Researchers from the Georgia Institute of Technology (Georgia Tech), funded by the US Office of Naval Research (ONR), have developed a program that teaches robots to read stories and more in an effort to educate them about humans. From a June 16, 2016 ONR news release by Warren Duffie Jr. (also on EurekAlert),
With support from the Office of Naval Research (ONR), researchers at the Georgia Institute of Technology have created an artificial intelligence software program named Quixote to teach robots to read stories, learn acceptable behavior and understand successful ways to conduct themselves in diverse social situations.
“For years, researchers have debated how to teach robots to act in ways that are appropriate, non-intrusive and trustworthy,” said Marc Steinberg, an ONR program manager who oversees the research. “One important question is how to explain complex concepts such as policies, values or ethics to robots. Humans are really good at using narrative stories to make sense of the world and communicate to other people. This could one day be an effective way to interact with robots.”
The rapid pace of artificial intelligence has stirred fears by some that robots could act unethically or harm humans. Dr. Mark Riedl, an associate professor and director of Georgia Tech’s Entertainment Intelligence Lab, hopes to ease concerns by having Quixote serve as a “human user manual” by teaching robots values through simple stories. After all, stories inform, educate and entertain–reflecting shared cultural knowledge, social mores and protocols.
For example, if a robot is tasked with picking up a pharmacy prescription for a human as quickly as possible, it could: a) take the medicine and leave, b) interact politely with pharmacists, c) or wait in line. Without value alignment and positive reinforcement, the robot might logically deduce robbery is the fastest, cheapest way to accomplish its task. However, with value alignment from Quixote, it would be rewarded for waiting patiently in line and paying for the prescription.
For their research, Riedl and his team crowdsourced stories from the Internet. Each tale needed to highlight daily social interactions–going to a pharmacy or restaurant, for example–as well as socially appropriate behaviors (e.g., paying for meals or medicine) within each setting.
The team plugged the data into Quixote to create a virtual agent–in this case, a video game character placed into various game-like scenarios mirroring the stories. As the virtual agent completed a game, it earned points and positive reinforcement for emulating the actions of protagonists in the stories.
Riedl’s team ran the agent through 500,000 simulations, and it displayed proper social interactions more than 90 percent of the time.
“These games are still fairly simple,” said Riedl, “more like ‘Pac-Man’ instead of ‘Halo.’ However, Quixote enables these artificial intelligence agents to immerse themselves in a story, learn the proper sequence of events and be encoded with acceptable behavior patterns. This type of artificial intelligence can be adapted to robots, offering a variety of applications.”
Within the next six months, Riedl’s team hopes to upgrade Quixote’s games from “old-school” to more modern and complex styles like those found in Minecraft–in which players use blocks to build elaborate structures and societies.
Riedl believes Quixote could one day make it easier for humans to train robots to perform diverse tasks. Steinberg notes that robotic and artificial intelligence systems may one day be a much larger part of military life. This could involve mine detection and deactivation, equipment transport and humanitarian and rescue operations.
“Within a decade, there will be more robots in society, rubbing elbows with us,” said Riedl. “Social conventions grease the wheels of society, and robots will need to understand the nuances of how humans do things. That’s where Quixote can serve as a valuable tool. We’re already seeing it with virtual agents like Siri and Cortana, which are programmed not to say hurtful or insulting things to users.”
This story brought to mind two other projects: RoboEarth (an internet for robots only) mentioned in my Jan. 14, 2014 which was an update on the project featuring its use in hospitals and RoboBrain, a robot learning project (sourcing the internet, YouTube, and more for information to teach robots) was mentioned in my Sept. 2, 2014 posting.
Machine-learning algorithms increasingly make decisions about credit, medical diagnoses, personalized recommendations, advertising and job opportunities, among other things, but exactly how usually remains a mystery. Now, new measurement methods developed by Carnegie Mellon University [CMU] researchers could provide important insights to this process.
Was it a person’s age, gender or education level that had the most influence on a decision? Was it a particular combination of factors? CMU’s Quantitative Input Influence (QII) measures can provide the relative weight of each factor in the final decision, said Anupam Datta, associate professor of computer science and electrical and computer engineering.
It’s reassuring to know that more requests for transparency of the decision-making process are being made. After all, it’s disconcerting that someone with the life experience of a gnat and/or possibly some issues might be developing an algorithm that could affection your life in some fundamental ways. Here’s more from the news release (Note: Links have been removed),
“Demands for algorithmic transparency are increasing as the use of algorithmic decision-making systems grows and as people realize the potential of these systems to introduce or perpetuate racial or sex discrimination or other social harms,” Datta said.
“Some companies are already beginning to provide transparency reports, but work on the computational foundations for these reports has been limited,” he continued. “Our goal was to develop measures of the degree of influence of each factor considered by a system, which could be used to generate transparency reports.”
These reports might be generated in response to a particular incident — why an individual’s loan application was rejected, or why police targeted an individual for scrutiny, or what prompted a particular medical diagnosis or treatment. Or they might be used proactively by an organization to see if an artificial intelligence system is working as desired, or by a regulatory agency to see whether a decision-making system inappropriately discriminated between groups of people.
Datta, along with Shayak Sen, a Ph.D. student in computer science, and Yair Zick, a post-doctoral researcher in the Computer Science Department, will present their report on QII at the IEEE Symposium on Security and Privacy, May 23–25 , in San Jose, Calif.
Generating these QII measures requires access to the system, but doesn’t necessitate analyzing the code or other inner workings of the system, Datta said. It also requires some knowledge of the input dataset that was initially used to train the machine-learning system.
A distinctive feature of QII measures is that they can explain decisions of a large class of existing machine-learning systems. A significant body of prior work takes a complementary approach, redesigning machine-learning systems to make their decisions more interpretable and sometimes losing prediction accuracy in the process.
QII measures carefully account for correlated inputs while measuring influence. For example, consider a system that assists in hiring decisions for a moving company. Two inputs, gender and the ability to lift heavy weights, are positively correlated with each other and with hiring decisions. Yet transparency into whether the system uses weight-lifting ability or gender in making its decisions has substantive implications for determining if it is engaging in discrimination.
“That’s why we incorporate ideas for causal measurement in defining QII,” Sen said. “Roughly, to measure the influence of gender for a specific individual in the example above, we keep the weight-lifting ability fixed, vary gender and check whether there is a difference in the decision.”
Observing that single inputs may not always have high influence, the QII measures also quantify the joint influence of a set of inputs, such as age and income, on outcomes and the marginal influence of each input within the set. Since a single input may be part of multiple influential sets, the average marginal influence of the input is computed using principled game-theoretic aggregation measures previously applied to measure influence in revenue division and voting.
“To get a sense of these influence measures, consider the U.S. presidential election,” Zick said. “California and Texas have influence because they have many voters, whereas Pennsylvania and Ohio have power because they are often swing states. The influence aggregation measures we employ account for both kinds of power.”
The researchers tested their approach against some standard machine-learning algorithms that they used to train decision-making systems on real data sets. They found that the QII provided better explanations than standard associative measures for a host of scenarios they considered, including sample applications for predictive policing and income prediction.
Now, they are seeking collaboration with industrial partners so that they can employ QII at scale on operational machine-learning systems.
Here’s a link to and a citation for a PDF of the paper presented at the May 2016 conference,
Hitachi Corporation has been exciting some interest with its announcement of the latest iteration of its artificial intelligence programme’s and its new ability to speak Japanese (from a June 5, 2016 news item on Nanotechnology Now),
Today, the social landscape changes rapidly and customer needs are becoming increasingly diversified. Companies are expected to continuously create new services and values. Further, driven by recent advancements in information & telecommunication and analytics technologies, interest is growing in technology that can extract valuable insight from big data which is generated on a daily basis.
Hitachi has been developing a basic AI technology that analyzes huge volumes of English text data and presents opinions in English to help enterprises make business decisions. The original technology required rules of grammar specific to the English language to be programmed, to extract sentences representing reasons and grounds for opinions. This process represented a hurdle in applying system to Japanese or any other language as it required dedicated programs correlated to the linguistic rules of the target language.
By applying deep learning, this issue was eliminated thus enabling the new technology to recognize sentences that have high probability of being reasons and grounds without relying on linguistic rules. More specifically, the AI system is presented with sentences which represent reasons and grounds extracted from thousands of articles. Learning from the rules and patterns, the system becomes discriminating of sentences which represent reasons and grounds in new articles. Hitachi added an attention mechanism” which support deep learning to estimate which words and phrases are worthy of attention in texts like news articles and research reports. The “attention mechanism” helps the system to grasp the points that require attention, including words and phrases related to topics and values. This method enables the system to distinguish sentences which have a high probability of being reasons and grounds from text data in any language.
They have plans for this technology,
The technology developed will be core technology in achieving a multi-lingual AI system capable of offering opinion. Hitachi will pursue further research to realize AI systems supporting business decision making by enterprises worldwide.
The June 2, 2016 Hitachi news release which originated the news item can be found here.
Deep learning seems to be a synonym for artificial intelligence if a May 24, 2016 Insilico Medicine news release on EurekAlert about its use in the fields of cosmetics and as an alternative to testing animals is to be believed (Note: Links have been removed),
In addition to heading Insilico Medicine, Inc, a big data analytics company focused on applying advanced signaling pathway activation analysis and deep learning methods to biomarker and drug discovery in cancer and age-related diseases, Alex Zhavoronkov, PhD is the co-founder and principal scientist of Youth Laboratories, a company focusing on applying machine learning methods to evaluating the condition of human skin and general health status using multimodal inputs. The company developed an app called RYNKL, a mobile app for evaluating the effectiveness of various anti-aging interventions by analyzing “wrinkleness” and other parameters. The app was developed using funds from a Kickstarter crowdfunding campaign and is now being extensively tested and improved. The company also developed a platform for running online beauty competitions, where humans are evaluated by a panel of robot judges. Teams of programmers also compete on the development of most innovative algorithms to evaluate humans.
“One of my goals in life is to minimize unnecessary animal testing in areas, where computer simulations can be even more relevant to humans. Serendipitously, some of our approaches find surprising new applications in the beauty industry, which has moved away from human testing and is moving towards personalizing cosmetics and beauty products. We are happy to present our research results to a very relevant audience at this major industry event”, said Alex Zhavoronkov, CEO of Insilico Medicine, Inc.
Artificial intelligence is entering every aspect of our daily life. Deep learning systems are already outperforming humans in image and text recognition and we would like to bring some of the most innovative players like Insilico Medicine, who dare to work with gene expression, imaging and drug data to find novel ways to keep us healthy, young and beautiful”, said Irina Kremlin, director of INNOCOS.
There’s an intriguing competition taking place at Dartmouth College (US) according to a May 2, 2016 piece on phys.org (Note: Links have been removed),
Algorithms help us to choose which films to watch, which music to stream and which literature to read. But what if algorithms went beyond their jobs as mediators of human culture and started to create culture themselves?
In 1950 English mathematician and computer scientist Alan Turing published a paper, “Computing Machinery and Intelligence,” which starts off by proposing a thought experiment that he called the “Imitation Game.” In one room is a human “interrogator” and in another room a man and a woman. The goal of the game is for the interrogator to figure out which of the unknown hidden interlocutors is the man and which is the woman. This is to be accomplished by asking a sequence of questions with responses communicated either by a third party or typed out and sent back. “Winning” the Imitation Game means getting the identification right on the first shot.
Turing then modifies the game by replacing one interlocutor with a computer, and asks whether a computer will be able to converse sufficiently well that the interrogator cannot tell the difference between it and the human. This version of the Imitation Game has come to be known as the “Turing Test.”
On May 18  at Dartmouth, we will explore a different area of intelligence, taking up the question of distinguishing machine-generated art. Specifically, in our “Turing Tests in the Creative Arts,” we ask if machines are capable of generating sonnets, short stories, or dance music that is indistinguishable from human-generated works, though perhaps not yet so advanced as Shakespeare, O. Henry or Daft Punk.
The piece on phys.org is a crossposting of a May 2, 2016 article by Michael Casey and Daniel N. Rockmore for The Conversation. The article goes on to describe the competitions,
The dance music competition (“Algorhythms”) requires participants to construct an enjoyable (fun, cool, rad, choose your favorite modifier for having an excellent time on the dance floor) dance set from a predefined library of dance music. In this case the initial random “seed” is a single track from the database. The software package should be able to use this as inspiration to create a 15-minute set, mixing and modifying choices from the library, which includes standard annotations of more than 20 features, such as genre, tempo (bpm), beat locations, chroma (pitch) and brightness (timbre).
In what might seem a stiffer challenge, the sonnet and short story competitions (“PoeTix” and “DigiLit,” respectively) require participants to submit self-contained software packages that upon the “seed” or input of a (common) noun phrase (such as “dog” or “cheese grater”) are able to generate the desired literary output. Moreover, the code should ideally be able to generate an infinite number of different works from a single given prompt.
To perform the test, we will screen the computer-made entries to eliminate obvious machine-made creations. We’ll mix human-generated work with the rest, and ask a panel of judges to say whether they think each entry is human- or machine-generated. For the dance music competition, scoring will be left to a group of students, dancing to both human- and machine-generated music sets. A “winning” entry will be one that is statistically indistinguishable from the human-generated work.
The competitions are open to any and all comers [competition is now closed; the deadline was April 15, 2016]. To date, entrants include academics as well as nonacademics. As best we can tell, no companies have officially thrown their hats into the ring. This is somewhat of a surprise to us, as in the literary realm companies are already springing up around machine generation of more formulaic kinds of “literature,” such as earnings reports and sports summaries, and there is of course a good deal of AI automation around streaming music playlists, most famously Pandora.
The authors discuss issues with judging the entries,
Evaluation of the entries will not be entirely straightforward. Even in the initial Imitation Game, the question was whether conversing with men and women over time would reveal their gender differences. (It’s striking that this question was posed by a closeted gay man [Alan Turing].) The Turing Test, similarly, asks whether the machine’s conversation reveals its lack of humanity not in any single interaction but in many over time.
It’s also worth considering the context of the test/game. Is the probability of winning the Imitation Game independent of time, culture and social class? Arguably, as we in the West approach a time of more fluid definitions of gender, that original Imitation Game would be more difficult to win. Similarly, what of the Turing Test? In the 21st century, our communications are increasingly with machines (whether we like it or not). Texting and messaging have dramatically changed the form and expectations of our communications. For example, abbreviations, misspellings and dropped words are now almost the norm. The same considerations apply to art forms as well.
The authors also pose the question: Who is the artist?
Thinking about art forms leads naturally to another question: who is the artist? Is the person who writes the computer code that creates sonnets a poet? Is the programmer of an algorithm to generate short stories a writer? Is the coder of a music-mixing machine a DJ?
Where is the divide between the artist and the computational assistant and how does the drawing of this line affect the classification of the output? The sonnet form was constructed as a high-level algorithm for creative work – though one that’s executed by humans. Today, when the Microsoft Office Assistant “corrects” your grammar or “questions” your word choice and you adapt to it (either happily or out of sheer laziness), is the creative work still “yours” or is it now a human-machine collaborative work?
That’s an interesting question and one I asked in the context of two ‘mashup’ art exhibitions in Vancouver (Canada) in my March 8, 2016 posting.
Getting back to back to Dartmouth College and its Neukom Institute Prizes in Computational Arts, here’s a list of the competition judges from the competition homepage,
The announcements will be made Wednesday, May 18, 2016. I can hardly wait!
Martin Robbins has written a rather amusing May 6, 2016 post for the Guardian science blogs on AI and art critics where he also notes that the question: What is art? is unanswerable (Note: Links have been removed),
Jonathan Jones is unhappy about artificial intelligence. It might be hard to tell from a casual glance at the art critic’s recent column, “The digital Rembrandt: a new way to mock art, made by fools,” but if you look carefully the subtle clues are there. His use of the adjectives “horrible, tasteless, insensitive and soulless” in a single sentence, for example.
The source of Jones’s ire is a new piece of software that puts… I’m so sorry… the ‘art’ into ‘artificial intelligence’. By analyzing a subset of Rembrandt paintings that featured ‘bearded white men in their 40s looking to the right’, its algorithms were able to extract the key features that defined the Dutchman’s style. …
Of course an artificial intelligence is the worst possible enemy of a critic, because it has no ego and literally does not give a crap what you think. An arts critic trying to deal with an AI is like an old school mechanic trying to replace the battery in an iPhone – lost, possessing all the wrong tools and ultimately irrelevant. I’m not surprised Jones is angry. If I were in his shoes, a computer painting a Rembrandt would bring me out in hives.
Can a computer really produce art? We can’t answer that without dealing with another question: what exactly is art? …
I wonder what either Robbins or Jones will make of the Dartmouth competition?
This story poses some interesting questions that touch on the uneasiness being felt as computers get ‘smarter’. From an April 13, 2016 news item on ScienceDaily,
The saying of philosopher René Descartes of what makes humans unique is beginning to sound hollow. ‘I think — therefore soon I am obsolete’ seems more appropriate. When a computer routinely beats us at chess and we can barely navigate without the help of a GPS, have we outlived our place in the world? Not quite. Welcome to the front line of research in cognitive skills, quantum computers and gaming.
Today there is an on-going battle between man and machine. While genuine machine consciousness is still years into the future, we are beginning to see computers make choices that previously demanded a human’s input. Recently, the world held its breath as Google’s algorithm AlphaGo beat a professional player in the game Go–an achievement demonstrating the explosive speed of development in machine capabilities.
But we are not beaten yet — human skills are still superior in some areas. This is one of the conclusions of a recent study by Danish physicist Jacob Sherson, published in the journal Nature.
“It may sound dramatic, but we are currently in a race with technology — and steadily being overtaken in many areas. Features that used to be uniquely human are fully captured by contemporary algorithms. Our results are here to demonstrate that there is still a difference between the abilities of a man and a machine,” explains Jacob Sherson.
At the interface between quantum physics and computer games, Sherson and his research group at Aarhus University have identified one of the abilities that still makes us unique compared to a computer’s enormous processing power: our skill in approaching problems heuristically and solving them intuitively. The discovery was made at the AU Ideas Centre CODER, where an interdisciplinary team of researchers work to transfer some human traits to the way computer algorithms work. ?
Quantum physics holds the promise of immense technological advances in areas ranging from computing to high-precision measurements. However, the problems that need to be solved to get there are so complex that even the most powerful supercomputers struggle with them. This is where the core idea behind CODER–combining the processing power of computers with human ingenuity — becomes clear. ?
Our common intuition
Like Columbus in QuantumLand, the CODER research group mapped out how the human brain is able to make decisions based on intuition and accumulated experience. This is done using the online game “Quantum Moves.” Over 10,000 people have played the game that allows everyone contribute to basic research in quantum physics.
“The map we created gives us insight into the strategies formed by the human brain. We behave intuitively when we need to solve an unknown problem, whereas for a computer this is incomprehensible. A computer churns through enormous amounts of information, but we can choose not to do this by basing our decision on experience or intuition. It is these intuitive insights that we discovered by analysing the Quantum Moves player solutions,” explains Jacob Sherson. ? [sic]
The laws of quantum physics dictate an upper speed limit for data manipulation, which in turn sets the ultimate limit to the processing power of quantum computers — the Quantum Speed ??Limit. Until now a computer algorithm has been used to identify this limit. It turns out that with human input researchers can find much better solutions than the algorithm.
“The players solve a very complex problem by creating simple strategies. Where a computer goes through all available options, players automatically search for a solution that intuitively feels right. Through our analysis we found that there are common features in the players’ solutions, providing a glimpse into the shared intuition of humanity. If we can teach computers to recognise these good solutions, calculations will be much faster. In a sense we are downloading our common intuition to the computer” says Jacob Sherson.
And it works. The group has shown that we can break the Quantum Speed Limit by combining the cerebral cortex and computer chips. This is the new powerful tool in the development of quantum computers and other quantum technologies.
After the buildup, the press release focuses on citizen science and computer games,
Science is often perceived as something distant and exclusive, conducted behind closed doors. To enter you have to go through years of education, and preferably have a doctorate or two. Now a completely different reality is materialising.? [sic]
In recent years, a new phenomenon has appeared–citizen science breaks down the walls of the laboratory and invites in everyone who wants to contribute. The team at Aarhus University uses games to engage people in voluntary science research. Every week people around the world spend 3 billion hours playing games. Games are entering almost all areas of our daily life and have the potential to become an invaluable resource for science.
“Who needs a supercomputer if we can access even a small fraction of this computing power? By turning science into games, anyone can do research in quantum physics. We have shown that games break down the barriers between quantum physicists and people of all backgrounds, providing phenomenal insights into state-of-the-art research. Our project combines the best of both worlds and helps challenge established paradigms in computational research,” explains Jacob Sherson.
The difference between the machine and us, figuratively speaking, is that we intuitively reach for the needle in a haystack without knowing exactly where it is. We ‘guess’ based on experience and thereby skip a whole series of bad options. For Quantum Moves, intuitive human actions have been shown to be compatible with the best computer solutions. In the future it will be exciting to explore many other problems with the aid of human intuition.
“We are at the borderline of what we as humans can understand when faced with the problems of quantum physics. With the problem underlying Quantum Moves we give the computer every chance to beat us. Yet, over and over again we see that players are more efficient than machines at solving the problem. While Hollywood blockbusters on artificial intelligence are starting to seem increasingly realistic, our results demonstrate that the comparison between man and machine still sometimes favours us. We are very far from computers with human-type cognition,” says Jacob Sherson and continues:
“Our work is first and foremost a big step towards the understanding of quantum physical challenges. We do not know if this can be transferred to other challenging problems, but it is definitely something that we will work hard to resolve in the coming years.”
Here’s a link to and a citation for the paper,
Exploring the quantum speed limit with computer games by Jens Jakob W. H. Sørensen, Mads Kock Pedersen, Michael Munch, Pinja Haikka, Jesper Halkjær Jensen, Tilo Planke, Morten Ginnerup Andreasen, Miroslav Gajdacz, Klaus Mølmer, Andreas Lieberoth, & Jacob F. Sherson. Nature 532, 210–213 (14 April 2016) doi:10.1038/nature17620 Published online 13 April 2016
Finally there’s an answer to the question: What (!!!) is the fourth industrial revolution? (I took a guess [wrongish] in my Nov. 20, 2015 post about a special presentation at the 2016 World Economic Forum’s IdeasLab.)
… an approach that focuses on combining technologies such as additive manufacturing, automation, digital services and the Internet of Things, and … is part of a growing movement towards exploiting the convergence between emerging technologies. This technological convergence is increasingly being referred to as the ‘fourth industrial revolution’, and like its predecessors, it promises to transform the ways we live and the environments we live in. (While there is no universal agreement on what constitutes an ‘industrial revolution’, proponents of the fourth industrial revolution suggest that the first involved harnessing steam power to mechanize production; the second, the use of electricity in mass production; and the third, the use of electronics and information technology to automate production.)
In anticipation of the the 2016 World Economic Forum (WEF), which has the fourth industrial revolution as its theme, Andrew explains how he sees the situation we are sliding into (from Andrew Maynard’s think piece),
As more people get closer to gaining access to increasingly powerful converging technologies, a complex risk landscape is emerging that lies dangerously far beyond the ken of current regulations and governance frameworks. As a result, we are in danger of creating a global ‘wild west’ of technology innovation, where our good intentions may be among the first casualties.
There are many other examples where converging technologies are increasing the gap between what we can do and our understanding of how to do it responsibly. The convergence between robotics, nanotechnology and cognitive augmentation, for instance, and that between artificial intelligence, gene editing and maker communities both push us into uncertain territory. Yet despite the vulnerabilities inherent with fast-evolving technological capabilities that are tightly coupled, complex and poorly regulated, we lack even the beginnings of national or international conceptual frameworks to think about responsible decision-making and responsive governance.
He also lists some recommendations,
Fostering effective multi-stakeholder dialogues.
Encouraging actionable empathy.
Providing educational opportunities for current and future stakeholders.
… The good news is that, in fields such as nanotechnology and synthetic biology, we have already begun to develop the skills to do this — albeit in a small way. We now need to learn how to scale up our efforts, so that our convergence in working together to build a better future mirrors the convergence of the technologies that will help achieve this.
It’s always a pleasure to read Andrew’s work as it’s thoughtful. I was surprised (since Andrew is a physicist by training) and happy to see the recommendation for “actionable empathy.”
Although, I don’t always agree with him on this occasion I don’t have any particular disagreements but I think that including a recommendation or two to cover the certainty we will get something wrong and have to work quickly to right things would be a good idea. I’m thinking primarily of governments which are notoriously slow to respond with legislation for new developments and equally slow to change that legislation when the situation changes.
The technological environment Andrew is describing is dynamic, that is fast-moving and changing at a pace we have yet to properly conceptualize. Governments will need to change so they can respond in an agile fashion. My suggestion is:
Develop policy task forces that can be convened in hours and given the authority to respond to an immediate situation with oversight after the fact
Getting back to Andrew Maynard, you can find his think piece in its entirety via this link and citation,
KAIST researchers will lead an IdeasLab on biotechnology for an aging society while HUBO, the winner of the 2015 DARPA Robotics Challenge, will interact with the forum participants, offering an experience of state-of-the-art robotics technology
Moving on from the news release’s subtitle, there’s more enlightenment,
Representatives from the Korea Advanced Institute of Science and Technology (KAIST) will attend the 2016 Annual Meeting of the World Economic Forum to run an IdeasLab and showcase its humanoid robot.
With over 2,500 leaders from business, government, international organizations, civil society, academia, media, and the arts expected to participate, the 2016 Annual Meeting will take place on Jan. 20-23, 2016 in Davos-Klosters, Switzerland. Under the theme of ‘Mastering the Fourth Industrial Revolution,’ [emphasis mine] global leaders will discuss the period of digital transformation [emphasis mine] that will have profound effects on economies, societies, and human behavior.
President Sung-Mo Steve Kang of KAIST will join the Global University Leaders Forum (GULF), a high-level academic meeting to foster collaboration among experts on issues of global concern for the future of higher education and the role of science in society. He will discuss how the emerging revolution in technology will affect the way universities operate and serve society. KAIST is the only Korean university participating in GULF, which is composed of prestigious universities invited from around the world.
Four KAIST professors, including Distinguished Professor Sang Yup Lee of the Chemical and Biomolecular Engineering Department, will lead an IdeasLab on ‘Biotechnology for an Aging Society.’
Professor Lee said, “In recent decades, much attention has been paid to the potential effect of the growth of an aging population and problems posed by it. At our IdeasLab, we will introduce some of our research breakthroughs in biotechnology to address the challenges of an aging society.”
In particular, he will present his latest research in systems biotechnology and metabolic engineering. His research has explained the mechanisms of how traditional Oriental medicine works in our bodies by identifying structural similarities between effective compounds in traditional medicine and human metabolites, and has proposed more effective treatments by employing such compounds.
KAIST will also display its networked mobile medical service system, ‘Dr. M.’ Built upon a ubiquitous and mobile Internet, such as the Internet of Things, wearable electronics, and smart homes and vehicles, Dr. M will provide patients with a more affordable and accessible healthcare service.
In addition, Professor Jun-Ho Oh of the Mechanical Engineering Department will showcase his humanoid robot, ‘HUBO,’ during the Annual Meeting. His research team won the International Humanoid Robotics Challenge hosted by the United States Defense Advanced Research Projects Agency (DARPA), which was held in Pomona, California, on June 5-6, 2015. With 24 international teams participating in the finals, HUBO completed all eight tasks in 44 minutes and 28 seconds, 6 minutes earlier than the runner-up, and almost 11 minutes earlier than the third-place team. Team KAIST walked away with the grand prize of USD 2 million.
Professor Oh said, “Robotics technology will grow exponentially in this century, becoming a real driving force to expedite the Fourth Industrial Revolution. I hope HUBO will offer an opportunity to learn about the current advances in robotics technology.”
President Kang pointed out, “KAIST has participated in the Annual Meeting of the World Economic Forum since 2011 and has engaged with a broad spectrum of global leaders through numerous presentations and demonstrations of our excellence in education and research. Next year, we will choreograph our first robotics exhibition on HUBO and present high-tech research results in biotechnology, which, I believe, epitomizes how science and technology breakthroughs in the Fourth Industrial Revolution will shape our future in an unprecedented way.”
Based on what I’m reading in the KAIST news release, I think the conversation about the ‘Fourth revolution’ may veer toward robotics and artificial intelligence (referred to in code as “digital transformation”) as developments in these fields are likely to affect various economies. Before proceeding with that thought, take a look at this video showcasing HUBO at the DARPA challenge,
I’m quite impressed with how the robot can recalibrate its grasp so it can pick things up and plug an electrical cord into an outlet and knowing whether wheels or legs will be needed to complete a task all due to algorithms which give the robot a type of artificial intelligence. While it may seem more like a machine than anything else, there’s also this version of a HUBO,
Description English: Photo by David Hanson Date 26 October 2006 (original upload date) Source Transferred from en.wikipedia to Commons by Mac. Author Dayofid at English Wikipedia
It’ll be interesting to note if the researchers make the HUBO seem more humanoid by giving it a face for its interactions with WEF attendees. It would be more engaging but also more threatening since there is increasing concern over robots taking work away from humans with implications for various economies. There’s more about HUBO in its Wikipedia entry.
As for the IdeasLab, that’s been in place at the WEF since 2009 according to this WEF July 19, 2011 news release announcing an ideasLab hub (Note: A link has been removed),
The World Economic Forum is publicly launching its biannual interactive IdeasLab hub on 19 July  at 10.00 CEST. The unique IdeasLab hub features short documentary-style, high-definition (HD) videos of preeminent 21st century ideas and critical insights. The hub also provides dynamic Pecha Kucha presentations and visual IdeaScribes that trace and package complex strategic thinking into engaging and powerful images. All videos are HD broadcast quality.
To share the knowledge captured by the IdeasLab sessions, which have been running since 2009, the Forum is publishing 23 of the latest sessions, seen as the global benchmark of collaborative learning and development.
So while you might not be able to visit an IdeasLab presentation at the WEF meetings, you could get a it to see them later.
Getting back to the robotics and artificial intelligence aspect of the 2016 WEF’s ‘digital’ theme, I noticed some reluctance to discuss how the field of robotics is affecting work and jobs in a broadcast of Canadian television show, ‘Conversations with Conrad’.
For those unfamiliar with the interviewer, Conrad Black is somewhat infamous in Canada for a number of reasons (from the Conrad Black Wikipedia entry), Note: Links have been removed,
Conrad Moffat Black, Baron Black of Crossharbour, KSG (born 25 August 1944) is a Canadian-born British former newspaper publisher and author. He is a non-affiliated life peer, and a convicted felon in the United States for fraud.[n 1] Black controlled Hollinger International, once the world’s third-largest English-language newspaper empire, which published The Daily Telegraph (UK), Chicago Sun Times (U.S.), The Jerusalem Post (Israel), National Post (Canada), and hundreds of community newspapers in North America, before he was fired by the board of Hollinger in 2004.
In 2004, a shareholder-initiated prosecution of Black began in the United States. Over $80 million in assets claimed to have been improperly taken or inappropriately spent by Black. He was convicted of three counts of fraud and one count of obstruction of justice in a U.S. court in 2007 and sentenced to six and a half years’ imprisonment. In 2011 two of the charges were overturned on appeal and he was re-sentenced to 42 months in prison on one count of mail fraud and one count of obstruction of justice. Black was released on 4 May 2012.
Despite or perhaps because of his chequered past, he is often a good interviewer and he definitely attracts interesting guests. n an Oct. 26, 2015 programme, he interviewed both former Canadian astronaut, Chris Hadfield, and Canadian-American David Frum who’s currently editor of Atlantic Monthly and a former speechwriter for George W. Bush.
It was Black’s conversation with Frum which surprised me. They discuss robotics without ever once using the word. In a section where Frum notes that manufacturing is returning to the US, he also notes that it doesn’t mean more jobs and cites a newly commissioned plant in the eastern US employing about 40 people where before it would have employed hundreds or thousands. Unfortunately, the video has not been made available as I write this (Nov. 20, 2015) but that situation may change. You can check here.
Final thought, my guess is that economic conditions are fragile and I don’t think anyone wants to set off panic by mentioning robotics and disappearing jobs.
An Oct. 20, 2015 posting by Lynn Bergeson on Nanotechnology Now announces a US White House challenge incorporating nanotechnology, computing, and brain research (Note: A link has been removed),
On October 20, 2015, the White House announced a grand challenge to develop transformational computing capabilities by combining innovations in multiple scientific disciplines. See https://www.whitehouse.gov/blog/2015/10/15/nanotechnology-inspired-grand-challenge-future-computing The Office of Science and Technology Policy (OSTP) states that, after considering over 100 responses to its June 17, 2015, request for information, it “is excited to announce the following grand challenge that addresses three Administration priorities — the National Nanotechnology Initiative, the National Strategic Computing Initiative (NSCI), and the BRAIN initiative.” The grand challenge is to “[c]reate a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain.”
Here’s where the Oct. 20, 2015 posting, which originated the news item, by Lloyd Whitman, Randy Bryant, and Tom Kalil for the US White House blog gets interesting,
While it continues to be a national priority to advance conventional digital computing—which has been the engine of the information technology revolution—current technology falls far short of the human brain in terms of both the brain’s sensing and problem-solving abilities and its low power consumption. Many experts predict that fundamental physical limitations will prevent transistor technology from ever matching these twin characteristics. We are therefore challenging the nanotechnology and computer science communities to look beyond the decades-old approach to computing based on the Von Neumann architecture as implemented with transistor-based processors, and chart a new path that will continue the rapid pace of innovation beyond the next decade.
There are growing problems facing the Nation that the new computing capabilities envisioned in this challenge might address, from delivering individualized treatments for disease, to allowing advanced robots to work safely alongside people, to proactively identifying and blocking cyber intrusions. To meet this challenge, major breakthroughs are needed not only in the basic devices that store and process information and the amount of energy they require, but in the way a computer analyzes images, sounds, and patterns; interprets and learns from data; and identifies and solves problems. [emphases mine]
Many of these breakthroughs will require new kinds of nanoscale devices and materials integrated into three-dimensional systems and may take a decade or more to achieve. These nanotechnology innovations will have to be developed in close coordination with new computer architectures, and will likely be informed by our growing understanding of the brain—a remarkable, fault-tolerant system that consumes less power than an incandescent light bulb.
Recent progress in developing novel, low-power methods of sensing and computation—including neuromorphic, magneto-electronic, and analog systems—combined with dramatic advances in neuroscience and cognitive sciences, lead us to believe that this ambitious challenge is now within our reach. …
This is the first time I’ve come across anything that publicly links the BRAIN initiative to computing, artificial intelligence, and artificial brains. (For my own sake, I make an arbitrary distinction between algorithms [artificial intelligence] and devices that simulate neural plasticity [artificial brains].)The emphasis in the past has always been on new strategies for dealing with Parkinson’s and other neurological diseases and conditions.
Vancouver-based (more accurately, Burnaby-based) D-Wave systems has scored a coup as key customers have upgraded from a 512-qubit system to a system with over 1,000 qubits. (The technical breakthrough and concomitant interest from the business community was mentioned here in a June 26, 2015 posting.) As for the latest business breakthrough, here’s more from a Sept. 28, 2015 D-Wave press release,
D-Wave Systems Inc., the world’s first quantum computing company, announced that it has entered into a new agreement covering the installation of a succession of D-Wave systems located at NASA’s Ames Research Center in Moffett Field, California. This agreement supports collaboration among Google, NASA and USRA (Universities Space Research Association) that is dedicated to studying how quantum computing can advance artificial intelligence and machine learning, and the solution of difficult optimization problems. The new agreement enables Google and its partners to keep their D-Wave system at the state-of-the-art for up to seven years, with new generations of D-Wave systems to be installed at NASA Ames as they become available.
“The new agreement is the largest order in D-Wave’s history, and indicative of the importance of quantum computing in its evolution toward solving problems that are difficult for even the largest supercomputers,” said D-Wave CEO Vern Brownell. “We highly value the commitment that our partners have made to D-Wave and our technology, and are excited about the potential use of our systems for machine learning and complex optimization problems.”
Cade Wetz’s Sept. 28, 2015 article for Wired magazine provides some interesting observations about D-Wave computers along with some explanations of quantum computing (Note: Links have been removed),
Though the D-Wave machine is less powerful than many scientists hope quantum computers will one day be, the leap to 1000 qubits represents an exponential improvement in what the machine is capable of. What is it capable of? Google and its partners are still trying to figure that out. But Google has said it’s confident there are situations where the D-Wave can outperform today’s non-quantum machines, and scientists at the University of Southern California [USC] have published research suggesting that the D-Wave exhibits behavior beyond classical physics.
A quantum computer operates according to the principles of quantum mechanics, the physics of very small things, such as electrons and photons. In a classical computer, a transistor stores a single “bit” of information. If the transistor is “on,” it holds a 1, and if it’s “off,” it holds a 0. But in quantum computer, thanks to what’s called the superposition principle, information is held in a quantum system that can exist in two states at the same time. This “qubit” can store a 0 and 1 simultaneously.
Two qubits, then, can hold four values at any given time (00, 01, 10, and 11). And as you keep increasing the number of qubits, you exponentially increase the power of the system. The problem is that building a qubit is a extreme difficult thing. If you read information from a quantum system, it “decoheres.” Basically, it turns into a classical bit that houses only a single value.
D-Wave claims to have a found a solution to the decoherence problem and that appears to be borne out by the USC researchers. Still, it isn’t a general quantum computer (from Wetz’s article),
… researchers at USC say that the system appears to display a phenomenon called “quantum annealing” that suggests it’s truly operating in the quantum realm. Regardless, the D-Wave is not a general quantum computer—that is, it’s not a computer for just any task. But D-Wave says the machine is well-suited to “optimization” problems, where you’re facing many, many different ways forward and must pick the best option, and to machine learning, where computers teach themselves tasks by analyzing large amount of data.
It takes a lot of innovation before you make big strides forward and I think D-Wave is to be congratulated on producing what is to my knowledge the only commercially available form of quantum computing of any sort in the world.
ETA Oct. 6, 2015* at 1230 hours PST: Minutes after publishing about D-Wave I came across this item (h/t Quirks & Quarks twitter) about Australian researchers and their quantum computing breakthrough. From an Oct. 6, 2015 article by Hannah Francis for the Sydney (Australia) Morning Herald,
For decades scientists have been trying to turn quantum computing — which allows for multiple calculations to happen at once, making it immeasurably faster than standard computing — into a practical reality rather than a moonshot theory. Until now, they have largely relied on “exotic” materials to construct quantum computers, making them unsuitable for commercial production.
But researchers at the University of New South Wales have patented a new design, published in the scientific journal Nature on Tuesday, created specifically with computer industry manufacturing standards in mind and using affordable silicon, which is found in regular computer chips like those we use every day in smartphones or tablets.
“Our team at UNSW has just cleared a major hurdle to making quantum computing a reality,” the director of the university’s Australian National Fabrication Facility, Andrew Dzurak, the project’s leader, said.
“As well as demonstrating the first quantum logic gate in silicon, we’ve also designed and patented a way to scale this technology to millions of qubits using standard industrial manufacturing techniques to build the world’s first quantum processor chip.”
According to the article, the university is looking for industrial partners to help them exploit this breakthrough. Fisher’s article features an embedded video, as well as, more detail.
*It was Oct. 6, 2015 in Australia but Oct. 5, 2015 my side of the international date line.
A two-qubit logic gate in silicon by M. Veldhorst, C. H. Yang, J. C. C. Hwang, W. Huang, J. P. Dehollain, J. T. Muhonen, S. Simmons, A. Laucht, F. E. Hudson, K. M. Itoh, A. Morello & A. S. Dzurak. Nature (2015 doi:10.1038/nature15263 Published online 05 October 2015