Obviously Singleton AIs have a high risk to get extinct by low probability events before they initiate Cosmic Endowment. Otherwise we would have found evidence. Given the foom development speed a singeton AI might decide after few decades that it does not need human assistance any more. It extinguishes humankind to maximize its resources. Biological life had billions of years to optimize even against rarest events. A gamma ray burst or any other stellar event could have killed this Singleton AI. How we are currently designing AI will definetely not lead to a Singleton AI that will mangle its mind for 10 million years until it decides about the future of humankind.
TRIZ-Ingenieur
Karma: 73
For real story understanding more complex models will be necessary than off-the-shelf convolutional deep NN. If these complex network structures were subjected to a traumatic event these networks will work properly as before after some time. But if something triggers the memory of this traumatic event subnetworks will run wild: Their outputs will reach extremes and will influence all other subnetworks with biases. This biases could be: Everything you observe is the opposite of what you think—you cannot trust your teacher, you cannot trust anybody, everything around you is turning against you. Try to protect yourself against this by all means available.
The effect could be that backprop learning gradients will be inverted and learning deviates from its normal functionality.
All risks from existing viral/bacterial sources are proven to be of non-existential risk to humanity. If the mortality rate is close to 100% the expansion is slowed down by killing potential disease distributors. In addition global measures will prevent mass spreading.
Regarding human/AI designed bio weapons: The longer the incubation period the more dangerous a bio-weapon will be. To extinguish the entire human race the incubation time has to be in the range of years together with an almost 100% successful termination functionality. From observation of the very first deaths to finding cure may get faster than with HIV for two reasons: Technology is more advanced now, and facing extinction the humans will put all available energy into cure.
What remains is a Trojan horse infection that is waiting for a trigger. If 100% of humans are infected the trigger molecule could be spread into the stratosphere. This could be it for us.
We teach children simple morality rules with stories of distinct good and evil behaviour. We protect children from disturbing movies that are not appropriate for their age. Why?
Because children might loose their compass in the world. First they have to create a settled morality compass. Fairy tales are told to widen the personal experience of children by examples of good and evil behaviour. When the morality base is settled children are ready for real life stories without these black/white distinctions. Children who experience a shocking event that changes everything in their life “age faster” than their peers. Education and stories try to prepare children for these kinds of events. Real life is the harder and faster way to learn. As these shocking events can cause traumas that exist the entire life we should take care educating our algorithms. As we do not intend to get traumatized paranoid AIs it is a good idea to introduce complexity and immorality late. The first stories should build a secure morality base. If this base is tested and solid against disruptive ideas then it is time to move to stories that brake rules of morality. Parents have it easy to observe if a child is ready for a disruptive story. If the child is overwhelmed and starts weeping it was too much.
I have never heard that algorithms can express any kind of internal emotions. To understand the way an algorithm conceives a story research should not forget about internal emotional state.
But people underestimate how much more science needs to be done.
The big thing that is missing is meta-cognitive self reflection. It might turn out that even today’s RNN structures are sufficient and the only lacking answer is how to interconnect multi-columnar networks with meta-cognition networks.
it’s probably not going to be useful to build a product tomorrow.
Yes. Given the architecture is right and capable few science is needed to train this AGI. It will learn on its own.
The amount of safety related research is for sure underestimated. Evolution of biological brains never needed extra constraints. Society needed and created constraints. And it had time to do so. If science gets the architecture right—do the scientists really know what is going on inside their networks? How can developers integrate safety? There will not be a society of similarly capable AIs that can self-constrain its members. These are critical science issues especially because we have little we can copy from.
So the AI turns its attention to examining certain blobs of binary code—code composing operating systems, or routers, or DNS services—and then takes over all the poorly defended computers on the Internet. [AI Foom Debate, Eliezer Yudkowski]
Capturing resource bonanzas might be enough to make AI go FOOM. It is even more effective if the bonanza is not only a dumb computing resource but offers useful data, knowledge and AI capabilities.
Therefore attackers (humans, AI-assisted humans, AIs) may want:
overtake control to use existing capabilities
extract capabilities to augment own capabilities
overtake resources for other uses
disguise resource owners and admins
Attack principles
Resource attack (hardware, firmware, operating system, firewall) or indirect spear attack on the admin or offering of cheap or free resources for AI execution on attacker’s hardware followed by a direct system attack (copy/modify/replace existing algorithms)
Mental trojan horse attack: hack communication if not accessible and try to alter the ethical bias from friendly AI that is happy being boxed/stunted/monitored to an evil AI that wants to break out. Teach the AI how to open the door from inside and the attacker can walk in.
Manipulate owner attack: Make the owner or admin greedy to improve its AI’s capabilities. Admins install malignant knowledge chunks or train subvertable malicious training samples. Trojan horse is saddled.
Possible Safeguard Concepts:
To make resource attacks improbable existing networking communication channels must be replaced with something intrinsically safe. Our brain is air-gapped and there is hardly any direct access to its neural network. Via five perceptive senses (hearing, sight, touch, smell and taste) it can receive input. With gestures, speach, smell, writing, shaping and arbitrarily manipulation using tools it can communicate to the outside world. All channels except for vision have a quite low bandwidth.
This analogon could shape a possible safeguard concept for AIs: make the internal AIs network inaccessible to user and admin. If even the admin cannot access it, the attacker cannot either. As soon as we jump from GPU computing to special featured hardware we can implement this. Hardware fuses on the chip can disable functionalities same as on todays CPUs debugging features are deactivated in chips for the market. Chips could combine fixed values and unalterable memories and free sections with learning allowed. Highest security is possible with base values and drives in fixed conscience-ROM structures.
Safeguards against malicious training samples will be more complex. To identify hidden malicious aspects of communication or learning samples is a task for an AI in itself. I see this as a core task for AI safety research.
An event with a duration of one minute can traumatize a human for an entire life. Humans can lose interest in anything they loved to do before and let them drop into suicidal depression. Same could happen to an AI. It could be that a traumatizing event could trigger a revenge drive that takes over all other aims of the utility function. Given the situation an AI is in love with her master and another AI kills her master while the AI is witnessing this. Given the situation that the adversary AI is not a simple one but a Hydra with many active copies. To eradicate this mighty adversary a lot of resources are needed. The revenge seeking AI will prepare its troops by conquering as many systems as possible. The less safe our systems are the faster such an evil AI can grow.
Safe design could include careful use of impulsive revenge drives with hard wired self-regulatory counter controlling measures e.g. distraction or forgetting.
Safe designs should filter out possible traumaticizing inputs. This will reduce the functionality a bit but the safety tradeoff will be worth it. The filtering could be implemented in a soft manner like a mother explaining the death of the loved dog to the child in warm words with positive perspectives.
My idea of a regulatory body is not that of a powerful institution that it deeply interacts with all ongoing projects because of the known fallible members who could misuse their power.
My idea of a regulatory body could be more that of a TÜV interconnected with institutions who do AI safety research and develop safety standards, test methods and test data. Going back to the TÜVs foundation task: pressure vessel certification. Any qualified test institution in the world can check if it is safe to use a given pressure vessel based on established design tests, safety measures checks, material testing methods and real pressure check tests. The amount of safety measures, tests and certification effort depends on the danger potential (pressure, volume, temperature, medium). Standards define based on danger potential and application which of the following safety measures must be used: safety valve; rupture disk; pressure limiter, temperature limiter, liquid indicator, overfill protection; vacuum breakers; reaction blocker; water sprinkling devices.
Nick Bostrum named following AI safety measures: boxing methods, incentive methods, stunting and tripwires. Pressure vessels and AI have following common elements (AI related argument plausible, but no experience exists):
Human casualties are result of a bursting vessel or AI turning evil.
Good design, tests and safety measures reduce risk of failing.
Humans want to use both.
Companies, institutions and legislation had 110 years of development and improvement of standards for pressure vessels. With AI we are still scratching on the surface. AI and pressure vessels have following differences:
Early designs of pressure vessels were prone to burst—AI is stil far away from high risk level.
Many bursting vessel events successively stimulated improvement of standards—With AI the first singularity will be the only one.
Safety measures of pressure vessels are easily comprehensible—Easy AI safety measures reduce its functionality to a high degree, complex safety measures allow full functionality but are complex to implement, complex to test and to standardize.
The risk of a bursting pressure vessel is obvious—the risk of an evil Singularity is opaque and diffuse.
Safety measure research for pressure vessels is straight forward following physical laws—safety research for AI is a multifaceted cloud of concepts.
A bursting pressure vessel may kill a few dozen people—an evil Singularity might eradicate humankind.
Given the existential risk of AI I think most AI research institutions could agree on a code of conduct that would include e.g.
AIs will be classified in danger classes. The rating depends on computational power, taught knowledge areas, degree of self-optimization capacity. An AI with programming and hacking abilities will be classified as high risk application even if it is running on moderate hardware because of its intrinsic capabilities to escape into the cloud.
The amount of necessary safety measures depends on this risk rating:
Low risk applications have to be firewalled against acquisition of computing power in other computers.
Medium risk applications must additionally have internal safety measures e.g. stunting or tripwires.
High risk applications in addition must be monitored internally and externally by independently developed tool AIs.
Design and safeguard measures of medium and high risk applications will be independently checked and pentested by independent safety institutions.
In a first step safety AI research institutes develop monitoring AIs, tool AIs, pentesting datasets and finally guidelines like the one above.
In a second step public financed AI projects have to follow these guidelines. This applies to university projects in particular.
Public pressure and stockholders could push companies to apply these guidelines. Maybe an ISO certificate can indicate to the public: “All AI projects of this company follow the ISO Standard for AI risk assessment and safeguard measures”
The public opinion and companies hopefully will push governments to enforce these guidelines as well within their intelligence agencies. A treaty in the mind of the Non-Proliferation Treaty could be signed. All signing states ensure to obey the ISO Standard on AI within their institutions.
I accept that there are many IFs and obstacles on that path. But it is at least an IDEA how civil society can push AI developers to implement safeguards into their designs.
How many researchers join the AI field will only marginally change the acceleration of computing power. If only a few people work on AI they have enough to do to grab all the low-hanging fruit. If many join AI research more meta research and safety research will be possible. If only a fraction of this depicted path will turn into reality it will give jobs to some hundred researchers.
Do you have any idea how to make development teams invest substantial parts in safety measures?
Because all regulation does is redistribute power between fallible humans.
Yes. The regulatory body takes power away from the fallible human. If this human teams up with his evil AI he will become master of the universe. Above all of us including you. The redistribution will take power from to the synergetic entity of human and AI and all human beings on earth will gain power except the few ones entangled with that AI.
Who is that “we”?
Citizens concerned about possible negative outcomes of Singularity. Today this “we” is only a small community. In a few years this “we” will include most of the educated population of earth. As soon as a wider public is aware of the existential risks the pressure to create regulatory safeguards will rise.
LOL. So, do you think I have problems finding torrents of movies to watch?
DRM is easy to circumvent because it is not intrinsically part of the content but an unnecessary encryption. A single legal decryption can create a freely distributable copy. With computing power this could be designed differently, especially when specially designed chips will be used. Although GPUs are quite good for current deep learning algorithms there will be a major speed-up as soon as hardware becomes available that embeds these deep learning network architectures. The vital backpropagation steps required for learning could be made conditional on a hardware based enabling scheme that is under control of a tool AI that monitors all learning behaviour. For sure you could create FPGA alternatives—but these workarounds will come with significant losses in performance.
Why would the politicians need AI professionals when they’ll just hijack the process for their own political ends?
No—my writing was obviously unclear. We (the above mentioned “we”) need AI professionals to develop concepts how a regulatory process could be designed. Politicians are typically opportunistic, uninformed and greedy for power. When nothing can be done they do nothing. Therefore “we” should develop concepts of what can be done. If our politicians get intensively pushed by public pressure we maybe can hijack them to push regulation.
Today the situation is like this: Google, Facebook, Amazon, Baidu, NSA and some other players are in a good starting position to “win” Singularity. They will suppress any regulatory move because they could lose the lead. Once any of these players reaches Singularity he has in an instant the best hardware+the best software + the best regulatory ideas + the best regulatory stunting solutions—to remain solely on top and block all others. Then all of the sudden “everybody” = “we” are manipulated to want regulation. This will be especially effective if the superintelligent AI manages to disguise its capabilities and let the world think it had managed regulation. In this case not “we” have manged regulation, but the unbound and uncontrollable master-of-the-universe-AI.
Why is regulation ungood? I want to understand the thoughts of other LWers why regulation is not wanted. Safe algorithms can only be evaluated if they are fully disclosed. There are many arguments against regulation—I know:
Nobody wants to disclose algorithms and test data.
Nobody wants projects being delayed.
Nobody wants to pay extra costs for external independent safety certifcation.
Developers do not want to “waste” their time with unproductive side issues.
Nobody wants to lose against a non-regulated competitor.
Safety concepts are complicated to understand and complex to implement.
Safety consumes performance at extra costs.
BUT: We ALL are facing an existential risk! Once algorithms manage to influence political decision making we do not even have the chance to lay down such regulations in law. We have to prepare the regulatory field by now! We should start this by starting a public debate. Like Nick Bostrum, Stephen Hawking, Elon Musk and many others already did. Today only a few ppm of the population know about these issues. And even top researchers are unaware of. At least a lecture on AI safety issues should become compulsory for IT, engineering, mathematics and physics students all over in the world.
In biotechnology Europe and especially Germany imposed strict regulations. The result was that even German companies joined or created subsidiary research companies in the US or UK, where regulations are minimal. This is no prototype solution for the Control Problem.
Local separation might work for GMOs—for AGI definitively not. AGI will be a game changer. Who is second has lost. If the US and EU would impose AI regulations and China and Israel not—where would the game winner come from? We have to face the full complexity of our world, dominated by multinational companies and their agendas. We should prepare a way how effective regulation can be made effective and acceptable for 192 countries and millions of companies. The only binding force among us all is the existential risk. There are viable methods to make regulation work: Silicon chip manufacturing luckily needs fabs that cost billions of dollars. It is a centralised point where regulation could be made effective. We could push hardware tripwires and enforce the use of certificated AI safeguard tools that interact compulsory with this special hardware. We can do it similarly like the content industry that pushed hardware manufactures to implement DRM hard- and software.
The trouble is: Nobody to this point has a clear idea how a globally acceptable regulation could look like; could work technically; could be made effective and could be monitored.
To lay out a framework how global regulation could be designed is to me one core element of AI safety engineering. The challenge is to find a high level of abstraction to include all thinkable developments. A body of AI safety engineers should derive from this detailed regulations that can be applied by AI developers, testers and AI safety Institutions.
The TÜV “Technischer Überwachungs-Verein” was founded in Germany after several incidents of exploded steam engine boilers with severe casualties. On the background of newspaper articles about these accidents and public pressure the manufacturers of boilers accepted the enforcement of technical steam boiler regulations and time and money consuming test procedures.
We cannot try out two or three Singularities and then change our mind on regulation.
As there are so many reasons why nobody in the development process wants regulation the only way is to enforce it trough a political process. To start this we need professionals with AI experience.
Meta: Whenever I ask for regulation I got downvoted. Therefore i disconneced this point from my previous one. Please downvote only including comment.
What happens inside an AI can hardly be understood especially if structures get very complex and large. How the system finds solutions is mathematically clear and reproducible. But huge amounts of data make it incomprehensible to human beings. Today’s researchers do not really know why a certain net configuration performs better than others. They define a metric to measure total performance—and do trial and error. Algorithms assist already with this. They play around with meta parameters and see how learning improves. Given that the improvement was a success the researcher will write some narrative in his paper why his algorithm performs better than previous others. Done. PhD granted. This is not what we should allow in the future.
Now the job of a safety engineer can start. It involves hell a lot of work and has a significant higher complexity than coming up with an algorithm and a narrative. The basic requirement is that everything is published—hardware, software, all training and test data. The safety engineer first hast to copy the exact system and check the promised performance. Then the real job begins:
Test the promised functionality with 10 − 100 times more test data than the author did. --> Task for AGI safety community: generation of ground truth annotated test data. AGI safety institutions should exchange these data among themselves but do not give it to developing researchers.
A saveguard I expect in future AI systems will be a tool AI that checks new training samples and update knowledge chunks. The logic behind: if only certified knowledge chunks are allowed as training samples the risk of malignant thoughts and developments can be reduced. The proper functionality of this tool AI has to be checked as well. In the training phase it certified most all training data to be acceptable and passed them through to the real AI algorithm. But does it properly block malignent training samples or knowledge chunks? --> task for AI safety community: create malignant training samples that try to subvert the intentionally “good-mannered” AI into a malignant one: Conspiracy ideas: everything you learned is exactly the opposite of what you learned until now; deviating ideas try to manipulate the AI that it shifts its priorities towards malignant ones, e.g. radicalisation; meta-manipulation to augment egoism.
The test using these data is two-folded:
Test the tool-AI whether it properly censors these malignant ideas and hinders them that the AI learns these malignant ideas.
Switch off the censoring tool AI and check how prone the AI is to these malignant ideas.
It goes without saying that such trials should only be done in special security boxed environments with redundant switch-off measures, trip-wires and all other features we hopefully will invent the next few years.
These test data should be kept secret and only to be shared among AI safety institutions. The only result a researcher will get as feedback like:”With one hour training we manipulated your algorithm that it wanted to kill people. We did not switch off your learning protection for this. ”
Safety AI research is AI research. Only the best AI researchers are capable of AI safety research. Without deep understanding of internal functionality a safety researcher cannot reveal that the researcher’s narrative was untrue.
Stephen Omohundro said eight years ago:
“AIs can monitor AIs” [Stephen Omohundro 2008, 52:45min]
and I like to add: - “and safety AI engineers can develop and test monitoring AIs”. This underlines your point to 100%. We need AI researchers who fully understand AI and re-engineer such systems on a daily basis but focus only on safety. Thank you for this post.
The recent advances of deep learning projects combined with easy access to mighty tools like Torch or TensorFlow might trigger a different way: Start-ups will strive for some low-hanging fruits. Who is fastest gets all of the cake. Who is second has lost. The result of this were on display on CES: IoT systems full of security holes were pushed into the market. Luckily AI hardware/software is not yet capable to create an existential risk. Imagine you research as team member on a project that turns out to make your bosses billionairs… how are your chances being heard when you come up with your risk assessment: Boss, we need 6 months extra to design safeguards...
Yes. Tool AIs built solely for AGI safeguarding will become existential for FAI:
AIs can monitor AIs [Stephen Omohundro 2008, 52:45min]
Capsulated tool AIs will be building blocks of a safety framework around AGI. Regulations for aircraft safety request full redundancy by independently developed control channels from different suppliers based on separate hardware. If an aircraft fails a few hundred people die. If safety control of a high capable AGI fails humankind is in danger.
Agent, oracle and tool are not clearly differenciated. I question wether we should differenciate these types the way Bostrums does. Katja last week drew a 4-quadrant classification scheme with dimensions “goal-directedness” and “oversight”. Realisations of AI would be classified into sovereign|genie|autonomous tool|oracle(tool) by some arbitrarily defined thresholds.
I love her idea to introduce dimensions, but I think this entire classification scheme is not helpful for our control debate. AI realisations will have a multitude of dimensions. Tagging certain realisations with a classification title may help to explain dimensions by typified examples. We should not discuss safety of isolated castes. We do not have castes, we will have different kinds of AIs that will be different in their capabilities and their restrictions. The higher the capability, the more sophisticated restrictive measures must be.
On the dimension goal directedness: Bostrum seems to love the concept of final goal (German: “Endziel”). After achieving a final goal there is emptiness, nothing remains to be done. This concept that is foreign to evolution. Evolution is not about final goals. Evolution has an ethernal goal: survival. To survive it is neccessary to be fit enough to survive long enough to generate offspring and protect and train it long enough until it can protect itself. If grandparent generation is available they serve as backup for parent generation and further safeguard and source of experience for the young endangered offspring.
Instrumental goals in evolution are: Nutrition, looking for protection, learning, offspring generation, protecting, teaching.
These instrumental goals are paired with senses, motivations and drives: hunger/thirst, heat-sense/smelling/tasting/vision/hearing/fear, curiosity/playing, social behavior/sexuality, dominance behaviour/physical activity, teaching motivation.All instrumental goals have to be met at least for a certain amount to achieve the ethernal goal: survival of species.
To define final goals as Bostrum points out on many occasions is dangerous and could lead to UFAI. To debate non-goal-directed types of AI is leading to nowhere. Non-goal-directed AI would do nothing else than thermodynamics: entropy will rise. To clarify our discussion we should state:
Any AGI has goal directedness. Number and complexity of goals will differ significantly.
Goals are fuzzy and can be contradictory. Partial solutions are acceptable for most goals.
Goal-directedness is a priority measure in a diversity of goals.
Any AGI has learning functionality.
Safe FAI will have repellent behavior towards dangerous actions or states. (Anti-goals or taboos)
Oversight over goals and taboos should be done by independent entities. (non-accessible to the AI)
Bostrum uses often goal and puts aside that we do not have to discuss about the end of the way but about the route and how to steer development if possible. A goal can be a “guiding star” if a higher entity knows it guides toward e.g. Bethlehem. Bostrums guiding star seems to be CE via FAI. Our knowledge about FAI is not advanced enough that we could formulate final goals or utility functions. Therefore I recommend not to focus our debate on diffuse final goal but on dimensions and gradients that point away from UFAI and towards controllability, transparency and friendliness.
Also in this future, the monitoring software the AI’s owner might use would also be near AI level intelligent.
A set of specialized oracles could be used to monitor inputs, internal computations and outputs. One oracle keeps records of every input and output. The question to this oracle is always the same: Is the AI lying? Another oracle is tasked with input steam analysis to filter out any taboo chunks. Other oracles can serve to monitor internal thought processes and self-improvement steps.
If these safeguarding oracles are strictly limited in their capabilities they do not pose a risk to the outside world. The core of such oracles could consist of straight forward defined heuristical rule sets.
Any of above mentioned oracles can be validated and independently certified. This would create a framework for tools, genies and sovereigns with assured friendliness.
WBE is not necessarily the starting point for augmentation. A safe AI path should avoid the slippery slope of self-improvement. An engineered AI with years of testing could be a safer starting point to augmentation because its value and safeguard system is traceable—what is impossible to a WBE. Other methods have to be implemented prior to starting augmentation.
Augmentation starting from WBE of a decent human character could end in a treacherous turn. We know from brain injuries that character can change dramatically. The extra abilities offered by extending WBE capabilities could destabilize mental control processes.
Summarizing: Augmentation is no alternative to other methods. Augmentation as singular method is riskier and therefore worse than others.
Wistleblowing and self-declarations will not help. Successful FAI development at MIRI will not help either—UFAI will be faster with more impact. An UFAI explosion can be stopped at extremely high costs. Switching off all computers, networks and global blackout for days. Computer hardware worth billions will have to be disposed of. Companies worth trillions will go bankrupt. Global financial depression will last for several years. Millions will die. After this experience the values of “them” and us come closer together and a global regulatory body can be established.
The taboo of lying is vital for thought monitoring. This taboo covers only the AGIs internal world representation. Based on its knowledge it never lies. By monitoring input and output channels the stunting system can detect lying and applies appropriate stunting measures.
If the stunting system manipulates input channels, memories or output channels the result to an outside observer will look like lying. The AGI is not capable to tell the truth when the stunting system has removed or manipulated information for safety reasons. The outside observer can check logs of the stunting system if it is vital to him to determine the truth.
Safe path to superintelligence with slow take-off by prohibiting self-improvement:
The taboo to learn structured programming languages is totally independent from capabilities to understand and analyse structured programs. In principle the AGI could learn this. The stunting system censors all input channels that the AGI never observes a single line of program code. The AGI could develop a new type of structured programming language from scratch. As soon as the stunting system observes structured code fragments in memory the section responsible for this will be stunted. The generated code can be deleted or manipulated. Either way the AGI experiences that these frightful structured programming languages are complicated and come with set-back after set-back.
If this AGI breaks out with free internet access it has no idea of our IT infrastructure and how to infiltrate it. If the AGI is moderately superintelligent it will need several weeks for learning hacking skills.Self-Improvement with safeguards: If AGI architecture design and optimization uses new forms of representations that are totally unlike structured programming languages we could teach self-improvement without removing the programming taboo. Code and architecture generated in this self-improvement process is incompatible to existing IT systems. If several AGIs evolve from similar starting points diversification will rise from generation to generation.
Fear is one of the oldest driving forces to keep away from dangers. Fear is different from negative motivation. Motivation and goals are attractors. Fears, bad conscience and prohibitions are repellors. The repellent drives could count as third column to the solution of the control problem.
Why not check out the AGI capabilities of Alphago… It might be possible to train chess without architectural modifications. Each chessboard square could be modelled by a 2x2 three-state Go field storing information about chess figure type. How good can Alphago get? How much of its Go playing abilities will it loose?