The 20-day poker tournament between four human pros and an artificial intelligence program concluded last night. The AI, named Libratus, was created by researchers at Carnegie Mellon University and bested its opponents by $1.76 million in chips. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," said Libratus co-creator Tuomas Sandholm in a press release. While the AI has so far only been applied to poker, CMU is eager to experiment with its abilities in other fields. "The computer can't win at poker if it can't bluff," said Frank Pfenning, head of the Computer Science Department at CMU. "Developing an AI that can do that successfully is a tremendous step forward scientifically and has numerous applications. Imagine that your smartphone will someday be able to negotiate the best price on a new car for you. There was intense speculation during the tournament about how Libratus was able to shift its strategy so dramatically from day to day. A number of poker pros weighed in to say that the researchers must be tinkering with it by hand at night. The event was surrounded by speculation about how Libratus was able to improve day to day during the competition. It turns out it was the pros themselves who taught Libratus about its weaknesses. "After play ended each day, a meta-algorithm analyzed what holes the pros had identified and exploited in Libratus' strategy," Sandholm said. This is very different than how learning has been used in the past in poker. Typically researchers develop algorithms that try to exploit the opponent's weaknesses. In contrast, here the daily improvement is about algorithmically fixing holes in our own strategy."

In the past human work was preferred over a machine's work because a human was more accurate than a machine. After all a human could look at all angles and make an informed decision, and a machine could not. But enter machine learning today, and a machine might be more useful than a human in shaping customer experiences. Today machine learning can help brands scale their engagement operations and provide increasingly relevant experiences. And the good news is now you don't have to be a software expert to use machine learning.

To manage complex web services with lots of calls to the cloud, many businesses have invested in Application Performance Management (APM) and Network Performance Management (NPM) tools. Together APM and NPM tools are essential aids in improving a business's infrastructure required to support an effective web experience... but they are missing a critical component - Internet visibility. Internet connectivity has always played a role in customer access to web presence, but in the past few years use of the Internet in the web experience has dramatically increased. The broadening use of Cloud Services sources means that web content is coming to the customer from all over the Internet in a multitude of paths. Relying solely on Network and Internet Service Providers will get you connected but at what cost to performance and cost.

Housed in the classic Denver Gas & Electric Building, 910 15th St., 910Telecom is a carrier-neutral telecom hotel located in the heart of Denver. Adjacent to CenturyLink, AT&T, and Denver Main, 910Telecom offers connectivity to all major carriers, Internet service providers, Internet backbones and exchanges. The 910Telecom Meet Me RoomTM facilities provide access to hundreds of customers as well as national and local carriers without incurring local loop fees. Passive and active panels support circuit connections from T1 to DS3, OC3-OC192, and GigE Ethernet. All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020.

Perhaps the most positive technical theme of 2016 was the long-delayed triumph of artificial intelligence, machine learning, and in particular deep learning. In this article we'll discuss what that means and how you might make use of deep learning yourself. Perhaps you noticed in the fall of 2016 that Google Translate suddenly went from producing, on the average, word salad with a vague connection to the original language to emitting polished, coherent sentences more often than not -- at least for supported language pairs, such as English-French, English-Chinese, and English-Japanese. That dramatic improvement was the result of a nine-month concerted effort by the Google Brain and Google Translate teams to revamp Translate from using its old phrase-based statistical machine translation algorithms to working with a neural network trained with deep learning and word embeddings employing Google's TensorFlow framework. The researchers working on the conversion had access to a huge corpus of translations from which to train their networks, but they soon discovered that they needed thousands of GPUs for training and would have to create a new kind of chip, a Tensor Processing Unit (TPU), to run Translate on their trained neural networks at scale.

Artificial Intelligence (AI) is undergoing a transformation from silo implementations to a utility function across many industry verticals as a form of Artificial General Intelligence (AGI) capability. This capability is becoming embedded and/or associated with many applications, services, products, and solutions. Mind Commerce sees AI innovation in a variety of areas including personalized AI to both support and protect end-users. This research evaluates the growth of AI, its application across diverse sectors, and the associated impact upon Public Safety, Security, and Privacy. Topics addressed in the report include: Assessment of how AI impacts public safety, security, and privacy Case study analysis of select AI initiatives related to public safety and security Views on the rise of AGI and its application related to public safety, security, and privacy Analysis of AI initiatives in Government, Businesses, Professional Bodies, and Universities Forecasts for Cognitive System and Content Analytics Software, AI Software & Analytics, AI Public Safety Software, and AI Software for Fraud Detection, Risk Management, and Automated Planning 2017 – 2022 All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing.

What if artificial intelligence actually made a difference in our everyday lives? If you think about it, technology for processing information like humans do is still at an early stage. It shows up in chatbots and on speakers like the Amazon Echo. Yet, many of the services we use each day are still not AI-enabled, which is unfortunate. It's one thing to make a car we can't afford or to make a speaker smarter, but what about common products and services?

Since originally writing this article, many people with far more expertise in these fields than myself have indicated that, while impressive, what Google have achieved is evolutionary, not revolutionary. In the very least, it's fair to say that I'm guilty of anthropomorphising in parts of the text. I've left the article's content unchanged, because I think it's interesting to compare the gut reaction I had with the subsequent comments from experts in the field. I strongly encourage readers to browse the comments beneath the version of this piece published on Medium.com In the closing weeks of 2016, Google published an article which quietly sailed under most people's radar.

Sharing emotion-driven narratives that resonate with other people is something humans are quite good at. We've been sitting around campfires telling stories for tens of thousands of years, and we still do it. One reason why is because it's an effective way to communicate: We remember stories. But what makes for good storytelling? Mark Magellan, a writer and designer at Ideo U, puts it this way: "To tell a story that someone will remember, it helps to understand his or her needs. The art of storytelling requires creativity, critical-thinking skills, self-awareness, and empathy."

Google is increasingly relying on machine learning to order search results, increasing concerns among travel brands about how they'll continue to be ranked. Sure, Google's use of machine learning might not be anything new, but understanding how it's using this technology is vital for the sector which can sometimes struggle to satisfy its notoriously fickle and savvy customer base. NB: This is an analysis by Daniel Bensley, travel industry lead at Qubit. To me, this report highlights a wider challenge for the industry around our approach to AI and machine learning techniques. Whether it's Google changing its search ranking metrics or technology's impact on jobs in the sector, headlines around AI are often more likely to spark fear rather than excitement.