Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps. This paper presents a data capture protocol that partially addresses these deficiencies by asking participants to specify ranges over which a grasp is valid. The protocol is verified both qualitatively through online survey questions (where within-range grasps are identified correctly with the nearest extreme grasp) and quantitatively by showing that there is small variation in grasps ranges from different participants as measured by joint angles and position. We demonstrate that these grasp ranges are valid through testing on a physical robot (93.75% of grasps interpolated from grasp ranges are successful).

While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users' past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors' profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.

As Hyperloop One continues its attempt at building the future of public transportation, it's moving on without the baggage of a messy lawsuit. The company announced today that it has reached a settlement with former employees, including co-founder and former CTO Brogan BamBrogan. No terms were disclosed, however, the lawsuit contained allegations of financial mismanagement, harassment and threats, which Hyperloop One had responded to with a $250 million suit of its own, claiming the exec had tried to lead a coup within the company. In a memo to current employees, CEO Rob Lloyd looked forward, citing the company's opening of a fabrication facility, acquiring $50 million in financing and new partnerships. Now the company can focus on more standard issues, like delivering on its vision of self-driving vehicles that turn into high-speed train cars.

It's been more than a decade since Jurassic Park hit theaters, but fans of the film have not yet given up hope that such a park could one day exist. Now, a Japanese firm is one step closer to making this a reality with an animatronic dinosaur park called Dino-A-Live where visitors see realistic replicas first hand. The theme park would contain full-sized dinosaur robots, and the firm set some of them lose in a Tokyo hotel to announce the park - to the horror of those in attendance. A Japanese firm announced an animatronic dinosaur park called Dino-A-Live where visitors see realistic replicas first hand. This fictitious Jurassic Park is the brainchild of Kazuya Kanemaru, who is the CEO of ON-ART Corp. Kanemaru and his team unveiled fully-painted man-operated robotic models of raptors, an allosaurus and a tyrannosaurus rex, in a performance at a hotel hall in Tokyo.

"The corridor connecting Cambridge, Milton Keynes, and Oxford could be the UK's Silicon Valley," the Infrastructure Commission said in a report published this week. The report recommended bringing forward £100 million in funding to create a western section of the East West Rail project by 2024, and that the government should commit up to a further £10 million in development funding to continue work on the central section, the part that would link Oxford with Cambridge. There used to be a rail link between Oxford and Cambridge but it was closed in 1967. It currently takes two and a half hours and two changes via London to travel by train between the university cities. The report describes the journey as "difficult, slow and unreliable," contrasting it to the strong north-south links to and from London.

One of the big trends we've seen over the last five years is automation. At the same time, we're also seeing more intelligence built into tools we already have, like phones and computers. Where do you see this process in five years? In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things. You will have more intelligent systems in the physical world, too -- not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world.

Decision trees can suffer from high variance which makes their results fragile to the specific training data used. Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated. Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance. In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.

CAMBRIDGE, MA - 10 Nov 2016: IBM Watson Health (NYSE: IBM) and the Broad Institute of MIT and Harvard today announced a research initiative aimed at discovering the basis of cancer drug resistance. The five year, $50 million project will study thousands of drug resistant tumors and draw on Watson's computational and machine learning methods to help researchers understand how cancers become resistant to therapies. The anonymized data will be made available to the scientific community to catalyze research worldwide. IBM Watson Health and the Broad Institute bring the data prowess of Watson to study cancer drug resistance in a $50M research collaboration. The goal is to identify patterns that could reveal clues into one of the greatest medical mysteries of cancer.

Investors believe it is'inevitable' that artificial intelligence will destroy millions of jobs and that governments are unprepared for such an impact, according to a new survey. Artificial intelligence (AI), or the process by which computers or robots take on tasks that need human intelligence, is one of the key themes of this week's Web Summit in Lisbon. The poll among 224 venture capitalists attending the conference showed 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The poll among 224 venture capitalists attending the Web summit in Lisbon found 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The survey also found that 83 percent of the investors canvassed expect Britain's exit from the European Union to damage Europe's economy and 77 percent believe it will damage British startups.

As part of a three-week pilot study this quarter, half of the students in STATS 60: "Introduction to Statistical Methods" were assigned a RoboTA, an artificially intelligent robot teaching assistant (TA), to answer their questions by email. Students emailed different addresses based on the TA they were assigned. The emails were then funneled through a program, stripped of any identifying information and relayed to Lucas Janson, the TA for the class. Janson replied to all the emails without knowing whether they were intended for the human TA or the artificial intelligence (AI) TA. This study is double-blind, so neither the students nor the experimenters have information about the other, which helps prevent bias.