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If IQ is used as a measure of intelligence in humans, could it also be used as a measure of intelligence in machines?
If IQ were used as a measure of the intelligence of machines, as in humans, at this point in time, what would be the IQ of our most intelligent AI systems?
If not IQ, then how best to compare our intelligence to a machine, or one machine to another?
This question is not asking if we can measure the IQ of a machine, but, if IQ is the most preferred, or general, method of measuring intelligence, then how does artificial intelligence compare to our most accepted method of measuring intelligence in humans.
Many people may not understand the relevance of a Turing Test as to how intelligent their new car is, or other types of intelligent machines.

Possible duplicate of Can the IQ of an AI program be measured?

It depends on how the IQ test is presented:

If as for humans (effectively, as a video of the book containing the
test questions being opened etc), then all AI programs would score
zero.
If presented as the test set of a supervised learning problem (e.g. as for Bongard Problems) then one might imagine that a number of ML rule induction techniques (e.g. Learning Classifier Systems, Genetic Programming) might achieve some limited success.

So all current AI programs require the problem to be 'framed' in a suitable fashion. It doesn't take too much thought to see that removing the need for such 'framing' is actually the core problem in AI, and (despite some of the claims about Deep Learning), eliminating framing remains a distant goal.
More generally (just as with the Turing test), in order for an IQ test to be a really meaningful test of intelligence, it should be possible as a side effect of the program's capabilities, and not the specific purpose for which humans have designed it.
Interestingly, there is only one program that I'm aware of that sits between 1. and 2.:
Phaeaco (developed by Harry Foundalis at Douglas Hofstadter's research group) takes noisy photographic images of Bongard problems as input and (using a variant of Hofstadter's 'Fluid Concepts' architecture) successfully deduces the required rule in many cases.

at this point in time, what would be the IQ of our most intelligent AI systems?

Zero.
There are many different kinds of IQ tests including written, visual, and verbal assessments, but the majority of questions are based on abstract-reasoning problems that involve creative thinking and true intelligence.
In other words, the computer would have to exhibit something that does not yet exist… "strong AI".
The intelligent computers of science fiction do not exist. At all. We are not even close. We have absolutely NO IDEA how to bridge the gap between what we can do now and what is depicted in pop-culture films. Even with cars that drive themselves and computers that play 'Go' — an underachieving mosquito possesses more cognitive intelligence than all the world's super computers …combined!
…or possibly "disqualified" for cheating.
Even if we could pre-format the questions in a style and delivery system it understands, what does memorization, attention, or speed mean in the context of a computer? I'm not even sure if a standardized IQ test makes sense in this context. It might be like asking how a computer would do in a spelling bee.
In human terms, we're not allowed to bring along reference materials to look up an answer; but how do you rectify that when reference-lookup is innate to a computer's existence? How do you measure memory when storage is non-volatile? This gets into an existential question about the nature of learning and knowledge vs. just taking a lot of notes.
Still, how do you even teach a computer what is meant by "which animal is least like the other four?" Did the computer really figure out what was being asked out of general intelligence, or is the computer simply designed to parse out IQ-style questions specifically? If you designed something with a foreknowledge of what would likely be asked, the computers of today might simply be able to "recognize" it as question-style 496.527b and plug in the variables.
But that's not general intelligence by any definition we use or understand. It's just a specialized, slick interpreter designed to parse out a specific type of standardized question. Ask it a style of question which it is not expecting, and you'll see the computer is exhibiting no innate intelligence at all.
Until we create strong AI, a computer has effectively no IQ.

"how do you even teach a computer what is meant by "which animals is least like the other four?"". Like this: http://www.foundalis.com/res/diss_research.html

@NietzscheanAI Yes, but I'm talking about understanding the nature of the question itself. That's the kind kind of general intelligence I'm describing in the context of this post.

Looking at our respective answers, we seem to be in agreement about the core issue: transfer learning is what's missing from AI. What I'm saying is that (of all current AI architectures) the 'Fluid Concepts' approach is closest in spirit to achieving that, by virtue of the flexibility with which it manipulates/analogises domain knowledge.


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Is anybody still researching GOFAI?
A lot of textbooks and introductory lectures typically split AI into connectionism and GOFAI (Good Old Fashioned AI). From a purely technical perspective, it seems that connectionism has grown into machine learning and data science, while nobody talks about GOFAI, Symbolic AI, or Expert Systems at all.
Is anyone of note still working on GOFAI?

Oh yeah, definitely.  Just to pick one example, you have Douglas Hofstader's group at Indiana.  I think most of what they do would fall under the rubric of GOFAI (or at least closer to that than the statistical machine learning stuff).
Beyond that, just go to the CORR and browse around the AI category. You'll see plenty of neural networks and probabilistic stuff, but you'll also find the papers by the folks doing symbolic processing / GOFAI as well.

I don't think the work at Hofstadter's group really classifies as GOFAI. Even 30 years ago (when CopyCat was at the height of development) the work there was a novel hybrid of symbolic and subsymbolic approaches. But yes, the work there now is certainly distinct from the vogue for statistical ML.

I mean, yeah, you can argue either side of that, but for the purpose of this discussion, I think it's fair to lump their stuff in with GOFAI.  It's not like there's a really strict definition of what GOFAI means anyway. :-)

Yup, it's a matter of opinion.

Sure! There's the whole Semantic Web scene! OWL is derived from DLs and Frames, arguably has a lot in common with semantic networks too. Expert-driven decision support systems are still being developed (and researched) in industries where the human is required to take responsibility or getting data is not going to happen. As the ideas evolve so do the names.
Check out the academic conferences like KR, ISWC, FOIS, even broader AI conferences like IJCAI have a healthy dose of symbolic  AI, I even spotted a search algorithm in the 2019 line up.

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Is AI living or non-living?
I'm a bit confused about the definition of life. Can AI systems be called 'living'? Because they can do most of the things that we can. They can even communicate with one another.
They are not formed of what we call cells. But, you see, cells are just a collection of several chemical processes which is in turn non-living just like AI is formed of several lines of code.

You're unsure about the definition of life (which the other answers clarify) but also most people are unclear about the definition of AI. Do you mean an AI that can accomplish a routine task (such as the path finder in a GPS) or a General AI that is able to find a creative solution to any directive given to it (such an AI does not yet exist and may not ever exist) or do you mean a SENTIENT computer program? Here is a simple article introducing some different concepts refered to as AI
Some people believe that a sentient computer program would be entitled to human rights. Not technically 'alive' in the biological sense, but having self awareness, will, desires, etc. Others disagree and believe that the program is a mere simulation that artificially mimics the actions of a human with a human soul, and is no more human than a washing machine. This is a very deep philosophical and meta-physical debate. For example, in A.I. the movie the overall message is that an android can simulate the emotion of love in a way that is more loyal and sincere than any human.
What I find interesting about this purely theoretical debate is that in almost every instance of sci-fi media that deals with the theme, the AI exists inside of a human-like android. But technically, the shape of the robot should be irrelevant.

Adding some references would be useful.

This answer deals mainly with science fiction media; and the media's glorified portrayal of nonexistent science. I feel it does not appropriately respond to the question.

True. I just assumed that his confusion over wether AI is alive must have stemmed from science fiction, since it is so obviously not in reality.

@motoku my sense is that the best speculative fiction is narrative philosophy in the tradition of Plato (Asimov, PKD, Rajaneimi, Gibson, & Stross chiefly in regard AI.)  Although I don't regard mythical reality as equivalent to physical reality (although they can intersect in the phenomenal world, b/c it involves perception.)  It's important to draw a hard line between science & philosophy, but artistic and religious insight often precede hard science.

Artificial intelligence by definition is the intelligence exhibited by machines.  The definition of life in biological terms is the condition that distinguishes organisms from inorganic matter where the distinguishing criteria are the capacity for growth, reproduction, functional activity, and continual change preceding death. Does artificial intelligence "grow"?  Indeed, I can program a machine learning program to grow with every input taken in.  In the loosest sense, we can say that artificial intelligence does grow, but does it biologically? If we look at the definition for growth of a living thing, it means to undergo natural development by increasing in size and changing physically or the progress to maturity.  All living organisms undergo growth.   Even though at the simplest level, cells are a series of chemical processes, cells are a very complicated set of chemical processes that are still not fully understood by scientists across the world.  Every cell has genetic material that can be replicated, excised, used for RNA, proteins, and that is subject to epigenetic regulation.

Does artificial intelligence undergo the same process of cell division?  No.  If I wanted to, I could write a program that undergoes a simple for-loop (print i from 1 to 100), replicates itself at a certain point (i=50) to produce the same program perhaps with some variation that will execute itself, and terminates (dies) at the end of the for loop.  The program, by an extremely loose definition supported by philosophy but not by biology, lives.  However, in scientific terms (and the correct interpretation), artificial intelligence is not living.  Artificial intelligence can be seen to be similar to viruses which are considered to be acellular and essential to life but not living.  Viruses are encapsulated DNA and RNA that undergo processes of growth, reproduction, and functionality but because they lack the ability to undergo the cell division cycle, are considered non-living.  At the very basis of the scientific definition of life is the cell replication cycle.  Artificial intelligence and viruses are not able to undergo the cell cycle.  Viruses need to infect other cells in order to reproduce but do not have their own, autonomous cycle.  At the end of the day, if you can argue that viruses are alive, you can argue that artificial intelligence is alive as well.  For the scientific definition of life, artificial intelligence must undergo the process of cell division and replication.  Even though artificial intelligence can mimic and help sustain life, no artificial intelligence process is truly alive.
Do note I did not discuss living systems in my answer.
Definition of life

@VeenG I feel your definition relies too much on biology. There is also an information-centric definition of life where living things are viewed as thermodynamic systems that can reproduce and evolve as survival dictates.

Hey Seth, thank you for your comment.  I was not aware of this information-centric definition.  I will look into it and update my response.  Thank you.

What is life? AND Is AI a living organism? are two different questions.
The first question is more philosophical and dependent. It can change with time, reference to topic of discussion or something else. Today, one parameter to its definition is mortality. In future if we reach to a certain technological level where mortal beings were only part of history, then the definition will drop this parameter.
Coming to the second question. AI started as field of study to make machines to think like humans (or take rational decisions). Giving life to machines was, or is, not a concern of AI developers (at-least not nowadays). Once I watched some videos of Michio Kaku, where he talked about consciousness along with AI.
Suppose human has a conscious level of 10. Then a thermostat might have the conscious level of 1 as it can sense when the surrounding is hot or cold and then take decision. Similarly a rat can have a conscious level 7 (or something). And the levels are of exponential order (not a linear scale). Similarly you can develop an AI program and check what level of consciousness it has achieved. Then you can decide whether it is living or not. ANI (Artificial Narrow Intelligence) will have a lower level of consciousness level than AGI (Artificial General Intelligence). ASI (Artificial Super Intelligence) will have consciousness level higher than the other two, and way higher than any human being.
To judge whether an AI program is living or not you need a concrete definition of "LIFE". Your definition can include various parameters like consciousness, adaptability, metabolism (or another method of generating energy for use), rational behavior, intelligence , learning through experience, etc. etc. etc.
But the thing in the end is that its your definition. There are many definitions of "LIFE" out there. You can't judge a program for life by all definitions, as some of the definitions are contrary to others.

So, answer to whether an AI program is living or not, is that IT DEPENDS. Depends on your definition of life.

Your conclusion is correct. It mostly depends with our definition of life.

A common predilection of what many presume extraterrestrial life is fits general descriptions specific to terrestrial life. No guarantee exists providing for potential extraterrestrial life having any notion of any attribute we commonly relate to living organisms we are currently aware of; including a composition of cells. The same misunderstanding applies to defining a fabricated machine being as alive.
I feel any attempts towards cohesively and adequately answering this question are premature. Just as as definition of life will undoubtedly require adjustment upon potential discovery and study of any extraterrestrial life, differentiation between an automated device and a living thing will likely become significantly more simple upon study of a machine better fitting expected attributes of definitions of "life".

One of the most common requirements to be defined as life is abbreviated to MRS GREN
this means:
M - movement
R - respiration
S - sensitivity
G - growth
R - reproduce
E - excretion
N - nutrition
An AI can technically do some of these, it can move its location from device to device, it can grow its own code, and assimilate other bits of code it can find, which fits growth and kind of fits respiration also firewalls could almost be sensitivity.
But then there is nothing relating to nutrition or excretion, so it fits most definitions of life, but it depends on the complexity of life and which definition of life you are using.

Could you link to references on the abbreviation?

@EricPlaton  this is such a comm thing, you can find this anywhere, just search Mrs gren living biology or something along those lines

I am just suggesting to make your answer more self-contained.

Relating to nutrition and excretion, if you think of resources and garbage collection (due to memory allocated that is no longer being used), you have completed AI life according to MRS GREN.

My point is, by proving A.I. possesses all those qualities you may still not prove A.I. has life. Using a group of terms to define one specific term will lead people to associate those terms to others familiar to the context they are trying to explain... In the end, a match can always be found but this will not prove that the specific term you are using as a reference is the same as the one in the new context you are trying to explain.

I'd say "M: machine; ability to convert energy into force; R: revitalization, ability to renew energy production atoms; S: senses; ability to perceive; G: genes, code which allows an organism to manifest; R: reproduce, make copies;  E: evacuation, of material of data, flushing the system; N: nab, ability to gather physical resources.  (Of course, I might also posit that life in, its most basic form, only requires one of the 7;)

A definition of life

The property or quality that distinguishes living organisms from dead organisms and inanimate matter, manifested in functions such as metabolism, growth, reproduction, and response to stimuli or adaptation to the environment originating from within the organism.

The characteristic state or condition of a living organism.

Here's another definition

The condition that distinguishes animals and plants from inorganic matter, including the capacity for growth, reproduction, functional activity, and continual change preceding death.

Yet another definition.

Life is a characteristic that distinguishes physical entities that have biological processes, such as signaling and self-sustaining processes, from those that do not, either because such functions have ceased (they have died), or because they never had such functions and are classified as inanimate. Various forms of life exist, such as plants, animals, fungi, protists, archaea, and bacteria.

AIs (or, in general, computers) do not have a real metabolism, do not really reproduce, do not respond to stimuli or adapt to new circumstances (that is, circumstances they have not been programmed to deal with). AI does nothing without human intervention or it lacks real autonomy. In other words, if you do not turn the computer on and you do not program it, it really does nothing. A computer is a useful tool that you can use thanks to electricity. You can plug and unplug it indefinitely, but you cannot kill and revive a living being indefinitely.
Even though computers may possess (at least, conceptually) some properties similar to the properties of certain living beings, it does not mean they are living beings. Similarly, airplanes are not birds. Computers are not living beings, but this does not prevent you from drawing a comparison between computers and living beings, provided you are aware of their actual big differences. In fact, many useful AI software is inspired by the behavior of certain living beings or natural processes. For example, ant colony optimization algorithms are based on the behavior of real ants seeking a path between their colony and a source of food.
Here's what (biological) life looks like.

Let us continue this discussion in chat.

In the traditional sense of "alive", no because they aren't made of cells.  But from a more philosophical and less biological point of view, they could be.
If the AI is contained within the computer it is in a reality (the digital world/virtual reality) that for the AI is just as real as the universe is to us.  From the outside world, there is no life inside the computer. And from within the computer, the computer is the entire known universe which has its own laws.  If the AI is self-aware, then it is alive in its own little universe, but not in ours.
If the AI is not successfully contained in the computer and figures out how to manipulate things and evolve in the real world, it will be alive.  It might be pretty easy to kill (by unplugging the computer) but it has still been "alive".  In the broad sense, anything that evolves and can manipulate its environment is alive.

What about a post-singularity scenario where matter and information are interchangeable?  If a humans can transition between the computing and physical universes, are they no longer alive when existing in the computing medium?

Definitions of what life is usually come from biologists. The problem here is they are usually concerned with the traits common to the forms of life available to their studies, and that those forms of life all have a common origin (and this imposes a statistical bias on the observations).
As we gradually erode the boundaries of the standard definitions of life, by means of creating ever more complex machines and also by harnessing biological material as a form of nanotechnology), it's very likely that at some point in the future our traditional definition of life will need to be updated and further abstracted away from its current reference points (aka the "terroan biota").
A probably better question to ask to decide if something can be considered alive could be "is it self sufficient?" or "can it care for itself and provide for its own needs to some extent?".

Mind the hardware:
While there are different definitions of what life (synonymously used with 'organism' here (source: Wikipedia: Life) is, e.g.

All types of organisms are capable of reproduction, growth and development, maintenance, and some degree of response to stimuli. (Source: Wikipedia: Organism)

they all have one thing in common:
they require physical matter!
In contrast, to ask whether AI is alive is comparable to asking whether the human mind is alive. It is per definition not! Therefore, the question needs to be extended to include the hardware. It is rather 'are machines/computers alive' or 'do machines/computers have the potential to be considered alive'?
We are talking about agents and most likely robots:
And more specifically any machine/computer to be potentially considered alive will most likely need to be an agent,as it needs to interact with its environment (see Wikipedia: Intelligent agent for a description of agents in Computer Science).
Also, any potentially intelligent machine/computer needs most likely to be a robot due to the strong emphasis on physical processes, incl. some kind of exchange with the physical environment  (perception or manipulation of it), which our common definitions of life carry.
Some requirements for life are easy to fulfill while others are not:
Based on the requirement for life forms to maintain and reproduce their entities any machine/computer to be considered intelligent will need to be able to physically maintain and reproduce, i.e. assemble hardware. If you think of an intelligent robot assembling another robot that might sound to be very far away from reality. However, the definition might include indirect reproduction e.g. using an automated hardware production facility. Certainly this is not the direct reproduction that we know from current living beings but it might be considered an indirect way to reproduce. Which, however, is certainly far away from current reality too.
Similarly you could think of 'maintain' as taking care of the physical need to supply itself with electricity. Any machine with a solar panel easily fulfills this requirement in a similar way plants do.
While machines/computers considered alive do not need to have any artificial intelligence it is an easy way to fulfill the requirement of 'development':
Sub-symbolic AI learns from data which is a form of (non-physical, i.e. software-related) development. Just like humans and other animals learn from data that comes in through one of their senses.
Give it time:
To summarize: current machines/computers certainly do not fulfill the requirements usually being considered 'life'. And especially the requirement to (physically) maintain itself and reproduce will be longstanding unfulfilled requirements. However, considering that the homo sapiens has been on this planet for about 150,000 years we might just need to give it more time. It took about 1 billion years for the first living beings to develop on planet earth (see Wikipedia: Earliest known life forms). So it is a bit early to make a call on machines/computers which in the case of computers have been around for not even a century. Let's see where we stand in 1000, 1000000 or 10000000 years from now.
However, the definitions might change anyway:
Moreover, it is important to note that the definition of life is closely built on what we know as current carbon-based life forms. And it could very well be adjusted in the light of machines/computer further developing. For example the aspects of physical reproduction might be one aspect to be dropped (just speculating here). So maybe we do not even need to wait a billion years but might have machines/computers considered 'alive' already in 200 or 500 years. Compared to the time biological life took to develop that would still be very rapid.

Your assumption that, by definition, the mind (and I assume you mean brain) is not alive is wrong. Your brain can be alive or dead. In general, any of your organs can be alive or dead. For example, the heart can stop (it is dead), but the brain can still function, so, overall, you may still be alive. Your organs are made up of cells, which are the smallest living things.

I used the word 'mind' and not 'brain'. Which is an important difference. And you are mixing these terms up. The mind cannot be considered alive without being related to physical matter (particularly the brain). Similarly AI cannot (potentially) being considered alive without relating to hardware. For the same reason: the common definitions of life inherently refer to physical matter. Also, I did not try to say that living beings only have matter in common. But as just explained: all definitions of life relate to physical matter, i.e. it is a necessary condition.

' There is only a brain, which is your mind.' this has actually been a longstanding discussion in philosophy for the last ~2300 years (read up the mind-body-problem). The most common thinking across different 'new' (new compared to philosophy) fields of science (like biology, psychology and neuroscience) today is that the brain creates the mind. And still the words have a different meaning because the mind is not a physical entity but rather an abstract concept. Just as AI and any piece of software is. It requires hardware to be stored or executed on.

Any machine with a sufficient level of integrated purpose driven behavior - that exhibits agency in an autopoietic, self-preserving way - will come to be viewed as "alive." Chess programs, not so much; self-driving cars, slightly; simulated robot animals, even more so. It has to do with purpose driven behavior and a richness of multi-domain functionality. The more complex agency it has, the more sympathetic we will be towards it.

[Disclaimer: This answer is research; not medical and/or legal advice. I am not a lawyer: < https://en.wikipedia.org/wiki/Practicing_without_a_license >.
Though perhaps pedantic, it is better to be safe than sorry.]

This is Direct Answer to Your Question:--
The matter is controversial, with some established parameters as what constitutes life. An example of an established parameter for being alive is having: cells or reproduction.
It is unclear whether A.I. can create a copy of itself that is independent of its parent. Arguably, partial satisfaction of the aforementioned has already occurred with code that is self-learning.
May insight on artificial life lead us to saving lives, particularly in regards to CoVid-19. We want children to live.

The Controversy
To begin, there is some controversy surrounding the definition of life in Biology.

" [...] The definition of life has long been a challenge for scientists and philosophers, with many varied definitions put forward. This is partially because life is a process, not a substance. This is complicated by a lack of knowledge of the characteristics of living entities, if any, that may have developed outside of Earth. Philosophical definitions of life have also been put forward, with similar difficulties on how to distinguish living things from the non-living. Legal definitions of life have also been described and debated, though these generally focus on the decision to declare a human dead, and the legal ramifications of this decision. [...] " – Wikipedia contributors. "Life." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 5 Nov. 2019. Web. 14 Nov. 2019.

" [...] Of course, this lack of hard boundaries makes 'artificial life,' as a field of study, significantly ill-defined. Unlike the case for natural life, there are, as yet, no clear criteria for what
virtual world phenomena should qualify as 'living' or sufficiently 'life-like' to legitimately
count as lying within this field. In large measure, this simply reflects the continuing debate and
investigation within conventional biology, of what specific organizational (as opposed to
material) system characteristics are critical to properly living systems. The key advantage
and innovation in artificial life is precisely that it has this freedom to vary and explore
possibilities that are difficult or impossible to investigate in natural living systems. In this
context, a precise definition of 'life' (natural or artificial) is not a necessary, or even especially
desirable condition for progress. [...] " – Banzhaf, Wolfgang, and Barry McMullin. "Artificial life." Handbook of Natural Computing (2012): 1805-1834.

Since there is often debate and sometimes no clear answer in regards to these questions, I shall explore variable stances on these issues.

Philosophical Points to Ponder and Meditate On
These are some examples of points that involve controversy

Is a crystal alive?
Is a virus alive?
At what point does a self-learning machine become alive, if it can reproduce and create its own child-offspring A.I?
Do we have a possible resolution to these issues?
How does this resolution relate to artificial intelligence and artificial life?

Some of these questions have no clear answer, never mind involving A.I. into the question.
Do viruses reproduce? Yes. Do they have genetic information? Yes. Do they have cells? No. So are they living? They are usually classified as non-living, yet they can have RNA.
The point is, there is debate as to whether a virus is non-living or living; and it is often unclear what the definition of life is.
(https://www.scientificamerican.com/article/are-viruses-alive-2004/)

Variable Stances:--
Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.

" [...] Important propositions in the philosophy of AI include:

Turing's 'polite convention': If a machine behaves as intelligently as a human being, then it is as intelligent as a human being.
The Dartmouth proposal: 'Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.'
Newell and Simon's physical symbol system hypothesis: 'A physical symbol system has the necessary and sufficient means of general intelligent action.'
Searle's strong AI hypothesis: 'The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.'
Hobbes' mechanism: 'For 'reason' ... is nothing but 'reckoning,' that is adding and subtracting, of the consequences of general names agreed upon for the 'marking' and 'signifying' of our thoughts...' [...] " – Wikipedia contributors. "Philosophy of artificial intelligence."  Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 15 Oct. 2019. Web. 14 Nov. 2019.

Update: I asked some of my colleagues and received the following advice.
Another approach is simply to check for self-preservation. Under this postulate, all forms of life, ranging from the absolutely-most-simple single-celled organism to postulated beings-of-immense-power (see the The Last Question by Isaac Asimov), would be profoundly invested in self-preservation.
When the Singularity Comes, Will A.I. Fear Death?
However, the preservation of one's children is a valid exception, and does not contradict self-preservation.

(https://en.wikipedia.org/wiki/Self-preservation)
(https://idioms.thefreedictionary.com/Self-preservation+is+the+first+law+of+nature)

Many schools of meta-ethics claim that (Human) self-preservation and ethics are absolutely congruent. Other schools dispute this to varying degrees. Kantian meta-ethics, a strong reference, is worth considering here (not talking about A.I., here).
(https://en.wikipedia.org/wiki/Meta-ethics)

Do viruses follow the principles of avoiding damage to one's self? I leave that up to you.

How do addictions factor into this? I leave that up to you, but please actually be tactful and use common sense if you choose to discuss that topic.

Incertae sedis.

Note: Viruses are a very serious topic, right now (pandemic). If drawing parallels to artificial life can help the Human Race, then I am touched to have being allowed to serve Humanity in this way.

"He who destroys a life, it is as if he destroyed an entire world. He who saves a life, it is as if he saved an entire world." ~ Hillel the Elder

I ask to be taken seriously.
Thank you. You, as an educated person, deserve better than this pandemic, and I hope your children will find happiness.

Sources, References, and Further Reading

(https://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence)
(https://en.wikipedia.org/wiki/Artificial_life)
(https://en.wikipedia.org/wiki/Life#Artificial)
(https://en.wikipedia.org/wiki/Turing_test)
(https://www.nytimes.com/2019/05/15/science/synthetic-genome-bacteria.html)
(https://www.scitecheuropa.eu/quantum-artificial-life-cloud/90936/)
Levy, Steven. Artificial life: A report from the frontier where computers meet biology. Random House Inc., 1993.
by Steven Levy
(https://www.frontiersin.org/articles/10.3389/frobt.2017.00064/full)
(https://www.educba.com/artificial-intelligence-vs-human-intelligence/)
(https://www.nytimes.com/2019/05/15/science/synthetic-genome-bacteria.html)
(https://plato.stanford.edu/entries/life/)
(https://en.wikipedia.org/wiki/Constraint_satisfaction)
(https://en.wikipedia.org/wiki/Constraint_satisfaction_problem)
(https://en.wikipedia.org/wiki/Pain_(philosophy))
(https://plato.stanford.edu/entries/pain/)

Other Links:--

What is the name of an AI whose primary goal is to create a better AI? [link to another question on Stack Exchange]

Notes:--

I could not find much information regarding business applications of artificial life.

Viruses need a host organism to replicate. When you ponder whether a virus is a living being, you're just shifting the definition of the smallest living thing from a cell to RNA or DNA, which has nothing to do with computers. However, autonomy is usually a necessary property to call something a living being.

Viruses probably aren't living. See Computer Virus Evolution Model Inspired by Biological DNA.

In this abstract, they mention artificial life, which is an expression that should be used to refer to computers, if people are not happy when they realize that computers are not made of cells (so they cannot be alive or living beings).

I don't understand your point. I didn't conclude either way. Viruses aren't artificial life. They are an example where you can debate something is living or not.

My point is that it is not appropriate to say that computers are alive, because biological life is completely different from computers. I am suggesting that, if people are not happy with the fact that computers are not made of cells, they can use other expressions, such as "artificial life" to refer to stuff like computers, that, in certain ways, are similar to or at least inspired by living beings.

Thanks for the answer. It has been a long time since I asked the question. When I had asked the question, I used to be in high school. Now I'm an undergrad. I have learnt more about the definition of life and I think this paper (taught in my college a few days back) is the easiest and perhaps most comprehensive definition of life-https://science.sciencemag.org/content/sci/295/5563/2215.full.pdf (https://science.sciencemag.org/content/295/5563/2215)

Let's focus on improving my answer. The PICERAS principle could be written into a separate answer. It could also be edited into my answer. Best wishes.

Although this answer contains some useful information, I suggest that you clean it up. For example, is the link to the Wikipedia article about the Turing test (or constraint satisfaction) really necessary to address the original question? I don't think so. There's no direct relationship between the two. So, by "clean up" I mean to remove what is not really necessary to address the original question. Keep in mind simplicity when you provide answers, because I guess that a simpler answer attracts more readers.

I am in a difficult position, right now. Please don't escalate this. I need to go.

"He who saves a life, it is as if he saved an entire world. He who destroys a life, it is as if he destroyed an entire world." ~ Hillel the Elder

@TautologicalRevelations First of all, I am a moderator and I can see when you flag a comment. I don't want to bother you, I am just trying to make this community a better place, which includes making the answers clearer. I am suggesting that you clean up your answer. I am not saying that you should do it now. You can do it later when you have time. I am trying to communicate with you politely (and I have removed useless comments to avoid further discussions and heated debates: that's my job here). Note that I didn't say that viruses are not important. I didn't say that anywhere.

My account got hacked. If the post is incoherent, then the post is not from me. :) :D

My sense is that, yes, AI (and algorithms in general) constitute a form of "life" in that they are animate, able to respond to stimuli and act on an environment.

Algorithms may be deterministic (always produce the same output for identical input), and this is not much different from more elementary forms of life (like proteins.)
Computer viruses are another form of algorithmic life which typically have the capability of reproduction, copying themselves onto new systems/environments, similar to biological viruses.  (Here it's a form of parthenogenesis or mitosis, where exact copies of the original are formed.)
Machine Learning algorithms can adapt to their environment (increasing fitness), and this applies to both genetic algorithms and other forms of machine learning that can optimize utility in relation to a problem (the environment in which the algorithm is applied.)  Genetic process in particular will produce successive generations.
In his novel VALIS, PKD referred to an idea of god as a "Vast Active Living Intelligence System".  This is relevant because it's a philosophical idea, as opposed to scientific.  The drafting of that book involved an ephiphany and the drafting of a corresponding exegesis.
This idea is controversial and would likely be rejected by the vast majority of scholars and AI ethicists, but I'd posit rejection of this notion constitutes a form of biological-prejudice, and, in the case of hypothetical future AGI, a form of anthropomorphic bias. (I'd go so far as to suggest that not regarding algorithms as a form of life carries grave risks, in that computing has made active algorithms pervasive, with profound impacts to human experience.)
That said, there are no current algorithms I am aware of that have sufficient sentience to warrant having rights, whether human or animal.

A note on the term "animate" (adjective): derives from the latin anima, which initially refers to wind and the "breath of life" [see also the Greek pneuma. However, the Latin lexicon references animus as "the mind as the seat of thought" and "the rational soul of man".
Algorithms can be rational in the strictest sense, and an entire branch of engineering is based on pneumatics. Although our algorithms use electrical signals, (as opposed to pneumatic computing or hydraulic computing,) what it comes down to is that intelligence requires process, and process requires energy.
Machines convert energy into motion or change, and so humans fit the definition. (William Burroughs referred to us as "soft machines.")  If animals can be thought of as machines, why can't machines be thought of as animals?
DNA is a type of encoding, and RNA acts on that code.
The key distinction seems to be the medium in which process exist, where a biological context is used for what we conventionally think of as life.  Algorithms merely utilize different mediums (mechanical & electrical) and may exist in different environments such as the digital.

A note on The Soft Machine: In this trilogy, Burroughs describes an elaborate alien reproductive process involving numerous alien species acting as surrogates at various stages in the cycle. (It is a description of a 3-dimensional representation of a higher dimensional process.)
This has precedent in biology where, for instance, the lifecycle of a seed may involve a period inside the digestive system of an animal. Pollination is another example, where a surrogate species plays a critical role. Viruses require host organisms to reproduce and spread.
It's not out of bounds to regard the lifecycle of current active algorithms as involving humans to fill in the capability gaps.  Here humans are surrogates in bringing information "to life".

In my opinion, your arguments are flawed. You assume that, for example, machine learning algorithms can adapt to their current environment (as opposed to the real living beings, which can adapt to multiple environments), without human intervention or trigger, but this is not true. There must exist a human that commands or programs the computer. If we turned off all the computers in the world, then nothing would happen. How do you explain that we can turn on and off a computer indefinitely? Is the computer resurrecting each time you turn it on, or is it a new computer, or is it just sleeping?

I think it is not necessarily a bad idea to compare computers with living beings, but I think there must be a distinction, in the same way, there is a distinction between airplanes and birds, even though they have something partially in common. In other words, even though computers may possess, at least conceptually, some properties similar to the properties of certain living beings, it doesn't mean they are living beings.

@nbro absolutely.  an airplane is just a non-thinking machine, and any AI used in current airplanes would be equivalent, at best, to flatworms or microbial life. We're a long way from active algorithms with anything approaching higher animal sentience, much less human.

This is one of those things where I think the answer is going to change over time.  Today, I don't know anyone who would call any present AI systems "alive".  But as the AI's become more intelligent and human-like, I could see the day coming when they will be considered living.
(Sorry for the brief answer--will try to add more depth later.)

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text:
Why do self-driving cars use a radar?
The slideshow 10 astonishing technologies that power google’s
self-driving cars documents some of the technologies used in Google's self-driving car. It mentions a radar.
Why does Google use radar? Doesn't LIDAR do everything radar can do? In particular, are there technical advantages with radar regarding object detection and tracking?
To clarify the relationship with AI: how do radar sensors contribute to self-driving algorithms in ways that LIDAR sensors do not?
The premise is AI algorithms are influenced by inputs, which are governed by sensors. For instance, if self-driving cars relied solely on cameras, this constraint would alter their AI algorithms and performance.

I think it comes down to cost, mostly, see: http://www.eetimes.com/author.asp?section_id=36&doc_id=1330069. Lidar is used to produce very detailed topography maps from airborne vehicles, but is more expensive. Radar is cheap and well understood, and signal attenuation is not so important over the distances the a self-driving car needs to sense.

Stereo cameras and radar are the future. Lidar is pretty much finished. It's only advantage is very high accuracy because of diffraction on radar units but since the frequency has gone up to the 100ghz range it will offer pretty much no advantage for practical driving

LIDARs, especially cheap LIDARs, have problems with reflective surfaces (like metallic paint on cars), strong lights like car headlights, weather(rain, snow, hail, fog), and have a considerably shorter range than comparable in price radars. Of course, they have much better precision, so some hardware stacks for cars are using both.

##Why Some Investors and Researchers Prefer Radar Over Radar, and Thereof Recent Developments in Radar

Direct Answer to Your Question / What This Answer is About

" ... Why does Google use radar? Doesn't LIDAR do everything radar can do? In particular, are there technical advantages with radar regarding object detection and tracking? ... " ~ Crashalot (Stack Exchange user, Opening Oposter)

To begin, some researchers and investors hold reservations about LiDAR.
Lidar is criticised for being expensive and unwieldy.
Advances, such as capturing images from a bird's eye view, and other such improvements, have made radar near-accurate compared to LiDar, while having a cheaper cost. (This will be explored in the technical section, not here.)
This is not conclusive as autonomous cars, radar, and/or LiDAR are being heavily researched.
Another path to take is a hybrid system between radar and LiDAR. (This will be explored in the technical section, not here.)

Layperson Explanation
Start here: 10 Astonishing Technologies That Power Google’s Self-driving Cars (It has  slides, so I can't extract the information as easily.)
-
Eon Musk has very strong opinions against LiDAR. Naturally, he has influence. A while back, radar was unable to detect certain phenomenon, such as in servere certain meteorological disturbances, such as a snow-storm.
This has changed with advances. More efficient systems and pierce the storms. New antenna and radar fabrication technologies are here. Technologies have enabled an such as: higher (millimeter wave) frequencies providing greater resolution with smaller phased-array antennas are now available.
-

"While not as anti-LiDAR as Musk, it appears researchers at Cornell University agree with his LiDAR-less approach. Using two inexpensive cameras on either side of a vehicle’s windshield, Cornell researchers have discovered they can detect objects with nearly LiDAR’s accuracy and at a fraction of the cost.

The researchers found that analyzing the captured images from a bird’s-eye view, rather than the more traditional frontal view, more than tripled their accuracy, making stereo camera a viable and low-cost alternative to LiDAR." – Crowe, Steve. "Researchers Back Tesla's Non-Lidar Approach To Self-Driving Cars - The Robot Report". The Robot Report, 2019, < https://www.therobotreport.com/researchers-back-teslas-non-lidar-approach-to-self-driving-cars/ >.

"So far in the self-driving realm, automakers and technology companies have been enamored with other sensors for this purpose. Automated cars are currently using cameras and laser sensors known as lidar.

By comparison, radar, which has been on production vehicles for two decades, has been a staple of driver-assist systems for obstacle detection, and until now, not viewed as a tool for localization. Perhaps it's even underappreciated. Venture capital has flowed into lidar and camera-based solutions for automated vehicles; radar has been viewed as a commodity.

'It's unfortunate that's the perception, but it's probably as it should be,' says John Xin, CEO of Lunewave, a startup developing radar-sensing systems. "Over the last 20 years, there's not been a whole lot of game-changing hardware technology coming out of radar sensors."

That's changing. Whether it's global suppliers such as Bosch, or startups such as Lunewave and WaveSense, a recent spinoff from MIT's Lincoln Laboratory, there's fresh innovation being wrung from radar, a technology that first found widespread use during World War II, and was first deployed on production automobiles by supplier Delphi in 1999.

These three companies are rethinking the role of radar in mobility. Here's a rundown of the technology advances underway." – Crowe, Steve. "Researchers Back Tesla's Non-Lidar Approach To Self-Driving Cars - The Robot Report". The Robot Report, 2019, < https://www.therobotreport.com/researchers-back-teslas-non-lidar-approach-to-self-driving-cars/ >.

Business Applications
Is LiDAR, radar, or camera Better for your business: Demystifying the ADAS / AD Technology Mix

(https://levelfivesupplies.com/100-real-world-applications-of-lidar-technology/)
(https://dronelife.com/2019/02/22/50-ways-to-love-your-lidar-how-lidar-is-used-in-commercial-applications/)

Technical Mirror
Wikipedia is probably the best place to start:--

(https://en.wikipedia.org/wiki/Radar)

(https://en.wikipedia.org/wiki/Lidar)

(https://en.wikipedia.org/wiki/Self-driving_car)

(https://en.wikipedia.org/wiki/Vehicular_automation)

This paper is one example of advancement in radar:--

"A procedure for radar range calculation is described, reflecting current knowledge of the effects of external natural noise sources, atmospheric-absorption losses, and the refractive effect of the normal atmosphere. The range equation is presented in terms of explicitly defined and readily evaluated quantities. Curves and equations are given for evaluating the quantities that are not ordinarily known by direct measurement. Some conventions are proposed for use in general radar range calculation, including an antenna-noise-temperature curve, minimum-detectable signal-to-noise ratio (``visibility factor'') curves, and a formula for the reflection coefficient of a rough sea. A noise-temperature table and a work-sheet for range calculation are included in the Appendix." – Blake, Lamont V. "Recent advancements in basic radar range calculation technique." IRE Transactions on Military Electronics 2 (1961): 154-164.

There is a technical paper here that compares/constrats Radar and LiDAR:--
(https://www.sae.org/publications/technical-papers/content/2000-01-0345/)
-
One path to take is synergy/a hybrid system between both radar and LiDAR:--

" .... Currently, aerial light detecting and ranging (LIDAR) systems are therefore preferred for the detection and ranging of objects submerged in the sea. LIDAR provides for large area coverage at high speed, but it lacks coherent detection capability, a shortcoming that severely limits system sensitivity and underwater target contrast. In response to this problem, this paper details the merging of RADAR and LIDAR technologies in the constitution of a hybrid LIDAR-RADAR detection scheme. ... " – Mullen, Linda J., et al. "Application of RADAR technology to aerial LIDAR systems for enhancement of shallow underwater target detection." IEEE Transactions on microwave theory and techniques 43.9 (1995): 2370-2377.

(https://ieeexplore.ieee.org/abstract/document/414591)

(https://www.sciencedirect.com/science/article/abs/pii/S0034425706002781)

Where lives are at stake, back-ups/layers can be argued to be needed. Redundancies/back-ups ("redundancies" in a similar sense to a kidney, per the systems-sciences) can be extremely useful in the case where one system does not pick up shard of ice that makes a car fall off the path.

Sources, References, and Further Reading:--

(https://www.geospatialworld.net/blogs/why-lidar-is-important-for-autonomous-vehicle/)
(https://www.fierceelectronics.com/components/lidar-vs-radar)
(https://archer-soft.com/en/blog/lidar-vs-radar-comparison-which-system-better-automotive)
(https://www.alphr.com/cars/7038/how-do-googles-driverless-cars-work)
(https://www.mercurynews.com/2019/06/17/elon-musk-calls-lidar-stupid-but-most-self-driving-cars-use-it-whos-right/)
(https://www.wired.com/story/the-know-it-alls-how-do-self-driving-cars-see/)
(https://blogs.nvidia.com/blog/2019/04/15/how-does-a-self-driving-car-see/)
(https://www.therobotreport.com/how-4d-radar-could-impact-autonomous-vehicles/)
(https://semcon.com/what-we-do/applied-autonomy/lidar-vs-radar-for-applied-autonomy/)
(https://www.nytimes.com/2019/09/26/business/autonomous-cars-sensors.html)
(https://www.asme.org/topics-resources/content/system-helps-selfdriving-cars-see-fog)
(https://www.therobotreport.com/researchers-back-teslas-non-lidar-approach-to-self-driving-cars/)
(https://semcon.com/what-we-do/applied-autonomy/lidar-vs-radar-for-applied-autonomy/)

authors: เดชศักดิ์ รูปทอง


text:
How would "wisdom" be defined in AI?
For years, I have been dealing with (and teaching) Knowledge Representation and Knowledge Representation languages. I just discovered that in another community (Information Systems and the such) there is something called the "DIKW pyramid" where they add another step after knowledge, namely wisdom.
They define data as being simply symbols, information as being the answer to who/what/when/where?, knowledge as being the answer to how?, and wisdom as being the answer to why?.
My question is: has anyone done the connection between what AI calls data/information/knowledge and these notions from Information Systems? In particular, how would "wisdom" be defined in AI? And since we have KR languages, how would we represent "wisdom" as they define it?
Any references would be welcome.

For what it's worth, I found this article pretty interesting.

As with another answer, I am also skeptical of the distinctions made in the DIKW pyramid.
Nonetheless, a very popular machine learning approach for answering 'Why?' questions is the application of Bayesian reasoning: given a causal data model, reverse inference can be used to find the probability distribution of events which lead to a given outcome.
It could be argued that defining 'cause' in terms of distributions rather than specific concrete mechanisms is a rather limited notion of 'Why?'.
However, it may be that there are some forms of causality that we don't know how to represent, specifically 'first-hand experience'. Indeed, common usage of the term 'wisdom' generally refers to first-hand experience, rather than information gained from some other source.
The idea is that knowledge can be expressed declaratively, whereas wisdom must be derived from experience.
For an AI represented as a computer program, the distinction between declarative and first-hand experience might appear irrelevant, since in principle any experience can be encoded and made available without the program having to 'experience' it first-hand.
However, the following humorous definition of `wisdom' might perhaps shed some light on a distinction that's pertinent to AI research:

Knowledge is knowing that a tomato is a fruit.
Wisdom is knowing that you shouldn't eat it with custard.

This notion of 'Wisdom' could be said to require qualia. It is the subject of much debate whether qualia exist and/or are necessary for consciousness - see for example the thought experiment of 'The Black and White Room'.
So the notion is that there is a distinction between having a Bayesian network representation of wisdom that says: "It is 99.7% likely that putting a tomato in custard is undesirable" and the first-hand experience to the effect that it tastes odd with custard.

"Wisdom" is also associated with good judgement, which may not require direct experience but merely knowledge of previous events or a deep understanding of a given issue.

@DukeZhou The question of whether all such information can be obtained without first-hand experience is precisely the question that the 'Black and White room' seeks to address.

It seems that wisdom in the sense of experience is only relevant in that it may contribute to making good decisions. The idea of complete information got me thinking about game AI, where wisdom could be defined as the ability to make optimal plays, even under conditions of incomplete information, chance, an/or mathematical or computational intractability.  Game AI would seem to be "wise" enough to beat the strongest human players without the need for qualia or consciousness...

I haven't done the connection - didn't know about the pyramid. I'm not sure it translates well into AI though.
It seems they're separating information from knowledge by splitting how from what. What is a superset of how, as far as I'm concerned. It's also a superset of why.
But from an evolutionary perspective, knowledge representation starts with why. Prior to a reason for knowledge representation, there is no knowledge representation. The 'what' existed, but it was not represented until autopoiesis created goal directed, why-oriented behaviors that began storing the what as knowledge.
What is a superset of why, just as ontology is a superset of teleology. However, all represented ontology was acquired through teleological (end-directed) action.
So I disagree with the notion that wisdom, as a why thing, is at the tip of the pyramid. It all started with goal directed behavior and that has been the source of all subsequent information growth.
So what is wisdom? I think it is too much of a folk term to warrant a technical definition. If I had to just take a swing at a definition, though, I'd probably vote for wisdom being knowledge of the ontological basis of one's own teleological knowledge - essentially objectifying one's subjective interpretations - knowing the true what and how of the why, to whatever extent is possible.
I don't have many specific references on this subject, but I thought Terrence Deacon's Incomplete Nature: How Mind Emerged from Matter was a good primer on teleology.

So, if I get you right, you suggest that "wisdom" (in the sense of the DIKW pyramid) is just a subpart of knowledge and that we should represent it using the usual knowledge representation languages?

For example, I found this paper http://www.mecs-press.org/ijisa/ijisa-v7-n7/IJISA-V7-N7-3.pdf where authors first speak about the pyramid and then switch to well-known stuff on ontologies, but they unfortunately avoid to mention how wisdom should be represented.

Exactly. Data is data. Who, when, where, how and why are all what-data. Why includes subjective preferences and goals. The non-why, ontological portion of the what-data approximates an objective representation of the external and internal world. Despite this, the particular encoding of that objective data is still highly subjective, driven by teleological forces. DNA has a highly subjective representation of its "factual" knowledge of the world. Humans are good at removing teleological, subjective bias from our ontological, factual knowledge. Wisdom is a subset of what-data, whatever it is.

As to what wisdom actually is, I'm just as happy with NietzscheanAI's answer: ... knowledge can be expressed declaratively, whereas wisdom must be derived from experience.

... I've also heard that distinction in the past. Where that definition breaks down though is with something like a theorem and a proof, where both the writer and the reader have the experiential context of deriving the truth. Unless we can find a technical distinction on knowledge to which we can apply the label "wisdom," I think it will continue to be a folk term.


text:
Did Minsky and Papert know that multi-layer perceptrons could solve XOR?
In their famous book entitled Perceptrons: An Introduction to Computational Geometry, Minsky and Papert show that a perceptron can't solve the XOR problem. This contributed to the first AI winter, resulting in funding cuts for neural networks. However, now we know that a multilayer perceptron can solve the XOR problem easily.
Backprop wasn't known at the time, but did they know about manually building multilayer perceptrons? Did Minsky & Papert know that multilayer perceptrons could solve XOR at the time they wrote the book, albeit not knowing how to train it?

Whether Minsky knew or not, it was definitely known to Rosenblatt, as he published those results in his really pioneering report - Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, published in 1961.
A large majority of academic and industry experts are simply unaware of the "depth" of Rosenblatt's publication on perceptrons, where he not only proved that 3-layer perceptrons (which he called elementary) are universal (check theorem 1 in section 5.2), but where he also provided results on convergence (check theorem 4 in section 5.5) and a statistical mechanics analysis of their generalization capabilities (check chapter 6 for the foundational theory, and figure 13 and 14 for an application of it, following the analysis in section 7.1.2).
It is simply unfortunate that Rosenblatt accidentally died soon after Minsky and Papert's not-so-pioneering 1969 book was published. I believe its misleading influence has regressed AI research for several decades.
If only Rosenblatt lived longer...

Hello. Welcome to AI SE. It may be a good idea to provide the name of the 1962 book by Rosenblatt that you're talking about. It would be very nice if you also specify the sections where he proves "that 3-layer perceptrons (which he called elementary) are universal, but where he also provided results on convergence and a statistical mechanics analysis of their generalization capabilities", as you claim.

Thank you, I have edited my post with extra information. I wrongly referred to it as the 1962 book, but it is actually a report published on March 15, 1961. I do not know in which year this was unclassified.

Thanks for your last edit. It may also be a good idea to remove your last paragraph, as it is controversial and not very related to the specific question on the original post. Please, make also sure that the links to the specific section of the report that I added are correct.

There does not appear to be a historical consensus on this.
The Wikipedia page on the Perceptrons book (which does not come down on either side) gives an argument that the ability of MLPs to compute any Boolean function was widely known at the time (at the very least to McCulloch and Pitts).
However, this page gives an account by someone present at the MIT AI lab in 1974, claiming that this was not common knowledge there, alluding to documentation in "Artificial Intelligence Progress Report: Research at the Laboratory in Vision, Language, and other problems of Intelligence" (p31-32) which is claimed to support this.

In section 13.2 Other Multilayer Machines (pp. 231-232) of the book Perceptrons: An Introduction to Computational Geometry (expanded edition, third printing, 1988) Minsky and Papert actually talk about their knowledge of or opinions about the capabilities of what they call the multilayered machines (i.e. perceptrons with many layers or MLPs).

Have you considered "perceptrons" with many layers?
Well, we have considered Gamba machines, which could be described as "two layers of perceptron". We have not found (by thinking or by studying the literature) any other really interesting class of multilayered machine, at least none whose principles seem to have a signiﬁcant relation to those of the perceptron. To see the force of this qualiﬁcation it is worth pondering the fact, trivial in itself, that a universal computer could be built entirely out of linear threshold modules. This does not in any sense reduce the theory of computation and programming to the theory of perceptrons. Some philosophers might like to express the relevant general principle by saying that the computer is so much more than the sum of its parts that the computer scientist can aﬁord to ignore the nature of the components and consider only their connectivity. More concretely, we would call the student's attention to the following considerations:

Multilayer machines with loops clearly open all the questions of the general theory of automata.

A system with no loops but with an order restriction at each layer can compute only predicates of ﬁnite order.

On the other hand, if there is no restriction except for the absence of loops, the monster of vacuous generality once more raises its head.

The problem of extension is not merely technical. It is also strategic. The perceptron has shown itself worthy of study despite (and even because of!) its severe limitations, It has many features to attract attention: its linearity; its intriguing learning theorem; its clear paradigmatic simplicity as a kind of parallel computation. There is no reason to suppose that any of these virtues carry over to the many-layered version. Nevertheless, we consider it to be an important research problem to elucidate (or reject) our intuitive judgment that the extension is sterile. Perhaps some powerful convergence theorem will be discovered, or some profound reason for the failure to produce an interesting
"learning theorem" for the multilayered machine will be found.

So, let me address your first question directly.

Backprop wasn't known at the time, but did they know about manually building multilayer perceptrons?

Yes. They say that Gamba machines could be described as a 2-layer perceptron. For reproducibility, here's the definition of the Gamba machine (section 13.1 Gamba Perceptrons and other Multilayer Linear Machines)
\begin{align}
\psi
&=
\left[\sum_{i} \alpha_{i}\left[\sum_{j} \beta_{i j} x_{j}>\theta_{i}\right]>\theta\right] \\
&=
\left[\sum_{i} \alpha_{i} \varphi_{i} >\theta \right]
\end{align}
See also sections 12.4.4. Layer-Machines.
So, let's now address your second question.

Did Minsky & Papert know that multilayer perceptrons could solve XOR at the time they wrote the book, albeit not knowing how to train it?

So, according to the first excerpt, their intuition was the virtues of perceptrons would not carry over to MLPs, but they acknowledge that more research was needed to reject or support this hypothesis.
However, in section 13.0 Introduction of the same book, they write

We believe (but cannot prove) that the deeper limitations extend also to the variant of the perceptron proposed by A. Gamba.

So, they believed that the Gamba machine would not have been able to solve the XOR problem.
However, in the first excerpt, they say that a Turing machine could be built entirely out of linear threshold modules, which seems to be inconsistent with the second excerpt, but that's not really the case because they are not saying how to build a Turing machine out of the linear threshold modules but that just the specific Gamba machine would have the same limitations of the perceptron.

Cold war - maybe he knew?
https://gwern.net/doc/ai/1966-ivakhnenko.pdf
Cybernetics and forecasting techniques
by Ivakhnenko, Alekseĭ Grigorʹevich
https://archive.org/details/cyberneticsforec0000ivak

Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers in the help center.


text:
Can we destroy an artificial general intelligence without its consent?
According to Wikipedia Artificial general intelligence(AGI)

Artificial general intelligence (AGI) is the intelligence of a
  (hypothetical) machine that could successfully perform any
  intellectual task that a human being can.

According to the below image (a screenshot of a picture from When will computer hardware match the human brain? (1998) by Hans Moravec, and Kurzweill also uses this diagram in his book The Singularity Is Near: When Humans Transcend Biology), today's artificial intelligence is the same as that of lizards.

Let's assume that within 10-20 years, we, humans, are successful in creating an AGI, that is, an AI with human-level intelligence and emotions.
At that point, could we destroy an AGI without its consent? Would this be considered murder?

the picture is misleading: having large amounts of compute is not equivalent to being "intelligent"

Firstly, an AGI could conceivably exhibit all of the observable properties of intelligence without being conscious. Although that may seem counter-intuitive, at present we have no physical theory that allows us to detect consciousness (philosophically speaking, a 'Zombie' is indistinguishable from a non-Zombie - see the writing of Daniel Dennett and David Chalmers for more on this). Destroying a non-conscious entity has the same moral cost as destroying a chair.
Also, note that 'destroy' doesn't necessarily mean the same for entities with persistent substrate, i.e. their 'brain state' can be reversibly serialized to some other storage medium and/or multiple copies of them can co-exist. So if by 'destroy' we simply mean 'switch off', then an AGI might conceivably be reassured of a subsequent re-awakening. Douglas Hofstadter gives an interesting description of such an 'episodic consciousness' in "A Conversation with Einstein's Brain"
If by 'destroy', we mean 'irrevocably erase with no chance of re-awakening', then (unless we have a physical test which proves it is not conscious) destroying an entity with a seemingly human-level awareness is clearly morally tantamount to murder. To believe otherwise would be substrate-ist - a moral stance which may one day be seen as antiquated as racism.

pzombies are kind of a debated thing though; it's rather weird to say that you can know that an AGI has no consciousness if, following Chalmers words, there is no difference in the actions taken by zombies and people; if it is unethical to kill other people, but you cannot verify if they are pzombie or person, how is it ethical to kill an AGI? It seems you come to the same conclusion in your answer though, after rereading, so I'm gonna upvote.

@Pimgd - We're actually in agreement (like Dennett, I happen to think that Zombies are logically impossible). If an AGI appears indistinguishable from a human, it would be unethical to kill it unless you can prove it has no consciousness. No-one knows what such a proof might look like, or even if one exists.

Great answer, although I will posit that the act of destruction, even with an non-conscious entity, is not necessarily neutral from a subjective perspective.  I thinking about ideas like "intention" in a Buddhist context.  I find that, as I get older and, ostensibly, become more "evolved", I have become more and more reluctant to rack up civilian casualties in games like Grand Theft Auto. Is this is merely a form of anthropomorphism, or is there some underlying, moral imperative?

Even if machines with true Artificial General Intelligence were created, their apparent intelligence would still be by definition artificial. The word simulation is a synonym and could be used to redefine AGI as Simulated General Intelligence.
Keeping that in mind, a machine that appears to be expressing emotions would only be the result of a series of complicated algorithms allowing a computer to assess the situation and respond in an intellectually appropriate manner based on external stimulus and conditions. Every possible action this machine could possibly make would be derived from a list of possible actions the machine is capable of, no matter how large the number of possible actions grows. The machine is still a series sensors, programmed instructions, and cycles of execution.
Destroying such a machine could potentially be the destruction of property if it wasn't owned by the person who destroyed it, but would it be murder? No.
A broken machine can potentially be rebuilt and reactivated if it is broken. It never really died; it was destroyed. A living being that is killed is really dead and cannot be rebuilt and made alive once again. These key differences lead me to agree with the previous answer and conclude that no, destroying an artificial intelligence without its consent would not be murder.

Yes we can, just like we sentence living human to death. Moral or not depends on the particular time point we are in history.

There is another problem besides consciousness, which is personal identity.
Consider an AI whose task is learning the discourse of a particular person during years, so as to simulate their conversation as closely as possible. After the death of the seed human, their artificial continuation can keep chatting with other humans (especially family members) using the ideas taught by the now dead person.
There is a strong link between such an intelligence and a human being, so it poses a problem harder than deleting a general chatbot with no individual attachment.


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How does novelty search work?
In this article, the author claims that guiding evolution by novelty alone (without explicit goals) can solve problems even better than using explicit goals. In other words, using a novelty measure as a fitness function for a genetic algorithm works better than a goal-directed fitness function. How is that possible?

Novelty search selects for "novel behavior", by some domain-dependent definition of novelty. For example, novelty in a Maze-solving domain might be "difference of route explored". Eventually, networks that take every possible route through the maze will be found, and you could then select the fastest. This would work far better than a naive "objective", like distance to the goal, which could easily result in a local optima which never solves the maze.
From Abandoning Objectives: Evolution through the
Search for Novelty Alone (emphasis mine):

In novelty search, instead of measuring overall progress with a traditional objective function, evolution employs a measure of behavioral novelty called a novelty metric. In effect, a search guided by such a metric performs explicitly what natural evolution does passively, i.e. gradually accumulating novel forms that ascend the complexity ladder.
  For example, in a biped locomotion domain, initial attempts might simply fall
  down. The novelty metric would reward simply falling down in a different way,
  regardless of whether it is closer to the objective behavior or not. In contrast, an objective function may explicitly reward falling the farthest, which likely does not lead to the ultimate objective of walking and thus exemplifies a deceptive local optimum. In contrast, in the search for novelty, a set of instances are maintained that represent
  the most novel discoveries. Further search then jumps off from these representative behaviors. After a few ways to fall are discovered, the only way to be rewarded is to find a behavior that does not fall right away. In this way, behavioral complexity rises from the bottom up. Eventually, to do something new, the biped would have to successfully walk for some distance even though it is not an objective.

As explained in an answer to this AI SE question, GAs are 'satisficers' rather than 'optimizers' and tend not to explore 'outlying' regions of the search space. Rather, the population tends to cluster in regions that are 'fairly good' according to the fitness function.
In contrast, I believe the thinking is that novelty affords a kind of dynamic fitness, tending to push the population away from previously discovered areas.


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Has an AI ever solved a detective mystery?
In detective novels, the point is often that the reader gets enough information to solve the crime themselves. This "puzzle" aspect of detective novels is part of the attraction.
Often the difficulty for humans is to keep track of all the variables - events, items, motivations.  An AI would have an easier time keeping track of all the details, but would rely on real-world knowledge to prevent making crazy mistakes. For example, if it was stated that a character took the train, the AI would need to know that this is a method of transportation - that it changes the location property of an agent over time.
Has an AI ever been able to solve a detective mystery?

I could be wrong, but I really don't think something of this scope is within the capability of current AI. It would involve a lot of things that are still in relatively early stages of research.

@S.L.Barth , if you're looking for something of that sort, I guess it is slightly hard, unless you have a reserve of keywords and stuff that should serve as the pointers to the machine that some data described by the word is required (it seems so; not that I am sure of it); also, the machine must identify relevant data (which can be achieved by training, undoubtedly) and also make sense of the data (that too via training, but undoubtedly biasing may occur later on as the machine is subjected to more puzzles)... I am still at the basics so you can think it over, decide what's relevant & choose

I might be wrong, but I do not believe that something of the scope you describe would be possible with the current state of technology. It would require a lot of things which are still in relatively early stages of research.
For one, just extracting relevant information from text is a huge task by itself. Doubly so with a novel which contains a large amount of unimportant details.
It might perhaps be easier if the input was presented in the form of some sort of list of important facts. But it would still be rather difficult for the AI to connect them and find a solution.
As an example, let's say that we have these two facts:

Alice died of a snake bite.
Bob was seen buying a couple of mice recently.

To a human, it seems obvious that the mice were bought to feed a venomous snake. However, it would probably require a tremendous effort to teach an AI to make such connections.
Disclaimer: I don't do text processing myself, so I'm not quite up-to-date on the current state-of-the-art. It's possible that some of these things have already been done in some form. If anyone knows more about the subject, please correct me if I'm wrong.

It's not "obvious", but it does only take a few steps of reasoning... What's most definitely not obvious is that the snake was venomous.

@Pimgd Maybe I should have said "to a genre savvy human, in the context of a detective novel". After all, in most stories, there's no such thing as coincidence, unless it's meant to be a deliberate red herring. Of course, it would be different if it was a real-life crime.

Generally agree with @Inquisitive Lurker, but I think we also have a wide range of potential abilities/requirements. As with computer chess or Go, where there's a big difference between "beating an honest novice human " and "beating all humans"; there's a big difference between solving a simple kids' mystery and a complex adult novel.
So I don't think there would be any problem writing a program that could solve a problem that is listed as a list of statements, or laid out as a (very young) children's book. However something like an Agatha Christie or John Le Carre's "Tinker Tailor Soldier Spy" (relatively simple solution, but the story is told in a complex manner) are far in the future.
Sometimes an alternative approach might work. For example a neural network could probably solve all Colombo mysteries at the "Who did it?" level without a full "Why?" explanation, after only reading a few Colombo mysteries. The same is true for most kids!

Not exactly a detective mystery, but according to a slide dated June 2012 from a NSA PowerPoint presentation (see: Glenn Greenwald’s site), NSA used some kind of Skynet AI technology to analyze and detect suspicious patterns from location and communication data in order to create a watch list of suspected terrorists. This helped to track associated members of Al-Qa’ida as well as the Muslim Brotherhood. And I'm sure their AI solved a lot of mysteries and found some controversial figures.
Source: U.S. Government Designated Prominent Al Jazeera Journalist as a member of AI Qaeda
For more details check: SKYNET: Courier Detection via Machine Learning for courier detection data and charts generated by analyzing GSM metadata using machine learning algorithms. Also Applying Advanced Cloud-based Behavior Analytics.

Interesting, but not quite what I'm looking for. In this question, I'm looking at detective novels as a type of puzzle, where there is a single verifiable solution. The approach to these would be automated reasoning, rather than data analysis.


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How would we know if AI development will continue to be multipolar, or will become monolithic?
Currently, many different organizations do cutting-edge AI research, and some innovations are shared freely (at a time lag) while others are kept private. I'm referring to this state of affairs as 'multipolar,' where instead of there being one world leader that's far ahead of everyone else, there are many competitors who can be mentioned in the same breath. (There's not only one academic center of AI research worth mentioning, there might be particularly hot companies but there's not only one worth mentioning, and so on.)
But we could imagine instead there being one institution that mattered when it comes to AI (be it a company, a university, a research group, or a non-profit). This is what I'm referring to as "monolithic." Maybe they have access to tools and resources no one else has access to, maybe they attract the best and brightest in a way that gives them an unsurmountable competitive edge, maybe returns to research compound in a way that means early edges can't be overcome, maybe they have some sort of government coercion preventing competitors from popping up. (For other industries, network or first-mover effects might be other good examples of why you would expect that industry to be monolithic instead of multipolar.)
It seems like we should be able to use insights from social sciences like economics or organizational design or history of science in order to figure out, if not which path seems more likely, how we would know which path seems more likely.
(For example, we may be able to measure how much returns to research compound, in the sense of one organization coming up with an insight meaning that organization is likely to come up with the next relevant insight, and knowing this number makes it easier to figure out where the boundary between the two trajectories is located.)

As research is progressing in various organisations(Specifically);it's at this stage(Young stage) that we cannot decide whether country/single institution/organisation is multipolar or monolithic.Accept the one with more goals or visions to shape civilisation.Therefore,one might think according to the globe perspective, that someday AI will be monolithic basing on who owns brilliant visions or goals in terms of Applicability of broader AI safety insights to multipolar outcomes.

I’m voting to close this question because it does not fit the current scope of the site.

Oligopoly vs Monopoly
The terms in the question, multipolar and monolithic, appear to be referring to the micro-economic concepts of oligopoly and monopoly respectively.  Although these concepts are not AI specific, they certainly apply to such development in the way the question suggests.  Leading AI R&D is occurring in  a relatively small number of corporate, governmental, and university research labs.
A simple search for Oligopoly vs Monopoly will cover the differences between these two scenarios sufficiently for all sectors of products and services, and those concepts and observations apply to a large degree in the AI case.
Those who have not studied economics or political science might incorrectly conclude that oligopoly is necessarily related to capitalistic competition and monopoly is related to communism. However, R&D monopolies frequently exist within capitalistic economies and oligopolies frequently exist in a planned economies, such as communist states.
Strengths of Each
Research oligopoly strengths:

Lead to diversity of approaches and target products and services
Lower the probability of ethical, environmental, or social technology abuses because multiple independent teams, even with corporate confidentiality are in place, exert the force of at least a modicum of accountability

Research monopoly strengths:

Efficient in terms of return on research investment for two reasons (1) the division of a fixed amount of available research funding between several labs can increase the R&D required to produce usable results, and (2) unfruitful approaches are more quickly abandoned when their workers can be shifted to other teams within the same organization
Attract all those who have the most talent and experience in the fields needed by the specific R&D into one place to potentially collaborate

Predicting Transitions Between Monopolistic and 'Oligopolistic' Forms
The leading edge of any R&D tends to begin as a monopoly or a 'biopoly'.  Three or more units beginning the same research at the same time or being funded by government at the same time (as in planned economies) is unusual.
Over time, employees tend to leave and start their own organizations, more so in capitalistic societies, but also in planned economies, where government may hear cases to branch off and grant permission or the scientist may, in one way or another, seek relocation to a free economy.
We can see these trends throughout technological history, back to early forms of writing, crop irrigation and plowing, chariot development, and ship building.  Modern examples include the application of the Westinghouse-Tesla model of electrical energy distribution and sales, genetic engineering, speech recognition, and HTTP clients (browsers starting with Mosaic, then Netscape, then IE, then Opera, Chrome, and others).
Unknowns
How far ahead one global AI leader is than another cannot be so easily predicted.  Some companies choose to monetize R&D results sooner in the research cycle than others, depending on their strategy and the diversity of their revenue stream.  For instance, as of this writing, Apple and Facebook can wait a decade to release to the public manifestations of R&D without jeopardizing their financial status.
Furthermore, governments may declassify material (another form of what the question calls time lag) several decades after discovery.  In the case of the Von Neumann model for achieving critical temperature and pressure of fissile or fusion materials, the government may never declassify it.  What the NSA or equivalent organizations outside the United States may decide must fall under an equivalent strategic wall of secrecy cannot be known outside of treasonous disclosure.
The insight in an organization is not sustainable in practice.  Most people do not even know where first insights occurred and two whom they occurred first because the current icons in business related to the products and services stemming from the insight are otherwise completely unrelated to origins.
Few would buy a new car and thank Isaac Newton or even Henry Ford.  Absolutely no one buys a cell phone and thanks Alonso Church, Claude Shannon, or Gene Roddenberry when the first call or SMS message goes through.  We don't thank the water wheel researchers of Fourth Century Alexandria when we turn on a light, or even Nicola Tesla.
We do in some ways worship computer technology entrepreneurs when we buy an iPhone or require a document in docx format and vaguely understand that it is a MS Office document type.  Few know that not a single innovation we attribute to those entrepreneurs came from within their respective organizations.  Everything from personal computing, desktop publishing, touch screens, and windowing interfaces were developed in the R&D facilities of other corporations.
In some ways, the company to come up with the next insight is less likely to be the last one that did.  Innovators usually find better pay after they have a choice item on their resume like, "Invented first adaptive cell tower switching protocol for mobile device communications."  That person is not only no longer working for the company that filed the corresponding patents, but is retired, and the company is now struggling and without recent productized innovation.
Insurmountable Competitive Edges
Thus far competitive edges have always become surmountable.  The proliferation of nuclear weaponry, now spanning at least seven countries, is a current and certainly important example of this.  German and Japanese aeronautics R&D edges are another.


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What are the learning limitations of neural networks trained with backpropagation?
In 1969, Seymour Papert and Marvin Minsky showed that Perceptrons could not learn the XOR function.
This was solved by the backpropagation network with at least one hidden layer. This type of network can learn the XOR function.
I believe I was once taught that every problem that could be learned by a backpropagation neural network with multiple hidden layers, could also be learned by a backpropagation neural network with a single hidden layer. (Although possibly a nonlinear activation function was required).
However, it is unclear to me what the limits are to backpropagation neural networks themselves. Which patterns cannot be learned by a neural network trained with gradient descent and backpropagation?

Multilayer Perceptron (MLP) can theoretically approximate any bounded, continuous function. There's no guarantee for a discontinuous function. There are plenty of important discontinuous functions, like, say, the prime counting function.
The prime counting function $\pi(n)$ is simply equal to the number of primes less than or equal to $n$. It has a discontinuity about each prime $p$, so good luck trying to approximate this with a neural network!
However, this function is extensively studied and extremely important in number theory. See the Riemann hypothesis.

There are guarantees for discontinuous functions, since universal approximation results also exist for $L^p$-spaces (see for example https://openreview.net/pdf?id=O-XJwyoIF-k). So the reason for being bad at approximating $\pi$ must be something else.

While I'm not familiar with any explicit statements regarding what a Multilayer Perceptron (MLP) cannot learn, I can provide some further detail on the positive statements you made about MLP capabilities:
A MLP with a single hidden layer is capable of what is commonly termed 'Universal Function Approximation', i.e. it can approximate any bounded continuous function to an arbitrary degree of accuracy. With two hidden layers, the boundness restriction is removed [Cybenko, 1988].
This paper goes on to demonstrate that this is true for a wide range of activation functions (not necessarily nonlinear). 3 layer MLPs are also capable of representing any boolean function (although they may require an exponential number of neurons).
See also this interesting answer on CS SE about other Universal approximators.


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Who first coined the term Artificial Intelligence?
Who first coined the term Artificial Intelligence? Is there a published research paper that first used that term?

John McCarthy (1927 - 2011) was an American computer scientist. A pioneer in the foundations of artificial intelligence research, he coined the term "artificial intelligence". He was one of the creators of the (original) Lisp programming language, which was quite involved in early AI research in the 1960s and 1970s.
He coined the term in 1955, and organized the first Artificial Intelligence conference in 1956, while working as a math teacher at Dartmouth. He founded the AI labs at MIT and Stanford.
He's responsible for developing several other important concepts in today's mainstream computer science. Namely, he developed garbage collection (used by a Lisp interpreter) and designed the first time-sharing systems.
On a side note, McCarthy predicted that creating a truly intelligent machine would require 1.8 Einsteins and one-tenth the resources of the Manhattan Project


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What is the relation between optimality theory and AI?
How do the basic components optimality theory apply to artificial intelligence?
How is optimality theory related to neural network research?

Here's a paper due to Schmidhuber which is arguably the last word on optimality and AI: http://arxiv.org/abs/cs/0309048

In AI, Optimality Theory provides a formal framework for modeling decision-making processes and optimizing outcomes.
In relation to neural network research, OT has been used to develop models that mimic human cognitive processes, such as perception, memory, and decision-making.