Artificial intelligence technologies can be used to help buildings and spaces track their waste in real-time and engage users by nudging them to correctly sort their waste. According to a study by the World Bank, 98% of the world's waste is sent to landfills, dumped into oceans or being incinerated, even though a high majority of daily consumables are recyclable. This is primarily due to the high level of contaminants found in recyclables, making previously clean material practically unrecyclable and financially unmarketable. In Toronto, for every percentage point decreased in contaminated waste can create up to $1 million in recycling cost savings every year, which can be attributed to the management and sorting costs incurred by the waste hauling and collection companies. Intuitive is a Canadian company which seeks to achieve zero waste through their AI solution, Oscar.

In my previous post I talked about using a portable EEG device to detect Event Related Potentials (ERP's) in the brain. Specifically, I was able to detect a Reward Positivity (RewP) signal after a puzzle was solved correctly. I did this by graphing the signal immediately after the event and comparing it with the average RewP signal from this paper. Using my human brain's visual pattern recognition, I confirmed that I was getting the same pattern. Wouldn't it be interesting to train a machine learning model to recognize the same pattern so we can monitor these events automatically.

Self-driving cars, meet your nemesis: the London roundabout. This strange piece of geometry, with tentacles shooting off at odd angles and cars nudging into impossible spaces, is one of the many headaches that will plague computer brains as the city's autonomous vehicle (AV) trials accelerate. In the US, Waymo and others boast fleets of self-driving cars that have racked up millions of miles of public road trials, across more than 25 cities. Billions of dollars of investment is flowing into AV units run by Uber and General Motors. Tesla is making bold promises about "robo taxis", and Ford plans to start building AVs in 2021. If you believe the latest McKinsey report, China will see mass deployment of fully autonomous vehicles within a decade.

We are facing one of the worst pandemics the world has suffered in recent decades. Evoking the famous words of Winston Churchill, weeks of strain, sweat, and tears await us. However, are preparing to stop such an illness, not only because we have the best doctors or brilliant scientists on our side. But because, for the first time, technology is going to become a fundamental ally. There is something called Artificial Intelligence that has already been shown to work is one of our best weapons.

IMAGE: Like any recipe, an ideal memristive neuromorphic computing system requires a special blend of CMOS circuits and memristive devices, as well as spatial resources and temporal dynamics that must be... view more WASHINGTON, March 24, 2020 -- During the 1990s, Carver Mead and colleagues combined basic research in neuroscience with elegant analog circuit design in electronic engineering. This pioneering work on neuromorphic electronic circuits inspired researchers in Germany and Switzerland to explore the possibility of reproducing the physics of real neural circuits by using the physics of silicon. The field of "brain-mimicking" neuromorphic electronics shows great potential not only for basic research but also for commercial exploitation of always-on edge computing and "internet of things" applications. In Applied Physics Letters, from AIP Publishing, Elisabetta Chicca, from Bielefeld University, and Giacomo Indiveri, from the University of Zurich and ETH Zurich, present their work to understand how neural processing systems in biology carry out computation, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials. One of the most distinctive computational features of neural networks is learning, so Chicca and Indiveri are particularly interested in reproducing the adaptive and plastic properties of real synapses.

Machine learning models are often pre-set with specific parameters for easy implementation.

Machine learning models are often pre-set with specific parameters for easy implementation.

Diseases know no borders and the threat of an epidemic is ever increasing with the number of all kinds of outbreaks tripling over the last forty years. While addressing a pandemic, experts usually want to know the following things about the disease: i) How quickly is it spreading? Since the first report of coronavirus (COVID-19) in Wuhan, China, it has spread to at least 100 other countries. Companies like Alibaba, Baidu, and Huawei accelerated their healthcare initiatives. Tech start-ups are getting more involved integrally with clinicians, academics and government entities around the world to activate technology against the fast-spreading pandemic.

The 2020 Call for Code Global Challenge has expanded its focus to tackle the effects of COVID-19. Technology solutions can help reduce the impact this pandemic has on our daily lives and the world. COVID-19, which is caused by the novel corona virus, has revealed the limits of the systems we take for granted in a very short period of time. Whether it's the massive increase in demand for information during a time of crisis, educating children when schools are closed, or helping communities best distribute limited resources, technology has a pivotal role to play. Through Call for Code, you can see your idea deployed by a global partner ecosystem.

To date, China's self-driving road test efforts have lagged behind other regions. The California Vehicle Administration (DMV) says 64 companies have been granted licenses for road tests with a human in the passenger seat, with former Google self-driving project Waymo the sole company in the state permitted to test without a human in the vehicle. Waymo has completed 2.34 million km of California road tests, followed by GM Cruise's 1.33 million km and others such as Pony.ai, It's not surprising that California is a world leader in self-driving road testing, considering the large number of AI companies located in the state. But a recent report suggests China has picked up speed, with Beijing emerging as a new self-driving vehicle hot spot.