When Apple released its software application, Siri, in 2011, iPhone users had high expectations for their intelligent personal assistants. Yet despite its impressive and growing capabilities, Siri often makes mistakes. The software's imperfections highlight the clear limitations of current AI: today's machine intelligence can't understand the varied and changing needs and preferences of human life. However, as artificial intelligence advances, experts believe that intelligent machines will eventually – and probably soon – understand the world better than humans. While it might be easy to understand how or why Siri makes a mistake, figuring out why a superintelligent AI made the decision it did will be much more challenging.

Back in September 2016, Google launched its Neural Machine Translation (GNMT) system, which uses deep learning to deliver more natural translations between languages. Google Translate originally supported only a handful of languages when it launched 10 years ago; today that number has risen to 103. Creating a computer system to translate multiple languages is complex. The people at Google who built it wanted to find out just how clever their system was. So they came up with a challenge.

Intel realizes there will be a post-Moore's Law era and is already investing in technologies to drive computing beyond today's PCs and servers. The chipmaker is "investing heavily" in quantum and neuromorphic computing, said Brian Krzanich, CEO of Intel, during a question-and-answer session at the company's investor day on Thursday. "We are investing in those edge type things that are way out there," Krzanich said. To give an idea of how far out these technologies are, Krzanich said his daughter would perhaps be running the company by then. Researching in these technologies, which are still in their infancy, is something Intel has to do to survive for many more decades.

Modern forensic DNA analyses are crucial to crime scene investigations; however the interpretation of the DNA profiles can be complex. Two researchers from the Forensics and National Security Sciences Institute (FNSSI) have turned to computer technology to assist complicated profile interpretation, specifically when it comes to samples containing DNA from multiple people. "There is a massive amount of data that is not being considered, simply due to our limited capability as human beings," says Michael Marciano, FNSSI research assistant professor, explaining why they're counting on computers to make data-driven predictions. Marciano and Jonathan Adelman, FNSSI research assistant professor, have developed a new method to predict the number of people contributing to mixed DNA samples, the results of which are published online in Forensic Science International: Genetics ahead of the journal's March issue. Additionally, the duo's method, dubbed Probabilistic Assessment for Contributor Estimate (PACE), is patent pending.

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There have been multiple reports recently which claim that a major part of the human workforce will be replaced by automatons and machines in the years to come. With excessive research and development being conducted in the field of artificial intelligence, many fear that a major job crisis will unfold since multiple jobs are more accurately and efficiently performed with the utilization of machines. With major names like Stephen Hawking already warning the world that development of robots and intelligent machines beyond a certain point could mark the end of humankind, the intimidation is real, to say the least. Is it possible for the machines to completely replace the human resources? Are humans really not going to find any job in upcoming decades, where every task is performed by the automatons and intelligent machines?

When AI first reached the ears of the legal market some years ago there was a flurry of stories about the end of lawyers. For years afterward and with Pavlov dogs-like automation any mention of legal AI summoned up the panicked refrain: 'The end of lawyers is coming, the end of lawyers is coming!' This was until law firms and corporates actually started to make use of legal AI systems, especially in the last two years and even more so last year. The clichéd refrain, now exposed to the cleansing light of real experience, seemed to die away upon contact. It turns out there were no androids or already out of date screen grabs from the 2004 Will Smith movie'iRobot' based on the late great Asimov novel.

Twitter's latest earnings were an unmitigated disaster. There was a huge revenue miss, coupled with a weak outlook that freaked out investors, who drove the stock down more than 12 percent. But part-time CEO Jack Dorsey insists that all is not lost. And to rally the troops and convince investors to give him more time, he tried his best yesterday to highlight the ways he believes the company's investments in machine learning offer reasons for optimism. "As I look into 2017 and we look at what we can do, I just think the superpower we really provide the world is we can break news and get information to people faster than any other service in the world," Dorsey said on an earnings call with analysts.

Arnold Schwarzenegger is a man of the future, he was onto the potential of Artificial Intelligence (AI) way before everyone else. Once again, something that seemed possible only in science-fiction films or a very distant future, turned out to become reality. That is, the Artificial Intelligence part, not the killer- robot-with-laser-gun part, thank god. Slowly but surely though, awareness about AI in HR is increasing, and there has been a lot of speculation about its future potential and applications. It's about time we had a closer look at Artificial Intelligence ourselves.

This article was originally published on International Business Times. Much of the current machine learning revolution originated around applications like computer vision that have nothing to do with finance. Financial data modeling is beset by a low signal to noise ratio, whereas data used to teach a computer to identify a picture of a cat, for example, is unambiguous. The financial universe is a non-stationary environment with variable patterns of correlation between stocks, bonds and other instruments. Not least, the task in hand is essentially about predicting things that haven't happened yet.