This story was published in partnership with The Moonshot Catalog. In the late 1960s, Nobel Prize-winning economist Herbert Simon posed the following thought exercise: Imagine you are an alien from Mars visiting a college on Earth, and you spend a day observing how professors teach their students. Simon argued that you would describe the process as "outrageous." "If we visited an organization responsible for designing, building and maintaining large bridges, we would expect to find employed there a number of trained and experienced professional engineers, thoroughly educated in mechanics and the other laws of nature that determine whether a bridge will stand or fall," he wrote in a 1967 issue of Education Record. "We find no one with a professional knowledge in the laws of learning, or the techniques for applying them," he wrote. Teaching at colleges is often done without any formal training. Mimicry of others who are equally untrained, instinct, and what feels right tend to provide the guidance. As a result, teaching is, to use another building metaphor, not up to code. There are widespread beliefs about the best way to teach and learn that have been proven wrong by science, yet they persist. Reading back over a textbook or taking lecture notes with a highlighter at the ready is often done by students, for instance, but these practices have proven of limited merit, and in some cases even counterproductive in aiding recall.

Summary: Explaining data science to a non-data scientist isn't as easy as it sounds. You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail. You might try this approach. You're at a party or maybe striking up a conversation with that pretty girl at the bar and sooner or later the question comes up, "what do you do?" Since you have what is reported to be the sexiest job in the world you proudly respond "I'm a data scientist". OK, what happens next depends on exactly what you say.

Digital generated image of data. When it comes to AI, much of the attention has been on deep learning. This part of the AI world has seen great strides, such as with image recognition. But of course, there are other areas of AI that look promising, such as reinforcement learning. Keep in mind that cutting-edge companies like Google's DeepMind and OpenAI have already made breakthroughs with this approach.

Nils J. Nilsson, a computer scientist who helped develop the first general-purpose robot and was a co-inventor of algorithms that made it possible for the machine to move about efficiently and perform simple tasks, died on Sunday at his home in Medford, Ore. His death was confirmed by his wife, Grace Abbott. Dr. Nilsson was a member of a small group of computer scientists and electrical engineers at the Stanford Research Institute (now known as SRI International) who pioneered technologies that have proliferated in modern life, whether in navigation software used in more than a billion smartphones or in such speech-control systems as Siri. The researchers had been recruited by Charles Rosen, a physicist at the institute, who had raised Pentagon funding in 1966 to design a robot that would be used as a platform for doing research in artificial intelligence. Although the project was intended to create a general-purpose mobile "automaton" and be a test bed for A.I. programs, Mr. Rosen had secured the funding by selling the idea to the Pentagon that the machine would be a mobile sentry for a military base.

According to an unofficial consensus, the birth of artificial intelligence as an independent research project can be dated to the summer of 1956, when John McCarthy at Dartmouth College, where he belonged to the Mathematical Department, was able to persuade the Rockefeller Foundation to finance an investigation " The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it". In addition to McCarthy (who was a professor at Stanford University until 2000 and is responsible for the coining of the term "artificial intelligence"), several other participants took part in the historical workshop at Dartmouth: Marvin Minsky (former professor at Stanford University), Claude Shannon (inventor of information theory); Herbert Simon (Nobel Prize winner in economics); Arthur Samuel (developer of the first chess computer program at world champion level); furthermore half a dozen experts from science and industry, who dreamed that it might be possible to produce a machine for coping with human tasks, which, according to the previous opinion, require intelligence. The Manifesto of Dartmouth (written at the dawn of the AI age) is both irritating and blurred. It is not clear whether the conference participants believed that one-day, machines would think or behave as if they could imagine. Both possible interpretations allow the word "simulate."

Back in August 2019, the BBC made some waves with the news that it was developing a voice assistant called Beeb, an English language "Alexa" of its own that could interact with and control its array of radio and TV services, and its on-demand catalogue, and able to understand the array of accents you find in across the BBC's national footprint to boot. Ten months on, it's releasing its first live version of the service in the form of a beta to a select group of early adopters: UK-based members of the Windows Insider Program, a beta-testing, bug-seeking, early-adopter group popular in the Windows community, with over 10 million users globally. The idea with the limited release beta -- according to Grace Boswood, COO of BBC Design and Engineering -- will be to get Insiders to try out various features and stress test Beeb in the early beta, while at the same time giving the BBC a trove of usage data that can help it continue to train Beeb further, ahead of a wider release. The BBC is not naming a date yet for the general release. When you are a member in the UK, you have to be using the latest release of Windows 10, and then you download Beeb BETA form the Windows App Store.)

Next generation virtual assistants are envisioned to handle multimodal inputs (e.g., vision, memories of previous interactions, etc., in addition to the user's utterances), and perform multimodal actions (e.g., displaying a route in addition to generating the system's utterance). We introduce Situated Interactive MultiModal Conversations (SIMMC) as a new direction aimed at training agents that take multimodal actions grounded in a co-evolving multimodal input context in addition to the dialog history. We provide two SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture (grounded in a shared virtual environment) and, (b) fashion (grounded in an evolving set of images). We also provide logs of the items appearing in each scene, and contextual NLU and coreference annotations, using a novel and unified framework of SIMMC conversational acts for both user and assistant utterances. Finally, we present several tasks within SIMMC as objective evaluation protocols, such as Structural API Prediction and Response Generation. We benchmark a collection of existing models on these SIMMC tasks as strong baselines, and demonstrate rich multimodal conversational interactions. Our data, annotations, code, and models will be made publicly available.

Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this paper, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that the accuracies of NEMA are up to 95%. It achieves 2%-10% higher accuracy and 5x-14x speedup over baseline methods.

Each month, Future Tense Fiction--a series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives--publishes a story on a theme. The evening before you sign and take delivery of your son, you call Charlie and tell him you think you've made a huge mistake. "Let me come on over and split a few with you," he says. "I haven't seen the fire pit yet." Charlie--a short, compact man with green eyes and a shaved head whom you met when he delivered groceries the first few weeks you were housebound--brings over a six-pack. You walk out into the complex's community garden together. It used to be a parking lot, and the path through the mushroom gardens under the solar panels is still faded gray asphalt and leftover white lines. You're careful with your right foot; you still haven't gotten used to the way your prosthetic moves. You and Sienna from 4B have a fire pit and stone circle dug out in your combined lots, and she's grown a privacy wall of rosebushes that surround the relaxing space. Charlie sits on one of the cedar benches as you fiddle with twigs to make a fire. This beats the awkwardness of sitting down to talk right away. Your parents didn't raise you to be direct about feelings. Neither did the army, nor the warehouse you drove a forklift in. Charlie will, if you let him. Making a fire gives you a moment to sort out all your feelings. Or maybe it just gives you an excuse to delay talking about them.

The IEEE Robotics & Automation Society has announced Allen School professor Dieter Fox as the recipient of a 2020 RAS Pioneer Award in recognition of his "pioneering contributions to probabilistic state estimation, RGB-D perception, machine learning in robotics, and bridging academic and industrial robotics research." The society will formally honor Fox, director of the University of Washington's Robotics and State Estimation Laboratory and senior director of robotics research at NVIDIA, during the International Conference on Robotics and Automation (ICRA 2020) next week. The RAS Pioneer Award honors individuals who have had a significant impact on the fields of robotics and automation by initiating new areas of research, development, or engineering. Fox's contributions have focused on enabling robots to interact with people and their environment in an intelligent way, with an emphasis on state estimation and perception problems such as 3D mapping, object detection and tracking, manipulation, and human activity recognition. "We are extremely proud that Dieter has been recognized with this prestigious award. It is truly deserved," said professor Magdalena Balazinska, director of the Allen School.