When sailors speak of "blue water," they anticipate taking a boat offshore, out of sight of land, and usually for an extended cruise – an adventure underwritten by a frisson of risk. Much of the chatter about artificial intelligence has the patina of blue-water thinking about it. Its promise is expansive – as broad as an ocean. There are unplumbed depths, certainly treasure and possibly danger. At the risk of overworking the metaphor, today Artificial Intelligence (AI) sits in the matrix of a perfect storm of coincidence.

Technology has a long and uneasy relationship with gender and identity. On one hand, technologies like social media allow people to connect across large distances to share their personal stories, build communities and find support. Yet there have also been troubling cases, such as Facebook's'Real Names' policy where users have been locked out of their accounts and left without recourse. As technology gets more complex and ubiquitous, these cases will continue to get worse. Widespread implementation of seemingly "objective" technology means less accessibility for those who do not fit developers' conceptions of an "ideal user" – usually white, male and affluent.

To resolve the issue, the industry need to understand why users are implementing ad blockers. The main reasons usually cited by the industry are normally data consumption, load times and battery use. These are all valid, but when looked at closely, they don't seem to be the main cause of ad blocking. Research by Global Web Index found that only one third of users state page load times are the reason for using an ad blocker, while just three in ten said battery life was the motive for installing the software. The real key to the problem lies in irrelevant advertising.

If a neural network can write Shakespeare, could it write a resume for you? Inspired by the remarkable results of Recurrent Neural Networks and using thousands of anonymized resumes from untapt, I've been experimenting with applying deep learning techniques to the CV. There's a seminal blog post called The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy, a PhD student and instructor at Stanford. Andrej is taken by surprise by the magical results from Recurrent Neural Networks, a particular type of neural network that can process arbitrary sequences of inputs. In one example, he feeds the Complete Works of Shakespeare into an RNN.

One of the most frustrating elements of the current healthcare environment is the administrative burden of prior authorizations for medications and procedures. It is a frustration for providers, for patients, and for payers. Is there any way to solve this dilemma? For physicians, an estimated 20 hours per week is spent in prior authorization activities, costing an average of 83,000 in excess annual overhead per physician. Is there an actual benefit for this effort? Most physicians say that payers (commercial, Medicare, Medicaid, and pharmacy benefit managers (PBMs)) use prior authorizations to keep costs down.

When Zinedine Zidane, the then French captain, headbutted Italy's Marco Materazzi during the 2006 World Cup final, the clash quickly became one of the most infamous incidents in football history. What was not clear was what sparked the Frenchman's ire – Zidane said his mother had been insulted, a charge that Materazzi vigorously denied. The head-butt got Zidane sent off and Italy won the game. However, had there been technology there to identify what was said, the result could have been very different, Helen Bear believes. "If a machine lip-reader was in existence, the other player [could] have got sent off too so it would have been 10 men against each other in a World Cup final," she argues.

KPMG's recent announcement was particularly noteworthy from my perspective, because it indicated that the audit firm would be deploying IBM's Watson "cognitive computing technology" to KPMG's professional services offerings.--According "One current initiative is focused on employing supervised cognitive capabilities to analyze much larger volumes of structured and unstructured data related to a company's financial information, as auditors'teach' the technology how to fine-tune assessments over time. This enables audit teams to have faster access to increasingly precise measurements that help them analyze anomalies and assess whether additional steps are necessary." IBM is, of course, one of KPMG's biggest software-auditing clients. All of these recent reports mention that the AI technologies currently are being contemplated for use in connection with financial audits.

Computer scientists from MIT and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts. At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules. But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway. To improve this, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2 artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.

Artificial Intelligence (AI) is a modern computing field that attempts to augment machine development to the point where it transcends human intelligence. The subject incorporates the concepts of computation and statistics with brain physiology, to create autonomous sentient machines capable of extrapolating independent conclusions. The success to creating a successful AI is contingent upon success to build machines that can learn and correct themselves. So, recent AI endeavors have been inclined toward Machine Learning Algorithms. These Algorithms use statistical and probabilistic approaches to create either supervised or unsupervised learning algorithm.

The finance world is cautiously optimistic about the future of artificial intelligence and how it can be used, but, there is more work needed on regulating the technology when it comes to world markets. "Financial institutions have been fined billions of dollars because of illegality and compliance breaches by traders. A logical response by banks is to automate as much decision-making as possible, hence the number of banks enthusiastically embracing AI and automation," said Baker and McKenzie head of financial services regulation Arun Srivastava. "But while conduct risk may be reduced, the unknown risks inherent in aspects of AI have not been eliminated." The law firm's research found more than 400 senior executives working in finance and fintech believe artificial intelligence (AI) will have the most impact on trading, financial analysis and IT over the next three years.