Western Australia has announced it will invest AU$1 million into nine initiatives that are aimed at reducing e-waste. The AU$1 million investment will come out of the state's AU$16.7 million New Industries Fund, and is expected to divert approximately 1,000 tonnes of e-waste annually from landfill. "The selected projects will support the recovery of high value material, while diverting materials which may have presented risks to human health and the environment if not disposed of appropriately," Environment Minister Stephen Dawson added. Among the grant recipients are Curtin University, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Epichem, which are all set to receive AU$200,000 apiece for their respective projects. Curtin University will use the funds to create a mini plant for recycling and metal recovery from printed circuit boards and integrated circuits; CSIRO will develop "innovative biotechnology" for extracting precious and base metals from e-waste; and Epichem has agreed to test whether oxidative hydrothermal dissolution can break down e-waste to produce a range of useful chemicals.
The news is full of stories and examples about how AI is impacting different industries. From manufacturing to finance, retail to pharmaceuticals, healthcare to insurance, and beyond, it's no doubt that AI is having a transformative impact on a wide range of industries. Likewise, various firms have been covering for years the impact AI is having on governments worldwide, with countries formulating strategic plans for AI and changing their way of operating in these remote-working reality days. However, not as much attention is placed on how local, state, city, and regional governments are implementing AI. After all, if AI is a transformative technology, shouldn't we be seeing its impact in our daily interactions with our local government officials?
Chatbots are one of the key opportunities to balance the budget-cuts, handle pressure to deliver services and manage the staff shortages. According to a survey by the Local Government Information Unit (LGiU), 64% of councillors in England believe that digital transformation will have a positive impact on the wellbeing of people in their areas over the next ten years. It strongly supports the increasing influence of artificial intelligence in public sector to streamline service delivery. By 2021, over 50% of enterprises will invest more on bots and Chatbot than traditional mobile app development, suggests Gartner. There are various applications to harness the Chatbot utility.
A blue-collar worker is a working class person who performs manual labor. Blue-collar work may involve skilled or unskilled manufacturing, mining, sanitation, custodial work, textile manufacturing, power plant operations, farming, commercial fishing, landscaping, pest control, food processing, oil field work, waste disposal, recycling, electrical, plumbing, construction, mechanic, maintenance, warehousing, shipping, technical installation, and many other types of physical work. Blue-collar work often involves something being physically built or maintained. In contrast, the white-collar worker typically performs work in an office environment and may involve sitting at a computer or desk. A third type of work is a service worker (pink collar) whose labor is related to customer interaction, entertainment, sales or other service-oriented work.
SINGAPORE/KUALA LUMPUR - The stench of curdled milk wafted from a shipping container of waste at Malaysia's Port Klang as Environment Minister Yeo Bee Yin told a group of journalists in May she would send the maggot-infested rubbish back where it came from. Yeo was voicing a concern that has spread across Southeast Asia, fueling a media storm over the dumping of rich countries' unwanted waste. About 5.8 million tons of trash was exported between January and November last year, led by shipments from the U.S., Japan and Germany, according to Greenpeace. Now governments across Asia are saying no to the imports, which for decades fed mills that recycled waste plastic. As more and more waste came, the importing countries faced a mounting problem of how to deal with tainted garbage that couldn't be easily recycled.
This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single- and multi-objective heuristics based on the PageRank method and two mutiobjective evolutionary algorithms are proposed. Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives. The computed results improve over the current planning in Montevideo and provide a reasonable budget cost and quality of service for Bahia Blanca.
Rolnick, David, Donti, Priya L., Kaack, Lynn H., Kochanski, Kelly, Lacoste, Alexandre, Sankaran, Kris, Ross, Andrew Slavin, Milojevic-Dupont, Nikola, Jaques, Natasha, Waldman-Brown, Anna, Luccioni, Alexandra, Maharaj, Tegan, Sherwin, Evan D., Mukkavilli, S. Karthik, Kording, Konrad P., Gomes, Carla, Ng, Andrew Y., Hassabis, Demis, Platt, John C., Creutzig, Felix, Chayes, Jennifer, Bengio, Yoshua
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
Filled with intricate mazes of high-speed conveyor belts carrying yesterday's garbage, high-tech recycling centers use sophisticated sensors to sort plastic from paper from aluminum. While this technology may streamline sorting, it's not smart or nimble enough to finish the job. Behind the scenes, recycling workers continue to sort the materials, making sure cereal boxes don't mix with soda cans. But the future of smart recycling is looking brighter. Spider-like robotic arms, guided by cameras and artificial intelligence (AI) -- think of it as facial-recognition technology for garbage -- are helping to make municipal recycling facilities (MRFs) run more efficiently.
Technology is powering the rise of smart cities, transforming everything from traffic management to waste collection. We dig into the digital revolution giving rise to cities that are more connected, sustainable, and efficient -- and what the future of urbanization might look like. Cities are evolving at a rapid pace. Over half the world's population currently lives in urban areas. By 2050, that number is expected to jump to 70%. Along with a growing population, new challenges are emerging as cities look to improve everything from infrastructure to connectivity. Many see this as a viable business opportunity, developing technology to help cities efficiently provide proper foundation, energy, transportation, resources, jobs, and services to their residents. As a result, cities are undergoing a digital transformation -- that is, they are turning into "smart" cities. Get a data-driven look at the startups and industry players developing smart city technologies.
Recycling facilities use robotic sorting stations and object-recognition technology to identify and put garbage in its proper place. Filled with intricate mazes of high-speed conveyor belts carrying yesterday's garbage, high-tech recycling centers use sophisticated sensors to sort plastic from paper from aluminum. While this technology may streamline sorting, it's not smart or nimble enough to finish the job. Behind the scenes, recycling workers continue to sort the materials, making sure cereal boxes don't mix with soda cans. But the future of smart recycling is looking brighter.