LONDON – 25 August 2016 – IP EXPO Europe, Europe's number one enterprise IT event, has today announced the addition of several influential industry speakers to this year's keynote and seminar programme. Attendees will have the opportunity to hear how key IT issues are affecting businesses and humanity alike, from Author & Founding Director of Oxford University's Future of Humanity Institute: Nick Bostrom; the'Father of Java': James Gosling; the creator of the'MySpace worm' and now an Independent Security Researcher: Samy Kamkar; and Independent Cyber Security Consultant Dr Jessica Barker. These additions to the 2016 IP EXPO Europe program are the latest in a list which already includes some of the world's most renowned technology innovators, from the likes of HPE, Microsoft, and Amazon Web Services. With his work influencing the likes of Bill Gates, Professor Stephen Hawking, and Elon Musk, keynote speaker Nick Bostrom, Author & Founding Director of Oxford University's Future of Humanity Institute, is one of the world's foremost authorities on Artificial Intelligence (AI). Opening Day One at this year's IP EXPO Europe, Bostrom will be discussing the impact that AI and intelligent machines will have on business and society, and sets out to answer the question: 'Will AI bring about the end of humanity?'

For centuries, physicians and healers focused primarily on treating acute problems such as broken bones, wounds, and infections. "If you had an infectious disease, you went to the doctor, the doctor treated you, and then you went home," says Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health. Today, the majority of healthcare revolves around treating chronic conditions such as heart disease, diabetes, and asthma. Treating chronic ailments often requires multiple visits to healthcare providers, over extended periods of time. In modern societies, "the old ways of delivering care will not work," says Krishnapuram. "We need to enable patients to take care of themselves to a far greater degree than before, and we need to move more treatment from the doctor's office or hospital to an outpatient setting or to the patient's home." Unlike traditional healthcare, which tends to be labor-intensive, emerging models of healthcare are knowledge-driven and data-intensive. Many of the newer healthcare delivery models will depend on a new generation of user-friendly, real-time big data analytics and artificial intelligence/machine learning (AI/ML) tools. Identifying risks, determining who is at risk, and identifying interventions that will reduce risk. Supporting and enabling customized self-care treatment plans for individual patients, monitoring patient health in real time, adjusting doses of medication, and providing incentives for behavioral changes leading to improved health. Optimizing healthcare processes (everything from medical treatment itself to the various ways insurers reimburse providers) through rigorous data analysis to improve outcomes and quality of care while reducing costs.

Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford University to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at The University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford's School of Engineering. AI100 formed a standing committee of scientists and charged it with commissioning reports on different aspects of AI over the ensuing century.

By the year 2030, artificial intelligence (A.I.) will have changed the way we travel to work and to parties, how we take care of our health and how our kids are educated. Focused on trying to foresee the advances coming to A.I., as well as the ethical challenges they'll bring, the panel yesterday released its first study. The 28,000-word report, "Artificial Intelligence and Life in 2030," looks at eight categories -- from employment to healthcare, security, entertainment, education, service robots, transportation and poor communities -- and tries to predict how smart technologies will affect urban life. "We believe specialized A.I. applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," Peter Stone, a computer scientist at the University of Texas at Austin and chair of the 17-member panel of international experts, said in a written statement. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of A.I. are broadly shared."

Rumors surfaced this week suggesting Google may soon drop the Nexus brand for its upcoming flagship phones. However, there has been no mention of a new product name to replaces the discontinued handsets, until now. A new report from Android Police reveals that Google has settled on Pixel and Pixel XL for the 5in handset, codenamed Sailfish and the 5.5in version, known as Marlin - and both will be unveiled on October 4th. A new report from Android Police reveals that Google has settled on Pixel and Pixel XL for the 5 in Sailfish and the 5.5 in Marlin, which will be unveiled on October 4th. A new report suggests Google is hosting an event on October 4th that is focused on its new hardware.

Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/or machines, for instance. Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios. In this paper, we propose a probabilistic-based Optimum Path Forest (OPF) classifier to handle with binary classification problems, and we show it can be more accurate than naive OPF in a number of datasets. In addition to being just more accurate or not, probabilistic OPF turns to be another useful tool to the scientific community.

In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance. The exact computation of EEM optimally balances exploration and exploitation. In practice, however, EEM-based algorithms employ various approximations due to the computational hardness of exact EEM. This can result in a lack of either exploration or exploitation, which can negatively impact the effectiveness of active learning. We propose a new algorithm TSA (Two-Step Approximation) that balances between exploration and exploitation efficiently while enjoying the same computational complexity as existing approximations. Finally, we empirically show the value of balancing between exploration and exploitation in both toy and real-world datasets where our method outperforms several state-of-the-art methods.

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.

Say what you will, but IBM Watson is one resourceful supercomputer. We've previously seen the AI describe the contents of photos, predict the most popular toys during Christmas season and gauge your emotional state – all of that with an exceptional accuracy. Now IBM Watson has added yet another skill to its arsenal as it just learned how to make movie trailers. Earlier this week, 20th Century Fox trusted the supercomputer with the task to create the trailer for its upcoming sci-fi drama Morgan. With this accomplishment, IBM Watson becomes the first-ever AI to produce a movie trailer.

Cancer is a tricky disease to treat. With more than one hundred known types, each responding differently to treatment depending on the person they're growing inside (and dozens of other factors), oncologists certainly have their work cut out for them. Machine learning could soon make their jobs a bit easier. IBM's supercomputer Watson has been applying machine learning to personalized cancer treatment for some time. After pouring over 600,000 medical reports and 1.5 million anonymized patient records and clinical trials, the data should help define the clearest path forward for doctors.