Artificial Intelligence · Information Technology · Technology

“The Technological Singularity” by Murray Shanahan 2015

This post is still in very rough shape, to say the least.  It is basically just a set of notes that I took on the book when I read it.  You might be able to get something out of these notes with a bit of struggle though.  I apologize.   I’ll get back to whipping this post into more readable shape just as soon as I can find the time to do it.  This blog is new, and there is currently more work than I can deal with.  Thanks for your patience.

The Technological Singularity by Murray Shanahan is a key book in the understanding of artificial intelligence and where AI is at right now.  The book is fairly objective as to whether AI is going to be a boon for mankind or its end.


Chapter 1

Ray Kurzweil

Law of accelerating Returns

Moores Law

CPU speed and bandwidth double every 18 months.

DNA sequencing & Brain Scan Resolution  double every 18 months

Also evolutionary rise of complexity


AGI= Artificial General intelligence.  AGI is the level of human intelligence.  

Deep Blue, Gary, Kasparov, 1997

Embodiment refers to the brain in a body

AGI exhibits both Creativity and common-sense

Crows are capable of learning: the making and use of hooks to grab food basket in pipe


Chapter 2: Whole Brain Emulation

Human Brain Biology


Dendrites, Input

Axons, Output

Interconnected like trees

Synapse, axon to dendrites

80 billion neurons in human brain

Neurotransmitters are Dopamine and Serotonin

Glial cells act as glue between neurons and do other things

Brain cells reach threshold levels and then fire

Brain plasticity, New Neural connections

Whole Brain Emulation is one way to build a AI that is good as the human brain


Whole Brain Emulation, Three Steps

1) Mapping

This is basically scanning these days.  It used to be slicing I think.

The goal is to make every neuron and all its connections

2) Simulation of the map on a computer

3) Embodiment in a body

The Model of the brain must emulate the original.


Brain Mapping

Mouse brain mapping.  369

It is possible to barcode every neuron genetically to see a neuron connectome

Bio-tech and Nano-tech are very important in brain mapping.

I will soon be possible to in inject nano-robots into the blood to monitor neutrons

Neonate Mouse brains are simpler


Neural Simulation

One way of simulating the brain is to use a digital computer

Serial computer processing is very slow

However, parallel computer processing is possible and powerful and fast

The Brain has massive parallel processing and to need same in computer

This was developed for Gamers who demanded high GPU

GPU = Graphic Processing Unit

Cray Titan Supercomputer has 17,000 GPUs (17000 parallel computers)

Human Brain Neural simulation not possible yet.


Neuro-morphic hardware is another way to simulate the brain:a type of wet brain


Summary of where brain simulation may be in the near future

Quantum computing won’t work for brain simulation

What is really needed is for Moore’s Law to go on forever

QDCA= Quantum Dot Cellular Automata

This is a nano scale semiconductor computer

This would be much better than the standard CMOS computer

This is still decades away

We are still not near the limits of Moore’s Law.  

Much more powerful  CMOS computers are still possible.


Is Robotics a possible way for embodiment of brains

In a real mouse the brain and body are not separate

In AI we have only the forebrain; AI throws the Cerebellum out

AI simulation is only half of the brain

Why not map the whole mouse


Both the Brain and Body can be simulated

Both the Brain and Body can be virtual in a virtual world

Gamers developed this

Physics engines can give both realistic input and output

virtual embodiment is thus advancing

Virtual embodiments could be an avatar of humans

Or Virtual embodiment might be general intelligence AIs

A whole virtual society could be built up that could be then speeded up

This could be a way to a singularity.


Mouse whole brain emulation is possible very soon

Once this is done, human AI may shortly follow

There are 3 orders or magnitude between mouse and human emulation

Can Moores’ law still produce a computer improvement of 3 magnitudes

Human brains have this power and are physical, computers can do this also

Mouse brain emulation is a very powerful tool, like particle accelerators

One problem; mouse brains have no language or symbol neurons

Does this mean a mouse brain cannot be interpolated up to the human level

Research on mouse General AI may be opening up


Chapter 3: Engineering AI

Engineering AI is not based on a biological brain.

It is not whole brain emulation

This may result in strange non-human AI


Computers cannot do many things that are very easy for human brains

They cannot understand everyday physics.  Dangle a rat by his tail and…

They cannot understand everyday human Psychology: fear, hope, trust

Humans can understand abstract concepts  and computers cannot

We can understand numbers, and money

This comes from our evolutionary endowment

This is why the temptation to emulate the entire human brain as above


Machine Learning

This is basically a learning algorithm plus data

Much progress has been made in this branch of AI in the 2000s

Increasing computer power and learning algorithms have brought this about

Marketing is used to profile customers and then predict the future

Find a pattern and predict the future

If data is 5, 10, 15, 20 then future is 25

Big Data is important here.


Deep Learning is part of Machine learning

The new facial recognition systems work thru deep learning

Learn the low level visual features first

Combine low level images to move up to more complex images


AI through big data

So far we have been looking at how ai can find hierarchical categories of objects and behaviors in a stream of data.  If you turned this algorithm loose on the internet you get surprising results.  The internet is a huge data base of almost everything in life.  Crowd-sourcing and social networking have led to massive data.  [Crowd-sourcing itself is fascinating.  It is using big data to find solutions.]  


To use big data learnings systems would not need to be embodied.

It would not be necessary to deal with the world directly’

An ai could learn common sense vicariously.

This could lead to better human level ai.  AGI (Artificial General Intelligence)

AI needs the ability to really understand what works mean.

If you dangle a rat by its tail, which is lower, nose or eyes.

It takes real human ai to answer this question

Maybe ai could learn math thru watching billions of online math lessons

Huge data works much better than just large data

Google study: “The unreasonable  effectiveness of data”

A messy data base of trillions works far better than a clean base of millions.

This was discovered only when very powerful computers were created.

AI not based on the human brain uses different principles than the brain

AGI does not need to be human like

AGI might be so fast, powerful using huge big data that it could get away from us.


Optimization and uncertainty

For AI to be general intelligence is has to understand goals and aims

Humans and animals use powers of prediction to attain goals

Goal of cat is to catch mouse.  Mouse goes being tree, Cat predicts mouse out.

AI tries to predict, plan and optimize

Cat and food problem

Cat visits places he will likely find food

He needs to maximize the expected reward, food

There is uncertainty it any particular site has food

Uncertainty is a part of life.

AI deals with uncertainty by using probability and statistics

This takes us into mathematical probability theory

Best AI can do is build probabilistic models.  [Domingos into probability]


Universal AI

[Domingos book the Master Algorithm is about this]

A real cat learns while it forages and forages while it learns

Maybe AI can do this also

Good AGI (human level AI)  is machine learning + optimization

This is called reinforcement learning.  This is a key phrase in AI

[The Master Algorithm and Bostrom’s book use this phrase constantly]

Universal AI is analogous to Turing’s universal computation

Universal AI maximizes expected reward, given the data it has, in any world

This above can be made mathematically precise

This universalness is theoretical like in Turing.  But still very, very valuable

Universal AI puts two things together

Machine learning to make probabilistic (statistical) predictive models

Optimization to maximize expected reward according to these models

Universal AI can analyze any intelligent agent, artificial or biological

Three questions need to be asked

What is the agents reward

How does the agent learn: data needed, learning tech, prior knowledge

How does agent maximize the reward

These three questions in terms of Crows

The crows reward is food.  

How do crows learn: sense data

How good are crows at maximizing expected reward:  Better than most

All this helps us predict crow behavior

We can ask the same questions to help us design AIs


Human-Level and Human-Like Intelligence


Crows are very smart but what we are interested in is human-level  artificial intelligence

How do real biological humans operate:   We need to ask the same three questions .

1-What is the human reward function:

What is it humans really want.  More than just food or sex.

We can decide what the Good Life is.  We can transcend our biology.

2-How do humans learn:

Language is the main means.  This allows us pass knowledge on.

3-How do humans maximize expected reward:

Human knowledge is collective and can be saved and passed on.

Human knowledge is the result of many individuals and generations.

Humans can innovate: Agriculture, writing, printing etc

Now, what would a human-level AI engineered from scratch be like.  

Not human-like but human-level.    People vary.  There can be various kinds of AI


Chapter 4, Super-intelligence


Here is a summary of where we are now in the book:


“We now have an overview of various enabling technologies, some biologically inspired, others the result of engineering from scratch, that could contribute to the creation of artificial general intelligence at the human level and beyond. The elements that can be made using these enabling technologies might be thought of as a set of building blocks that can be assembled in different combinations to make a variety of forms of artificial intelligence. To gain some understanding of what the resulting systems might be capable of and how they might behave, we can use the framework of three questions proposed in the previous chapter. What is the system’s reward function? How and what does it learn? How does it optimize for expected reward?”

Shanahan, Murray (2015-08-07). The Technological Singularity (The MIT Press Essential Knowledge series) (Kindle Locations 978-983). The MIT Press. Kindle Edition. Location 977

[It seems to me that machine understanding is never going to be like human understanding.  Machines are just not anthropomorphic.  Human understanding has been formed by evolution and thus it has all kinds of stuff like emotions, empathy, fear, greed, love, joy, pleasure, domination, embarrassment, sex, drives that machines are just not going to have.  But machines can still be powerful and useful and dangerous and possibly capable of totally bypassing human beings.  Machines will never be able to communicate with humans on purely human  terms; they can give the illusion of doing so but it will never really happen.  This illusion can be helpful and dangerous both.  Machines can talk to each other tho: in code, in mathematics in abstract symbols different from ours.]

[Evolution has added all kinds of stuff to humans.,things that are embedded in our brains, things that make us human.  

 Some of these things are highly desirable: the ability to feel empathy, joy, beauty, sadness, social sharing, the ability too the individual to sacrifice for the whole, etc.  

  And some of these things are no longer so valuable in the 21st century as they were in the long ago ages when the human brain was being formed: selfishness, greed, desire to kill and destroy, etc.  

 And some of these things are still double edges: competitiveness, aggression, jealousy., tribal community and closeness and trust.  

 The Bottom line is that humans are human.  They have to learn to deal with their evolutionary heritage:  they have to learn to overcome the bad, think about the double edged and rejoice in the good.]  


4.1 Toward Super-intelligence:

If an AI suffers it should have rights, If an AI has morality it should be held responsible

If and when a human -level AI is achieved super-intelligent AI will be almost inevitable.

This is so because all digital programs can be copied and speeded up, AI included

This gives AI an insurmountable advantage.  The Motor bike example.

It may take many AIs working together to be super-intelligence but its still SI


4.2 Brain-inspired, human-like Superintelligence (Not artificially engineered AI)

AIs just working at accelerated speeds (as above) could lead to Super-int.

AIs don’t need food, sleep, rest, vacations: this gives a big advantage alone

Adding more prefrontal cortex would make AI superior to humans

A simulated brain could be copied and thus increase parallelism

Parallelism is always faster than trying out one possibility at a time serially

Slightly faster more efficient AI systems could build even better systems

This could lead to an intelligence explosion

4.3 Optimization and Creativity (AI engineered from scratch, not human-brain-like)

This would likely not be anthropomorphic.  These forms could look weird to us.

How could a machine be innovative, create new things

We can look at evolution to understand this

Evolution is simply: mechanical replication, variation, competition

There is also massive parallelism and long running times

This generated all life on earth without reason or designer

Evolution explores blindly, with lack of direction or goal

Yet evolution has solved huge problems and is hugely creative

This shows creativity can emerge from a mechanical  process

Not just any old algorithm can do this though.  Not all alg will create a hand, eye

A brute force system like evolution needs several things:

Most importantly the raw materials must be the right kind

Raw materials must be amenable to open-ended recombination like legos

Life can do this: it consists of organic molecules that all fit together

Also important is an Universal Reward Function food or money in short supply

An incentive to amass as much as possible leads to endless creativity

Also the optimizing algorithm has to be very powerful: massive computing power

The algorithm has to be able to invent whole new classes of things

But a brute force system like evolution would not have  genuine intelligence

It would not conduct rational arguments or use human design principles

“In nature, the brute-force approach has bootstrapped its way to intelligence by evolving the brain. But the goal of AI research is to endow systems with intelligence directly.”

Shanahan, Murray (2015-08-07). The Technological Singularity (The MIT Press Essential Knowledge series) (Kindle Locations 1146-1147). The MIT Press. Kindle Edition.


A brute force system like evolution uses a lot of trail and error.  Data is dumped

The sort of AI we are looking for doesn’t use simple trial and error

It uses the results of its experiments to build a build a model that can predict

It tests ideas out in theory or in simulation before trying them in practice

Machine learning is needed to build and maintain this model


4.4 Engineering Super-intelligence


“The take-home message of the previous section is that even a crude optimization algorithm may be enough for human-level AI given sufficient computing power. Even creativity, one the most difficult qualities to realize in a computer, can emerge from a brute-force search if enough processing time is available. But if (as we might expect) the enormous computing power required is beyond the reach of Moore’s law, then the shortfall can be made up by endowing the AI with sophisticated cognitive capacities—rational enquiry, principled design, theoretical analysis, and simulation. Very well, let’s suppose this is sufficient to achieve human-level AI by the engineering route (as opposed to the brain-inspired route). What about going beyond human-level intelligence? Can superintelligence be achieved this way?”

Shanahan, Murray (2015-08-07). The Technological Singularity (The MIT Press Essential Knowledge series) (Kindle Locations 1160-1166). The MIT Press. Kindle Edition.

Speedup and Parallelism can get to engineered Super-AI just as in  brain-based.

Also, engineering approach may skip human-level AI and go direct to Super AI.

Super AI could be super in some ways but pretty dumb in others.  Just like men.

Some humans have Dyslexia but can compensate.  Sure AI the same

There is also a distinction between Purview and Performance in humans

Same in AI.  Super AI has to have human General Skills

But it can be much better than humans in one specific skill & still be super AI

Actually to have human AI, AI might have to be  already Super AI in some way

For instance to be general human AI, AI might have to be super in data reading

It takes super AI to deal with big data & big data exposure needed for human AI

Genetics and Neuroscience so reliant of Big Data, its not humanly possible


4.5 User Illusion or Anthropomorphism?



The article is still in progress.  It will be finished soon.

J6,Pier1 Winter Pier and Dawn, OOB,2-8-14-copy,PSS,Sized-16x20,WhiteGLowCorrected-B
Wharf and Beach Shacks, Old Orchard Beach, Maine.  Picture by Hanselmann Photography. 

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