We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively.

Driscoll's is so secretive about its robotic strawberry picker it won't let photographers within telephoto range of it. But if you do get a peek, you won't see anything humanoid or space-aged. AgroBot is still more John Deere than C-3PO -- a boxy contraption moving in fits and starts, with its computer-driven sensors, graspers and cutters missing 1 in 3 berries. Such has been the progress of ag-tech in California, where despite the adoption of drones, iPhone apps and satellite-driven sensors, the hand and knife still harvest the bulk of more than 200 crops. Now, the $47-billion agriculture industry is trying to bring technological innovation up to warp speed before it runs out of low-wage immigrant workers.

To say that radiology in the NHS is drowning in work volume is an understatement. In May 2016 the Royal College of Radiologists highlighted the results of a national workforce survey, including the fact that 230,000 studies a month were stuck in a backlog of over 31 days and that spending to reduce these backlogs had increased by 57%. The story is the same in Scotland, where over £5.25 million per annum is spent on outsourcing. No-one seems to have the answer to this crisis, and with the current state of NHS underfunding it doesn't look likely that one will emerge any time soon. Over the pond, however, there is a huge push towards solving radiology's problems -- and the UK is being left behind.

Posted By C. M. Rubin on Jun 20, 2017 "We need courageous cathedral builders! We also need to address traditional experts' biases clinging to their narrow domains, parents' old personal experiences biasing their views, and teachers' and administrators' lack of training and leadership, respectively." All around us we are witnessing disruptive automation that is changing lives and taking away the jobs many have relied on to make a living. According to a recent report by PwC, within 15 years, artificial intelligence will take over 38% of U.S. jobs. But will this trend continue even further, and to what extent does AI pose a threat to most of our jobs?

Tanmay Bakshi fell in love with computers at five, released his first iPhone app at nine, and now at just 13 years old is working with IBM on artificial intelligence. The Canadian teen has become a global force in programming and commands more than 20,000 subscribers on his YouTube channel that teaches computer coding. He is currently in Australia for the IBM Watson Summit, which brings together experts in artificial intelligence to discuss how the technology can help people and businesses in the future. "If you think about it, really anything would fascinate a five-year-old, especially a computer," Tanmay told News Breakfast. "Just looking at the colours change on screen or even displaying my name on screen, whatever it might be, as a five-year-old that really fascinated me."

More than 70% of Australians play video games, and I'm one of them. With its spread from computers to TVs to mobile phones, the global games industry is now worth over $100bn, with an Australian market alone worth $3bn. But the value of games goes beyond just money. Interactive entertainment and the "serious games" that share lessons and skills have psychological and social benefits. We play to bond with other players, to build communities, to learn, to experience worlds beyond our imagination, and – as in the Dionysian theatres of old – to enjoy a temporary catharsis and channel feelings that otherwise preoccupy us into something vicarious.

While data science has emerged as an ambitious new scientific field, related debates and discussions have sought to address why science in general needs data science and what even makes data science a science. Following a comprehensive literature review,5,6,10,11,12,15,18 I offer a number of observations concerning big data and the data science debate. For example, discussion has covered not only data-related disciplines and domains like statistics, computing, and informatics but traditionally less data-related fields and areas like social science and business management as well. Data science has thus emerged as a new inter- and cross-disciplinary field. Although many publications are available, most (likely over 95%) concern existing concepts and topics in statistics, data mining, machine learning, and broad data analytics. This limited view demonstrates how data science has emerged from existing core disciplines, particularly statistics, computing, and informatics. The abuse, misuse, and overuse of the term "data science" is ubiquitous, contributing to the hype, and myths and pitfalls are common.4 While specific challenges have been covered,13,16 few scholars have addressed the low-level complexities and problematic nature of data science or contributed deep insight about the intrinsic challenges, directions, and opportunities of data science as an emerging field. Data science promises new opportunities for scientific research, addressing, say, "What can I do now but could not do before, as when processing large-scale data?"; "What did I do before that does not work now, as in methods that view data objects as independent and identically distributed variables (IID)?"; "What problems not solved well previously are becoming even more complex, as when quantifying complex behavioral data?"; and "What could I not do better before, as in deep analytics and learning?"

Whether your interaction with artificial intelligence (AI) is limited to science-fiction or you spend more time in your day talking to Siri and Alexa than actual humans, you can't hide from the fact AI is changing the world. This week, the UK's new digital strategy was launched, which dedicated £17.3m to research and development of robotics and AI. Out of the industries welcoming this technology with open arms, finance and banking is one of the biggest. It's not hard to see why: when companies are dealing with large amounts of data, handing over control to a machine learning system that can analyse and understand information much faster than a human being is an obvious benefit. Where is this new technology having an impact in the finance sector?

On an average, more than 50 percent of projects fail. Without touching on failure factors this staggering number applies across all sorts of project environments – IT, construction, engineering, or transformation projects. One would assume we'd figured out by now how to successfully deliver projects on time, on a budget, and to the desired scope and benefits; But apparently, we haven't. AI is one of the buzzwords du jour, thanks to frequent tech media news about voice-enabled personal assistants and self-driving cars. However, the concept of AI is older than project management itself -- going as far back as 380 BC to Greek philosophers such as Aristotles who described the syllogism, a method of mechanical thought.

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.