This article was first published on the IEC e-tech website. Advances in sensors have increased the ability of assistive robots to perform domestic handling and mobility assistance tasks as well as to analyze the environment and individuals around them so that they can carry out monitoring functions. About 4 700 elderly assistance robots were sold globally in 2015, according to the International Federation of Robotics in Frankfurt, which forecasts that sales will increase to 37 500 units between 2016 and 2019. Robots assisting with healthcare delivery in the home not only increase their users' autonomy but also have the potential to relieve the burgeoning demands that elderly populations place on health and care services and informal caregivers. Meanwhile, increasingly sophisticated "companion robots" are being developed to interact with users, make social connections with them and even provide companionship and emotional support, subject to personal choice.

From drone warfare to self-checkout lines at the grocery store, the change is clear. Sales of automated industrial "robots" rose 15% year-over-year in 2015, to reach an annual record of 253,748 units, according to the International Federation of Robotics. Those units were valued at $11 billion, 9% more than the year before. That lifted the total installed base, worldwide, to 1.6 million industrial bots, a number that the IFR projects will reach 2.6 million in 2019. For investors, it smells like opportunity.

The Future Group has launched a Future Consumer and Digital (C&D) Lab in Bengaluru that will focus on the consumer and digital space to bring in next-generation innovations using Artificial Intelligence, Big Data Analytics, Blockchains, Internet of Things, Robotics, and allied technologies. If you are a startup working in these areas, then the Future Group will love to work with you as your primary customer and support your growth with infrastructure and funding. "We want to collaborate with Indian companies in developing the next-generation of technologies in the consumer space. We have data on over 30 million loyal customers and the infrastructure to prototype new products and ideas. We are looking to partner with founders who are ambitious to create game-changing businesses leveraging these capabilities."

BEIJING - China has great potential in applications of artificial intelligence (AI), a senior official said Sunday. "Chinese researchers and entrepreneurs are among the best in the world, with technological innovations and good earnings in the sector," said Liu Lihua, vice minister of industry and information technology. Researchers with Chinese companies such as iFlytek, Alibaba and Baidu participated in the study of the world's leading AI technologies, said Liu, referring to technologies of reinforcement learning, paying with your face and self-driving trucks. A couple of weeks ago, the "MIT Technology Review" listed the above three and another seven technologies as its 10 breakthrough technologies in 2017. AI research started more than 60 years ago and there have been some major ups and downs.

Volkswagen really, really wants to prove that it's moving past its diesel emissions scandal, and to that end it's unveiling yet another electric car concept following the I.D. and I.D. Buzz. This third model will be VW's first crossover EV, blending a "four-door coupé" with an SUV. You'll get the full scoop on the third I.D. at Auto Shanghai, which kicks off April 19th. As with the other prototypes, the CUV is a statement of purpose. VW wants to fully embrace EVs by 2020 (not to mention autonomous driving by 2025), and that means having a whole stable of mainstream models rather than one or two niche cars.

AI refers to systems that act intelligently, whether in a specific domain (narrow AI), or in general (strong AI). Designing such systems is no easy task. The human brain, consisting of about 86 billion neurons, has been postulated to be the most complex object in the known universe; naturally, recreating even a portion of that complexity has proven to be challenging. Does this mean the current interest in AI is just the latest in a series of hype cycles? Consider these recent AI accomplishments: in 2011, IBM's question-answering Watson program bested Jeopardy!

We talked a bit about the Software Development Life Cycle (SDLC) in a recent post, but today, we're going to dig a little deeper into one specific and crucial element in the testing phase, particularly for Agile development: regression testing. Definition of Regression Testing Regression testing refers to the process of testing a changed or updated computer program to make sure the older software features - which were previously developed and tested - still performs exactly as they did before. One way to think about software regression is to think about somebody who implements a new air conditioning system in their home only to find that while their new air conditioning system works as expected, the lights no longer work. Regression testing will often involve running existing tests against the modified code to make sure that the new code did not break anything that worked before the update. Regression testing can eliminate much of the risk associated with software updates.

As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework. First, we design a two-pathway deep network pretrained with contrastive loss, and employ double triplet similarity loss for fine-tuning to learn the shared representation for each media type by modeling the relative semantic similarity. Second, the metric network is designed for effectively calculating the cross-media similarity of the shared representation, by modeling the pairwise similar and dissimilar constraints. Compared to the existing methods which mostly ignore the dissimilar constraints and only use sample distance metric as Euclidean distance separately, our UNCSM approach unifies the representation learning and distance metric to preserve the relative similarity as well as embrace more complex similarity functions for further improving the cross-media retrieval accuracy. The experimental results show that our UNCSM approach outperforms 8 state-of-the-art methods on 4 widely-used cross-media datasets.

Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions. In this paper, we propose a close yet discriminative domain adaptation method, namely CDDA, which generates a latent feature representation with two interesting properties. First, the discrepancy between the source and target domain, measured in terms of both marginal and conditional probability distribution via Maximum Mean Discrepancy is minimized so as to attract two domains close to each other. More importantly, we also design a repulsive force term, which maximizes the distances between each label dependent sub-domain to all others so as to drag different class dependent sub-domains far away from each other and thereby increase the discriminative power of the adapted domain. Moreover, given the fact that the underlying data manifold could have complex geometric structure, we further propose the constraints of label smoothness and geometric structure consistency for label propagation. Extensive experiments are conducted on 36 cross-domain image classification tasks over four public datasets. The Comprehensive results show that the proposed method consistently outperforms the state-of-the-art methods with significant margins. These first two authors contributed equally.

Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear, global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However, so far for the references we know, few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper, we propose an adaptive neighboring selection algorithm, which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry, Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results. By quantitative analysis, the embedding quality measured by residual variance is increased 45.45% after using the proposed algorithm in LLE.