The greatest contribution that artificial intelligence could make to humanity might be in health care. According to the consultancy firm Frost & Sullivan, AI has the potential to improve medical treatment outcomes by 30-40 per cent and reduce costs by as much as 50 per cent. This is particularly important for China, with its population of 1.4 billion people. Medical services can be scarce in China's rural areas while, in urban areas, services are highly strained due to the sheer volume of patients. According to the latest data from the Organisation for Economic Cooperation and Development, China has 1.8 practising doctors per 1,000 people, compared with 2.56 for the United States and 5.1 for Australia.

Aggregated information would then be interpreted according to defined rules that help choose the next course of action.

The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e.g., GoogLeNet, ResNet and Wide ResNet). Further, the high energy consumption of convolutions limits its deployment on mobile devices. To this end, we proposed a simple yet effective scheme for compressing convolutions though applying k-means clustering on the weights, compression is achieved through weightsharing, by only recording K cluster centers and weight assignment indexes. We then introduced a novel spectrally relaxed k-means regularization, which tends to make hard assignments of convolutional layer weights to K learned cluster centers during retraining. We additionally propose an improved set of metrics to estimate energy consumption of CNN hardware implementations, whose estimation results are verified to be consistent with previously proposed energy estimation tool extrapolated from actual hardware measurements. We finally evaluated Deep k-Means across several CNN models in terms of both compression ratio and energy consumption reduction, observing promising results without incurring accuracy loss. The code is available at https://github.

Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, the deep neural network has advantages in handling high dimensional data. In view that a differentiable neural decision forest can be added to the neural network to fully exploit the benefits of both models, in our work, we further combine convolutional autoencoder with neural decision forest, where autoencoder has its advantages in finding the hidden representations of the input data. We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance. The idea of gradient boost is to learn and use the residual in the prediction. In addition, we design a structure to learn the parameters of the neural decision forest and gradient boost module at contiguous steps. The extensive experiments on several public datasets demonstrate that our proposed model achieves good efficiency and prediction performance compared with a series of baseline methods.

We present here a new probabilistic inference algorithm that gives exact results in the domain of discrete probability distributions. This algorithm, named the Statues algorithm, calculates the marginal probability distribution on probabilistic models defined as direct acyclic graphs. These models are made up of well-defined primitives that allow to express, in particular, joint probability distributions, Bayesian networks, discrete Markov chains, conditioning and probabilistic arithmetic. The Statues algorithm relies on a variable binding mechanism based on the generator construct, a special form of coroutine; being related to the enumeration algorithm, this new algorithm brings important improvements in terms of efficiency, which makes it valuable in regard to other exact marginalization algorithms. After introduction of several definitions, primitives and compositional rules, we present in details the Statues algorithm. Then, we briefly discuss the interest of this algorithm compared to others and we present possible extensions. Finally, we introduce Lea and MicroLea, two Python libraries implementing the Statues algorithm, along with several use cases.

WATERLOO – An exciting wave of tech industry investment is set to make Kitchener-Waterloo the Canadian hub of creating an uncontrollable line of code that goes on to wreak incalculable economic and human damage. "AI is the future, and it's important for Canada to be part of that future," said Prime Minister Trudeau. "Even if that future includes the complete destruction of all life on earth." While some experts worry that the increasing integration of tech into the realms of economics, policing, personal finance, transit, health, and parenting, exponentially increases the ability of rogue code to create human suffering, other experts are paid a lot to design it, and say that things will'probably be fine'. "On our current course, it's inevitable that some country will invent a piece of AI with the power to destroy the world in the next 20 years," said Trudeau.

WATERLOO – An exciting wave of tech industry investment is set to make Kitchener-Waterloo the Canadian hub of creating an uncontrollable line of code that goes on to wreak incalculable economic and human damage. "AI is the future, and it's important for Canada to be part of that future," said Prime Minister Trudeau. "Even if that future includes the complete destruction of all life on earth." While some experts worry that the increasing integration of tech into the realms of economics, policing, personal finance, transit, health, and parenting, exponentially increases the ability of rogue code to create human suffering, other experts are paid a lot to design it, and say that things will'probably be fine'. "On our current course, it's inevitable that some country will invent a piece of AI with the power to destroy the world in the next 20 years," said Trudeau.

Asset managers are spending tens of millions on data science. The idea is to use lots of data and machine-learning tools in search of trading ideas and blind spots, the Journal reports. "Most of it is at an early stage, and I don't think it's matured yet," says Onur Erzan, a senior partner at consultant McKinsey & Co. "There will be new sources of data and it will help with investment decisions, but the real question is can an asset manager sustain an edge on those kinds of insights." This is how it starts. "Petter Stensland's foray into data began in late 2012 when, as a junk-bond analyst at AllianceBernstein Holding LP, he needed to analyze the prospects of the many oil-and-gas companies emerging in the nation's energy boom," the Journal says.

Machine learning and artificial intelligence (AI) have amazing potential to simplify, accelerate, and improve many aspects of our everyday lives. Early results have simultaneously created huge excitement and demonstrated its frightening potential. In one example, Facebook was forced to shut down an AI engine after developers discovered that the AI had created its own unique language that humans could not interpret. Researchers at Facebook discovered that the chatbots had deviated from the script and were communicating in a new language developed without human input or intervention. Despite this, the positive hype surrounding these technologies and the level of investment are set to grow exponentially, impacting every part of our personal and professional lives.

In 2015, a worried father asked Rhema Vaithianathan a question that still weighs on her mind. A small crowd had gathered in a basement room in Pittsburgh, Pennsylvania, to hear her explain how software might tackle child abuse. Each day, the area's hotline receives dozens of calls from people who suspect that a child is in danger; some of these are then flagged by call-centre staff for investigation. But the system does not catch all cases of abuse. Vaithianathan and her colleagues had just won a half-million-dollar contract to build an algorithm to help. Vaithianathan, a health economist who co-directs the Centre for Social Data Analytics at the Auckland University of Technology in New Zealand, told the crowd how the algorithm might work. For example, a tool trained on reams of data -- including family backgrounds and criminal records -- could generate risk scores when calls come in. That could help call screeners to flag which families to investigate.