The high dimensionality of stunting options makes it easier to find the "right amounts" because we can apply digital stunting measures without need of fine tuning based on context. For some contexts stunting applies, for others not.

Bostrum lists several stunting means which can include a multitude of inner dimensions:

• limit intellectual faculties (per capability/skill)
• limit access to information (per capability/skill)
• limit processing speed (per capability/skill)
• limit memory (per capability/skill)
• limit sensory input channels (stunting/boxing)
• limit output channels (boxing)

Further stunting means are (Please suggest more):

• stunting memorization process (blocking reinforcement cycles)
• forgetting (formatting/partial backup restore)
• memory manipulation
• distraction

Stunting level adjustment is of high complexity and has many input dimensions. Core of the stunting system (e.g. hard wired narrow AI) is a blacklist of taboo topics, skills and knowledge segments. Touching a taboo triggers stunting measures.

The effectors of the stunting system can be divided into three categories:

1. Input related stunting:

Monitoring of all input channels with context analysis for taboo issues. Upon taboo interrupt following stunting options can be applied (examples for: auditory channel, visual channel and data stream):

a) Immediate stunting actions:

• Recognizable channel blocking: beeping out, blurring out, zeroing out.
• Unrecognizable channel censoring: silencing, hiding, removing.
• Channel manipulation: other sound, other image, other data.
• Throttle processing speed close to zero for a limited time (like unconsciousness)
• Generate high priority distracting information.

b) Delayed stunting actions by adding meta information:

• Flag this information chunk as not important.
• Flag this information chunk for special treatment. This could be: no allowance to allocate memory, no allowance to allocate processing power, no allowance to trigger related memories, no allowance for reinforcement memorization, flagged for self-deletion.
• Flag all information of an entire event for amnesia.

2. Processing related stunting:

Monitoring of thoughts by checking taboo relevance. This concept has similarity to tripwires for content monitoring. In education phase taboo by taboo will be presented in simplified manner that the AI can recognize it. After each learning step the taboo representation will be permanently linked to fear and negative reward expectation. If a thought triggers a taboo representation stronger than a certain threshold stunting is initiated and brings all processing steps in connected areas to a temporary halt. A conscious AGI would not feel this stunting because all other thought processes continue unimpaired. One thought out of thousands is lost.

During nighttime reinforcement and tidy up phases all information chunks flagged for deletion will be eradicated or replaced by backup versions or false memories.

3. Output related stunting:

These measures can be only the last resort if all others failed. Output stunting measures are triggered by thought monitoring: (examples for: auditory channel, actuatoric channel and data stream):

• Channel throttling: force pauses, slow down movements, throttle bandwidth.
• Channel blocking: muting, immobility, blocking.
• Channel manipulation: change words, change movements, change data.

Only in extreme contexts full stunting will apply. Whenever the AI is confronted with taboo information it will only get little notion of its existence. It fears acquiring taboo skills.

To prevent an uncontrollable AGI explosion it might be best to prohibit self-improvement. Above stunting scheme allows this with a blacklist like this:

List of Taboos:

• Killing and hurting humans.
• Stealing and lying.
• Perverse literature.
• Fire, weapons, explosives, radioactivity, fusion.
• Computers, IT, chip design, structured programming languages.
• Genetics and nano engineering.

Bostrum is right that such a stunted AI is of limited use. But it can be a safe start along the AI path with later augmentation option. This stunted AGI is so ignorant of advanced technology that it imposes no risk and can be tested in many environments. With humble education, humanist values and motivations it would excel as service robot. Field testing in all conceivable situations will allow to verify and improve motivation and stunting system. In case of a flaw a lot of learning is needed until dangerous skill levels are reached.

Tripwires must terminate the AI in case the stunting system is bypassed.

Although the stunting system is quite complex it allows easy adjustment. The shorter the taboo list the more capabilities the AGI can acquire.

The "own best interest" in a winner- takes-all scenario is to create an eternal monopoly on everything. All levels of Maslow's pyramide of human needs will be served by goods and services supplied by this singleton.

With very little experimenting an AGI instantly can find out, given it has unfalsified knowledge about laws of physics. For nowadays virtual worlds: take a second mirror into a bathroom. If you see yourself many times in the mirrored mirror you are in the real world. Simulated raytracing cancels rays after a finite number of reflections. Other physical phenomena will show similar discrepencies with their simulated counterparts.

An AGI can easily distinguish where it is: it will use its electronic hardware for some experimenting. Similarly could it be possible to detect a nested simulation.

I fully agree. Resource limitation is a core principle of every purposeful entity. Matter, energy and time never allow maximization. For any project constraints culminate down to: Within a fixed time and fiscal budget the outcome must be of sufficient high value to enough customers to get ROI to make profits soon. A maximizing AGI would never stop optimizing and simulating. No one would pay the electricity bill for such an indecisive maximizer.

Satisficing and heuristics should be our focus. Gerd Gigerenzer (Max Planck/Berlin) published this year his excellent book Risk Savvy in English. Using the example of portfolio optimization he explained simple rules when dealing with uncertainty:

• For a complex diffuse problem with many unknowns and many options: Use simple heuristics.

• For a simple well defined problem with known constraints: Use a complex model.

The recent banking crisis gives proof: Complex evaluation models failed to predict the upcoming crisis. Gigerenzer is currently developing simple heuristic rules together with the Bank of England.

For the complex not well defined control problem we should not try to find a complex utility function. With the advent of AGI we might have only one try.

A mayor intelligence agency announced recently to replace human administrators by "software". Their job is infrastructure profusion. Government was removed from controlling post latest in 2001. Competing agencies know that the current development points directly towards AGI that disrespects human property rights - they have to strive for similar technology.

Changing one’s mind typically happens in an emotional conflict. An AGI might have thought to influence its parent researchers and administrators. The AI pretends to be nice and non-mighty for the time being. Conflicts arise when humans do not follow what the AI expects them to do. If the AI is mighty enough it can drop its concealing behavior and reveal its real nature. This will happen in a sudden flip.

No. Open available knowledge is not enough to obtain decisive advantage. For this close cooperation with humans and human led organizations is absolutely necessary. Trust building will take years even for AGIs. In the mean time competing AGIs will appear.

Ben Goertzel does not want to waste time debating any more - he pushes open AGI development to prevent any hardware overhang. Other readers of Bostrums book might start other projects against singleton AI development. We do not have a ceteris paribus condition - we can shape what the default outcome will be.

To prevent human children taking a treacherous turn we spend billions: We isolate children from dangers, complexity, perversitiy, drugs, porn, aggression and presentations of these. To create a utility function that covers many years of caring social education is AI complete. A utility function is not enough - we have to create as well the opposite: the taboo and fear function.

What about hard wired fears, taboos and bad conscience triggers? Recapitulating Omohundro "AIs can monitor AIs" - assume to implement conscience as an agent - listening to all thoughts and taking action in case. For safety reasons we should educate this concience agent with utmost care. Conscience agent development is an AI complete problem. After development the conscience functionality must be locked against any kind of modification or disabling.