As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of $100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.

A recent study led by a Tallahassee-based Florida State University psychology researcher investigated whether artificial intelligence can assist in suicide prevention. The researchers, led by Jessica Ribeiro, PhD, identified the EHRs of 2 million Tennessee patients, more than 3,200 of whom had attempted suicide. The researchers used machine learning on these patients' medical histories to determine which combination of risk factors most accurately predicted future suicide attempts. The machine learning algorithm could predict suicide attempts with between 80 percent and 90 percent accuracy as far as two years into the future. The algorithm's accuracy increased based on closeness to the time of the suicide attempt; accuracy was as high as 92 percent when identifying general hospital patients at risk for a suicide attempt within one week.

Capital One Financial has developed a "chatbot" named Eno, an automated program that can communicate with the bank's customers via text message to give them information on their accounts and help them make credit-card payments from their smartphone. Capital One's (cof) gender-neutral virtual assistant, which is being rolled out as a pilot to a segment of its customers, uses artificial intelligence to respond to natural language text messages from users about their money, the bank said on Friday. For example, customers might ask Eno to show them their recent account balances or pay off a credit-card bill. Ken Dodelin, Capital One's vice president of digital product development, said the bank deliberately chose a gender-neutral name. The predominance of female names among popular digital assistants has provoked criticism recently.

The company created a technology that recognises spoken words ever closer to human parity. IBM reached a new AI milestone in speech recognition, achieving an industry record of 5.5% word error rate using the Switchboard linguistic corpus. The company broke the industry record by extending its deep learning technologies and incorporating an acoustic model that learns from positive examples while taking advantage of negative ones. The model gets smarter and performs better when similar speech patterns are repeated. IBM achieved another major AI milestone in conversational speech recognition last year with a computer system that reached a word error rate of 6.9%.

Every year the Mobile World Congress in Barcelona features the updates and innovations likely to sweep the mobile industry in the year ahead. What will it take to get you jazzed about smartphones again? The industry appears mostly stuck in neutral, as was evident at the Mobile World Congress trade show that took place in Barcelona, Spain, last week. There was a me-too-ness to most of the handsets on display, and it's been this way for a while. It was equally telling that one of the biggest stories to come out of MWC was about the relaunch of the Nokia 3310, a cheap compact throwback phone that debuted around the turn of century.

The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.

Here's a glimpse into the future of AI and HR via Rob May, CEO and Co-Founder of Talla, which builds intelligent assistants. While human resources has always viewed itself as the means by which an organization maximizes the value of its most crucial asset -- its people -- too many actual employees view HR as "the triage unit for bad workforce practices." HR is who you run to when a coworker is causing a problem, or there's an issue with your employment benefits. Strategic functions never seem to enter the HR equation. Artificial intelligence -- most specifically a combination of machine learning and natural language processing -- has matured to the point that it is practically useful in a workplace setting.

In my 25 years of working with large datasets, from developing early machine learning algorithms for multimedia systems in the 1990s to optimizing the email marketing infrastructure at GSI Commerce in the 2000s and now applying machine learning to big data to find actionable insights in real time, I've seen the convergence of machine learning and marketing firsthand. This year, I'm excited to see how machine learning (ML), an artificial intelligence (AI) discipline geared toward the technological development of human knowledge, has impacted the marketing big data ecosystem. I'm also intrigued by how much room I see for growth in the future. Machine learning techniques are being used to solve many diverse problems, and we stand to benefit as we move towards a world of hyper-converged data, channels, content, and context -- having the right conversation at the right time with the right person in the right way. For us marketers, ML is about finding nuggets of "predictive" knowledge in the waves of structured and unstructured data.

We rarely go to the bank anymore. Our main interaction with these money-tending edifices is through the tiny money-spitting mouths of ATMs. But we miss the human interaction. It's unlikely we'll ever get that same-level of human-banker contact back, but at least Capital One is giving us a banking chat bot, the first, they claim, SMS-based one in the U.S. The bank unveiled its new mobile technology, called Eno, at SXSW in Austin, Texas, on Friday. And yes, "Eno" is "One" spelled backwards.

Nice and steady does it. A video analysis system uses motion tracking and machine learning to assess how good surgeons are at suturing a wound. "Different surgeons all have different styles of suturing," says Aneeq Zia at the Georgia Institute of Technology in Atlanta, whose team developed the system. The team first captured footage of 41 surgeons and nurses practising their suturing and knot-tying skills on foam boards. Participants also wore an accelerometer on each hand to capture motion data, and a clinician then watched the videos and rated each person's skills.