This article is part of KrASIA's partnership with Web Summit. The last 12 months have seen decisive change in the way we spend our free time. Mobility solutions are becoming increasingly popular, with driverless vehicles popping up across the world, while our urban spaces are evolving into smart city projects. Web Summit's lifestyle content covers it all. What CNN calls "Europe's largest tech event" gathers experts from the industries that play vital roles in our lifestyles.

Artificial intelligence (AI) has both surpassed and replaced humans in many fields. Will AI overpower humanity in the near or distant future? Will AI control humans and replace governments? Or will AI remain a tool that humans will use to improve their performances? Research on brain-computer interface (BCI) has begun and suggests that we implant chips or connect devices to our brain to increase computing power.

Becoming a physicist was not Maria Schuld's life goal. As an undergrad, she started out studying political science, taking physics in parallel. Her plan was to work for a nonprofit organization in a capacity that had a very clear benefit to society. But then, she says, "life happened"--jobs fell through and other opportunities opened up--and she found herself with a career in quantum machine learning. Today Schuld, who works for the Canadian quantum computing company Xanadu from her home in South Africa, says that she has matured in what she thinks it means for a person to benefit society.

Hyderabad, November 23, 2020 –– Analytics Insight conducted a survey "The Global Artificial Intelligence Trends 2020" to understand the global adoption of Artificial Intelligence (AI) amongst enterprises and recognize the business perceptions of AI across sectors. Analytics Insight reached out to 2,200 professionals online located in different geographic regions across a wide range of industries to explore different views toward AI and its current implications among enterprises. Receiving 256 responses for the survey, Analytics Insight articulated a detailed report, which can be indicative of the market as a whole. Out of the 256 respondents, 48.5% were working at small-scale companies with the company size of fewer than 100 employees. About 29.8% of the respondents were employed at companies which had total employees ranging from 100-1000, while 21.7% of respondents had a company size of over 1000 employees.

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping. This implies that there is obfuscation in the way concepts are captured in these embedding spaces. Its effects are seen in many representations and tasks, one particularly problematic one being in language representations where the societal biases, learned from underlying data, are captured and occluded in unknown dimensions and subspaces. As a result, invalid associations (such as different races and their association with a polar notion of good versus bad) are made and propagated by the representations, leading to unfair outcomes in different tasks where they are used. This work addresses some of these problems pertaining to the transparency and interpretability of such representations. A primary focus is the detection, quantification, and mitigation of socially biased associations in language representation.

The future will be replete with scenarios where humans are robots will be working together in complex environments. Teammates interact, and the robot's interaction has to be about getting useful information about the human's (teammate's) model. There are many challenges before a robot can interact, such as incorporating the structural differences in the human's model, ensuring simpler responses, etc. In this paper, we investigate how a robot can interact to localize the human model from a set of models. We show how to generate questions to refine the robot's understanding of the teammate's model. We evaluate the method in various planning domains. The evaluation shows that these questions can be generated offline, and can help refine the model through simple answers.

In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) -- an MLP that predicts an RGB pixel value given its (x,y) coordinate. In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and use them to build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs for image generation were limited to MNIST-like datasets and do not scale to complex real-world data. Our proposed architectural design improves the performance of continuous image generators by x6-40 times and reaches FID scores of 6.27 on LSUN bedroom 256x256 and 16.32 on FFHQ 1024x1024, greatly reducing the gap between continuous image GANs and pixel-based ones. To the best of our knowledge, these are the highest reported scores for an image generator, that consists entirely of fully-connected layers. Apart from that, we explore several exciting properties of INR-based decoders, like out-of-the-box superresolution, meaningful image-space interpolation, accelerated inference of low-resolution images, an ability to extrapolate outside of image boundaries and strong geometric prior. The source code is available at https://github.com/universome/inr-gan

The market size of the military robot market will rise considerably in the coming years owing to improving border surveillance and patrolling. The growth of inefficient border research and growing ISR functions has led to military robot industry propulsion. The robots are for defensive drive purposes and are remotely controlled which can perform dangerous activities making them more efficient than the other tools. The wider market will be influenced by a growing need for advanced monitoring, targeting and information-gathering systems in the military due to the presence of challenging and life-risking activities. Furthermore, the rising need for isolated processes for a long period and technological developments in unused systems worldwide is another factor that significantly increases the growth of the market.

The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth's climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA's Sentinel-2 satellites. Using a modified ResNet-50, we can detect smoke plumes of different sizes with an accuracy of 94.3%. The model correctly ignores natural clouds and focuses on those imaging channels that are related to the spectral absorption from aerosols and water vapor, enabling the localization of smoke. We exploit this localization ability and train a U-Net segmentation model on a labeled sub-sample of our data, resulting in an Intersection-over-Union (IoU) metric of 0.608 and an overall accuracy for the detection of any smoke plume of 94.0%; on average, our model can reproduce the area covered by smoke in an image to within 5.6%. The performance of our model is mostly limited by occasional confusion with surface objects, the inability to identify semi-transparent smoke, and human limitations to properly identify smoke based on RGB-only images. Nevertheless, our results enable us to reliably detect and qualitatively estimate the level of smoke activity in order to monitor activity in industrial plants across the globe. Our data set and code base are publicly available.

We present a Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression). The method builds representations of multivariate functions with lower-dimensional terms, either as an expansion over orders of coupling or using terms of only a given dimensionality. This facilitates, in particular, recovering functional dependence from sparse data. The code also allows imputation of missing values of the variables. The capabilities of this regression tool are demonstrated on test cases involving synthetic analytic functions, the potential energy surface of the water molecule, and financial market data.