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Kindle Notes & Highlights
by
Calum Chace
Read between
January 4 - January 14, 2018
Automation could lead to an economic singularity. “Singularity” is a term borrowed from maths and physics, and means a point where the normal rules cease to apply, and what lies beyond is un-knowable to anyone this side of the event horizon.
Today’s computers use processors made of silicon, but in future other materials like graphene may come into play.
We do need to discriminate between two very different types of artificial intelligence: artificial narrow intelligence (ANI) and artificial general intelligence (AGI (4)), which are also known as weak AI and strong AI, and as ordinary AI and full AI.
The important distinction between narrow AI and AGI is about more than successful competition across a wide range of domains. It involves goal-setting. Ordinary AI simply does what we tell it to. An AGI will have has the ability to reflect on its goals and decide whether to adjust them. It will have volition.
(That word “algorithm” crops up a lot in computer science. It simply means a set of rules, or instructions, for a computer to follow.
An algorithm is not a programme which tells a computer how to handle a particular situation such as opening a spreadsheet, or calculating the sum of a column of figures. Rather it is a general set of instructions which can be applied to a wide range of data inputs.
While working on ENIAC’s successor, EDVAC (Electronic Discrete Variable Automatic Computer), the brilliant mathematician and polymath John von Neumann wrote a paper describing an architecture for computers which remains the basis for today’s machines.
Expert systems limit themselves to solving narrowly-defined problems from single domains of expertise (for instance, litigation) using vast data banks. They avoid the messy complications of everyday life, and do not tackle the perennial problem of trying to inculcate common sense.
. to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses.” It had access to 200 million pages of information, including the full text of Wikipedia, but it was not online during the contest. The difficulty of the challenge is illustrated by the answer, “A long, tiresome speech delivered by a frothy pie topping” to which the target question (which Watson got right) was “What is a meringue harangue?” After the game, the losing human contestant Ken Jennings famously quipped, “I for one welcome our new robot overlords.”
LIDAR sensing system
executive Rick Rashid demonstrated the company’s simultaneous translation software
It has been observed that our healthcare systems are really sick-care systems, often spending 90% of the amount they ever spend on an individual during the final year of their lives. We all know that prevention is better than cure, and that problems are most easily solved when identified early on, but we don’t run our healthcare systems that way.
We needed to understand epigenetics too: the changes in our cells that are caused by factors above and beyond our DNA sequence.
Nitrogen is a basic nutrient for plants, and it makes up 78% of the atmosphere, but its gaseous form is hard for farmers to use.
On 13th October 1908, a German chemist named Fritz Haber filed a patent for ammonia, having managed to solidify nitrogen in a useful and stable form for the first time: three atoms of hydrogen and one of nitrogen.
AirBnB, with 13 members of staff, owns no hotels and its revenues in March 2015 were around $250m. Uber’s rise has been even more dramatic: its market cap reached $50bn in May 2015.
A sub-industry of authors and consultants has sprung up, offering to help businesses cope with this disruption. One of its leading figures is Peter Diamandis, who is also a co-founder of Silicon Valley’s Singularity University.
In the 15th century, Dutch workers threw their shoes into textile looms to break them. (Their shoes were called sabots, which is a possible etymology for the word “saboteur”.)
Robots are peripherals – physical extensions of AI systems. Despite the recession, sales of robots grew at 10% a year from 2008 to 2013, when 178,000 industrial robots were sold worldwide. China became the biggest market, installing 37,000 robots compared with 30,000 in the USA. (18)
In 1930, the British economist John Maynard Keynes wrote “We are being afflicted with a new disease of which some readers may not yet have heard the name,
The idea that each job lost to automation equates to a person rendered permanently unemployed is known as the Luddite Fallacy.
Car-sharing is expected to become more common, and parking should become much easier.
Probably not. Martin Ford, who has written extensively on technological unemployment, argues that in 2014, 90% of the USA’s 150m workers were doing jobs which already existed 100 years earlier. (28) Even if we can keep inventing new types of employment, will the rate of churn be too fast for us to keep up? Will we all be able to change our career annually or every six months as computers keep stealing our old ones? The rapid growth of online education (the MOOC, or Massive Open Online Courses revolution) means that employees can re-skill themselves faster than ever before, and for free. But we
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UBI is a noble vision, but it leaves three large problems outstanding: the allocation of scarce resources, the creation of meaning, and the transition.
These thoughts are leading some people to worry that a dystopia is inevitable, in which an elite preys on the majority, using powerfully intrusive technologies to impose a truly nightmarish 1984 scenario.
Evolution does not have a purpose or a goal. It is merely a by-product of the struggle for survival by billions and billions of living creatures.
American philosopher John Searle
In a phenomenon known as synaptic plasticity, when two neurons communicate often enough their link becomes stronger and each becomes more likely to fire in response to the other.
Computational capacity is the second challenge, and it is another big one. It is calculated that the human brain operates at the exaflop scale, meaning that it carries out one to the billion billion floating point operations per second – that is, one with eighteen zeroes after
The Human Brain Project (HBP) and Obama’s BRAIN initiative Henry Markram, an Israeli / South African neuroscientist, has become a controversial figure in his field while attracting
Machine learning is the process of creating and refining algorithms which can produce conclusions based on data without being explicitly programmed to do so.
Data mining is the process of discovering previously unknown properties in large data sets, whereas machine learning systems usually make predictions based on information which is already known to the experimenter, using training data.
A special case is reinforcement learning, where the computer gets feedback from the environment – for instance by playing a video game.
Machine learning employs a host of clever statistical techniques. Two of the most commonly cited are “Bayesian networks”, and “Hidden Markov Models”, which have achieved almost sacred status in AI research circles.
Computational neuroscientist Dr Dan Goodman of Imperial College, London
The veteran AI researcher Geoff Hinton, now working at Google,
The eminent AI researcher Christof Koch, chief scientific officer of the Allen Institute for Brain Science
DeepMind and Vicarious, another AI pioneer, in order to keep up to speed with their progress.
Stuart Russell is a British computer scientist and AI researcher who is, along with Peter Norvig, a director of research at Google, co-author of one of the field’s standard university textbooks, “Artificial Intelligence: A Modern Approach”.
In 1965 Moore observed that his company was cramming twice as many transistors on each chip every two years. The observation was subsequently re-framed as a “law”, refined to a period of 18 months, and broadened to mean that the processing power of $1,000-worth of computer was doubling every period.
Exponential curves do not generally last for long: they are just too powerful. In most contexts, fast-growing phenomena start off slowly, pick up speed to an exponential rate, and then after a few periods they tail off to form an S-shaped curve. This is as true of the population growth in animal species as it is of the life cycle of products and services.
One of the milestones anticipated by AI researchers is the availability of exascale computing. We saw in the last chapter that the brain operates at the exaflop scale,
We noted earlier in this chapter that we are curiously nostalgic about the future that we once thought we would have. We haven’t got hover boards, flying cars or personal jetpacks, but we have got pretty close to omniscience at the touch of a button. What’s coming next is no less amazing, but we tend to focus on what we didn’t get more than what we did.
The fastest speed that signals travel within neurons is around 100 metres per second. Signals travel between neurons at junctions called synapses, where the axon (the longest part of a neuron) of one neuron meets the dendrite of another one. This crossing takes the form of chemicals jumping across the gap, which is why neuron signalling is described as an electro-chemical process. The synapse jumping part is much slower than the electrical part. Signals within computers typically travel at 200 million metres per second – well over half the speed of light.
It is interesting to speculate whether this AGI, if conscious, would experience life at 2 million times the speed of a human. If so it would find waiting around for us to do something very boring.
Once the first AGI is created, its intelligence could be expanded by adding extra hardware in a way that is sadly impossible for human brains.
Our superior intelligence seems to be generated by our neocortex, the deeply folded area of our brains which were the last part to evolve.
The AGI may run tests itself, and design its successor, which would in turn design its own successor. Again, this cannot be done with human brains.
Thus the first ultra-intelligent machine is the last invention that man need ever make.”
