Robot Brains Catch Humans in 25 Years, Then Speed Right On By

An android Repliee S1, produced by Japan's Osaka University professor Hiroshi Ishiguro, performing during a dress rehearsal of Franz Kafka's "The Metamorphosis." Phototographer: Yoshikazu Tsuno/AFP via Getty Images

We’ve been wrong about these robots before.

Soon after modern computers evolved in the 1940s, futurists started predicting that in just a few decades machines would be as smart as humans. Every year, the prediction seems to get pushed back another year. The consensus now is that it’s going to happen in ... you guessed it, just a few more decades.

There’s more reason to believe the predictions today. After research that’s produced everything from self-driving cars to Jeopardy!-winning supercomputers, scientists have a much better understanding of what they’re up against. And, perhaps, what we’re up against.

Nick Bostrom, director of the Future of Humanity Institute at Oxford University, lays out the best predictions of the artificial intelligence (AI) research community in his new book, “Superintelligence: Paths, Dangers, Strategies.” Here are the combined results of four surveys of AI researchers, including a poll of the most-cited scientists in the field, totalling 170 respondents.

Human-level machine intelligence is defined here as “one that can carry out most human professions at least as well as a typical human.”

By that definition, maybe we shouldn’t be so surprised about these predictions. Robots and algorithms are already squeezing the edges of our global workforce. Jobs with routine tasks are getting digitized: farmers, telemarketers, stock traders, loan officers, lawyers, journalists -- all of these professions have already felt the cold steel nudge of our new automated colleagues. 

Related: How to Keep Your Job When Your Boss Is a Robot

Replication of routine isn't the kind of intelligence Bostrom is interested in. He’s talking about an intelligence with intuition and logic, one that can learn, deal with uncertainty and sense the world around it. The most interesting thing about reaching human-level intelligence isn’t the achievement itself, says Bostrom; it’s what comes next. Once machines can reason and improve themselves, the skynet is the limit.

Computers are improving at an exponential rate. In many areas -- chess, for example -- machine skill is already superhuman. In others -- reason, emotional intelligence -- there’s still a long way to go. Whether human-level general intelligence is reached in 15 years or 150, it’s likely to be a little-observed mile marker on the road toward superintelligence.

Superintelligence: one that “greatly exceeds the cognitive performance of humans in virtually all domains of interest.”

Inventor and Tesla CEO Elon Musk warns that superintelligent machines are possibly the greatest existential threat to humanity. He says the investments he's made in artificial-intelligence companies are primarily to keep an eye on where the field is headed.

“Hope we’re not just the biological boot loader for digital superintelligence,” Musk Tweeted in August. “Unfortunately, that is increasingly probable.”

There are lots of caveats before we prepare to hand the keys to our earthly kingdom over to robot offspring.

• First, humans have a terrible track record of predicting the future. 
• Second, people are notoriously optimistic when forecasting the future of their own industries. 
• Third, it’s not a given that technology will continue to advance along its current trajectory, or even with its current aims.

Still, the brightest minds devoted to this evolving technology are predicting the end of human intellectual supremacy by midcentury. That should be enough to give everyone pause. The direction of technology may be inevitable, but the care with which we approach it is not.

“Success in creating AI would be the biggest event in human history,” wrote theoretical physicist Stephen Hawking, in an Independent column in May. “It might also be the last.”

ORIGINAL: Bloomberg

By Tom Randall 

Nov 10, 2014

The Myth Of AI. A Conversation with Jaron Lanier

The idea that computers are people has a long and storied history. It goes back to the very origins of computers, and even from before. There's always been a question about whether a program is something alive or not since it intrinsically has some kind of autonomy at the very least, or it wouldn't be a program. There has been a domineering subculture—that's been the most wealthy, prolific, and influential subculture in the technical world—that for a long time has not only promoted the idea that there's an equivalence between algorithms and life, and certain algorithms and people, but a historical determinism that we're inevitably making computers that will be smarter and better than us and will take over from us. ...

That mythology, in turn, has spurred a reactionary, perpetual spasm from people who are horrified by what they hear. You'll have a figure say, "The computers will take over the Earth, but that's a good thing, because people had their chance and now we should give it to the machines." Then you'll have other people say, "Oh, that's horrible, we must stop these computers." Most recently, some of the most beloved and respected figures in the tech and science world, including Stephen Hawking and Elon Musk, have taken that position of: "Oh my God, these things are an existential threat. They must be stopped."

In the history of organized religion, it's often been the case that people have been disempowered precisely to serve what was perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity. ... That looks an awful lot like the new digital economy to me, where you have (natural language) translators and everybody else who contributes to the corpora that allows the data schemes to operate, contributing to the fortunes of whoever runs the computers. You're saying, "Well, but they're helping the AI, it's not us, they're helping the AI." It reminds me of somebody saying, "Oh, build these pyramids, it's in the service of this deity," and, on the ground, it's in the service of an elite. It's an economic effect of the new idea. The new religious idea of AI is a lot like the economic effect of the old idea, religion.

[39:47]

JARON LANIER is a Computer Scientist; Musician; Author of Who Owns the Future? 

Jaron Lanier's Edge Bio Page

INTRODUCTION 

by John Brockman

This past weekend, during a trip to San Francisco, Jaron Lanier stopped by to talk to me for an Edge feature. He had something on his mind: news reports about comments by Elon Musk and Stephen Hawking, two of the most highly respected and distinguished members of the science and technology communiity, on the dangers of AI. ("Elon Musk, Stephen Hawking and fearing the machine" by Alan Wastler, CNBC 6.21.14). He then talked, uninterrupted, for an hour.

As Lanier was about to depart, John Markoff, the Pulitzer Prize-winning technology correspondent for THE NEW YORK TIMES, arrived. Informed of the topic of the previous hour's conversation, he said, "I have a piece in the paper next week. Read it." A few days later, his article, "Fearing Bombs That Can Pick Whom to Kill" (11.12.14), appeared on the front page. It's one of a continuing series of articles by Markoff pointing to the darker side of the digital revolution.

This is hardly new territory. Cambridge cosmologist Martin Rees, the former Astronomer Royal and President of the Royal Society, addressed similar topics in his 2004 book, Our Final Hour: A Scientist's Warning, as did computer scientist, Bill Joy, co-founder of Sun Microsystems, in his highly influential 2000 article in Wired, "Why The Future Doesn't Need Us: Our most powerful 21st-century technologies — robotics, genetic engineering, and nanotech — are threatening to make humans an endangered species".

But these topics are back on the table again, and informing the conversation in part is Superintelligence: Paths, Dangers, Strategies, the recently published book by Nick Bostrom, founding director of Oxford University’s Institute for the Future of Humanity. In his book, Bostrom asks questions such as "what happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us?"

I am encouraging, and hope to publish, a Reality Club conversation, with comments (up to 500 words) on, but not limited to, Lanier's piece. This is a very broad topic that involves many different scientific fields and I am sure the Edgies will have lots of interesting things to say.

—JB

Related on Edge:

Jaron Lanier: 

• "Digital Maoism: The Hazards of the New Online Collectivism" (2006) ;
• "One Half A Manifesto" (2000)

Kevin Kelly: 

• "The Technium" (2014)

George Dyson: 

• "Turing's Cathedral" (2004)

THE MYTH OF AI (Transcript)

A lot of us were appalled a few years ago when the American Supreme Court decided, out of the blue, to decide a question it hadn't been asked to decide, and declare that corporations are people. That's a cover for making it easier for big money to have an influence in politics. But there's another angle to it, which I don't think has been considered as much: the tech companies, which are becoming the most profitable, the fastest rising, the richest companies, with the most cash on hand, are essentially people for a different reason than that. They might be people because the Supreme Court said so, but they're essentially algorithms.

10 IBM Watson-Powered Apps That Are Changing Our World

IBM is investing $1 billion in its IBM Watson Group with the aim of creating an ecosystem of startups and businesses building cognitive computing applications with Watson. Here are 10 examples that are making an impact.

IBM considers Watson to represent a new era of computing — a step forward to cognitive computing, where apps and systems interact with humans via natural language and help us augment our own understanding of the world with big data insights.

Big Blue isn't playing small ball with that claim. It has opened a new IBM Watson Global Headquarters in the heart of New York City's Silicon Alley and is investing $1 billion into the Watson Group, focusing on development and research as well as bringing cloud-delivered cognitive applications and services to the market. That includes $100 million available for venture investments to support IBM's ecosystem of start-ups and businesses building cognitive apps with Watson.

Here are 10 examples of Watson-powered cognitive apps that are already starting to shake things up.

Image courtesy USAA/IBM Feature Photo Service/Flickr

USAA and Watson Help Military Members Transition to Civilian Life

USAA, a financial services firm dedicated to those who serve or have served in the military, has turned to IBM's Watson Engagement Advisor in a pilot program to help military men and women transition to civilian life.

According to the U.S. Bureau of Labor Statistics, about 155,000 active military members transition to civilian life each year. This process can raise many questions, like "Can I be in the reserve and collect veteran's compensation benefits?" or "How do I make the most of the Post-9/11 GI Bill?" Watson has analyzed and understands more than 3,000 documents on topics exclusive to military transitions, allowing members to ask it questions and receive answers specific to their needs.

Wayblazer

Sofie 

LifeLearn Sofie is an intelligent treatment support tool for veterinarians of all backgrounds and levels of experience. Sofie is powered by IBM WatsonTM, the world’s leading cognitive computing system. She can understand and process natural language, enabling interactions that are more aligned with how humans think and interact.

Implement Watson

Dive deeper into subjects. Find insights where no one ever thought to look before. From Healthcare to Retail, there's an IBM Watson Solution that's right for your enterprise.

Healthcare

Helping doctors identify treatment options

The challenge

Google CEO: Computers Are Going To Take Our Jobs, And 'There's No Way Around That'

Google+/Larry Page Google CEO Larry Page

When Google co-founders Larry Page and Sergey Brin formed the company in 1998, they sought to package all the information on the internet into an index that's simple to use.

Today, Google is much more than a search engine. The company appears to be involved in every type of new technology ranging from self-driving cars to contact lenses that can test for disease.

In a recent interview with the Financial Times, CEO Larry Page provided some insight as to why the company has decided to take on so many different tasks.

Part of the reason is because Page believes there's this inevitable shift coming in which computers will be much better-suited to take on most jobs. 

"You can't wish away these things from happening, they are going to happen," he told the Financial Times on the subject of artificial intelligence infringing on the job market. "You're going to have some very amazing capabilities in the economy. When we have computers that can do more and more jobs, it's going to change how we think about work. There's no way around that. You can't wish it away."

But people shouldn't fear computers taking over their occupations, according to Page, who says it "doesn't make sense" for people to work so much.

"The idea that everyone should slavishly work so they do something inefficiently so they keep their job — that just doesn't make any sense to me," he told the Financial Times. "That can't be the right answer." 

Based on Page's quotes in the Financial Times, it sounds as if he feels like Google has an obligation to invest in forward-thinking technologies.

"...We have all these billions we should be investing to make people's lives better," Page said to the Financial Times. "If we just do the same thing we did before and don't do something new, it seems like a crime to me."

You can read the full interview with Page here >>

ORIGINAL: BusinessInsider

By LISA EADICICCO

OCT. 31, 2014

Meet Amelia: the computer that's after your job

A new artificially intelligent computer system called 'Amelia' – that can read and understand text, follow processes, solve problems and learn from experience – could replace humans in a wide range of low-level jobs

Amelia aims to answer the question, can machines think? Photo: IPsoft

In February 2011 an artificially intelligent computer system called IBM Watson astonished audiences worldwide by beating the two all-time greatest Jeopardy champions at their own game.

Thanks to its ability to apply 

• advanced natural language processing, 
• information retrieval, 
• knowledge representation, 
• automated reasoning, and 
• machine learning technologies, Watson consistently outperformed its human opponents on the American quiz show Jeopardy.

Watson represented an important milestone in the development of artificial intelligence, but the field has been progressing rapidly – particularly with regard to natural language processing and machine learning.

In 2012, Google used 16,000 computer processors to build a simulated brain that could correctly identify cats in YouTube videos; the Kinect, which provides a 3D body-motion interface for Microsoft's Xbox, uses algorithms that emerged from artificial intelligence research, as does the iPhone's Siri virtual personal assistant.

Today a new artificial intelligence computing system has been unveiled, which promises to transform the global workforce. Named 'Amelia' after American aviator and pioneer Amelia Earhart, the system is able to shoulder the burden of often tedious and laborious tasks, allowing human co-workers to take on more creative roles.

"Watson is perhaps the best data analytics engine that exists on the planet; it is the best search engine that exists on the planet; but IBM did not set out to create a cognitive agent. It wanted to build a program that would win Jeopardy, and it did that," said Chetan Dube, chief executive Officer of IPsoft, the company behind Amelia.

"Amelia, on the other hand, started out not with the intention of winning Jeopardy, but with the pure intention of answering the question posed by Alan Turing in 1950 – can machines think?"

Amelia learns by following the same written instructions as her human colleagues, but is able to absorb information in a matter of seconds. She understands the full meaning of what she reads rather than simply recognising individual words. This involves 

• understanding context, 
• applying logic and 
• inferring implications.

When exposed to the same information as any new employee in a company, Amelia can quickly apply her knowledge to solve queries in a wide range of business processes. Just like any smart worker she learns from her colleagues and, by observing their work, she continually builds her knowledge.

While most ‘smart machines’ require humans to adapt their behaviour in order to interact with them, Amelia is intelligent enough to interact like a human herself. She speaks more than 20 languages, and her core knowledge of a process needs only to be learned once for her to be able to communicate with customers in their language.

Independently, rather than through time-intensive programming, Amelia creates her own 'process map' of the information she is given so that she can work out for herself what actions to take depending on the problem she is solving.

"Intelligence is the ability to acquire and apply knowledge. If a system claims to be intelligent, it must be able to read and understand documents, and answer questions on the basis of that. It must be able to understand processes that it observes. It must be able to solve problems based on the knowledge it has acquired. And when it cannot solve a problem, it must be capable of learning the solution through noticing how a human did it," said Dube.

IPsoft has been working on this technology for 15 years with the aim of developing a platform that does not simply mimic human thought processes but can comprehend the underlying meaning of what is communicated – just like a human.

Just as machines transformed agriculture and manufacturing, IPsoft believes that cognitive technologies will drive the next evolution of the global workforce, so that in the future companies will have digital workforces that comprise a mixture of human and virtual employees.

Amelia has already been trialled within a number of Fortune 1000 companies, in areas such as manning technology help desks, procurement processing, financial trading operations support and providing expert advice for field engineers.

In each of these environments, she has learnt not only from reading existing manuals and situational context but also by observing and working with her human colleagues and discerning for herself a map of the business processes being followed.

In a help desk situation, for example, Amelia can understand what a caller is looking for, ask questions to clarify the issue, find and access the required information and determine which steps to follow in order to solve the problem.

As a knowledge management advisor, she can help engineers working in remote locations who are unable to carry detailed manuals, by diagnosing the cause of failed machinery and guiding them towards the best steps to rectifying the problem.

During these trials, Amelia was able to go from solving very few queries independently to 42 per cent of the most common queries within one month. By the second month she could answer 64 per cent of those queries independently.

"That’s a true learning cognitive agent. Learning is the key to the kingdom, because humans learn from experience. A child may need to be told five times before they learn something, but Amelia needs to be told only once," said Dube.

"Amelia is that Mensa kid, who personifies a major breakthrough in cognitive technologies."

Analysts at Gartner predict that, by 2017, managed services offerings that make use of autonomics and cognitive platforms like Amelia will drive a 60 per cent reduction in the cost of services, enabling organisations to apply human talent to higher level tasks requiring creativity, curiosity and innovation.

IPsoft even has plans to start embedding Amelia into humanoid robots such as Softbank's Pepper, Honda's Asimo or Rethink Robotics' Baxter, allowing her to take advantage of their mechanical functions.

"The robots have got a fair degree of sophistication in all the mechanical functions – the ability to climb up stairs, the ability to run, the ability to play ping pong. What they don’t have is the brain, and we’ll be supplementing that brain part with Amelia," said Dube.

"I am convinced that in the next decade you’ll pass someone in the corridor and not be able to discern if it’s a human or an android."

Given the premise of IPsoft's artificial intelligence system, it seems logical that the ultimate measure of Amelia's success would be passing the Turing Test – which sets out to see whether humans can discern whether they are interacting with a human or a machine.

Earlier this year, a chatbot named Eugene Goostman became the first machine to pass the Turing Test by convincingly imitating a 13-year-old boy. In a five-minute keyboard conversation with a panel of human judges, Eugene managed to convince 33 per cent that it was human.

Interestingly, however, IPsoft believes that the Turing Test needs reframing, to redefine what it means to 'think'. While Eugene was able to immitate natural language, he was only mimicking understanding. He did not learn from the interaction, nor did he demonstrate problem solving skills.

"Natural language understanding is a big step up from parsing. Parsing is syntactic, understanding is semantic, and there’s a big cavern between the two," said Dube.

"The aim of Amelia is not just to get an accolade for managing to fool one in three people on a panel. The assertion is to create something that can answer to the fundamental need of human beings – particularly after a certain age – of companionship. That is our intent."

ORIGINAL: Telegraph

By Sophie Curtis
29 Sep 2014

Danko Nikolic on Singularity 1 on 1: Practopoiesis Tells Us Machine Learning Is Not Enough!

If there’s ever been a case when I just wanted to jump on a plane and go interview someone in person, not because they are famous but because they have created a totally unique and arguably seminal theory, it has to be Danko Nikolic. I believe Danko’s theory of Practopoiesis is that good and he should and probably eventually would become known around the world for it. Unfortunately, however, I don’t have a budget of thousands of dollars per interview which will allow me to pay for my audio and video team to travel to Germany and produce the quality that Nikolic deserves. So, I’ve had to settle with Skype. And Skype refused to cooperate on that day even though both me and Danko have pretty much the fastest internet connections money can buy. Luckily, despite the poor video quality, our audio was very good and I would urge that if there’s ever been an interview where you ought to disregard the video quality and focus on the content – it has to be this one.

During our 67 min conversation with Danko we cover a variety of interesting topics such as:

• his personal journey into psychology and cognitive science; 
• writing a manual for the mind; 
• practopoiesis, AI and learning how to learn; 
• consciousness and free will; 
• the Penrose-Hameroff Quantum Theory of consciousness; 
• the brain-mind distinction; 
• the Human Brain Project, 
• whole brain simulation and 
• mind uploading…

As always you can listen to or download the audio file above or scroll down and watch the video interview in full.

To show your support you can write a review on iTunes or make a donation.

Who is Danko Nikolic?

The main motive for my studies is the explanatory gap between the brain and the mind. My interest is in how the physical world of neuronal activity produces the mental world of perception and cognition. I am associated with

• the Max-Planck Institute for Brain Research, 
• Ernst Strüngmann Institute, 
• Frankfurt Institute for Advanced Studies, and
• the University of Zagreb.

I approach the problem of explanatory gap from both sides, bottom-up and top-down. The bottom-up approach investigates brain physiology. The top-down approach investigates the behavior and experiences. Each of the two approaches led me to develop a theory: The work on physiology resulted in the theory of practopoiesis. The work on behavior and experiences led to the phenomenon of ideasthesia.

The empirical work in the background of those theories involved

• simultaneous recordings of activity of 100+ neurons in the visual cortex (extracellular recordings), 
• behavioral and imaging studies in visual cognition (attention, working memory, long-term memory), and 
• empirical investigations of phenomenal experiences (synesthesia).

The ultimate goal of my studies is twofold.

• First, I would like to achieve conceptual understanding of how the dynamics of physical processes creates the mental ones. I believe that the work on practopoiesis presents an important step in this direction and that it will help us eventually address the hard problem of consciousness and the mind-body problem in general. 
• Second, I would like to use this theoretical knowledge to create artificial systems that are biologically-like intelligent and adaptive. This would have implications for our technology.

A reason why one would be interested in studying the brain in the first place is described here: Why brain?

ORIGINAL: Singularity 1 on 1

by SOCRATES

Practopoiesis: How cybernetics of biology can help AI

By creating any form of AI we must copy from biology. The argument goes as follows. A brain is a biological product. And so must be then its products such as perception, insight, inference, logic, mathematics, etc. By creating AI we inevitably tap into something that biology has already invented on its own. It follows thus that the more we want the AI system to be similar to a human—e.g., to get a better grade on the Turing test—the more we need to copy the biology.

When it comes to describing living systems, traditionally, we assume the approach of different explanatory principles for different levels of system organization.

1. One set of principles is used for “low-level” biology such as the evolution of our genome through natural selection, which is a completely different set of principles than the one used for describing the expression of those genes. 
2. A yet different type of story is used to explain what our neural networks do. Needless to say, 
3. the descriptions at the very top of that organizational hierarchy—at the level of our behavior—are made by concepts that again live in their own world.

But what if it was possible to unify all these different aspects of biology and describe them all by a single set of principles? What if we could use the same fundamental rules to talk about the physiology of a kidney and the process of a conscious thought? What if we had concepts that could give us insights into mental operations underling logical inferences on one hand and the relation between the phenotype and genotype on the other hand? This request is not so outrageous. After all, all those phenomena are biological.

One can argue that such an all-embracing theory of the living would be beneficial also for further developments of AI. The theory could guide us on what is possible and what is not. Given a certain technological approach, what are its limitations? Maybe it could answer the question of what the unitary components of intelligence are. And does my software have enough of them?

For more inspiration, let us look into Shannon-Wiener theory of information and appreciate how much helpful this theory is for dealing with various types of communication channels (including memory storage, which is also a communication channel, only over time rather than space). We can calculate how much channel capacity is needed to transmit (store) certain contents. Also, we can easily compare two communication channels and determine which one has more capacity. This allows us to directly compare devices that are otherwise incomparable. For example, an interplanetary communication system based on satellites can be compared to DNA located within a nucleus of a human cell. Only thanks to the information theory can we calculate whether a given satellite connection has enough capacity to transfer the DNA information about human person to a hypothetical recipient at another planet. (The answer is: yes, easily.) Thus, information theory is invaluable in making these kinds of engineering decisions.

So, how about intelligence? Wouldn’t it be good to come into possession of a similar general theory for adaptive intelligent behavior? Maybe we could use certain quantities other than bits that could tell us why the intelligence of plants is lagging behind that of primates? Also, we may be able to know better what the essential ingredients are that distinguish human intelligence from that of a chimpanzee? Using the same theory we could compare

• an abacus, 
• a hand-held calculator, 
• a supercomputer, and 
• a human intellect.

The good news is that, since recently, such an overarching biological theory exists, and it is called practopoiesis. Derived from Ancient Greek praxis + poiesis, practopoiesis means creation of actions. The name reflects the fundamental presumption on what the common property can be found across all the different levels of organization of biological systems: 

• Gene expression mechanisms act; 
• bacteria act; 
• organs act; 
• organisms as a whole act.

Due to this focus on biological action, practopoiesis has a strong cybernetic flavor as it has to deal with the need of acting systems to close feedback loops. Input is needed to trigger actions and to determine whether more actions are needed. For that reason, the theory is founded in the basic theorems of cybernetics, namely that of requisite variety and good regulator theorem.

The key novelty of practopoiesis is that it introduces the mechanisms explaining how different levels of organization mutually interact. These mechanisms help explain how genes create anatomy of the nervous system, or how anatomy creates behavior.

When practopoiesis is applied to human mind and to AI algorithms, the results are quite revealing.

To understand those, we need to introduce the concept of practopoietic traverse. Without going into details on what a traverse is, let us just say that this is a quantity with which one can compare different capabilities of systems to adapt. Traverse is a kind of a practopoietic equivalent to the bit of information in Shannon-Wiener theory. If we can compare two communication channels according to the number of bits of information transferred, we can compare two adaptive systems according to the number of traverses. Thus, a traverse is not a measure of how much knowledge a system has (for that the good old bit does the job just fine). It is rather a measure of how much capability the system has to adjust its existing knowledge for example, when new circumstances emerge in the surrounding world.

To the best of my knowledge no artificial intelligence algorithm that is being used today has more than two traverses. That means that these algorithms interact with the surrounding world at a maximum of two levels of organization. For example, an AI algorithm may receive satellite images at one level of organization and the categories to which to learn to classify those images at another level of organization. We would say that this algorithm has two traverses of cybernetic knowledge. In contrast, biological behaving systems (that is, animals, homo sapiens) operate with three traverses.

This makes a whole lot of difference in adaptive intelligence. Two-traversal systems can be super-fast and omni-knowledgeable, and their tech-specs may list peta-everything, which they sometimes already do, but these systems nevertheless remain comparably dull when compared to three-traversal systems, such as a three-year old girl, or even a domestic cat.

To appreciate the difference between two and three traverses, let us go one step lower and consider systems with only one traverse. An example would be a PC computer without any advanced AI algorithm installed.

This computer is already light speed faster than I am in calculations, way much better in memory storage, and beats me in spell checking without the processor even getting warm. And, paradoxically, I am still the smarter one around. Thus, computational capacity and adaptive intelligence are not the same.

Importantly, this same relationship “me vs. the computer” holds for “me vs. a modern advanced AI-algorithm”. I am still the more intelligent one although the computer may have more computational power. But also the relationship holds “AI-algorithm vs. non-AI computer”. Even a small AI algorithm, implemented say on a single PC, is in many ways more intelligent than a petaflop supercomputer without AI. Thus, there is a certain hierarchy in adaptive intelligence that is not determined by memory size or the number of floating point operations executed per second but by the ability to learn and adapt to the environment.

A key requirement for adaptive intelligence is the capacity to observe how well one is doing towards a certain goal combined with the capacity to make changes and adjust in light of the feedback obtained. Practopoiesis tells us that there is not only one step possible from non-adaptive to adaptive, but that multiple adaptive steps are possible. Multiple traverses indicate a potential for adapting the ways in which we adapt.

We can go even one step further down the adaptive hierarchy and consider the least adaptive systems e.g., a book: Provided that the book is large enough, it can contain all of the knowledge about the world and yet it is not adaptive as it cannot for example, rewrite itself when something changes in that world. Typical computer software can do much more and administer many changes, but there is also a lot left that cannot be adjusted without a programmer. A modern AI-system is even smarter and can reorganize its knowledge to a much higher degree. Still, nevertheless, these systems are incapable of doing a certain types of adjustments that a human person can do, or that animals can do. Practopoisis tells us that these systems fall into different adaptive categories, which are independent of the raw information processing capabilities of the systems. Rather, these adaptive categories are defined by the number of levels of organization at which the system receives feedback from the environment — also referred to as traverses.

We can thus make the following hierarchical list of the best exemplars in each adaptive category:

• A book: dumbest; zero traverses
• A computer: somewhat smarter; one traverse
• An AI system: much smarter; two traverses
• A human: rules them all; three traverses

Most importantly for creation of strong AI, practopoiesis tells us in which direction the technological developments should be heading:
Engineering creativity should be geared towards empowering the machines with one more traverse. To match a human, a strong AI system has to have three traverses.

Practopoietic theory explains also what is so special about the third traverse. Systems with three traverses (referred to as T3-systems) are capable of storing their past experiences in an abstract, general form, which can be used in a much more efficient way than in two-traversal systems. This general knowledge can be applied to interpretation of specific novel situations such that quick and well-informed inferences are made about what is currently going on and what actions should be executed next. This process, unique for T3-systems, is referred to as anapoiesis, and can be generally described as a capability to reconstruct cybernetic knowledge that the system once had and use this knowledge efficiently in a given novel situation.

If biology has invented T3-systems and anapoiesis and has made a good use of them, there is no reason why we should not be able to do the same in machines.

5 Robots Booking It to a Classroom Near You: 

Danko Nikolić is a brain and mind scientist, running an electrophysiology lab at the Max Planck Institute for Brain Research, and is the creator of the concept of ideasthesia. More about practopoiesis can be read here

Tagged as: AI, Artificial Intelligence, Practopoiesis

ORIGINAL: Singularity Web

by DANKO NIKOLIĆ

Everybody Relax: An MIT Economist Explains Why Robots Won't Steal Our Jobs

Living together in harmony. Photo by Oli Scarff/Getty Images

If you’ve ever found yourself fretting about the possibility that software and robotics are on the verge of thieving away all our jobs, renowned MIT labor economist David Autor is out with a new paper that might ease your nerves. Presented Friday at the Federal Reserve Bank of Kansas City’s big annual conference in Jackson Hole, Wyoming, the paper argues that humanity still has two big points in its favor: People have "common sense,” and they’re "flexible."

Neil Irwin already has a lovely writeup of the paper at the New York Times, but let’s run down the basics. There’s no question machines are getting smarter, and quickly acquiring the ability to perform work that once seemed uniquely human. Think self-driving cars that might one day threaten cabbies, or computer programs that can handle the basics of legal research.

But artificial intelligence is still just that: artificial. We haven’t untangled all the mysteries of human judgment, and programmers definitely can’t translate the way we think entirely into code. Instead, scientists at the forefront of AI have found workarounds like machine-learning algorithms. As Autor points out, a computer might not have any abstract concept of a chair, but show it enough Ikea catalogs, and it can eventually suss out the physical properties statistically associated with a seat. Fortunately for you and me, this approach still has its limits.

For example, both a toilet and a traffic cone look somewhat like a chair, but a bit of reasoning about their shapes vis-à-vis the human anatomy suggests that a traffic cone is unlikely to make a comfortable seat. Drawing this inference, however, requires reasoning about what an object is “for” not simply what it looks like. Contemporary object recognition programs do not, for the most part, take this reasoning-based approach to identifying objects, likely because the task of developing and generalizing the approach to a large set of objects would be extremely challenging.

That’s what Autor means when he says machines lack for common sense. They don’t think. They just do math.

And that leaves lots of room for human workers in the future.

Technology has already whittled away at middle class jobs, from factory workers replaced by robotic arms to secretaries made redundant by Outlook, over the past few decades. But Autor argues that plenty of today's middle-skill occupations, such as construction trades and medical technicians, will stick around, because “many of the tasks currently bundled into these jobs cannot readily be unbundled … without a substantial drop in quality.”

These aren’t jobs that require performing a single task over and over again, but instead demand that employees handle some technical work while dealing with other human beings and improvising their way through unexpected problems. Machine learning algorithms can’t handle all of that. Human beings, Swiss-army knives that we are, can. We’re flexible.

Just like the dystopian arguments that machines are about to replace a vast swath of the workforce, Autor’s paper is very much speculative. It’s worth highlighting, though, because it cuts through the silly sense of inevitability that sometimes clouds this subject. Predictions about the future of technology and the economy are made to be dashed. And while Noah Smith makes a good point that we might want to be prepared for mass, technology-driven unemployment even if there’s just a slim chance of it happening, there’s also no reason to take it for granted.

Jordan Weissmann is Slate's senior business and economics correspondent.

ORIGINAL: Slate

By Jordan Weissmann

It's Time to Take Artificial Intelligence Seriously

No Longer an Academic Curiosity, It Now Has Measurable Impact on Our Lives

A still from "2001: A Space Odyssey" with Keir Dullea reflected in the lens of HAL's "eye." MGM / POLARIS / STANLEY KUBRICK

The age of intelligent machines has arrived—only they don't look at all like we expected. Forget what you've seen in movies; this is no HAL from "2001: A Space Odyssey," and it's certainly not Scarlett Johansson's disembodied voice in "Her." It's more akin to what happens when insects, or even fungi, do when they "think." (What, you didn't know that slime molds can solve mazes?)

Artificial intelligence has lately been transformed from an academic curiosity to something that has measurable impact on our lives. Google Inc. used it to increase the accuracy of voice recognition in Android by 25%. The Associated Press is printing business stories written by it. Facebook Inc. is toying with it as a way to improve the relevance of the posts it shows you.

What is especially interesting about this point in the history of AI is that it's no longer just for technology companies. Startups are beginning to adapt it to problems where, at least to me, its applicability is genuinely surprising.

Take advertising copywriting. Could the "Mad Men" of Don Draper's day have predicted that by the beginning of the next century, they would be replaced by machines? Yet a company called Persado aims to do just that.

Persado does one thing, and judging by its client list, which includes Citigroup Inc. and Motorola Mobility, it does it well. It writes advertising emails and "landing pages" (where you end up if you click on a link in one of those emails, or an ad).

Here's an example: Persado's engine is being used across all of the types of emails a top U.S. wireless carrier sends out when it wants to convince its customers to renew their contracts, upgrade to a better plan or otherwise spend money.

Traditionally, an advertising copywriter would pen these emails; perhaps the company would test a few variants on a subset of its customers, to see which is best.

But Persado's software deconstructs advertisements into five components, including emotion words, characteristics of the product, the "call to action" and even the position of text and the images accompanying it. By recombining them in millions of ways and then distilling their essential characteristics into eight or more test emails that are sent to some customers, Persado says it can effectively determine the best possible come-on.

"A creative person is good but random," says Lawrence Whittle, head of sales at Persado. "We've taken the randomness out by building an ontology of language."

The results speak for themselves: In the case of emails intended to convince mobile subscribers to renew their plans, initial trials with Persado increased click-through rates by 195%, the company says.

Here's another example of AI becoming genuinely useful: X.ai is a startup aimed, like Persado, at doing one thing exceptionally well. In this case, it's scheduling meetings. X.ai's virtual assistant, Amy, isn't a website or an app; she's simply a "person" whom you cc: on emails to anyone with whom you'd like to schedule a meeting. Her sole "interface" is emails she sends and receives—just like a real assistant. Thus, you don't have to bother with back-and-forth emails trying to find a convenient time and available place for lunch. Amy can correspond fluidly with anyone, but only on the subject of his or her calendar. This sounds like a simple problem to crack, but it isn't, because Amy must communicate with a human being who might not even know she's an AI, and she must do it flawlessly, says X.ai founder Dennis Mortensen.

E-mail conversations with Amy are already quite smooth. Mr. Mortensen used her to schedule our meeting, naturally, and it worked even though I purposely threw in some ambiguous language about the times I was available. But that is in part because Amy is still in the "training" stage, where anything she doesn't understand gets handed to humans employed by X.ai.

It sounds like cheating, but every artificially intelligent system needs a body of data on which to "train" initially. For Persado, that body of data was text messages sent to prepaid cellphone customers in Europe, urging them to re-up their minutes or opt into special plans. For Amy, it's a race to get a body of 100,000 email meeting requests. Amusingly, engineers at X.ai thought about using one of the biggest public database of emails available, the Enron emails, but there is too much scheming in them to be a good sample.

Both of these systems, and others like them, work precisely because their makers have decided to tackle problems that are as narrowly defined as possible. Amy doesn't have to have a conversation about the weather—just when and where you'd like to schedule a meeting. And Persado's system isn't going to come up with the next "Just Do It" campaign.

This is where some might object that the commercialized vision for AI isn't intelligent at all. But academics can't even agree on where the cutoff for "intelligence" is in living things, so the fact that these first steps toward economically useful artificial intelligence lie somewhere near the bottom of the spectrum of things that think shouldn't bother us.

We're also at a time when it seems that advances in the sheer power of computers will lead to AI that becomes progressively smarter. So-called deep-learning algorithms allow machines to learn unsupervised, whereas both Persado and X.ai's systems require training guided by humans.

Last year Google showed that its own deep-learning systems could learn to recognize a cat from millions of images scraped from the Internet, without ever being told what a cat was in the first place. It's a parlor trick, but it isn't hard to see where this is going—the enhancement of the effectiveness of knowledge workers. Mr. Mortensen estimates there are 87 million of them in the world already, and they schedule 10 billion meetings a year. As more tools tackling specific portions of their job become available, their days could be filled with the things that only humans can do, like creativity.

"I think the next Siri is not Siri; it's 100 companies like ours mashed into one," says Mr. Mortensen.

—Follow Christopher Mims on Twitter @Mims or write to him atchristopher.mims@wsj.com.

ORIGINAL: Wall Street Journal

By CHRISTOPHER MIMS

Aug. 24, 2014

Why a deep-learning genius left Google & joined Chinese tech shop Baidu (interview)

Image Credit: Jordan Novet/VentureBeat

SUNNYVALE, California — Chinese tech company Baidu has yet to make its popular search engine and other web services available in English. But consider yourself warned: Baidu could someday wind up becoming a favorite among consumers.

The strength of Baidu lies not in youth-friendly marketing or an enterprise-focused sales team. It lives instead in Baidu’s data centers, where servers run complex algorithms on huge volumes of data and gradually make its applications smarter, including not just Web search but also Baidu’s tools for music, news, pictures, video, and speech recognition.

Despite lacking the visibility (in the U.S., at least) of Google and Microsoft, in recent years Baidu has done a lot of work on deep learning, one of the most promising areas of artificial intelligence (AI) research in recent years. This work involves training systems called artificial neural networks on lots of information derived from audio, images, and other inputs, and then presenting the systems with new information and receiving inferences about it in response.

Two months ago, Baidu hired Andrew Ng away from Google, where he started and led the so-called Google Brain project. Ng, whose move to Baidu follows Hugo Barra’s jump from Google to Chinese company Xiaomi last year, is one of the world’s handful of deep-learning rock stars.

Ng has taught classes on machine learning, robotics, and other topics at Stanford University. He also co-founded massively open online course startup Coursera.

He makes a strong argument for why a person like him would leave Google and join a company with a lower public profile. His argument can leave you feeling like you really ought to keep an eye on Baidu in the next few years.

“I thought the best place to advance the AI mission is at Baidu,” Ng said in an interview with VentureBeat.

Baidu’s search engine only runs in a few countries, including China, Brazil, Egypt, and Thailand. The Brazil service was announced just last week. Google’s search engine is far more popular than Baidu’s around the globe, although Baidu has already beaten out Yahoo and Microsoft’s Bing in global popularity, according to comScore figures.

And Baidu co-founder and chief executive Robin Li, a frequent speaker on Stanford’s campus, has said he wants Baidu to become a brand name in more than half of all the world’s countries. Presumably, then, Baidu will one day become something Americans can use.

Above: Baidu co-founder and chief executive Robin Li.

Image Credit: Baidu

Now that Ng leads Baidu’s research arm as the company’s chief scientist out of the company’s U.S. R&D Center here, it’s not hard to imagine that Baidu’s tools in English, if and when they become available, will be quite brainy — perhaps even eclipsing similar services from Apple and other tech giants. (Just think of how many people are less than happy with Siri.)

A stable full of AI talent

But this isn’t a story about the difference a single person will make. Baidu has a history in deep learning.

A couple years ago, Baidu hired Kai Yu, a engineer skilled in artificial intelligence. Based in Beijing, he has kept busy.

“I think Kai ships deep learning to an incredible number of products across Baidu,” Ng said. Yu also developed a system for providing infrastructure that enables deep learning for different kinds of applications.

“That way, Kai personally didn’t have to work on every single application,” Ng said.

In a sense, then, Ng joined a company that had already built momentum in deep learning. He wasn’t starting from scratch.

Above: Baidu’s Kai Yu.

Image Credit: Kai Yu

Only a few companies could have appealed to Ng, given his desire to push artificial intelligence forward. It’s capital-intensive, as it requires lots of data and computation. Baidu, he said, can provide those things.

Baidu is nimble, too. Unlike Silicon Valley’s tech giants, which measure activity in terms of monthly active users, Chinese Internet companies prefer to track usage by the day, Ng said.

“It’s a symptom of cadence,” he said. “What are you doing today?” And product cycles in China are short; iteration happens very fast, Ng said.

Plus, Baidu is willing to get infrastructure ready to use on the spot.

“Frankly, Kai just made decisions, and it just happened without a lot of committee meetings,” Ng said. “The ability of individuals in the company to make decisions like that and move infrastructure quickly is something I really appreciate about this company.”

That might sound like a kind deference to Ng’s new employer, but he was alluding to a clear advantage Baidu has over Google.

“He ordered 1,000 GPUs [graphics processing units] and got them within 24 hours,” Adam Gibson, co-founder of deep-learning startup Skymind, told VentureBeat. “At Google, it would have taken him weeks or months to get that.”

Not that Baidu is buying this type of hardware for the first time. Baidu was the first company to build a GPU cluster for deep learning, Ng said — a few other companies, like Netflix, have found GPUs useful for deep learning — and Baidu also maintains a fleet of servers packing ARM-based chips.

Above: Baidu headquarters in Beijing.

Image Credit: Baidu

Now the Silicon Valley researchers are using the GPU cluster and also looking to add to it and thereby create still bigger artificial neural networks.

But the efforts have long since begun to weigh on Baidu’s books and impact products. “We deepened our investment in advanced technologies like deep learning, which is already yielding near term enhancements in user experience and customer ROI and is expected to drive transformational change over the longer term,” Li said in a statement on the company’s earnings the second quarter of 2014.

Next step: Improving accuracy

What will Ng do at Baidu? The answer will not be limited to any one of the company’s services. Baidu’s neural networks can work behind the scenes for a wide variety of applications, including those that handle text, spoken words, images, and videos. Surely core functions of Baidu like Web search and advertising will benefit, too.

“All of these are domains Baidu is looking at using deep learning, actually,” Ng said.

Ng’s focus now might best be summed up by one word: accuracy.

That makes sense from a corporate perspective. Google has the brain trust on image analysis, and Microsoft has the brain trust on speech, said Naveen Rao, co-founder and chief executive of deep-learning startup Nervana. Accuracy could potentially be the area where Ng and his colleagues will make the most substantive progress at Baidu, Rao said.

Matthew Zeiler, founder and chief executive of another deep learning startup, Clarifai, was more certain. “I think you’re going to see a huge boost in accuracy,” said Zeiler, who has worked with Hinton and LeCun and spent two summers on the Google Brain project.

One thing is for sure: Accuracy is on Ng’s mind.

Above: The lobby at Baidu’s office in Sunnyvale, Calif.

Image Credit: Jordan Novet/VentureBeat

“Here’s the thing. Sometimes changes in accuracy of a system will cause changes in the way you interact with the device,” Ng said. For instance, more accurate speech recognition could translate into people relying on it much more frequently. Think “Her”-level reliance, where you just talk to your computer as a matter of course rather than using speech recognition in special cases.

“Speech recognition today doesn’t really work in noisy environments,” Ng said. But that could change if Baidu’s neural networks become more accurate under Ng.

Ng picked up his smartphone, opened the Baidu Translate app, and told it that he needed a taxi. A female voice said that in Mandarin and displayed Chinese characters on screen. But it wasn’t a difficult test, in some ways: This was no crowded street in Beijing. This was a quiet conference room in a quiet office.

“There’s still work to do,” Ng said.

‘The future heroes of deep learning’

Meanwhile, researchers at companies and universities have been hard at work on deep learning for decades.

Google has built up a hefty reputation for applying deep learning to images from YouTube videos, data center energy use, and other areas, partly thanks to Ng’s contributions. And recently Microsoft made headlines for deep-learning advancements with its Project Adam work, although Li Deng of Microsoft Research has been working with neural networks for more than 20 years.

In academia, deep learning research groups all over North America and Europe. Key figures in the past few years include Yoshua Bengio at the University of Montreal, Geoff Hinton of the University of Toronto (Google grabbed him last year through its DNNresearch acquisition), Yann LeCun from New York University (Facebook pulled him aboard late last year), and Ng.

But Ng’s strong points differ from those of his contemporaries. Whereas Bengio made strides in training neural networks, LeCun developed convolutional neural networks, and Hinton popularized restricted Boltzmann machines, Ng takes the best, implements it, and makes improvements.

“Andrew is neutral in that he’s just going to use what works,” Gibson said. “He’s very practical, and he’s neutral about the stamp on it.”

Not that Ng intends to go it alone. To create larger and more accurate neural networks, Ng needs to look around and find like-minded engineers.

“He’s going to be able to bring a lot of talent over,” Dave Sullivan, co-founder and chief executive of deep-learning startup Ersatz Labs, told VentureBeat. “This guy is not sitting down and writing mountains of code every day.”

And truth be told, Ng has had no trouble building his team.

“Hiring for Baidu has been easier than I’d expected,” he said.

“A lot of engineers have always wanted to work on AI. … My job is providing the team with the best possible environment for them to do AI, for them to be the future heroes of deep learning.”

More information:

Google 

Baidu

Google's innovative search technologies connect millions of people around the world with information every day. Founded in 1998 by Stanford Ph.D. students Larry Page and Sergey Brin, Google today is a top web property in all major glob... read more »

Powered by VBProfiles

ORIGINAL: VentureBeat

Jordan Novet

July 30, 2014 8:03 AM