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.

"Gone are the days of data engineers manually copying data around again and again, delivering datasets weeks after a data scientist requests it"-these are Steven Mih's words about the revolution that artificial intelligence is bringing about, in the scary world of big data. By the time the term "big data" was coined, data had already accumulated massively with no means of handling it properly. In 1880, the US Census Bureau estimated that it would take eight years to process the data it received in that year's census. The government body also predicted that it would take more than 10 years to process the data it would receive in the following decade. Fortunately, in 1881, Herman Hollerith created the Hollerith Tabulating Machine, inspired by a train conductor's punch card.

I chose artificial intelligence as my next topic, as it can be considered as one of the most known technologies, and people imagine it when they talk about the future. But the right question would be: What is artificial intelligence? Artificial intelligence is not something that just happened in 2015 and 2016. It's been around for a hundred years as an idea, but as a science, we started seeing developments from the 1950s. So, this is quite an old tech topic already, but because of the kinds of technology that we have access to today -- specifically, processing performance and storage -- we're starting to see significant leaps in AI development. When I started the course entitled, "Foundations of the Fourth Industrial Revolution (Industry 4.0)," I got deeper into the topic of artificial intelligence. One of the differences between the third industrial revolution -- defined by the microchip and digitization -- and the fourth industrial revolution is the scope, velocity and breakthroughs in medicine and biology, as well as widespread use of artificial intelligence across our society. Thus, AI is not only a product of Industry 4.0 but also an impetus as to why the fourth industrial revolution is currently happening and will continue to do so. I think there are two ways to understand AI: the first way is to try giving a quick definition of what it is, but the second is to also think about what it is not.

It's time for big business to embrace the public chain – as a coordination tool, rather than as a place to carry out large-scale financial transactions. The group has come up with a new way of using the public ethereum mainnet to connect firms' internal systems for resource planning. Generative adversarial networks (GANs) -- two-part AI models consisting of a generator that creates samples and a discriminator that attempts to differentiate between the generated samples and real-world samples -- have been applied to tasks ranging from video, artwork, and music synthesis to drug discovery and misleading media detection. They've also made their way into ecommerce, as Amazon revealed in a blog post this morning. Frontline healthcare employees -- nurses, call center agents, among others -- have one of the toughest jobs in America.

A paper published by researchers at Carnegie Mellon University, San Francisco research firm OpenAI, Facebook AI Research, the University of California at Berkeley, and Shanghai Jiao Tong University describes a paradigm that scales up multi-agent reinforcement learning, where AI models learn by having agents interact within an environment such that the agent population increases in size over time. By maintaining sets of agents in each training stage and performing mix-and-match and fine-tuning steps over these sets, the coauthors say the paradigm -- Evolutionary Population Curriculum -- is able to promote agents with the best adaptability to the next stage. In computer science, evolutionary computation is the family of algorithms for global optimization inspired by biological evolution. Instead of following explicit mathematical gradients, these models generate variants, test them, and retain the top performers. They've shown promise in early work by OpenAI, Google, Uber, and others, but they're somewhat tough to prototype because there's a dearth of tools targeting evolutionary algorithms and natural evolution strategies (NES).

Episode 80: The featured guests are Machine Learning Tokyo (MLT) members Suzana Ilić (co-founder), Dimitris Katsios, and Asir Saeed. MLT is a Tokyo-based nonprofit organization dedicated to democratizing Machine Learning (ML). They are a team of engineers and researchers--now with a community of 5,000 people--and the winner of the Rakuten Technology & Innovation Silver Award 2019.

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When an epidemic spreads, it's vital to alert people who may have been infected but are yet to show symptoms. This process is called contact tracing, and across the world officials are spending long nights and weekends interviewing patients to identify people at risk. "The challenge is that, right now, it takes a significant amount of time to identify close contacts of the person who has been infected," says Akshay Saigal, Head of Innovation Labs for Asia at DXC Technology. "The track and trace is very manual and takes huge teams of dedicated officials to perform." He thinks that tech innovation can make a big difference to nations in need and spoke with GovInsider about three steps that officials can use for faster contact tracing.

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