Winners of an AI-judged beauty contest, which displayed bias against non-white faces. AI has seen a renaissance over the last year, with developments in driverless vehicle technology, voice recognition, and the mastery of the game "Go," revealing how much machines are capable of. But with all of the successes of AI, it's also important to pay attention to when, and how, it can go wrong, in order to prevent future errors. A recent paper by Roman Yampolskiy, director of the Cybersecurity Lab at the University of Louisville, outlines a history of AI failures which are "directly related to the mistakes produced by the intelligence such systems are designed to exhibit." According to Yampolskiy, these types of failures can be attributed to mistakes during the learning phase or mistakes in the performance phase of the AI system.

In the late sixties while working at Ericsson he invented both sequence diagrams and use cases, and in later years worked on the SDL, UML and the RUP. We caught up with Dr Ivar Jacobson to hear his thoughts on where the industry is today, and where it will head in the future. Builder AU: What do you think of the state of software engineering today? Ivar Jacobson: What I see when I travel and talk to customers, participate in conferences and have discussions with experts around the world is that software development is very much still an immature discipline. We still rely on too much old work. Personally I am convinced we will change that dramatically, but it is a very slow process. I have been working on process improvement and new technologies for many years now, starting with component based development and then adding to that object orientation and now aspect orientation. I've been involved with new technology since the sixties, and I've been more optimistic than most -- it's my nature, but we are still struggling with basic stuff for many reasons.

In the movie Her, which was nominated for the Oscar for Best Picture this year, a middle-aged writer named Theodore Twombly installs and rapidly falls in love with an artificially intelligent operating system who christens herself Samantha. Samantha lies far beyond the faux "artificial intelligence" of Google Now or Siri: she is as fully and unambiguously conscious as any human. The film's director and writer, Spike Jonze, employs this premise for limited and prosaic ends, so the film limps along in an uncanny valley, neither believable as near-future reality nor philosophically daring enough to merit suspension of disbelief. Nonetheless, Her raises questions about how humans might relate to computers. Twombly is suffering a painful separation from his wife; can Samantha make him feel better?

Manufacturing, finance, and the communications industry have in the last decade all come to rely upon artificial intelligence. But there's one industry that continues to put up resistance: the legal profession. The idea of a machine making legal decisions was long considered by opponents to be dangerous and ethically untenable. That's about to change, says John Zeleznikow, a computer scientist at Victoria University in Melbourne, Australia. Zeleznikow believes AI is about to improve people's access to justice and massively reduce the costs of running legal services.

Siri was not the first speech recognition application to grace our phones, but its success will help increase expectations for more artificial intelligence in business. Siri combines speech recognition and natural language processing with artificial intelligence and search algorithms in the cloud to behave like an intelligent human assistant. It stops short of predicting, but it sounds like it does. Even more predictive technologies are starting to be applied to business applications to help us wade through the seemingly unsurmountable volume of data compounding every day. Edith Cowan University in Perth is using a predictive model developed by IBM to identify students at risk of leaving a course before they finish.

The Massachusetts Institute of Technology brain sciences department and, separately, a group of some 200 neuroscientists from around the world have written letters to The New York Times claiming that a book excerpt in the newspaper's Sunday magazine this week contains important errors, misinterpretations of scientific disputes, and unfair characterizations of an MIT neuroscientist who did groundbreaking research on human memory. In particular, the excerpt contains a 36-volley verbatim exchange between author Luke Dittrich and MIT's Suzanne Corkin in which she says that key documents from historic experiments were "shredded." "Most of it has gone, is in the trash, was shredded," Corkin is quoted as telling Dittrich before she died in May, explaining, "there's no place to preserve it." Destroying files related to historic scientific research would raise eyebrows, but Corkin's colleagues say it never happened. "We believe that no records were destroyed and, to the contrary, that professor Corkin worked in her final days to organize and preserve all records," said the letter that Dr. James DiCarlo, head of the MIT Department of Brain and Cognitive Sciences, sent to the Times late Tuesday.

Can training to become ambidextrous improve brain function? Although teaching people to become ambidextrous has been popular for centuries, this practice does not appear to improve brain function, and it may even harm our neural development. Calls for ambidexterity were especially prominent in the late 19th and early 20th centuries. For instance, in the early 20th century English propagandist John Jackson established the Ambidextral Culture Society in pursuit of universal ambidexterity and "two-brainedness" for the betterment of society. This hype died down in the mid-20th century as benefits of being ambidextrous failed to materialize.

The brain performs its canonical task -- learning -- by tweaking its myriad connections according to a secret set of rules. To unlock these secrets, scientists 30 years ago began developing computer models that try to replicate the learning process. Now, a growing number of experiments are revealing that these models behave strikingly similar to actual brains when performing certain tasks. Researchers say the similarities suggest a basic correspondence between the brains' and computers' underlying learning algorithms. The algorithm used by a computer model called the Boltzmann machine, invented by Geoffrey Hinton and Terry Sejnowski in 1983, appears particularly promising as a simple theoretical explanation of a number of brain processes, including development, memory formation, object and sound recognition, and the sleep-wake cycle.

We've long been fans of the self-sustaining, wave-powered Waveglider, a maritime robot that can harvest propulsion energy from the rolling motion of ocean waves to operate far from shores indefinitely--or at least until something breaks down. Durability, then, is key to Waveglider's seafaring success, and this this year it proved its hardiness several times over. First, the National Oceanic and Atmospheric Administration began custom-fitting the Waveglider to be a long-endurance weather monitoring platform capable of staying at sea for many months at a time to intercept hurricanes and other storms gathering far from shore--a major vote of confidence for the technology. Then we learned in early November that one of NOAA's Wavegliders had deployed (and survived) off the coast of New Jersey during Hurricane Sandy, withstanding the full brunt of her destructive power as she came ashore (while beaming reams of valuable data to meteorologist back onshore). And finally, we learned that the first of four Wavegliders that set off from San Francisco last year on a trans-Pacific crossing had reached Australia, proving these robots are ready for long-duration science missions at sea with virtually no human help or interference.

How do you use a scientific method to measure the intelligence of a human being, an animal, a machine or an extra-terrestrial? So far this has not been possible, but a team of Spanish and Australian researchers have taken a first step towards this by presenting the foundations to be used as a basis for this method in the journal Artificial Intelligence, and have also put forward a new intelligence test. "We have developed an'anytime' intelligence test, in other words a test that can be interrupted at any time, but that gives a more accurate idea of the intelligence of the test subject if there is a longer time available in which to carry it out", José Hernández-Orallo, a researcher at the Polytechnic University of Valencia (UPV), tells SINC. This is just one of the many determining factors of the universal intelligence test. "The others are that it can be applied to any subject whether biological or not at any point in its development (child or adult, for example), for any system now or in the future, and with any level of intelligence or speed", points out Hernández-Orallo.