With more than 2000 spoken languages, Africa's linguistic diversity is second only to Asia. A third of the world's languages is spoken by the 1.2 billion people living within her 54 countries. But the language of artificial intelligence is yet to gain fluency. It has become hackneyed to weave AI into every conversation about technology and society. AI will take away jobs.

Taiwan is a small island off the coast of China that is roughly one fourth the size of North Carolina. Despite its size, Taiwan has made significant waves in the fields of science and technology. In the 2019 Global Talent Competitiveness Index Taiwan (labeled as Chinese Taipei) ranked number 1 in Asia and 15th globally. However, despite being ahead of many countries in terms of technological innovation, Taiwan was still looking for further ways to improve and support research within the country. Therefore, in 2017 the Taiwan Ministry of Science and Technology (MOST), initiated an AI innovation research program in order to promote the development of AI technologies and attract top AI professionals to work in Taiwan.

Artificial Intelligence (AI) has undoubtedly been the technology story of the 2010s, and it doesn't look like the excitement is going to wear off as a new decade dawns. The past decade will be remembered as the time when machines that can truly be thought of as "intelligent" – as in capable of thinking, and learning, like we do – started to become a reality outside of science fiction. While no prediction engine has yet been built that can plot the course of AI over the coming decade, we can be fairly certain about what might happen over the next year. Spending on research, development, and deployment continues to rise, and debate over the wider social implications rages on. Meanwhile, the incentives only get bigger for those looking to roll out AI-driven innovation into new areas of industry, fields of science, and our day-to-day lives. Here are my predictions for what we're likely to see continue or emerge in the first year of the 2020s.

For a child prodigy, learning didn't always come easily to Derek Haoyang Li. When he was three, his father – a famous educator and author – became so frustrated with his progress in Chinese that he vowed never to teach him again. "He kicked me from here to here," Li told me, moving his arms wide. Yet when Li began school, aged five, things began to click. Five years later, he was selected as one of only 10 students in his home province of Henan to learn to code. At 16, Li beat 15 million kids to first prize in the Chinese Mathematical Olympiad. Among the offers that came in from the country's elite institutions, he decided on an experimental fast-track degree at Jiao Tong University in Shanghai. It would enable him to study maths, while also covering computer science, physics and psychology. In his first year at university, Li was extremely shy.

Recently, Charikar and Siminelakis (2017) presented a framework for kernel density estimation in provably sublinear query time, for kernels that possess a certain hashing-based property. However, their data structure requires a significantly increased super-linear storage space, as well as super-linear preprocessing time. These limitations inhibit the practical applicability of their approach on large datasets. In this work, we present an improvement to their framework that retains the same query time, while requiring only linear space and linear preprocessing time. We instantiate our framework with the Laplacian and Exponential kernels, two popular kernels which possess the aforementioned property.

We propose a novel reinforcement learning algorithm, AlphaNPI, that incorpo- rates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural biases in the form of modularity, hierarchy and recursion, which are helpful to reduce sample complexity, improve generalization and in- crease interpretability. AlphaZero contributes powerful neural network guided search algorithms, which we augment with recursion. AlphaNPI only assumes a hierarchical program specification with sparse rewards: 1 when the program execution satisfies the specification, and 0 otherwise. This specification enables us to overcome the need for strong supervision in the form of execution traces and consequently train NPI models effectively with reinforcement learning.

The Kalman filter (KF) is one of the most widely used tools for data assimilation and sequential estimation. In this work, we show that the state estimates from the KF in a standard linear dynamical system setting are equivalent to those given by the KF in a transformed system, with infinite process noise (i.e., a flat prior'') and an augmented measurement space. This reformulation---which we refer to as augmented measurement sensor fusion (SF)---is conceptually interesting, because the transformed system here is seemingly static (as there is effectively no process model), but we can still capture the state dynamics inherent to the KF by folding the process model into the measurement space. Further, this reformulation of the KF turns out to be useful in settings in which past states are observed eventually (at some lag). Here, when the measurement noise covariance is estimated by the empirical covariance, we show that the state predictions from SF are equivalent to those from a regression of past states on past measurements, subject to particular linear constraints (reflecting the relationships encoded in the measurement map).

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

After a passenger infected with the novel coronavirus boarded the Diamond Princess cruise ship in January, the virus quickly spread, eventually infecting at least 712 and killing seven. Critics labeled the ship quarantined in Yokohama a floating petri dish, and at least one Japanese expert attributed the explosion of cases to food trays passed out by infected crew. Could robots have made a difference? As countries around the world grapple with COVID-19, front line medical workers are deploying robots, telemedicine and other technologies to help contain the pandemic. China and Spain have used drones to monitor people during lockdown campaigns, while South Korea has deployed them to help disinfect areas in Daegu, an epidemic hotspot.

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban traffic body.