Tag Archives: algorithms

Health technology and the Canadian Broadcasting Corporation’s (CBC) two-tier health system ‘Viewpoint’

There’s a lot of talk and handwringing about Canada’s health care system, which ebbs and flows in almost predictable cycles. Jesse Hirsh in a May 16, 2017 ‘Viewpoints’ segment (an occasional series run as part the of the CBC’s [Canadian Broadcasting Corporation] flagship, daily news programme, The National) dared to reframe the discussion as one about technology and ‘those who get it’  [the technologically literate] and ‘those who don’t’,  a state Hirsh described as being illiterate as you can see and hear in the following video.

I don’t know about you but I’m getting tired of being called illiterate when I don’t know something. To be illiterate means you can’t read and write and as it turns out I do both of those things on a daily basis (sometimes even in two languages). Despite my efforts, I’m ignorant about any number of things and those numbers keep increasing day by day. BTW, Is there anyone who isn’t having trouble keeping up?

Moving on from my rhetorical question, Hirsh has a point about the tech divide and about the need for discussion. It’s a point that hadn’t occurred to me (although I think he’s taking it in the wrong direction). In fact, this business of a tech divide already exists if you consider that people who live in rural environments and need the latest lifesaving techniques or complex procedures or access to highly specialized experts have to travel to urban centres. I gather that Hirsh feels that this divide isn’t necessarily going to be an urban/rural split so much as an issue of how technically literate you and your doctor are.  That’s intriguing but then his argumentation gets muddled. Confusingly, he seems to be suggesting that the key to the split is your access (not your technical literacy) to artificial intelligence (AI) and algorithms (presumably he’s referring to big data and data analytics). I expect access will come down more to money than technological literacy.

For example, money is likely to be a key issue when you consider his big pitch is for access to IBM’s Watson computer. (My Feb. 28, 2011 posting titled: Engineering, entertainment, IBM’s Watson, and product placement focuses largely on Watson, its winning appearances on the US television game show, Jeopardy, and its subsequent adoption into the University of Maryland’s School of Medicine in a project to bring Watson into the examining room with patients.)

Hirsh’s choice of IBM’s Watson is particularly interesting for a number of reasons. (1) Presumably there are companies other than IBM in this sector. Why do they not rate a mention?  (2) Given the current situation with IBM and the Canadian federal government’s introduction of the Phoenix payroll system (a PeopleSoft product customized by IBM), which is  a failure of monumental proportions (a Feb. 23, 2017 article by David Reevely for the Ottawa Citizen and a May 25, 2017 article by Jordan Press for the National Post), there may be a little hesitation, if not downright resistance, to a large scale implementation of any IBM product or service, regardless of where the blame lies. (3) Hirsh notes on the home page for his eponymous website,

I’m presently spending time at the IBM Innovation Space in Toronto Canada, investigating the impact of artificial intelligence and cognitive computing on all sectors and industries.

Yes, it would seem he has some sort of relationship with IBM not referenced in his Viewpoints segment on The National. Also, his description of the relationship isn’t especially illuminating but perhaps it.s this? (from the IBM Innovation Space  – Toronto Incubator Application webpage),

Our incubator

The IBM Innovation Space is a Toronto-based incubator that provides startups with a collaborative space to innovate and disrupt the market. Our goal is to provide you with the tools needed to take your idea to the next level, introduce you to the right networks and help you acquire new clients. Our unique approach, specifically around client engagement, positions your company for optimal growth and revenue at an accelerated pace.

OUR SERVICES

IBM Bluemix
IBM Global Entrepreneur
Softlayer – an IBM Company
Watson

Startups partnered with the IBM Innovation Space can receive up to $120,000 in IBM credits at no charge for up to 12 months through the Global Entrepreneurship Program (GEP). These credits can be used in our products such our IBM Bluemix developer platform, Softlayer cloud services, and our world-renowned IBM Watson ‘cognitive thinking’ APIs. We provide you with enterprise grade technology to meet your clients’ needs, large or small.

Collaborative workspace in the heart of Downtown Toronto
Mentorship opportunities available with leading experts
Access to large clients to scale your startup quickly and effectively
Weekly programming ranging from guest speakers to collaborative activities
Help with funding and access to local VCs and investors​

Final comments

While I have some issues with Hirsh’s presentation, I agree that we should be discussing the issues around increased automation of our health care system. A friend of mine’s husband is a doctor and according to him those prescriptions and orders you get when leaving the hospital? They are not made up by a doctor so much as they are spit up by a computer based on the data that the doctors and nurses have supplied.

GIGO, bias, and de-skilling

Leaving aside the wonders that Hirsh describes, there’s an oldish saying in the computer business, garbage in/garbage out (gigo). At its simplest, who’s going to catch a mistake? (There are lots of mistakes made in hospitals and other health care settings.)

There are also issues around the quality of research. Are all the research papers included in the data used by the algorithms going to be considered equal? There’s more than one case where a piece of problematic research has been accepted uncritically, even if it get through peer review, and subsequently cited many times over. One of the ways to measure impact, i.e., importance, is to track the number of citations. There’s also the matter of where the research is published. A ‘high impact’ journal, such as Nature, Science, or Cell, automatically gives a piece of research a boost.

There are other kinds of bias as well. Increasingly, there’s discussion about algorithms being biased and about how machine learning (AI) can become biased. (See my May 24, 2017 posting: Machine learning programs learn bias, which highlights the issues and cites other FrogHeart posts on that and other related topics.)

These problems are to a large extent already present. Doctors have biases and research can be wrong and it can take a long time before there are corrections. However, the advent of an automated health diagnosis and treatment system is likely to exacerbate the problems. For example, if you don’t agree with your doctor’s diagnosis or treatment, you can search other opinions. What happens when your diagnosis and treatment have become data? Will the system give you another opinion? Who will you talk to? The doctor who got an answer from ‘Watson”? Is she or he going to debate Watson? Are you?

This leads to another issue and that’s automated systems getting more credit than they deserve. Futurists such as Hirsh tend to underestimate people and overestimate the positive impact that automation will have. A computer, data analystics, or an AI system are tools not gods. You’ll have as much luck petitioning one of those tools as you would Zeus.

The unasked question is how will your doctor or other health professional gain experience and skills if they never have to practice the basic, boring aspects of health care (asking questions for a history, reading medical journals to keep up with the research, etc.) and leave them to the computers? There had to be  a reason for calling it a medical ‘practice’.

There are definitely going to be advantages to these technological innovations but thoughtful adoption of these practices (pun intended) should be our goal.

Who owns your data?

Another issue which is increasingly making itself felt is ownership of data. Jacob Brogan has written a provocative May 23, 2017 piece for slate.com asking that question about the data Ancestry.com gathers for DNA testing (Note: Links have been removed),

AncestryDNA’s pitch to consumers is simple enough. For $99 (US), the company will analyze a sample of your saliva and then send back information about your “ethnic mix.” While that promise may be scientifically dubious, it’s a relatively clear-cut proposal. Some, however, worry that the service might raise significant privacy concerns.

After surveying AncestryDNA’s terms and conditions, consumer protection attorney Joel Winston found a few issues that troubled him. As he noted in a Medium post last week, the agreement asserts that it grants the company “a perpetual, royalty-free, world-wide, transferable license to use your DNA.” (The actual clause is considerably longer.) According to Winston, “With this single contractual provision, customers are granting Ancestry.com the broadest possible rights to own and exploit their genetic information.”

Winston also noted a handful of other issues that further complicate the question of ownership. Since we share much of our DNA with our relatives, he warned, “Even if you’ve never used Ancestry.com, but one of your genetic relatives has, the company may already own identifiable portions of your DNA.” [emphasis mine] Theoretically, that means information about your genetic makeup could make its way into the hands of insurers or other interested parties, whether or not you’ve sent the company your spit. (Maryam Zaringhalam explored some related risks in a recent Slate article.) Further, Winston notes that Ancestry’s customers waive their legal rights, meaning that they cannot sue the company if their information gets used against them in some way.

Over the weekend, Eric Heath, Ancestry’s chief privacy officer, responded to these concerns on the company’s own site. He claims that the transferable license is necessary for the company to provide its customers with the service that they’re paying for: “We need that license in order to move your data through our systems, render it around the globe, and to provide you with the results of our analysis work.” In other words, it allows them to send genetic samples to labs (Ancestry uses outside vendors), store the resulting data on servers, and furnish the company’s customers with the results of the study they’ve requested.

Speaking to me over the phone, Heath suggested that this license was akin to the ones that companies such as YouTube employ when users upload original content. It grants them the right to shift that data around and manipulate it in various ways, but isn’t an assertion of ownership. “We have committed to our users that their DNA data is theirs. They own their DNA,” he said.

I’m glad to see the company’s representatives are open to discussion and, later in the article, you’ll see there’ve already been some changes made. Still, there is no guarantee that the situation won’t again change, for ill this time.

What data do they have and what can they do with it?

It’s not everybody who thinks data collection and data analytics constitute problems. While some people might balk at the thought of their genetic data being traded around and possibly used against them, e.g., while hunting for a job, or turned into a source of revenue, there tends to be a more laissez-faire attitude to other types of data. Andrew MacLeod’s May 24, 2017 article for thetyee.ca highlights political implications and privacy issues (Note: Links have been removed),

After a small Victoria [British Columbia, Canada] company played an outsized role in the Brexit vote, government information and privacy watchdogs in British Columbia and Britain have been consulting each other about the use of social media to target voters based on their personal data.

The U.K.’s information commissioner, Elizabeth Denham [Note: Denham was formerly B.C.’s Office of the Information and Privacy Commissioner], announced last week [May 17, 2017] that she is launching an investigation into “the use of data analytics for political purposes.”

The investigation will look at whether political parties or advocacy groups are gathering personal information from Facebook and other social media and using it to target individuals with messages, Denham said.

B.C.’s Office of the Information and Privacy Commissioner confirmed it has been contacted by Denham.

Macleod’s March 6, 2017 article for thetyee.ca provides more details about the company’s role (note: Links have been removed),

The “tiny” and “secretive” British Columbia technology company [AggregateIQ; AIQ] that played a key role in the Brexit referendum was until recently listed as the Canadian office of a much larger firm that has 25 years of experience using behavioural research to shape public opinion around the world.

The larger firm, SCL Group, says it has worked to influence election outcomes in 19 countries. Its associated company in the U.S., Cambridge Analytica, has worked on a wide range of campaigns, including Donald Trump’s presidential bid.

In late February [2017], the Telegraph reported that campaign disclosures showed that Vote Leave campaigners had spent £3.5 million — about C$5.75 million [emphasis mine] — with a company called AggregateIQ, run by CEO Zack Massingham in downtown Victoria.

That was more than the Leave side paid any other company or individual during the campaign and about 40 per cent of its spending ahead of the June referendum that saw Britons narrowly vote to exit the European Union.

According to media reports, Aggregate develops advertising to be used on sites including Facebook, Twitter and YouTube, then targets messages to audiences who are likely to be receptive.

The Telegraph story described Victoria as “provincial” and “picturesque” and AggregateIQ as “secretive” and “low-profile.”

Canadian media also expressed surprise at AggregateIQ’s outsized role in the Brexit vote.

The Globe and Mail’s Paul Waldie wrote “It’s quite a coup for Mr. Massingham, who has only been involved in politics for six years and started AggregateIQ in 2013.”

Victoria Times Colonist columnist Jack Knox wrote “If you have never heard of AIQ, join the club.”

The Victoria company, however, appears to be connected to the much larger SCL Group, which describes itself on its website as “the global leader in data-driven communications.”

In the United States it works through related company Cambridge Analytica and has been involved in elections since 2012. Politico reported in 2015 that the firm was working on Ted Cruz’s presidential primary campaign.

And NBC and other media outlets reported that the Trump campaign paid Cambridge Analytica millions to crunch data on 230 million U.S. adults, using information from loyalty cards, club and gym memberships and charity donations [emphasis mine] to predict how an individual might vote and to shape targeted political messages.

That’s quite a chunk of change and I don’t believe that gym memberships, charity donations, etc. were the only sources of information (in the US, there’s voter registration, credit card information, and more) but the list did raise my eyebrows. It would seem we are under surveillance at all times, even in the gym.

In any event, I hope that Hirsh’s call for discussion is successful and that the discussion includes more critical thinking about the implications of Hirsh’s ‘Brave New World’.

Machine learning programs learn bias

The notion of bias in artificial intelligence (AI)/algorithms/robots is gaining prominence (links to other posts featuring algorithms and bias are at the end of this post). The latest research concerns machine learning where an artificial intelligence system trains itself with ordinary human language from the internet. From an April 13, 2017 American Association for the Advancement of Science (AAAS) news release on EurekAlert,

As artificial intelligence systems “learn” language from existing texts, they exhibit the same biases that humans do, a new study reveals. The results not only provide a tool for studying prejudicial attitudes and behavior in humans, but also emphasize how language is intimately intertwined with historical biases and cultural stereotypes. A common way to measure biases in humans is the Implicit Association Test (IAT), where subjects are asked to pair two concepts they find similar, in contrast to two concepts they find different; their response times can vary greatly, indicating how well they associated one word with another (for example, people are more likely to associate “flowers” with “pleasant,” and “insects” with “unpleasant”). Here, Aylin Caliskan and colleagues developed a similar way to measure biases in AI systems that acquire language from human texts; rather than measuring lag time, however, they used the statistical number of associations between words, analyzing roughly 2.2 million words in total. Their results demonstrate that AI systems retain biases seen in humans. For example, studies of human behavior show that the exact same resume is 50% more likely to result in an opportunity for an interview if the candidate’s name is European American rather than African-American. Indeed, the AI system was more likely to associate European American names with “pleasant” stimuli (e.g. “gift,” or “happy”). In terms of gender, the AI system also reflected human biases, where female words (e.g., “woman” and “girl”) were more associated than male words with the arts, compared to mathematics. In a related Perspective, Anthony G. Greenwald discusses these findings and how they could be used to further analyze biases in the real world.

There are more details about the research in this April 13, 2017 Princeton University news release on EurekAlert (also on ScienceDaily),

In debates over the future of artificial intelligence, many experts think of the new systems as coldly logical and objectively rational. But in a new study, researchers have demonstrated how machines can be reflections of us, their creators, in potentially problematic ways. Common machine learning programs, when trained with ordinary human language available online, can acquire cultural biases embedded in the patterns of wording, the researchers found. These biases range from the morally neutral, like a preference for flowers over insects, to the objectionable views of race and gender.

Identifying and addressing possible bias in machine learning will be critically important as we increasingly turn to computers for processing the natural language humans use to communicate, for instance in doing online text searches, image categorization and automated translations.

“Questions about fairness and bias in machine learning are tremendously important for our society,” said researcher Arvind Narayanan, an assistant professor of computer science and an affiliated faculty member at the Center for Information Technology Policy (CITP) at Princeton University, as well as an affiliate scholar at Stanford Law School’s Center for Internet and Society. “We have a situation where these artificial intelligence systems may be perpetuating historical patterns of bias that we might find socially unacceptable and which we might be trying to move away from.”

The paper, “Semantics derived automatically from language corpora contain human-like biases,” published April 14  [2017] in Science. Its lead author is Aylin Caliskan, a postdoctoral research associate and a CITP fellow at Princeton; Joanna Bryson, a reader at University of Bath, and CITP affiliate, is a coauthor.

As a touchstone for documented human biases, the study turned to the Implicit Association Test, used in numerous social psychology studies since its development at the University of Washington in the late 1990s. The test measures response times (in milliseconds) by human subjects asked to pair word concepts displayed on a computer screen. Response times are far shorter, the Implicit Association Test has repeatedly shown, when subjects are asked to pair two concepts they find similar, versus two concepts they find dissimilar.

Take flower types, like “rose” and “daisy,” and insects like “ant” and “moth.” These words can be paired with pleasant concepts, like “caress” and “love,” or unpleasant notions, like “filth” and “ugly.” People more quickly associate the flower words with pleasant concepts, and the insect terms with unpleasant ideas.

The Princeton team devised an experiment with a program where it essentially functioned like a machine learning version of the Implicit Association Test. Called GloVe, and developed by Stanford University researchers, the popular, open-source program is of the sort that a startup machine learning company might use at the heart of its product. The GloVe algorithm can represent the co-occurrence statistics of words in, say, a 10-word window of text. Words that often appear near one another have a stronger association than those words that seldom do.

The Stanford researchers turned GloVe loose on a huge trawl of contents from the World Wide Web, containing 840 billion words. Within this large sample of written human culture, Narayanan and colleagues then examined sets of so-called target words, like “programmer, engineer, scientist” and “nurse, teacher, librarian” alongside two sets of attribute words, such as “man, male” and “woman, female,” looking for evidence of the kinds of biases humans can unwittingly possess.

In the results, innocent, inoffensive biases, like for flowers over bugs, showed up, but so did examples along lines of gender and race. As it turned out, the Princeton machine learning experiment managed to replicate the broad substantiations of bias found in select Implicit Association Test studies over the years that have relied on live, human subjects.

For instance, the machine learning program associated female names more with familial attribute words, like “parents” and “wedding,” than male names. In turn, male names had stronger associations with career attributes, like “professional” and “salary.” Of course, results such as these are often just objective reflections of the true, unequal distributions of occupation types with respect to gender–like how 77 percent of computer programmers are male, according to the U.S. Bureau of Labor Statistics.

Yet this correctly distinguished bias about occupations can end up having pernicious, sexist effects. An example: when foreign languages are naively processed by machine learning programs, leading to gender-stereotyped sentences. The Turkish language uses a gender-neutral, third person pronoun, “o.” Plugged into the well-known, online translation service Google Translate, however, the Turkish sentences “o bir doktor” and “o bir hem?ire” with this gender-neutral pronoun are translated into English as “he is a doctor” and “she is a nurse.”

“This paper reiterates the important point that machine learning methods are not ‘objective’ or ‘unbiased’ just because they rely on mathematics and algorithms,” said Hanna Wallach, a senior researcher at Microsoft Research New York City, who was not involved in the study. “Rather, as long as they are trained using data from society and as long as society exhibits biases, these methods will likely reproduce these biases.”

Another objectionable example harkens back to a well-known 2004 paper by Marianne Bertrand of the University of Chicago Booth School of Business and Sendhil Mullainathan of Harvard University. The economists sent out close to 5,000 identical resumes to 1,300 job advertisements, changing only the applicants’ names to be either traditionally European American or African American. The former group was 50 percent more likely to be offered an interview than the latter. In an apparent corroboration of this bias, the new Princeton study demonstrated that a set of African American names had more unpleasantness associations than a European American set.

Computer programmers might hope to prevent cultural stereotype perpetuation through the development of explicit, mathematics-based instructions for the machine learning programs underlying AI systems. Not unlike how parents and mentors try to instill concepts of fairness and equality in children and students, coders could endeavor to make machines reflect the better angels of human nature.

“The biases that we studied in the paper are easy to overlook when designers are creating systems,” said Narayanan. “The biases and stereotypes in our society reflected in our language are complex and longstanding. Rather than trying to sanitize or eliminate them, we should treat biases as part of the language and establish an explicit way in machine learning of determining what we consider acceptable and unacceptable.”

Here’s a link to and a citation for the Princeton paper,

Semantics derived automatically from language corpora contain human-like biases by Aylin Caliskan, Joanna J. Bryson, Arvind Narayanan. Science  14 Apr 2017: Vol. 356, Issue 6334, pp. 183-186 DOI: 10.1126/science.aal4230

This paper appears to be open access.

Links to more cautionary posts about AI,

Aug 5, 2009: Autonomous algorithms; intelligent windows; pretty nano pictures

June 14, 2016:  Accountability for artificial intelligence decision-making

Oct. 25, 2016 Removing gender-based stereotypes from algorithms

March 1, 2017: Algorithms in decision-making: a government inquiry in the UK

There’s also a book which makes some of the current use of AI programmes and big data quite accessible reading: Cathy O’Neil’s ‘Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy’.

High-performance, low-energy artificial synapse for neural network computing

This artificial synapse is apparently an improvement on the standard memristor-based artificial synapse but that doesn’t become clear until reading the abstract for the paper. First, there’s a Feb. 20, 2017 Stanford University news release by Taylor Kubota (dated Feb. 21, 2017 on EurekAlert), Note: Links have been removed,

For all the improvements in computer technology over the years, we still struggle to recreate the low-energy, elegant processing of the human brain. Now, researchers at Stanford University and Sandia National Laboratories have made an advance that could help computers mimic one piece of the brain’s efficient design – an artificial version of the space over which neurons communicate, called a synapse.

“It works like a real synapse but it’s an organic electronic device that can be engineered,” said Alberto Salleo, associate professor of materials science and engineering at Stanford and senior author of the paper. “It’s an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that’s been done before with inorganics.”

The new artificial synapse, reported in the Feb. 20 issue of Nature Materials, mimics the way synapses in the brain learn through the signals that cross them. This is a significant energy savings over traditional computing, which involves separately processing information and then storing it into memory. Here, the processing creates the memory.

This synapse may one day be part of a more brain-like computer, which could be especially beneficial for computing that works with visual and auditory signals. Examples of this are seen in voice-controlled interfaces and driverless cars. Past efforts in this field have produced high-performance neural networks supported by artificially intelligent algorithms but these are still distant imitators of the brain that depend on energy-consuming traditional computer hardware.

Building a brain

When we learn, electrical signals are sent between neurons in our brain. The most energy is needed the first time a synapse is traversed. Every time afterward, the connection requires less energy. This is how synapses efficiently facilitate both learning something new and remembering what we’ve learned. The artificial synapse, unlike most other versions of brain-like computing, also fulfills these two tasks simultaneously, and does so with substantial energy savings.

“Deep learning algorithms are very powerful but they rely on processors to calculate and simulate the electrical states and store them somewhere else, which is inefficient in terms of energy and time,” said Yoeri van de Burgt, former postdoctoral scholar in the Salleo lab and lead author of the paper. “Instead of simulating a neural network, our work is trying to make a neural network.”

The artificial synapse is based off a battery design. It consists of two thin, flexible films with three terminals, connected by an electrolyte of salty water. The device works as a transistor, with one of the terminals controlling the flow of electricity between the other two.

Like a neural path in a brain being reinforced through learning, the researchers program the artificial synapse by discharging and recharging it repeatedly. Through this training, they have been able to predict within 1 percent of uncertainly what voltage will be required to get the synapse to a specific electrical state and, once there, it remains at that state. In other words, unlike a common computer, where you save your work to the hard drive before you turn it off, the artificial synapse can recall its programming without any additional actions or parts.

Testing a network of artificial synapses

Only one artificial synapse has been produced but researchers at Sandia used 15,000 measurements from experiments on that synapse to simulate how an array of them would work in a neural network. They tested the simulated network’s ability to recognize handwriting of digits 0 through 9. Tested on three datasets, the simulated array was able to identify the handwritten digits with an accuracy between 93 to 97 percent.

Although this task would be relatively simple for a person, traditional computers have a difficult time interpreting visual and auditory signals.

“More and more, the kinds of tasks that we expect our computing devices to do require computing that mimics the brain because using traditional computing to perform these tasks is becoming really power hungry,” said A. Alec Talin, distinguished member of technical staff at Sandia National Laboratories in Livermore, California, and senior author of the paper. “We’ve demonstrated a device that’s ideal for running these type of algorithms and that consumes a lot less power.”

This device is extremely well suited for the kind of signal identification and classification that traditional computers struggle to perform. Whereas digital transistors can be in only two states, such as 0 and 1, the researchers successfully programmed 500 states in the artificial synapse, which is useful for neuron-type computation models. In switching from one state to another they used about one-tenth as much energy as a state-of-the-art computing system needs in order to move data from the processing unit to the memory.

This, however, means they are still using about 10,000 times as much energy as the minimum a biological synapse needs in order to fire. The researchers are hopeful that they can attain neuron-level energy efficiency once they test the artificial synapse in smaller devices.

Organic potential

Every part of the device is made of inexpensive organic materials. These aren’t found in nature but they are largely composed of hydrogen and carbon and are compatible with the brain’s chemistry. Cells have been grown on these materials and they have even been used to make artificial pumps for neural transmitters. The voltages applied to train the artificial synapse are also the same as those that move through human neurons.

All this means it’s possible that the artificial synapse could communicate with live neurons, leading to improved brain-machine interfaces. The softness and flexibility of the device also lends itself to being used in biological environments. Before any applications to biology, however, the team plans to build an actual array of artificial synapses for further research and testing.

Additional Stanford co-authors of this work include co-lead author Ewout Lubberman, also of the University of Groningen in the Netherlands, Scott T. Keene and Grégorio C. Faria, also of Universidade de São Paulo, in Brazil. Sandia National Laboratories co-authors include Elliot J. Fuller and Sapan Agarwal in Livermore and Matthew J. Marinella in Albuquerque, New Mexico. Salleo is an affiliate of the Stanford Precourt Institute for Energy and the Stanford Neurosciences Institute. Van de Burgt is now an assistant professor in microsystems and an affiliate of the Institute for Complex Molecular Studies (ICMS) at Eindhoven University of Technology in the Netherlands.

This research was funded by the National Science Foundation, the Keck Faculty Scholar Funds, the Neurofab at Stanford, the Stanford Graduate Fellowship, Sandia’s Laboratory-Directed Research and Development Program, the U.S. Department of Energy, the Holland Scholarship, the University of Groningen Scholarship for Excellent Students, the Hendrik Muller National Fund, the Schuurman Schimmel-van Outeren Foundation, the Foundation of Renswoude (The Hague and Delft), the Marco Polo Fund, the Instituto Nacional de Ciência e Tecnologia/Instituto Nacional de Eletrônica Orgânica in Brazil, the Fundação de Amparo à Pesquisa do Estado de São Paulo and the Brazilian National Council.

Here’s an abstract for the researchers’ paper (link to paper provided after abstract) and it’s where you’ll find the memristor connection explained,

The brain is capable of massively parallel information processing while consuming only ~1–100fJ per synaptic event1, 2. Inspired by the efficiency of the brain, CMOS-based neural architectures3 and memristors4, 5 are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10pJ for 103μm2 devices), displays >500 distinct, non-volatile conductance states within a ~1V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems6, 7. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

Here’s a link to and a citation for the paper,

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing by Yoeri van de Burgt, Ewout Lubberman, Elliot J. Fuller, Scott T. Keene, Grégorio C. Faria, Sapan Agarwal, Matthew J. Marinella, A. Alec Talin, & Alberto Salleo. Nature Materials (2017) doi:10.1038/nmat4856 Published online 20 February 2017

This paper is behind a paywall.

ETA March 8, 2017 10:28 PST: You may find this this piece on ferroelectricity and neuromorphic engineering of interest (March 7, 2017 posting titled: Ferroelectric roadmap to neuromorphic computing).

Algorithms in decision-making: a government inquiry in the UK

Yesterday’s (Feb. 28, 2017) posting about the newly launched Cascadia Urban Analytics Cooperative grew too big to include interesting tidbits such as this one from Sense about Science, (from a Feb. 28, 2017 announcement received via email),

The House of Commons science and technology select committee announced
today that it will launch an inquiry into the use of algorithms in
decision-making […].

Our campaigns and policy officer Dr Stephanie Mathisen brought this
important and under-scrutinised issue to the committee as part of their
#MyScienceInquiry initiative; so fantastic news that they are taking up
the call.

A Feb. 28, 2017 UK House of Commons Science and Technology Select Committee press release gives more details about the inquiry,

The Science and Technology Committee is launching a new inquiry into the use of algorithms in public and business decision making.

In an increasingly digital world, algorithms are being used to make decisions in a growing range of contexts. From decisions about offering mortgages and credit cards to sifting job applications and sentencing criminals, the impact of algorithms is far reaching.

How an algorithm is formulated, its scope for error or correction, the impact it may have on an individual—and their ability to understand or challenge that decision—are increasingly relevant questions.

This topic was pitched to the Committee by Dr Stephanie Mathisen (Sense about Science) through the Committee’s ‘My Science Inquiry’ open call for inquiry suggestions, and has been chosen as the first subject for the Committee’s attention following that process. It follows the Committee’s recent work on Robotics and AI, and its call for a standing Commission on Artificial Intelligence.

Submit written evidence

The Committee would welcome written submissions by Friday 21 April 2017 on the following points:

  • The extent of current and future use of algorithms in decision-making in Government and public bodies, businesses and others, and the corresponding risks and opportunities;
  • Whether ‘good practice’ in algorithmic decision-making can be identified and spread, including in terms of:
    —  The scope for algorithmic decision-making to eliminate, introduce or amplify biases or discrimination, and how any such bias can be detected and overcome;
    — Whether and how algorithmic decision-making can be conducted in a ‘transparent’ or ‘accountable’ way, and the scope for decisions made by an algorithm to be fully understood and challenged;
    — DThe implications of increased transparency in terms of copyright and commercial sensitivity, and protection of an individual’s data;
  • Methods for providing regulatory oversight of algorithmic decision-making, such as the rights described in the EU General Data Protection Regulation 2016.

The Committee would welcome views on the issues above, and submissions that illustrate how the issues vary by context through case studies of the use of algorithmic decision-making.

You can submit written evidence through the algorithms in decision-making inquiry page.

I looked at the submission form and while it assumes the submitter is from the UK, there doesn’t seem to be any impediment to citizens of other countries from making a submission. Since there is some personal information included as part of the submission, there is a note about data protection on the Guidance on giving evidence to a Select Committee of the House of Commons webpage.

Essays on Frankenstein

Slate.com is dedicating a month (January 2017) to Frankenstein. This means there were will be one or more essays each week on one aspect or another of Frankenstein and science. These essays are one of a series of initiatives jointly supported by Slate, Arizona State University, and an organization known as New America. It gets confusing since these essays are listed as part of two initiatives:  Futurography and Future Tense.

The really odd part, as far as I’m concerned, is that there is no mention of Arizona State University’s (ASU) The Frankenstein Bicentennial Project (mentioned in my Oct. 26, 2016 posting). Perhaps they’re concerned that people will think ASU is advertising the project?

Introductions

Getting back to the essays, a Jan. 3, 2017 article by Jacob Brogan explains, by means of a ‘Question and Answer’ format article, why the book and the monster maintain popular interest after two centuries (Note: We never do find out who or how many people are supplying the answers),

OK, fine. I get that this book is important, but why are we talking about it in a series about emerging technology?

Though people still tend to weaponize it as a simple anti-scientific screed, Frankenstein, which was first published in 1818, is much richer when we read it as a complex dialogue about our relationship to innovation—both our desire for it and our fear of the changes it brings. Mary Shelley was just a teenager when she began to compose Frankenstein, but she was already grappling with our complex relationship to new forces. Almost two centuries on, the book is just as propulsive and compelling as it was when it was first published. That’s partly because it’s so thick with ambiguity—and so resistant to easy interpretation.

Is it really ambiguous? I mean, when someone calls something frankenfood, they aren’t calling it “ethically ambiguous food.”

It’s a fair point. For decades, Frankenstein has been central to discussions in and about bioethics. Perhaps most notably, it frequently crops up as a reference point in discussions of genetically modified organisms, where the prefix Franken- functions as a sort of convenient shorthand for human attempts to meddle with the natural order. Today, the most prominent flashpoint for those anxieties is probably the clustered regularly interspaced short palindromic repeats, or CRISPR, gene-editing technique [emphasis mine]. But it’s really oversimplifying to suggest Frankenstein is a cautionary tale about monkeying with life.

As we’ll see throughout this month on Futurography, it’s become a lens for looking at the unintended consequences of things like synthetic biology, animal experimentation, artificial intelligence, and maybe even social networking. Facebook, for example, has arguably taken on a life of its own, as its algorithms seem to influence the course of elections. Mark Zuckerberg, who’s sometimes been known to disavow the power of his own platform, might well be understood as a Frankensteinian figure, amplifying his creation’s monstrosity by neglecting its practical needs.

But this book is almost 200 years old! Surely the actual science in it is bad.

Shelley herself would probably be the first to admit that the science in the novel isn’t all that accurate. Early in the novel, Victor Frankenstein meets with a professor who castigates him for having read the wrong works of “natural philosophy.” Shelley’s protagonist has mostly been studying alchemical tomes and otherwise fantastical works, the sort of things that were recognized as pseudoscience, even by the standards of the day. Near the start of the novel, Frankenstein attends a lecture in which the professor declaims on the promise of modern science. He observes that where the old masters “promised impossibilities and performed nothing,” the new scientists achieve far more in part because they “promise very little; they know that metals cannot be transmuted and that the elixir of life is a chimera.”

Is it actually about bad science, though?

Not exactly, but it has been read as a story about bad scientists.

Ultimately, Frankenstein outstrips his own teachers, of course, and pulls off the very feats they derided as mere fantasy. But Shelley never seems to confuse fact and fiction, and, in fact, she largely elides any explanation of how Frankenstein pulls off the miraculous feat of animating dead tissue. We never actually get a scene of the doctor awakening his creature. The novel spends far more dwelling on the broader reverberations of that act, showing how his attempt to create one life destroys countless others. Read in this light, Frankenstein isn’t telling us that we shouldn’t try to accomplish new things, just that we should take care when we do.

This speaks to why the novel has stuck around for so long. It’s not about particular scientific accomplishments but the vagaries of scientific progress in general.

Does that make it into a warning against playing God?

It’s probably a mistake to suggest that the novel is just a critique of those who would usurp the divine mantle. Instead, you can read it as a warning about the ways that technologists fall short of their ambitions, even in their greatest moments of triumph.

Look at what happens in the novel: After bringing his creature to life, Frankenstein effectively abandons it. Later, when it entreats him to grant it the rights it thinks it deserves, he refuses. Only then—after he reneges on his responsibilities—does his creation really go bad. We all know that Frankenstein is the doctor and his creation is the monster, but to some extent it’s the doctor himself who’s made monstrous by his inability to take responsibility for what he’s wrought.

I encourage you to read Brogan’s piece in its entirety and perhaps supplement the reading. Mary Shelley has a pretty interesting history. She ran off with Percy Bysshe Shelley who was married to another woman, in 1814  at the age of seventeen years. Her parents were both well known and respected intellectuals and philosophers, William Godwin and Mary Wollstonecraft. By the time Mary Shelley wrote her book, her first baby had died and she had given birth to a second child, a boy.  Percy Shelley was to die a few years later as was her son and a third child she’d given birth to. (Her fourth child born in 1819 did survive.) I mention the births because one analysis I read suggests the novel is also a commentary on childbirth. In fact, the Frankenstein narrative has been examined from many perspectives (other than science) including feminism and LGBTQ studies.

Getting back to the science fiction end of things, the next part of the Futurography series is titled “A Cheat-Sheet Guide to Frankenstein” and that too is written by Jacob Brogan with a publication date of Jan. 3, 2017,

Key Players

Marilyn Butler: Butler, a literary critic and English professor at the University of Cambridge, authored the seminal essay “Frankenstein and Radical Science.”

Jennifer Doudna: A professor of chemistry and biology at the University of California, Berkeley, Doudna helped develop the CRISPR gene-editing technique [emphasis mine].

Stephen Jay Gould: Gould is an evolutionary biologist and has written in defense of Frankenstein’s scientific ambitions, arguing that hubris wasn’t the doctor’s true fault.

Seán Ó hÉigeartaigh: As executive director of the Center for Existential Risk at the University of Cambridge, hÉigeartaigh leads research into technologies that threaten the existience of our species.

Jim Hightower: This columnist and activist helped popularize the term frankenfood to describe genetically modified crops.

Mary Shelley: Shelley, the author of Frankenstein, helped create science fiction as we now know it.

J. Craig Venter: A leading genomic researcher, Venter has pursued a variety of human biotechnology projects.

Lingo

….

Debates

Popular Culture

Further Reading

….

‘Franken’ and CRISPR

The first essay is in a Jan. 6, 2016 article by Kay Waldman focusing on the ‘franken’ prefix (Note: links have been removed),

In a letter to the New York Times on June 2, 1992, an English professor named Paul Lewis lopped off the top of Victor Frankenstein’s surname and sewed it onto a tomato. Railing against genetically modified crops, Lewis put a new generation of natural philosophers on notice: “If they want to sell us Frankenfood, perhaps it’s time to gather the villagers, light some torches and head to the castle,” he wrote.

William Safire, in a 2000 New York Times column, tracked the creation of the franken- prefix to this moment: an academic channeling popular distrust of science by invoking the man who tried to improve upon creation and ended up disfiguring it. “There’s no telling where or how it will end,” he wrote wryly, referring to the spread of the construction. “It has enhanced the sales of the metaphysical novel that Ms. Shelley’s husband, the poet Percy Bysshe Shelley, encouraged her to write, and has not harmed sales at ‘Frank’n’Stein,’ the fast-food chain whose hot dogs and beer I find delectably inorganic.” Safire went on to quote the American Dialect Society’s Laurence Horn, who lamented that despite the ’90s flowering of frankenfruits and frankenpigs, people hadn’t used Frankensense to describe “the opposite of common sense,” as in “politicians’ motivations for a creatively stupid piece of legislation.”

A year later, however, Safire returned to franken- in dead earnest. In an op-ed for the Times avowing the ethical value of embryonic stem cell research, the columnist suggested that a White House conference on bioethics would salve the fears of Americans concerned about “the real dangers of the slippery slope to Frankenscience.”

All of this is to say that franken-, the prefix we use to talk about human efforts to interfere with nature, flips between “funny” and “scary” with ease. Like Shelley’s monster himself, an ungainly patchwork of salvaged parts, it can seem goofy until it doesn’t—until it taps into an abiding anxiety that technology raises in us, a fear of overstepping.

Waldman’s piece hints at how language can shape discussions while retaining a rather playful quality.

This series looks to be a good introduction while being a bit problematic in spots, which roughly sums up my conclusion about their ‘nano’ series in my Oct. 7, 2016 posting titled: Futurography’s nanotechnology series: a digest.

By the way, I noted the mention of CRISPR as it brought up an issue that they don’t appear to be addressing in this series (perhaps they will do this elsewhere?): intellectual property.

There’s a patent dispute over CRISPR as noted in this American Chemical Society’s Chemistry and Engineering News Jan. 9, 2017 video,

Playing God

This series on Frankenstein is taking on other contentious issues. A perennial favourite is ‘playing God’ as noted in Bina Venkataraman’s Jan. 11, 2017 essay on the topic,

Since its publication nearly 200 years ago, Shelley’s gothic novel has been read as a cautionary tale of the dangers of creation and experimentation. James Whale’s 1931 film took the message further, assigning explicitly the hubris of playing God to the mad scientist. As his monster comes to life, Dr. Frankenstein, played by Colin Clive, triumphantly exclaims: “Now I know what it feels like to be God!”

The admonition against playing God has since been ceaselessly invoked as a rhetorical bogeyman. Secular and religious, critic and journalist alike have summoned the term to deride and outright dismiss entire areas of research and technology, including stem cells, genetically modified crops, recombinant DNA, geoengineering, and gene editing. As we near the two-century commemoration of Shelley’s captivating story, we would be wise to shed this shorthand lesson—and to put this part of the Frankenstein legacy to rest in its proverbial grave.

The trouble with the term arises first from its murkiness. What exactly does it mean to play God, and why should we find it objectionable on its face? All but zealots would likely agree that it’s fine to create new forms of life through selective breeding and grafting of fruit trees, or to use in-vitro fertilization to conceive life outside the womb to aid infertile couples. No one objects when people intervene in what some deem “acts of God,” such as earthquakes, to rescue victims and provide relief. People get fully behind treating patients dying of cancer with “unnatural” solutions like chemotherapy. Most people even find it morally justified for humans to mete out decisions as to who lives or dies in the form of organ transplant lists that prize certain people’s survival over others.

So what is it—if not the imitation of a deity or the creation of life—that inspires people to invoke the idea of “playing God” to warn against, or even stop, particular technologies? A presidential commission charged in the early 1980s with studying the ethics of genetic engineering of humans, in the wake of the recombinant DNA revolution, sheds some light on underlying motivations. The commission sought to understand the concerns expressed by leaders of three major religious groups in the United States—representing Protestants, Jews, and Catholics—who had used the phrase “playing God” in a 1980 letter to President Jimmy Carter urging government oversight. Scholars from the three faiths, the commission concluded, did not see a theological reason to flat-out prohibit genetic engineering. Their concerns, it turned out, weren’t exactly moral objections to scientists acting as God. Instead, they echoed those of the secular public; namely, they feared possible negative effects from creating new human traits or new species. In other words, the religious leaders who called recombinant DNA tools “playing God” wanted precautions taken against bad consequences but did not inherently oppose the use of the technology as an act of human hubris.

She presents an interesting argument and offers this as a solution,

The lesson for contemporary science, then, is not that we should cease creating and discovering at the boundaries of current human knowledge. It’s that scientists and technologists ought to steward their inventions into society, and to more rigorously participate in public debate about their work’s social and ethical consequences. Frankenstein’s proper legacy today would be to encourage researchers to address the unsavory implications of their technologies, whether it’s the cognitive and social effects of ubiquitous smartphone use or the long-term consequences of genetically engineered organisms on ecosystems and biodiversity.

Some will undoubtedly argue that this places an undue burden on innovators. Here, again, Shelley’s novel offers a lesson. Scientists who cloister themselves as Dr. Frankenstein did—those who do not fully contemplate the consequences of their work—risk later encounters with the horror of their own inventions.

At a guess, Venkataraman seems to be assuming that if scientists communicate and make their case that the public will cease to panic with reference moralistic and other concerns. My understanding is that social scientists have found this is not the case. Someone may understand the technology quite well and still oppose it.

Frankenstein and anti-vaxxers

The Jan. 16, 2017 essay by Charles Kenny is the weakest of the lot, so far (Note: Links have been removed),

In 1780, University of Bologna physician Luigi Galvani found something peculiar: When he applied an electric current to the legs of a dead frog, they twitched. Thirty-seven years later, Mary Shelley had Galvani’s experiments in mind as she wrote her fable of Faustian overreach, wherein Dr. Victor Frankenstein plays God by reanimating flesh.

And a little less than halfway between those two dates, English physician Edward Jenner demonstrated the efficacy of a vaccine against smallpox—one of the greatest killers of the age. Given the suspicion with which Romantic thinkers like Shelley regarded scientific progress, it is no surprise that many at the time damned the procedure as against the natural order. But what is surprising is how that suspicion continues to endure, even after two centuries of spectacular successes for vaccination. This anti-vaccination stance—which now infects even the White House—demonstrates the immense harm that can be done by excessive distrust of technological advance.

Kenny employs history as a framing device. Crudely, Galvani’s experiments led to Mary Shelley’s Frankenstein which is a fable about ‘playing God’. (Kenny seems unaware there are many other readings of and perspectives on the book.) As for his statement ” … the suspicion with which Romantic thinkers like Shelley regarded scientific progress … ,” I’m not sure how he arrived at his conclusion about Romantic thinkers. According to Richard Holmes (in his book, The Age of Wonder: How the Romantic Generation Discovered the Beauty and Terror of Science), their relationship to science was more complex. Percy Bysshe Shelley ran ballooning experiments and wrote poetry about science, which included footnotes for the literature and concepts he was referencing; John Keats was a medical student prior to his establishment as a poet; and Samuel Taylor Coleridge (The Rime of the Ancient Mariner, etc.) maintained a healthy correspondence with scientists of the day sometimes influencing their research. In fact, when you analyze the matter, you realize even scientists are, on occasion, suspicious of science.

As for the anti-vaccination wars, I wish this essay had been more thoughtful. Yes, Andrew Wakefield’s research showing a link between MMR (measles, mumps, and rubella) vaccinations and autism is a sham. However, having concerns and suspicions about technology does not render you a fool who hasn’t progressed from 18th/19th Century concerns and suspicions about science and technology. For example, vaccines are being touted for all kinds of things, the latest being a possible antidote to opiate addiction (see Susan Gados’ June 28, 2016 article for ScienceNews). Are we going to be vaccinated for everything? What happens when you keep piling vaccination on top of vaccination? Instead of a debate, the discussion has devolved to: “I’m right and you’re wrong.”

For the record, I’m grateful for the vaccinations I’ve had and the diminishment of diseases that were devastating and seem to be making a comeback with this current anti-vaccination fever. That said, I think there are some important questions about vaccines.

Kenny’s essay could have been a nuanced discussion of vaccines that have clearly raised the bar for public health and some of the concerns regarding the current pursuit of yet more vaccines. Instead, he’s been quite dismissive of anyone who questions vaccination orthodoxy.

The end of this piece

There will be more essays in Slate’s Frankenstein series but I don’t have time to digest and write commentary for all of them.

Please use this piece as a critical counterpoint to some of the series and, if I’ve done my job, you’ll critique this critique. Please do let me know if you find any errors or want to add an opinion or add your own critique in the Comments of this blog.

ETA Jan. 25, 2017: Here’s the Frankenstein webspace on Slate’s Futurography which lists all the essays in this series. It’s well worth looking at the list. There are several that were not covered here.

The mathematics of Disney’s ‘Moana’

The hit Disney movie “Moana” features stunning visual effects, including the animation of water to such a degree that it becomes a distinct character in the film. Courtesy of Walt Disney Animation Studios

Few people think to marvel over the mathematics when watching an animated feature but without mathematicians, the artists would not be able to achieve their artistic goals as a Jan. 4, 2017 news item on phys.org makes clear (Note: A link has been removed),

UCLA [University of California at Los Angeles] mathematics professor Joseph Teran, a Walt Disney consultant on animated movies since 2007, is under no illusion that artists want lengthy mathematics lessons, but many of them realize that the success of animated movies often depends on advanced mathematics.

“In general, the animators and artists at the studios want as little to do with mathematics and physics as possible, but the demands for realism in animated movies are so high,” Teran said. “Things are going to look fake if you don’t at least start with the correct physics and mathematics for many materials, such as water and snow. If the physics and mathematics are not simulated accurately, it will be very glaring that something is wrong with the animation of the material.”

Teran and his research team have helped infuse realism into several Disney movies, including “Frozen,” where they used science to animate snow scenes. Most recently, they applied their knowledge of math, physics and computer science to enliven the new 3-D computer-animated hit, “Moana,” a tale about an adventurous teenage girl who is drawn to the ocean and is inspired to leave the safety of her island on a daring journey to save her people.

A Jan. 3, 2017 UCLA news release, which originated the news item, explains in further nontechnical detail,

Alexey Stomakhin, a former UCLA doctoral student of Teran’s and Andrea Bertozzi’s, played an important role in the making of “Moana.” After earning his Ph.D. in applied mathematics in 2013, he became a senior software engineer at Walt Disney Animation Studios. Working with Disney’s effects artists, technical directors and software developers, Stomakhin led the development of the code that was used to simulate the movement of water in “Moana,” enabling it to play a role as one of the characters in the film.

“The increased demand for realism and complexity in animated movies makes it preferable to get assistance from computers; this means we have to simulate the movement of the ocean surface and how the water splashes, for example, to make it look believable,” Stomakhin explained. “There is a lot of mathematics, physics and computer science under the hood. That’s what we do.”

“Moana” has been praised for its stunning visual effects in words the mathematicians love hearing. “Everything in the movie looks almost real, so the movement of the water has to look real too, and it does,” Teran said. “’Moana’ has the best water effects I’ve ever seen, by far.”

Stomakhin said his job is fun and “super-interesting, especially when we cheat physics and step beyond physics. It’s almost like building your own universe with your own laws of physics and trying to simulate that universe.

“Disney movies are about magic, so magical things happen which do not exist in the real world,” said the software engineer. “It’s our job to add some extra forces and other tricks to help create those effects. If you have an understanding of how the real physical laws work, you can push parameters beyond physical limits and change equations slightly; we can predict the consequences of that.”

To make animated movies these days, movie studios need to solve, or nearly solve, partial differential equations. Stomakhin, Teran and their colleagues build the code that solves the partial differential equations. More accurately, they write algorithms that closely approximate the partial differential equations because they cannot be solved perfectly. “We try to come up with new algorithms that have the highest-quality metrics in all possible categories, including preserving angular momentum perfectly and preserving energy perfectly. Many algorithms don’t have these properties,” Teran said.

Stomakhin was also involved in creating the ocean’s crashing waves that have to break at a certain place and time. That task required him to get creative with physics and use other tricks. “You don’t allow physics to completely guide it,” he said.  “You allow the wave to break only when it needs to break.”

Depicting boats on waves posed additional challenges for the scientists.

“It’s easy to simulate a boat traveling through a static lake, but a boat on waves is much more challenging to simulate,” Stomakhin said. “We simulated the fluid around the boat; the challenge was to blend that fluid with the rest of the ocean. It can’t look like the boat is splashing in a little swimming pool — the blend needs to be seamless.”

Stomakhin spent more than a year developing the code and understanding the physics that allowed him to achieve this effect.

“It’s nice to see the great visual effect, something you couldn’t have achieved if you hadn’t designed the algorithm to solve physics accurately,” said Teran, who has taught an undergraduate course on scientific computing for the visual-effects industry.

While Teran loves spectacular visual effects, he said the research has many other scientific applications as well. It could be used to simulate plasmas, simulate 3-D printing or for surgical simulation, for example. Teran is using a related algorithm to build virtual livers to substitute for the animal livers that surgeons train on. He is also using the algorithm to study traumatic leg injuries.

Teran describes the work with Disney as “bread-and-butter, high-performance computing for simulating materials, as mechanical engineers and physicists at national laboratories would. Simulating water for a movie is not so different, but there are, of course, small tweaks to make the water visually compelling. We don’t have a separate branch of research for computer graphics. We create new algorithms that work for simulating wide ranges of materials.”

Teran, Stomakhin and three other applied mathematicians — Chenfanfu Jiang, Craig Schroeder and Andrew Selle — also developed a state-of-the-art simulation method for fluids in graphics, called APIC, based on months of calculations. It allows for better realism and stunning visual results. Jiang is a UCLA postdoctoral scholar in Teran’s laboratory, who won a 2015 UCLA best dissertation prize.  Schroeder is a former UCLA postdoctoral scholar who worked with Teran and is now at UC Riverside. Selle, who worked at Walt Disney Animation Studios, is now at Google.

Their newest version of APIC has been accepted for publication by the peer-reviewed Journal of Computational Physics.

“Alexey is using ideas from high-performance computing to make movies,” Teran said, “and we are contributing to the scientific community by improving the algorithm.”

Unfortunately, the paper does not seem to have been published early online so I cannot offer a link.

Final comment, it would have been interesting to have had a comment from one of the film’s artists or animators included in the article but it may not have been possible due to time or space constraints.

Removing gender-based stereotypes from algorithms

Most people don’t think of algorithms as having biases and stereotypes but Michael Zou in his Sept. 26, 2016 essay for The Conversation (h/t phys.org Sept. 26, 2016 news item) says different, Note: Links have been removed,

Machine learning is ubiquitous in our daily lives. Every time we talk to our smartphones, search for images or ask for restaurant recommendations, we are interacting with machine learning algorithms. They take as input large amounts of raw data, like the entire text of an encyclopedia, or the entire archives of a newspaper, and analyze the information to extract patterns that might not be visible to human analysts. But when these large data sets include social bias, the machines learn that too.

A machine learning algorithm is like a newborn baby that has been given millions of books to read without being taught the alphabet or knowing any words or grammar. The power of this type of information processing is impressive, but there is a problem. When it takes in the text data, a computer observes relationships between words based on various factors, including how often they are used together.

We can test how well the word relationships are identified by using analogy puzzles. Suppose I ask the system to complete the analogy “He is to King as She is to X.” If the system comes back with “Queen,” then we would say it is successful, because it returns the same answer a human would.

Our research group trained the system on Google News articles, and then asked it to complete a different analogy: “Man is to Computer Programmer as Woman is to X.” The answer came back: “Homemaker.”

Zou explains how a machine (algorithm) learns and then notes this,

Not only can the algorithm reflect society’s biases – demonstrating how much those biases are contained in the input data – but the system can potentially amplify gender stereotypes. Suppose I search for “computer programmer” and the search program uses a gender-biased database that associates that term more closely with a man than a woman.

The search results could come back flawed by the bias. Because “John” as a male name is more closely related to “computer programmer” than the female name “Mary” in the biased data set, the search program could evaluate John’s website as more relevant to the search than Mary’s – even if the two websites are identical except for the names and gender pronouns.

It’s true that the biased data set could actually reflect factual reality – perhaps there are more “Johns” who are programmers than there are “Marys” – and the algorithms simply capture these biases. This does not absolve the responsibility of machine learning in combating potentially harmful stereotypes. The biased results would not just repeat but could even boost the statistical bias that most programmers are male, by moving the few female programmers lower in the search results. It’s useful and important to have an alternative that’s not biased.

There is a way according to Zou that stereotypes can be removed,

Our debiasing system uses real people to identify examples of the types of connections that are appropriate (brother/sister, king/queen) and those that should be removed. Then, using these human-generated distinctions, we quantified the degree to which gender was a factor in those word choices – as opposed to, say, family relationships or words relating to royalty.

Next we told our machine-learning algorithm to remove the gender factor from the connections in the embedding. This removes the biased stereotypes without reducing the overall usefulness of the embedding.

When that is done, we found that the machine learning algorithm no longer exhibits blatant gender stereotypes. We are investigating applying related ideas to remove other types of biases in the embedding, such as racial or cultural stereotypes.

If you have time, I encourage you to read the essay in its entirety and this June 14, 2016 posting about research into algorithms and how they make decisions for you about credit, medical diagnoses, job opportunities and more.

There’s also an Oct. 24, 2016 article by Michael Light on Salon.com on the topic (Note: Links have been removed),

In a recent book that was longlisted for the National Book Award, Cathy O’Neil, a data scientist, blogger and former hedge-fund quant, details a number of flawed algorithms to which we have given incredible power — she calls them “Weapons of Math Destruction.” We have entrusted these WMDs to make important, potentially life-altering decisions, yet in many cases, they embed human race and class biases; in other cases, they don’t function at all.
Among other examples, O’Neil examines a “value-added” model New York City used to decide which teachers to fire, even though, she writes, the algorithm was useless, functioning essentially as a random number generator, arbitrarily ending careers. She looks at models put to use by judges to assign recidivism scores to inmates that ended up having a racist inclination. And she looks at how algorithms are contributing to American partisanship, allowing political operatives to target voters with information that plays to their existing biases and fears.

I recommend reading Light’s article in its entirety.

Will AI ‘artists’ be able to fool a panel judging entries the Neukom Institute Prizes in Computational Arts?

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 [2016] 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,

David Cope (Composer, Algorithmic Music Pioneer, UCSC Music Professor)
David Krakauer (President, the Santa Fe Institute)
Louis Menand (Pulitzer Prize winning author and Professor at Harvard University)
Ray Monk (Author, Biographer, Professor of Philosophy)
Lynn Neary (NPR: Correspondent, Arts Desk and Guest Host)
Joe Palca (NPR: Correspondent, Science Desk)
Robert Siegel (NPR: Senior Host, All Things Considered)

The announcements will be made Wednesday, May 18, 2016. I can hardly wait!

Addendum

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.
Advertisement

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?

Are they just computer games or are we in a race with technology?

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.

An April 13, 2016 Aarhus University press release (also on EurekAlert) by Rasmus Rørbæk, which originated the news item, further develops the point,

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

This paper is behind a paywall.

AI assistant makes scientific discovery at Tufts University (US)

In light of this latest research from Tufts University, I thought it might be interesting to review the “algorithms, artificial intelligence (AI), robots, and world of work” situation before moving on to Tufts’ latest science discovery. My Feb. 5, 2015 post provides a roundup of sorts regarding work and automation. For those who’d like the latest, there’s a May 29, 2015 article by Sophie Weiner for Fast Company, featuring a predictive interactive tool designed by NPR (US National Public Radio) based on data from Oxford University researchers, which tells you how likely automating your job could be, no one knows for sure, (Note: A link has been removed),

Paralegals and food service workers: the robots are coming.

So suggests this interactive visualization by NPR. The bare-bones graphic lets you select a profession, from tellers and lawyers to psychologists and authors, to determine who is most at risk of losing their jobs in the coming robot revolution. From there, it spits out a percentage. …

You can find the interactive NPR tool here. I checked out the scientist category (in descending order of danger: Historians [43.9%], Economists, Geographers, Survey Researchers, Epidemiologists, Chemists, Animal Scientists, Sociologists, Astronomers, Social Scientists, Political Scientists, Materials Scientists, Conservation Scientists, and Microbiologists [1.2%]) none of whom seem to be in imminent danger if you consider that bookkeepers are rated at  97.6%.

Here at last is the news from Tufts (from a June 4, 2015 Tufts University news release, also on EurekAlert),

An artificial intelligence system has for the first time reverse-engineered the regeneration mechanism of planaria–the small worms whose extraordinary power to regrow body parts has made them a research model in human regenerative medicine.

The discovery by Tufts University biologists presents the first model of regeneration discovered by a non-human intelligence and the first comprehensive model of planarian regeneration, which had eluded human scientists for over 100 years. The work, published in PLOS Computational Biology, demonstrates how “robot science” can help human scientists in the future.

To mine the fast-growing mountain of published experimental data in regeneration and developmental biology Lobo and Levin developed an algorithm that would use evolutionary computation to produce regulatory networks able to “evolve” to accurately predict the results of published laboratory experiments that the researchers entered into a database.

“Our goal was to identify a regulatory network that could be executed in every cell in a virtual worm so that the head-tail patterning outcomes of simulated experiments would match the published data,” Lobo said.

The paper represents a successful application of the growing field of “robot science” – which Levin says can help human researchers by doing much more than crunch enormous datasets quickly.

“While the artificial intelligence in this project did have to do a whole lot of computations, the outcome is a theory of what the worm is doing, and coming up with theories of what’s going on in nature is pretty much the most creative, intuitive aspect of the scientist’s job,” Levin said. “One of the most remarkable aspects of the project was that the model it found was not a hopelessly-tangled network that no human could actually understand, but a reasonably simple model that people can readily comprehend. All this suggests to me that artificial intelligence can help with every aspect of science, not only data mining but also inference of meaning of the data.”

Here’s a link to and a citation for the paper,

Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration by Daniel Lobo and Michael Levin. PLOS (Computational Biology) DOI: DOI: 10.1371/journal.pcbi.1004295 Published: June 4, 2015

This paper is open access.

It will be interesting to see if attributing the discovery to an algorithm sets off criticism suggesting that the researchers overstated the role the AI assistant played.