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 ...

Many people have this doubt, what's the difference between statistics and machine learning? Is there something like machine learning vs. statistics? From a traditional data analytics standpoint, the answer to the above question is simple. Machine learning is all about predictions, supervised learning, unsupervised learning, etc. Statistics is about sample, population, hypothesis, etc. Well, let's see if they are actually that different!

We have experimented with CaffeOnSpark on a 5 node MapR 5.1 cluster running Spark 1.5.2 and will share our experience, difficulties, and solutions on this blog post. Deep learning is getting a lot of attention recently, with AlphaGo beating a top world player at a game that was thought so complicated as to be out of reach of computers just five years ago. Deep learning is not just beating humans at Go, but also at pretty much every Atari computer game. But the fact is, deep learning is also useful for tasks with clear enterprise applications in the fields of image classification and speech recognition, AI chat bots and machine translation, just to name a few. Caffe is a C /CUDA deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC).

IBM plans to launch a cloud-based version of Watson's cognitive computing technology, designed solely to zero in on cybersecurity language, as a part of a year-long research project, the company announced Tuesday. The Watson for Cyber Security platform is touted as the first technology to offer cognition of security data. Watson will pull the majority of its cognitive data from the X-Force research library: a threat intelligence platform with 20 years of security research, details on 8 million spam and phishing attacks and more than 100,000 documented vulnerabilities. "Even if the industry was able to fill the estimated 1.5 million open cybersecurity jobs by 2020, we'd still have a skills crisis in security," Marc van Zadelhoff, general manager of IBM Security said in a statement. "The volume and velocity of data in security is one of our greatest challenges in dealing with cybercrime."

A Law Firm Just Hired Its First Artificial Intelligence Attorney . While only a portion of the typical attorney's work involves legal research and creation of memoranda of law and tables of authorities, AI Ross can apparently understand natural language queries, perform legal research, and spit out memoranda of law and answers without human intervention. A human lawyer can then evaluate the response, and advise clients accordingly. Will AI lawyers take over the practice, or will they just enhance the ability of the lawyers still working? What if clients decide that the quaility of the work product and the answers of the AI computer give them the option to cut out the middleman?

Artificial neural networks (ANNs) are a type of information processing system based on mimicking the principles of biological brains, and have been broadly applied in application domains such as pattern recognition, automatic control, signal processing, decision support systems and artificial intelligence. Spiking neural networks (SNNs) are a type of biologically inspired ANN that perform information processing based on discrete time spikes. They are more biologically realistic than classic ANNs, and can potentially achieve a much better performance-power ratio. Recently, researchers from Zhejiang University and Hangzhou Dianzi University in Hangzhou, China successfully developed the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on spiking neural networks, fabricated by standard CMOS technology. With the rapid development of the "Internet of Things" and intelligent hardware systems, intelligent devices are pervasive in today's society, providing many services and conveniences to people's lives. But they also raise challenges of running complex intelligent algorithms on small devices.

Leading industry experts are launching a Cognitive Computing Consortium to focus on furthering innovation in cognitive computing. The consortium is an interactive forum for researchers, developers, and practitioners of cognitive computing and its allied technologies. The consortium was co-founded by Sue Feldman, CEO, Synthexis; and Hadley Reynolds, principal analyst at NextEra Research, to fill a gap in the industry. Its mission is to enable professionals to exchange ideas and insights to conduct research and to educate buyers, users and the public on cognitive computing technologies, their uses, and potential impacts. The group was inspired to form after vendors told various experts that they needed an unbiased source to which they can refer potential clients for validation, advice, and background information.

Google is about to set its developer agenda for the next year. So, what kind of news should you expect from Google I/O 2016? Google, as it normally does, has organized I/O around three distinct categories: development, monetization and the future. The conference will have 190 sessions for developers to learn how to make fast and efficient Web apps, optimize Android development and learn about the tools and features that will progressively make the Internet a more intelligent place. If you've never experienced a Google I/O before, the sessions can be very technical.