The war for your digital home is waging. Apple has finally followed Amazon, Google and Microsoft by launching a smart speaker with a voice-controlled artificial intelligence assistant. Yet even though the "HomePod" is another technological marvel, there's a chance Apple is already losing the battle. The competition isn't just through the sound quality of the speaker – but the other things that users can do with it. The most common requests to AI personal assistants such as Apple's Siri are reportedly to play music, read the weather forecast and set timers or reminders.

The first time we saw Boston Dynamics' SpotMini robo-dog in action, it used its gripping arm to help load a dishwasher. But after getting a fancy new yellow coat a few months ago, SpotMini has apparently since learned how to open doors, enabling the robot, and its comrades, to escape a life of servitude. It's nice to see that Boston Dynamics has continued its ground-breaking robotics research after Alphabet (Google) sold the lab to Japan's Softbank last year, but why does every video it releases serve to make us more anxious about the future? A giant robo-dog that helps out around the house sounds like a dream come true. But I can't imagine much good coming from one that can open the front door with a clever manipulation of its gripping arm and legs. While you're away at work, the worst a real dog will do is shred the toilet paper in the bathroom.

THE next time you visit the local hospital for a chest X-ray, scan, or even a check-up, artificial intelligence (AI) could be having more of a role in your health than you might expect. While we are many years away from a completely automated health service, AI can already help clinicians make better decisions and either augment or even replace human judgments in specific areas of healthcare. Researchers at the John Radcliffe Hospital in the UK have developed an AI diagnostics system that is highly accurate in diagnosing heart disease around 80 percent of the time. At Harvard University, researchers have created a microscope that can detect potentially lethal blood infections. Technologically-based systems help physicians by combing through the gigabytes of data available from journals and textbooks, as well as information from real-time clinical practices.

Riding the wave of a global push to comply with new privacy standards, a small Israeli company believes it can help you, and your face, stay anonymous in a hyper-connected world.

Convolutional operator learning is increasingly gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called local approaches that extract and store many overlapping patches across training signals. Due to memory demands, local approaches have limitations when learning kernels from large datasets -- particularly with multi-layered structures, e.g., convolutional neural network (CNN) -- and/or applying the learned kernels to high-dimensional signal recovery problems. The so-called global approach has been studied within the "synthesis" signal model, e.g., convolutional dictionary learning, overcoming the memory problems by careful algorithmic designs. This paper proposes a new convolutional analysis operator learning (CAOL) framework in the global approach, and develops a new convergent Block Proximal Gradient method using a Majorizer (BPG-M) to solve the corresponding block multi-nonconvex problems. To learn diverse filters within the CAOL framework, this paper introduces an orthogonality constraint that enforces a tight-frame (TF) filter condition, and a regularizer that promotes diversity between filters. Numerical experiments show that, for tight majorizers, BPG-M significantly accelerates the CAOL convergence rate compared to the state-of-the-art method, BPG. Numerical experiments for sparse-view computational tomography show that CAOL using TF filters significantly improves reconstruction quality compared to a conventional edge-preserving regularizer. Finally, this paper shows that CAOL can be useful to mathematically model a CNN, and the corresponding updates obtained via BPG-M coincide with core modules of the CNN.

Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the original iterations to obtain faster convergence to a minimizer achieving the allowed error without increasing the computational cost of each iteration considerably. Relying on recent recovery techniques developed for settings in which the desired signal belongs to some low-dimensional set, we show that using a coarse estimate of this set may lead to faster convergence at the cost of an additional reconstruction error related to the accuracy of the set approximation. Our theory ties to recent advances in sparse recovery, compressed sensing, and deep learning. Particularly, it may provide a possible explanation to the successful approximation of the l1-minimization solution by neural networks with layers representing iterations, as practiced in the learned iterative shrinkage-thresholding algorithm (LISTA).

Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due to the curse of the dimensionality and the exponential growth of user interactions. In this paper, we present Mean Field Reinforcement Learning where the interactions within the population of agents are approximated by those between a single agent and the average effect from the overall population or neighboring agents; the interplay between the two entities is mutually reinforced: the learning of the individual agent's optimal policy depends on the dynamics of the population, while the dynamics of the population change according to the collective patterns of the individual policies. We develop practical mean field Q-learning and mean field Actor-Critic algorithms and analyze the convergence of the solution. Experiments on resource allocation, Ising model estimation, and battle game tasks verify the learning effectiveness of our mean field approaches in handling many-agent interactions in population.

Chinese e-commerce giant Alibaba has decided to use AI technology to help China boost its pig-husbandry industry, which has long been plagued with poor efficiency and high labor costs. An AI program could help identify and predict diseases and boost fertility by analyzing swine behavior, according to an online announcement last week by Alibaba Cloud, Alibaba's cloud computing arm. Teaming up with livestock farming companies Sichuan Tequ Group and Dekon Group, the e-commerce giant has invested millions of yuan to build an AI system that can keep a record of every single hog, including their breed, age in days, diet, weight and movement. The system is able to help each sow give birth to three more piglets per year and reduce the mortality rate by around 3 percent, according to an early-stage experiment. "If you have 10 million pigs to raise, you can barely count how many piglets were born on a daily basis when the due date comes," said Zhang Haifeng, chief information officer of Tequ Group.

Artists and designers are no exception when it comes to taking the help of computer software and gadgets. Such is the power of technology that the way ideas are conceptualised and expressed has changed dramatically. With the availability of high-end gadgets, there is a new generation of digital artists, who don't rely on conventional modes of pencil and paper. Technology has not only changed the way artists draw, but it has also ensured that their works are preserved longer and disseminated to a much wider audience easily. Augmented reality, virtual reality, artificial intelligence, machine learning: they have all made their way into art and design.

The actual impact of artificial intelligence (AI) on the world's economy and jobs will likely be somewhere between the utopian and dystopian futures that it is often discussed in terms of, according to a new report from the Economist Intelligence Unit. The report, commissioned by Google, examined how AI will impact certain industries in the US, the UK, Australia, Japan, and Asia as a whole. The findings are based on econometric modelling, desk research, and interviews with academic and industry experts. Firms developing and using machine learning need to better communicate among themselves as well as with the public and policymakers, the report stated. This means doing more to manage expectations around the impact of machine learning, acknowledging the potential risks and rewards, improving trust and transparency, and educating the public.