The scene in your mind's eye is likely set in a rural area with farmhands doing back-breaking work. Hundreds of pigs are raised together, perhaps in a cramped space: they eat, they sleep, they play, they breed; and when the time comes, they are sent to the slaughterhouse by the truckload. But the nature of pig farms is changing in China. Some of the country's biggest names in the tech industry–Alibaba, JD–are lining up to become disruptors of this traditional business. In late 2017, Laozhang's family pig farm in a Beijing suburb received an unusual group of visitors--20 engineers from JD Finance's artificial intelligence (AI) department.

Imagine an advanced fighter aircraft is patrolling a hostile conflict area and a bogie suddenly appears on radar accelerating aggressively at them. The pilot, with the assistance of an Artificial Intelligence co-pilot, has a fraction of a second to decide what action to take – ignore, avoid, flee, bluff, or attack. The costs associated with False Positive and False Negative are substantial – a wrong decision that could potentially provoke a war or lead to the death of the pilot. What is one to do…and why? No one less than the Defense Advanced Research Projects Agency (DARPA) and the Department of Defense (DoD) are interested in not only applying AI to decide what to do in hostile, unstable and rapidly devolving environments but also want to understand why an AI model recommended a particular action.

Pro Football Focus Majority owner and former Cincinnati Bengals wide receiver Cris Collinsworth discuss how his company is using artificial intelligence to help football teams determine players' salaries. Chinese e-commerce billionaire Jack Ma discussed the possibility of a 12-hour work week during an appearance with Tesla chief executive Elon Musk at the World Artificial Intelligence Conference in Shanghai on Thursday. "For the next 10, 20 years, every human being, country, government should focus on reforming the education system, making sure our kids can find a job, a job that only requires three days a week, four hours a day," Ma said, according to Bloomberg. "If we don't change the education system we are in, we will all be in trouble." "I think because of artificial intelligence, people will have more time enjoying being human beings," Ma said.

OYO Hotels & Homes has announced that it has acquired Danamica, a Copenhagen-based data science company, with machine learning and business intelligence capabilities, specialised in dynamic pricing. This acquisition is in line with OYO's continued commitment to the company's global vacation rentals business through strategic investments in technology products, processes, and people. Earlier in August, the company had committed to invest EUR 300 million (USD 328.94 million) in the vacation homes business in Europe, with a special focus on strengthening the relationship with homeowners and enabling them with the resources, including technology investments, required to deliver chic hospitality experiences. With the acquisition of Danamica, OYO will be able to drive top-line growth by leveraging dynamic pricing across all its brands – OYO Home, Belvilla and DanCenter, all of them already at the forefront of vacation rental pricing in Europe. Additionally, OYO and its real estate partners around the world will benefit using data sciences for improved yield.

Over the past few years, brands have been toying with different ways AI can double as a wordsmith. While these experiments have proven that AI's ability to "learn" mass amounts of information give it a unique advantage when it comes to churning out copy, it's also become increasingly clear that there's only so much the technology can provide from a creative perspective. Take Saatchi & Saatchi Los Angeles, which trained IBM Watson to spit out copy for Toyota Mirai ads in 2017 as part of a campaign geared toward tech and science enthusiasts. While Watson was eventually able to unearth some interesting insights and string together clever lines of copy, getting there was a laborious process that involved months of training. Last year, Alibaba's digital marketing arm unveiled an AI-powered copywriting tool for brands to leverage on its ecommerce sites.

All signs point toward a future affected by climate change. From higher temperatures to droughts and more extreme weather, experts are searching for ways to sustain our growing population, as well as our planet. Some analysts say machine learning and artificial intelligence offer promising strategies to respond to the effects of climate change. AI can work faster than a human being, can forecast further into the future, has a low error rate and has 24/7 availability. This allows it to better predict extreme weather, flooding, natural disasters and other destruction linked to climate change.

India's Oyo said on Monday it has acquired Copenhagen-based data science firm Danamica as the fast-growing lodging startup works to expand its business in Europe. Neither of the parties disclosed financial terms of the deal, but a source familiar with the matter told TechCrunch that Oyo paid about $10 million to acquire the Danish firm. Danamica, which was founded in 2016, has built machine learning tools and "business intelligence capabilities" to specialize in dynamic pricing of rental properties. The firm's algorithm analyzes 144,000 data points every hour and makes 60 million price changes every day with a prediction accuracy of 97% to help hotels boost their revenue, Oyo said. The Indian startup said Danamica would help it scale its technical expertise as it expands its footprint in overseas markets.

The latest neural network to impress is DeepCubeA from Forest Agostinelli, Stephen McAleer, Alexander Shmakov and Pierre Baldi of the University of California, Irvine. This is a deep neural network that learns a range of combinatorial puzzles - sliding block15, 24, 35, 48 puzzles, Lights Out, Sokoban and, of course, Rubik's cube. The network learns a reinforcment value function, but it does this "backwards". That is, it starts from a solution and randomly takes moves away from the goal. As it steps away from the goal, the moves and configurations become increasingly low in value, - i.e. they are moving away from the goal.

In this issue of Maine Policy Review, authors provided insights into pressing concerns for Maine's policymakers, business leaders, educators, and citizens. They highlight research regarding critical challenges faced by Maine--and the nation--in industry, workforce, health, education, and politics. This "Maine Policy Minute" provides a synopsis of the authors and arguments contained in Volume 28, Number 1 of Maine Policy Review. In, "Local Politics from Away," transplanted Mainer and college student Matthew Bourque reflects on the strength and character of Maine's political traditions including independent thinking and politicians who put ideas over political party. Joseph W. McDonnell, a professor of public policy in management at the Muskie School at the University of Southern Maine, argues in "Maine's Workforce Challenges in an Age of Artificial Intelligence" that to accommodate changes wrought by increased automation and the move towards artificial intelligence, Maine needs to upgrade the skills of its workforce in what is becoming a rapidly changing economy.

While the backpropagation of error algorithm allowed for a rapid rise in the development and deployment of artificial neural networks, two key issues currently preclude biological plausibility: (i) symmetry is required between forward and backward weights, which is known as the weight transport problem, and (ii) updates are locked before both the forward and backward passes have been completed. The feedback alignment (FA) algorithm uses fixed random feedback weights to release the weight transport problem. The direct feedback alignment (DFA) variation directly propagates the output error to each hidden layer through fixed random connectivity matrices. In this work, we show that using only the error sign is sufficient to maintain feedback alignment and to provide learning in the hidden layers. As in classification problems the error sign information is already contained in the target vector, using the latter as a proxy for the error brings three advantages: (i) it solves the weight transport problem by eliminating the requirement for an explicit feedback pathway, which also reduces the computational workload, (ii) it reduces memory requirements by removing update locking, allowing for weight updates to be computed in each layer independently without requiring a full forward pass, and (iii) it leads to a purely feedforward and low-cost algorithm that only requires a label-dependent random vector selection to estimate the layerwise loss gradients. Therefore, in this work, we propose the direct random target projection (DRTP) algorithm and demonstrate on the MNIST and CIFAR-10 datasets that, despite the absence of an explicit error feedback, DRTP performance can still lie close to the one of BP, FA and DFA. The low memory and computational cost of DRTP and its reliance only on layerwise feedforward computation make it suitable for deployment in adaptive edge computing devices.