US space agency NASA has collaborated with Google to help local government monitor and predict the air quality. The duo will build advanced machine learning-based algorithms and link space data with Google Earth Engine data streams to generate high-resolution air quality maps in near real-time. Signed under the Space Act Agreement, Google and Nasa have committed to a 2 years Annex agreement under which they will leverage their expertise to help local governments make informed decisions about daily air quality monitoring and forecasts. The results will create city-scale, near real-time estimation and forecasting of harmful pollutants, such as nitrogen dioxide and fine particulate matter present in the atmosphere. Data will be collected from Google's Street View mapping vehicles, surface monitoring stations and other Earth monitoring satellites.

Industry observers say we're about to enter the next AI Winter. Artificial Intelligence emerged more than 60 years ago, and in that timeframe we have seen many such seasons. While I don't believe the coming winter will be as severe as the others, by all accounts, it's coming. The first AI Winter happened in the 1970s, when, after more than a decade of heavy funding of academic research by the U.S. Department of Defense, the government pulled back. It became clear that advancing AI would be much more challenging and expensive than originally foreseen.

Now more than ever, every company is a data company. By 2025, individuals and companies around the world will produce an estimated 463 exabytes of data each day, 1 1. Jeff Desjardins, "How much data is generated each day?" World Economic Forum, April 17, 2019. With that in mind, most businesses have begun to address the operational aspects of data management--for instance, determining how to build and maintain a data lake or how to integrate data scientists and other technology experts into existing teams. Fewer companies have systematically considered and started to address the ethical aspects of data management, which could have broad ramifications and responsibilities. If algorithms are trained with biased data sets or data sets are breached, sold without consent, or otherwise mishandled, for instance, companies can incur significant reputational and financial costs. Board members could even be held personally liable.

KT is looking to improve its artificial intelligence competitiveness through a partnership with Canada's Vector Institute, a nonprofit firm dedicated to AI research. According to KT, the two sides signed an agreement on research, development and business cooperation at the Vector Institute in Toronto on Thursday. The signing marked the first partnership between a South Korean company and the Vector Institute. KT said the partnership will focus on three main areas in the field of AI: joint R&D projects, foster professional talents and expanding the global AI ecosystem. For starters, the two sides will collaborate on applying voice recognition based on big data AI to KT AI services.

Some two weeks ago, Jason M Allen of Pueblo West won first prize in the digital category at the Colorado State Fair for his art work named work "ThéâtreD'opéra Spatial". It was no ordinary art work. Allen used Midjourney, an artificial intelligence (AI) programme, for creating the artwork by converting text into hyper-realistic graphics. The art world remained divided over the ethics of such an AI-generated art, with some purists expressing indignation at the way technology is taking dominance over human artistry and originality. Do purists have a point? Will we see machines overtaking humans in every sphere, including the sublime realm of art?

AUSTIN, Texas – Rep. Tony Gonzales, a Republican who represents a district in Texas that spans more than 800 miles along the border, warned that the surge of migrants crossing into the US illegally won't stop until Congress takes action. Tomorrow, it's your city, whether that's Chicago, New York, San Francisco, Florida," Gonzales told Fox News Digital on Saturday. There have been more than two million migrant encounters this fiscal year, including more than 203,000 just last month. House Republicans unveiled their "Commitment to America" agenda this week, which calls for ending catch-and-release loopholes, requiring proof of legal status for a job, and increasing funding for infrastructure and advanced technology at the border. Autonomous surveillance towers are a key piece of technology that Congress should fund for Border Patrol, Gonzales said. The towers, which can be erected in just a few hours and reach 33 feet in height, scan the surrounding area and use artificial intelligence to detect both migrants and the human smugglers who traffic them. "Every border sector is asking for more of these," Gonzales said. "What you don't hear too much about – the'gotaways' – these are people that we know entered the country illegally, but we don't know where they went.

Can a machine be an inventor? After the courts said no, a computer scientist is once more trying to have an artificial intelligence considered an inventor in the eyes of the law. In August, the U.S. Federal Circuit Court of Appeals issued a decision that AI cannot be listed as the inventor on a patent registration. The case before the court--Thaler v. Vidal--was either a gimmick that could be dismissed with a simple reading of U.S. patent law or one that strikes at the heart of a metaphysical question with crucial implications for the future of innovation. In Thaler v. Vidal, Stephen Thaler challenged the refusal of the U.S. Patent and Trademark Office to issue a patent registration for an invention Thaler claims was created by an artificial intelligence device called Device for Autonomous Bootstrapping of Unified Sentience, or DABUS.

Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction. However, to date the majority of the approaches to detect these attacks have been designed for image processing systems. Many popular image adversarial detection approaches are able to identify adversarial examples from embedding feature spaces, whilst in the NLP domain existing state of the art detection approaches solely focus on input text features, without consideration of model embedding spaces. This work examines what differences result when porting these image designed strategies to Natural Language Processing (NLP) tasks - these detectors are found to not port over well. This is expected as NLP systems have a very different form of input: discrete and sequential in nature, rather than the continuous and fixed size inputs for images. As an equivalent model-focused NLP detection approach, this work proposes a simple sentence-embedding "residue" based detector to identify adversarial examples. On many tasks, it out-performs ported image domain detectors and recent state of the art NLP specific detectors.

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings (Feller et al. 2016), medical diagnoses (Rajkomar et al. 2018; Esteva et al. 2019) and recruitment (Heilweil 2019, Van Esch et al. 2019). Academic articles (Floridi et al. 2018), policy texts (HLEG 2019), and popularizing books (O'Neill 2016, Eubanks 2018) alike warn that such algorithms tend to be _opaque_: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation (Lombrozo 2011, Hitchcock 2012), I formulate a moral concern for opaque algorithms that is yet to receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges.

A sustainable future: How AI and digital twins are helping ESG and sustainability efforts in construction, industry and city planning.