Solar Alliance Energy has launched a photovoltaic charging station for robotic lawnmowers. The Powershed system allows users to cut the cord and place a robotic mower anywhere the sun shines, the company said. Solar Alliance developed the design in cooperation with a researcher from the University of Tennessee and a provisional patent application has been filed with the US Patent office. The first Powershed unit has been installed at the University of Tennessee and is currently operating. Solar Alliance said the unit is designed to meet demand through a scalable production model and will initially be offered through commercial distribution partners and direct sales.

At the Diversity, Equity, and Inclusion breakfast at VentureBeat's AI-focused Transform 2020 event, a panel of AI practitioners, leaders, and academics discussed the changes that need to happen in the industry to make AI safer, more equitable, and more representative of the people to whom AI is applied. The wide-ranging conversation was hosted by Krystal Maughan, a Ph.D. candidate at the University of Vermont, who focuses on machine learning, differential privacy, and provable fairness. The group discussed the need for higher accountability from tech companies, inclusion of multiple stakeholders and domain experts in AI decision making, practical ways to adjust AI project workflows, and representation at all stages of AI development and at all levels -- especially where the power brokers meet. In other words, although there are systemic problems, there are systemic solutions as well. The old Silicon Valley mantra "move fast and break things" has not aged well in the era of AI.

As AI becomes increasingly adopted in more industries, its users attempt to achieve the delicate balance of making efficient use of its utility while striving to protect the privacy of its customers. A common best practice of AI is to be transparent about its use and how it reaches certain outcomes. However, there is a good and bad side to this transparency. Here is what you should know about the pros and cons of AI transparency, and possible solutions to achieve this difficult balance. The number of uses of AI has continued to expand over the last several years.

However, manually copy data from multiple sources for retrieval in a central place can be very tedious and time-consuming. "Web scraping," also called crawling or spidering, is the automated gathering of data from an online source usually from a website. While scraping is a great way to get massive amounts of data in relatively short timeframes, it does add stress to the server where the source hosted. However, as long as it does not disrupt the primary function of the online source, it is relatively acceptable. Despite its legal challenges, web scraping remains popular even in 2019.

Now is a particularly opportune time to drive towards this goal. As the world moves towards a COVID-19 post-pandemic recovery, the UN has called on governments to heed the "unprecedented wake-up call" and "build back better" by creating more sustainable, resilient and inclusive societies. There are two approaches to Green AI – using AI to solve sustainability challenges and using AI in a more sustainable way. How can AI solve sustainability challenges? Delivering societal and environmental well-being through AI are key strategic considerations of the European Commission, who acknowledge that "AI systems promise to help [tackle] the most pressing concerns, including climate change and environmental degradation".

In the light of the Black Lives Matter protests, AI and facial recognition vendors and users are taking notice of concerns over racial bias and privacy, reports Ron Alalouff. The use of artificial intelligence (AI) has come under the spotlight recently, especially how algorithms can be biased against people of colour or women. And most recently, in the wake of the Black Lives Matter campaigns following the death of George Floyd in May, tech giants such as Amazon and IBM have suspended or withdrawn their facial recognition technologies which are based on AI algorithms. In the United States the issue of bias in AI is most explosive. Miriam Vogel, President and CEO of Equal AI, believes that while racism has its historical roots, "AI now plays a role in creating, exacerbating and hiding these disparities behind the facade of a seemingly neutral, scientific machine".

Sharing, downloading, and reusing software is common-place, some of which is carried out legally with open source software. When it is not legal, it is unclear just how many copyright infringements and trade secret violations have taken place: does an infringement count for the artefact as a whole or perhaps for each file of the program? To answer this question, it must first be established whether a program should be considered as an integral whole, a collection, or a mere set of distinct files, and why. We argue that a program is a functional whole, availing of, and combining, arguments from mereology, granularity, modularity, unity, and function to substantiate the claim. The argumentation and answer contributes to the ontology of software artefacts, may assist industry in litigation cases, and demonstrates that the notion of unifying relation is operationalisable. Indirectly, it provides support for continued modular design of artefacts following established engineering practices.

Uber has been hit with a lawsuit by two British drivers in a bid to reveal how the company's algorithm works. The headquarters for Uber in Europe is in Amsterdam, so the drivers have taken their case to a Dutch court. Uber's drivers want to know what data is being collected about them – and how it's being used. The drivers are concerned that Uber's algorithm isn't entirely neutral in how it decides who to allocate rides to. "They want to prove that Uber is in fact acting as an employer," their lawyer, Anton Ekker, said to Dutch outlet NOS.

Minicab drivers will launch a legal bid to uncover secret computer algorithms used by Uber to manage their work in a test case that could increase transparency for millions of gig economy workers across Europe. Two UK drivers are demanding to see the huge amounts of data the ride-sharing company collects on them and how this is used to exert management control, including through automated decision-making that invisibly shapes their jobs. The case is being brought on Monday by the UK-based App Drivers and Couriers Union in the district court in Amsterdam, where the international headquarters of the $56bn (£44.5bn) The union said transparency was essential in checking if Uber was exercising discrimination or unequal treatment between drivers. It will also allow drivers to organise and build collective bargaining power over terms of work and pay in a way that is currently impossible.

It is a complex process, one that involves data mining, machine learning, and predictive modeling.