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New machine learning tool tracks urban traffic congestion

#artificialintelligence

This display was computed in less than one hour.... view more A new machine learning algorithm is poised to help urban transportation analysts relieve bottlenecks and chokepoints that routinely snarl city traffic. The tool, called TranSEC, was developed at the U.S. Department of Energy's Pacific Northwest National Laboratory to help urban traffic engineers get access to actionable information about traffic patterns in their cities. WATCH: https://www.youtube.com/watch?v 8S4bLv9CtOo (Video by Graham Bourque Pacific Northwest National Laboratory) Currently, publicly available traffic information at the street level is sparse and incomplete. Traffic engineers generally have relied on isolated traffic counts, collision statistics and speed data to determine roadway conditions. The new tool uses traffic datasets collected from UBER drivers and other publicly available traffic sensor data to map street-level traffic flow over time.


As AI transforms the power sector ethics and governance take centre stage

#artificialintelligence

Artificial intelligence applications are transforming business operations and processes in the power sector, leading to greater cost savings, increased efficiency and new services for consumers. But further developments rely on the ability to foster and support innovation, addressing outstanding matters related to investments, data access and governance, as well as ethics. This is according to an assessment released by Eurelectric, AI Insights: The Power Sector in a Post-Digital Age. Eurelectric believes that by 2025, 81% of the energy companies will have adopted artificial intelligence, reaping the numerous benefits of accelerated developments in this field and fast tracking the clean energy transition. First, AI can enable a faster decarbonisation of the power sector.


AI's carbon footprint problem

#artificialintelligence

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions -- about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.


Amazon announces new employee tracking tech, and customers are lining up

Mashable

Amazon-powered employee tracking is coming to a warehouse, and possibly a store, near you. The ecommerce, logistics, and (among other things) cloud computing giant quietly previewed Tuesday new hardware and software development kits (SDK) which add machine learning and computer vision capabilities to companies' existing surveillance camera networks. And in what should come as no surprise as companies around the world ramp up employee monitoring, customers are already champing at the bit to sic Amazon's tech on their own workers. Amazon, of course, is notorious for monitoring its fulfillment center workers' movements in excruciating detail. From social-distance tracking systems to automatic tools that keep tabs on "the rates of each individual associate's productivity," Amazon has a well deserved reputation for invasiveness.


Welcome

#artificialintelligence

AI can benefit society in many ways but, given the energy needed to support the computing behind AI, these benefits can come at a high environmental price. CodeCarbon is a lightweight software package that seamlessly integrates into your Python codebase. It estimates the amount of carbon dioxide (CO2) produced by the cloud or personal computing resources used to execute the code.


Beginning by hacking Tesla .. Is the world witnessing a global war for artificial intelligence?

#artificialintelligence

At the height of the exchange of accusations between the United States and China regarding the "Covid-19" disease, new signs of a war between the two countries appeared, the Artificial intelligence War, which lead us to ask: Is this technology ready to work in safety? And can military AI be deceived easily? Although military AI technologies dominate military strategy in the US and China; But what sparked the crisis was that last March, Chinese researchers launched a brilliant, and potentially devastating, attack against one of America's most valuable technological assets, the Tesla electric car. A research team from the security laboratory of the Chinese technology giant "Tencent" has succeeded in finding several ways to deceive the artificial intelligence algorithms in the Tesla electric car by carefully changing the data, which are fed to the car's sensors, and the team managed to trick and confuse the vehicle's AI. The team tricked Tesla's brilliant algorithms capable of detecting raindrops on the windshield or following the lines on the road, operating the windshield wipers to act as if there was rain, and the lane markings on the road were modified to confuse the autonomous driving system so that it passed in the opposite traffic lane in violation of traffic rules.


Artificial Intelligence Will Revolutionize Energy, Earning Billions For Investors

#artificialintelligence

As the world is anticipating the end of the COVID-19 pandemic, energy consumption in industry and services is likely to grow. In the longer term, the developing world will increase its energy utilization, leading to growth of global primary energy demand by of 0.4% - 0.6% per year, or a 25% increase by 2050. According to scenarios calculated by energy giant Total SE, massive electrification of transportation will lead to decarbonization, and will require a rapid growth in renewables as a source of electricity. This energy transformation will see an explosion of growth in Artificial Intelligence (AI) utilization in the sector – up 50% between 2020 and 2024 – to allow smart, 21st century grids to become the gold standard, gradually replacing the "dumb" grids laid down in the late 19th – early 20th century in Europe, North America, Japan, China and beyond. The grid is a meta-system of generation facilities, be it nuclear, gas, coal, solar, wind, and hydro, connected by high voltage wire networks to transformers, and then to sub-stations and individual buildings, households, and apartments.


Reinforcement Learning to Reduce Building Energy Consumption

#artificialintelligence

The need for Energy Savings has become increasily foundamental to fight Climate Change. We have been working on a cloud-based RL algorithm that can retrofit existing HVAC controls to obtain substantial results. In the last decade, a new class of controls which relies on Artificial Intelligence have been proposed. In particular, we are going to highlight data-driven controls based on Reinforcement Learning (RL), since they showed from the very beginning promising results as HVAC controls [2]. There are two main ways to upgrade with RL the air conditioning systems: to implement RL on new systems or to retrofit the existing ones.


Increasing Solar Energy Adoption Through AI Roof Detection

#artificialintelligence

Solar AI, a Singapore based startup incubated as a part of ENGIE Factory, collaborated with Omdena, to hyper-scale the deployment of distributed solar and the transition towards 100% renewables by modernizing the way rooftop solar is sold. Solar energy is a promising and freely available resource for managing the forthcoming energy crisis, without hurting the environment. There's enough solar energy hitting the Earth every hour to meet all of humanity's power needs for an entire year. The rooftop solar assessment process can be time consuming and expensive, taking anywhere between 1 hour to 2 full days to calculate the solar potential of each rooftop. In the solar industry, this has resulted in the cost of sales taking up to 30–40% of total project costs, significantly worsening the unit economics of solar projects.


Global Big Data Conference

#artificialintelligence

As the world is anticipating the end of the COVID-19 pandemic, energy consumption in industry and services is likely to grow. In the longer term, the developing world will increase its energy utilization, leading to growth of global primary energy demand by of 0.4% - 0.6% per year, or a 25% increase by 2050. According to scenarios calculated by energy giant Total SE, massive electrification of transportation will lead to decarbonization, and will require a rapid growth in renewables as a source of electricity. This energy transformation will see an explosion of growth in Artificial Intelligence (AI) utilization in the sector – up 50% between 2020 and 2024 – to allow smart, 21st century grids to become the gold standard, gradually replacing the "dumb" grids laid down in the late 19th – early 20th century in Europe, North America, Japan, China and beyond. The grid is a meta-system of generation facilities, be it nuclear, gas, coal, solar, wind, and hydro, connected by high voltage wire networks to transformers, and then to sub-stations and individual buildings, households, and apartments.