As a young researcher tasked with pioneering AI in my corporate research lab, this is an exciting opportunity. We are at the very peak of the AI hype curve. This is no ordinary academic conference. The Japanese had announced their 5th Generation computing project that promised fundamental logical processing. The USA responded with a 10 year research project CyC to give computers common sense reasoning power.

In March, a computer achieved what many thought impossible when it won a best of five series against world-class go champion Lee Sedol. The victory by the DeepMind computer was the most significant milestone in artificial intelligence (AI) since Deep Blue beat chess Grandmaster Garry Kasparov in 1997, and once again sparked many predictable headlines about humans being knocked off our IQ perch. The question is, what do such human versus computer matches tell us about AI? Is it the harbinger of a machine-led future or are computers just very good at playing board games? To see how this might play out, we first need to look to the past. Despite being extremely bad at playing board games as complicated as chess and go for decades, there was almost a sense of inevitability to computers eventually surpassing the abilities of their human creators in this area.

The safety of critical maritime operations and the power output of wave energy converters can both be improved by measuring and predicting the shape and motion of sea waves. The aim of this project is extract information from monoscopic video footage of the sea's surface that enable its shape and motion to be modelled. The models will then be used to predict its future motion up to two minutes ahead. Making observations of the shapes of sea waves is difficult. We have been working with wave profiling radar, which is relatively expensive and difficult to install and run.

Even if human-like emotional intelligence is necessary, and even if it depends on our limited low bandwidth communication, machine intelligences could just emulate that and then speed it up. Furthermore, it seems unlikely that removing the constraint of low communication bandwidth between minds wouldn't permit any advantages. Furthermore any general AI would have to interact with humans and operate in an anthropized environment at least at the initial stages of its development, which would require it to effectively communicate over low-bandwith interfaces, limited by the human communication bandwith, most likely by using natural language. I found this point one of the weakest of the analysis. Yes uncertainty propagates, but prediction doesn't require absolute detail and precision. Humans can roughly predict the long term future by giving up detail and precision, because even extremely low-detail, abstract, low-precision distributions over the future are useful for planning.

Meet the Woman Who's Created the 21st Century Finance Model for Emerging Technologies Riva-Melissa Tez is the CEO and co-founder of Permutation in San Francisco. A London native, she runs an artificial intelligence platform and incubator. In her spare time, she works on The Longevity Cookbook, alongside Maria Konovalenko and Steve Aoki, which is a book that distills academic research into practical measures for slowing the aging process. This is an edited transcript of a recorded interview. I learned important lessons about money at a very early age. At 10, I moved into a homeless shelter after my father left my mother. My mother is severely schizophrenic -- which can be both chaotically fun and devastatingly traumatic -- and was not well enough to look after herself, let alone me at the time. Amongst other things, she used to make me drink the milk in the morning first to check if it had poison in it. A few years later, at 14, we moved into social housing.

When new trends and technologies burst onto the marketing scene, there's always a frantic effort to either keep up or provide guidance, especially when serious amounts of money are involved. It happened with social media, it happened with personalisation and big data, and it's happening now with artificial intelligence. We're approaching the top of the hype cycle where, like teenage sex, everyone is talking about it but very few are actually doing it. Conditions are perfect for the snake oil salesmen to move in. But there's real substance behind some of work being done in this field, and in this post I'll try to navigate through the fog of rhetoric to understand what's required to make the most of the significant opportunities.

Last week, the White House released a report chronicling the Obama administration's concerns over Big Data and artificial intelligence. Many prominent thinkers and scientists have come out recently with warnings about the dangers of unchecked artificial intelligence. However, the A.I. the White House report refers to is not of the Terminator ilk -- rather, Obama has concerns over algorithmic artificial intelligence operating without human oversight. The report, "Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights," catalogs the growing sphere of influence represented by Big Data in society, including employment, higher education, and criminal justice. "As data-driven services become increasingly ubiquitous, and as we come to depend on them more and more, we must address concerns about intentional or implicit biases that may emerge from both the data and the algorithms used as well as the impact they may have on the user and society. Questions of transparency arise when companies, institutions, and organizations use algorithmic systems and automated processes to inform decisions that affect our lives, such as whether or not we qualify for credit or employment opportunities, or which financial, employment and housing advertisements we see." "If feedback loops are not thoughtfully constructed, a predictive algorithmic system built in this manner could perpetuate policing practices that are not sufficiently attuned to community needs and potentially impede efforts to improve community trust and safety. For example, machine learning systems that take into account past arrests could indicate that certain communities require more policing and oversight, when in fact the communities may be changing for the better over time."

The company is continuing its review of the potential to use drones someday in its global package delivery system, teaming with two partners to deliver blood supplies later this year in Rwanda. The company, through its UPS Foundation, has committed 800,000 toward the project with Zipline, a California robotics company; and Gavi, a Swiss-based group that works to bring vaccines to children in poor countries. UPS said that starting later this year, the Rwandan government intends to begin using Zipline drones to delivery blood to 21 transfusing facilities in the western half of the country. The goal is to step up the battle against the deaths of women who hemorrhage after giving birth. The additional blood can allow for life-saving transfusions on a continent known for the world's high rates of maternal death, according to the World Health Organization.

IT services provider Accenture announced its deal to acquire OPS Rules, an analytics consulting company based in the US. The acquisition of OPS Rules enables Accenture to expand its machine learning and operations analytics capabilities. Last year, Accenture also acquired Gapso, an analytics services and solutions provider in Brazil that assists enterprises to solve supply chain and logistics challenges. OPS Rules, founded in 2012, specializes in the application of data science to create supply chain and operations analytics solutions. The US head-quartered Accenture aims to add new operations analytics professionals to its team that apply machine learning and optimization techniques to develop innovative analytics approaches for clients.

Picking the winner from the 20 horse field at the Kentucky Derby is hard. So hard, in fact, that no expert polled by SBNation was able to do it. That's why the holy grail at the racetrack is the Superfecta, where bettors are asked not only to pick the winner, but the second, third and fourth horses to finish the Derby. This is fiendishly difficult task that, not surprisingly, defeated every expert at Churchill Downs, where no one predicted the top four horses correctly, much less in the correct order. In the world of AI, even Bing Predicts blew it, picking only heavily favored Nyquist to win the race, but missing the other 3 picks entirely.