As technology evolves at a rapid rate – especially technology that incorporates artificial intelligence (AI) capabilities – so too does the potential for bias, disconnect, misuse of data, and the automation of impersonal actions or decisions. With the vast amounts of data collected, stored, and exchanged, capitalist societies risk the commoditization of personal data at the expense of the individual, instead of using personal data to foster valuable individual and societal relationships. In business, AI and machine learning are increasingly used as part of smart systems that analyze large amounts of data to identify trends that will benefit the business, like capturing more consumers and increasing profits, as opposed to building long-lasting relationships. AI shouldn't only be focused on the business' bottom line. In fact, a recent AI and empathy survey by our company of 6,000 consumers from North America, the United Kingdom, Australia, Japan, Germany, and France found that 69% of consumers think businesses have a moral obligation to do what's right for the consumer, beyond what is legally required.

New Zealand Police has recruited an unusual new officer to the force: an AI cop called Ella. Ella is a life-like virtual assistant that uses real-time animation to emulate face-to-face interaction in an empathetic way. Its first day of work will be next Monday, when Ella will be stationed in the lobby of the force's national headquarters in Wellington. Its chief duties there will be welcoming visitors to the building, telling staff that they've arrived, and directing them to collect their passes. It can also talk to visitors about certain issues, such as the force's non-emergency number and police vetting procedures. After three months on the job, Ella's future on the force will be evaluated.

Immune systems across the globe have been working overtime this winter as a devastating flu season has taken hold. More than 180,000 Americans have been hospitalized and 10,000 more have died in recent months, according to the CDC, while the coronavirus (now officially designated COVID-19) has spread across the globe at an alarming rate. Fears of a growing worldwide flu outbreak have even prompted the precautionary cancelling of MWC 2020 -- barely a week before it was slated to open in Barcelona. But in the near future, AI-augmented drug development could help produce vaccines and treatments fast enough to halt the spread of deadly viruses before they mutate into global pandemics. Conventional methods for drug and vaccine development are wildly inefficient.

People in India watch a lot of videos on the internet. According to a report from The Wall Street Journal, Indians spend more than 8.5GB of mobile data on average, and most of it on video. Last year, YouTube said more than 95% of content consumption is in regional languages. So naturally, there's a lot of appetite for vernacular videos, but not all creators know all Indic languages. Last week, just after Parasite won the Oscar award, Mother Jones claimed dubbing is superior than translated subtitles.

The findings were discussed at the annual meeting of the American Association for the Advancement of Science in Seattle on Friday. Sex robots integrate artificial intelligence and traditional as well as novel technologies that may result in widely unknown and unpredictable risks. Scientists are concerned these sex robots (or love dolls) are being designed to look like children or even programmed to protest and simulate a rape scenario. According to tech expert Chris Riddell, stricter regulation of sex robots is needed immediately, "otherwise it's going to be the wild west." "Until now, we've only had human-to-human relationships. We're heading into an era where humans are having relationships with technology systems, and that's disturbing us," Riddell told 10 daily.

Fuel poverty affects between 50 and 125 million households in Europe and is a significant issue for both developed and developing countries globally. This means that fuel poor residents are unable to adequately warm their home and run the necessary energy services needed for lighting, cooking, hot water, and electrical appliances. The problem is complex but is typically caused by three factors; low income, high energy costs, and energy inefficient homes. In the United Kingdom (UK), 4 million families are currently living in fuel poverty. Those in series financial difficulty are either forced to self-disconnect or have their services terminated by energy providers. Fuel poverty contributed to 10,000 reported deaths in England in the winter of 2016-2107 due to homes being cold. While it is recognized by governments as a social, public health and environmental policy issue, the European Union (EU) has failed to provide a common definition of fuel poverty or a conventional set of indicators to measure it. This chapter discusses current fuel poverty strategies across the EU and proposes a new and foundational behavior measurement indicator designed to directly assess and monitor fuel poverty risks in households using smart meters, Consumer Access Device (CAD) data and machine learning. By detecting Activities of Daily Living (ADLS) through household appliance usage, it is possible to spot the early signs of financial difficulty and identify when support packages are required.

In the past few years, several new matching models have been proposed and studied that take into account complex distributional constraints. Relevant lines of work include (1) school choice with diversity constraints where students have (possibly overlapping) types and (2) hospital-doctor matching where various regional quotas are imposed. In this paper, we present a polynomial-time reduction to transform an instance of (1) to an instance of (2) and we show how the feasibility and stability of corresponding matchings are preserved under the reduction. Our reduction provides a formal connection between two important strands of work on matching with distributional constraints. We then apply the reduction in two ways. Firstly, we show that it is NP-complete to check whether a feasible and stable outcome for (1) exists. Due to our reduction, these NP-completeness results carry over to setting (2). In view of this, we help unify some of the results that have been presented in the literature. Secondly, if we have positive results for (2), then we have corresponding results for (1). One key conclusion of our results is that further developments on axiomatic and algorithmic aspects of hospital-doctor matching with regional quotas will result in corresponding results for school choice with diversity constraints.

We explore the benefits of augmenting state-of-the-art model-free deep reinforcement algorithms with simple object representations. Following the Frostbite challenge posited by Lake et al. (2017), we identify object representations as a critical cognitive capacity lacking from current reinforcement learning agents. We discover that providing the Rainbow model (Hessel et al.,2018) with simple, feature-engineered object representations substantially boosts its performance on the Frostbite game from Atari 2600. We then analyze the relative contributions of the representations of different types of objects, identify environment states where these representations are most impactful, and examine how these representations aid in generalizing to novel situations.

One often reads that the complexities of anatomical pathology are now, or are soon to be unraveled by the latest machine learning technologies. Such incredible claims are bolstered by the experience of seeing a system classify histology images (or better, training one's own). It truly is remarkable that this is even possible. Yet, as this becomes a more common experience for the pathology community, it is likely that our current expectations and ambitions will be tempered by the constraints of reality. I remember being awe-struck at how realistic computer graphics were in the late 80's and early 90's.

Oracle supercharged its efforts to take on cloud giants Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) and today launched a data science platform that runs as a native service on Oracle Cloud Infrastructure. The announcement marks the company's second cloud push of the new year. Last week Oracle announced its Generation 2 Cloud was available in five new regions including Jeddah, Saudi Arabia; Melbourne, Australia; Osaka, Japan; Montreal; and Amsterdam. The new Oracle's Cloud Infrastructure Data Science Platform uses elements of DataScience.com, The vendor claims the new offering can bring data scientists together and aid analysis with capabilities like shared projects, model catalogs, team security policies, reproducibility, and auditability.