Category: AGI

I’ve been writing about AGI (see part-0 [ish], part-1 [ish], part-2 [ish], part-3ish, part-4 and part 5) and the dangers that come with it (part-0 in the above list) for a number of years now. My last post on the subject I expected to be writing a post discussing the book Human compatible AI and the problem of control which is a great book on the subject. But since then I ran across another paper that perhaps is a better brief introduction into the topic and some of the current thought and research into developing safe AI.

The article I found is Concrete problems in AI, written by a number of researchers at Google, Stanford, Berkley, and OpenAI. It essentially lays out the AI safety problem in 5 dimensions and these are:

Avoiding negative side effects – these can be minor or major and is probably the one thing that scares humans the most, some toothpick generating AI that strips the world to maximize toothpick making.

Avoiding reward hacking – this is more subtle but essentially it’s having your AI fool you in that it’s doing what you want but doing something else. This could entail actually changing the reward logic itself to being able to convince/manipulate the human overseer into seeing things it’s way. Also a pretty bad thing from humanity’s perspective

Scalable oversight – this is the problem where human(s) overseers aren’t able to keep up and witness/validate what some AI is doing, 7×24, across the world, at the speed of electronics. So how can AI be monitored properly so that it doesn’t go and do something it’s not supposed to (see the prior two for ideas on how bad this could be).

Safe exploration – this is the idea that reinforcement learning in order to work properly has to occasionally explore a solution space, e.g. a Go board with moves selected at random, to see if they are better then what it currently believes are the best move to make. This isn’t much of a problem for game playing ML/AI but if we are talking about helicopter controlling AI, exploration at random could destroy the vehicle plus any nearby structures, flora or fauna, including humans of course.

Robustness to distributional shifts – this is the perrennial problem where AI or DNNs are trained on one dataset but over time the real world changes and the data it’s now seeing has shifted (distribution) to something else. This often leads to DNNs not operating properly over time or having many more errors in deployment than it did during training. This is probably the one problem in this list that is undergoing more research to try to rectify than any of the others because it impacts just about every ML/AI solution currently deployed in the world today. This robustness to distributional shifts problem is why many AI DNN systems require periodic retraining.

So now we know what to look for, now what

Each of these deserves probably a whole book or more to understand and try to address. The paper talks about all of these and points to some of the research or current directions trying to address them.

The researchers correctly point out that some of the above problems are more pressing when more complex ML/AI agents have more autonomous control over actions in the real world.

We don’t want our automotive automation driving us over a cliff just to see if it’s a better action than staying in the lane. But Go playing bots or article summarizers might be ok to be wrong occasionally if it could lead to better playing bots/more concise article summaries over time. And although exploration is mostly a problem during training, it’s not to say that such activities might not also occur during deployment to probe for distributional shifts or other issues.

However, as we start to see more complex ML AI solutions controlling more activities, the issue of AI safety are starting to become more pressing. Autonomous cars are just one pressing example. But recent introductions of sorting robots, agricultural bots, manufacturing bots, nursing bots, guard bots, soldier bots, etc. are all just steps down a -(short) path of increasing complexity that can only end in some AGI bots running more parts (or all) of the world.

So safety will become a major factor soon, if it’s not already

Scares me the most

The first two on the list above scare me the most. Avoiding negative or unintentional side effects and reward hacking.

I suppose if we could master scalable oversight we could maybe deal with all of them better as well. But that’s defense. I’m all about offense and tackling the problem up front rather than trying to deal with it after it’s broken.

Negative side effects

Negative side effects is a rather nice way of stating the problem of having your ML destroy the world (or parts of it) that we need to live.

One approach to dealing with this problem is to define or train another AI/ML agent to measure impacts the environment and have it somehow penalize the original AI/ML for doing this. The learning approach has some potential to be applied to numerous ML activities if it can be shown to be safe and fairly all encompassing.

Another approach discussed in the paper is to inhibit or penalize the original ML actions for any actions which have negative consequences. One approach to this is to come up with an “empowerment measure” for the original AI/ML solution. The idea would be to reduce, minimize or govern the original ML’s action set (or potential consequences) or possible empowerment measure so as to minimize its ability to create negative side effects.

The paper discusses other approaches to the problem of negative side effects, one of which is having multiple ML (or ML and human) agents working on the problem it’s trying to solve together and having the ability to influence (kill switch) each other when they discover something’s awry. And the other approach they mention is to reduce the certainty of the reward signal used to train the ML solution. This would work by having some function that would reduce the reward if there are random side effects, which would tend to have the ML solution learn to avoid these.

Neither of these later two seem as feasible as the others but they are all worthy of research.

Reward hacking

This seems less of a problem to our world than negative side effects until you consider that if an ML agent is able to manipulate its reward code, it’s probably able to manipulate any code intending to limit potential impacts, penalize it for being more empowered or manipulate a human (or other agent) with its hand over the kill switch (or just turn off the kill switch).

So this problem could easily lead to a break out of any of the other problems present on the list of safety problems above and below. An example of reward hacking is a game playing bot that detects a situation that leads to buffer overflow and results in win signal or higher rewards. Such a bot will no doubt learn how to cause more buffer overflows so it can maximize its reward rather than learn to play the game better.

But the real problem is that a reward signal used to train a ML solution is just an approximation of what’s intended. Chess programs in the past were trained by masters to use their opening to open up the center of the board and use their middle and end game to achieve strategic advantages. But later chess and go playing bots just learned to checkmate their opponent and let the rest of the game take care of itself.

Moreover, (board) game play is relatively simple domain to come up with proper reward signals (with the possible exception of buffer overflows or other bugs). But car driving bots, drone bots, guard bots, etc., reward signals are not nearly as easy to define or implement.

One approach to avoid reward hacking is to make the reward signaling process its own ML/AI agent that is (suitably) stronger than the ML/AI agent learning the task. Most reward generators are relatively simple code. For instance in monopoly, one that just counts the money that each player has at the end of the game could be used to determine the winner (in a timed monopoly game). But rather than having a simple piece of code create the reward signal use ML to learn what the reward should be. Such an agent might be trained to check to see if more or less money was being counted than was physically possible in the game. Or if property was illegally obtained during the game or if other reward hacks were done. And penalize the ML solution for these actions. These would all make the reward signal depend on proper training of that ML solution. And the two ML solutions would effectively compete against one another.

Another approach is to “sandbox” the reward code/solution so that it is outside of external and or ML/AI influence. Possible combining the prior approach with this one might suffice.

Yet another approach is to examine the ML solutions future states (actions) to determine if any of them impact the reward function itself and penalize it for doing this. This assumes that the future states are representative of what it plans to do and that some code or some person can recognize states that are inappropriate.

Another approach discussed in the paper is to have multiple reward signals. These could use multiple formulas for computing the multi-faceted reward signal and averaging them or using some other mathematical function to combine them into something that might be more accurate than one reward function alone. This way any ML solution reward hacking would need to hack multiple reward functions (or perhaps the function that combines them) in order to succeed.

The one IMHO that has the most potential but which seems the hardest to implement is to somehow create “variable indifference” in the ML/AI solution. This means having the ML/AI solution ignore any steps that impact the reward function itself or other steps that lead to reward hacking. The researchers rightfully state that if this were possible then many of the AI safety concerns could be dealt with.

There are many other approaches discussed and I would suggest reading the paper to learn more. None of the others, seem simple or a complete solution to all potential reward hacks.

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The paper goes into the same or more level of detail with the other three “concrete safety” issues in AI.

In my last post (see part 5 link above) I thought I was going to write about Human Compatible (AI) by S. Russell book’s discussion AI safety. But then I found the “Concrete problems in AI safety paper (see link above) and thought it provided a better summary of AI safety issues and used it instead. I’ll try to circle back to the book at some later date.

Photo Credit(s):

• From “Artificial Intelligence & AI & Machine Learning” by mikemacmarketing is licensed under CC BY 2.0 
• From The face of a robot with human-like features, Penn State
• From Deepmind’s pre-print, Competition-Level Code Generation with AlphaCode
• From [280] Easy come, easy go.” by Linh H. Nguyen is licensed under CC BY-NC-ND 2.0
• From “1968- ‘2001’ – Hal’s eye” by x-ray delta one is licensed under CC BY-NC-SA 2.0.