When it comes to robots stealing our jobs, there's one task that plenty of people would be happy to relinquish: assembling flat-pack furniture. The day we can handball that job off is a step closer. In Science Robotics today, a trio of roboticists from Nanyang Technological University in Singapore unveiled a semi-autonomous pair of robotic arms that can put together the frame of an IKEA chair. Made from off-the-shelf gear, their invention can look at chair parts scattered around, grasp and lift the right bit, carefully insert wooden pins in pre-drilled holes and slot the pieces together. And while the robot's repertoire of furniture-building skills is limited -- it can't yet screw in metal screws, for instance -- the technology could soon be ready for jobs that require human-like dexterity, such as electronics and aircraft manufacturing.

These days, you hear a lot about machine learning (or ML) and artificial intelligence (or AI) – both good or bad depending on your source. Many of us immediately conjure up images of HAL from 2001: A Space Odyssey, the Terminator cyborgs, C-3PO, or Samantha from Her when the subject turns to AI. And many may not even be familiar with machine learning as a separate subject. The phrases are often tossed around interchangeably, but they're not exactly the same thing. In the most general sense, machine learning has evolved from AI. In the Google Trends graph above, you can see that AI was the more popular search term until machine learning passed it for good around September 2015.

It's also going to be a long time before Indians get to see fully autonomous cars, the poster boys of artificial intelligence, on their roads. Currently, there are not many automobile manufacturers training such cars in India. Reasons include badly maintained roads, scarce road signage, unruly drivers and pedestrians, and, most importantly, lack of government support. None of the Indian states have clear laws for such vehicles. Furthermore, in order to avoid worsening the already bad unemployment numbers, the current Union Minister for Road Transport and Highways has even suggested banning them altogether.

With the rise of artificial intelligence, several of today's larger organizations face the issue of restructuring. Several global companies believe their organizational design is less than ideal, but few know how to go about remedying it. The answer to this is to make sure the global workforce "upskills" to be qualified enough to handle fresh positions. Adapting to a rapidly-changing world of work will be the defining task of our time - organizations ought to invest in their workers to ensure they aren't left behind. Those with the right skills will be at an advantage to choose how, where and when they work.

Those who fear the rise of the machines, look away now. In a laboratory in Singapore two robots have mastered a task that roundly defeats humans every weekend: they have successfully assembled an Ikea chair. Engineers at Nanyang Technological University used a 3D camera and two industrial robot arms fitted with grippers and force sensors to take on the challenge of building an £18 "Stefan" chair from the furniture company. Working together, the robots completed the job in 20 minutes and 19 seconds after having the parts placed in front of them. More than half of the time was spent planning moves, with the execution taking nine minutes in total.

Student performance prediction - where a machine forecasts the future performance of students as they interact with online coursework - is a challenging problem. Reliable early-stage predictions of a student's future performance could be critical to facilitate timely educational interventions during a course. However, very few prior studies have explored this problem from a deep learning perspective. In this paper, we recast the student performance prediction problem as a sequential event prediction problem and propose a new deep learning based algorithm, termed GritNet, which builds upon the bidirectional long short term memory (BLSTM). Our results, from real Udacity students' graduation predictions, show that the GritNet not only consistently outperforms the standard logistic-regression based method, but that improvements are substantially pronounced in the first few weeks when accurate predictions are most challenging.

Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesting parts in large amounts of data, including video and text data. In opposition to existing techniques for detecting isolated anomalous data points, we propose the "Maximally Divergent Intervals" (MDI) framework for unsupervised detection of coherent spatial regions and time intervals characterized by a high Kullback-Leibler divergence compared with all other data given. In this regard, we define an unbiased Kullback-Leibler divergence that allows for ranking regions of different size and show how to enable the algorithm to run on large-scale data sets in reasonable time using an interval proposal technique. Experiments on both synthetic and real data from various domains, such as climate analysis, video surveillance, and text forensics, demonstrate that our method is widely applicable and a valuable tool for finding interesting events in different types of data.

We show a sufficient condition on the dissimilarity measure that leads to the consistency of the algorithms for clustering offline and online data settings, respectively. As an example of application, clustering synthetic data sampled from multifractional Brownian motions is provided.

This paper presents a technical approach to robot learning of motor skills which combines active intrinsically motivated learning with imitation learning. Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies that solve a corresponding distribution of parameterised goals/tasks. This is made possible by the technical integration of imitation learning techniques within an algorithm for learning inverse models that relies on active goal babbling. After reviewing social learning and intrinsic motivation approaches to action learning, we describe the general framework of our algorithm, before detailing its architecture. In an experiment where a robot arm has to learn to use a flexible fishing line , we illustrate that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation and benefits from human demonstration properties to learn how to produce varied outcomes in the environment, while developing more precise control policies in large spaces.

Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference is incorporated into the MCS process for reducing sensing costs while its quality is guaranteed. Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i.e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of quality. To address this issue, this paper proposes a Deep Reinforcement learning based Cell selection mechanism for Sparse MCS, called DR-Cell. First, we properly model the key concepts in reinforcement learning including state, action, and reward, and then propose to use a deep recurrent Q-network for learning the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Furthermore, we leverage the transfer learning techniques to reduce the amount of data required for training the Q-function if there are multiple correlated MCS tasks that need to be conducted in the same target area. Experiments on various real-life sensing datasets verify the effectiveness of DR-Cell over the state-of-the-art cell selection mechanisms in Sparse MCS by reducing up to 15% of sensed cells with the same data inference quality guarantee.