My idea of a regulatory body is not that of a powerful institution that it deeply interacts with all ongoing projects because of the known fallible members who could misuse their power.

My idea of a regulatory body could be more that of a TÜV interconnected with institutions who do AI safety research and develop safety standards, test methods and test data. Going back to the TÜVs foundation task: pressure vessel certification. Any qualified test institution in the world can check if it is safe to use a given pressure vessel based on established design tests, safety measures checks, material testing methods and real pressure check tests. The amount of safety measures, tests and certification effort depends on the danger potential (pressure, volume, temperature, medium). Standards define based on danger potential and application which of the following safety measures must be used: safety valve; rupture disk; pressure limiter, temperature limiter, liquid indicator, overfill protection; vacuum breakers; reaction blocker; water sprinkling devices.

Nick Bostrum named following AI safety measures: boxing methods, incentive methods, stunting and tripwires. Pressure vessels and AI have following common elements (AI related argument plausible, but no experience exists):

  • Human casualties are result of a bursting vessel or AI turning evil.
  • Good design, tests and safety measures reduce risk of failing.
  • Humans want to use both.

Companies, institutions and legislation had 110 years of development and improvement of standards for pressure vessels. With AI we are still scratching on the surface. AI and pressure vessels have following differences:

  • Early designs of pressure vessels were prone to burst - AI is stil far away from high risk level.
  • Many bursting vessel events successively stimulated improvement of standards - With AI the first singularity will be the only one.
  • Safety measures of pressure vessels are easily comprehensible - Easy AI safety measures reduce its functionality to a high degree, complex safety measures allow full functionality but are complex to implement, complex to test and to standardize.
  • The risk of a bursting pressure vessel is obvious - the risk of an evil Singularity is opaque and diffuse.
  • Safety measure research for pressure vessels is straight forward following physical laws - safety research for AI is a multifaceted cloud of concepts.
  • A bursting pressure vessel may kill a few dozen people - an evil Singularity might eradicate humankind.

Given the existential risk of AI I think most AI research institutions could agree on a code of conduct that would include e.g.

  • AIs will be classified in danger classes. The rating depends on computational power, taught knowledge areas, degree of self-optimization capacity. An AI with programming and hacking abilities will be classified as high risk application even if it is running on moderate hardware because of its intrinsic capabilities to escape into the cloud.
  • The amount of necessary safety measures depends on this risk rating:
    • Low risk applications have to be firewalled against acquisition of computing power in other computers.
    • Medium risk applications must additionally have internal safety measures e.g. stunting or tripwires.
    • High risk applications in addition must be monitored internally and externally by independently developed tool AIs.
  • Design and safeguard measures of medium and high risk applications will be independently checked and pentested by independent safety institutions.

In a first step safety AI research institutes develop monitoring AIs, tool AIs, pentesting datasets and finally guidelines like the one above.

In a second step public financed AI projects have to follow these guidelines. This applies to university projects in particular.

Public pressure and stockholders could push companies to apply these guidelines. Maybe an ISO certificate can indicate to the public: "All AI projects of this company follow the ISO Standard for AI risk assessment and safeguard measures"

The public opinion and companies hopefully will push governments to enforce these guidelines as well within their intelligence agencies. A treaty in the mind of the Non-Proliferation Treaty could be signed. All signing states ensure to obey the ISO Standard on AI within their institutions.

I accept that there are many IFs and obstacles on that path. But it is at least an IDEA how civil society can push AI developers to implement safeguards into their designs.

How many researchers join the AI field will only marginally change the acceleration of computing power. If only a few people work on AI they have enough to do to grab all the low-hanging fruit. If many join AI research more meta research and safety research will be possible. If only a fraction of this depicted path will turn into reality it will give jobs to some hundred researchers.

To contribute to AI safety, consider doing AI research

by Vika 1 min read16th Jan 201639 comments

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Among those concerned about risks from advanced AI, I've encountered people who would be interested in a career in AI research, but are worried that doing so would speed up AI capability relative to safety. I think it is a mistake for AI safety proponents to avoid going into the field for this reason (better reasons include being well-positioned to do AI safety work, e.g. at MIRI or FHI). This mistake contributed to me choosing statistics rather than computer science for my PhD, which I have some regrets about, though luckily there is enough overlap between the two fields that I can work on machine learning anyway. I think the value of having more AI experts who are worried about AI safety is far higher than the downside of adding a few drops to the ocean of people trying to advance AI. Here are several reasons for this:

  1. Concerned researchers can inform and influence their colleagues, especially if they are outspoken about their views.
  2. Studying and working on AI brings understanding of the current challenges and breakthroughs in the field, which can usefully inform AI safety work (e.g. wireheading in reinforcement learning agents).
  3. Opportunities to work on AI safety are beginning to spring up within academia and industry, e.g. through FLI grants. In the next few years, it will be possible to do an AI-safety-focused PhD or postdoc in computer science, which would hit two birds with one stone.

To elaborate on #1, one of the prevailing arguments against taking long-term AI safety seriously is that not enough experts in the AI field are worried. Several prominent researchers have commented on the potential risks (Stuart Russell, Bart Selman, Murray Shanahan, Shane Legg, and others), and more are concerned but keep quiet for reputational reasons. An accomplished, strategically outspoken and/or well-connected expert can make a big difference in the attitude distribution in the AI field and the level of familiarity with the actual concerns (which are not about malevolence, sentience, or marching robot armies). Having more informed skeptics who have maybe even read Superintelligence, and fewer uninformed skeptics who think AI safety proponents are afraid of Terminators, would produce much needed direct and productive discussion on these issues. As the proportion of informed and concerned researchers in the field approaches critical mass, the reputational consequences for speaking up will decrease.

A year after FLI's Puerto Rico conference, the subject of long-term AI safety is no longer taboo among AI researchers, but remains rather controversial. Addressing AI risk on the long term will require safety work to be a significant part of the field, and close collaboration between those working on safety and capability of advanced AI. Stuart Russell makes the apt analogy that "just as nuclear fusion researchers consider the problem of containment of fusion reactions as one of the primary problems of their field, issues of control and safety will become central to AI as the field matures". If more people who are already concerned about AI safety join the field, we can make this happen faster, and help wisdom win the race with capability.

(Cross-posted from my blog. Thanks to Janos Kramar for his help with editing this post.)

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