In sub-Saharan Africa, home electricity is a 50-50 prospect and bank accounts can be rare, but most people have some kind of cellphone. The phones provide information often tough to come by in rural areas--the latest commodity prices, for example. And even in places where pastoral tribesmen tend livestock in very old-school ways, they may also chat over WhatsApp and use money-transfer apps to settle debts. To charge the phones without access to an electrical grid, Africans spend more than $17 billion a year on such fuels as kerosene and firewood to power sometimes primitive generators. Simon Bransfield-Garth is pitching a cleaner and, he says, smarter alternative.

Understanding the representational power of Restricted Boltzmann Machines (RBMs) with multiple layers is an ill-understood problem and is an area of active research. Motivated from the approach of \emph{Inherent Structure formalism} (Stillinger & Weber, 1982), extensively used in analysing Spin Glasses, we propose a novel measure called \emph{Inherent Structure Capacity} (ISC), which characterizes the representation capacity of a fixed architecture RBM by the expected number of modes of distributions emanating from the RBM with parameters drawn from a prior distribution. Though ISC is intractable, we show that for a single layer RBM architecture ISC approaches a finite constant as number of hidden units are increased and to further improve the ISC, one needs to add a second layer. Furthermore, we introduce \emph{Lean} RBMs, which are multi-layer RBMs where each layer can have at-most $O(n)$ units with the number of visible units being n. We show that for every single layer RBM with $\Omega(n^{2+r}), r \ge 0$, hidden units there exists a two-layered \emph{lean} RBM with $\Theta(n^2)$ parameters with the same ISC, establishing that 2 layer RBMs can achieve the same representational power as single-layer RBMs but using far fewer number of parameters. To the best of our knowledge, this is the first result which quantitatively establishes the need for layering.

I'll never forget the time I watched the trailer for 300, the iconic fantasy war film from 2006 about King Leonidas of Sparta and his clash against the Persians with a heavily outnumbered army. As soon as I saw the scene where Leonidas, played by Gerard Butler, screamed "This is Sparta" at the top of his lungs and then kicked a dude down a deep concrete well, I knew the movie was going to be an instant classic. Less than a year after touring Egypt with Origins, Ubisoft is taking us to King Leonidas' world in Assassin's Creed: Odyssey. The new game takes place in 431BC, right at the start of the Peloponnesian war between the competing cities of Sparta and Athens. As these two empires fight to establish broader power, your play as a young Spartan mercenary who goes on quest to help his people defeat the Athenians.

That's why the technology team at the well-known nonprofit Crisis Text Line in New York City analyzed some 65 million text messages to determine what words were most statistically associated with a high risk of suicide. This scale of analysis would clearly be infeasible without some form of automated analysis, and its results surprised the team. Use of the term "EMS" in a text, for example, is five times more predictive of a high risk of suicide than the actual word "suicide." As a result, the organization is now able to respond to 94 percent of high-risk texters in fewer than five minutes. This is just one example of "mission-driven artificial intelligence"--the responsible application of artificial intelligence (AI) to solve societal and ecological challenges. Sometimes dubbed "AI for good," mission-driven AI is the use of machine-learning techniques to streamline operations and enhance programs at nonprofits, nongovernmental organizations (NGOs), and social enterprises.

Researchers have developed a deep learning algorithm that can automatically identify, count and describe animals in their natural habitats. A new paper, published in Proceedings of the National Academy of Sciences (PNAS), decribes how the cutting-edge artificial intelligence technique can automatically describe photographs that have been collected by motion-sensor cameras on deep neural networks. The result is a system that can automate animal identification for up to 99.3 per cent of images while still performing at the same 96.6 per cent accuracy rate of crowd-sourced teams of human volunteers. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behaviour into'big data' sciences," explained Jeff Clune, the senior author of the paper and Harris Associate Professor at the University of Wyoming. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems."

Remark Holdings (MARK) is what one would call a contested company. It has long supporters with strong conviction, but there has also been some kind of a short drive, and the present short count is almost 16% of the nearly 25M float. That short count is far from the highest we've seen, a company like Applied Optoelectronics (AAOI) still has 78% of the float shorted (at least according to the latest figures) but it seems to have done major damage already. Who to believe, the conviction longs or the shorts who put out a troubling report. Questions like these are very difficult to solve, especially if you're not a forensic accountant. Since we're no forensic accountants ourselves, we'll try to gather some stylized facts and see what these add up to, and whether there is some chance for the longs to recoup some of their losses. This company was one of two highest conviction longs for SA contributor Yale Bock, who is the President of Y H & C Investments (see here).

South African construction sites may experience a drone hovering above them. Gauteng province's department of infrastructure development has launched a fleet of drones that'll be used to monitor building projects including those in and around Johannesburg. "It is possible for the public sector to be efficient and to be productive in what we do," says Jacob Mamabolo, Head of Provincial Department. The drones would monitor the quality of work and also ensure that the projects stick to their schedules. The unmanned aerial vehicles (UAVs) are just the latest effort by Mamabolo to make his department more tech-savvy and to'leap frog' with opportunities from the'fourth industrial revolution'.

Banks That Have Implemented Social Media Payments Through Facebook and Twitter, Let's Talk Payments

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.