Vis consili expers mole ruit
sua
Concept of the State of the Future
What Should Be Done to Turn it into Reality?
Every person in any state of the world wants to be
proud of his or her country, belonging to the nation and his or her identity:
culture, faith, mentality, language, etc. This goes without saying and this is
a joint benefit of civilisation, because the multifaceted mental, spiritual and
cultural wealth of all humankind is reflected in our diversity.
But what is needed, what should be done to make such
sensations materialise in the real quality of life, in harmony between society
and the individual?!
In order for my state to become an object of true pride
precisely in terms of worthy achievements of the national economy, the level of
the nation’s welfare, and spiritual and material wealth of the people, and not
just when national-patriotic feelings run high (which
are often cultivated in the interests of the respective political regimes). In
order to have an objective basis to be proud of the leaders of the state who
have managed to mobilise and use efficiently the intellectual and creative
potential of the nation and the available natural resources in the common
interests of society. Have been capable of providing all this, wisely managing their
country and its inhabitants.
At the moment, most people still do not realise that
humanity is on the threshold of a new era (or
destruction!). Not wanting to perceive this and accept a new reality, we
choke our future prospects. Being in a wait-and-see position or even resisting
objective changes, we cause pain to ourselves and contemporaries, and inevitably
create and multiply tragedies.
The
Narrow Corridor: States, Societies, and the Fate of Liberty
'A must-read. Acemoglu and
Robinson are intellectual heavyweights of the first rank . . . erudite and
fascinating' Paul Collier, Guardian
By the authors of the
international bestseller Why Nations Fail, based on decades of
research, this powerful new big-picture framework explains how some countries
develop towards and provide liberty while others fall to despotism, anarchy or
asphyxiating norms- and explains how liberty can thrive despite new threats.
Liberty is hardly the
'natural' order of things; usually states have been either too weak to protect
individuals or too strong for people to protect themselves from despotism.
There is also a happy Western myth that where liberty exists, it's a steady
state, arrived at by 'enlightenment'. But liberty emerges only when a delicate
and incessant balance is struck between state and society - between elites and
citizens. This struggle becomes self-reinforcing, inducing both state and
society to develop a richer array of capacities, thus affecting the
peacefulness of societies, the success of economies and how people experience
their daily lives.
Explaining this new
framework through compelling stories from around the world, in history and from
today - and through a single diagram on which the development of any state can
be plotted - this masterpiece helps us understand the past and present, and
analyse the future.
'An intellectually rich
book that develops an important thesis with verve' Martin Wolf, Financial
Times, on Why Nations Fail
Decentralized Society: Finding Web3's Soul
by EG
Weyl · 2022
We call this richer,
pluralistic ecosystem “Decentralized Society” (DeSoc)—a co-determined
sociality, where Souls and communities come ... :
Artificial
Intelligence Set: What You Need to Know About AI
April 25, 2018
What do you really need to know about the
Artificial Intelligence (AI) revolution? This specially priced 4 item set will
make it easier for you to understand how your company, industry, and career can
be transformed by AI. It is a must-have for managers who need to recognize the
potential impact of AI, how it is driving future growth, and how they can make
the most of it. This collection includes: "Human + Machine: Reimagining
Work in the Age of AI" by Paul Daugherty and H. James Wilson;
which reveals how companies are using the new rules of AI to leap ahead on
innovation and profitability, as well as what you can do to achieve similar
results. Based on the authors' experience and research with 1,500
organizations, this book describes six new types of hybrid human + machine
roles that every company must develop, and it includes a "leader's guide"
with the principals required to become an AI-fueled business. "Prediction
Machines: The Simple Economics of Artificial Intelligence" by Ajay
Agrawal, Joshua Gans, and Avi Goldfarb; the authors lift the curtain on
the AI-is-magic hype and show how basic tools from economics provide clarity
about the AI revolution and a basis for action by CEOs, managers, policy
makers, investors, and entrepreneurs. "Artificial Intelligence for the
Real World" (Article PDF), based on a survey of 250 executives
familiar with their companies' use of cognitive technology and a study of 152
projects show that companies do better by developing an incremental approach to
AI, and by focusing on augmenting rather than replacing human capabilities. And
"Reshaping Business with Artificial Intelligence" (Article
PDF); provides baseline information on the strategies used by companies leading
in AI, the prospects for its growth, and the steps executives need to take to
develop a strategy for their business.
08 Oct 2019 | 15:00 GMT
Many
Experts Say We Shouldn’t Worry About Superintelligent AI. They’re Wrong
By Stuart Russell
This article
is based on a chapter of the author’s newly released book,
Human Compatible: Artificial Intelligence and the Problem of Control.
AI research is making
great strides toward its long-term goal of human-level or superhuman
intelligent machines. If it succeeds in its current form, however, that could
well be catastrophic for the human race. The reason is that the “standard
model” of AI requires machines to pursue a fixed objective specified by humans.
We are unable to specify the objective completely and correctly, nor can we
anticipate or prevent the harms that machines pursuing an incorrect objective
will create when operating on a global scale with superhuman capabilities. Already,
we see examples such as social-media algorithms that learn to optimize
click-through by manipulating human preferences, with disastrous consequences
for democratic systems.
Nick Bostrom’s 2014
book Superintelligence:
Paths, Dangers, Strategies presented a detailed case for taking the
risk seriously. In what most would consider a classic example of British
understatement, The Economist magazine’s review
of Bostrom’s book ended with: “The implications of introducing
a second intelligent species onto Earth are far-reaching enough to deserve hard
thinking.”
Switching the machine off
won’t work for the simple reason that a superintelligent entity
will already have thought of that possibility and taken
steps to prevent it.
Surely, with so much at
stake, the great minds of today are already doing this hard thinking—engaging
in serious debate, weighing up the risks and benefits, seeking solutions,
ferreting out loopholes in solutions, and so on. Not yet, as far as I am aware.
Instead, a great deal of effort has gone into various forms of denial.
Some well-known AI
researchers have resorted to arguments that hardly merit refutation. Here are
just a few of the dozens that I have read in articles or heard at conferences:
Electronic calculators
are superhuman at arithmetic. Calculators didn’t take over the world;
therefore, there is no reason to worry about superhuman AI.
Historically, there are
zero examples of machines killing millions of humans, so, by induction, it
cannot happen in the future.
No physical quantity in
the universe can be infinite, and that includes intelligence, so concerns about
superintelligence are overblown.
Perhaps the most common
response among AI researchers is to say that “we can always just switch it
off.” Alan Turing himself raised
this possibility, although he did not put much faith in it:
If a machine can think,
it might think more intelligently than we do, and then where should we be? Even
if we could keep the machines in a subservient position, for instance by
turning off the power at strategic moments, we should, as a species, feel
greatly humbled.... This new danger...is certainly something which can give us
anxiety.
Switching the machine off
won’t work for the simple reason that a superintelligent entity will already
have thought of that possibility and taken steps to prevent it. And it
will do that not because it “wants to stay alive” but because
it is pursuing whatever objective we gave it and knows that it will fail if it
is switched off. We can no more “just switch it off” than we can beat AlphaGo
(the world-champion Go-playing program) just by putting stones on the right
squares.
Other forms of denial
appeal to more sophisticated ideas, such as the notion that intelligence is
multifaceted. For example, one person might have more spatial intelligence than
another but less social intelligence, so we cannot line up all humans in strict
order of intelligence. This is even more true of machines: Comparing the
“intelligence” of AlphaGo with that of the Google search engine is quite
meaningless.
Kevin Kelly, founding
editor of Wired magazine and a remarkably perceptive technology commentator,
takes this argument one step further. In “The Myth of a
Superhuman AI,” he writes, “Intelligence is not a single dimension,
so ‘smarter than humans’ is a meaningless concept.” In a single stroke, all
concerns about superintelligence are wiped away.
Now, one obvious response
is that a machine could exceed human capabilities in all relevant
dimensions of intelligence. In that case, even by Kelly’s strict standards, the
machine would be smarter than a human. But this rather strong assumption is not
necessary to refute Kelly’s argument.
Consider the chimpanzee.
Chimpanzees probably have better
short-term memory than humans, even on human-oriented tasks such as
recalling sequences of digits. Short-term memory is an important dimension of
intelligence. By Kelly’s argument, then, humans are not smarter than
chimpanzees; indeed, he would claim that “smarter than a chimpanzee” is a
meaningless concept.
This is cold comfort to
the chimpanzees and other species that survive only because we deign to allow
it, and to all those species that we have already wiped out. It’s also cold
comfort to humans who might be worried about being wiped out by machines.
The risks of
superintelligence can also be dismissed by arguing that superintelligence
cannot be achieved. These claims are not new, but it is surprising now to see
AI researchers themselves claiming that such AI is impossible. For example, a
major report from the AI100 organization, “Artificial
Intelligence and Life in 2030 [PDF],” includes the following
claim: “Unlike in the movies, there is no race of superhuman robots on the
horizon or probably even possible.”
To my knowledge, this is
the first time that serious AI researchers have publicly espoused the view that
human-level or superhuman AI is impossible—and this in the middle of a period
of extremely rapid progress in AI research, when barrier after barrier is being
breached. It’s as if a group of leading cancer biologists announced that they
had been fooling us all along: They’ve always known that there will never be a
cure for cancer.
What could have motivated
such a volte-face? The report provides no arguments or evidence whatever.
(Indeed, what evidence could there be that no physically possible arrangement
of atoms outperforms the human brain?) I suspect that the main reason is
tribalism—the instinct to circle the wagons against what are perceived to be
“attacks” on AI. It seems odd, however, to perceive the claim that
superintelligent AI is possible as an attack on AI, and even odder to defend AI
by saying that AI will never succeed in its goals. We cannot insure against
future catastrophe simply by betting against human ingenuity.
If superhuman AI is not strictly
impossible, perhaps it’s too far off to worry about? This is the gist of Andrew
Ng’s assertion that it’s like
worrying about “overpopulation on the planet Mars.” Unfortunately, a
long-term risk can still be cause for immediate concern. The right time to
worry about a potentially serious problem for humanity depends not just on when
the problem will occur but also on how long it will take to prepare and
implement a solution.
For example, if we were
to detect a large asteroid on course to collide with Earth in 2069, would we
wait until 2068 to start working on a solution? Far from it! There would be a
worldwide emergency project to develop the means to counter the threat, because
we can’t say in advance how much time is needed.
Ng’s argument also
appeals to one’s intuition that it’s extremely unlikely we’d even try to move
billions of humans to Mars in the first place. The analogy is a false one,
however. We are already devoting huge scientific and technical
resources to creating ever more capable AI systems, with very little thought
devoted to what happens if we succeed. A more apt analogy, then, would be a
plan to move the human race to Mars with no consideration for what we might
breathe, drink, or eat once we arrive. Some might call this plan unwise.
Another way to avoid the
underlying issue is to assert that concerns about risk arise from ignorance.
For example, here’s Oren Etzioni, CEO of the Allen Institute for AI, accusing
Elon Musk and Stephen Hawking of Luddism because of their calls to recognize
the threat AI could pose:
At the rise of every
technology innovation, people have been scared. From the weavers throwing their
shoes in the mechanical looms at the beginning of the industrial era to today’s
fear of killer robots, our response has been driven by not knowing what impact
the new technology will have on our sense of self and our livelihoods. And when
we don’t know, our fearful minds fill in the details.
Even if we take this
classic ad hominem argument at face value, it doesn’t hold water. Hawking was
no stranger to scientific reasoning, and Musk has supervised and invested in
many AI research projects. And it would be even less plausible to argue that
Bill Gates, I.J. Good, Marvin Minsky, Alan Turing, and Norbert Wiener, all of
whom raised concerns, are unqualified to discuss AI.
The accusation of Luddism
is also completely misdirected. It is as if one were to accuse nuclear
engineers of Luddism when they point out the need for control of the fission
reaction. Another version of the accusation is to claim that mentioning risks
means denying the potential benefits of AI. For example, here
again is Oren Etzioni:
Doom-and-gloom
predictions often fail to consider the potential benefits of AI in preventing
medical errors, reducing car accidents, and more.
And here
is Mark Zuckerberg, CEO of Facebook, in a recent media-fueled
exchange with Elon Musk:
If you’re arguing against
AI, then you’re arguing against safer cars that aren’t going to have accidents.
And you’re arguing against being able to better diagnose people when they’re
sick.
The notion that anyone
mentioning risks is “against AI” seems bizarre. (Are nuclear safety engineers
“against electricity”?) But more importantly, the entire argument is precisely
backwards, for two reasons. First, if there were no potential benefits, there
would be no impetus for AI research and no danger of ever achieving human-level
AI. We simply wouldn’t be having this discussion at all. Second, if the
risks are not successfully mitigated, there will be no benefits.
The potential benefits of
nuclear power have been greatly reduced because of the catastrophic events at
Three Mile Island in 1979, Chernobyl in 1986, and Fukushima in 2011. Those
disasters severely curtailed the growth of the nuclear industry. Italy
abandoned nuclear power in 1990, and Belgium, Germany, Spain, and Switzerland
have announced plans to do so. The net new capacity per year added from 1991 to
2010 was about a tenth of what it was in the years immediately before
Chernobyl.
Strangely, in light of
these events, the renowned cognitive scientist Steven
Pinker has argued [PDF] that it is inappropriate to call
attention to the risks of AI because the “culture of safety in advanced
societies” will ensure that all serious risks from AI will be eliminated. Even
if we disregard the fact that our advanced culture of safety has produced
Chernobyl, Fukushima, and runaway global warming, Pinker’s argument entirely
misses the point. The culture of safety—when it works—consists precisely of
people pointing to possible failure modes and finding ways to prevent them. And
with AI, the standard model is the failure mode.
Pinker also argues that problematic AI
behaviors arise from putting in specific kinds of objectives;
if these are left out, everything will be fine:
AI dystopias project a
parochial alpha-male psychology onto the concept of intelligence. They assume
that superhumanly intelligent robots would develop goals like deposing their
masters or taking over the world.
Yann LeCun, a pioneer of
deep learning and director of AI research at Facebook, often
cites the same idea when downplaying the risk from AI:
There is no reason for
AIs to have self-preservation instincts, jealousy, etc.... AIs will not have
these destructive “emotions” unless we build these emotions into them.
Unfortunately, it doesn’t
matter whether we build in “emotions” or “desires” such as self-preservation,
resource acquisition, knowledge discovery, or, in the extreme case, taking over
the world. The machine is going to have those emotions anyway, as subgoals of
any objective we do build in—and regardless of its gender. As we saw with the
“just switch it off” argument, for a machine, death isn’t bad per se. Death is
to be avoided, nonetheless, because it’s hard to achieve objectives if you’re
dead.
A common variant on the
“avoid putting in objectives” idea is the notion that a sufficiently
intelligent system will necessarily, as a consequence of its intelligence,
develop the “right” goals on its own. The 18th-century philosopher David Hume
refuted this idea in A Treatise of Human Nature. Nick Bostrom,
in Superintelligence, presents Hume’s position as an orthogonality
thesis:
Intelligence and final
goals are orthogonal: more or less any level of intelligence could in principle
be combined with more or less any final goal.
For example, a
self-driving car can be given any particular address as its destination; making
the car a better driver doesn’t mean that it will spontaneously start refusing
to go to addresses that are divisible by 17.
By the same token, it is
easy to imagine that a general-purpose intelligent system could be given more
or less any objective to pursue—including maximizing the number of paper clips
or the number of known digits of pi. This is just how reinforcement learning
systems and other kinds of reward optimizers work: The algorithms are
completely general and accept any reward signal. For engineers
and computer scientists operating within the standard model, the orthogonality
thesis is just a given.
The most explicit
critique of Bostrom’s orthogonality thesis comes from the noted roboticist
Rodney Brooks, who
asserts that it’s impossible for a program to be “smart enough
that it would be able to invent ways to subvert human society to achieve goals
set for it by humans, without understanding the ways in which it was causing
problems for those same humans.”
Those who argue the risk
is negligible have failed to explain why superintelligent AI will necessarily
remain under human control.
Unfortunately, it’s not
only possible for a program to behave like this; it is, in fact, inevitable,
given the way Brooks defines the issue. Brooks posits that the optimal plan for
a machine to “achieve goals set for it by humans” is causing problems for
humans. It follows that those problems reflect things of value to humans that
were omitted from the goals set for it by humans. The optimal plan being
carried out by the machine may well cause problems for humans, and the machine
may well be aware of this. But, by definition, the machine will not recognize
those problems as problematic. They are none of its concern.
In summary, the
“skeptics”—those who argue that the risk from AI is negligible—have failed to
explain why superintelligent AI systems will necessarily remain under human
control; and they have not even tried to explain why superintelligent AI
systems will never be developed.
Rather than continue the
descent into tribal name-calling and repeated exhumation of discredited
arguments, the AI community must own the risks and work to mitigate them. The
risks, to the extent that we understand them, are neither minimal nor
insuperable. The first step is to realize that the standard model—the AI system
optimizing a fixed objective—must be replaced. It is simply bad engineering. We
need to do a substantial amount of work to reshape and rebuild the foundations
of AI.
This article appears in the October 2019 print issue
as “It’s Not Too Soon to Be Wary of AI.”
About the Author
Stuart
Russell, a computer scientist, founded and directs the Center for
Human-Compatible Artificial Intelligence at the University of
California, Berkeley.
https://spectrum.ieee.org/computing/software/many-experts-say-we-shouldnt-worry-about-superintelligent-ai-theyre-wrong?
Nav komentāru:
Ierakstīt komentāru