We are fast moving from the app era to the era of the intelligent agent. By definition, these agents complete entire jobs by themselves, which means they must learn to understand us and our objectives. Therein lies the technical challenge--for humans often don't say what they mean. Worse, we believe that we're being clear when our communications are riddled with ambiguity. Amy's job is to schedule meetings.

Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately. The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you.

In a series of videos posted on Kellogg Insight, David Ferrucci, the lead scientist behind IBM's Watson computer, sits down with Kellogg School of Management professor Brian Uzzi to discuss how machine learning and artificial intelligence will become central to the future of business. The discussion took place at the Kellogg School's first Computational Social Science Summit.

Last week Facebook unveiled chatbots, its artificial intelligence-powered messaging system where people can text businesses inside Facebook Messenger and receive natural language responses to customer service inquiries. But after a week of use, many people have found Facebook's chatbots less than helpful. The problem is the chatbots don't talk like you'd speak to someone in a normal conversation, and you end up with responses like you see below. Bots are *amazing* Mind blown. Facebook has acknowledged the rocky rollout and urged people to give it time to improve.

At last week's F8 developer conference, Mark Zuckerberg showed off the company's ten-year roadmap. Zuckerberg's intention here was to show Facebook's three-stage gameplan in action: First, you take a neat cutting-edge technology. Then, you build a product based on it. Then, you turn it into an ecosystem where developers and outside companies can use that technology to build their own businesses. Maybe I'm weird, though, because I looked at this slide and said "okay...then what happens?" Facebook is too busy with the short term to provide handy answers.

Updated April 12, 2016 16:26:57 With oil and gas prices hovering at decade lows, companies are turning to artificial intelligence to cut costs and boost productivity. The technology, which gives companies the ability to predict future problems, is estimated to save the industry trillions of dollars and lead to a new wave of highly sophisticated jobs. At a time when the banking industry needs to become increasingly focused on creating better customer experiences, the importance of distributing personalized communications that provide real value has never been greater. Artificial intelligence (AI) can help make this possible - both automatically and at scale. The banking industry is undergoing a major transformation.

Computer scientists from the Michigan Institute of Technology (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.

Mobileye, the Israeli car automation company that came onto the self-driving car scene as sort of an anti-Google, is now looking at the future in terms that seem a bit closer to Google's than used to be the case. Speaking Friday at a conference organized by Goldman Sachs (which owned a chunk of Mobileye's shares when the company first became publicly traded in 2014), Amnon Shashua, Mobileye's founder and chief technical officer, placed a lot of emphasis on mapping, something Google has done all along.

For a long time search engines relied on static ranking factors. Those webmasters and SEOs who knew what to pay attention for were able to reach the best positions on Google's SERPs. This has changed recently and will be changing in the future: The increasing usage of machine learning techniques leads to both dynamic ranking criteria and – as confusing as it may sound – a greater influence of human signals. Machine learning is nothing new. Its roots go back to the 50s of the last century.

The rise of both structured and unstructured data has created a booming market that is expected to be worth around 41.5 billion by 2018. The rapid growth of the big data market has resulted in the creation of a large crop of vendors that are all looking to take a slice. Amid the plethora of vendors competing for market position are a number of start-ups that are aiming to help organisations collect and analyse data. CBR identifies 10 companies that are worth watching. Founded in 2014, the company has over 30 million in capital raised so far from investors such as LinkedIn, Index Ventures, Benchmark Capital and The Data Collective.