Talent Gap Is a Main Roadblock as Agencies Eye Emerging Tech

Curated on Posted on by Stefaan Verhulst

Theo Douglas in GovTech: “U.S. public service agencies are closely eyeing emerging technologies, chiefly advanced analytics and predictive modeling, according to a new report from Accenture, but like their counterparts globally they must address talent and complexity issues before adoption rates will rise.

The report, Emerging Technologies in Public Service, compiled a nine-nation survey of IT officials across all levels of government in policing and justice, health and social services, revenue, border services, pension/Social Security and administration, and was released earlier this week.

It revealed a deep interest in emerging tech from the public sector, finding 70 percent of agencies are evaluating their potential — but a much lower adoption level, with just 25 percent going beyond piloting to implementation….

The revenue and tax industries have been early adopters of advanced analytics and predictive modeling, he said, while biometrics and video analytics are resonating with police agencies.

In Australia, the tax office found using voiceprint technology could save 75,000 work hours annually.

Closer to home, Utah Chief Technology Officer Dave Fletcher told Accenture that consolidating data centers into a virtualized infrastructure improved speed and flexibility, so some processes that once took weeks or months can now happen in minutes or hours.

Nationally, 70 percent of agencies have either piloted or implemented an advanced analytics or predictive modeling program. Biometrics and identity analytics were the next most popular technologies, with 29 percent piloting or implementing, followed by machine learning at 22 percent.

Those numbers contrast globally with Australia, where 68 percent of government agencies have charged into piloting and implementing biometric and identity analytics programs; and Germany and Singapore, where 27 percent and 57 percent of agencies respectively have piloted or adopted video analytic programs.

Overall, 78 percent of respondents said they were either underway or had implemented some machine-learning technologies.

The benefits of embracing emerging tech that were identified ranged from finding better ways of working through automation to innovating and developing new services and reducing costs.

Agencies told Accenture their No. 1 objective was increasing customer satisfaction. But 89 percent said they’d expect a return on implementing intelligent technology within two years. Four-fifths, or 80 percent, agreed intelligent tech would improve employees’ job satisfaction….(More).

The ethical impact of data science

Curated on Posted on by Stefaan Verhulst

Theme issue of Phil. Trans. R. Soc. A compiled and edited by Mariarosaria Taddeo and Luciano Floridi: “This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations—the interactions among hardware, software and data—rather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments….(More)”

Table of Contents:

  • The dynamics of big data and human rights: the case of scientific research; Effy Vayena, John Tasioulas
  • Facilitating the ethical use of health data for the benefit of society: electronic health records, consent and the duty of easy rescue; Sebastian Porsdam Mann, Julian Savulescu, Barbara J. Sahakian
  • Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions; Luciano Floridi
  • Compelling truth: legal protection of the infosphere against big data spills; Burkhard Schafer
  • Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems; Sabina Leonelli
  • Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy; Deirdre K. Mulligan, Colin Koopman, Nick Doty
  • Beyond privacy and exposure: ethical issues within citizen-facing analytics; Peter Grindrod
  • The ethics of smart cities and urban science; Rob Kitchin
  • The ethics of big data as a public good: which public? Whose good? Linnet Taylor
  • Data philanthropy and the design of the infraethics for information societies; Mariarosaria Taddeo
  • The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics; Olivia Varley-Winter, Hetan Shah
  • Data science ethics in government; Cat Drew
  • The ethics of data and of data science: an economist’s perspective; Jonathan Cave
  • What’s the good of a science platform? John Gallacher

 

Teaching an Algorithm to Understand Right and Wrong

Curated on Posted on by Stefaan Verhulst

Greg Satell at Harvard Business Review: “In his Nicomachean Ethics, Aristotle states that it is a fact that “all knowledge and every pursuit aims at some good,” but then continues, “What then do we mean by the good?” That, in essence, encapsulates the ethical dilemma. We all agree that we should be good and just, but it’s much harder to decide what that entails.

Since Aristotle’s time, the questions he raised have been continually discussed and debated. From the works of great philosophers like Kant, Bentham, andRawls to modern-day cocktail parties and late-night dorm room bull sessions, the issues are endlessly mulled over and argued about but never come to a satisfying conclusion.

Today, as we enter a “cognitive era” of thinking machines, the problem of what should guide our actions is gaining newfound importance. If we find it so difficult to denote the principles by which a person should act justly and wisely, then how are we to encode them within the artificial intelligences we are creating? It is a question that we need to come up with answers for soon.

Designing a Learning Environment

Every parent worries about what influences their children are exposed to. What TV shows are they watching? What video games are they playing? Are they hanging out with the wrong crowd at school? We try not to overly shelter our kids because we want them to learn about the world, but we don’t want to expose them to too much before they have the maturity to process it.

In artificial intelligence, these influences are called a “machine learning corpus.”For example, if you want to teach an algorithm to recognize cats, you expose it to thousands of pictures of cats and things that are not cats. Eventually, it figures out how to tell the difference between, say, a cat and a dog. Much as with human beings, it is through learning from these experiences that algorithms become useful.

However, the process can go horribly awry, as in the case of Microsoft’s Tay, aTwitter bot that the company unleashed on the microblogging platform. In under a day, Tay went from being friendly and casual (“Humans are super cool”) to downright scary (“Hitler was right and I hate Jews”). It was profoundly disturbing.

Francesca Rossi, an AI researcher at IBM, points out that we often encode principles regarding influences into societal norms, such as what age a child needs to be to watch an R-rated movie or whether they should learn evolution in school. “We need to decide to what extent the legal principles that we use to regulate humans can be used for machines,” she told me.

However, in some cases algorithms can alert us to bias in our society that we might not have been aware of, such as when we Google “grandma” and see only white faces. “There is a great potential for machines to alert us to bias,” Rossi notes. “We need to not only train our algorithms but also be open to the possibility that they can teach us about ourselves.”…

Another issue that we will have to contend with is that we will have to decide not only what ethical principles to encode in artificial intelligences but also how they are coded. As noted above, for the most part, “Thou shalt not kill” is a strict principle. Other than a few rare cases, such as the Secret Service or a soldier, it’s more like a preference that is greatly affected by context….

As pervasive as artificial intelligence is set to become in the near future, the responsibility rests with society as a whole. Put simply, we need to take the standards by which artificial intelligences will operate just as seriously as those that govern how our political systems operate and how are children are educated.

It is a responsibility that we cannot shirk….(More)

Understanding the four types of AI, from reactive robots to self-aware beings

Curated on Posted on by Stefaan Verhulst

 at The Conversation: “…We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

Type I AI: Reactive machines

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn’t have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn’t rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a “representation” of the world….

Type II AI: Limited memory

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel…;

Type III AI: Theory of mind

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

Type IV AI: Self-awareness

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it….

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences….(More)”

AI Ethics: The Future of Humanity 

Curated on Posted on by Stefaan Verhulst

Report by sparks & honey: “Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans.

This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of beauty and aesthetics to how we interact with one another – and our AI.

Infused with technology, we’re asking: what does it mean to be human?

Our report examines:

• The evolution of our empathy from humans to animals and robots
• How we treat AI in its infancy like we do a child, allowing it space to grow
• The spectrum of our emotional comfort in a world embracing AI
• The cultural contexts fueling AI biases, such as gender stereotypes, that drive the direction of AI
• How we place an innate trust in machines, more than we do one another (Download for free)”

 

Power to the People: Addressing Big Data Challenges in Neuroscience by Creating a New Cadre of Citizen Neuroscientists

Curated on Posted on by Stefaan Verhulst
Jane Roskams and Zoran Popović in Neuron: “Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire—and now a new neuroscience game, Mozak—are fueling a people-powered research science (PPRS) revolution, creating a global community of “new experts” that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain….(More)”

Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective

Curated on Posted on by Stefaan Verhulst

 et al at Peer J. Computer Science: “Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average). Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis….(More)”

Artificial Intelligence can streamline public comment for federal agencies

Curated on Posted on by Stefaan Verhulst

John Davis at the Hill: “…What became immediately clear to me was that — although not impossible to overcome — the lack of consistency and shared best practices across all federal agencies in accepting and reviewing public comments was a serious impediment. The promise of Natural Language Processing and cognitive computing to make the public comment process light years faster and more transparent becomes that much more difficult without a consensus among federal agencies on what type of data is collected – and how.

“There is a whole bunch of work we have to do around getting government to be more customer friendly and making it at least as easy to file your taxes as it is to order a pizza or buy an airline ticket,” President Obama recently said in an interview with WIRED. “Whether it’s encouraging people to vote or dislodging Big Data so that people can use it more easily, or getting their forms processed online more simply — there’s a huge amount of work to drag the federal government and state governments and local governments into the 21st century.”

…expanding the discussion around Artificial Intelligence and regulatory processes to include how the technology should be leveraged to ensure fairness and responsiveness in the very basic processes of rulemaking – in particular public notices and comments. These technologies could also enable us to consider not just public comments formally submitted to an agency, but the entire universe of statements made through social media posts, blogs, chat boards — and conceivably every other electronic channel of public communication.

Obviously, an anonymous comment on the Internet should not carry the same credibility as a formally submitted, personally signed statement, just as sworn testimony in court holds far greater weight than a grapevine rumor. But so much public discussion today occurs on Facebook pages, in Tweets, on news website comment sections, etc. Anonymous speech enjoys explicit protection under the Constitution, based on a justified expectation that certain sincere statements of sentiment might result in unfair retribution from the government.

Should we simply ignore the valuable insights about actual public sentiment on specific issues made possible through the power of Artificial Intelligence, which can ascertain meaning from an otherwise unfathomable ocean of relevant public conversations? With certain qualifications, I believe Artificial Intelligence, or AI, should absolutely be employed in the critical effort to gain insights from public comments – signed or anonymous.

“In the criminal justice system, some of the biggest concerns with Big Data are the lack of data and the lack of quality data,” the NSTC report authors state. “AI needs good data. If the data is incomplete or biased, AI can exacerbate problems of bias.” As a former federal criminal prosecutor and defense attorney, I am well familiar with the absolute necessity to weigh the relative value of various forms of evidence – or in this case, data…(More)“

Crowdsourcing Gun Violence Research

Curated on Posted on by Stefaan Verhulst

Penn Engineering: “Gun violence is often described as an epidemic, but as visible and shocking as shooting incidents are, epidemiologists who study that particular source of mortality have a hard time tracking them. The Centers for Disease Control is prohibited by federal law from conducting gun violence research, so there is little in the way of centralized infrastructure to monitor where, how,when, why and to whom shootings occur.

Chris Callison-Burch, Aravind K.Joshi Term Assistant Professor in Computer and InformationScience, and graduate studentEllie Pavlick are working to solve this problem.

They have developed the GunViolence Database, which combines machine learning and crowdsourcing techniques to produce a national registry of shooting incidents. Callison-Burch and Pavlick’s algorithm scans thousands of articles from local newspaper and television stations,determines which are about gun violence, then asks everyday people to pullout vital statistics from those articles, compiling that information into a unified, open database.

For natural language processing experts like Callison-Burch and Pavlick, the most exciting prospect of this effort is that it is training computer systems to do this kind of analysis automatically. They recently presented their work on that front at Bloomberg’s Data for Good Exchange conference.

The Gun Violence Database project started in 2014, when it became the centerpiece of Callison-Burch’s “Crowdsourcing and Human Computation”class. There, Pavlick developed a series of homework assignments that challenged undergraduates to develop a classifier that could tell whether a given news article was about a shooting incident.

“It allowed us to teach the things we want students to learn about datascience and natural language processing, while giving them the motivation to do a project that could contribute to the greater good,” says Callison-Burch.

The articles students used to train their classifiers were sourced from “TheGun Report,” a daily blog from New York Times reporters that attempted to catalog shootings from around the country in the wake of the Sandy Hook massacre. Realizing that their algorithmic approach could be scaled up to automate what the Times’ reporters were attempting, the researchers began exploring how such a database could work. They consulted with DouglasWiebe, a Associate Professor of Epidemiology in Biostatistics andEpidemiology in the Perelman School of Medicine, to learn more about what kind of information public health researchers needed to better study gun violence on a societal scale.

From there, the researchers enlisted people to annotate the articles their classifier found, connecting with them through Mechanical Turk, Amazon’scrowdsourcing platform, and their own website, http://gun-violence.org/…(More)”

The Promise of Artificial Intelligence: 70 Real-World Examples

Curated on Posted on by Stefaan Verhulst

Report by the Information Technology & Innovation Foundation: “Artificial intelligence (AI) is on a winning streak. In 2005, five teams successfully completed the DARPA Grand Challenge, a competition held by the Defense Advanced Research Projects Agency to spur development of autonomous vehicles. In 2011, IBM’s Watson system beat out two longtime human champions to win Jeopardy! In 2016, Google DeepMind’s AlphaGo system defeated the 18-time world-champion Go player. And thanks to Apple’s Siri, Microsoft’s Cortana, Google’s Google Assistant, and Amazon’s Alexa, consumers now have easy access to a variety of AI-powered virtual assistants to help manage their daily lives. The potential uses of AI to identify patterns, learn from experience, and find novel solutions to new challenges continue to grow as the technology advances.

Moreover, AI is already having a major positive impact in many different sectors of the global economy and society.  For example, humanitarian organizations are using intelligent chatbots to provide psychological support to Syrian refugees, and doctors are using AI to develop personalized treatments for cancer patients. Unfortunately, the benefits of AI, as well as its likely impact in the years ahead, are vastly underappreciated by policymakers and the public. Moreover, a contrary narrative—that AI raises grave concerns and warrants a precautionary regulatory approach to limit the damages it could cause—has gained prominence, even though it is both wrong and harmful to societal progress.

To showcase the overwhelmingly positive impact of AI, this report describes the major uses of AI and highlights 70 real-world examples of how AI is already generating social and economic benefits. Policymakers should consider these benefits as they evaluate the steps they can take to support the development and adoption of AI….(More)”