September 5, 2023
Last updated: October 17, 2023
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Unless you are a Luddite or an ardent follower of Neo-Luddism, you are already using AI-based services every day. Also, if you are a fan of the science fiction genre, you’d be curious about how AI will change the world. Either way, you probably know that AI is not just a cool concept or a sci-fi fantasy anymore.
Artificial intelligence is a powerful technology that can shape our world in profound ways, for better or for worse. That’s why I believe we need to think carefully about how we create and use AI, and what kind of impact it has on us and others. That’s what AI safety and alignment is all about.
I guess we have reached a point where we should figure out how to ensure that AI systems are on our side, that they do what we want them to do, and that they don’t cause any harm or trouble along the way. I am well aware that it won’t be an easy task, but it’s not something we should overlook.
In this blog, I’ll talk about some of the theoretical and practical aspects of AI safety and alignment, and why you should care about them too.
Artificial intelligence safety and alignment is an idea that summarises the functions of AI systems by which they will be on track with human values and goals. Although it might sound simple, it is the ultimate challenge of every AI developer.
Artificial intelligence systems are not simple, they are pretty complex and adaptive. I have personally used the leading generative AI platforms currently and all of them provide different results for the same prompts. So, it is clear that they have different objectives, preferences, and perspectives toward everything. Read more about this in this blog where I wrote about the intersection of technology and creativity with Generative AI.
Have I missed something in it?
Are people serious about AI safety and alignment? Well, it was once at a philosophical level but now it has transitioned into a real-world engineering problem. Needless to say, we haven’t figured out a solid solution to escape from the existential risk of artificial general intelligence.
Hold it. We have indeed figured out some approaches to overcome it.
They can be broadly classified into four main types:
Specification is basically instructing the artificial intelligence system about what to do and how to do it. It would be as simple as setting up the objectives and constraints for the system. On the other hand, we will also be developing metrics or criteria to measure its performance and gauge its behavior.
But, specification is not an easy task because it would be likely impossible to predict every scenario and outcome that the AI system might encounter. No matter how much of an overthinker you are, you just cannot anticipate every scenario, can you?
Verification is an extension of the specification as we will have to keep verifying whether the artificial intelligence system is abiding by the instructions given to it. This will be helpful to keep track of its robustness and consistency which will be helpful for you to look for errors or issues that will compromise its purpose and functionality.
Verification also has its own challenges in terms of dealing with complexity and uncertainty. Given the limitations of our thinking and methods, we might reach a level where we create the next level – artificial superintelligence, which is simply the total autonomous level of AI.
Either way, we do have some ideas to control the situation even when the hypothetical situation arises. You can learn more about artificial superintelligence, its ethical dilemma, purpose, types, benefits, etc., in my blog. I have discussed the ethical dilemma that surrounds the ‘super-advanced’ AI system.
What do you think about the ASI situation and should we need that?
Validation is the process of evaluating whether the artificial intelligence system is doing the right thing. It involves cross-checking and observing its behavior. I believe that validation is very important because the system should deal with values and goals that are different on various levels. As our society is very dynamic and the variability of several factors will be hard to keep track of, validation will be the ideal approach for feedback loops. Our feedback will directly affect the AI system and its environment.
Now that we are done with validation of how the artificial intelligence system works, it is time to adjust its performance. It is up to us how we want to do it as it involves several levels of updating, refining, and improving the system. Feedback can give us a good idea about our previous approaches and rectify the issues in them. Needless to say, this part can also be a little tricky as we have to strike a balance between exploration and exploitation and autonomy and control.
Artificial intelligence alignment is not restricted to technical issues as it is also an ethical and social problem. Will it act fair? Although the term itself is subjective, we might as well find a sweet spot that is fair and equitable. I really look forward to witnessing a society without any inequalities or injustice.
Given, the numerous misconceptions and challenges in this sector, I want to stress the fact that AI alignment theory is not a binary or static problem. As I mentioned earlier, it cannot be reduced to a single formula or criterion. This means it is impossible to come up with a one-size-fits-all solution for this issue. It requires continuous consultation and customization from different domains and contexts. It will involve countless stakeholders and won’t be a straightforward approach.
I hope we get a proper solution for the AI safety and alignment issues I discussed in this blog. I believe that proper regulations in place are crucial for the future of humanity. The transformation is inevitable but we can decide how things move forward and can be in control of the ‘necessary intelligence’.
Until next time, stay safe and aligned!
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