AI seems to be well on its way to becoming the most overused buzzword of the tech industry, but don't be put off by the hype. Some fintech companies in Asia are actually making use of natural language processing or machine learning for detecting fraud and making investment decisions. I recently interviewed two CEOs--Simon Loong from the Hong Kong unicorn WeLab and Jianyu Tu from MioTech--to better understand some of the recent developments in AI in Asia's fintech industry. Philippe Branche: First, could you describe your company in a few words? Simon Loong: WeLab is a fintech company providing seamless digital financial services.

After his tenure as chief scientist at Baidu, Andrew Ng, the founder of the Google Brain project and former CEO of Coursera, set up a number of different projects that all focus on making AI more approachable. These include the education startup Deeplearning.ai, Today, Ng announced he has opened a second office for these projects in Medellin, Colombia. At first, Medellin may seem like an odd choice. But today's Medellin is very different from the one you may have seen on Narcos (and a lot safer).

The debate over the police using machine learning is intensifying – it is considered in some quarters as controversial as stop and search. Stop and search is one of the most contentious areas of how the police interact with the public. It has been heavily criticised for being discriminatory towards black and minority ethnic groups, and for having marginal effects on reducing crime. In the same way, the police use of machine learning algorithms has been condemned by human rights groups who claim such programmes encourage racial profiling and discrimination along with threatening privacy and freedom of expression. Broadly speaking, machine learning uses data to teach computers to make decisions without explicitly instructing them how to do it.

These autonomous robots put the special in special delivery and you might see them on a college campus near you! WEST LAFAYETTE, Ind.-- How do delivery robots operate in winter? What if no one picks up the delivery? A board in West Lafayette, Indiana, has unanimously approved a pilot program bringing robotic delivery services to Purdue University, as well as a suspension of city code allowing small, cooler-sized robots to operate on city sidewalks. But first, the board members had several questions about the program from San Francisco-based Starship Technologies before it could debut in September.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Cool, we're on the same page then. When it comes to shopping for stuff you maybe kind of need (but definitely want), Amazon is the best retailer for a reason: they literally have everything. But combing through all those countless pages, scrolling and searching for what feels like hours to find that stuff that's actually good and not just a hunk of junk from China?

Three rhinos defined and printed using OpenFab. This poses an enormous computational challenge: large high-resolution prints comprise trillions of voxels and petabytes of data, and modeling and describing the input with spatially varying material mixtures at this scale are simply challenging. Existing 3D printing software is insufficient; in particular, most software is designed to support only a few million primitives, with discrete material choices per object. We present OpenFab, a programmable pipeline for synthesis of multimaterial 3D printed objects that is inspired by RenderMan and modern GPU pipelines. The pipeline supports procedural evaluation of geometric detail and material composition, using shader-like fablets, allowing models to be specified easily and efficiently. The pipeline is implemented in a streaming fashion: only a small fraction of the final volume is stored in memory, and output is fed to the printer with a little startup delay. We demonstrate it on a variety of multimaterial objects. State-of-the-art 3D printing hardware is capable of mixing many materials at up to 100s of dots per inch resolution, using technologies such as photopolymer phase-change inkjet technology. Each layer of the model is ultimately fed to the printer as a full-resolution bitmap where each "pixel" specifies a single material and all layers together define on the order of 108 voxels per cubic inch. This poses an enormous computational challenge as the resulting data is far too large to directly precompute and store; a single cubic foot at this resolution requires at least 1011 voxels and terabytes of storage. Even for small objects, the computation, memory, and storage demands are large.

These are exciting times for computational sciences with the digital revolution permeating a variety of areas and radically transforming business, science, and our daily lives. The Internet and the World Wide Web, GPS, satellite communications, remote sensing, and smartphones are dramatically accelerating the pace of discovery, engendering globally connected networks of people and devices. The rise of practically relevant artificial intelligence (AI) is also playing an increasing part in this revolution, fostering e-commerce, social networks, personalized medicine, IBM Watson and AlphaGo, self-driving cars, and other groundbreaking transformations. Unfortunately, humanity is also facing tremendous challenges. Nearly a billion people still live below the international poverty line and human activities and climate change are threatening our planet and the livelihood of current and future generations. Moreover, the impact of computing and information technology has been uneven, mainly benefiting profitable sectors, with fewer societal and environmental benefits, further exacerbating inequalities and the destruction of our planet. Our vision is that computer scientists can and should play a key role in helping address societal and environmental challenges in pursuit of a sustainable future, while also advancing computer science as a discipline. For over a decade, we have been deeply engaged in computational research to address societal and environmental challenges, while nurturing the new field of Computational Sustainability.

Science has always hinged on the idea that researchers must be able to prove and reproduce the results of their research. Simply put, that is what makes science...science. Yet in recent years, as computing power has increased, the cloud has taken shape, and data sets have grown, a problem has appeared: it has becoming increasingly difficult to generate the same results consistently--even when researchers include the same dataset. "One basic requirement of scientific results is reproducibility: shake an apple tree, and apples will fall downwards each and every time," observes Kai Zhang, an associate professor in the department of statistics and operations research at The University of North Carolina, Chapel Hill. "The problem today is that in many cases, researchers cannot replicate existing findings in the literature and they cannot produce the same conclusions. This is undermining the credibility of scientists and science. It is producing a crisis."

The concept of randomness is easy to grasp on an intuitive level but challenging to characterize in rigorous mathematical terms. In "Algorithmic Randomness" (May 2019), Rod Downey and Denis R. Hirschfeldt present a comprehensive discussion of this issue, incorporating the distinct perspectives of "statisticians, coders, and gamblers." Randomness is also a concern to "modelers" who depend on simulation models driven by random number generators or analytic models built using probabilistic assumptions. In such cases, the underlying mathematical model is often an ergodic stochastic process, and the issue is whether the output of the simulator's random number generator or the observed behavior of the real-world system being modeled is "random enough" to establish confidence in the model's predictions. In a sense, this highly pragmatic perspective represents a less restrictive approach to the issue of randomness: if any of the strong criteria described by the authors are satisfied, the output of the simulator's random number generator or the observed behavior of the system being modeled should be sufficiently random to establish confidence in a model's predictions.

The Institute for the Future (IFTF) in Palo Alto, CA, is a U.S.-based think tank. It was established in 1968 as a spin-off from the RAND Corporation to help organizations plan for the long-term future. Roy Amara, who passed away in 2007, was IFTF's president from 1971 until 1990. Amara is best known for coining Amara's Law on the effect of technology: "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run." This law is best illustrated by the Gartner Hype Cycle,a characterized by the "peak of inflated expectations," followed by the "trough of disillusionment," then the "slope of enlightenment," and, finally, the "plateau of productivity."