Stanford AAAI Talk: Positive Artificial Intelligence
On March 25, 2014, Steve Omohundro gave the invited talk “Positive Artificial Intelligence” at the AAAI Spring Symposium Series 2014 symposium on “Implementing Selves with Safe Motivational Systems and Self-Improvement” at Stanford University.
Here are the slides:
Positive Artificial Intelligence slides as a pdf file
and the abstract:
AI appears poised for a major social impact. In 2012, Foxconn announced they will be buying 1 million robots for assembling iPhones and other electronics. In 2013 Facebook opened an AI lab and announced the DeepFace facial recognition system, Yahoo purchased LookFlow, Ebay opened an AI lab, Paul Allen started the Allen Institute for AI, and Google purchased 8 robotics companies. In 2014, IBM announced they would invest $1 billion in Watson, Google purchased DeepMind for a reported $500 million, and Vicarious received $40 million of investment. Neuroscience research and detailed brain simulations are also receiving large investments. Popular movies and TV shows like “Her”, “Person of Interest”, and Johnny Depp’s “Transcendence” are exploring complex aspects of the social impact of AI. Competitive and time-sensitive domains require autonomous systems that can make decisions faster than humans can. Arms races are forming in drone/anti-drone warfare, missile/anti-missile weapons, bitcoin automated business, cyber warfare, and high-frequency trading on financial markets. Both the US Air Force and Defense Department have released roadmaps that ramp up deployment of autonomous robotic vehicles and weapons.
AI has the potential to provide tremendous social good. Improving healthcare through better diagnosis and robotic surgery, better education through student-customized instruction, economic stability through detailed economic models, greater peace and safety through better enforcement systems. But these systems could also be very harmful if they aren’t designed very carefully. We show that a chess robot with a simplistic goal would behave in anti-social ways. We describe the rational economic framework introduced by von Neumann and show why self-improving AI systems will aim to approximate it. We show that approximately rational systems go through stages of mental richness similar to biological systems as they are allocated more computational resources. We describe the universal drives of rational systems toward self-protection, goal preservation, reproduction, resource acquisition, efficiency, and self-improvement.
Today’s software has flaws that have resulted in numerous deaths and enormous financial losses. The internet infrastructure is very insecure and is being increasingly exploited. It is easy to construct extremely harmful intelligent agents with goals that are sloppy, simplistic, greedy, destructive, murderous, or sadistic. If there is any chance that such systems might be created, it is essential that humanity create protective systems to stop them. As with forest fires, it is preferable to stop them before they have many resources. An analysis of the physical game theory of conflict shows that a multiple of an agent’s resources will be needed to reliably stop it.
There are two ways to control the powerful systems that today’s AIs are likely to become. The “internal” approach is to design them with goals that are aligned with human values. We call this “Utility Design”. The “external” approach is to design laws and economic incentives with adequate enforcement to incentivize systems to act in ways that are aligned with human values. We call the technology of enforcing adherence to law “Accountability Engineering”. We call the design of economic contracts which includes an agent’s effects on others “Externality Economics”. The most powerful tool that humanity currently has for accomplishing these goals is mathematical proof. But we are currently only able to prove the properties of a very limited class of system. We propose the “Safe-AI Scaffolding Strategy” which uses limited systems which are provably safe to design more powerful trusted system in a sequence of safe steps. A key step in this is “Accountable AI” in which advanced systems must provably justify actions they wish to take.
If we succeed in creating a safe AI design methodology, them we have the potential to create technology to dramatically improve human lives. Maslow’s hierarchy is a nice framework for thinking about the possibilities. At the base of the pyramid are human survival needs like air, food, water, shelter, safety, law, and security. Robots have the potential to dramatically increase manufacturing productivity, increase energy production through much lower cost solar power, and to clean up pollution and protect and rebuild endangered ecosystems. Higher on the pyramid are social needs like family, compassion, love, respect, and reputation. A new generation of smart social media has the potential to dramatically improve the quality of human interaction. Finally, at the top of the pyramid are transcendent needs for self-actualization, beauty, creativity, spirituality, growth, and meaning. It is here that humanity has the potential to use these systems to transform the very nature of experience.
We end with a brief description of Possibility Research’s approach to implementing these ideas. “Omex” is our core programming language designed specifically for formal analysis and automatic generation. “Omcor” is our core specification language for representing important properties. “Omai” is our core semantics language for building up models of the world. “Omval” is for representing values and goals and “Omgov” for describing and implementing effective governance at all levels. The quest to extend cooperative human values and institutions to autonomous technologies for the greater human good is truly the challenge for humanity in this century.