A Stanford robotics team has built a humanoid robot that can stand in as "the physical representation" of a remote pilot -- a swimming avatar for dangerous aquatic expeditions. The robot, OceanOne, is a two-handed, anthropomorphic'bot that provides haptic feedback, meaning the pilot can "feel" what the robot reaches out and touches. OceanOne's first trip was to the wreck of the La Lune, a ship that sank off the coast of France in 1664, where the robot recovered artifacts. The goal is for OceanOne to help out on missions that are too dangerous for human divers. While OceanOne is still a prototype, the project could eventually become a fleet of robotic divers, working together as human pilots guide them from afar.

Looking at what early-stage startups are working on is not only entertaining -- and sometimes concerning -- it can also be a good indicator of where tech is headed. Since we have data on these young ventures at my company, Startup Tracker, we have the opportunity to glimpse emergent product trends in the startup space. We decided to put together a list of the 100 most interesting little-known startups in existence right now and to analyze the underlying patterns. We specifically focused on companies that are building something unique or unconventional. One interesting trend we spotted was the apparent birth of a startup meta-industry -- startups building products for other startups is becoming a thing.

Another week, another IBM acquisition: This time, a cloud consulting and implementation services specialist called Bluewolf Group. IBM (NYSE: IBM) said Thursday (March 31) the acquisition would help extend its analytics, cloud consulting and "experience design" capabilities. Financial details of the acquisition were not disclosed, but reports pegged the deal at about 200 million. Upon completion of the transaction, which is expected by the end of the second quarter of this year, IBM said Bluewolf would become part of its Interactive Experience unit focusing on offering consulting services for clients adopting Salesforce offerings via the cloud. The deal is intended to boost the IBM unit's customer experience and data integration platforms while adding a cloud consulting capability.

February's batch of Scripts of the Week highlights some of the month's best content produced by Kagglers on our public datasets. It also includes a great getting started script predicting outcomes of the 2016 NCAA basketball tournaments for March Machine Learning Mania 2016. Actually, I'm quite new to Kaggle, and before entering into the jungle of competitions, I wanted to train on datasets. I believe that training on datasets is a good way to start on Kaggle: there is no deadline, no competition. I chose this dataset because it was typically calling for sentiment analysis, which is a classical exercise for text-mining.

Everybody wants to look beautiful. We all like to be well dressed and keep up with fashion trends, but most times this is not possible. We are constrained by time, money and the skill to put together trendy outfits. The problem gets compounded when we go shopping online. Every store has 1000's of items in each category.

This week, Ford and Volvo announced they are forming a "coaliton" – along with Google – to push not only for the development of self-driving cars, but for federal "action" (their term) to force-feed them to us. The reasons are obvious: There's money – and control – in it. To understand what's going on, to grok the tub-thumping for these things, it is first of all necessary to deconstruct the terminology. The cars are not "self-driving." The "self-driving" car does what it has been programmed to do by the people who control it.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning. Its victory is a stunning achievement and another milestone in the inexorable march of AI research.

Microsoft Research is more important to the tech titan than ever. Crucial futuristic products like the HoloLens holographic goggles and Skype Translate came straight out of the company's science labs. But research wasn't always this central to Microsoft -- in fact, there used to be a "barrier" between the more academia-like Microsoft Research and the rest of the company, recalls Microsoft researcher emeritus and Silicon Valley icon Gordon Bell. For Microsoft Research, it was considered "not appropriate to be a part of what Microsoft was selling," Bell tells Business Insider. Bell, who helped set up Microsoft Research in the early 1990's, says that the team did great work on the very cutting edge of computer science and interaction design.

The stage and grade of psoriasis severity is clinically relevant and important for dermatologists as it aids them lead to a reliable and an accurate decision making process for better therapy. This paper proposes a novel psoriasis risk assessment system (pRAS) for stratification of psoriasis severity from colored psoriasis skin images having Asian Indian ethnicity. Machine learning paradigm is adapted for risk stratification of psoriasis disease grades utilizing offline training and online testing images. It uses two kinds of classifiers (support vector machines (SVM) and decision tree (DT)) during training and testing phases and two kinds of feature selection criteria (Principal Component Analysis (PCA) and Fisher Discriminant Ratio (FDR)), thus, leading to an exhaustive comparison between these four systems. Our database consisted of 848 psoriasis images with five severity grades: healthy, mild, moderate, severe and very severe, consisting of 383, 47, 245, 145, and 28 images respectively.

Berg is taking its tech-enabled approach to drug development into the clinic. The biotech, which is known for making brash statements about its ability to slash preclinical timelines, has incorporated its machine-learning technology into a Phase II pancreatic cancer trial in an attempt to identify the patients who are most likely to respond to the treatment. In its early years, Berg, which was cofounded in 2006 by real estate billionaire Carl Berg, applied a combination of genomics, systems biology, computational modeling and artificial intelligence to the discovery of a pipeline of products. Now, with two candidates in the clinic in multiple indications, the biotech is aiming to use similar capabilities to improve its odds of success in human trials. A Phase II study combining Berg's lead candidate, BPM 31510, with gemcitabine is acting as an early testing ground for the concept.