves and paradigms hit an exponential wall and explode, but DL/DRL still have not.
Don’t the scaling laws use logarithmic axis? That would suggest that the phenomenon is indeed exponential in it nature. If we need to get X times more compute with X times more data for additional improvements, we will hit the wall quite soon. There is only that much useful text on the Web and only that much compute that labs are willing to spend on this considering the diminishing returns.
According to current understanding of scaling laws most tasks follow a sigmoid with their performance w.r.t. model size. As we increase model size, we have a slow start followed by a rapid improvement, followed by a slow saturation towards maximum performance. But each task has different shape based on its difficulty. Therefore in some tasks you might be in the rapid improvement phase when you do one comparison and then you might he in saturated phase when you do another. The results you are seeing are to be expected so far. I would visualize absolute performance for each task for a series of models to see how the performance actually behaves.
There is already a sizable amount of research done in this direction, the so called bertology. I believe the methodology that is being developed is useful, but knowing about specific models is probably superfluous. In few months / years we will have new models and anything that you know will not generalize.
You might enjoy reading _The Structure of Scientific Revolutions_. #9 is explicitly discussed there. It is often a case when the old incorrect theory has a lot of work in it and many of the anomalies are explained by additional mechanism, e.g. the geocentric theory had a lot of bells and whistles in the end and it was quite precise in some cases. When the heliocentric theory was created, it was actually worse at predicting the movement of celestial bodies because it was too simplistic and was not able to handle various edge cases. Related to your remark about gravity, it took more than 50 years to successfully apply the theory of gravity to predict how Moon will behave.
Yeah, that is somewhat my perception.
Thanks, this looks very good.
Are you being passive-aggressive or am I reading this wrong? :)
The user Hickey is making a different argument. He is arguing about the falsifiability of the superintelligence is coming claim. This is also an interesting question, but I was not talking about this claim in particular.
I think that AI Safety can be a subfield of AI Alignment, however I see a distinction between AI as current ML models and AI as theoretical AGI.
One additional maxim to consider is that the AI community in general can only barely conceptualize and operationalize difficult concepts, such as safety. Historically, the AI community was good at maximizing some measure of performance, usually pretty straight forward test set metrics such as classification accuracy. Culturally this is how the community approaches all the problems—by aggregating complex phenomena into a single number. Note that this approach is not used in that many fields outside of AI and math, as you always have to make some lossy simplifications.
We can observe this malpractice in AI safety as well. There is a cottage industry of datasets and papers collecting “safety” samples, and we use these to measure some safety metric. We can then compare the numbers for different models and this makes AI folks happy. But there is barely any discussion about how representative these datasets really are for real-life risks, how comprehensive the data collection process is, or how sound it is to use random crowd-sourced workers or LLMs to generate such samples. The threats and risks are also rarely described in more detail—often it’s just a lot of hand-waving.
Based on my pretty deep experience with one aspect of AI safety (societal biases), I have very little confidence in our ability to understand AI behavior. Compared to measuring performance on well-defined NLP tasks, once we involve societal context, the intricacies of what we are trying to measure are beyond simple benchmarks. Note that we have entire fields that are trying to understand some of these problem in human societies, but we are to believe that we can collect a test set with few thousand samples and this should be enough to understand how AI works.