AI

[Lord Protector]

Google’s AI can create better machine-learning code than the researchers who made it

 

Google’s AutoML system recently produced a series of machine-learning codes with higher rates of efficiency than those made by the researchers themselves. In this latest blow to human superiority the robot student has become the self-replicating master.

AutoML was developed as a solution to the lack of top-notch talent in AI programming. There aren’t enough cutting edge developers to keep up with demand, so the team came up with a machine learning software that can create self-learning code.  The system runs thousands of simulations to determine which areas of the code can be improved, makes the changes, and continues the process ad infinitum, or until its goal is reached.

 

This is a fabulous representation of the infinite monkey theorum, but instead of a monkey with a keyboard creating Shakespeare, Google made machines capable of replicating their own programming. And those machines can do in hours what takes the best human programmers weeks or months.

Even scarier, AutoML is better at coding machine-learning systems than the researchers who made it. In an image recognition task it reached record a high 82 percent accuracy. Even in some of the most complex AI tasks, its self-created code is superior to humans; it can mark multiple points within an image with 42 percent accuracy compared to human-made software’s 39.

This isn’t the beginning of Skynet or anything spooky like that, we’re not on the verge of the singularity (self-aware machines), but we are leaps closer to revealing AI’s potential to accelerate the technology timeline.

Google only announced AutoML five months ago. It’s remarkable the company created an AI capable of creating better machine-learning systems than researchers have, in such a short time – imagine where the same team will be in a year. More, the team is likely to share their research with others both inside and outside of the company, as Google’s AI researchers often do.

I can’t be the only person who wants to see what Deep Mind can do with the ability to create a better Deep Mind — a Deeper Mind, if you will?

The AI revolution is currently underway and its future is brighter than ever. AutoML is the genesis for the next generation of machine-learning. Tomorrow’s machines won’t just learn, they’ll self-update and be capable of creating custom programs to solve unforeseen problems.

Our hope lies in a future where AI takes care of time-consuming tasks like programming, thus freeing humans to do the things machines can’t do – like enjoy tacos and beer.

 

Edited October 18, 2017 by slow

[hazard]

https://www.wired.com/story/greedy-brittle-opaque-and-shallow-the-downsides-to-deep-learning/

 

Quote

GREEDY, BRITTLE, OPAQUE, AND SHALLOW: THE DOWNSIDES TO DEEP LEARNING
We've been promised a revolution in how and why nearly everything happens. But the limits of modern artificial intelligence are closer than we think.

AUTHOR: JASON PONTINBY JASON PONTIN

 

SUNDAR PICHAI, THE chief executive of Google, has said that AI “is more profound than … electricity or fire.” Andrew Ng, who founded Google Brain and now invests in AI startups, wrote that “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”

 

Their enthusiasm is pardonable. There have been remarkable advances in AI, after decades of frustration. Today we can tell a voice-activated personal assistant like Alexa to “Play the band Television,” or count on Facebook to tag our photographs; Google Translate is often almost as accurate as a human translator. Over the last half decade, billions of dollars in research funding and venture capital have flowed towards AI; it is the hottest course in computer science programs at MIT and Stanford. In Silicon Valley, newly minted AI specialists command half a million dollars in salary and stock.

 

But there are many things that people can do quickly that smart machines cannot. Natural language is beyond deep learning; new situations baffle artificial intelligences, like cows brought up short at a cattle grid. None of these shortcomings is likely to be solved soon. Once you’ve seen you’ve seen it, you can’t un-see it: deep learning, now the dominant technique in artificial intelligence, will not lead to an AI that abstractly reasons and generalizes about the world. By itself, it is unlikely to automate ordinary human activities.

 

To see why modern AI is good at a few things but bad at everything else, it helps to understand how deep learning works. Deep learning is math: a statistical method where computers learn to classify patterns using neural networks. Such networks possess inputs and outputs, a little like the neurons in our own brains; they are said to be “deep” when they possess multiple hidden layers that contain many nodes, with a blooming multitude of connections. Deep learning employs an algorithm called backpropagation, or backprop, that adjusts the mathematical weights between nodes, so that an input leads to the right output. In speech recognition, the phonemes c-a-t should spell the word “cat;” in image recognition, a photograph of a cat must not be labeled “a dog;” in translation, qui canem et faelem ut deos colunt should spit out “who worship dogs and cats as gods.” Deep learning is “supervised” when neural nets are trained to recognize phonemes, photographs, or the relation of Latin to English using millions or billions of prior, laboriously labeled examples.

 

Deep learning’s advances are the product of pattern recognition: neural networks memorize classes of things and more-or-less reliably know when they encounter them again. But almost all the interesting problems in cognition aren’t classification problems at all. “People naively believe that if you take deep learning and scale it 100 times more layers, and add 1000 times more data, a neural net will be able to do anything a human being can do,” says François Chollet, a researcher at Google. “But that’s just not true.”

 

Gary Marcus, a professor of cognitive psychology at NYU and briefly director of Uber’s AI lab, recently published a remarkable trilogy of essays, offering a critical appraisal of deep learning. Marcus believes that deep learning is not “a universal solvent, but one tool among many.” And without new approaches, Marcus worries that AI is rushing toward a wall, beyond which lie all the problems that pattern recognition cannot solve. His views are quietly shared with varying degrees of intensity by most leaders in the field, with the exceptions of Yann LeCun, the director of AI research at Facebook, who curtly dismissed the argument as “all wrong,” and Geoffrey Hinton, a professor emeritus at the University of Toronto and the grandfather of backpropagation, who sees “no evidence” of a looming obstacle.

 

According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow. The systems are greedy because they demand huge sets of training data. Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks. They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases. Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology.

 

These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. “A self-driving car can drive millions of miles, but it will eventually encounter something new for which it has no experience,” explains Pedro Domingos, the author of The Master Algorithm and a professor of computer science at the University of Washington. “Or consider robot control: A robot can learn to pick up a bottle, but if it has to pick up a cup, it starts from scratch.” In January, Facebook abandoned M, a text-based virtual assistant that used humans to supplement and train a deep learning system, but never offered useful suggestions or employed language naturally.

 

What’s wrong? “It must be that we have a better learning algorithm in our heads than anything we’ve come up with for machines,” Domingos says. We need to invent better methods of machine learning, skeptics aver. The remedy for artificial intelligence, according to Marcus, is syncretism: combining deep learning with unsupervised learning techniques that don’t depend so much on labeled training data, as well as the old-fashioned description of the world with logical rules that dominated AI before the rise of deep learning. Marcus claims that our best model for intelligence is ourselves, and humans think in many different ways. His young children could learn general rules about language, and without many examples, but they were also born with innate capacities. “We are born knowing there are causal relationships in the world, that wholes can be made of parts, and that the world consists of places and objects that persist in space and time,” he says. “No machine ever learned any of that stuff using backprop.”

 

Other researchers have different ideas. “We’ve used the same basic paradigms [for machine learning] since the 1950s,” says Pedro Domingos, “and at the end of the day, we’re going to need some new ideas.” Chollet looks for inspiration in program synthesis, programs that automatically create other programs. Hinton’s current research explores an idea he calls “capsules,” which preserves backpropagation, the algorithm for deep learning, but addresses some of its limitations.

 

“There are a lot of core questions in AI that are completely unsolved,” says Chollet, “and even largely unasked.” We must answer these questions because there are tasks that a lot of humans don’t want to do, such as cleaning toilets and classifying pornography, or which intelligent machines would do better, such as discovering drugs to treat diseases. More: there are things that we can’t do at all, most of which we cannot yet imagine.

 

[mei]

22 hours ago, hazard said:

The systems are greedy because they demand huge sets of training data. Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks. They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases. Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology.

Za "greedy", pa ovo je poznato, to je jedan od razloga zasto je doslo do ponovljene AI zime tokom 90-ih kada su SVM postale #1. CNNs (Convolutional Neural Networks) koje se koriste za prepoznavanje slika (i audio signala u zavisnosti od aplikacije) su tu od 1989., i jos uvek se koriste. Deep Learning (rebranded NN) je dosao u centar paznje upravo zbog digitalne ere i dostupnosti ogromne kolicine podaka, koji su potrebni da be se NN modeli dobro istrenirali, kao i sa computer power rastom. Za transfer "test", naravno da uvek postoje primeri koji model nikad nije video, ali model nekada uspeva da generalizuje dobro, nekada ne, zato se modeli i testiraju na nevidjenim setovima podataka pre nego sto odu u production mode. Za "opaque", opet i ovo svi znaju, Hinton kaze nesto kao: ovi modeli se razvijaju za probleme u kojim je ljudska percepcija dobra (vizuelna i audio na primer), i gde je gotovo nemoguce napisati dobar klasicni program jer je nemoguce znati sve parametre, kojih ima previse, dok se klasicno programiranje se koristi za probleme gde su ljudske performanse losije (ne primer, racunanje da uzmem najprostiji primer). Za "shallow", pa zato se istrazivanja jos uvek sprovode u ovom polju, kako bi doslo do poboljsanja, te profesori sa univerziteta ujedno vode labove po ovim korporacijama. Ljudi pokusavaju da razvijaju nove metode (na pr. Hinton sa kapsulama).

 

AI je postao buzz word, ali za one kojima deep learning nije struka. Ako je kritika ovog profa okrenuta ka ovom hajpu, onda je ok, ali ako je okrenuta ka istrazivacima, pa istrazivaci sve ovo u neku ruku vec znaju, zato se i bave daljim istrazivanjem. 

Edited February 10, 2018 by mei
slovo

[hazard]

Rekao bih da je ovaj članak pisan onako više za širu publiku  

 

Nisam čitao spomenute eseje profe s NYU (baciću pogled kad budem imao vremena), no čini mi se da je glavna ideja ovde da inteligencija nije samo pattern matching/recognition. Siguran sam da će duboko učenje/neuronske mreže još napredovati, i da će programi koji ih koriste uspeti da rade nove & ,,neverovatnije" stvari, ali opet sam prilično siguran da DL/NN ne mogu proizvesti kompjutera koji ,,misli" (kao što misli čovek). Čak i to stranu, neće raditi (bar ne bez poteškoća) i neke druge stvari koje ljudi rade olako. Ljudski (pa i životinjski) mozak radi mnogo više od pukog prepoznavanja ,,šablona" (kako se uopšte prevodi pattern recognition na srpski, stručno?).

 

Meni se ovako sa strane čini da je Big Data najzaslužniji za trenutnu eksploziju ,,AI" putem DL/NN - nikada nije bilo dostupno toliko podataka u kojim se mogu tražiti ,,obrasci" i na osnovu kojih se mogu ,,trenirati" neuronske mreže. Da se primetiti da i kad ljudski mozak radi pattern recognition, on to radi dosta brže i efikasnije nego DL/NN. Detetu je generalno dovoljno da vidi jednu mačku (ili bar jednocifren broj njih) da bi shvatilo da je sledeća mačka koju vidi ista životinja kao i ona od pre. Naravno kompjuteri su bolji u pronalaženju obrazaca u nekim drugim slučajevima (ali ne bih rekao da su efikasniji) jer mogu da procesiraju količinu numeričkih podataka koje ljudski mozak ne može.

[mei]

3 hours ago, hazard said:

Meni se ovako sa strane čini da je Big Data najzaslužniji za trenutnu eksploziju ,,AI" putem DL/NN - nikada nije bilo dostupno toliko podataka u kojim se mogu tražiti ,,obrasci" i na osnovu kojih se mogu ,,trenirati" neuronske mreže. Da se primetiti da i kad ljudski mozak radi pattern recognition, on to radi dosta brže i efikasnije nego DL/NN. Detetu je generalno dovoljno da vidi jednu mačku (ili bar jednocifren broj njih) da bi shvatilo da je sledeća mačka koju vidi ista životinja kao i ona od pre. Naravno kompjuteri su bolji u pronalaženju obrazaca u nekim drugim slučajevima (ali ne bih rekao da su efikasniji) jer mogu da procesiraju količinu numeričkih podataka koje ljudski mozak ne može.

Slažem se . Mogućnost da se dobro generalizuje (na pr., pomenuta mačka) je daleko bolja kod mozga nego kod DL/NN. Problem je što mi ne znamo kako ta generalizacija u mozgu radi da bi je reprodukovali modelima. Ovo je za sada najefikasniji pristup, zahvaljući količini označenih podataka, razvoju tehnika i snage GPU-a koje neko ima da trenira svoje modele. 

 

Ako shvatimo DL/NN kao tehniku za prepoznavanje određenih obrazaca (govor, rak na medicinskim slikama, bolesne ćelije, objekte u saobraćaju  itd), bar na nivou ljudske greške, ili bolje od toga, a ne kao jedini (ili pravi) put za razvoj AI-a, onda nema problema za dalji razvoj ovih tehinka. Ovi modeli nam već olakšavaju kako funkcionišemo, pronalazimo podatke, zarađujemo novac, koristimo mobilne telefone itd. Zato me pomalo nervira što se sve ovo reklamira kao AI od strane određenih kompanija i medija, naročito pošto AI još uvek ne postoji, ali je vizija koja inspiriše dalja istraživanja u ovom smeru. Mada, možda je čitav ovaj marketing sa AI buzzword dobar kako bi se obezbedilo neprekidno finansiranje za dalja istraživanja...

Edited February 10, 2018 by mei

[bigvlada]

21 hours ago, mei said:

Slažem se . Mogućnost da se dobro generalizuje (na pr., pomenuta mačka) je daleko bolja kod mozga nego kod DL/NN. Problem je što mi ne znamo kako ta generalizacija u mozgu radi da bi je reprodukovali modelima. Ovo je za sada najefikasniji pristup, zahvaljući količini označenih podataka, razvoju tehnika i snage GPU-a koje neko ima da trenira svoje modele. 

 

Ako shvatimo DL/NN kao tehniku za prepoznavanje određenih obrazaca (govor, rak na medicinskim slikama, bolesne ćelije, objekte u saobraćaju  itd), bar na nivou ljudske greške, ili bolje od toga, a ne kao jedini (ili pravi) put za razvoj AI-a, onda nema problema za dalji razvoj ovih tehinka. Ovi modeli nam već olakšavaju kako funkcionišemo, pronalazimo podatke, zarađujemo novac, koristimo mobilne telefone itd. Zato me pomalo nervira što se sve ovo reklamira kao AI od strane određenih kompanija i medija, naročito pošto AI još uvek ne postoji, ali je vizija koja inspiriše dalja istraživanja u ovom smeru. Mada, možda je čitav ovaj marketing sa AI buzzword dobar kako bi se obezbedilo neprekidno finansiranje za dalja istraživanja...

Oh, imaju oni^tm mnogo prizemnije namere. 

 

Ai asistenti se sastoje od hardvera i softvera koji su dizajnirani da hvataju zvučne frekvencije van načeg čulnog opsega radi smanjivanja smetnji i poboljšanja razumevanja pa dolazimo u situaciju da asistenti u teoriji mogu da primaju komande koje čovek ne može da čuje. 

 

Imamo istraživanje Future of Privacy Forum-a i Ernest & Young-a koje je pokazalo da su svi fizički ai asistenti tipa Amazon Echo uvek uključeni i da uvek slušaju, bez obzira na to da li su isključeni ili ne (blagi naklon za Microsoft Kinect). Na to dodajte činjenicu da se na proizvođače vrši pritisak da uvrste i mogućnost praćenja ključnih reči tipa bomba. Nenagradno pitanje: Koliko će biti potrebno vremena da se lista ključnih reči proširi sa izjavama koje spadaju u kritiku vladajuće stranke?

 

Ne samo da će korporacije da vas špijuniraju , već će da vam i uvaljuju reklame (Amazon već pregovara oko uvođenja tog vida oglašavanja u svog ai asistenta). Ebay daily deals, svuda i na svakom mestu.  

 

Facebook ima problem sa lažnim vestima i cenzurom suprotnih mišljenja. AI asistenti će vam ukazivati za kog političara treba glasati i ubeđivati vaše dete da je ta i ta igračka baš ono što mu sada treba. To nije budućnost, to se već dešava. Bundesnetzagentur je naredio, pod pretnjom kazne od 25.000 evra svim kupcima da fizički unište lutke My friend Kayla jer se ispostavilo da je firma iza tog ai asistenta snimala decu i sve podatke prodavala kompaniji koja se bavi analizom ljudskog glasa u bezbednosne svrhe. Matel je morao da povuče Hello Barbie iz sličnih razloga (lako hakovanje i korišćenje lutke kao bubice za prisluškivanje). Dizni je trenutno na sudu jer se ispostavilo da 42 aplikacije namenjene deci istu špijuniraju. 

 

Vrli novi svet, kombinacija liberalno-demokratskog uređenja, Omni Consumer Products poslovne politike i Jučea, gde svako može da vas prisluškuje kada, gde i kako želi i to posle proda zainteresovanim stranama.  

[hazard]

On 2/10/2018 at 11:28 PM, mei said:

Mada, možda je čitav ovaj marketing sa AI buzzword dobar kako bi se obezbedilo neprekidno finansiranje za dalja istraživanja...

 

Pa, može biti mač s dve oštrice...trenutno verovatno sve što ima ,,AI" u nazivu lako dobija pare (bilo da je istraživački projekat ili startap), ali kada neminovno hajp ne isporuči sve što obećava, možda naglo presuši novac. Zaključak bi bio da ljudi koji se bave time treba dobro da iskoriste postojeći trenutak

 

Inače daleko od toga da sam stručnjak, ali igrao sam se sa NN par puta. Moj utisak je da su super za određenu klasu problema koja ne može da se reši ,,klasičnim" metodama (pod uslovom da taj problem može da se definiše kao nekakvo pronalaženje tj. poklopanja ponavljajućih obrazaca) - no ako problem može da se reši na neki klasičan način, npr. da se opiše sistemom jednačina, klasičan pristup bolji je od NN. Podsceća me na to kada sam pohađao predmet ,,nelinearna kontrola": otišao sam tamo misleći da će sada neko da mi pokaže neke ultra-giga napredne metode kontrole, da ću reći zbogom tupavom PID kontroleru zauvek. Kad ono...zaključak je bio ,,ako možeš da linearizuješ svoj sistem oko operacione tačke i primeniš klasičnu linearnu kontrolu, to je uvek najbolje; ako ne, ovo što smo te naučili će ti možda pomoći, nekada će da radi, nekad baš i ne..."

[hazard]

Evo još jedan interesantan članak na temu inteligencije, veštačke i prirodne:

 

https://www.wired.com/2017/04/the-myth-of-a-superhuman-ai/

 

Quote

THE AI CARGO CULT: THE MYTH OF A SUPERHUMAN AI

KEVIN KELL

 

I’ve heard that in the future computerized AIs will become so much smarter than us that they will take all our jobs and resources, and humans will go extinct. Is this true? That’s the most common question I get whenever I give a talk about AI. The questioners are earnest; their worry stems in part from some experts who are asking themselves the same thing. These folks are some of the smartest people alive today, such as Stephen Hawking, Elon Musk, Max Tegmark, Sam Harris, and Bill Gates, and they believe this scenario very likely could be true. Recently at a conference convened to discuss these AI issues, a panel of nine of the most informed gurus on AI all agreed this superhuman intelligence was inevitable and not far away.

 

Yet buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence. These claims might be true in the future, but there is no evidence to date to support them. The assumptions behind a superhuman intelligence arising soon are:

 

1. Artificial intelligence is already getting smarter than us, at an exponential rate.
2. We’ll make AIs into a general purpose intelligence, like our own.
3. We can make human intelligence in silicon.
4. Intelligence can be expanded without limit.
5. Once we have exploding superintelligence it can solve most of our problems.

In contradistinction to this orthodoxy, I find the following five heresies to have more evidence to support them.

 

1. Intelligence is not a single dimension, so “smarter than humans” is a meaningless concept.
2. Humans do not have general purpose minds, and neither will AIs.
3. Emulation of human thinking in other media will be constrained by cost.
4. Dimensions of intelligence are not infinite.
5. Intelligences are only one factor in progress.

 

If the expectation of a superhuman AI takeover is built on five key assumptions that have no basis in evidence, then this idea is more akin to a religious belief — a myth. In the following paragraphs I expand my evidence for each of these five counter-assumptions, and make the case that, indeed, a superhuman AI is a kind of myth.

 

1.

The most common misconception about artificial intelligence begins with the common misconception about natural intelligence. This misconception is that intelligence is a single dimension. Most technical people tend to graph intelligence the way Nick Bostrom does in his book, Superintelligence — as a literal, single-dimension, linear graph of increasing amplitude. At one end is the low intelligence of, say, a small animal; at the other end is the high intelligence, of, say, a genius—almost as if intelligence were a sound level in decibels. Of course, it is then very easy to imagine the extension so that the loudness of intelligence continues to grow, eventually to exceed our own high intelligence and become a super-loud intelligence — a roar! — way beyond us, and maybe even off the chart.

 

This model is topologically equivalent to a ladder, so that each rung of intelligence is a step higher than the one before. Inferior animals are situated on lower rungs below us, while higher-level intelligence AIs will inevitably overstep us onto higher rungs. Time scales of when it happens are not important; what is important is the ranking—the metric of increasing intelligence.

 

The problem with this model is that it is mythical, like the ladder of evolution. The pre-Darwinian view of the natural world supposed a ladder of being, with inferior animals residing on rungs below human. Even post-Darwin, a very common notion is the “ladder” of evolution, with fish evolving into reptiles, then up a step into mammals, up into primates, into humans, each one a little more evolved (and of course smarter) than the one before it. So the ladder of intelligence parallels the ladder of existence. But both of these models supply a thoroughly unscientific view.

A more accurate chart of the natural evolution of species is a disk radiating outward, like this one (above) first devised by David Hillis at the University of Texas and based on DNA. This deep genealogy mandala begins in the middle with the most primeval life forms, and then branches outward in time. Time moves outward so that the most recent species of life living on the planet today form the perimeter of the circumference of this circle. This picture emphasizes a fundamental fact of evolution that is hard to appreciate: Every species alive today is equally evolved. Humans exist on this outer ring alongside cockroaches, clams, ferns, foxes, and bacteria. Every one of these species has undergone an unbroken chain of three billion years of successful reproduction, which means that bacteria and cockroaches today are as highly evolved as humans. There is no ladder.

 

Likewise, there is no ladder of intelligence. Intelligence is not a single dimension. It is a complex of many types and modes of cognition, each one a continuum. Let’s take the very simple task of measuring animal intelligence. If intelligence were a single dimension we should be able to arrange the intelligences of a parrot, a dolphin, a horse, a squirrel, an octopus, a blue whale, a cat, and a gorilla in the correct ascending order in a line. We currently have no scientific evidence of such a line. One reason might be that there is no difference between animal intelligences, but we don’t see that either. Zoology is full of remarkable differences in how animals think. But maybe they all have the same relative “general intelligence?” It could be, but we have no measurement, no single metric for that intelligence. Instead we have many different metrics for many different types of cognition.

Instead of a single decibel line, a more accurate model for intelligence is to chart its possibility space, like the above rendering of possible forms created by an algorithm written by Richard Dawkins. Intelligence is a combinatorial continuum. Multiple nodes, each node a continuum, create complexes of high diversity in high dimensions. Some intelligences may be very complex, with many sub-nodes of thinking. Others may be simpler but more extreme, off in a corner of the space. These complexes we call intelligences might be thought of as symphonies comprising many types of instruments. They vary not only in loudness, but also in pitch, melody, color, tempo, and so on. We could think of them as ecosystem. And in that sense, the different component nodes of thinking are co-dependent, and co-created.

 

Human minds are societies of minds, in the words of Marvin Minsky. We run on ecosystems of thinking. We contain multiple species of cognition that do many types of thinking: deduction, induction, symbolic reasoning, emotional intelligence, spacial logic, short-term memory, and long-term memory. The entire nervous system in our gut is also a type of brain with its own mode of cognition. We don’t really think with just our brain; rather, we think with our whole bodies.

 

These suites of cognition vary between individuals and between species. A squirrel can remember the exact location of several thousand acorns for years, a feat that blows human minds away. So in that one type of cognition, squirrels exceed humans. That superpower is bundled with some other modes that are dim compared to ours in order to produce a squirrel mind. There are many other specific feats of cognition in the animal kingdom that are superior to humans, again bundled into different systems.

 

Likewise in AI. Artificial minds already exceed humans in certain dimensions. Your calculator is a genius in math; Google’s memory is already beyond our own in a certain dimension. We are engineering AIs to excel in specific modes. Some of these modes are things we can do, but they can do better, such as probability or math. Others are type of thinking we can’t do at all — memorize every single word on six billion web pages, a feat any search engine can do. In the future, we will invent whole new modes of cognition that don’t exist in us and don’t exist anywhere in biology. When we invented artificial flying we were inspired by biological modes of flying, primarily flapping wings. But the flying we invented — propellers bolted to a wide fixed wing — was a new mode of flying unknown in our biological world. It is alien flying. Similarly, we will invent whole new modes of thinking that do not exist in nature. In many cases they will be new, narrow, “small,” specific modes for specific jobs — perhaps a type of reasoning only useful in statistics and probability.

 

In other cases the new mind will be complex types of cognition that we can use to solve problems our intelligence alone cannot. Some of the hardest problems in business and science may require a two-step solution. Step one is: Invent a new mode of thought to work with our minds. Step two: Combine to solve the problem. Because we are solving problems we could not solve before, we want to call this cognition “smarter” than us, but really it is different than us. It’s the differences in thinking that are the main benefits of AI. I think a useful model of AI is to think of it as alien intelligence (or artificial aliens). Its alienness will be its chief asset.

 

At the same time we will integrate these various modes of cognition into more complicated, complex societies of mind. Some of these complexes will be more complex than us, and because they will be able to solve problems we can’t, some will want to call them superhuman. But we don’t call Google a superhuman AI even though its memory is beyond us, because there are many things we can do better than it. These complexes of artificial intelligences will for sure be able to exceed us in many dimensions, but no one entity will do all we do better. It’s similar to the physical powers of humans. The industrial revolution is 200 years old, and while all machines as a class can beat the physical achievements of an individual human (speed of running, weight lifting, precision cutting, etc.), there is no one machine that can beat an average human in everything he or she does.

 

Even as the society of minds in an AI become more complex, that complexity is hard to measure scientifically at the moment. We don’t have good operational metrics of complexity that could determine whether a cucumber is more complex than a Boeing 747, or the ways their complexity might differ. That is one of the reasons why we don’t have good metrics for smartness as well. It will become very difficult to ascertain whether mind A is more complex than mind B, and for the same reason to declare whether mind A is smarter than mind B. We will soon arrive at the obvious realization that “smartness” is not a single dimension, and that what we really care about are the many other ways in which intelligence operates — all the other nodes of cognition we have not yet discovered.

 

2.
The second misconception about human intelligence is our belief that we have a general purpose intelligence. This repeated belief influences a commonly stated goal of AI researchers to create an artificial general purpose intelligence (AGI). However, if we view intelligence as providing a large possibility space, there is no general purpose state. Human intelligence is not in some central position, with other specialized intelligence revolving around it. Rather, human intelligence is a very, very specific type of intelligence that has evolved over many millions of years to enable our species to survive on this planet. Mapped in the space of all possible intelligences, a human-type of intelligence will be stuck in the corner somewhere, just as our world is stuck at the edge of vast galaxy.

 

We can certainly imagine, and even invent, a Swiss-army knife type of thinking. It kind of does a bunch of things okay, but none of them very well. AIs will follow the same engineering maxim that all things made or born must follow: You cannot optimize every dimension. You can only have tradeoffs. You can’t have a general multi-purpose unit outperform specialized functions. A big “do everything” mind can’t do everything as well as those things done by specialized agents. Because we believe our human minds are general purpose, we tend to believe that cognition does not follow the engineer’s tradeoff, that it will be possible to build an intelligence that maximizes all modes of thinking. But I see no evidence of that. We simply haven’t invented enough varieties of minds to see the full space (and so far we have tended to dismiss animal minds as a singular type with variable amplitude on a single dimension.)

 

3.
Part of this belief in maximum general-purpose thinking comes from the concept of universal computation. Formally described as the Church-Turing hypothesis in 1950, this conjecture states that all computation that meets a certain threshold is equivalent. Therefore there is a universal core to all computation, whether it occurs in one machine with many fast parts, or slow parts, or even if it occurs in a biological brain, it is the same logical process. Which means that you should be able to emulate any computational process (thinking) in any machine that can do “universal” computation. Singularitans rely on this principle for their expectation that we will be able to engineer silicon brains to hold human minds, and that we can make artificial minds that think like humans, only much smarter. We should be skeptical of this hope because it relies on a misunderstanding of the Church-Turing hypothesis.

 

The starting point of the theory is: “Given infinite tape [memory] and time, all computation is equivalent.” The problem is that in reality, no computer has infinite memory or time. When you are operating in the real world, real time makes a huge difference, often a life-or-death difference. Yes, all thinking is equivalent if you ignore time. Yes, you can emulate human-type thinking in any matrix you want, as long as you ignore time or the real-life constraints of storage and memory. However, if you incorporate time, then you have to restate the principal in a significant way: Two computing systems operating on vastly different platforms won’t be equivalent in real time. That can be restated again as: The only way to have equivalent modes of thinking is to run them on equivalent substrates. The physical matter you run your computation on — particularly as it gets more complex — greatly influences the type of cognition that can be done well in real time.

 

I will extend that further to claim that the only way to get a very human-like thought process is to run the computation on very human-like wet tissue. That also means that very big, complex artificial intelligences run on dry silicon will produce big, complex, unhuman-like minds. If it would be possible to build artificial wet brains using human-like grown neurons, my prediction is that their thought will be more similar to ours. The benefits of such a wet brain are proportional to how similar we make the substrate. The costs of creating wetware is huge and the closer that tissue is to human brain tissue, the more cost-efficient it is to just make a human. After all, making a human is something we can do in nine months.

 

Furthermore, as mentioned above, we think with our whole bodies, not just with our minds. We have plenty of data showing how our gut’s nervous system guides our “rational” decision-making processes, and can predict and learn. The more we model the entire human body system, the closer we get to replicating it. An intelligence running on a very different body (in dry silicon instead of wet carbon) would think differently.

 

I don’t see that as a bug but rather as a feature. As I argue in point 2, thinking differently from humans is AI’s chief asset. This is yet another reason why calling it “smarter than humans” is misleading and misguided.

 

4.
At the core of the notion of a superhuman intelligence — particularly the view that this intelligence will keep improving itself — is the essential belief that intelligence has an infinite scale. I find no evidence for this. Again, mistaking intelligence as a single dimension helps this belief, but we should understand it as a belief. There is no other physical dimension in the universe that is infinite, as far as science knows so far. Temperature is not infinite — there is finite cold and finite heat. There is finite space and time. Finite speed. Perhaps the mathematical number line is infinite, but all other physical attributes are finite. It stands to reason that reason itself is finite, and not infinite. So the question is, where is the limit of intelligence? We tend to believe that the limit is way beyond us, way “above” us, as we are “above” an ant. Setting aside the recurring problem of a single dimension, what evidence do we have that the limit is not us? Why can’t we be at the maximum? Or maybe the limits are only a short distance away from us? Why do we believe that intelligence is something that can continue to expand forever?

 

A much better way to think about this is to see our intelligence as one of a million types of possible intelligences. So while each dimension of cognition and computation has a limit, if there are hundreds of dimensions, then there are uncountable varieties of mind — none of them infinite in any dimension. As we build or encounter these uncountable varieties of mind we might naturally think of some of them as exceeding us. In my recent book The Inevitable I sketched out some of that variety of minds that were superior to us in some way. Here is an incomplete list:

 

Some folks today may want to call each of these entities a superhuman AI, but the sheer variety and alienness of these minds will steer us to new vocabularies and insights about intelligence and smartness.

 

Second, believers of Superhuman AI assume intelligence will increase exponentially (in some unidentified single metric), probably because they also assume it is already expanding exponentially. However, there is zero evidence so far that intelligence — no matter how you measure it — is increasing exponentially. By exponential growth I mean that artificial intelligence doubles in power on some regular interval. Where is that evidence? Nowhere I can find. If there is none now, why do we assume it will happen soon? The only thing expanding on an exponential curve are the inputs in AI, the resources devoted to producing the smartness or intelligences. But the output performance is not on a Moore’s law rise. AIs are not getting twice as smart every 3 years, or even every 10 years.

 

I asked a lot of AI experts for evidence that intelligence performance is on an exponential gain, but all agreed we don’t have metrics for intelligence, and besides, it wasn’t working that way. When I asked Ray Kurzweil, the exponential wizard himself, where the evidence for exponential AI was, he wrote to me that AI does not increase explosively but rather by levels. He said: “It takes an exponential improvement both in computation and algorithmic complexity to add each additional level to the hierarchy…. So we can expect to add levels linearly because it requires exponentially more complexity to add each additional layer, and we are indeed making exponential progress in our ability to do this. We are not that many levels away from being comparable to what the neocortex can do, so my 2029 date continues to look comfortable to me.”

 

What Ray seems to be saying is that it is not that the power of artificial intelligence is exploding exponentially, but that the effort to produce it is exploding exponentially, while the output is merely raising a level at a time. This is almost the opposite of the assumption that intelligence is exploding. This could change at some time in the future, but artificial intelligence is clearly not increasing exponentially now.

 

Therefore when we imagine an “intelligence explosion,” we should imagine it not as a cascading boom but rather as a scattering exfoliation of new varieties. A Cambrian explosion rather than a nuclear explosion. The results of accelerating technology will most likely not be super-human, but extra-human. Outside of our experience, but not necessarily “above” it.

 

5.
Another unchallenged belief of a super AI takeover, with little evidence, is that a super, near-infinite intelligence can quickly solve our major unsolved problems.

Many proponents of an explosion of intelligence expect it will produce an explosion of progress. I call this mythical belief “thinkism.” It’s the fallacy that future levels of progress are only hindered by a lack of thinking power, or intelligence. (I might also note that the belief that thinking is the magic super ingredient to a cure-all is held by a lot of guys who like to think.)

 

Let’s take curing cancer or prolonging longevity. These are problems that thinking alone cannot solve. No amount of thinkism will discover how the cell ages, or how telomeres fall off. No intelligence, no matter how super duper, can figure out how the human body works simply by reading all the known scientific literature in the world today and then contemplating it. No super AI can simply think about all the current and past nuclear fission experiments and then come up with working nuclear fusion in a day. A lot more than just thinking is needed to move between not knowing how things work and knowing how they work. There are tons of experiments in the real world, each of which yields tons and tons of contradictory data, requiring further experiments that will be required to form the correct working hypothesis. Thinking about the potential data will not yield the correct data.

 

Thinking (intelligence) is only part of science; maybe even a small part. As one example, we don’t have enough proper data to come close to solving the death problem. In the case of working with living organisms, most of these experiments take calendar time. The slow metabolism of a cell cannot be sped up. They take years, or months, or at least days, to get results. If we want to know what happens to subatomic particles, we can’t just think about them. We have to build very large, very complex, very tricky physical structures to find out. Even if the smartest physicists were 1,000 times smarter than they are now, without a Collider, they will know nothing new.

 

There is no doubt that a super AI can accelerate the process of science. We can make computer simulations of atoms or cells and we can keep speeding them up by many factors, but two issues limit the usefulness of simulations in obtaining instant progress. First, simulations and models can only be faster than their subjects because they leave something out. That is the nature of a model or simulation. Also worth noting: The testing, vetting and proving of those models also has to take place in calendar time to match the rate of their subjects. The testing of ground truth can’t be sped up.

 

These simplified versions in a simulation are useful in winnowing down the most promising paths, so they can accelerate progress. But there is no excess in reality; everything real makes a difference to some extent; that is one definition of reality. As models and simulations are beefed up with more and more detail, they come up against the limit that reality runs faster than a 100 percent complete simulation of it. That is another definition of reality: the fastest possible version of all the details and degrees of freedom present. If you were able to model all the molecules in a cell and all the cells in a human body, this simulation would not run as fast as a human body. No matter how much you thought about it, you still need to take time to do experiments, whether in real systems or in simulated systems.

 

To be useful, artificial intelligences have to be embodied in the world, and that world will often set their pace of innovations. Without conducting experiments, building prototypes, having failures, and engaging in reality, an intelligence can have thoughts but not results. There won’t be instant discoveries the minute, hour, day or year a so-called “smarter-than-human” AI appears. Certainly the rate of discovery will be significantly accelerated by AI advances, in part because alien-ish AI will ask questions no human would ask, but even a vastly powerful (compared to us) intelligence doesn’t mean instant progress. Problems need far more than just intelligence to be solved.

 

Not only are cancer and longevity problems that intelligence alone can’t solve, so is intelligence itself. The common trope among Singularitans is that once you make an AI “smarter than humans” then all of sudden it thinks hard and invents an AI “smarter than itself,” which thinks harder and invents one yet smarter, until it explodes in power, almost becoming godlike. We have no evidence that merely thinking about intelligence is enough to create new levels of intelligence. This kind of thinkism is a belief. We have a lot of evidence that in addition to great quantities of intelligence we need experiments, data, trial and error, weird lines of questioning, and all kinds of things beyond smartness to invent new kinds of successful minds.

 

I’d conclude by saying that I could be wrong about these claims. We are in the early days. We might discover a universal metric for intelligence; we might discover it is infinite in all directions. Because we know so little about what intelligence is (let alone consciousness), the possibility of some kind of AI singularity is greater than zero. I think all the evidence suggests that such a scenario is highly unlikely, but it is greater than zero.

 

So while I disagree on its probability, I am in agreement with the wider aims of OpenAI and the smart people who worry about a superhuman AI — that we should engineer friendly AIs and figure out how to instill self-replicating values that match ours. Though I think a superhuman AI is a remote possible existential threat (and worthy of considering), I think its unlikeliness (based on the evidence we have so far) should not be the guide for our science, policies, and development. An asteroid strike on the Earth would be catastrophic. Its probability is greater than zero (and so we should support the B612 Foundation), but we shouldn’t let the possibility of an asteroid strike govern our efforts in, say, climate change, or space travel, or even city planning.

 

Likewise, the evidence so far suggests AIs most likely won’t be superhuman but will be many hundreds of extra-human new species of thinking, most different from humans, none that will be general purpose, and none that will be an instant god solving major problems in a flash. Instead there will be a galaxy of finite intelligences, working in unfamiliar dimensions, exceeding our thinking in many of them, working together with us in time to solve existing problems and create new problems.

 

I understand the beautiful attraction of a superhuman AI god. It’s like a new Superman. But like Superman, it is a mythical figure. Somewhere in the universe a Superman might exist, but he is very unlikely. However myths can be useful, and once invented they won’t go away. The idea of a Superman will never die. The idea of a superhuman AI Singularity, now that it has been birthed, will never go away either. But we should recognize that it is a religious idea at this moment and not a scientific one. If we inspect the evidence we have so far about intelligence, artificial and natural, we can only conclude that our speculations about a mythical superhuman AI god are just that: myths.

 

Many isolated islands in Micronesia made their first contact with the outside world during World War II. Alien gods flew over their skies in noisy birds, dropped food and goods on their islands, and never returned. Religious cults sprang up on the islands praying to the gods to return and drop more cargo. Even now, fifty years later, many still wait for the cargo to return. It is possible that superhuman AI could turn out to be another cargo cult. A century from now, people may look back to this time as the moment when believers began to expect a superhuman AI to appear at any moment and deliver them goods of unimaginable value. Decade after decade they wait for the superhuman AI to appear, certain that it must arrive soon with its cargo.

 

Yet non-superhuman artificial intelligence is already here, for real. We keep redefining it, increasing its difficulty, which imprisons it in the future, but in the wider sense of alien intelligences — of a continuous spectrum of various smartness, intelligences, cognition, reasonings, learning, and consciousness — AI is already pervasive on this planet and will continue to spread, deepen, diversify, and amplify. No invention before will match its power to change our world, and by century’s end AI will touch and remake everything in our lives. Still the myth of a superhuman AI, poised to either gift us super-abundance or smite us into super-slavery (or both), will probably remain alive—a possibility too mythical to dismiss.

 

[Lord Protector]

23 hours ago, hazard said:

A more accurate chart of the natural evolution of species is a disk radiating outward, like this one (above) first devised by David Hillis at the University of Texas and based on DNA. This deep genealogy mandala begins in the middle with the most primeval life forms, and then branches outward in time. Time moves outward so that the most recent species of life living on the planet today form the perimeter of the circumference of this circle. This picture emphasizes a fundamental fact of evolution that is hard to appreciate: Every species alive today is equally evolved. Humans exist on this outer ring alongside cockroaches, clams, ferns, foxes, and bacteria. Every one of these species has undergone an unbroken chain of three billion years of successful reproduction, which means that bacteria and cockroaches today are as highly evolved as humans. There is no ladder.

 

 

Ima lestvica, samo je beskonačna , i to u hiperboličnoj ravni...neka mu neko javi, trebao je da kaže da se evolucija se odvija u neeuklidskoj ravni i ima svoju kejli-klajn metriku

 

Autor fino poređa pretpostavke ali dođe do pogrešnog zaključka... 

 

 

 

 

Edited February 13, 2018 by slow

[hazard]

Koliko je nama poznato, ništa u ovom univerzumu nije beskonačno

[Lord Protector]

7 minutes ago, hazard said:

Koliko je nama poznato, ništa u ovom univerzumu nije beskonačno

 

imaš neprebrojivo beskonačno brojeva u intervalu [0,1] 

 

u kosmičkim singularitetima se nalaze beskonačno velike veličine

 

Ono što autor zastupa je rigidni finitarizam, a on je odavno napušten u prirodnim naukama.  Beskonačnost postoji.

 

 

 

 

Edited February 13, 2018 by slow

[hazard]

Brojevi u intervalu [0,1] su apstraktne, da ne kažem fizički nepostojeće stvari. Šta je u kosmičkim singularitetima možemo samo da nagađamo. Možda jednog dana saznamo. Takođe, fizičari zapravo nisu ni sigurno da kosmički singulariteti uopšte postoje. To je nešto što proizilazi iz trenutno vladajućih teorija. Može biti da toga ima, a može biti da su teorije neadekvatne (kao što se pokazalo mnogo puta u istoriji nauke).

 

Autor ne zastupa ,,rigidni finitarizam", nego kaže - ne postoji nikakav dokaz da je inteligencija beskonačna. Na kraju ostavlja mogućnost da nije u pravu.

 

Takođe, svaka inteligencija za koju mi znamo zahteva nekakav fizički supstrat. Možda možemo da imamo beskonačnu inteligenciju kada bi imali beskonačni supstrat. Mi to ne umemo da napravimo (niti ćemo umeti ,,skoro", gde se ,,skoro" meri u milenijumima).

 

No to o konačnosti ili beskonačnosti inteligencije nije jedina niti glavna poenta tog dela teksta. Poenta je da inteligencija nije jednodimenzionalna.

[bigvlada]

Hey Alexa, is it true a TV advert made Amazon Echo order cat food?
Advertising watchdog rejects Echo Dot owner’s complaint that TV spot was irresponsible

 

An Amazon Echo owner has tried to get a television advertising campaign for the smart speaker banned after the Alexa virtual assistant attempted to order cat food when it heard its name on an ad.

An Amazon TV ad for the Echo Dot, which can perform functions such as make shopping lists and play music with voice commands, features people using the device in different situations. In one a man’s voice says: “Alexa, reorder Purina cat food.” Alexa responds: “I’ve found Purina cat food. Would you like to buy it?”

A viewer lodged a complaint with the Advertising Standards Authority (ASA), saying that the ad was irresponsible because it caused their Echo Dot to order cat food. Amazon confirmed that the complainant’s device did place an order for the cat food but it had been cancelled by the customer.

Amazon said it was aware of the potential issue and “marks” ads so that Alexa is not triggered. In addition, customers are required to confirm a purchase, which is automatically cancelled if they do not do so, the company said.

Earlier this month Amazon used its technology to stop devices from interacting with its Super Bowl TV spot, which featured celebrities including Gordon Ramsay, Rebel Wilson and Anthony Hopkins taking over from Alexa when she “loses her voice”.

 

The word “Alexa” was mentioned 10 times in the commercial, made by the London agency Lucky Generals, but it did not trigger action from devices in viewers’ homes.

The ASA assessed the complaint about the phantom cat food order but did not find it in breach of the UK advertising code.

It is not the first time that Amazon has run into trouble with Alexa taking orders from the TV. Last year an episode of South Park that featured the characters repeatedly yelling commands at cartoon versions of Alexa and rival Google Home wreaked havoc with some viewers’ devices. 

Similarly, a TV presenter in San Diego commented on a story about a six-year-old girl who had asked Alexa to order her a dollhouse, which triggered orders for the dollhouse by Alexa on devices owned by viewers.

“The real problem, I think, is that it’s much harder for manufacturers of this kind of device to guard against ads created by a third parties,” said Geraint Lloyd-Taylor, of the law firm Lewis Silkin. “There’s not much Amazon can do to proactively guard against that.”

 

https://www.theguardian.com/technology/2018/feb/14/amazon-alexa-ad-avoids-ban-after-viewer-complaint-ordered-cat-food

 

[eumeswil]

 

[Lord Protector]

Ovo će još malo da prođe Tjuringov test (na gornjem primeru već prolazi)... a to jeste AI po definiciji

Edited May 14, 2018 by slow