I've just run my second briefing on intelligent assistance. Much happened in the few months between sessions. This time, the second half of the day was dominated with stories about bots and their use cases on messaging platforms. It also included the amazing things now possible via automated voice which Amazon's Alexa Challenge exemplifies. Meanwhile IBM's Watson continues its conquest of carbon life forms with the trashing of an extraordinarily talented Go grand master.

Artificial Intelligence - is it something that's closer to reality than we realize? The answer is most certainly yes. Up until now AI has been the stuff of science fiction, but that's not going to be the case for much longer. Megacorporation Google is stepping in to bring on the future with its moves towards creating AI. SEO Leads To More Than Search Rankings Google is creating the foundation for AI by enticing companies to create high quality content to rank high in SEO.

Episode Summary: When we think about applying AI and data science to different areas of business, we often think about those domains that offer a wide swath of quantitative metrics that we can feed a machine, like marketing or finance. Human resources (HR) normally doesn't fit the bill. How we hired someone, how we felt about them when we hired them, how they perform qualitatively, these are things that are often difficult to discern in team dynamics. That being said, big teams like Google are applying machine learning (ML) to some of their HR choices, and our guest today believes more companies will be doing the same in future. CEO of Humanyze Ben Waber applies ML to HR decision-making, helping people get better employees and better performance by measuring and improving using data science in new ways.

Even though you may not realize it, machine learning-powered matchmaking is present everywhere in our daily lives, from the type of content shown on our Facebook news feeds to the suggested TV shows that come up on Netflix, and even to the matches suggested on dating sites/apps like Match.com and Tinder. As machine learning continues to advance, it will start to make its way to the hiring process, driving efficiencies in connecting employers and candidates, especially for technical jobs. Analyzing large amounts of data on candidates will become increasingly important during the hiring process for many companies. Today, matching algorithms use strings and keywords in resumes to filter candidates. This enables companies to get more accurate results, quicker, during the hiring process.

Recent advances in artificial intelligence have made it clear that our computers need to have a moral code. Consider this: A car is driving down the road when a child on a bicycle suddenly swerves in front of it. Does the car swerve into an oncoming lane, hitting another car that is already there? Does the car swerve off the road and hit a tree? Does it continue forward and hit the child?

One day, filled with a mix of invited talks and presentations of peer-reviewed papers. Invited speaker Karl Branting The MITRE Corporation, USA Accepted papers (full, position and short) Sudhir Agarwal, Kevin Xu and John Moghtader Toward Machine-Understandable Contracts Trevor Bench-Capon Value-Based Reasoning and the Evolution of Norms Trevor Bench-Capon and Sanjay Modgil Rules are Made to be Broken Markus Fatalin Product Liability for Autonomous Systems in Europe Raghav Kalyanasundaram, Krishna Reddy P and Balakista Reddy V Analysis for Extracting Relevant Legal Judgments using Paragraph-level and Citation Information Niels Netten, Susan van Den Braak, Sunil Choenni and Frans Leeuw The Rise of Smart Justice: on the Role of AI in the Future of Legal Logistics Marc van Opijnen and Cristiana Santos On the Concept of Relevance in Legal Information Retrieval Livio Robaldo and Xin Sun Reified Input/Output logic - a position paper Olga Shulayeva, Advaith Siddharthan and Adam Wyner Recognizing Cited Facts and Principles in Legal Judgements Pieter Slootweg, Lloyd Rutledge, Lex Wedemeijer and Stef Joosten The Implementation of Hohfeldian Legal Concepts with Semantic Web Technologies Floris Bex, Joeri Peters and Bas Testerink A.I for Online Criminal Complaints: from Natural Dialogues to Structured Scenarios Robert van Doesburg, Tijs van der Storm and Tom van Engers CALCULEMUS: Towards a Formal Language for the Interpretation of Normative Systems Henry Prakken On how AI & law can help autonomous systems obey the law: a position paper Giovanni Sileno, Alexander Boer and Tom Van Engers Reading Agendas Between the Lines, an Exercise Bart Verheij Formalizing Correct Evidential Reasoning with Arguments, Scenarios and Probabilities

Hi PK and all, I'm organising this Bothaton and am working around the clock to lock in the venue, generate more awarness and get more local or remote participants from Australia as well as some sponsors. There will be more info coming soon, drafting a page on http://devpost.com/ha..., planning leads catch up over this weekend and a Hangout/webinar a few days in prior the next weekend. If you or anyone is interested in a project leadership or some other involvement, please do not hesitate to msg me!:)

This small and seemingly unimportant description of a mug represents the core construction of neural networks. A logic tree is predetermined and therefore would require knowing and then manually inputting how likely it was that a mug would be hot to touch. A neural network, simply responds to data confirming or denying the frequency of the neuron "heat" being connected to the neuron "mug." This concept is the closest we've come up with for how our own brain works. We touch a mug on a table -- it's hot.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.