And what kinds of accused offenders could this facial recognition trawl for? It's one thing to set body cameras to identify wanted violent offenders that come into view of body cameras; it's another to scan the streets for those accused of petty offenses. Many cities have sizable numbers of active arrest warrants for minor crimes. A judge has since withdrawn thousands.) Facial recognition software could give police officers unprecedented abilities to exercise "arrest at will" authority over a large proportion of the population. It's not hard to imagine how, without curbs on its use, police could abuse this power against protesters, minorities, or others an individual officer could have bias against.

As the presence of artificial intelligence continues to grow in the world, industry leaders and scholars are starting to explore the ethics surrounding the science. As the presence of artificial intelligence continues to grow in the world, industry leaders and scholars are starting to explore the ethics surrounding the science. In 1941, science-fiction writer Isaac Asimov stated "The Three Laws of Robotics," in his short story "Runaround." Law One: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Law Two: A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

Very few applications and devices so far are truly intelligent. However, innovation in the field of machine intelligence is high and accelerating rapidly. We're approaching a new age in which there actually will be an intelligent assistant for every part of our lives and, accordingly, the future digital landscape will look very different. When Dag Kittlaus (creator of Siri and founder of Viv) demonstrated Viv's new speech-based user interface in May of this year, we were given a glimpse into a future with radically different dynamics. One of the demonstrations was booking a vacation.

Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways. Over the past several months, we and some colleagues have been trying to address these goals by crafting a set of principles for accountable algorithms.

How could they see anything but the shadows if they were never allowed to move their heads? Python and high level libraries like Scikit-learn, TensorFlow, NLTK, PyBrain, Theano, and MLPY have made machine learning accessible to a broad programming community that might never have found it otherwise. With the democratization of these tools, there is now a large, and growing, population of machine learning practitioners who are primarily self-taught. At the same time, the stakes of machine learning have never been higher; predictive tools are driving decision-making in every sector, from business, art, and engineering to education, law, and defense. How do we ensure our predictions are valid and robust in a time when these few lines of Python can instantiate and fit a model?

They generate not just answers to numerical problems, but hypotheses, reasoned arguments and recommendations about more complex -- and meaningful -- bodies of data. What's more, cognitive systems can make sense of the 80 percent of the world's data that computer scientists call "unstructured." This enables them to keep pace with the volume, complexity and unpredictability of information and systems in the modern world. None of this involves either sentience or autonomy on the part of machines. Rather, it consists of augmenting the human ability to understand -- and act upon -- the complex systems of our society. This augmented intelligence is the necessary next step in our ability to harness technology in the pursuit of knowledge, to further our expertise and to improve the human condition. That is why it represents not just a new technology, but the dawn of a new era of technology, business and society: the Cognitive Era. The success of cognitive computing will not be measured by Turing tests or a computer's ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved. It's not surprising that the public's imagination has been ignited by Artificial Intelligence since the term was first coined in 1955. In the ensuing 60 years, we have been alternately captivated by its promise, wary of its potential for abuse and frustrated by its slow development. But like so many advanced technologies that were conceived before their time, Artificial Intelligence has come to be widely misunderstood --co-opted by Hollywood, mischaracterized by the media, portrayed as everything from savior to scourge of humanity. Those of us engaged in serious information science and in its application in the real world of business and society understand the enormous potential of intelligent systems. The future of such technology -- which we believe will be cognitive, not "artificial"-- has very different characteristics from those generally attributed to AI, spawning different kinds of technological, scientific and societal challenges and opportunities, with different requirements for governance, policy and management. Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans naturally. Rather than being explicitly programmed, they learn and reason from their interactions with us and from their experiences with their environment. They are made possible by advances in a number of scientific fields over the past half-century, and are different in important ways from the information systems that preceded them. Here at IBM, we have been working on the foundations of cognitive computing technology for decades, combining more than a dozen disciplines of advanced computer science with 100 years of business expertise. Now we are seeing first hand its potential to transform businesses, governments and society.

On 20 November, 1945 the Nuremberg trials began - the military tribunals called to prosecute Nazi war criminals closely involved in the Holocaust. Now, 71 years later, that work continues through the Bavarian State criminal office (LKA) in Munich, that has created a virtual reality version of the Auschwitz concentration camp to assist with the continued prosecutions. Digital imaging expert Ralf Breker is behind the project: "We spent five days in Auschwitz taking laser scans of the buildings and the whole project to complete took about six months." About 1.1 million people, mostly Jews, were killed at Auschwitz, most deceived into entering gas chambers where cyanide-based pesticide Zyklon B was released, killing those inside. Their bodies were then burned in the camp's many crematoria.

Contributes Intel Apache Spark* Spark Users *Other names and brands may be claimed as the property of others 3. Sparse data is almost everywhere • Data Source: – Movie ratings – Purchase history • Feature engineering: – NLP: CountVectorizer, HashingTF – Categorical: OneHotEncoder – Image, video 0 1 2 3 4 5 6 7 8 9 10 0 2 4 6 8 10 Customers products Purchase History 4. Sparse data support in MLlib new DenseVector( values Array(1.0, Sparse data support in MLlib • Supporting Sparse data since v1.0 – Load / Save, Sparse Vector, LIBSVM – Supporting sparse vector is one of the primary review focus. KMeans • Pick initial cluster centers – Random – KMeans • Iterative training – Points clustering, find nearest center for each point – Re-compute center in each cluster (avg.) MLlib iteration 2. Compute a sum table for each partition of data val sum new Array[Vector](k) for (each point in the partition) { val bestCenter traverse() sum(bestCenter) point } Training dataset Executor 1 Executor 2 Executor 3 Sums: 16G Centers: 16G *Other names and brands may be claimed as the property of others 14. Analysis: Data • Are the cluster centers dense?

I don't want to tell data scientists and AI developers what to do in any given situation. I want to give scientists and engineers tools for thinking about problems. We surely can't predict all the problems and ethical issues in advance; we need to be the kind of people who can have effective discussions about these issues as we anticipate and discover them. What are some of the ethical questions that AI developers and researchers should be thinking about? Even though we're still in the earliest days of AI, we're already seeing important issues rise to the surface: issues about the kinds of people we want to be, and the kind of future we want to build.