An artificial intelligence agent named Tala may open the door on a new way of gathering feedback from New Zealand's Samoan community. The Talanoa Project is a pilot project that uses IBM's artificial intelligence virtual agent solution, Watson, to interact in real time in Samoan for public consultation and community engagement. Developed and designed by Beca, business director Matthew Ensor said it was about consulting with'the silent majority' in the public on projects and community facilities. "We don't hear so much from the people where language is a barrier, where culturally there's no tradition of responding to public consultation. "We then created a conversational agent, it's like a chat-bot and what it does is it mimics the kind of conversation that you would have with a consultation expert," Mr Ensor said. "It will ask open questions about your thoughts on different things and really lets the person lead the conversation rather than a survey form where the questions are completely scripted." Steve O'Donnell from IBM New Zealand's Managing Partner for Global Business Services said this was the first time IBM Watson Assistant had been used for public consultation in New Zealand in a language other than English. "What we are seeing now is AI being able to scale down, and drive value in many industries," he said. "IBM Watson has already transformed the world of customer service, due largely to its ability to understand human sentiment and interact naturally with people and Tala is a promising first step towards that." The Talanoa Project, part funded by Callaghan Innovation, tested Tala among a few dozen Samoan speakers, asking them for their thoughts on their local community facilities. The focus group of Samoans ranged from 19-years of age to 77 being the oldest and included Samoan elders, law students, psychologists and sociologists. "It was overwhelmingly positive the response we got back from the Samoan community," Mr Ensor said. "We had a few people share that it was great to hear technology using their native language.

AI is taking off in all areas of business and in our daily lives – from improving agriculture and predicting where forest fires might erupt to determining who is likely to return to a hospital after discharge. With advanced GPUs that can crunch more data faster and growing demand from companies looking to increase competitive advantage, machine learning and other forms of AI are expected to become more pervasive. Today, many companies are relying on smart apps to provide the insight needed to make decisions that can affect people's lives, such as who qualifies for a mortgage or who will be insured. Because of this responsibility, it's more important than ever that data professionals don't inadvertently automate any biases into the AI algorithm because of the data they use or don't use, and how they use it. While AI should be regulated to ensure the fair and ethical use of data, particularly as it impacts decision-making and people's lives, unfortunately, we still have a long way to go before this happens.

The Austrian Research Promotion Agency (FFG) has officially announced to support the development of an AI (Artificial intelligence) forecasting project with MarineXchange (MXP), which provides enterprise software solutions to the cruise industry. "Accurate supply chain forecasting is a huge challenge for the cruise industry," MXP said, in a press release ."Because Consumption patterns depend on guest profiles, seasonal items, substitutions and menu cycles. Vendor performance, freight and currency exchange rates need to be considered. The overall CO2 impact, waste reduction and fair trade are additional factors. To aid cruise companies in this complex decision-making process, more accurate forecasts are needed."

Moritz Lipp is a Ph.D. candidate at Graz University of Technology, Flanders, Austria. Michael Schwarz is a postdoctoral researcher at Graz University of Technology, Flanders, Austria. Daniel Gruss is an assistant professor at Graz University of Technology, Flanders, Austria. Thomas Prescher is a chief architect at Cyberus Technology GmbH, Dresden, Germany. Werner Haas is the Chief Technology Officer at Cyberus Technology GmbH, Dresden, Germany.

Such a disappearance is a fundamental consequence not of technology but of human psychology. Whenever people learn something sufficiently well, they cease to be aware of it. Thus, Weiser's vision is even broader: as this technology becomes truly embedded in human activity we won't be aware of it at all. As the field of ubiquitous computing has evolved, with computation embedded in walls, clothes, and so forth, the materiality to support it is often physically and intentionally hidden from the user. Indeed, this material disappearance is often considered evidence of good design. The "agent" metaphor, in particular in its early presentations such as the Knowledge Navigator and Starfire, is also another utopian vision. These virtual agents are typically accessible via peripherals such as screens or phones, doing the bidding of those they serve.

Artificial intelligence could play a decisive role in stopping the Covid-19 pandemic. To give the technology a push, the MIT-IBM Watson AI Lab is funding 10 projects at MIT aimed at advancing AI's transformative potential for society. The research will target the immediate public health and economic challenges of this moment. But it could have a lasting impact on how we evaluate and respond to risk long after the crisis has passed. The 10 research projects are highlighted below.

Not long ago US President Donald Trump quoted that, "Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States and to shaping the global evolution of AI in a manner consistent with our Nation's values, policies, and priorities." President Donald J. Trump launched the American Artificial Intelligence Initiative, the Nation's strategy for promoting American leadership in AI, by signing Executive Order 13859 in February 2019. Reportedly, the American AI Initiative focuses the resources of the Federal Government to support AI innovation that will increase prosperity, enhance national security, and improve quality of life for the American people. The United States has decided to promote Federal investment in AI R&D in collaboration with industry, academia, international partners and allies, and other non-Federal entities to generate technological breakthroughs in AI. President Trump called for a 2-year doubling of non-defense AI R&D in his fiscal year (FY) 2021 budget proposal, and in 2019 the Administration updated its AI R&D strategic plan, developed the first progress report describing the impact of Federal R&D investments, and published the first-ever reporting of government-wide non-defense AI R&D spending.

Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it. However, learning models for these tasks is difficult partly because generating the necessary supervision signals for it is costly and does not scale. This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck. It will illustrate their benefit in the context of multiple problems, all pertaining to inducing various levels of semantic representations from text.

Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of accuracy, which are not always available. One technique researchers use when labelled training data are scarce is domain adaptation (DA) -- where data from an alternate region, known as the source domain, are used to train a classifier and this model is adapted to map the study region, or target domain. The scenario we address in this paper is known as semi-supervised DA, where some labelled samples are available in the target domain. In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data. The technique takes a convolutional neural network trained on a source domain and then trains further on the available target domain with a novel regularizer applied to the model weights. The regularizer adjusts the degree to which the model is modified to fit the target data, limiting the degree of change when the target data are few in number and increasing it as target data quantity increases. Our experiments on Sentinel-2 time series images compare Sourcerer with two state-of-the-art semi-supervised domain adaptation techniques and four baseline models. We show that on two different source-target domain pairings Sourcerer outperforms all other methods for any quantity of labelled target data available. In fact, the results on the more difficult target domain show that the starting accuracy of Sourcerer (when no labelled target data are available), 74.2%, is greater than the next-best state-of-the-art method trained on 20,000 labelled target instances.

A New York Times, Wall Street Journal, and USA Today Bestseller! Featured on CBS 60 Minutes Kai-Fu Lee named a Wired Icon, as part of Wired Magazine's 25th Anniversary Feature Publishers Weekly Fall 2018 Top 10 in Business & Economics Featured in the New York Times, the Wall Street Journal, the Washington Post, Wired, Financial Times, Bloomberg Businessweek, Business Insider, Forbes, and more. "After thirty years of pioneering work in artificial intelligence at Google China, Microsoft, Apple and other companies, Lee says he's figured out the blueprint for humans to thrive in the coming decade of massive technological disruption: 'Let us choose to let machines be machines, and let humans be humans.'"--Forbes "Kai-Fu Lee believes China will be the next tech-innovation superpower and in his new (and first) book, AI Superpowers: China, Silicon Valley, and the New World Order, he explains why. Taiwan-born Lee is perfectly positioned for the task."--New Times "AI Superpowers: China, Silicon Valley, and the New World Order, by Kai-Fu Lee, about the ways that artificial intelligence is reshaping the world and the economic upheaval new technology will generate. We need to start thinking now about how to address these gigantic changes."--Senator