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For that iconic tragic photo, click HERE. In many ways soldiers today are like paid mercenaries: service in the military is most often an economic choice made more attractive by the perennial GI Bill, VA medical care and other post-service perquisites.
Military service also offers a path to citizenship for immigrants which is a significant pay-off, indeed. French feminist writer Virginie Despentes. The response by pesticide companies and the fossil fuel industry was deafening; however, some legislation was passed and the Environmental Protection Agency EPA was set up, under Richard Nixon no less. Money is an important part of the answer: In the current cycle Republicans have received 97 percent of political contributions from the coal industry, 88 percent from oil and gas.
From divided, the population became atomized. The War in Indo-China continued, entire countries were bombed back to the stone-age. A milestone in political stupidity was reached when Nixon authorized the Watergate break-in as he was coasting to a huge victory in the presidential election over anti-war candidate George McGovern.
The divided country was struggling economically. By the mid s powerful companies from Japan, Germany et al. But American capitalism had resources and was itself ready to react. To start there was the Almighty Dollar which made the banks and financial markets of New York so dominant. And finance was becoming more mathematical and more bold, armed with options and other financial derivatives such as futures, forwards, swaps, mortgage-backed securities and other trendy securitizations.
The deal was becoming more important than the factory or the product. Big Industry was yielding to Big Capital. And the s and s would see the country fracture along new fault lines. It was a period when the state invested in young people — e. In his work The Power Elite , C. Big Labor too had some influence in this period. And political and economic leadership also had to be shared with emerging powerful Western states like California and Texas and with new groups such as Catholics and Jews.
With the Bretton-Woods monetary accords and the founding of the United Nations , the US was assuming a new role in the world. Intervention abroad was not new to the US foreign policy playbook. But now with the Cold War, this tactic was expanded way beyond the mare nostrum that was the Caribbean — there soon came dramatic regime change in Iran, in the Congo and in Indonesia, replacing left leaning leaders with amenable dictators Pahlavi, Mobutu, Suharto.
Big Industry, Big Government and Big Military together created a new force in the country, the Military Industrial Complex — the one Eisenhower warned about in his farewell address. This new nexus of power and influence came complete with its revolving doors involving congressmen, generals, lobbyists and all kinds of government officials; add to that cost overruns and massive subsidies of underperforming contractors.
Worse the benefits of capitalist competition were further diluted as the defense industry was subject to more and more mergers, making it a most non-competitive oligopoly — and it continues, the most recent merger being defense giants Raytheon and United Technologies ; somehow anti-trust does not apply to this world.
The military budget has grown incessantly, in the process starving other departments most dangerously the State department. Paradoxically, despite its dismal performance in endless wars, the military enjoys a position of prestige in the country and is even forced to take money for projects it itself does not want!
Another development that followed in the wake of the dazzling success of the wartime Manhattan Project was an extraordinary bonding of science and the Pentagon. Big Military became the major funder of university science research some of which has spilled over into the civilian realm such as the Internet, GPS and Artificial Intelligence.
Although these achievements are impressive, they have come at the cost of redirecting research dollars and personnel to military ends as opposed to projects that would have addressed civilian needs much more responsively such as the environment. On the home front, the FBI was unleashed to combat anything and anybody whom the untouchable J. Blacklisting became a spectator sport bringing dangerous grandstanders like Joseph McCarthy and Roy Cohn onto the national stage.
Big Government was threatening civil liberties as never before. This was accompanied by the growing influence of the religious right as the US positioned itself as the bulwark against atheistic communism.
Big Religion was represented faithfully by evangelists like Jerry Falwell on the fundamentalist side and by eminences like Francis Cardinal Spellman of New York on the high church side. The political cause of Big Industry was taken up by men born wealthy like the Koch brothers and Joseph Coors. University professors being too liberal and too intellectually honest, these wealthy backers of Barry Goldwater and later Ronald Reagan created right-wing think tanks like the Charles Koch Institute, the Heritage Foundation, the Cato Institute, etc.
There even was a Nobel Prize in the new category of Economics for libertarian economist and propagandist James Buchanan White, rural vs. The unions were strong and industry was churning, the sons and grandsons of immigrants had fought valiantly in two world wars, the military and professional baseball had been integrated, price supports and electrification buoyed the countryside, the 19th Amendment was the law of the land and Rosie the Riveter a national heroine.
How did this happen? My late friend Alex was a holocaust survivor who landed in New York as a refugee after World War II, a young man who then went on to create a new life for himself here. A teenager when the War broke out, Alex shared the story of how he was shipped across Europe from camp to camp with Joanna Wiszniewicz of the Polish-Jewish Institute in Warsaw.
Not an easy read, but one that reveals the best in people along with the worst. There is a point to having a ruling class. The ship of state is steady and transitions of power peaceful something that is of concern lately in the US. Family ties, concern for future generations and responsibility for the nation in their charge makes a ruling class a dynasty which directs the country with a long term view as opposed to the short-term logic of the stock market and with a nationalistic view as opposed to that of striving politicians who are beholden to self-interested donors and lobbyists.
Until not very long ago, the WASPs were visible everywhere — in business, government and academe; their leadership role in American life was universally acknowledged. Today they have almost disappeared from view; indeed, for some years now, not a single person even raised Protestant has been on the Supreme Court, a court where the WASPs were accustomed to being the majority.
At the outset, they had to share power with the rich, well-educated, planter class of the South. After the Civil War the Republicans in Congress moved quickly to pass the 13th Amendment which ended slavery as such in the US and then went on to pass the 14th citizenship, due process and equal protection under the law, and the 15th the right to vote, Except for those three amendments, their post-Civil War record on race relations proved disastrous.
Grant were plagued by corruption. The Republicans engineered a deal with the Southern states to secure the presidency although Hayes had lost the popular vote to Democratic candidate Samuel Tilden. A break in the Republican suite of presidents occurred with the election of Grover Cleveland in As for family tree, on both sides his forebears Scots-Irish and English went back to the early s in Pennsylvania. The excesses of the Gilded Age and the robber barons were reined in some by the Progressive Era policies enacted during the presidency of Teddy Roosevelt, a Harvard man with impeccable patroon credentials.
Roosevelt was followed by William H. Taft Yale whose family tree went back to the s in Massachusetts. Following Woodrow Wilson a Democrat of vintage Scots-Irish heritage, Princeton , Warren Harding a Republican from Ohio became the next president —- no surprise there: in the sequence from Grant to Harding, 7 of the 10 elected presidents were from the great state of Ohio!
The Harding administration was another low point, however, for the WASP Ascendancy and their reputation for honest living. Moral order was restored with the accession to the presidency of Vice-President Calvin Coolidge in , a Yale Law graduate whose Puritan ancestry traced back to in Massachusetts. There was another brief break with the Ivy League tradition with the election of Herbert Hoover Stanford Hoover was from Iowa originally making him the only president till then from West of the Mississippi.
Hoover even carried 5 states from the Solid South, a bloc which had voted Democratic unerringly since the end of Reconstruction. Roosevelt Harvard to the office of President. With the New Deal and pro-labor policies, his administration forestalled threats from the far Left and, on the other hand, subsidized industry with huge public works projects — many of them iconic such as the Golden Gate Bridge, the Hoover Dam and the Tennessee Valley Authority.
In the post-war world, the WASP political class still stood tall. The Cabots, the Lodges, the Harrimans, the Rockefellers, the Tafts were still politically active and visible. But the dynasty was coming to an end. The New Deal empowered labor unions — a development long resisted by the establishment. And this villainy continued after World War II as the Great Migration of 5 million black Americans to the Northern cities gained momentum, spurred in part by the invention of the cotton harvesting machine in — Nicholas Lemann, The Promised Land The response was high-rise ghettos as federal funds were directed to ensure the Levittowns of the post-War suburbs would be segregated.
The integration of the military and of major league baseball during the Truman administration were the only bright spots. Where would the new leadership of the new colossus take us? Work on Artificial Intelligence systems continues apace. But one must ask whether this juggernaut could run into obstacles that would seriously slow it down?
But maybe down the road the AI Bots or the enhanced humans will be able to create the software science of the future that will rationalize the development of the phenomenal new software projects that will be undertaken. But for now, programming is more art than science: in fact, neuroscience research reveals that it is the language area of the brain that is activated while programming and not the mathematical, logical part.
For one article, click HERE. BTW, development at legendary Silicon Valley research center Xerox PARC pioneered features like wysiwyg editors what you see is what you get , the GUI graphical user interface with windows and icons, the mouse, even the Ethernet the backbone of computer networks.
As a case in point, Google has developed its own Tensor Processing Unit TCU as an application specific chip for neural network machine learning It performs most impressively on NLP tasks such as translation and answering questions; it even writes articles and blog posts that pass for human-made nothing is sacred anymore, alas. In the years following WWII, for the most part AI research was done in universities, largely sponsored by the military.
Business interest grew with the development of Expert System aka Rule Based Systems and then with the progress made with Connectionist models based on neural nets. In this century, the role of industry and business in the development of AI has become paramount.
Not only is there money to be made but, thanks in large part to the Internet, business now has access to extraordinarily rich data sets with abundant information on millions and millions of people. Interestingly, China has gone from a cash economy to a mobile device payment economy without passing through the credit card stage, a development accelerated by these AI technologies.
A key to the success of these Chinese companies is their access to limitless consumer data — their customer base is huge to begin with and the vertically integrated apps log about everything those customers do. He sums things up with a dash of humor by saying that data is the oil of the AI era and that China is the Saudi Arabia of data. Indeed, the subject of data is much in the papers these days as we see the Trump administration acting to ban the Chinese apps Tik-Tok and WeChat in the US, charging that the mass of data these apps collect could be used nefariously by the Chinese government or other actors.
Such government interference could slow things down considerably — perhaps for the good — but this is not likely in the US and unthinkable in China. As another example of this superpower AI relationship, we have the TU Simple company, a leader in driverless trucks: it is headquartered in Tucson AZ but its research lab is in Beijing.
Looked at as a race between the two, Lee gives China an edge because of access to data, its ferociously competitive business culture and supportive government policy. But as is happening now, this duel will give these two superpowers a significant advantage over all the others.
The side-effects will be widespread: e. The road to the Singularity where machine intelligence catches up to human intelligence is still open. The next decade will see the 3rd Wave of AI and the futurologists are bullish. Georges Bataille was a prolific French writer, philosopher and activist from the s through the s.
He influenced later philosophers such as postmodernist Michel Foucault, deconstructionist Jacques Derrida, accelerationist Nick Land, …. In his Psychology of Fascism , Bataille analyzes the phenomenon of Fascism in Italy and Germany in the s and 30s. To start, Bataille emphasizes the role of a base formed from a segment of society that was once part of the main-stream population but which has lost its status and has become part of a discontented, violence-prone, alienated class. Not long ago, there was the New Deal that empowered industrial unions; there was the Second World War where this class provided the bulk of the GIs; post WWII, people worked for powerful, successful, competitive, giant companies.
Lewis coal miners and Walter Reuther automobile workers made regular appearances on TV. The white working class were fully part of main-stream America. But then by the late s things began to change as US industry became hollowed out when confronted with keen competition from Europe and Asia. American capitalism switched from Industrial Capitalism to Financial Capitalism — more money was to be made by financing the deal rather than by actually making anything!
The unions were crushed, the factories were closed. Except for those who managed to move into the middle class mostly through a college education , the white working class has since been turned into an angry proletariat: economic insecurity, food insecurity, the opioid crisis, the breakdown of family structure, college unaffordable — all without the safety net provided to their own citizens by European countries. Characteristically, fascists promise a break with the present by means of reviving a mythologized past to restore the base to its position of prestige; thus Make America Great Again is the motto of the Trump ascendancy.
Bataille emphasizes the role of organized violence in the rise of fascism. In Europe, the fascists had para-military gangs from the outset — the blackshirts in Italy, the brownshirts in Germany. Trump is cultivating para-military support by bringing gun wielding haters to his convention, by shout-outs to white supremacists, to QAnon and other conspiracy theory groups.
He has deployed federal troops and Homeland Security agents to police the citizenry in violation of law and protocol. Bataille discusses the all-important role of the leader for which he employs the absolutely quaint French term meneur : a word one would use for, say, a goatherd. The Republicans have nominated him without a party platform — loyalty to the leader is the only thing required.
This concentration of power gives the movement transcendence as Bataille puts it; Bataille notes further that the leader and his people must repeatedly break the law to firm up the belief that they are above the law, something Trump and his minions excel at — the Emoluments Clause, the Hatch Act, etc. In fact, this is a reason that people take seriously the threat the Trump will not honor the results of the upcoming election if the returns do not suit him. Bataille argues that for fascism to triumph, the regime to overturn must be a weakened, liberal, democratic one: this certainly applies to the present day US where we have watched the legislature have its power eroded constantly in favor of the executive — reminiscent of the Senate of the Roman Republic.
Bataille emphasizes the role of esthetics — the uniforms, the rallies, the symbols, the cult rituals, the myths, the music and marches, the heroic architecture, the films. Somehow, this misogynist adulterer has co-opted the votes and symbols of Evangelical Christians and uses a Bible as a stage prop at every opportunity.
Now Trump and his staff lie all the time. Bataille addresses racism as a way to rile up the base and to define a scapegoat. Trump outrageously uses immigrants as a target: putting babies in cages, separating children from parents — sadism as theater.
More and more he is appealing to out-and-out anti-Black racism, posing as the defender of Law and Order. For Bataille, racism is a tool for changing the nature of the host state. The moral of the story: if Bataille is your guide, Trump is a fascist. On the road to the Technological Singularity where machine intelligence catches up to human intelligence, we are seeing a symbiosis of mankind and machinekind taking place what with nanobots, brain implants, genetic engineering, etc.
Man becomes, as it were, the sex organs of the machine world, as the bee of the plant world, enabling it to fecundate and to evolve ever new forms. For example: except for those working on numerical algorithms like Linear Programming, research mathematicians were indifferent to the development of computing power — until they learned that computers could draw! Then with visualization yielding insight, challenging long-open problems were solved — notably ones dealing with fractals and chaos!
But all this co-evolution is just the latest chapter in a long saga that goes back millions of years. For example, mastery of fire was a technological breakthrough that transformed humans from folivores into omnivores and from fearful prey into fearsome predators. There he argues that mastery of fire was responsible for the extraordinary development of the human brain: apes spend their whole day eating raw food which requires enormous caloric expenditure to digest; cooked food is quickly eaten, easily digested and cooked meat especially is a marvelous source of protein; as a result, energy was liberated from the task of digestion and reallocated to power a larger brain which itself requires enormous energy ; the resulting free time was reallocated to a wider range of activities such as tool making which in turn drove technology further along.
The significance of human control of fire was not lost on the ancient Greeks: this empowerment of humankind by the titan Prometheus so shocked the Olympian gods that Zeus had him chained to a rock where vultures would pick at his liver for all eternity! Luckily for Prometheus, he was eventually set free by Hercules. More recently so to speak , the development of agriculture began but some years ago with the Neolithic Revolution and this powerful technology has directly impacted human evolution in multiple ways.
Indeed, when hunter gatherers metamorphosed into farmers and began civilization as we know it, they traded a life-style where they were taller, healthier and longer-lived for one in which they were shorter, less healthy, had a shorter life span and to make it worse had to endure the economic and social hierarchy of a structure built on inequality, private property, slavery and a class system dominated by priests, nobles, chiefs and kings.
To this list of negative developments, add warfare on an increasingly deadly scale and poxes from domestic animals. It is hard to divine what true advantages agriculture and a sedentary life-style might have held for humans. Could population growth be an end in itself from an evolutionary biology point of view — genes selfishly seeking reproduction? Thinking cosmically, agriculture and sedentarism tapped into the extraordinary power of the sun to create surplus production.
Agriculture and wasteful expense indeed did lead to more complex societies and to constantly improving technologies. As examples of such wasteful expense, nobles and kings flaunt their wealth most spectacularly; warfare provides a most dramatic example. Moreover, archaeological evidence points to the fact that one of the first things these new farmers did do was to make beer by fermenting grains — eventually tied to religious Bacchanalian rites. Indeed, religion offers a rich set of examples of wasteful, excessive expense from priestly raiment to magnificent temples to Homeric hecatombs to human sacrifice.
Organized religion has the power to mobilize large numbers of people in nations and empires — even beyond national or imperial boundaries. Technological acceleration and evolutionary acceleration are intertwined processes. Even after branching off from apes, human evolution proceeded very slowly, sped up in the last 2. All this means that technology is interacting with the ongoing process of human biological evolution, a process that itself is very much alive. For example, the mutation in Northern Europe for lactose tolerance in adults only goes back years.
A similar mutation for lactose tolerance in East Africa only goes back years! The technology associated with these recent evolutionary events is cattle husbandry. Interestingly, along with all this technological acceleration, the size of the human brain has been shrinking for thousands of years now and technology is needed to bolster it.
It turns out that progress and modern life are contributing to this. Indeed, civilization is a form of domestication which dulls intelligence. Just as dogs are less intelligent than wolves and have smaller brains than wolves, we have measurably smaller brains than our cousins the Neanderthals did.
Environmental and cultural factors also influence natural selection and stone-age people encounter more challenges than the couch potatoes of the civilized world. The evolutionary explanation for this decrease in human brain power is simple — large brains are great consumers of energy and, as life gets easier and as technology assists us in our intellectual endeavors, brains can lessen in size and power as the requirements for survival of the species change.
The difficulty of human birth also has something to do with it. Large head size means fewer surviving children and mothers so this is resisted by evolution. In the end, this is a tricky topic to discuss. Even the most rational scientists probably believe in their hearts that there has been some Design and Purpose in the evolution of human intelligence — especially their own.
The nihilism of modernity serves up a gruel too thin. Futurist doctrines which hold that humanity is serving as a pass-through to introduce intelligence to the cosmos provide little comfort. Things have moved too far. Going forward, technology can serve to make up for the dulling of the human intellect. Human intellection will likely become a hybrid of biological and non-biological intelligence that risks becoming increasingly dominated by its non-biological component.
Even short of reaching the Singularity, the social implications of all this are exhilarating for a futurist and frightening for a humanist. Given the dazzling progress during the 2nd Wave, expectations are high. For the coming 3rd Wave, the futurolgists predict that new systems will be able to learn, to reason, to converse in natural language and to generalize!
A tall order, indeed. One problem for this kind of progress is that current deep learning systems are muscle-bound and highly specialized for a specific domain. They depend on huge training sets and are supervised to master specific tasks. This is an especial problem for self-driving vehicles: human drivers with their ability to deal with the unexpected can handle an unusual situation while self-driving systems still cannot. Although quite impressive, this performance by the person still would not mean that the he or she understood the Chinese language in any real way — the corollary being that that the AI system cannot be said to understand it either.
Another serious issue is that biases can be embedded into a system too easily. Well known examples are face-recognition systems which perform badly on images of people of color. Google itself faced a public relations nightmare in when its photo-tagger cruelly mislabeled images of African-Americans. In her thoughtful and well-written book Artificial Intelligence: A Guide for Thinking Humans , AI researcher Melanie Mitchell describes how machine learning systems can fall victim to attacks.
In fact, a whole field known as adversarial learning has developed where researchers seek out weaknesses in deep learning systems. And they are good at it! Indeed, it is often the case that changes imperceptible to humans in a photo, for example, can force a well trained system to reverse direction and completely misclassify an image. Adversarial learning exploits the fact that we do not know how these AI systems are reaching the conclusions they do: a tweak to the data that would not affect our judgment can confuse an AI system which is reacting to clues in the data that are completely different from the ones humans react to.
A related issue is that the systems cannot explain why and how they arrive at their results. This is an issue for mathematical algorithms as well — there it is a deep problem because extracting an explanation can bring us right back to the dreaded specter of Combinatorial Explosion! Its special skill at responding to single-answer queries does not carry over well to other areas.
However, futurologists hold that the 3rd Wave of AI will break through these barriers, developing systems that will be proficient at perceiving, learning, reasoning and generalization; they will not require such huge training sets or such extensive human supervision. To start, life expectancy will increase. We have cochlear implants that restore hearing. A retinal implant has been developed that provides some visual perception for some blind individuals, basically by replacing certain visual-processing circuits of the brain.
Indeed, in the future we will be permanently connected to the internet for monitoring and for cognitive enhancement, the surveillance state on steroids. We have already reached the point where there are AI based prostheses such as artificial hands which communicate with receptors implanted in the brain.
Indeed, Kobe Bryant was a man of parts. Nanotechnology is the manipulation of matter at the atomic and molecular scale, down to the level of one billionth of a meter: to track the pioneering basic research in this field, round up some of the usual suspects — Cal Tech, Bell Labs, IBM Research, MIT. This technology promises a suite of miracles far into the future.
By way of example, a nanobot will sail around the insides of the body searching out and destroying cancer cells. It is expected that nanobots embedded in the brain will be capable of reprogramming neural connections to enhance human intellectual power. Indeed, we are at the dawn of a new era, where biology, mathematics, physics, AI and computer science more generally all converge and combine — a development heralded in Prof.
Thus, viruses are being employed to build lithium-ion batteries using nanomaterials — to boot, these new batteries will be environmentally safe! So the momentum is there. A confluence of sciences is thrusting humanity forward to a Brave New World, to the Technological Singularity where machine intelligence catches up to human intelligence.
The run up to the Singularity already will have a profound impact on human life and human social structure. Historically, humanity and its technology have co-evolved, as seen in the record of human biological evolution and its accompanying ever accelerating technological progress.
But this time there is reason to fear that dystopia awaits us and not a better world. The surveillance state is here to stay. The new developments in Bionics are bringing us to the point where the specter of a caste of Nietzschean supermen looms large — there is no reason to suppose that Bionics will be uniformly available to the human population as a whole; worse, many think that race as well as class will play a role in future developments.
The list goes on. As children, we first encounter randomness in flipping a coin to see who goes first in a game or in shuffling cards for Solitaire — nothing terribly dramatic. Biological evolution, on the other hand, uses randomness in some of the most important processes of life such as the selection of genes for transfer from parent to child. And then randomness is a key concept in Quantum Mechanics, the physics of the subatomic realm: the deterministic laws of Newton are replaced by assertions about likely outcomes formulated using mathematical statistics and probability theory — worse, the fundamental axiom of Quantum Mechanics is called the Heisenberg Uncertainty Principle.
But randomness has given Artificial Intelligence AI researchers and others a rich set of new algorithmic tools and some real help in dealing with the issues raised by exponential growth and combinatorial explosion. Indeed, with the advent of modern computing machinery, mathematicians and programmers quickly introduced randomization into algorithms; this is indeed coeval with the Computer Age — mathematicians working laboriously by hand just are not physically able to weave randomness into an algorithmic process.
Randomized algorithms require long sequences of random numbers bits actually. Randomized algorithms are employed in machine learning, robotics and Bayesian networks. For example, the process of evolution itself uses randomness for mutations and gene crossover to drive natural selection; genetic algorithms, introduced in by John Holland, use these stratagems to develop codes for all sorts of practical challenges — e.
The phenomenon of exponential growth continues to impede progress in AI, in Operations Research and other fields. But hope springs eternal and perhaps the physical world can provide a way around these problems. And, in fact, quantum computing is a technology that has the potential to overcome the combinatorial explosion that limits the practical range of mathematical algorithms.
The role of randomness in Quantum Mechanics is the key to the way Quantum Mechanics itself can deal with the problem of an exponential number of possibilities. Just as the Theory of Relativity is modern physics at a cosmic scale, Quantum Mechanics is modern physics at the atomic and sub-atomic scale. Planck solved this complicated problem by postulating that energy levels could only increase in whole number multiples of a minimal increment, now called a quantum.
Then there were the laser, the transistor, the computer chip, etc. Digital computers are not able to simulate quantum systems of any complexity since they are confronted with an exponential number of possibilities to consider, another example of combinatorial explosion. In a paper published in , Simulating Physics with Computers , Nobel laureate Richard Feynman looked at this differently; since simulating a quantum experiment is a computational task too difficult for a digital computer, a quantum experiment itself must be actually performing a prodigious act of computation; he concluded that to simulate quantum systems you should try to build a new kind of computational engine, one based on Quantum Mechanics — quantum computers!
Moreover, for the experts, the existing field of quantum interferometry with its multiparticle interference experiments would provide a starting point! In other words, they knew where to begin. The phenomenon of entanglement refers to the fact that two particles in the same state can be separated in space but later a change to one will force the same state change in the other; so although there is no physical link between them, the particles still influence each other.
However, entanglement has been verified experimentally with photons, neutrinos, and other sub-atomic particles. The phenomenon of superposition also applies to sub-atomic particles. A cat is in a box and an apparatus is set up so that the cat will be poisoned if and only if the radioactive material actually decays; an outside observer will not know if any of this has happened. At the end of the hour, the apparatus that would poison the cat is turned off.
Naively, when the hour is up, one would think that the cat in the box is either alive or dead. Ironically, this fable was intended to poke fun at the interpretation of Quantum Mechanics put forth by the Copenhagen School led by Niels Bohr but it has become the classic story for illustrating superposition.
Recently, Google announced a milestone in quantum computing: in seconds their quantum system solved a problem that would take a traditional digital supercomputer about 10, years to complete! The problem was to verify the randomness of a very long sequence of numbers. The latter contested this claim, of course, saying among other things that their digital computers could solve that problem in only 2. So the jury is still out on all this — but something is happening and both companies are investing most seriously in the field.
With quantum computing, Mathematical Logic might still play an important role in reaching the Technological Singularity the point where machine intelligence will surpass human intelligence if the impasse presented by combinatorial explosion can indeed be broken through. Scientists have long marveled at the extraordinary fit between mathematics and physics, from the differential equation to the geometry of space-time — to the extent that today theoretical physicists start by looking at the mathematics to point them in new directions.
So what can be expected in the next decade, in the 3rd Wave of AI? The phenomenon of exponential growth is having an impact on the way Artificial Intelligence AI is bringing us to the Technological Singularity, the point at which machine intelligence will catch up to human intelligence — and surpass it quickly thereafter.
The phenomenon of exponential growth is also much with us today because of the Corona virus whose spread gives a simple example: 1 person infects 2, 2 infect 4, 4 infect 8, and so on; after 20 iterations of this, over 4 million people are infected. Each step doubles the number of new patients and when you get to 20 the number of patients is huge — and all that from one initial case. A recent New York Times article April 25 uses the example of a pond being overrun by lily pads to illustrate the exponential spread of the virus.
At first there is only one lily pad but once the pond is half-covered with lily pads, the next day the entire pond is covered. For another example, in a classic tale from medieval India, the King wants to reward the man who has just invented Chess; the man requests 1 grain of wheat on a corner square of the chess board and then twice the previous amount on each successive square until all squares are accounted for; naively, the King agrees thinking he is getting a bargain of sorts; but after a few squares, they realize that soon there would be no wheat left in the kingdom!
BTW, in the end the total number of grains of wheat on the chessboard would come to 18,,,,,, — over 18 quintillion, way more than current annual world wheat production. For yet another historical note, the word algorithm is derived from the name of the medieval Persian mathematician Al-Khwarizmi, who wrote a treatise on the Hindu-Arabic numerals in the early 9th Century.
The interest feeds on itself. Interestingly, when Fibonacci popularized the magical Hindu-Arabic numerals in Europe with his Book of Calculation Liber Abaci , , he included the example of compound interest nigh impossible using Roman numerals — in any case, charging compound interest was outlawed in the Roman Empire. Yet another example is due to the English country parson Thomas Malthus. In An Essay on the Principle of Population , writing in opposition to the feel-good optimism of the Enlightenment, he argued that the food supply will only grow at a slow pace but that the population will increase exponentially leading to an eventual catastrophe.
Malthus employed the terminology of infinite series, describing the growth of the food supply as arithmetic and that of the population as geometric. Especially appalling from an historical viewpoint are the packed feed lots where beef cattle are fed corn which makes them sick to fatten them for market.
From the Caspian Steppe, some went East to India where today the cow is iconic; others went to Europe and then as far as the North American West to find grazing land where the cattle could eat grass and ruminate. The root of the Latin word for money pecunia is the word for cow: pecus ; the connection persists to this day in English words like pecuniary and impecunious. So deep is the connection that certainly feeding corn to beef cattle would bring tears to the pagan gods of Mt. Olympus and Valhalla.
Moore, a founder of INTEL, noted that the number of transistors on a microchip was doubling every two years even as the cost was being halved — adding that this was likely to continue. Amazingly this prediction has held true into the 21st Century and the number of transistors on an integrated circuit has gone from 5 thousand to 1 billion. Indeed, exponential bursts are part of the evolutionary process that is technology.
Like compound interest, progress feeds on itself. The future is getting closer all the time — advances that once would have been the stuff of science fiction can now be expected in a decade or two. This is an important element in the march toward the Technological Singularity. However, exponential growth of a different kind can be a stumbling block for AI and other areas of Computer Science because it leads to Combinatorial Explosion : situations where the time for an algorithm to return a solution would surpass the life-expectancy of our solar system if not of the galaxy.
For example, consider the archetypical Traveling Salesman Problem TSP : given a list of cities, the distances between them and the city to start from, find the shortest route that visits all the cities and returns to the start city. For n cities, the number of possible routes that any computer algorithm has to reckon with in order to return the optimal solution is the product of all the numbers from 1 through n , aka n factorial , a quantity that grows much faster than the 2 n of the examples above.
One reason that we resort often to the TSP as an example is that it is representative of a large class of important combinatorial problems — a good algorithm for one can translate into a good algorithm for all of them. These challenging problems are known in the trade as NP-Complete. As a result, Mathematical Logic as such does not play the role in AI that was once predicted for it; the stunning progress in machine learning and other areas has relied on Connectionism, Bayesian Networks, emulating biological and physical processes etc.
One side effect of this humbling of Logic is that we are beginning to look with greater respect at models of intelligence different from our own conscious way of thinking. Up till now, our intelligence has been the gold standard — for philosophers the definition itself of intelligence, for theologians even the model for the mind of God. While a direct attack on the phenomenon of Combinatorial Explosion seems unlikely to yield results, researchers and developers have turned to techniques that use randomness, Statistics and Probability to help in decision making in applications.
Introducing uncertainty into algorithms might well make Al-Khwarizmi turn in his grave, but it has worked for Quantum Mechanics — the physics that brought us television and the transistor. And it has also worked in AI systems for decision making that deal with uncertainty, notably with Bayesian Networks. So perhaps randomized algorithms and even Quantum Mechanics can open the way to some further progress in AI on this front. Then too the creativity of researchers in exploiting the genius of nature knows no limits and can boggle the imagination: on the horizon are virus built lithium batteries and amoeba inspired algorithms.
The 1st Wave of AI went from to the turn of the millennium. The 2nd Wave of AI went from to the present. During this period advances continued in fields like expert systems and Bayesian networks; search based software for games like chess also advanced considerably. However, it is in this period that Connectionism — imitating the way neurons are connected in the brain — came into its own.
Connectionism proper begins in with a paper by Warren McCulloch and Walter Pitts which provided the first mathematical model of an artificial neuron. This inspired the single layer perceptron network of Frank Rosenblatt whose Perceptron Learning Theorem showed that machines could learn! However, this cognitive model was soon shown to be very limited in what it could do, which did dull the enthusiasm of AI funding sources— but the idea of machine learning by means of neuron-like networks was established and research went on.
So, already by the s, the connectionist model was expanded to include more complex neural networks, composed of large numbers of units together with weights that measure the strength of the connections between the units — in the brain, if enough input accumulates at a neuron, it then sends a signal along the synapses extending from it. These weights model the effects of the synapses that link one neuron to another. These net architectures have multiplied and there are now not only classical neural nets but also convolutional neural nets, recurrent neural nets, neural Turing Machines, etc.
Along with that, there are multiple new machine learning methods such as deep learning, reinforcement learning, competitive learning, etc. These methods are constantly improving and constitute true engineering achievements. Accordingly, there has been progress in the handling of core applications like text comprehension and translation, vision, sensor technology, voice recognition, face recognition, etc.
Popular apps such as eHarmony, Tinder, ancestry. These algorithms are purported to have learned what makes for a happy marriage and how Italian you really can claim to be. First there was AlphaGo which stunned the world by beating champion player Lee Se-dol in a five game match in March, — something that was thought to be yet years away as the number of possible positions in Go dwarfs that of Chess.
In fact, AlphaZero can learn how to play multiple games such as Chess and Shogi Japanese Chess as well as Go; what is more, AlphaZero does not learn by playing against human beings or other systems: it learns by playing against itself — playing against humans would just be a waste of precious time! Applying machine learning to create a computer that can win at Go is a milestone. For example, per a recent NY Times article, an AI trained robot has been deployed in Europe to sort articles for packing and shipping for eCommerce.
The robot is trained using reinforcement learning, an engineering extension of the mathematical optimization technique of dynamic programming the one used by GPS systems to find the best route. Another way AI is having an impact on society is through surveillance technology: from NSA eavesdropping to hovering surveillance drones to citywide face recognition cameras. London, once the capital of civil liberties and individual freedom, has become the surveillance capital of the world — but breaking news Shanghai has already overtaken London in this dystopian competition.
What is more we are now subjecting our own selves to constant monitoring: our movements traced by our cellphones, our keystrokes logged by social media. In the process, the surveillance state has created it own surveillance capitalism : our personal behavorial data are amassed by AI enhanced software — Fitbit, Alexa, Siri, Google, FaceBook, … ; the data are analyzed and sold for targeted advertising and other feeds to guide us in our lives; an example: as one googles work on machine intelligence, Amazon drops ads for books on the topic e.
This is only going to get worse as the internet of things puts sensors and listening devices throughout the home and machines start to shepherd us through our day — a GPS for everything, adieu free will! Moreover, there is the emerging field of telehealth: the provision of healthcare remotely by means of telecommunications technology.
Such devices are typically worn for a week or so and then have to be carefully returned. Indeed, in the future we will be permanently connected to the internet for monitoring — the surveillance state on steroids.
In fact, all this information about us lives a life parallel to our own out in the cloud — it has become our avatar, and for many purposes it is more important than we are. To update a metaphor from French post-modernist philosopher Michel Foucault, with surveillance technology we have created our own panopticon — one in which we dwell quietly and willingly as our every keystroke, every move is observed.
So what can be expected in the next decade, in this 3rd Wave? And then what? After the Chinese invasion and takeover of Tibet in the s, China became a practitioner of panda diplomacy where it would send those cuddly bears to zoos around the world to improve relations with various countries. But back then, China was still something of a sleepy economic backwater.
In recent times, China has emerged as the most dynamic country on the planet: Shanghai has long replaced New York and Chicago as the place for daring sky-scraper architecture, the Chinese economy is the second largest in the world, the Silk Road project extends from Beijing across Asia and into Europe itself.
Indeed, China is the center of a new kind of capitalist system, state controlled capitalism where the government is the principal actor. So has the party of the people lost its bearings or is something else going on? The Maoist era in China saw the economy mismanaged, saw educated cadres and scientists exiled to rural areas to learn the joys of farming, saw the Great Leap Forward lead to the deaths of millions.
However, the idea that capitalist economic practices were going to prove necessary on the road to Communism was not new, far from it. Marx himself wrote that further scientific and industrial progress under capitalism was going to be necessary to have the tools in place for the transition to communism. So, there is nothing new here: in China, Communism is alive and well — just taking a deep breath, getting its bearings.
Normally we associate capitalism with agile democracies like the US and the UK rather than autocratic monoliths like China. But capitalism has worked its wonders before in autocratic societies: prior to the World Wars of the 20th century, there was a thriving capitalist system in Europe in the imperial countries of Germany and Austria-Hungary which created the society that brought us the internal combustion engine and the Diesel engine, Quantum Mechanics and the Theory of Relativity, Wagner and Mahler, Freud and Nietzsche.
All of which bodes well for the new China — to some libertarian thinkers, democracy just inhibits capitalism. There was the return of Japan and Germany to dominant positions in high-end manufacturing by the s: machine tools, automobiles, microwave ovens, cameras, and so on — with Korea, Taiwan and Singapore following close behind. Industrial production was de-emphasized as more money could be made in financing things than in making them.
This created a vacuum and China was poised to fill the void — rural populations were uprooted to work in manufacturing plants in often brutal conditions, ironically creating in China itself the kind of social upheaval and exploitation of labor that Marx and Engels denounced in the 19th century. But the resulting boom in the Chinese economy led to membership in the World Trade Organization in ! The displaced rural populations were crammed into ever more crowded cities.
The fact that China is no longer isolated as it once was but a huge exporter and importer of goods and services from all over the world has made these virus transmissions a frightening global menace. The corona pandemic is raging as the world finds itself in a situation eerily like that of August in Europe: two powerful opposing capitalist systems — one led by democracies, the other by an autocratic central government. The idea of a full-scale war between nuclear powers is unthinkable.
Hope springs eternal, perhaps naively — but consider the alternative. Alan Turing was a Computer Science pioneer whose brilliant and tragic life has been the subject of books, plays and films — most recently The Imitation Game with Benedict Cumberbatch. Futurologist Ray Kurzweil has predicted that a machine will pass the Turing Test by the year Historically, the practice of war has hewn closely to developments in technology.
And warfare, in turn, has made demands on technology. Indeed, even men of genius like Archimedes and Leonardo da Vinci developed weapons systems. However, the relationship between matters military and matters technological became almost symbiotic with WWII. Technological feats such as radar, nuclear power, rockets, missiles, jet planes and the digital computer are all associated with the war efforts of the different powers of that conflict.
Certainly, the fundamental research behind these achievements was well underway by the s but the war determined which areas of technology should be prioritized, thereby creating special concentrations of brilliant scientific talent. Never before had science been applied at so grand a scale to such a variety of warfare problems.
Perhaps this is why, instead of addressing the environmental crisis, post WWII technological progress has perfected drones and fueled the growth of organizations such as the NSA and its surveillance prowess. The field of AI was only founded formally in , at a conference at Dartmouth College, in Hanover, New Hampshire, where the term Artificial Intelligence itself was coined. The 1st Wave saw the development of four fundamental approaches to AI — one based on powerful Search Algorithms, one on Mathematical Logic, one on algorithms drawn form the natural world and one on Connectionism , imitating the structure of neurons in the human brain.
Connectionism develops slowly in the First Wave but explodes in the 2nd Wave We are now entering the 3rd Wave. Claude Shannon, a scientist at the legendary Bell Labs, was a participant at the Dartmouth conference. In , Shannon published a beautiful paper Programming a Computer for Playing Chess , which laid the groundwork for games playing algorithms based on searching ahead and evaluating the quality of possible moves.
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