Brainlike Computers, Learning From Experience

Curated on Posted on by Stefaan Verhulst

The New York Times: “Computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.

The first commercial version of the new kind of computer chip is scheduled to be released in 2014. Not only can it automate tasks that now require painstaking programming — for example, moving a robot’s arm smoothly and efficiently — but it can also sidestep and even tolerate errors, potentially making the term “computer crash” obsolete.

The new computing approach, already in use by some large technology companies, is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information. It allows computers to absorb new information while carrying out a task, and adjust what they do based on the changing signals.

In coming years, the approach will make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control. That can hold enormous consequences for tasks like facial and speech recognition, navigation and planning, which are still in elementary stages and rely heavily on human programming.

Designers say the computing style can clear the way for robots that can safely walk and drive in the physical world, though a thinking or conscious computer, a staple of science fiction, is still far off on the digital horizon.

“We’re moving from engineering computing systems to something that has many of the characteristics of biological computing,” said Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centers devoted to developing these new kinds of computer circuits.

Conventional computers are limited by what they have been programmed to do. Computer vision systems, for example, only “recognize” objects that can be identified by the statistics-oriented algorithms programmed into them. An algorithm is like a recipe, a set of step-by-step instructions to perform a calculation.

But last year, Google researchers were able to get a machine-learning algorithm, known as a neural network, to perform an identification task without supervision. The network scanned a database of 10 million images, and in doing so trained itself to recognize cats.

In June, the company said it had used those neural network techniques to develop a new search service to help customers find specific photos more accurately.

The new approach, used in both hardware and software, is being driven by the explosion of scientific knowledge about the brain. Kwabena Boahen, a computer scientist who leads Stanford’s Brains in Silicon research program, said that is also its limitation, as scientists are far from fully understanding how brains function.”

Crowdsourcing drug discovery: Antitumour compound identified

Curated on Posted on by Stefaan Verhulst

David Bradley in Spectroscopy.now: “American researchers have used “crowdsourcing” – the cooperation of a large number of interested non-scientists via the internet – to help them identify a new fungus. The species contains unusual metabolites, isolated and characterized, with the help of vibrational circular dichroism (VCD). One compound reveals itself to have potential antitumour activity.
So far, a mere 7 percent of the more than 1.5 million species of fungi thought to exist have been identified and an even smaller fraction of these have been the subject of research seeking bioactive natural products. …Robert Cichewicz of the University of Oklahoma, USA, and his colleagues hoped to remedy this situation by working with a collection of several thousand fungal isolates from three regions: Arctic Alaska, tropical Hawaii, and subtropical to semiarid Oklahoma. Collaborator Susan Mooberry of the University of Texas at San Antonio carried out biological assays on many fungal isolates looking for antitumor activity among the metabolites in Cichewicz’s collection. A number of interesting substances were identified…
However, the researchers realized quickly enough that the efforts of a single research team were inadequate if samples representing the immense diversity of the thousands of fungi they hoped to test were to be obtained and tested. They thus turned to the help of citizen scientists in a “crowdsourcing” initiative. In this approach, lay people with an interest in science, and even fellow scientists in other fields, were recruited to collect and submit soil from their gardens.
As the samples began to arrive, the team quickly found among them a previously unknown fungal strain – a Tolypocladium species – growing in a soil sample from Alaska. Colleague Andrew Miller of the University of Illinois did the identification of this new fungus, which was found to be highly responsive to making new compounds based on changes in its laboratory growth conditions. Moreover, extraction of the active chemicals from the isolate revealed a unique metabolite which was shown to have significant antitumour activity in laboratory tests. The team suggests that this novel substance may represent a valuable new approach to cancer treatment because it precludes certain biochemical mechanisms that lead to the emergence of drug resistance in cancer with conventional drugs…
The researchers point out the essential roles that citizen scientists can play. “Many of the groundbreaking discoveries, theories, and applied research during the last two centuries were made by scientists operating from their own homes,” Cichewicz says. “Although much has changed, the idea that citizen scientists can still participate in research is a powerful means for reinvigorating the public’s interest in science and making important discoveries,” he adds.”

The Postmodernity of Big Data

Curated on Posted on by Stefaan Verhulst

Essay by in the New Inquiry: “Big Data fascinates because its presence has always been with us in nature. Each tree, drop of rain, and the path of each grain of sand, both responds to and creates millions of data points, even on a short journey. Nature is the original algorithm, the most efficient and powerful. Mathematicians since the ancients have looked to it for inspiration; techno-capitalists now look to unlock its mysteries for private gain. Playing God has become all the more brisk and profitable thanks to cloud computing.
But beyond economic motivations for Big Data’s rise, are there also epistemological ones? Has Big Data come to try to fill the vacuum of certainty left by postmodernism? Does data science address the insecurities of the postmodern thought?
It turns out that trying to explain Big Data is like trying to explain postmodernism. Neither can be summarized effectively in a phrase, despite their champions’ efforts. Broad epistemological developments are compressed into cursory, ex post facto descriptions. Attempts to define Big Data, such as IBM’s marketing copy, which promises “insights gleaned” from “enterprise data warehouses that implement massively parallel processing,” “real-time scalability” and “parsing structured and unstructured sources,” focus on its implementation at the expense of its substance, decontextualizing it entirely . Similarly, definitions of postmodernism, like art critic Thomas McEvilley’s claim that it is “a renunciation that involves recognition of the relativity of the self—of one’s habit systems, their tininess, silliness, and arbitrariness” are accurate but abstract to the point of vagueness….
Big Data might come to be understood as Big Postmodernism: the period in which the influx of unstructured, non-teleological, non-narrative inputs ceased to destabilize the existing order but was instead finally mastered processed by sufficiently complex, distributed, and pluralized algorithmic regime. If Big Data has a skepticism built in, how this is different from the skepticism of postmodernism is perhaps impossible to yet comprehend”.

Big Data Becomes a Mirror

Curated on Posted on by Stefaan Verhulst

Book Review of ‘Uncharted,’ by Erez Aiden and Jean-Baptiste Michel in the New York Times: “Why do English speakers say “drove” rather than “drived”?

As graduate students at the Harvard Program for Evolutionary Dynamics about eight years ago, Erez Aiden and Jean-Baptiste Michel pondered the matter and decided that something like natural selection might be at work. In English, the “-ed” past-tense ending of Proto-Germanic, like a superior life form, drove out the Proto-Indo-European system of indicating tenses by vowel changes. Only the small class of verbs we know as irregular managed to resist.

To test this evolutionary premise, Mr. Aiden and Mr. Michel wound up inventing something they call culturomics, the use of huge amounts of digital information to track changes in language, culture and history. Their quest is the subject of “Uncharted: Big Data as a Lens on Human Culture,” an entertaining tour of the authors’ big-data adventure, whose implications they wildly oversell….

Invigorated by the great verb chase, Mr. Aiden and Mr. Michel went hunting for bigger game. Given a large enough storehouse of words and a fine filter, would it be possible to see cultural change at the micro level, to follow minute fluctuations in human thought processes and activities? Tiny factoids, multiplied endlessly, might assume imposing dimensions.

By chance, Google Books, the megaproject to digitize every page of every book ever printed — all 130 million of them — was starting to roll just as the authors were looking for their next target of inquiry.

Meetings were held, deals were struck and the authors got to it. In 2010, working with Google, they perfected the Ngram Viewer, which takes its name from the computer-science term for a word or phrase. This “robot historian,” as they call it, can search the 30 million volumes already digitized by Google Books and instantly generate a usage-frequency timeline for any word, phrase, date or name, a sort of stock-market graph illustrating the ups and downs of cultural shares over time.

Mr. Aiden, now director of the Center for Genome Architecture at Rice University, and Mr. Michel, who went on to start the data-science company Quantified Labs, play the Ngram Viewer (books.google.com/ngrams) like a Wurlitzer…

The Ngram Viewer delivers the what and the when but not the why. Take the case of specific years. All years get attention as they approach, peak when they arrive, then taper off as succeeding years occupy the attention of the public. Mentions of the year 1872 had declined by half in 1896, a slow fade that took 23 years. The year 1973 completed the same trajectory in less than half the time.

“What caused that change?” the authors ask. “We don’t know. For now, all we have are the naked correlations: what we uncover when we look at collective memory through the digital lens of our new scope.” Someone else is going to have to do the heavy lifting.”

Web Science: Understanding the Emergence of Macro-Level Features on the World Wide Web

Curated on Posted on by Stefaan Verhulst

Monograph by Kieron O’Hara, Noshir S. Contractor, Wendy Hall, James A. Hendler and Nigel Shadbolt in Foundations and Trends in Web Sciences: “Web Science considers the development of Web Science since the publication of ‘A Framework for Web Science’ (Berners-Lee et al., 2006). This monograph argues that the requirement for understanding should ideally be accompanied by some measure of control, which makes Web Science crucial in the future provision of tools for managing our interactions, our politics, our economics, our entertainment, and – not least – our knowledge and data sharing…
In this monograph we consider the development of Web Science since the launch of this journal and its inaugural publication ‘A Framework for Web Science’ [44]. The theme of emergence is discussed as the characteristic phenomenon of Web-scale applications, where many unrelated micro-level actions and decisions, uninformed by knowledge about the macro-level, still produce noticeable and coherent effects at the scale of the Web. A model of emergence is mapped onto the multitheoretical multilevel (MTML) model of communication networks explained in [252]. Four specific types of theoretical problem are outlined. First, there is the need to explain local action. Second, the global patterns that form when local actions are repeated at scale have to be detected and understood. Third, those patterns feed back into the local, with intricate and often fleeting causal connections to be traced. Finally, as Web Science is an engineering discipline, issues of control of this feedback must be addressed. The idea of a social machine is introduced, where networked interactions at scale can help to achieve goals for people and social groups in civic society; an important aim of Web Science is to understand how such networks can operate, and how they can control the effects they produce on their own environment.”

Are Smart Cities Empty Hype?

Curated on Posted on by Stefaan Verhulst

Irving Wladawsky-Berger in the Wall Street Journal: “A couple of weeks ago I participated in an online debate sponsored by The Economist around the question: Are Smart Cities Empty Hype? Defending the motion was Anthony Townsend, research director at the Institute for the Future and adjunct faculty member at NYU’s Wagner School of Public Service. I took the opposite side, arguing the case against the motion.
The debate consisted of three phases spread out over roughly 10 days. We each first stated our respective positions in our opening statements, followed a few days later by our rebuttals, and then finally our closing statements.  It was moderated by Ludwig Siegele, online business and finance editor at The Economist. Throughout the process, people were invited to vote on the motion, as well as to post their own comments.
The debate was inspired, I believe, by The Multiplexed Metropolis, an article Mr. Siegele published in the September 7 issue of The Economist which explored the impact of Big Data on cities. He wrote that the vast amounts of data generated by the many social interactions taking place in cities might lead to a kind of second electrification, transforming 21st century cities much as electricity did in the past. “Enthusiasts think that data services can change cities in this century as much as electricity did in the last one,” he noted. “They are a long way from proving their case.”
In my opening statement, I said that I strongly believe that digital technologies and the many data services they are enabling will make cities smarter and help transform them over time. My position is not surprising, given my affiliations with NYU’s Center for Urban Science and Progress (CUSP) and Imperial College’s Digital City Exchange, as well as my past involvements with IBM’s Smarter Cities and with Citigroup’s Citi for Cities initiatives. But, I totally understand why so many– almost half of those voting and quite a few who left comments–feel that smart cities are mostly hype. The case for smart cities is indeed far from proven.
Cities are the most complex social organisms created by humans. Just about every aspect of human endeavor is part of the mix of cities, and they all interact with each other leading to a highly dynamic system of systems. Moreover, each city has its own unique style and character. As is generally the case with transformative changes to highly complex systems, the evolution toward smart cities will likely take quite a bit longer than we anticipate, but the eventual impact will probably be more transformative than we can currently envision.
Electrification, for example, started in the U.S., Britain and other advanced nations around the 1880s and took decades to deploy and truly transform cities. The hype around smart cities that I worry the most about is underestimating their complexity and the amount of research, experimentation, and plain hard work that it will take to realize the promise. Smart cities projects are still in their very early stages. Some will work and some will fail. We have much to learn. Highly complex systems need time to evolve.
Commenting on the opening statements, Mr. Siegele noted: “Despite the motion being Are smart cities empty hype?, both sides have focused on whether these should be implemented top-down or bottom-up. Most will probably agree that digital technology can make cities smarter–meaning more liveable, more efficient, more sustainable and perhaps even more democratic.  But the big question is how to get there and how smart cities will be governed.”…

Philosophical Engineering: Toward a Philosophy of the Web

Curated on Posted on by Stefaan Verhulst
New book by Harry Halpin (Editor) and Alexandre Monnin (Editor) : “This is the first interdisciplinary exploration of the philosophical foundations of the Web, a new area of inquiry that has important implications across a range of domains.

  • Contains twelve essays that bridge the fields of philosophy, cognitive science, and phenomenology
  • Tackles questions such as the impact of Google on intelligence and epistemology, the philosophical status of digital objects, ethics on the Web, semantic and ontological changes caused by the Web, and the potential of the Web to serve as a genuine cognitive extension
  • Brings together insightful new scholarship from well-known analytic and continental philosophers, such as Andy Clark and Bernard Stiegler, as well as rising scholars in “digital native” philosophy and engineering
  • Includes an interview with Tim Berners-Lee, the inventor of the Web”…

Participation Dynamics in Crowd-Based Knowledge Production: The Scope and Sustainability of Interest-Based Motivation

Curated on Posted on by Stefaan Verhulst

New paper by Henry Sauermann and Chiara Franzoni: “Crowd-based knowledge production is attracting growing attention from scholars and practitioners. One key premise is that participants who have an intrinsic “interest” in a topic or activity are willing to expend effort at lower pay than in traditional employment relationships. However, it is not clear how strong and sustainable interest is as a source of motivation. We draw on research in psychology to discuss important static and dynamic features of interest and derive a number of research questions regarding interest-based effort in crowd-based projects. Among others, we consider the specific versus general nature of interest, highlight the potential role of matching between projects and individuals, and distinguish the intensity of interest at a point in time from the development and sustainability of interest over time. We then examine users’ participation patterns within and across 7 different crowd science projects that are hosted on a shared platform. Our results provide novel insights into contribution dynamics in crowd science projects. Moreover, given that extrinsic incentives such as pay, status, self-use, or career benefits are largely absent in these particular projects, the data also provide unique insights into the dynamics of interest-based motivation and into its potential as a driver of effort.”

Google Global Impact Award Expands Zooniverse

Curated on Posted on by Stefaan Verhulst

Press Release: “A $1.8 million Google Global Impact Award will enable Zooniverse, a nonprofit collaboration led by the Adler Planetarium and the University of Oxford, to make setting up a citizen science project as easy as starting a blog and could lead to thousands of innovative new projects around the world, accelerating the pace of scientific research.
The award supports the further development of the Zooniverse, the world’s leading ‘citizen science’ platform, which has already given more than 900,000 online volunteers the chance to contribute to science by taking part in activities including discovering planets, classifying plankton or searching through old ship’s logs for observations of interest to climate scientists. As part of the Global Impact Award, the Adler will receive $400,000 to support the Zooniverse platform.
With the Google Global Impact Award, Zooniverse will be able to rebuild their platform so that research groups with no web development expertise can build and launch their own citizen science projects.
“We are entering a new era of citizen science – this effort will enable prolific development of science projects in which hundreds of thousands of additional volunteers will be able to work alongside professional scientists to conduct important research – the potential for discovery is limitless,” said Michelle B. Larson, Ph.D., Adler Planetarium president and CEO. “The Adler is honored to join its fellow Zooniverse partner, the University of Oxford, as a Google Global Impact Award recipient.”
The Zooniverse – the world’s leading citizen science platform – is a global collaboration across several institutions that design and build citizen science projects. The Adler is a founding partner of the Zooniverse, which has already engaged more than 900,000 online volunteers as active scientists by discovering planets, mapping the surface of Mars and detecting solar flares. Adler-directed citizen science projects include: Galaxy Zoo (astronomy), Solar Stormwatch (solar physics), Moon Zoo (planetary science), Planet Hunters (exoplanets) and The Milky Way Project (star formation). The Zooniverse (zooniverse.org) also includes projects in environmental, biological and medical sciences. Google’s investment in the Adler and its Zooniverse partner, the University of Oxford, will further the global reach, making thousands of new projects possible.”

Selected Readings on Crowdsourcing Data

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of crowdsourcing data was originally published in 2013.

As institutions seek to improve decision-making through data and put public data to use to improve the lives of citizens, new tools and projects are allowing citizens to play a role in both the collection and utilization of data. Participatory sensing and other citizen data collection initiatives, notably in the realm of disaster response, are allowing citizens to crowdsource important data, often using smartphones, that would be either impossible or burdensomely time-consuming for institutions to collect themselves. Civic hacking, often performed in hackathon events, on the other hand, is a growing trend in which governments encourage citizens to transform data from government and other sources into useful tools to benefit the public good.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Baraniuk, Chris. “Power Politechs.” New Scientist 218, no. 2923 (June 29, 2013): 36–39. http://bit.ly/167ul3J.

  • In this article, Baraniuk discusses civic hackers, “an army of volunteer coders who are challenging preconceptions about hacking and changing the way your government operates. In a time of plummeting budgets and efficiency drives, those in power have realised they needn’t always rely on slow-moving, expensive outsourcing and development to improve public services. Instead, they can consider running a hackathon, at which tech-savvy members of the public come together to create apps and other digital tools that promise to enhace the provision of healthcare, schools or policing.”
  • While recognizing that “civic hacking has established a pedigree that demonstrates its potential for positive impact,” Baraniuk argues that a “more rigorous debate over how this activity should evolve, or how authorities ought to engage in it” is needed.

Barnett, Brandon, Muki Hansteen Izora, and Jose Sia. “Civic Hackathon Challenges Design Principles: Making Data Relevant and Useful for Individuals and Communities.” Hack for Change, https://bit.ly/2Ge6z09.

  • In this paper, researchers from Intel Labs offer “guiding principles to support the efforts of local civic hackathon organizers and participants as they seek to design actionable challenges and build useful solutions that will positively benefit their communities.”
  • The authors proposed design principles are:
    • Focus on the specific needs and concerns of people or institutions in the local community. Solve their problems and challenges by combining different kinds of data.
    • Seek out data far and wide (local, municipal, state, institutional, non-profits, companies) that is relevant to the concern or problem you are trying to solve.
    • Keep it simple! This can’t be overstated. Focus [on] making data easily understood and useful to those who will use your application or service.
    • Enable users to collaborate and form new communities and alliances around data.

Buhrmester, Michael, Tracy Kwang, and Samuel D. Gosling. “Amazon’s Mechanical Turk A New Source of Inexpensive, Yet High-Quality, Data?” Perspectives on Psychological Science 6, no. 1 (January 1, 2011): 3–5. http://bit.ly/H56lER.

  • This article examines the capability of Amazon’s Mechanical Turk to act a source of data for researchers, in addition to its traditional role as a microtasking platform.
  • The authors examine the demographics of MTurkers and find that “MTurk participants are slightly more demographically diverse than are standard Internet samples and are significantly more diverse than typical American college samples; (b) participation is affected by compensation rate and task length, but participants can still be recruited rapidly and inexpensively; (c) realistic compensation rates do not affect data quality; and (d) the data obtained are at least as reliable as those obtained via traditional methods.”
  • The paper concludes that, just as MTurk can be a strong tool for crowdsourcing tasks, data derived from MTurk can be high quality while also being inexpensive and obtained rapidly.

Goodchild, Michael F., and J. Alan Glennon. “Crowdsourcing Geographic Information for Disaster Response: a Research Frontier.” International Journal of Digital Earth 3, no. 3 (2010): 231–241. http://bit.ly/17MBFPs.

  • This article examines issues of data quality in the face of the new phenomenon of geographic information being generated by citizens, in order to examine whether this data can play a role in emergency management.
  • The authors argue that “[d]ata quality is a major concern, since volunteered information is asserted and carries none of the assurances that lead to trust in officially created data.”
  • Due to the fact that time is crucial during emergencies, the authors argue that, “the risks associated with volunteered information are often outweighed by the benefits of its use.”
  • The paper examines four wildfires in Santa Barbara in 2007-2009 to discuss current challenges with volunteered geographical data, and concludes that further research is required to answer how volunteer citizens can be used to provide effective assistance to emergency managers and responders.

Hudson-Smith, Andrew, Michael Batty, Andrew Crooks, and Richard Milton. “Mapping for the Masses Accessing Web 2.0 Through Crowdsourcing.” Social Science Computer Review 27, no. 4 (November 1, 2009): 524–538. http://bit.ly/1c1eFQb.

  • This article describes the way in which “we are harnessing the power of web 2.0 technologies to create new approaches to collecting, mapping, and sharing geocoded data.”
  • The authors examine GMapCreator and MapTube, which allow users to do a range of map-related functions such as create new maps, archive existing maps, and share or produce bottom-up maps through crowdsourcing.
  • They conclude that “these tools are helping to define a neogeography that is essentially ‘mapping for the masses,’ while noting that there are many issues of quality, accuracy, copyright, and trust that will influence the impact of these tools on map-based communication.”

Kanhere, Salil S. “Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces.” In Distributed Computing and Internet Technology, edited by Chittaranjan Hota and Pradip K. Srimani, 19–26. Lecture Notes in Computer Science 7753. Springer Berlin Heidelberg. 2013. https://bit.ly/2zX8Szj.

  • This paper provides a comprehensive overview of participatory sensing — a “new paradigm for monitoring the urban landscape” in which “ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communications infrastructure.”
  • In addition to examining a number of innovative applications of participatory sensing, Kanhere outlines the following key research challenges:
    • Dealing with incomplete samples
    •  Inferring user context
    • Protecting user privacy
    • Evaluating data trustworthiness
    • Conserving energy