Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards. Annually since 1963, these international R&D awards recognize 100 technologies that a panel of expert judges selects as the most revolutionary of the past year. Six of the laboratory's winning technologies are software systems, a number of which take advantage of artificial intelligence techniques. The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity. Another technology is a process designed to assure secure fabrication of integrated circuits, and the eighth winner is an optical communications technology that may enable future space missions to transmit error-free data to Earth at significantly higher rates than currently possible.

In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. Here we consider for the first time open QA over both tabular and textual data and present a new large-scale dataset Open Table-Text Question Answering (OTT-QA) to evaluate performance on this task. Most questions in OTT-QA require multi-hop inference across tabular data and unstructured text, and the evidence required to answer a question can be distributed in different ways over these two types of input, making evidence retrieval challenging---our baseline model using an iterative retriever and BERT-based reader achieves an exact match score less than 10%. We then propose two novel techniques to address the challenge of retrieving and aggregating evidence for OTT-QA. The first technique is to use "early fusion" to group multiple highly relevant tabular and textual units into a fused block, which provides more context for the retriever to search for. The second technique is to use a cross-block reader to model the cross-dependency between multiple retrieved evidences with global-local sparse attention. Combining these two techniques improves the score significantly, to above 27%.

In 2016 and 2017, Andrea Silenzi hosted and produced the hit dating podcast Why Oh Why, with the mission to chronicle her hilarious, maddening, and sometimes disastrous expedition into online dating. For guy listeners like me, it was also a window into what single women had to put up with when they were looking for love (or even just a decent date) on the internet. Her excruciatingly detailed exploration of how men and women approach digital courtship led Vulture to dub her "a genius of the cringe." After the show went on hiatus, Silenzi continued to post about the horrors of online dating on her Instagram account, a lifeline for fans who missed the show. But then this week, something else appeared on the account: She posted a very sweet engagement story, announcing her impending nuptials to a man reportedly from Hinge. Who will post screenshots of men saying things like, "Yeah i got the cure for coronavirus! I called Silenzi to ask.

I'm underground, back where it all started. Sitting at the hidden cafe where I first met Mike. I'd been studying in my bedroom for the past 9-months and decided to step out of the cave. Half of me was concerned about having to pay $19 for breakfast (unless it's Christmas, driving Uber on the weekends isn't very lucrative), the other half about whether any of this study I'd been doing online meant anything. In 2017, I left Apple, tried to build a web startup, failed, discovered machine learning, fell in love, signed up to a deep learning course with zero coding experience, emailed the support team asking what the refund policy was, didn't get a refund, spent the next 3-months handing in the assignments four to six days late, somehow passed, decided to keep going and created my own AI Masters Degree.

Each week, it packs recent break-throughs and noteworthy news alongside a short intro by Andrew Ng himself. The best thing about The Batch is that it is quick, self-contained, and always includes some perspective on the news, such as "why it matters." This is the best place to start, as it is not overwhelming, is self-contained, and is not focused on any particular sub-topic. Yeah, this place is incredible. Make sure you check AI and Data Science as topics of interest and start following people and publications that have similar interests as you.

Drone connectivity in the sky is an indispensable part of the Internet of Things (IoT): Anywhere, Anytime, Anything. In a recent summer internship project at Ericsson, we explored how Artificial Intelligence (AI) can empower drone mobility support in 5G networks. Our work received the Best Paper Award at the 2020 IEEE Wireless Communications and Networking Conference (WCNC 2020). The award is a recognition of the Ericsson internship program, which offers candidates a chance to learn about the world of work while working on projects that are changing the world of communications. Drones have many applications, ranging from package delivery and surveillance to remote sensing and IoT scenarios.

I'm underground, back where it all started. Sitting at the hidden cafe where I first met Mike. I'd been studying in my bedroom for the past 9-months and decided to step out of the cave. Half of me was concerned about having to pay $19 for breakfast (unless it's Christmas, driving Uber on the weekends isn't very lucrative), the other half about whether any of this study I'd been doing online meant anything. In 2017, I left Apple, tried to build a web startup, failed, discovered machine learning, fell in love, signed up to a deep learning course with zero coding experience, emailed the support team asking what the refund policy was, didn't get a refund, spent the next 3-months handing in the assignments four to six days late, somehow passed, decided to keep going and created my own AI Masters Degree.

You might say the brain is our most photogenic organ. We are, thanks to modern neuroimaging, living amid an explosion of brain data. Just consider: We can zoom into the brain's connectivity to the most minute, molecular level. We can trace individual cells as well as entire cell populations. We can turn neurons on and off just like a light switch.

It is well-known nowadays that robots do much of the work making a car, their giant arms swinging in precise motion to bolt on doors and weld metal. Less well-known is one of the major figures behind that assembly-line transformation, a Japanese engineer who built an empire at the base of Mount Fuji where his own robots churned out robots for the world's factories. Seiuemon Inaba, who died at age 95 on Oct. 2, led robot maker Fanuc Corp. from its start as a Fujitsu Ltd. spinoff in 1972. Today it is one of the principal industrial-robot makers in the world with a market value of some $40 billion, helping make products as diverse as cars and smartphones. Born March 5, 1925, in Chikusei, a small city some 50 miles north of Tokyo, Mr. Inaba was the son of a local landowner.

Technology companies provide much of the critical infrastructure of the modern state and develop products that affect fundamental rights. Search and social media companies, for example, have set de facto norms on privacy, while facial recognition and predictive policing software used by law enforcement agencies can contain racial bias. In this episode of Deep Tech, Marietje Schaake argues that national regulators aren't doing enough to enforce democratic values in technology, and it will take an international effort to fight back. Schaake--a Dutch politician who used to be a member of the European parliament and is now international policy director at Stanford University's Cyber Policy Center--joins our editor-in-chief, Gideon Lichfield, to discuss how decisions made in the interests of business are dictating the lives of billions of people. Also this week, we get the latest on the hunt to locate an air leak aboard the International Space Station--which has grown larger in recent weeks. Elsewhere in space, new findings suggest there is even more liquid water on Mars than we thought. It's located in deep underground lakes and there's a chance it could be home to Martian life. Space reporter Neel Patel explains how we might find out. Back on Earth, the US election is heating up. Data reporter Tate Ryan-Mosley breaks down how technologies like microtargeting and data analytics have improved since 2016. Check out more episodes of Deep Tech here. Gideon Lichfield: There's a situation playing out onboard the International Space Station that sounds like something out of Star Trek… But there is an air leak in the space station.