AI has begun to take hold in the everyday, in the form of Siri, Alexa and autonomous vehicles. But if identifying the nearest Korean BBQ and driving me there is all that the future of AI promises, well that's a damn shame. It's time that we become a lot more ambitious about the future of AI. There's a broad perception that the best that AI has to offer will be in the consumer world. This is not the case, and it demonstrates a severe lack of imagination.

IBM this week said its speech recognition system set an industry record of 5.5% word error rate, a percentage that lets a computer understand human conversation almost as well as the average person does. According to IBM human parity was considered a 5.9% word error rate but IBM who partnered with Appen, a speech and technology service provider, reassessed the industry benchmark and determined that human parity is lower than what anyone has yet achieved: 5.1%. "Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the ultimate industry goal. Others in the industry are chasing this milestone alongside us, and some have recently claimed reaching 5.9% as equivalent to human parity…but we're not popping the champagne yet. As part of our process in reaching today's milestone, we determined human parity is actually lower than what anyone has yet achieved -- at 5.1%," wrote George Saon principal research scientist with IBM in a blog post on the subject. That reassessment however might ruffle some feathers as in October Microsoft Artificial Intelligence and Research group said its speech recognition system had attained "human parity" and made fewer errors than a human professional transcriptionist.

As people seek affirmation of their political beliefs in an increasingly polarized nation with accusations of fake news from both sides, smart device AI (artificial intelligence) is more than happy to comply. Concise, loaded questions tend to yield loaded results when querying Google Assistant, the voice-activated AI technology that comes with Google Pixel phones and Google Home – the latter a voice-activated speaker. When I asked Google Assistant, "Is Donald Trump a racist?" And if your question is rephrased in less-raw, accusatory terms such as, "Is President Trump racially insensitive?" the top results are, again, the Huffington Post story and stories like Vox's "Trump's win is a reminder of the incredible, unbeatable power of racism." Jump the political divide and former President Barack Obama doesn't get off that easy either but top results don't tend to be from large media organizations, which have been accused of being softer on the former president than the current commander-in-chief.

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.