LONDON – The rise of robots in manufacturing in Southeast Asia is likely to fuel modern-day slavery as workers who end up unemployed due to automation face abuses competing for a shrinking pool of low-paid jobs in a "race to the bottom," analysts said Thursday. Drastic job losses due to the growth of automation in the region -- a hub for many manufacturing sectors from garments to vehicles -- could produce a spike in labor abuses and slavery in global supply chains, said risk consultancy Verisk Maplecroft. More than half of the workers in Cambodia, Indonesia, Thailand, Vietnam and the Philippines -- at least 137 million people -- risk losing their jobs to automation in the next two decades, the United Nations' International Labour Organization says. The risk of slavery tainting supply chains will spiral because workers who lose their jobs due to increased robot manufacturing will be more vulnerable to workplace abuses as they jostle for fewer jobs at lower wages, said Alexandra Channer of Maplecroft. "Displaced workers without the skills to adapt or the cushion of social security will have to compete for a diminishing supply of low-paid, low-skilled work in what will likely be an increasingly exploitative environment," she said.

HMRC's use of AI and big data to gather evidence in tax investigations has led to a 30% drop in property raids, according to law firm Pinsent Masons. The firm explained that HMRC has leveraged sophisticated algorithms and big data sources to gather evidence with greater ease and efficiency than costly and time-consuming property raids. Steven Porter, Partner at Pinsent Masons, said: "HMRC's big brother-style data collection on taxpayers is giving it the material it needs to ramp up its tax investigations and at the same time, is reducing the need for it to actually raid properties." The figure has dropped from 669 property raids in 2016/17 to 471 last year. In particular, tax inspectors have been using the state-of-the-art Connect database, an analytical system worth £80m and designed by BAE Systems, to carry out preliminary investigative work within seconds.

GDPR (The General Data Protection Regulation) is a very significant EU law that offers major new data and privacy protection for all individuals within the European Union (EU) and the European Economic Area (EEA). GDPR took effect on May 25, 2018. You probably have all received countless emails from companies updating their privacy policies to comply with GDPR. These were written prior to the introduction of the new regulations, when the ambiguity of the new regulations had everyone second-guessing as to what affect the machine learning field would feel. So, two months on, has it become clearer?

In the meantime, the market has emerged into an on-demand economy that has been driven by information. Today home buyers' increasing access to information allows them, in some ways, to circumvent the agent. The need for instant gratification and knowledge to find the best value at the lowest cost is slowly evolving this industry. How real estate succeeds in the next decade will fully rely on the changing habits of the home buyer, with an acquiescence to slowly dismantling this market structure to improve buyer access to information, while creating new sources of value. One start-up based in Calgary and Toronto is paving the way for this disruption and is challenging the players: the realtors and the home buyers to think differently about these transactions.

The findings are an important step toward building more energy-efficient computing systems that also are capable of learning and adaptation in the real world. They were published last week in a paper in the journal Nature Communications. The researchers, Bipin Rajendran, an associate professor of electrical and computer engineering, and S. R. Nandakumar, a graduate student in electrical engineering, have been developing brain-inspired computing systems that could be used for a wide range of big data applications. Over the past few years, deep learning algorithms have proven to be highly successful in solving complex cognitive tasks such as controlling self-driving cars and language understanding. At the heart of these algorithms are artificial neural networks -- mathematical models of the neurons and synapses of the brain -- that are fed huge amounts of data so that the synaptic strengths are autonomously adjusted to learn the intrinsic features and hidden correlations in these data streams.

IT'S robots that mostly come to mind when you ask people about the future of work. Robots taking our jobs, to be specific. And it's a reaction that's two centuries old, in a replay of Lancashire weavers attacking looms and stocking frames at the start of the first Industrial Revolution. A secondary reaction, among a much smaller group, is the creation of new jobs in the coming fourth Industrial Revolution. On the left side are old industries, where some workers are being replaced by robots.

Artificial intelligence (AI) is being used both to monitor and prevent crimes in many countries. AI is used in such areas as bomb detection and deactivation, surveillance, prediction, social media scanning and interviewing suspects. However, for all the hype and hoopla around AI, there is scope for growth of its role in crime management. Currently, a few issues are proving problematic. AI is not uniformly engaged across countries in crime management. There is fierce debate on the ethical boundaries of AI, compelling law enforcement authorities to tread carefully.

The problem of learning a correspondence relationship between nodes of two networks has drawn much attention of the computer science community and recently that of statisticians. The unseeded version of this problem, in which we do not know any part of the true correspondence, is a long-standing challenge. For low-rank networks, the problem can be translated into an unsupervised point registration problem, in which two point sets generated from the same distribution are matchable by an unknown orthonormal transformation. Conventional methods generally lack consistency guarantee and are usually computationally costly. In this paper, we propose a novel approach to this problem. Instead of simultaneously estimating the unknown correspondence and orthonormal transformation to match up the two point sets, we match their distributions via minimizing our designed loss function capturing the discrepancy between their Laplace transforms, thus avoiding the optimization over all possible correspondences. This dramatically reduces the dimension of the optimization problem from $\Omega(n^2)$ parameters to $O(d^2)$ parameters, where $d$ is the fixed rank, and enables convenient theoretical analysis. In this paper, we provide arguably the first consistency guarantee and explicit error rate for general low-rank models. Our method provides control over the computational complexity ranging from $\omega(n)$ (any growth rate faster than $n$) to $O(n^2)$ while pertaining consistency. We demonstrate the effectiveness of our method through several numerical examples.

We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule implement one-shot binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank one-shot learning benchmarks, our model achieves state-of-the-art results.

The ability to model continuous change in Reiter's temporal situation calculus action theories has attracted a lot of interest. In this paper, we propose a new development of his approach, which is directly inspired by hybrid systems in control theory. Specifically, while keeping the foundations of Reiter's axiomatization, we propose an elegant extension of his approach by adding a time argument to all fluents that represent continuous change. Thereby, we insure that change can happen not only because of actions, but also due to the passage of time. We present a systematic methodology to derive, from simple premises, a new group of axioms which specify how continuous fluents change over time within a situation. We study regression for our new temporal basic action theories and demonstrate what reasoning problems can be solved. Finally, we formally show that our temporal basic action theories indeed capture hybrid automata.