Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.

I'm an AI researcher (based outside Oxford UK) at eXvisory.ai. It's an AI web chat application that guides consumers through finding and fixing problems with their mobile phones or tablets. Instead of chatting with a scripted human support engineer consumers chat with a scripted AI. Machine learning AI is amazing for matching single questions to single answers (given lots of high quality Q&A training data) but not so hot with conversational or back-and-forth Q&A. It's not an algorithmic problem -- it's just much harder to obtain high quality conversational Q&A training data.

By nature, human language is complex. To understand human speech, a technology must also understand the grammatical rules, meaning and context, but also colloquialisms, slang and acronyms used in a language. Natural language processing algorithms support computers by simulating the human ability to understand language. Many NLP algorithms are based on statistics and may be combined with deep learning. This approach is superficial in its analysis of language, however, because it isn't able to understand the meaning of words.

I made a automated skin disease diagnosis DEMO website based on deep learning algorithm (Model Dermatology; http://ModelDerm.com). ResNet152 and VGG19 were used as a CNN model, around 300,000 images (179 class;176 skin disorders) were used as a trainining dataset. The training images were collected from 4 university hospitals in Korea. This CNN model is the successor to my onychomycosis model (http://nail.medicalphoto.org). The web-based test platform provides 3 differential diagnosis after analyzing image.

U.S. District Judge Wendy Beetlestone in Philadelphia issued a preliminary injunction preventing enforcement of rules the administration announced in October that allowed businesses or non-profits to obtain exemptions on moral or religious grounds.

IBM researchers have developed a program that can predict the products of organic chemistry reactions.1 Modelled on the latest language translation systems – like Google's artificial neural network – the AI picked the right product 80% of the time despite not having been taught any organic chemistry rules. 'What this tool is trying to do is imitate a top pro chemist in more or less the entire domain of organic chemistry,' says Teodoro Laino, one of the researchers involved in the study at IBM in Zurich, Switzerland. His ambitious goal is shared by other chemists who have been attempting to create a functioning AI chemist since the 1970s, when organic chemist E J Corey kick-started the field by creating a chemical knowledge database. However, making a tool based on chemistry knowledge can be time-consuming; Bartosz Grzybowski's team took 10 years to encode their Chematica retrosynthesis program with 20,000 chemical rules. Moreover, a knowledge-based AI has difficulty tackling reactions that lie outside of its rule set. 'There's a way to learn organic chemistry that's not memorising chemical rules, by just trying to find out the underlying patterns in reactions and trying to rationalise them,' Laino says, explaining the approach that his team took.

The contraception mandate was implemented as part of the 2010 Affordable Care Act, former Democratic President Barack Obama's signature healthcare legislation, popularly known as Obamacare. Republicans, who control the U.S. House of Representatives, Senate and White House, have so far failed to repeal the law, a top presidential campaign promise of Trump.

Managed multi-context systems (mMCSs) allow for the integration of heterogeneous knowledge sources in a modular and very general way. They were, however, mainly designed for static scenarios and are therefore not well-suited for dynamic environments in which continuous reasoning over such heterogeneous knowledge with constantly arriving streams of data is necessary. In this paper, we introduce reactive multi-context systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources and data streams. We show that rMCSs are indeed well-suited for this purpose by illustrating how several typical problems arising in the context of stream reasoning can be handled using them, by showing how inconsistencies possibly occurring in the integration of multiple knowledge sources can be handled, and by arguing that the potential non-determinism of rMCSs can be avoided if needed using an alternative, more skeptical well-founded semantics instead with beneficial computational properties. We also investigate the computational complexity of various reasoning problems related to rMCSs. Finally, we discuss related work, and show that rMCSs do not only generalize mMCSs to dynamic settings, but also capture/extend relevant approaches w.r.t.

It's kind of a no-brainer that Dr. Keith Dreyer would be among those who lead the advance of artificial intelligence into healthcare. Dreyer is a rare breed, a radiologist who teaches at Harvard Medical School, but he also holds a degree in mathematics and has a doctorate in computer science. So it's fitting that Dreyer serves as the chief data science officer at Partners HealthCare, a healthcare network that includes Brigham and Women's Hospital and Massachusetts General Hospital, two of America's most prestigious medical institutions. Earlier this year, Partners and GE Healthcare signed a 10-year agreement to "integrate artificial intelligence into every aspect of the patient journey." A hospital generates some 50 petabytes of data per year on average, enough to fill 20 million four-drawer filing cabinets with standard pages of text.

The Basel committee rules have been an ongoing international response to the 2007-2009 financial crisis that saw the bankruptcy of U.S. investment bank Lehman Brothers and taxpayer bailouts of big banks. The financial crisis was the prelude to the Great Recession that saw many people lose their jobs and homes. Governments in the United States, Europe and elsewhere were pushed to rescue banks to prevent a cutoff of credit to businesses that would further harm the economy and increase unemployment.