As Torrance observes: '[c]reativity defies precise definition... even if we had a precise conception of creativity, I am certain we would have difficulty putting it into words' [15, p. 43]. Many other authors have expressed similar difficulties [7, 10, 16]. In their review of research into human creativity, Hennessey and Amabile ask a significant follow-on question: 'Even if this mysterious phenomenon can be isolated, quantified, and dissected, why bother? Wouldn't it make more sense to revel in the mystery and wonder of it all?' [11, p. 570] Two answers to this question are offered by Hennessey and Amabile, both of which are identified as desirable: to gain a deeper understanding of creativity and to learn how to boost people's creativity. Creativity can and should be studied and measured scientifically, but the lack of a commonly-agreed understanding causes problems for measurement [10]. Plucker et al. make recommendations about best practice based on their own survey of the creativity literature: 'we argue that creativity researchers must (a) explicitly define what they mean by creativity, (b) avoid using scores of creativity measures as the sole definition of creativity (e.g., creativity is what creativity tests measure and creativity tests measure creativity, therefore we will use a score on a creativity test as our outcome variable), (c) discuss how the definition they are using is similar to or different from other definitions, and (d) address the question of creativity for whom and in what context.' [9, p.92] In short, we need to specify and justify the standards that we use to judge creativity. A more objective and well-articulated account of how creativity is manifested enables researchers to make a worthwhile contribution [8-10]. Particularly, in research we would like to focus on what processes and concepts relevant to creativity are'sufficiently important to warrant study' [17, p. 15], based on an accumulation of the body of work on creativity to date [17].

Google's WaveNet uses neural nets to generate eerily convincing speech and music http://tcrn.ch/2ccjLjY Physicists have discovered what makes neural networks so extraordinarily powerful http://bit.ly/2cikJeB Will This "Neural Lace" Brain Implant Help Us Compete with AI? http://bit.ly/2c75cPH These nightmare videos are generated from still baby photos by a neural network http://gizmo.do/G476Lyi Mathematicians have been searching, but the answer lies in physics.

Facebook continues its efforts to create artificial intelligence capable of outclassing all humans at the ancient Chinese strategy board game Go. The social media company recently published a research paper showcasing the progress it made with the DarkForest bots, which use a synergy of methods to be the best Go players available. Yuandong Tian and Yan Zhu, AI researchers at Facebook, explain how the computer program behaves in the abstract of the paper. "Against human players, [darkfores2 achieves] a stable 3d level on KGS Go Server as a ranked bot," the duo points out [pdf]. This is a visible improvement over the predicted 4k-5k ranks for DCNN that Clark & Storkey (2015) reported after studying matches against other machine players.

Watson APIs are now available for public use, albeit only through IBM's Bluemix cloud services platform. IBM's Watson Developer Cloud now offers eight services for building what IBM describes as cognitive apps, with more services promised later on. The Relationship Extraction system seems less limited by available data than Machine Translation, but it is limited in different ways. When the Relationship Extraction system is fed the sentence "Nick Cave's new film '20,000 Days on Earth' debuted yesterday," it understood that "Nick Cave" was a person and that "yesterday" was a date, but didn't understand that "20,000 Days" referred to the title of a work.

Those who have been chomping at the bit to use IBM's Watson machine-intelligence service with their apps need gnaw no longer. Watson APIs are now available for public use, albeit only through IBM's Bluemix cloud services platform. IBM's Watson Developer Cloud now offers eight services for building what IBM describes as cognitive apps, with more services promised later on. Of the services offered so far, Visualization Rendering seems the most immediately useful and powerful, since it isn't limited by data training many of Watson's other services rely on. Most of the services rely on a "corpus," or cultivated body of data that Watson can use as raw material, so the breadth of several Watson offerings is limited by the size of their existing corpora.

Although deep neural networks (DNNs) have achieved impressive performance in several non-trivial machine learning (ML) tasks, many challenges remain. One class of challenges has to do with understanding how and why DNNs obtain their improved performance, given that they do not exhibit properties such as convexity that are common among ML methods. A second class of challenges has to do with using this understanding to develop DNN methods that have better statistical/inferential properties and/or better algorithmic/running time properties. In this work, the team will pursue two related directions. First, to develop a model to make theoretically precise certain intuitions that might explain the performance of DNNs.

The AI startup landscape report has been divided into two sub-sectors -- infrastructure companies, which provide cloud infrastructure, algorithms, libraries for creating AI based applications. Examples of infrastructure services include AI as a service (Data Robot, Sentient), NLP as a service (Nuance, SpinVox), and computer vision as a service (Face). The second sub-sector -- AI applications looks at companies developing applications based on artificial intelligence technologies for different verticals. These include companies leverage AI techniques to build applications tailored for end use in enterprise (Palantir, Dataminr, Cylance), industry (ButterFly Network, Zest Finance) and consumer (X.ai, Anki) markets. Over 1.2B has been invested in AI infrastructure startups since 2010 with 540M being invested in 2015-16.

A clever hack by Alexander Crosson and Naveen Kulandaivelu at today's TechCrunch Disrupt SF Hackathon may be giving us tech journalists early warning signs of our forthcoming replacement by AI. While nowhere near advanced enough to conduct independent due diligence or investigative reporting, their project, CrunchLetter, is aimed squarely at automating the way we get news. Newsletters from publications like TechCrunch and StrictlyVC, as well as platforms like CrunchBase, are painstakingly assembled by hand, but the team's machine learning experiment leveraging Google's Tensor Flow is already able to generate rudimentary venture capital deal summaries. The tool uses unsupervised machine learning to analyze the CrunchBase data set, alongside articles from major VC publications, to generate newsletters for folks following the startup funding beat. During the team's presentation, the two showed a work in progress that could assemble a 30 word summary of a funding round.

When you're a firefighter, your life is always on the brink of danger. Now, NASA has developed an artificial intelligence system called AUDREY that could help save the lives of firefighters by nagivating them in smoke-filled burning establishments. Meet AUDREY (Assistant for Understanding Data through Reasoning, Extraction, and sYnthesis), an artificial intelligence system being developed by NASA's Jet Propulsion Laboratory. According to a report from Smithsonian, AUDREY is a technology that tracks the firefighters movements and guides them to safety during rescue. The technology originated from the Jet Propulsion Laboratory's work on space rovers kused on Mars.

The session is comprised of a panel discussion with leading researchers debating the current scientific trends in AI and its applications. That is followed by a broader discussion that dives into how the developments in AI affect our lives and society. The Heidelberg Laureate Forum Foundation (HLFF) is driven to foster the opportunity for progressive discourse, and the Hot Topic session is a crucial component of that goal. Today, AI is no longer a brash, cryptic concept taken directly from the pages of science fiction. The developments owed to the technology based on AI have altered what we thought possible and has done so in a much quicker fashion than was predicted.