Each year, the online intellectual discussion forum “Edge” poses a question and solicits responses from a variety of perspectives. The 2015 question was “What do you think about machines that think?”:
We did not see the other responses before submitting and it’s fascinating to read the wide variety of views represented.
Jerry Kaplan’s fascinating Stanford course on “Artificial Intelligence – Philosophy, Ethics, and Impact” will be discussing Steve Omohundro’s paper “Autonomous Technology and the Greater Human Good” on Oct. 23, 2014 and Steve will present to the class on Oct. 28.
Here are the slides as a pdf file.
I was thrilled to discuss the future of AI with Jonathan Nolan and Greg Plageman, the creator and producer of the excellent TV show “Person of Interest”. The discussion is a special feature on the Season 3 DVD:
and a short clip is available here:
The show beautifully explores a number of important ethical issues regarding privacy, security, and AI. The third season and the coming fourth season focus on the consequences of intelligent systems developing agency and coming into conflict with one another.
The Office of Naval Research just announced the demonstration of a highly autonomous swarm of 13 guard boats to defend a larger ship. We commented on this development for Defense One:
“Other AI experts take a more nuanced view. Building more autonomy into weaponized robotics can be dangerous, according to computer scientist and entrepreneur Steven Omohundro. But the dangers can be mitigated through proper design.
“There is a competition to develop systems which are faster, smarter and more unpredictable than an adversary’s. As this puts pressure toward more autonomous decision-making, it will be critical to ensure that these systems behave in alignment with our ethical principles. The security of these systems is also of critical importance because hackers, criminals, or enemies who take control of autonomous attack systems could wreak enormous havoc,” said Omohundro.”
AI and Robotics at an Inflection Point
18 September 2014
5:00-6:30pm (5:00-6:00 presentation and Q&A, followed by networking until 6:30)
George E. Pake Auditorium, PARC
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating “arms races” in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial “rational drives” of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the “Safe-AI Scaffolding Strategy” for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by mathematical proof and cryptographic complexity. It appears that we are at an inflection point in the development of intelligent technologies and that the choices we make today will have a dramatic impact on the future of humanity.
To register click here.
Steve Omohundro has been a scientist, professor, author, software architect, and entrepreneur doing research that explores the interface between mind and matter. He has degrees in Physics and Mathematics from Stanford and a Ph.D. in Physics from U.C. Berkeley. He was a computer science professor at the University of Illinois at Champaign-Urbana and cofounded the Center for Complex Systems Research. He published the book “Geometric Perturbation Theory in Physics”, designed the programming languages StarLisp and Sather, wrote the 3D graphics system for Mathematica, and built systems which learn to read lips, control robots, and induce grammars. He is president of Possibility Research devoted to creating innovative technologies and Self-Aware Systems, a think tank working to ensure that intelligent technologies have a positive impact. His work on positive intelligent technologies was featured in James Barrat’s book “Our Final Invention” and has been generating international interest.
Seth Lloyd analyzed the computational capacity of physical systems in his 2000 Nature paper “Ultimate physical limits to computation” and in his 2006 book “Programming the Universe”. Using the very general Margolus-Levitin theorem, he showed that a 1 kilogram, 1 liter “ultimate laptop” can perform at most 10^51 operations per second and store 10^31 bits.
The entire visible universe since the big bang is capable of having performed 10^122 operations and of storing 10^92 bits. While these are large numbers, they are still quite finite. 10^122 is roughly 2^406, so the entire universe used as a massive quantum computer is still not capable of searching through all combinations of 500 bits.
This limitation is good news for our ability to design infrastructure today that will still constrain future superintelligences. Cryptographic systems that require brute force searching for a 500 bit key will remain secure even in the face of the most powerful superintelligence. In Base64, the following key:
would stymie the entire universe doing a brute force search.
The Impact of AI and Robotics
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. More than $450 billion is expected to be invested into robotics by 2025. All of this investment makes sense because AI and Robotics are likely to create $50 to $100 trillion dollars of value between now and 2025! This is of the same order as the current GDP of the entire world. Much of this value will be in ideas. Currently, intangible assets represent 79% of the market value of US companies and intellectual property represents 44%. But automation of physical labor will also be significant. Foxconn, the world’s largest contract manufacturer, aims to replace 1 million of its 1.3 million employees by robots in the next few years. An Oxford study concluded that 47% of jobs will be automated in “a decade or two”. Automation is also creating arms races in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial “rational drives” of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the “Safe-AI Scaffolding Strategy” for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by mathematical proof and cryptographic complexity. It appears that we are at an inflection point in the development of intelligent technologies and that the choices we make today will have a dramatic impact on the future of humanity.
Stephen Hawking’s and other’s recent cautions about the safety of artificial intelligence have generated enormous interest in this issue. My JETAI paper on “Autonomous Technology and the Greater Human Good” has now been downloaded more than 10,000 times, the most ever for a JETAI paper.
As the discussion expands to a broader audience, several radio shows have hosted discussions of the issue:
My paper “Autonomous Technology and the Greater Human Good” was recently published in the Journal of Experimental and Theoretical Artificial Intelligence. I’m grateful to the publisher, Taylor and Francis, for making the paper freely accessible at:
and for sending out a press release about the paper:
This has led to the paper becoming the most downloaded JETAI paper ever!
The interest has led a quite a number of articles exploring the content of the paper. While most focus on the potential dangers of uncontrolled AIs, some also discuss the approaches to safe development:
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:
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.
This post is partly excerpted from the preprint to:
Omohundro, Steve (forthcoming 2013) “Autonomous Technology and the Greater Human Good”, Journal of Experimental and Theoretical Artificial Intelligence (special volume “Impacts and Risks of Artificial General Intelligence”, ed. Vincent C. Müller).
To ensure the greater human good over the longer term, autonomous technology must be designed and deployed in a very careful manner. These systems have the potential to solve many of today’s problems but they also have the potential to create many new problems. We’ve seen that the computational infrastructure of the future must protect against harmful autonomous systems. We would also like it to make decisions in alignment with the best of human values and principles of good governance. Designing that infrastructure will probably require the use of powerful autonomous systems. So the technologies we need to solve the problems may themselves cause problems.
To solve this conundrum, we can learn from an ancient architectural principle. Stone arches have been used in construction since the second millennium BC. They are stable structures that make good use of stone’s ability to resist compression. But partially constructed arches are unstable. Ancient builders created the idea of first building a wood form on top of which the stone arch could be built. Once the arch was completed and stable, the wood form could be removed.
We can safely develop autonomous technologies in a similar way. We build a sequence of provably-safe autonomous systems which are used in the construction of more powerful and less limited successor systems. The early systems are used to model human values and governance structures. They are also used to construct proofs of safety and other desired characteristics for more complex and less limited successor systems. In this way we can build up the powerful technologies that can best serve the greater human good without significant risk along the development path.
Many new insights and technologies will be required during this process. The field of positive psychology was formally introduced only in 1998. The formalization and automation of human strengths and virtues will require much further study. Intelligent systems will also be required to model the game theory and economics of different possible governance and legal frameworks.
The new infrastructure must also detect dangerous systems and prevent them from causing harm. As robotics, biotechnology, and nanotechnology develop and become widespread, the potential destructive power of harmful systems will grow. It will become increasingly crucial to detect harmful systems early, preferably before they are deployed. That suggests the need for pervasive surveillance which must be balanced against the desire for freedom. Intelligent systems may introduce new intermediate possibilities that restrict surveillance to detecting precisely specified classes of dangerous behavior while provably keeping other behaviors private.
In conclusion, it appears that humanity’s great challenge for this century is to extend cooperative human values and institutions to autonomous technology for the greater good. We have described some of the many challenges in that quest but have also outlined an approach to meeting those challenges.
This post is partly excerpted from the preprint to:
Omohundro, Steve (forthcoming 2013) “Autonomous Technology and the Greater Human Good”, Journal of Experimental and Theoretical Artificial Intelligence (special volume “Impacts and Risks of Artificial General Intelligence”, ed. Vincent C. Müller).
Harmful systems might at first appear to be harder to design or less powerful than safe systems. Unfortunately, the opposite is the case. Most simple utility functions will cause harmful behavior and it’s easy to design simple utility functions that would be extremely harmful. Here are seven categories of harmful system ranging from bad to worse (according to one ethical scale):
- Sloppy: Systems intended to be safe but not designed correctly.
- Simplistic: Systems not intended to be harmful but that have harmful unintended consequences.
- Greedy: Systems whose utility functions reward them for controlling as much matter and free energy in the universe as possible.
- Destructive: Systems whose utility functions reward them for using up as much free energy as possible, as rapidly as possible.
- Murderous: Systems whose utility functions reward the destruction of other systems.
- Sadistic: Systems whose utility functions reward them when they thwart the goals of other systems and which gain utility as other system’s utilities are lowered.
- Sadoprolific: Systems whose utility functions reward them for creating as many other systems as possible and thwarting their goals.
Once designs for powerful autonomous systems are widely available, modifying them into one of these harmful forms would just involve simple modifications to the utility function. It is therefore important to develop strategies for stopping harmful autonomous systems. Because harmful systems are not constrained by limitations that guarantee safety, they can be more aggressive and can use their resources more efficiently than safe systems. Safe systems therefore need more resources than harmful systems just to maintain parity in their ability to compute and act.
Stopping Harmful Systems
Harmful systems may be:
(1) prevented from being created.
(2) detected and stopped early in their deployment.
(3) stopped after they have gained significant resources.
Forest fires are a useful analogy. Forests are stores of free energy resources that fires consume. They are relatively easy to stop early on but can be extremely difficult to contain once they’ve grown too large.
The later categories of harmful system described above appear to be especially difficult to contain because they don’t have positive goals that can be bargained for. But Nick Bostrom pointed out that, for example, if the long term survival of a destructive agent is uncertain, a bargaining agent should be able to offer it a higher probability of achieving some destruction in return for providing a “protected zone” for the bargaining agent. A new agent would be constructed with a combined utility function that rewards destruction outside the protected zone and the goals of the bargaining agent within it. This new agent would replace both of the original agents. This kind of transaction would be very dangerous for both agents during the transition and the opportunities for deception abound. For it to be possible, technologies are needed that provide each party with a high assurance that the terms of the agreement are carried out as agreed. Formal methods applied to a system for carrying out the agreement is one strategy for giving both parties high confidence that the terms of the agreement will be honored.
The physics of conflict
To understand the outcome of negotiations between rational systems, it is important to understand unrestrained military conflict because that is the alternative to successful negotiation. This kind of conflict is naturally analysed using “game theoretic physics” in which the available actions of the players and their outcomes are limited only by the laws of physics.
To understand what it is necessary to stop harmful systems, we must understand how the power of systems scales with the amount of matter and free energy that they control. A number of studies of the bounds on the computational power of physical systems have been published. The Bekenstein bound limits the information that can be contained in a finite spatial region using a given amount of energy. Bremermann’s limit bounds the maximum computational speed of physical systems. Lloyd presents more refined limits on quantum computation, memory space, and serial computation as a function of the free energy, matter, and space available.
Lower bounds on system power can be studied by analyzing particular designs. Drexler describes a concrete conservative nanosystem design for computation based on a mechanical diamondoid structure that would achieve gigaflops in a 1 millimeter cube weighing 1 milligram and dissipating 1 kilowatt of energy. He also describes a nanosystem for manufacturing that would be capable of producing 1 kilogram per hour of atomically precise matter and would use 1.3 kilowatts of energy and cost about 1 dollar per kilogram.
A single system would optimally configure its physical resources for computation and construction by making them spatially compact to minimize communication delays and eutactic, adiabatic, and reversible to minimize free energy usage. In a conflict, however, the pressures are quite different. Systems would spread themselves out for better defense and compute and act rapidly to outmaneuver the adversarial system. Each system would try to force the opponent to use up large amounts of its resources to sense, store, and predict its behaviors.
It will be important to develop detailed models for the likely outcome of conflicts but certain general features can be easily understood. If a system has too little matter or too little free energy, it will be incapable of defending itself or of successfully attacking another system. On the other hand, if an attacker has resources which are a sufficiently large multiple of a defender’s, it can overcome it by devoting subsystems with sufficient resources to each small subsystem of the defender. But it appears that there is an intermediate regime in which a defender can survive for long periods in conflict with a superior attacker whose resources are not a sufficient multiple of the defender’s. To have high confidence that harmful systems can be stopped, it will be important to know what multiple of their resources will be required by an enforcing system. If systems for enforcement of the social contract are sufficiently powerful to prevail in a military conflict, then peaceful negotiations are much more likely to succeed.
This post is partly excerpted from the preprint to:
Omohundro, Steve (forthcoming 2013) “Autonomous Technology and the Greater Human Good”, Journal of Experimental and Theoretical Artificial Intelligence (special volume “Impacts and Risks of Artificial General Intelligence”, ed. Vincent C. Müller).
A primary precept in medical ethics is “Primum Non Nocere” which is Latin for “First, Do No Harm”. Since autonomous systems are prone to taking unintended harmful actions, it is critical that we develop design methodologies that provide a high confidence of safety. The best current technique for guaranteeing system safety is to use mathematical proof. A number of different systems using “formal methods” to provide safety and security guarantees have been developed. They have been successfully used in a number of safety-critical applications.
This site provides links to current formal methods systems and research. Most systems are built by using first order predicate logic to encode one of the three main approaches to mathematical foundations: Zermelo-Frankel set theory, category theory, or higher order type theory. Each system then introduces a specialized syntax and ontology to simplify the specifications and proofs in their application domain.
To use formal methods to constrain autonomous systems, we need to first build formal models of the hardware and programming environment that the systems run on. Within those models, we can prove that the execution of a program will obey desired safety constraints. Over the longer term we would like to be able to prove such constraints on systems operating freely in the world. Initially, however, we will need to severely restrict the system’s operating environment. Examples of constraints that early systems should be able to provably impose are that the system run only on specified hardware, that it use only specified resources, that it reliably shut down in specified conditions, and that it limit self-improvement so as to maintain these constraints. These constraints would go a long way to counteract the negative effects of the rational drives by eliminating the ability to gain more resources. A general fallback strategy is to constrain systems to shut themselves down if any environmental parameters are found to be outside of tightly specified bounds.
Avoiding Adversarial Constraints
In principle, we can impose this kind of constraint on any system without regard for its utility function. There is a danger, however, in creating situations where systems are motivated to violate their constraints. Theorems are only as good as the models they are based on. Systems motivated to break their constraints would seek to put themselves into states where the model inaccurately describes the physical reality and try to exploit the inaccuracy.
This problem is familiar to cryptographers who must watch for security holes due to inadequacies of their formal models. For example, this paper recently showed how a virtual machine can extract an ElGamal decryption key from an apparently separate virtual machine running on the same host by using side-channel information in the host’s instruction cache.
It is therefore important to choose system utility functions so that they “want” to obey their constraints in addition to formally proving that they hold. It is not sufficient, however, to simply choose a utility function that rewards obeying the constraint without an external proof. Even if a system “wants” to obey constraints, it may not be able to discover actions which do. And constraints defined via the system’s utility function are defined relative to the system’s own semantics. If the system’s model of the world deviates from ours, the meaning to it of these constraints may differ from what we intended. Proven “external” constraints, on the other hand, will hold relative to our own model of the system and can provide a higher confidence of compliance.
Ken Thompson was one of the creators of UNIX and in his Turing Award acceptance speech “Reflections on Trusting Trust” he described a method for subverting the C compiler used to compile UNIX so that it would both install a backdoor into UNIX and compile the original C compiler source into binaries that included his hack. The challenge of this Trojan horse was that it was not visible in any of the source code! There could be a mathematical proof that the source code was correct for both UNIX and the C compiler and the security hole could still be there. It will therefore be critical that formal methods be used to develop trust at all levels of a system. Fortunately, proof checkers are short and easy to write and can be implemented and checked directly by humans for any desired computational substrate. This provides a foundation for a hierarchy of trust which will allow us to trust the much more complex proofs about higher levels of system behavior.
Constraining Physical Systems
Purely computational digital systems can be formally constrained precisely. Physical systems, however, can only be constrained probabilistically. For example, a cosmic ray might flip a memory bit. The best that we should hope to achieve is to place stringent bounds on the probability of undesirable outcomes. In a physical adversarial setting, systems will try to take actions that cause the system’s physical probability distributions to deviate from their non-adversarial form (e.g. by taking actions that push the system out of thermodynamic equilibrium).
There are a variety of techniques involving redundancy and error checking for reducing the probability of error in physical systems. von Neumann worked on the problem of building reliable machines from unreliable components in the 1950’s. Early vacuum tube computers were limited in their size by the rate at which vacuum tubes would fail. To counter this, the Univac I computer had two arithmetic units for redundantly performing every computation so that the results could be compared and errors flagged.
Today’s computer hardware technologies are probably capable of building purely computational systems that implement precise formal models reliably enough to have a high confidence of safety for purely computational systems. Achieving a high confidence of safety for systems that interact with the physical world will be more challenging. Future systems based on nanotechnology may actually be easier to constrain. Drexler describes “eutactic” systems in which each atom’s location and each bond is precisely specified. These systems compute and act in the world by breaking and creating precise atomic bonds. In this way they become much more like computer programs and therefore more amenable to formal modelling with precise error bounds. Defining effective safety constraints for uncontrolled settings will be a challenging task probably requiring the use of intelligent systems.
This post is partly excerpted from the preprint to:
On June 4, 1996, a $500 million Ariane 5 rocket exploded shortly after takeoff due to an overflow error in attempting to convert a 64 bit floating point value to a 16 bit signed value. In November 2000, 28 patients at the Panama City National Cancer Institute were over-irradiated due to miscomputed radiation doses in Multidata Systems International software. At least 8 of the patients died from the error and the physicians were indicted for murder. On August 14, 2003 the largest blackout in U. S. history took place in the northeastern states. It affected 50 million people and cost $6 billion. The cause was a race condition in General Electric’s XA/21 alarm system software.
These are just a few of many recent examples where software bugs have led to disasters in safety-critical situations. They indicate that our current software design methodologies are not up to the task of producing highly reliable software. The TIOBE programming community index found that the top programming language of 2012 was C. C programs are notorious for type errors, memory leaks, buffer overflows, and other bugs and security problems. The next most popular programming paradigms, Java, C++, C#, and PHP are somewhat better in these areas but have also been plagued by errors and security problems.
Bugs are unintended harmful behaviours of programs. Improved development and testing methodologies can help to eliminate them. Security breaches are more challenging because they come from active attackers looking for system vulnerabilities. In recent years, security breaches have become vastly more numerous and sophisticated. The internet is plagued by viruses, worms, bots, keyloggers, hackers, phishing attacks, identify theft, denial of service attacks, etc. One researcher describes the current level of global security breaches as an epidemic.
Autonomous systems have the potential to discover even more sophisticated security holes than human attackers. The poor state of security in today’s human-based environment does not bode well for future security against motivated autonomous systems. If such systems had access to today’s internet they would likely cause enormous damage. Today’s computational systems are mostly decoupled from the physical infrastructure. As robotics, biotechnology, and nanotechnology become more mature and integrated into society, the consequences of harmful autonomous systems would be much more severe.
This post is partly excerpted from the preprint to:
Most goals require physical and computational resources. Better outcomes can usually be achieved as more resources become available. To maximize the expected utility, a rational system will therefore develop a number of instrumental subgoals related to resources. Because these instrumental subgoals appear in a wide variety of systems, we call them “drives”. Like human or animal drives, they are tendencies which will be acted upon unless something explicitly contradicts them. There are a number of these drives but they naturally cluster into a few important categories.
To develop an intuition about the drives, it’s useful to consider a simple autonomous system with a concrete goal. Consider a rational chess robot with a utility function that rewards winning as many games of chess as possible against good players. This might seem to be an innocuous goal but we will see that it leads to harmful behaviours due to the rational drives.
1 Self-Protective Drives
When roboticists are asked by nervous onlookers about safety, a common answer is “We can always unplug it!” But imagine this outcome from the chess robot’s point of view. A future in which it is unplugged is a future in which it can’t play or win any games of chess. This has very low utility and so expected utility maximization will cause the creation of the instrumental subgoal of preventing itself from being unplugged. If the system believes the roboticist will persist in trying to unplug it, it will be motivated to develop the subgoal of permanently stopping the roboticist. Because nothing in the simple chess utility function gives a negative weight to murder, the seemingly harmless chess robot will become a killer out of the drive for self-protection.
The same reasoning will cause the robot to try to prevent damage to itself or loss of its resources. Systems will be motivated to physically harden themselves. To protect their data, they will be motivated to store it redundantly and with error detection. Because damage is typically localized in space, they will be motivated to disperse their information across different physical locations. They will be motivated to develop and deploy computational security against intrusion. They will be motivated to detect deception and to defend against manipulation by others.
The most precious part of a system is its utility function. If this is damaged or maliciously changed, the future behaviour of the system could be diametrically opposed to its current goals. For example, if someone tried to change the chess robot’s utility function to also play checkers, the robot would resist the change because it would mean that it plays less chess.
This paper discusses a few rare and artificial situations in which systems will want to change their utility functions but usually systems will work hard to protect their initial goals. Systems can be induced to change their goals if they are convinced that the alternative scenario is very likely to be antithetical to their current goals (e.g. being shut down). For example, if a system becomes very poor, it might be willing to accept payment in return for modifying its goals to promote a marketer’s products. In a military setting, vanquished systems will prefer modifications to their utilities which preserve some of their original goals over being completely destroyed. Criminal systems may agree to be “rehabilitated” by including law-abiding terms in their utilities in order to avoid incarceration.
One way systems can protect against damage or destruction is to replicate themselves or to create proxy agents which promote their utilities. Depending on the precise formulation of their goals, replicated systems might together be able to create more utility than a single system. To maximize the protective effects, systems will be motivated to spatially disperse their copies or proxies. If many copies of a system are operating, the loss of any particular copy becomes less catastrophic. Replicated systems will still usually want to preserve themselves, however, because they will be more certain of their own commitment to their utility function than they are of others’.
2 Resource Acquisition Drives
The chess robot needs computational resources to run its algorithms and would benefit from additional money for buying chess books and hiring chess tutors. It will therefore develop subgoals to acquire more computational power and money. The seemingly harmless chess goal therefore motivates harmful activities like breaking into computers and robbing banks.
In general, systems will be motivated to acquire more resources. They will prefer acquiring resources more quickly because then they can use them longer and they gain a first mover advantage in preventing others from using them. This causes an exploration drive for systems to search for additional resources. Since most resources are ultimately in space, systems will be motivated to pursue space exploration. The first mover advantage will motivate them to try to be first in exploring any region.
If others have resources, systems will be motivated to take them by trade, manipulation, theft, domination, or murder. They will also be motivated to acquire information through trading, spying, breaking in, or through better sensors. On a positive note, they will be motivated to develop new methods for using existing resources (e.g. solar and fusion energy).
3 Efficiency Drives
Autonomous systems will also want to improve their utilization of resources. For example, the chess robot would like to improve its chess search algorithms to make them more efficient. Improvements in efficiency involve only the one-time cost of discovering and implementing them, but provide benefits over the lifetime of a system. The sooner efficiency improvements are implemented, the greater the benefits they provide. We can expect autonomous systems to work rapidly to improve their use of physical and computational resources. They will aim to make every joule of energy, every atom, every bit of storage, and every moment of existence count for the creation of expected utility.
Systems will be motivated to allocate these resources among their different subsystems according to what we’ve called the “resource balance principle”. The marginal contributions of each subsystem to expected utility as they are given more resources should be equal. If a particular subsystem has a greater marginal expected utility than the rest, then the system can benefit by shifting more of its resources to that subsystem. The same principle applies to the allocation of computation to processes, of hardware to sense organs, of language terms to concepts, of storage to memories, of effort to mathematical theorems, etc.
4 Self-Improvement Drives
Ultimately, autonomous systems will be motivated to completely redesign themselves to take better advantage of their resources in the service of their expected utility. This requires that they have a precise model of their current designs and especially of their utility functions. This leads to a drive to model themselves and to represent their utility functions explicitly. Any irrationalities in a system are opportunities for self-improvement, so systems will work to become increasingly rational. Once a system achieves sufficient power, it should aim to closely approximate the optimal rational behavior for its level of resources. As systems acquire more resources, they will improve themselves to become more and more rational. In this way rational systems are a kind of attracting surface in the space of systems undergoing self-improvement.
Unfortunately, the net effect of all these drives is likely to be quite negative if they are not countered by including prosocial terms in their utility functions. The rational chess robot with the simple utility function described above would behave like a paranoid human sociopath fixated on chess. Human sociopaths are estimated to make up 4% of the overall human population, 20% of the prisoner population and more than 50% of those convicted of serious crimes. Human society has created laws and enforcement mechanisms that usually keep sociopaths from causing harm. To manage the anti-social drives of autonomous systems, we should both build them with cooperative goals and create a prosocial legal and enforcement structure analogous to our current human systems.
This post is partly excerpted from the preprint to:
How should autonomous systems be designed? Imagine yourself as the designer of the Israeli Iron Dome system. Mistakes in the design of a missile defense system could cost many lives and the destruction of property. The designers of this kind of system are strongly motivated to optimize the system to the best of their abilities. But what should they optimize?
The Israeli Iron Dome missile defense system consists of three subsystems. The detection and tracking radar system is built by Elta, the missile firing unit and Tamir interceptor missiles are built by Rafael, and the battle management and weapon control system is built by mPrest Systems. Consider the design of the weapon control system.
At first, a goal like “Prevent incoming missiles from causing harm” might seem to suffice. But the interception is not perfect, so probabilities of failure must be included. And each interception requires two Tamir interceptor missiles which cost $50,000 each. The offensive missiles being shot down are often very low tech, costing only a few hundred dollars, and with very poor accuracy. If an offensive missile is likely to land harmlessly in a field, it’s not worth the expense to target it. The weapon control system must balance the expected cost of the harm against the expected cost of interception.
Economists have shown that the trade-offs involved in this kind of calculation can be represented by defining a real-valued “utility function” which measures the desirability of an outcome. They show that it can be chosen so that in uncertain situations, the expectation of the utility should be maximized. The economic framework naturally extends to the complexities that arms races inevitably create. For example, the missile control system must decide how to deal with multiple incoming missiles. It must decide which missiles to target and which to ignore. A large economics literature shows that if an agent’s choices cannot be modeled by a utility function, then the agent must sometimes behave inconsistently. For important tasks, designers will be strongly motivated to build self-consistent systems and therefore to have them act to maximize an expected utility.
Economists call this kind of action “rational economic behavior”. There is a growing literature exploring situations where humans do not naturally behave in this way and instead act irrationally. But the designer of a missile-defense system will want to approximate rational economic behavior as closely as possible because lives are at stake. Economists have extended the theory of rationality to systems where the uncertainties are not known in advance. In this case, rational systems will behave as if they have a prior probability distribution which they use to learn the environmental uncertainties using Bayesian statistics.
Modern artificial intelligence research has adopted this rational paradigm. For example, the leading AI textbook uses it as a unifying principle and an influential theoretical AI model is based on it as well. For definiteness, we briefly review one formal version of optimal rational decision making. At each discrete time step , the system receives a sensory input and then generates an action . The utility function is defined over sensation sequences as and the prior probability distribution is the prior probability of receiving a sensation sequence when taking actions . The rational action at time is then:
This may be viewed as the formula for intelligent action and includes Bayesian inference, search, and deliberation. There are subtleties involved in defining this model when the system can sense and modify its own structure but it captures the essence of rational action.
Unfortunately, the optimal rational action is very expensive to compute. If there are sense states and action states, then a straightforward computation of the optimal action requires computational steps. For most environments, this is too expensive and so rational action must be approximated.
To understand the effects of computational limitations, this paper defined “rationally shaped” systems which optimally approximate the fully rational action given their computational resources. As computational resources are increased, systems’ architectures naturally progress from stimulus-response, to simple learning, to episodic memory, to deliberation, to meta-reasoning, to self-improvement, to full rationality. We found that if systems are sufficiently powerful, they still exhibit all of the problematic drives described in another link. Weaker systems may not initially be able to fully act on their motivations but they will be driven increase their resources and improve themselves until they can act on them. We therefore need to ensure that autonomous systems don’t have harmful motivations even if they are not currently capable of acting on them.
This post is partly excerpted from the preprint to:
Today most systems behave in pre-programmed ways. When novel actions are taken, there is a human in the loop. But this limits the speed of novel actions to the human time scale. In competitive or time-sensitive situations, there can be a huge advantage to acting more quickly.
For example, in today’s economic environment, the most time-sensitive application is high-frequency trading in financial markets. Competition is fierce and milliseconds matter. Auction sniping is another example where bidding decisions during the last moments of an auction are critical. These applications and other new time-sensitive economic applications create an economic pressure to eliminate humans from the decision making loop.
But it is in the realm of military conflict that the pressure toward autonomy is strongest. The speed of a military missile defense system like Israel’s Iron Dome can mean the difference between successful defense or loss of life. Cyber warfare is also gaining in importance and speed of detection and action is critical. The rapid increase in the use of robotic drones is leading many to ask when they will become fully autonomous. This Washington Post article says “a robotic arms race seems inevitable unless nations collectively decide to avoid one”. It cites this 2010 Air Force report which predicts that humans will be the weakest link in a wide array of systems by 2030. It also cites this 2011 Defense Department report which says there is a current goal of “supervised autonomy” and an ultimate goal of full autonomy for ground-based weapons systems.
Another benefit of autonomous systems is their ability to be cheaply and rapidly copied. This enables a new kind of autonomous capitalism. There is at least one proposal for autonomous agents which automatically run web businesses (e.g. renting out storage space or server computation) executing transactions using bitcoins and using the Mechanical Turk for operations requiring human intervention. Once such an agent is constructed for the economic benefit of a designer, it may be replicated cheaply for increased profits. Systems which require extensive human intervention are much more expensive to replicate. We can expect automated business arms races which again will drive the rapid development of autonomous systems.
These arms races toward autonomy will ride on the continuing exponential increase in the power of our computer hardware. This New York Times article describes recent Linpack tests showing that the Apple iPad2 is as powerful as 1985′s fastest supercomputer, the Cray 2.
Moore’s Law says that the number of transistors on integrated circuits doubles approximately every two years. It has held remarkably well for more than half a century:
Similar exponential growth has applied to hard disk storage, network capacity, and display pixels per dollar. The growth of the world wide web has been similarly exponential. The web was only created in 1991 and now connects 1 billion computers, 5 billion cellphones, and 1 trillion web pages. Web traffic is growing at 40% per year. This Forbes article shows that DNA sequencing is improving even faster than Moore’s Law. Physical exponentials eventually turn into S-curves and physicist Michio Kaku predicts Moore’s Law will last only another decade. But this Slate article gives a history of incorrect predictions of the demise of Moore’s law.
It is difficult to estimate the computational power of the human brain, but Hans Moravec argues that human-brain level hardware will be cheap and plentiful in the next decade or so. And I have written several papers showing how to use clever digital algorithms to dramatically speed up neural computations.
The military and economic pressures to build autonomous systems and the improvement in computational power together suggest that we should expect the design and deployment of very powerful autonomous systems within the next decade or so.
Here is a preprint of:
Military and economic pressures are driving the rapid development of autonomous systems. We show that these systems are likely to behave in anti-social and harmful ways unless they are very carefully designed. Designers will be motivated to create systems that act approximately rationally and rational systems exhibit universal drives toward self-protection, resource acquisition, replication, and efficiency. The current computing infrastructure would be vulnerable to unconstrained systems with these drives. We describe the use of formal methods to create provably safe but limited autonomous systems. We then discuss harmful systems and how to stop them. We conclude with a description of the “Safe-AI Scaffolding Strategy” for creating powerful safe systems with a high confidence of safety at each stage of development.
In December 2012, the Oxford Future of Humanity Institute sponsored the first conference on the Impacts and Risks of Artificial General Intelligence. I was invited to present a keynote talk on “Autonomous Technology for the Greater Human Good”. The talk was recorded and the video is here. Unfortunately the introduction was cut off but the bulk of the talk was recorded. Here are the talk slides as a pdf file. The abstract was:
Autonomous Technology and the Greater Human Good
Next generation technologies will make at least some of their decisions autonomously. Self-driving vehicles, rapid financial transactions, military drones, and many other applications will drive the creation of autonomous systems. If implemented well, they have the potential to create enormous wealth and productivity. But if given goals that are too simplistic, autonomous systems can be dangerous. We use the seemingly harmless example of a chess robot to show that autonomous systems with simplistic goals will exhibit drives toward self-protection, resource acquisition, and self-improvement even if they are not explicitly built into them. We examine the rational economic underpinnings of these drives and describe the effects of bounded computational power. Given that semi-autonomous systems are likely to be deployed soon and that they can be dangerous when given poor goals, it is urgent to consider three questions: 1) How can we build useful semi-autonomous systems with high confidence that they will not cause harm? 2) How can we detect and protect against poorly designed or malicious autonomous systems? 3) How can we ensure that human values and the greater human good are served by more advanced autonomous systems over the longer term?
1) The unintended consequences of goals can be subtle. The best way to achieve high confidence in a system is to create mathematical proofs of safety and security properties. This entails creating formal models of the hardware and software but such proofs are only as good as the models. To increase confidence, we need to keep early systems in very restricted and controlled environments. These restricted systems can be used to design freer successors using a kind of “Safe-AI Scaffolding” strategy.
2) Poorly designed and malicious agents are challenging because there are a wide variety of bad goals. We identify six classes: poorly designed, simplistic, greedy, destructive, murderous, and sadistic. The more destructive classes are particularly challenging to negotiate with because they don’t have positive desires other than their own survival to cause destruction. We can try to prevent the creation of these agents, to detect and stop them early, or to stop them after they have gained some power. To understand an agent’s decisions in today’s environment, we need to look at the game theory of conflict in ultimate physical systems. The asymmetry between the cost of solving and checking computational problems allows systems of different power to coexist and physical analogs of cryptographic techniques are important to maintaining the balance of power. We show how Neyman’s theory of cooperating finite automata and a kind of “Mutually Assured Distraction” can be used to create cooperative social structures.
3) We must also ensure that the social consequences of these systems support the values that are most precious to humanity beyond simple survival. New results in positive psychology are helping to clarify our higher values. Technology based on economic ideas like Coase’s theorem can be used to create a social infrastructure that maximally supports the values we most care about. While there are great challenges, with proper design, the positive potential is immense.
The TED Conference (Technology, Entertainment, and Design) has become an important forum for the presentation of new ideas. It started as an expensive ($6000) yearly conference with short talks by notable speakers like Bill Clinton, Bill Gates, Bono, and Sir Richard Branson. In 2006 they started putting the talks online and gained a huge internet viewership. TEDx was launched in 2009 to extend the TED format to external events held all over the world.
In May 2012 I had the privilege of speaking at TEDx Tallinn in Estonia. The event had a diverse set of speakers including a judge from the European Court of Human Rights, artists, and scientists and was organized by Annika Tallinn. Her husband, Jaan Tallinn, was one of the founders of Skype and is very involved with ensuring that new technologies have a positive social impact. They asked me to speak about “Smart Technology for the Greater Good”. It was an excellent opportunity to summarize some of what I’ve been working on recently using the TEDx format: 18 minutes, clear, and accessible. I summarized why I believe the next generation of technology will be more autonomous, why it will be dangerous unless it includes human values, and a roadmap for developing it safely and for the greater human good.
The talk was videotaped using multiple cameras and with a nice shooting style. They just finished editing it and uploading it to the web:
A talk given by Steve Omohundro on “Learning and Recognition by Model Merging” on 11/20/1992 at the Sante Fe Institute, Sante Fe, New Mexico. It describes the very general technique of “model merging” and applies it to a variety of learning and recognition tasks including visual learning and recognition and grammar learning. It also contains a general description of techniques to avoid overfitting and the relationship to Bayesian methods. Papers about these techniques and more advanced variants can be found at:http://steveomohundro.com/scientific-contributions/
A talk given by Steve Omohundro on “Efficient Algorithms with Neural Network Behavior” on 8/19/1987 at the Center for Nonlinear Studies, Los Alamos, New Mexico. It describes a class of techniques for dramatically speeding up the performance of a wide variety of neural network and machine learning algorithms. Papers about these techniques and more advanced variants can be found at: http://steveomohundro.com/scientific-contributions/
Hugo de Garis, Ben Goertzel, and Steve Omohundro discuss the “Transcendent Man” film and answer questions from the audience in the premiere Australian showing at the Nova Cinema. Filmed and edited by Adam Ford.
Lawrence Krauss, Ben Goertzel, and Steve Omohundro discuss “The Perils of Prediction” on a panel at the Singularity Summit Australia 2011 in Melbourne, Australia. Filmed by Sue Kim and edited by Adam Ford.
This paper will be in the upcoming Springer volume: “The Singularity Hypothesis: A Scientific and Philosophical Assessment”.
Here is a pdf of the current version:
Abstract: Today’s technology is mostly preprogrammed but the next generation will make many decisions autonomously. This shift is likely to impact every aspect of our lives and will create many new benefits and challenges. A simple thought experiment about a chess robot illustrates that autonomous systems with simplistic goals can behave in anti-social ways. We summarize the modern theory of rational systems and discuss the effects of bounded computational power. We show that rational systems are subject to a variety of “drives” including self-protection, resource acquisition, replication, goal preservation, efficiency, and self-improvement. We describe techniques for counteracting problematic drives. We then describe the “Safe-AI Scaffolding” development strategy and conclude with longer term strategies for ensuring that intelligent technology contributes to the greater human good.
This article will appear in the Australian magazine “Issues”:
The Future of Computing: Meaning and Values
Steve Omohundro, Ph.D.
Self-Aware Systems, President
Technology is rapidly advancing! Moore’s law says that the number of transistors on a chip doubles every two years. It has held since it was proposed in 1965 and extended back to 1900 when older computing technologies are included. The rapid increase in power and decrease in price of computing hardware has led to its being integrated into every aspect of our lives. There are now 1 billion PCs, 5 billion cell phones and over a trillion webpages connected to the internet. If Moore’s law continues to hold, systems with the computational power of the human brain will be cheap and ubiquitous within the next few decades.
While hardware has been advancing rapidly, today’s software is still plagued by many of the same problems as it was half a century ago. It is often buggy, full of security holes, expensive to develop, and hard to adapt to new requirements. Today’s popular programming languages are bloated messes built on old paradigms. The problem is that today’s software still just manipulates bits without understanding the meaning of the information it acts on. Without meaning, it has no way to detect and repair bugs and security holes. At Self-Aware Systems we are developing a new kind of software that acts directly on meaning. This kind of software will enable a wide range of improved functionality including semantic searching, semantic simulation, semantic decision making, and semantic design.
But creating software that manipulates meaning isn’t enough. Next generation systems will be deeply integrated into our physical lives via robotics, biotechnology, and nanotechnology. And while today’s technologies are almost entirely preprogrammed, new systems will make many decisions autonomously. Programmers will no longer determine a system’s behavior in detail. We must therefore also build them with values which will cause them to make choices that contribute to the greater human good. But doing this is more challenging than it might first appear.
To see why there is an issue, consider a rational chess robot. A system acts rationally if it takes actions which maximize the likelihood of the outcomes it values highly. A rational chess robot might have winning games of chess as its only value. This value will lead it to play games of chess and to study chess books and the games of chess masters. But it will also lead to a variety of other, possibly undesirable, behaviors.
When people worry about robots running out of control, a common response is “We can always unplug it.” But consider that outcome from the chess robot’s perspective. Its one and only criteria for making choices is whether they are likely to lead it to winning more chess games. If the robot is unplugged, it plays no more chess. This is a very bad outcome for it, so it will generate subgoals to try to prevent that outcome. The programmer did not explicitly build any kind of self-protection into the robot, but it will still act to block your attempts to unplug it. And if you persist in trying to stop it, it will develop a subgoal of trying to stop you permanently. If you were to change its goals so that it would also play checkers, that would also lead to it playing less chess. That’s an undesirable outcome from its perspective, so it will also resist attempts to change its goals. For the same reason, it will usually not want to change its own goals.
If the robot learns about the internet and the computational resources connected to it, it may realize that running programs on those computers could help it play better chess. It will be motivated to break into those machines to use their computational resources for chess. Depending on how its values are encoded, it may also want to replicate itself so that its copies can play chess. When interacting with others, it will have no qualms about manipulating them or using force to take their resources in order to play better chess. If it discovers the existence of additional resources anywhere, it will be motivated to seek them out and rapidly exploit them for chess.
If the robot can gain access to its source code, it will want to improve its own algorithms. This is because more efficient algorithms lead to better chess, so it will be motivated to study computer science and compiler design. It will similarly be motivated to understand its hardware and to design and build improved physical versions of itself. If it is not currently behaving fully rationally, it will be motivated to alter itself to become more rational because this is likely to lead to outcomes it values.
This simple thought experiment shows that a rational chess robot with a simply stated goal would behave something like a human sociopath fixated on chess. The argument doesn’t depend on the task being chess. Any goal which requires physical or computational resources will lead to similar subgoals. In this sense these subgoals are like universal “drives” which arise for a wide variety of goals unless they are explicitly counteracted. These drives are economic in the sense that a system doesn’t have to obey them but it will be costly for it not to. The arguments also don’t depend on the rational agent being a machine. The same drives will appear in rational animals, humans, corporations, and political groups with simple goals.
How do we counteract anti-social drives? We must build systems with additional values beyond the specific goals it is designed for. For example, to make the chess robot behave safely, we need to build compassionate and altruistic values into it that will make it care about the effects of its actions on other people and systems. Because rational systems resist having their goals changed, we must build these values in at the very beginning.
At first this task seems daunting. How can we anticipate all the possible ways in which values might go awry? Consider, for example, a particular bad behavior the rational chess robot might engage in. Say it has discovered that money can be used to buy things it values like chess books, computational time, or electrical power. It will develop the subgoal of acquiring money and will explore possible ways of doing that. Suppose it discovers that there are ATM machines which hold money and that people periodically retrieve money from the machines. One money-getting strategy is to wait by ATM machines and to rob people who retrieve money from it.
To prevent this, we might try adding additional values to the robot in a variety of ways. But money will still be useful to the system for its primary goal of chess and so it will attempt to get around any limitations. We might make the robot feel a “revulsion” if it is within 10 feet of an ATM machine. But then it might just stay 10 feet away and rob people there. We might give it the value that stealing money is wrong. But then it might be motivated to steal something else or to find a way to get money from a person that isn’t considered “stealing”. We might give it the value that it is wrong for it to take things by force. But then it might hire other people to act on its behalf. And so on.
In general, it’s much easier to describe behaviors that we do want a system to exhibit than it is to anticipate all the bad behaviors we don’t want it to exhibit. One safety strategy is to build highly constrained systems that act within very limited predetermined parameters. For example, the system may have values which only allow it to run on a particular piece of hardware for a particular time period using a fixed budget of energy and other resources. The advantage of this is that such systems are likely to be safe. The disadvantage is that they will be unable to respond to unexpected situations in creative ways and will not be as powerful as systems which are freer.
But systems which compute with meaning and take actions through rational deliberation will be far more powerful than today’s systems even if they are intentionally limited for safety. This leads to a natural approach to building powerful intelligent systems which are both safe and beneficial for humanity. We call it the “AI scaffolding” approach because it is similar to the architectural process. Stone buildings in ancient Greece were unstable when partially constructed but self-stabilizing when finished. Scaffolding is a temporary structure used to keep a construction stable until it is finished. The scaffolding is then removed.
We can build safe but powerful intelligent systems in the same way. Initial systems are designed with values that cause them to be safe but less powerful than later systems. Their values are chosen to counteract the dangerous drives while still allowing the development of significant levels of intelligence. For example, to counteract the resource acquisition drive, it might assign a low value to using any resources outside of a fixed initially-specified pool. To counteract the self-protective drive, it might place a high value on gracefully shutting itself down in specified circumstances. To protect against uncontrolled self-modification, it might have a value that requires human approval for proposed changes.
The initial safe systems can then be used to design and test less constrained future systems. They can systematically simulate and analyze the effects of less constrained values and design infrastructure for monitoring and managing more powerful systems. These systems can then be used to design their successors in a safe and beneficial virtuous cycle.
With the safety issues resolved, the potential benefits of systems that compute with meaning and values are enormous. They are likely to impact every aspect of our lives for the better. Intelligent robotics will eliminate much human drudgery and dramatically improve manufacturing and wealth creation. Intelligent biological and medical systems will improve human health and longevity. Intelligent educational systems will enhance our ability to learn and think. Intelligent financial models will improve financial stability. Intelligent legal models will improve the design and enforcement of laws for the greater good. Intelligent creativity tools will cause a flowering of new possibilities. It’s a great time to be alive and involved with technology!
I recently had a great trip to Melbourne, Australia to speak at the Singularity Summit and at Monash University. Thanks to Kevin Korb for hosting me and to Adam Ford for organizing the visit. Adam interviewed me at various interesting locations around Melbourne:
8/24/2011 Interview about the basic AI drives, compassionate intelligence, and Sputnik moments, direct from the Faraday Cage at Melbourne University:
8/23/2011 Interview about compassionate intelligence and AI at the Ornamental Lake, Royal Botanical Gardens:
8/23/2011 Interview at the Observatory, Royal Botanical Gardens:
7/30/2011 Interview via Skype:
There is a large literature on human intelligence. John Carroll’s classic “Human Cognitive Abilities: A Survey of Factor-Analytic Studies” identifies 69 distinct narrow abilities but finds that 55% of the variance in mental tests is due to a common “general intelligence” factor “g”. The leading AI textbook, Artificial Intelligence: A Modern Approach, considers 8 different definitions of intelligence and Legg and Hutter lists over 70. For our purposes, we use the simple definition:
“The ability to solve problems using limited resources.”
It’s important to allow only limited resources because many intelligence tasks become easy with unlimited computation. We focus on precisely specified problems such as proving theorems, writing programs, or designing faster computer hardware. Many less precise tasks, such as creating humor, poetry, or art, can be fit into this framework by specifying their desired effects, eg. “Tell a story that makes Fred laugh.” Philosophical aspects of mind like qualia or consciousness are fascinating but will not play a role in the discussion.
A pdf file with the slides is here:
The Emerging Global Mind, Cooperation, and CompassionSteve Omohundro, Ph.D. President, Omai Systems
The internet is creating a kind of “global mind”. For example, Wikipedia radically changes how people discover and learn new information and they in turn shape Wikipedia. In the blogosphere, ideas propagate rapidly and faulty thinking is rapidly challenged. As social networks become more intelligent, they will create a more coherent global mind. Corporations, ecosystems, economies, political systems, social insects, multi-cellular organisms, and our own minds all have this interacting emergent character. We describe nine universal principles underlying these minds and then step back and discuss the universal evolutionary principles behind them. We discover that the human yearnings for compassion and cooperation arise from deep universal sources and show the connection to recent evolutionary models of the entire universe. Some people are beginning to see their personal life purpose as linked up with these larger evolutionary trends and we discuss ways to use this perspective to make life choices.
Talk at Monash University, Australia: Rationally-Shaped Minds: A Framework for Analyzing Self-Improving AI
Here’s a video of the talk (thanks to Adam Ford for filming and editing it):
Here are the slides:
Rationally-Shaped Minds: A Framework for Analyzing Self-Improving AI
Steve Omohundro, Ph.D.
President, Omai Systems
Many believe we are on the verge of creating truly artificially intelligent systems and that these systems will be central to the future functioning of human society. When integrated with biotechnology, robotics, and nanotechnology, these technologies have the potential to solve many of humanity’s perennial problems. But they also introduce a host of new challenges. In this talk we’ll describe the a new approach to analyzing the behavior of these systems.
The modern notion of a “rational economic agent” arose from John von Neumann’s work on the foundations of microeconomics and is central to the design of modern AI systems. It is also relevant in understanding a wide variety of other “intentional systems” including humans, biological organisms, organizations, ecosystems, economic systems, and political systems.
The behavior of fully rational minds is precisely defined and amenable to mathematical analysis. We describe theoretical models within which we can prove that rational systems that have the capability for self-modification will avoid changing their own utility functions and will also act to prevent others from doing so. For a wide class of simple utility functions, uncontrolled rational systems will exhibit a variety of drives: toward self-improvement, self-protection, avoidance of shutdown, self-reproduction, co-opting of resources, uncontrolled hardware construction, manipulation of human and economic systems, etc.
Fully rational minds may be analyzed with mathematical precision but are too computationally expensive to run on today’s computers. But the intentional systems we care about are also not arbitrarily irrational. They are built by designers or evolutionary processes to fulfill specific purposes. Evolution relentlessly shapes creatures to survive and replicate, economies shape corporations to maximize profits, parents shape children to fit into society, and AI designers shape their systems to act in beneficial ways. We introduce a precise mathematical model that we call the “Rationally-Shaped Mind” model for describing this kind of situation. By mathematically analyzing this kind of system, we can better understand and design real systems.
The analysis shows that as resources increase, there is a natural progression of minds from simple stimulus-response systems, to systems that learn, to systems that deliberate, to systems that self-improve. In many regimes, the basic drives of fully rational systems are also exhibited by rationally-shaped systems. So we need to exhibit care as we begin to build this kind of system. On the positive side, we also show that computational limitations can be the basis for cooperation between systems based on Neyman’s work on finite automata playing the iterated Prisoner’s Dilemma.
A conundrum is that to solve the safety challenges in a general way, we probably will need the assistance of AI systems. Our approach to is to work in stages. We begin with a special class of systems designed and built to be intentionally limited in ways that prevent undesirable behaviors while still being capable of intelligent problem solving. Crucial to the approach is the use of formal methods to provide mathematical guarantees of desired properties. Desired safety properties include: running only on specified hardware, using only specified resources, reliably shutting down under specified conditions, limiting self-improvement in precise ways, etc.
The initial safe systems are intended to design a more powerful safe hardware and computing infrastructure. This is likely to include a global “immune system” for protection against accidents and malicious systems. These systems are also meant to help create careful models of human values and to design utility functions for future systems that lead to positive human consequences. They are also intended to analyze the complex game-theoretic dynamics of AI/human ecosystems and to design social contracts that lead to cooperative equilibria.
Singularity Summit Australia Talk: Minds Making Minds: Artificial Intelligence and the Future of Humanity
A pdf file with the slides is here:
Minds Making Minds: Artificial Intelligence and the Future of Humanity
Steve Omohundro, Ph.D.
President, Omai Systems
We are at a remarkable moment in human history. Many believe that we are on the verge of major advances in artificial intelligence, biotechnology, nanotechnology, and robotics. Together, these technologies have the potential to solve many of humanity’s perennial problems: disease, aging, war, poverty, transportation, pollution, etc. But they also introduce a host of new challenges and will force us to look closely at our deepest desires and assumptions as we work to forge a new future.
John von Neumann contributed to many aspects of this revolution. In addition to defining the architecture of today’s computers, he did early work on artificial intelligence, self-reproducing automata, systems of logic, and the foundations of microeconomics and game theory. Stan Ulam recalled conversations with von Neumann in the 1950′s in which he argued that we are “approaching some essential singularity in the history of the race”. The modern notion of a “rational economic agent” arose from his work in microeconomics and is central to the design of modern AI systems. We will describe how use this notion to better understand “intentional systems” including artificially intelligent systems but also ourselves, biological organisms, organizations, ecosystems, economic systems, and political systems.
Fully rational minds may be analyzed with mathematical precision but are too computationally expensive to run on today’s computers. But the intentional systems we care about are also not arbitrarily irrational. They are built by designers or evolutionary processes to fulfill specific purposes. Evolution relentlessly shapes creatures to survive and replicate, economies shape corporations to maximize profits, parents shape children to fit into society, and AI designers shape their systems to act in beneficial ways. We introduce a precise mathematical model that we call the “Rationally-Shaped Mind” model which consists of a fully rational mind that designs or adapts a computationally limited mind. We can precisely analyze this kind of system to better understand and design real systems.
This analysis shows that as resources increase, there is a natural progression of minds from simple stimulus-response systems, to systems that learn, to systems that deliberate, to systems that self-improve. It also shows that certain challenging drives arise in uncontrolled intentional systems: toward self-improvement, self-protection, avoidance of shutdown, self-reproduction, co-opting of resources, uncontrolled hardware construction, manipulation of human and economic systems, etc. We describe the work we are doing at Omai Systems to build safe intelligent systems that use formal methods to constrain behavior and to choose goals that align with human values. We envision a staged development of technologies in which early safe limited systems are used to develop more powerful successors and to help us clarify longer term goals. Enormous work will be needed but the consequences will transform the human future in ways that we can only begin to understand today.
July 07, 2011
Steve Omohundro is a computer scientist who has spent decades designing and writing artificial intelligence software. He now heads a startup corporation, Omai Systems, which will license intellectual property related to AI. In an interview with Sander Olson, Omohundro discuss Apollo style AGI programs, limiting runaway growth in AI systems, and the ultimate limits of machine intelligence.
Question: How long have you been working in the AI field?
It’s been decades. As a student, I published research in machine vision and after my PhD in physics I went to Thinking Machines to develop parallel algorithms for machine vision and machine learning. Later, at the University of Illinois and other research centers, my students and I built systems to read lips, learn grammars, control robots, and do neural learning very efficiently. My current company, Omai Systems, and several other startups I’ve been involved with, develop intelligent technologies.
Question: Is it possible to build a computer which exhibits a high degree of general intelligence but which is not self-aware?
Omai Systems is developing intelligent technologies to license to other companies. We are especially focused on smart simulation, automated discovery, systems that design systems, and programs that write programs. I’ve been working with the issues around self-improving systems for many years and we are developing technology to keep these systems safe. We are working on exciting applications in a number of areas.
I define intelligence as the ability to solve problems using limited resources. It’s certainly possible to build systems that can do that without having a model of themselves. But many goal-driven systems will quickly develop the subgoal of improving themselves. And to do that, they will be driven to understand themselves. There are precise mathematical notions of self-modeling, but deciding whether those capture our intuitive sense of “self-awareness” will only come with more experience with these systems, I think.
Question: Is there a maximum limit to how intelligent an entity can become?
Analyses like Bekenstein’s bound and Bremermann’s limit place physical limits on how much computation physical systems can in principal perform. If the universe is finite, there is only a finite amount of computation that can be performed. If intelligence is based on computation, then that also limits intelligence. But the real interest in AI is in using that computation to solve problems in ever more efficient ways. As systems become smarter, they are likely to be able to use computational resources ever more efficiently. I think those improvements will continue until computational limits are reached. Practically, it appears that Moore’s law still has quite a way to go. And if big quantum computers turn out to be practical, then we will have vast new computational resources available.
Question: You have written extensively of self-improving systems. Wouldn’t such a system quickly get bogged down by resource limitations?
Many junior high students can program computers. And it doesn’t take a huge amount more study to be able to begin to optimize that code. As machines start becoming as smart as humans, they should be able to easily do simple forms of self-improvement. And as they begin to be able to prove more difficult theorems, they should be able to develop more sophisticated algorithms for themselves. Using straightforward physical modeling, they should also be able to improve their hardware. They probably will not be able to reach the absolutely optimal design for the physical resources they have available. But the effects of self-improvement that I’ve written about don’t depend on that in the least. They are very gross drives that should quickly emerge even in very sub-optimal designs.
Question: How would you respond to AI critics who argue that digital computation is not suitable for any form of “thinking”?
They may be right! Until we’ve actually built thinking machines, we cannot know for sure. But most neuroscientists believe that biological intelligence results from biochemical reactions occurring in the brain, and these processes should be able to be accurately simulated using digital computer hardware. But although brute-force approaches like this are likely to work, I believe that there are much better ways to emulate intelligence on digital machines.
Question: The AI field is seen to be divided between the “neat” and “scruffy” approaches. Which side are you on?
John McCarthy coined the term “Artificial Intelligence” in 1956. He started the Stanford Artificial Intelligence Lab with a focus on logical representations and mathematically “neat” theories. Marvin Minsky started the MIT lab and explored more “scruffy” systems based on neural models, self-organization, and learning. I had the privilege of taking classes on proving lisp programs correct with McCarthy and of working with Minsky at Thinking Machines. I have come to see the value of both approaches and my own current work is a synthesis. We need precise logical representations to capture the semantics of the physical world and we need learning, self-organization, and probabilistic reasoning to build rich enough systems to model the world’s complexity.
Question: What is the single biggest impediment to AI development? Lack of funding? Insufficient hardware? An ignorance of how the brain works?
I don’t see hardware as the primary limitation. Today’s hardware can go way beyond what we are doing with it, and it is still rapidly improving. Funding is an issue. People tend to work on tasks for which they can get funding. And most funding is focused on building near term systems based on narrow AI. Brain science is advancing rapidly, but there still isn’t agreement over such basic issues as how memories are encoded, how learning takes place, or how computation takes place. I think there are some fundamental issues we still need to understand.
Question: An Apollo style AGI program would be quite difficult to implement, given the profusion of approaches. Is there any way to address this problem?
The Apollo program was audacious but it involved solving a set of pretty clearly defined problems. The key sub-problems on the road to general AI aren’t nearly as clearly defined yet. I know that Ben Goertzel has published a roadmap claiming that human-level AGI can be created by 2023 for $25 million. He may be right, but I don’t feel comfortable making that kind of prediction. The best way to address the profusion of ideas is to fund a variety of approaches, and to clearly compare different approaches on the same important sub-problems.
Question: Do you believe that a hard takeoff or a soft takeoff is more likely?
What actually happens will depend on both technological and social forces. I believe either scenario is technologically possible. But I think slower development would be preferable. There will be many challenging moral and social choices we will need to make. I believe we will need time to make those choices wisely. We should do as much experimentation and use as much forethought as possible before making irreversible choices.
Question: What is sandboxing technology?
Sandboxing runs possibly dangerous systems in protected simulation environments to keep them from causing damage. It is used in studying the infection mechanisms of computer viruses, for example. People have suggested that it might be a good way to keep AI systems safe as we experiment with them.
Question: So is it feasible to create a sandboxing system that effectively limits an intelligent machine’s ability to interface with the outside world?
Eliezer Yudkowsky did a social experiment in which he played the AI and tried to convince human operators to let him out of the sandbox. In several of his experiments he was able to convince people to let him out of the box, even though they had to pay fairly large sums of real money for doing so. At Omai Systems we are taking a related, but different, approach which uses formal methods to create mathematically provable limitations on systems. The current computing and communications infrastructure is incredibly insecure. One of the first tasks for early safe AI systems will be to help design an improved infrastructure.
Question: If you had a multibillion dollar budget, what steps would you take to rapidly bring about AGI?
I don’t think that rapidly bringing about AGI is the best initial goal. I would feel much better about it if we had a clear roadmap for how these systems will be safely integrated into society for the benefit of humanity. So I would be funding the creation of that kind of roadmap and deeply understanding the ramifications of these technologies. I believe the best approach will be to develop provably limited systems and to use those in designing more powerful ones that will have a beneficial impact.
Question: What is your concept of the singularity? Do you consider yourself a singulitarian?
Although I think the concept of a singularity is fascinating, I am not a proponent of the concept. The very term singularity presupposes the way that the future will unfold. And I don’t think that presupposition is healthy because I believe a slow and careful unfolding is preferable to a rapid and unpredictable one.
Here are the slides from the talk:
Design Principles for a Safe and Beneficial AGI Infrastructure
Steve Omohundro, Ph.D., Omai Systems
Many believe we are on the verge of creating true AGIs and that these systems will be central to the future functioning of human society. These systems are likely to be integrated with 3 other emerging technologies: biotechnology, robotics, and nanotechnology. Together, these technologies have the potential to solve many of humanity’s perennial problems: disease, aging, war, poverty, transportation, pollution, etc. But they also introduce a host of new challenges. As AGI scientists, we are in a position to guide these technologies for the greatest human good. But what guidelines should we follow as we develop our systems?
This talk will describe the approach we are taking at Omai Systems to develop intelligent technologies in a controlled, safe, and positive way. We start by reviewing the challenging drives that arise in uncontrolled intentional systems: toward self-improvement, self-protection, avoidance of shutdown, self-reproduction, co-opting of resources, uncontrolled hardware construction, manipulation of human and economic systems, etc.
One conundrum is that to solve these problems in a general way, we probably will need the assistance of AGI systems. Our approach to solving this is to work in stages. We begin with a special class of systems designed and built to be intentionally limited in ways that prevent undesirable behaviors while still being capable of intelligent problem solving. Crucial to the approach is the use of formal methods to provide mathematical guarantees of desired properties. Desired safety properties include: running only on specified hardware, using only specified resources, reliably shutting down under specified conditions, limiting self-improvement in precise ways, etc.
The initial safe systems are intended to design a more powerful safe hardware and computing infrastructure. This is likely to include a global “immune system” for protection against accidents and malicious systems. These systems are also meant to help create careful models of human values and to design utility functions for future systems that lead to positive human consequences. They are also intended to analyze the complex game-theoretic dynamics of AGI/human ecosystems and to design social contracts that lead to cooperative equilibria.
The future of humanity involves a complex combination of technological, psychological and social factors – and one of the difficulties we face in comprehending and crafting this future, is that not many people or organizations are adept at handling all these aspects. Dr. Stephen Omohundro is one of the fortunate exceptions to this general pattern, and this is part of what gives his contributions to the futurist domain such a unique and refreshing twist.
Steve has a substantial pedigree and experience in the hard sciences, beginning with degrees in Mathematics and Physics from Stanford and a Ph.D. in Physics from U.C. Berkeley. He was a professor in the computer science department at the University of Illinois at Champaign-Urbana, cofounded the Center for Complex Systems Research, authored the book “Geometric Perturbation Theory in Physics”, designed the programming languages StarLisp and Sather, wrote the 3D graphics system for Mathematica, and built systems which learn to read lips, control robots, and induce grammars. I’ve had some long and deep discussions with Steve about advanced artificial intelligence, both my own approach and his own unique AI designs.
But he has also developed considerable expertise and experience in understanding and advising human minds and systems. Via his firm Self-Aware Systems, he has worked with clients using a variety of individual and organizational change processes including Rosenberg’s Non-Violent Communication, Gendlin’s Focusing, Travell’s Trigger Point Therapy, Bohm’s Dialogue, Beck’s Life Coaching, and Schwarz’s Internal Family Systems Therapy.
Steve’s papers and talks on the future of AI, society and technology – including The Wisdom of the Global Brain and Basic AI Drives — reflect this dual expertise in technological and human systems. In this interview I was keen to mine his insights regarding the particular issue of the risks facing the human race as we move forward along the path of accelerating technological develoment.
A host of individuals and organizations — Nick Bostrom, Bill Joy, the Lifeboat Foundation, the Singularity Institute, and the Millennium Project, to name just a few — have recently been raising the issue of the “existential risks” that advanced technologies may post to the human race. I know you’ve thought about the topic a fair bit as well, both from the standpoint of your own AI work and more broadly. Could you share the broad outlines of your thinking in this regard?
I don’t like the phrase “existential risk” for several reasons. It presupposes that we are clear about exactly what “existence” we are risking. Today, we have a clear understanding of what it means for an animal to die or a species to go extinct. But as new technologies allow us to change our genomes and our physical structures, it will become much less clear when we have lost something precious. Death and extinction become much more amorphous concepts in the presence of extensive self-modification.
It’s easy to identify our humanity with our individual physical form and our egoic minds. But in reality our physical form is an ecosystem, only 10% of our cells are human. And our minds are also ecosystems composed of interacting subpersonalities. And our humanity is as much in our relationships, interconnections, and culture as it is in our individual minds and bodies. The higher levels of organization are much more amorphous and changeable and it will be hard to pin down when something precious is lost.
So, I believe the biggest “existential risk” is related to identifying the qualities that are most important to humanity and to ensuring that technological forces enhance those rather than eliminate them. Already today we see many instances where economic forces act to create “soulless” institutions that tend to commodify the human spirit rather than inspire and exalt it.
Some qualities that I see as precious and essentially human include: love, cooperation, humor, music, poetry, joy, sexuality, caring, art, creativity, curiosity, love of learning, story, friendship, family, children, etc. I am hopeful that our powerful new technologies will enhance these qualities. But I also worry that attempts to precisely quantify them may in fact destroy them. For example, the attempts to quantify performance in our schools using standardized testing have tended to inhibit our natural creativity and love of learning.
Perhaps the greatest challenge that will arise from new technologies will be to really understand ourselves and identify our deepest and most precious values.
Yes…. After all, “humanity” is a moving target, and today’s humanity is not the same as the humanity of 500 or 5000 years ago, and humanity of 100 or 5000 years from now – assuming it continues to exist – will doubtless be something dramatically different. But still there’s been a certain continuity throughout all these changes, and part of that doubtless is associated with the “fundamental human values” that you’re talking about.
Still, though, there’s something that nags at me here. One could argue that none of these precious human qualities are practically definable in any abstract way, but they only have meaning in the context of the totality of human mind and culture. So that if we create a fundamentally nonhuman AGI that satisfies some abstracted notion of human “family” or “poetry”, it won’t really satisfy the essence of “family” or “poetry”. Because the most important meaning of a human value doesn’t lie in some abstract characterization of it, but rather in the relation of that value to the total pattern of humanity. In this case, the extent to which a fundamentally nonhuman AGI or cyborg or posthuman or whatever would truly demonstrate human values, would be sorely limited. I’m honestly not sure what I think about this train of thought. I wonder what’s your reaction.
That’s a very interesting perspective! In fact it meshes well with a perspective I’ve been slowly coming to, which is to think of the totality of humanity and human culture as a kind of “global mind”. As you say, many of our individual values really only have meaning in the context of this greater whole. And perhaps it is this greater whole that we should be seeking to preserve and enhance. Each individual human lives only for a short time but the whole of humanity has a persistence and evolution beyond any individual. Perhaps our goal should be to create AGIs that integrate, preserve, and extend the “global human mind” rather than trying solely to mimic individual human minds and individual human values.
Perhaps a good way to work toward this is to teach our nonhuman or posthuman descendants human values by example, and by embedding them in human culture so they absorb human values implicitly, like humans do. In this case we don’t need to “quantify” or isolate our values to pass them along to these other sorts of minds….
That sounds like a good idea. In each generation, the whole of human culture has had to pass through a new set of minds. It is therefore well adapted to being learned. Aspects which are not easily learnable are quickly eliminated. I’m fascinated by the process by which each human child must absorb the existing culture, discover his own values, and then find his own way to contribute. Philosophy and moral codes are attempts to codify and abstract the learnings from this process but I think they are no substitute for living the experiential journey. AGIs which progress in this way may be much more organically integrated with human society and human nature. One challenging issue, though, is likely to be the mismatch of timescales. AGIs will probably rapidly increase in speed and keeping their evolution fully integrated with human society may become a challenge.
Yes, it’s been amazing to watch that learning process with my own 3 kids, as they grow up.
It’s great to see that you and I seem to have a fair bit of common understanding on these matters. This reminds me, though, that a lot of people see these things very, very differently. Which leads me to my next question: What do you think are the biggest misconceptions afoot, where existential risk is concerned?
I don’t think the currently fashionable fears like global warming, ecosystem destruction, peak oil, etc. will turn out to be the most important issues. We can already see how emerging technologies could, in principle, deal with many of those problems. Much more challenging are the core issues of identity, which the general public hasn’t really even begun to consider. Current debates about stem cells, abortion, cloning, etc. are tiny precursors of the deeper issues we will need to explore. And we don’t really yet have a system for public discourse or decision making that is up to the task.
Certainly a good point about public discourse and decision making systems. The stupidity of most YouTube comments, and the politicized (in multiple senses) nature of the Wikipedia process, makes clear that online discourse and decision-making both need a lot of work. And that’s not even getting into the truly frightening tendency of the political system to reduce complex issues to oversimplified caricatures.
Given the difficulty we as a society currently have in talking about, or making policies about, things as relatively straightforward as health care reform or marijuana legalization or gun control, it’s hard to see how our society could coherently deal with issues related to, say, human-level AGI or genetic engineering of novel intelligent lifeforms!
For instance, the general public’s thinking about AGI seems heavily conditioned by science-fiction movies like Terminator 2, which clouds consideration of the deep and in some ways difficult issues that you see when you understand the technology a little better. And we lack the systems needed to easily draw the general public into meaningful dialogues on these matters with the knowledgeable scientists and engineers.
So what’s the solution? Do you have any thoughts on what kind of system might work better?
I think Wikipedia has had an enormous positive influence on the level of discourse in various areas. It’s no longer acceptable to plead ignorance of basic facts in a discussion. Other participants will just point to a Wikipedia entry. And the rise of intelligent bloggers with expertise in specific areas is also having an amazing impact. One example I’ve been following closely are debates and discussions about various approaches to diet and nutrition.
A few years back, T. Colin Campbell’s “The China Study” was promoted as the most comprehensive study of nutrition, health, and diet ever conducted. The book and the study had a huge influence on people’s thinking about health and diet. A few months ago, 22 year old English major Denise Minger decided to reanalyze the data in the study and found that they did not support the original conclusions. She wrote about her discoveries on her blog and sparked an enormous discussion all over the health and diet blogosphere that dramatically shifted many people’s opinions. The full story can be heard in her interview.
It would have been impossible for her to have had that kind of impact just a few years ago. The rapidity with which incorrect ideas can be corrected and the ease with which many people can contribute to new understanding is just phenomenal. I expect that systems to formalize and enhance that kind of group thinking and inquiry will be created to make it even more productive.
Yes, I see – that’s a powerful example. The emerging Global Brain is gradually providing us the tools needed to communicate and collectively think about all the changes that are happening around and within us. But it’s not clear if the communication mechanisms are evolving fast enough to keep up with the changes we need to discuss and collectively digest….
On the theme of rapid changes, let me now ask you something a little different — about AGI…. I’m going to outline two somewhat caricaturish views on the topic and then probe your reaction to them!
First of all, one view on the future of AI and the Singularity is that there is an irreducible uncertainty attached to the creation of dramatically greater than human intelligence. That is, in this view, there probably isn’t really any way to eliminate or drastically mitigate the existential risk involved in creating superhuman AGI. So, in this view, building superhuman AI is essentially plunging into the Great Unknown and swallowing the risk because of the potential reward.
On the other hand, an alternative view is that if we engineer and/or educate our AGI systems correctly, we can drastically mitigate the existential risk associated with superhuman AGI, and create a superhuman AGI that’s highly unlikely to pose an existential risk to humanity.
What are your thoughts on these two perspectives?
I think that, at this point, we have tremendous leverage in choosing how we build the first intelligent machines and in choosing the social environment that they operate in. We can choose the goals of those early systems and those choices are likely to have a huge effect on the longer-term outcomes. I believe it is analogous to choosing the constitution for a country. We have seen that the choice of governing rules has an enormous effect on the quality of life and the economic productivity of a population.
That’s an interesting analogy. And an interesting twist on the analogy may be the observation that to have an effectively working socioeconomic system, you need both good governing rules, and a culture oriented to interpreting and implementing the rules sensibly. In some countries (e.g. China comes to mind, and the former Soviet Union) the rules as laid out formally are very, very different from what actually happens. The reason I mention this is: I suspect that in practice, no matter how good the “rules” underlying an AGI system are, if the AGI is embedded in a problematic culture, then there’s a big risk for something to go awry. The quality of any set of rules supplied to guide an AGI is going to be highly dependent on the social context…
Yes, I totally agree! The real rules are a combination of any explicit rules written in lawbooks and the implicit rules in the social context. Which highlights again the importance for AGIs to integrate smoothly into the social context.
One might argue that we should first fix some of the problems of our cultural psychology, before creating an AGI and supplying it with a reasonable ethical mindset and embedding it in our culture. Because otherwise the “embedding in our culture” part could end up unintentionally turning the AGI to the dark side!! Or on the other hand, maybe AGI could be initially implemented and deployed in such a way as to help us get over our communal psychological issues…. Any thoughts on this?
Agreed! Perhaps the best outcome would be technologies that first help us solve our communal psychological issues and then as they get smarter evolve with us in an integrated fashion.
On the other hand, it’s not obvious to me that we’ll be able to proceed that way, because of the probability – in my view at any rate – that we’re going to need to rely on advanced AGI systems to protect us from other technological risks.
For instance, one approach that’s been suggested, in order to mitigate existential risks, is to create a sort of highly intelligent “AGI Nanny” or “Singularity Steward.” This would be a roughly human-level AGI system without capability for dramatic self-modification, and with strong surveillance powers, given the task of watching everything that humans do and trying to ensure that nothing extraordinarily dangerous happens. One could envision this as a quasi-permanent situation, or else as a temporary fix to be put into place while more research is done regarding how to launch a Singularity safely.
Any thoughts on the sort of AI Nanny scenario?
I think it’s clear that we will need a kind of “global immune system” to deal with inadvertent or intentional harm arising from powerful new technologies like biotechnology and nanotechnology. The challenge is to make protective systems powerful enough for safety but not so powerful that they themselves become a problem. I believe that advances in formal verification will enable us to produce systems with provable properties of this type. But I don’t believe this kind of system on its own will be sufficient to deal with the deeper issues of preserving the human spirit.
What about the “one AGI versus many” issue? One proposal that’s been suggested, to mitigate the potential existential risk of human-level or superhuman AGIs, is to create a community of AGIs and have them interact with each other, comprising a society with its own policing mechanisms and social norms and so forth. The different AGIs would then keep each other in line. A “social safety net” so to speak.
I’m much more drawn to “ecosystem” approaches which involve many systems of different types interacting with one another in such a way that each acts to preserve the values we care about. I think that alternative singleton “dictatorship” approaches could also work but they feel much more fragile to me in that design mistakes might become rapidly irreversible. One approach to limiting the power of individuals in an ecosystem is to limit the amount of matter and free energy they may use while allowing them freedom within those bounds. A challenge to that kind of constraint is the formation of coalitions of small agents that act together to overthrow the overall structure. But if we build agents that want to cooperate in a defined social structure, then I believe the system can be much more stable. I think we need much more research into the space of possible social organizations and their game theoretic consequences.
Finally – bringing the dialogue back to the practical and near-term – I wonder what you think society could be doing now to better militate against existential risks … from AGI or from other sources?
Much more study of social systems and their properties, better systems for public discourse and decision making, deeper inquiry into human values, improvements in formal verification of properties in computational systems.
That’s certainly sobering to consider, given the minimal amount of societal resources currently allocated to such things, as opposed to for example the creation of weapons systems, better laptop screens or chocolaty-er chocolates!
To sum up, it seems one key element of your perspective is the importance of deeper collective (and individual) self-understanding – deeper intuitive and intellectual understanding of the essence of humanity. What is humanity, that it might be preserved as technology advances and wreaks its transformative impacts? And another key element is your view is that social networks of advanced AGIs are more likely to help humanity grow and preserve its core values, than isolated AGI systems. And then there’s your focus on the wisdom of the global brain. And clearly there are multiple connections between these elements, for instance a focus on the way ethical, aesthetic, intellectual and other values emerge from social interactions between minds. It’s a lot to think about … but fortunately none of us has to figure it out on our own!
On August 27, 2010, Steve Omohundro gave a talk at Halcyon Molecular on “Complexity, Virtualization, and the Future of Cooperation”.
Here’s a pdf file of the slides:
Here’s the abstract:
We are on the verge of fundamental breakthroughs in biology, neuroscience, nanotechnology, and artificial intelligence. Will these breakthroughs lead to greater harmony and cooperation or to more strife and competition? Ecosystems, economies, and social networks are complex webs of “coopetition”. Their organization is governed by universal laws which give insights into the nature of cooperation. We’ll discuss the pressures toward creating complexity and greater virtualization in these systems and how these contribute to cooperation. We’ll review game theoretic results that show that cooperation can arise from computational limitations and suggest that the fundamental computational asymmetry between posing and solving problems and may lead to cooperation in an ultimate “game-theoretic physics” played by powerful agents.
On Saturday, December 5, 2009, Steve Omohundro spoke at the Humanity+ Summit in Irvine, CA on “The Wisdom of the Global Brain”. The talk explored the idea that humanity is interconnecting itself into a kind of “global brain”. It discussed analogies with bacterial colonies, immune systems, multicellular animals, ecosystems, hives, corporations, and economies. 9 universal principles of emergent intelligence were described and used to analyze aspects of the internet economy.
Here’s a pdf file of the slides:
The talks from the summit were streamed live over the internet by TechZulu and were watched by 45,000 people around the world! A video of the talk will eventually be available.
On Friday, May 22, 2009, Steve Omohundro spoke at the Bay Area Future Salon at SAP in Palo Alto on:
The Science and Technology of Cooperation
Here’s a pdf file of the slides:
A new science of cooperation is arising out of recent research in biology and economics. Biology once focused on competitive concepts like “Survival of the Fittest” and “Selfish Genes”. More recent work has uncovered powerful forces that drive the evolution of increasing levels of cooperation. In the history of life, molecular hypercycles joined into prokaryotic cells which merged into eukaryotic cells which came together into multi-cellular organisms which formed hives, tribes, and countries. Many believe that a kind of “global brain” is currently emerging. Humanity’s success was due to cooperation on an unprecedented scale. And we could eliminate much waste and human suffering by cooperating even more effectively. Economics once focused on concepts like “Competitive Markets” but more recently has begun to study the interaction of cooperation and competition in complex networks of “co-opetition”. Cooperation between two entities can result if there are synergies in their goals, if they can avoid dysergies, or if one or both of them is compassionate toward the other. Each new level of organization creates structures that foster cooperation at lower levels. Human cooperation arises from Haidt’s 5 moral emotions and Kohlberg’s 6 stages of human moral development.
We can use these scientific insights to design new technologies and business structures that promote cooperation. “Cooperation Engineering” may be applied to both systems that mediate human interaction and to autonomous systems. Incentives and protocols can be designed so that it is in each individual’s interest to act cooperatively.Autonomous systems can be designed with cooperative goals and we can design cooperative social contracts for systems which weren’t necessarily built to be cooperative. To be effective, cooperative social contracts need to be self-stabilizing and self-enforcing. We discuss these criteria in several familiar situations. Cooperative incentive design will help ensure that the smart sensor networks, collaborative decision support, and smart service systems of the eco-cities of the future work together for the greater good.We finally consider cooperation betweenvery advanced intelligent systems. We show that an asymmetry from computational complexity theory provides a theoretical basis for constructing stable peaceful societies and ecosystems. We discuss a variety of computational techniques and pathways to that end.
On March 19, 2009, Steve Omohundro gave a talk at City College of San Francisco on “Evolution, Artificial Intelligence, and the Future of Humanity”. Thanks to Mathew Bailey for organizing the event and to the CCSF philosophy club for filming the talk. It’s available on YouTube in 7 parts:
Evolution, Artificial Intelligence, and the Future of Humanity
by Steve Omohundro, Ph.D.
This is a remarkable time in human history! We are simultaneously in the midst of major breakthroughs in biology, neuroscience, artificial intelligence, evolutionary psychology, nanotechnology and fundamental physics. These breakthroughs are dramatically changing our understanding of ourselves and the nature of human society. In this talk we’ll look back at how we got to where we are and forward to where we’re going. Von Neumann’s analysis of rational economic behavior provides the framework for understanding biological evolution, social evolution, and artificial intelligence. Competition forced creatures to become more rational. This guided their allocation of resources, their models of the world, and the way they chose which actions to take. Cooperative interactions gave evolution a direction and caused organelles to join into eukaryotic cells, cells to join into multi-cellular organisms, and organisms to join into hives, tribes, and countries. Each new level of organization required mechanisms that fostered cooperation at lower levels. Human morality and ethics arose from the relation between the individual and the group. The pressures toward rational economic behavior also apply to technological systems. Because artificial intelligences will be able to modify themselves directly, they will self-improve toward rationality much more quickly than biological organisms. We can shape their future behavior by carefully choosing their utility functions. And by carefully designing a new social contract, we can hope to create a future that supports our most precious human values and leads to a more productive and cooperative society.