Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. The transportation industry is associated with high maintenance costs, disasters, accidents, injuries and loss of life. Hundreds of thousands of people across the world are losing their lives to car accidents and road disasters every year. According to the National Safety Council, 38,300 people were killed and 4.4 million injured on U.S. roads alone in 2015.

Some water utilities have so much data at their disposal it is hard to sift through it all and use it effectively, but software has the potential to change that. "One tool for working with potentially valuable truck loads [of data] is an artificial neural network -- a software system that uses machine learning techniques to process tons of data and intelligently answer questions," Ars Technica reported. OmniEarth, a geographic analytics company, is using IBM's Watson technology to give meaning to water data. Watson, a machine learning system, became a household name when it managed to outdo some formidable Jeopardy contestants. The project has deployed this technology to help water managers understand whether ratepayers are using water efficiently.

Artificial Intelligence is the latest hotness. We've heard how bots are going to be the new apps allowing natural conversational interfaces. Siri, Cortana and Google Now are no longer a gimmick and are becoming more useful every day. Machine Learning sits behind artificial intelligence and on the surface it may seem like a deeply technical, scientific topic. We are here to tell you that it is more accessible than you think.

As a UK-focused venture capital investor, Oxford Capital is fortunate that the UK is a world leader in AI (although to avoid topical accusations of British exceptionalism, it's definitely worth mentioning that there are centres of excellence in AI and machine learning (ML) throughout Europe). We have just completed an as-yet undisclosed investment in the AI space, and we're evaluating several other opportunities. Along with taking an interest in AI companies that approach us for funding, we've mapped out recent activity in the sector across the UK. Innovation starts with talented individuals, and AI is no exception. Many of the UK's most respected universities have AI research groups, such as the Intelligent Systems group within UCL and the joint Oxford-Cambridge Strategic AI Research Centre.

The perception of what artificial intelligence was capable of began to change when chess grand master and world champion Garry Kasparov lost to Deep Blue, IBM's chess-playing program, in 1997. Significantly more complex, requiring even more strategic thinking, and featuring an intricate interweaving of tactical and strategical components, it posed an even greater challenge to artificial intelligence. With a number of possible moves per turn an order of magnitude greater than chess, any algorithm trying to evaluate all possible future moves was expected to fail. Led by Hiroaki Kitano and Manuela Veloso, the ambitious goal set that year was to have by 2050 a team of humanoid robots able to play a game of football against the world champion team according to FIFA rules, and win.

The new plugin gives businesses a simple way to create and deploy customer support messenger bots; providing their customers with a real-time chat experience that combines a hybrid of AI self-service support and on-demand live agent chat. Integrated with Inbenta's powerful semantic search technology, the Chatbot Creation Platform enables a company to leverage its existing knowledge base to create the chatbot experience. The knowledge base -- or customer FAQs -- are no more than a sequence of answers to common questions; because the content is founded on natural language, the customer exchange becomes a two-way conversation. Inbenta specializes in Natural Language Processing and semantic search to improve the customer experience online through Artificial Intelligence-powered technology that helps businesses increase the efficiency of its customer service, call centers, e-Commerce, FAQs and social media platforms. Support services such as dynamic FAQs, knowledge management and and virtual assistants improve business website searches, customer self-service, and e-Commerce conversions.

With the rising anxiety about robots taking over human jobs, now even lawyers may have a reason to be on the edge of their seats. A 19-year-old Stanford University student, Joshua Browder, impressively taught himself how to code and ended up creating what he claims is the "world's first robot lawyer," according to The Guardian. Over the past 21 months, the artificial intelligence (AI) lawyer chatbot has successfully helped void 160,000 parking tickets in London and New York. The bot has done it all for free. Remarkably, DoNotPay chatbot has taken on a quarter of a million parking ticket cases, and the AI lawyer has won 64 percent of them. To quantify the impact, the chatbot has saved about 4 million in fines that no longer have to be paid.

Last week New York University hosted the final workshop of a series sponsored by the White House on the transformative potential of Artificial Intelligence. Rather than focusing on the technical bits and bytes, the NYU-hosted schedule centered around the near-term social and economic impact of automation, mass data collection and new analytics. This leads directly into the White House's July 22nd deadline for its Request for Information on "Preparing for the Future of Artificial Intelligence." While we often fantasize about the fallout from the coming robot apocalypse, that is simply not today's challenge. Today, we need to focus on the near-term impact of smart-er automation systems on labor and social structures.

Allison Parrish is a computer programer, educator, and poet whose work deals with the materiality of language. With a background in both the formal study of language (she received her BA in Linguistics from UC Berkeley) and years of experience as a software developer, Parrish's work has lead her to explore words not only as symbols but also as objects in their own right, teasing the boundaries between medium and message to ask questions about how communication happens in a digital context. Electronic media open up entirely new possibilities for the manipulation of language, but they also lay bare some of the thorniest problems of textual interpretation. If we are writing for other humans we can assume a baseline understanding of how language works, an understanding which computers still do not share. If this seems abstract, consider the way that language comes to be represented as a text file in a computer: words are compressed into ASCII characters and stored as bits on a hard drive; the fundaments of human communication, having emerged out of obscure prehistory and evolved for centuries, accumulating layers of connotation and nuance along the way, are now encoded as tiny electrical charges in a matrix of transistors.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The funders had no role in the preparation of the article. Competing interests: The authors have declared that no competing interests exist.