But you probably know this already, if only from the preponderance of conference presentations, blogs and trade articles on the topic. Yes, text analytics are all the rage these days. You may feel under the gun to catch up, but if you're late to the game, you may be comforted to know that for many people, text analytics aren't living up to the hype. Nearly every researcher I come in contact with at conferences and through my professional network is at least actively investigating text analysis if they haven't already adopted a solution. And in either case, they're frequently underwhelmed. It's my experience that there are two primary reasons for this: How Do I Know Before I Buy?

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.

Artificial intelligence (AI) is already becoming entrenched in many facets of everyday life, and is being tapped for a growing array of core business applications, including predicting market and customer behavior, automating repetitive tasks and providing alerts when things go awry. As technology becomes more sophisticated, the use of AI will continue to grow quickly in the coming years, as explored in a new eMarketer report, "Artificial Intelligence 2016: What's Now, What's New and What's Next" (eMarketer PRO customers only). In its most widely understood definition, AI involves the ability of machines to emulate human thinking, reasoning and decision-making. A May 2015 survey of US business executives by Narrative Science found that 31% of respondents believed AI was "technology that thinks and acts like humans." Other conceptions included "technology that can learn to do things better over time," "technology that can understand language" and "technology that can answer questions for me."

Chatbots are the new black in UX design. Facebook already has over 11,000 chatbots since the official announcement of chatbot support made earlier this year. The question is: Should you build a chatbot for your business? Yes, if your goal is to create a more personalized experience with your customers without hiring additional staff. You shouldn't be perceived just as "faceless" website or a product page.

For example, if you visit the landing page for the Poncho weatherbot, it clearly states how to use it -- as a virtual weather agent.

A study published last Monday, heralded as an historic achievement by Microsoft, details a new speech recognition technology that's able to transcribe conversational speech as well as humans -- or at least, as best as professional human transcriptionists (which is better than most humans). The technology scored a word error rate (WER) of 5.9%, which was lower than the 6.3% WER reported just last month. "[I]t's the lowest ever recorded against the industry standard Switchboard speech recognition task," Microsoft reports. The rate is the same as (or even lower than) the human professional transcriptionists who transcribed the same conversation. "We've reached human parity," says Xuedong Huang, Microsoft's chief speech scientist.

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Using Artificial intelligence (AI) or machine learning technology, a team of researchers has predicted outcomes in judicial decisions at the European Court of Human Rights (EctHR) with 79 per cent accuracy. The AI method, developed by researchers from University College London (UCL), University of Sheffield and US-based University of Pennsylvania is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm. "We don't see AI replacing judges or lawyers but we think they will find it useful for rapidly identifying patterns in cases that lead to certain outcomes," said Nikolaos Aletras, who led the study at UCL's computer science department. "It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," Aletras added. In developing the method, the team found that judgements by the ECtHR are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are'realists' rather than'formalists'.

Soon, police officers won't need to pull you over if you're caught texting while driving. The machine vision technology company Movidius has teamed up with the Chinese security camera maker Hikvision to create smart cameras that can catch behavior such as leaving a suspicious package in a public place, driving while distracted by mobile devices, and break ins. Advanced visual analytics will also allow cameras to identify car models and detect seat belts. The partnership will debut a new line of cameras with higher accuracy than traditional computing techniques in China this week. Both search giant Google and the world's leading drone manufacturer DJI have relied on Movidius to enhance spatial awareness in virtual reality and engineer the sense and avoid features inside drones respectively.

Researchers were able to predict the results of human rights cases with 79 percent accuracy by using artificial intelligence to analyze the factual sections of published human rights judgments. The study, published in PeerJ Computer Science, found that the outcomes were best predicted by analyzing the "circumstances" section of a case--which includes factual background--along with the topics covered by the case and the language used, according to a press release. Publications covering the findings include the Wall Street Journal Law Blog, Law.com The researchers examined 584 cases before the European Court of Human Rights with a machine-learning algorithm. They found that the court's judgments were highly correlated to facts rather than legal arguments.