IS BIG DATA TAKING US CLOSER TO THE DEEPER QUESTIONS IN ARTIFICIAL INTELLIGENCE? What I'm worried about and what I'm thinking about these days is if we're really making progress in AI. I'm also interested in the same kind of question in neuroscience, which is that we feel like we're making progress, but are we? There's huge progress in AI, or at least huge interest in AI--a bigger interest than there's ever been in my lifetime. I've been interested in AI since I was a little kid trying to program computers to play chess, and do natural language databases, and things like that, though not very well. I've watched the field and there have been ups and downs. There were a couple of AI winters where people stopped paying attention to AI altogether. People who were doing AI stopped saying that they were in the field of AI. They say, "Yes, I do artificial intelligence," where two years ago they would have said, "I do statistics." Even though there's a lot of hype about AI and a lot of money being invested in AI, I feel like the field is headed in the wrong direction. There's been a local maximum where there's a lot of low-hanging fruit right now in a particular direction, which is mainly deep learning and big data. People are very excited about the big data and what it's giving them right now, but I'm not sure it's taking us closer to the deeper questions in artificial intelligence, like how we understand language or how we reason about the world. The big data paradigm is great in certain scenarios. One of the most impressive advances is in speech recognition. You can now dictate into your phone and it will transcribe most of what you say right most of the time. That doesn't mean it understands what you're saying. Each new update of Siri adds a new feature. First, you could ask about movie times, then sports, and so forth. The natural language understanding is coming along slowly. You wouldn't be able to dictate this conversation into Siri and expect it to come out with anything whatsoever.

Back in October 2014, Taylor Swift opened an account on Line, the messaging app that is massively popular in Asia. The account currently doesn't do much. If you communicate with it, you can hear a funny voice message from Swift, for instance. Paul McCartney has one, too. Burberry and Selfridges also have accounts on WeChat and Line.

Machine learning has invaded and challenged traditional methods in unimaginable ways in the recent past. Continuing in the same vein, today we look at some of the innovative ways machine learning is addressing alcohol drinking related issues. Be it drunken outburst on social media or drunken driving, new age algorithms are preparing to help you save both your life as well as your reputation. Detecting outbursts on social media More than harmful, drunk tweeting or social media updates are regretful. And a group of researchers at the University of Rochester have been building a machine learning tool just to address this issue.

AstraZeneca's announcement today that they have joined forces with Human Longevity, a US sequencing and machine learning company, to sequence 2 million genomes is therefore not a surprise as such, but it does represent a step up in terms of the scale of such projects. AstraZeneca will be able to use Human Longevity's database of 1 million genomic and heath records alongside 500,000 DNA samples from their own clinical trials. The creation of this new database is likely to take as long as a decade, but the project will also include sequences from samples donated in the past 15 years.

Accenture has acquired OPS Rules, a boutique analytics consulting company that specializes in the application of data science to create supply chain and operations analytics solutions. When the acquisition is completed, Accenture will add new operations analytics professionals to its team to apply machine learning and optimization techniques to develop analytics approaches for clients across many industries. Founded in 2012, OPS Rules has offices in Waltham, Mass., and Richardson, Texas. OPS Rules is led by David Simchi-Levi, a professor of engineering systems at the Massachusetts Institute of Technology (MIT) and renowned supply chain and operations analytics expert. Simchi-Levi and his team will join Accenture Analytics, part of Accenture Digital, and will also be a part of Accenture's Data Science Center of Excellence, an innovation team that focuses on solving immediate and complex client problems through advanced analytics approaches, including machine learning, deep learning, text analytics and more.

One of the most difficult parts of creating Artificial Intelligence that actually appears intelligent is having it read, hear, understand and process human language. Human language is incredibly ambiguous and every sentence we speak, though perhaps clear to us because we have the ability to apply context and lived experience, could have tens of thousands of possible syntactic structures which drastically alter its meaning, making things much harder for a computer system. For example in the sentence "Alice drove down the street in her car" we can quickly establish what that means but a machine might not find it so simple and instead interpret that the street is located in Alice's car. This ambiguity might be silly to us but it isn't entirely impossible and a machine without our lived experiences would not be so quick to discard it as a possible interpretation. To help AI overcome this problem and provide a solid foundation for Natural Language Understanding systems, Google has developed SyntaxNet, an open-source neural network framework that helps AI figure out all the different ways a sentence could be understood before scoring them and establishing the most likely option.

Artificial Intelligence has been prominent in tech news recently, and was a hot topic at SXSW. The technology has massive potential for use in customer communication, yet will it ever be able to completely replace the need for a human element? Facebook has already launched itself into the AI arena by introducing chatbots to its messenger app. Facebook Messenger users will now be able to chat to select partners and check on the status of their orders, receiving replies from AI, rather than an actual customer service agent. The question is, is this a feasible solution for the majority of brands at the moment?

In experiments involving a simulation of the human esophagus and stomach, researchers at MIT, the University of Sheffield, and the Tokyo Institute of Technology have demonstrated a tiny origami robot that can unfold itself from a swallowed capsule and, steered by external magnetic fields, crawl across the stomach wall to remove a swallowed button battery or patch a wound. The new work, which the researchers are presenting this week at the International Conference on Robotics and Automation, builds on a long sequence of papers on origami robots from the research group of Daniela Rus, the Andrew and Erna Viterbi Professor in MIT's Department of Electrical Engineering and Computer Science. "It's really exciting to see our small origami robots doing something with potential important applications to health care," says Rus, who also directs MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "For applications inside the body, we need a small, controllable, untethered robot system. It's really difficult to control and place a robot inside the body if the robot is attached to a tether."

Artificial intelligence taking jobs has been the topic of much debate on several levels. Financial markets have perhaps been most impacted by computer automation over the past decade, spawning tremendous efficiency. But computer automation and artificial intelligence intelligence taking jobs has also been an issue. Concerns for high paying job loss in the banking industry as well as other C Suite jobs have been expressed alongside low skill workers. At the Milken Institute Conference in Beverly Hills, CA a group of automated hedge fund leaders weighed in on the topic.

On May 8, a group of Danish researchers publicly released a dataset of nearly 70,000 users of the online dating site OkCupid, including usernames, age, gender, location, what kind of relationship (or sex) they're interested in, personality traits, and answers to thousands of profiling questions used by the site. When asked whether the researchers attempted to anonymize the dataset, Aarhus University graduate student Emil O. W. Kirkegaard, who was lead on the work, replied bluntly: "No. This sentiment is repeated in the accompanying draft paper, "The OKCupid dataset: A very large public dataset of dating site users," posted to the online peer-review forums of Open Differential Psychology, an open-access online journal also run by Kirkegaard: Some may object to the ethics of gathering and releasing this data. However, all the data found in the dataset are or were already publicly available, so releasing this dataset merely presents it in a more useful form. For those concerned about ...