Dr. Rebecca Fiebrink is a Lecturer in Computing at Goldsmiths, University of London. She creates new technologies for digital music and art, and she designs new ways for humans to interact with computers in creative practice. Much of her current research combines techniques from human-computer interaction, machine learning, and signal processing to allow people to apply machine learning more effectively to new problems, such as the design of new digital musical instruments and gestural interfaces for gaming and health. She is also involved in projects developing rich interactive technologies for digital humanities scholarship, and in designing new approaches to integrating the arts into computer science teaching and outreach. Rebecca is the developer of the Wekinator system for interactive machine learning.

Wolfram Alpha has been a huge hit with students. Whether in college or high school, Wolfram Alpha has become a ubiquitous way for students to get answers. But it's a one-shot process: a student enters the question they want to ask (say in math) and Wolfram Alpha gives them the (usually richly contextualized) answer. It's incredibly useful--especially when coupled with its step-by-step solution capabilities. But what if one doesn't want just a one-shot answer? What if one wants to build up (or work through) a whole computation?

To operate successfully in a complex and changing environment, learning agents must be able to acquire new skills quickly. Humans display remarkable skill in this area -- we can learn to recognize a new object from one example, adapt to driving a different car in a matter of minutes, and add a new slang word to our vocabulary after hearing it once. Meta-learning is a promising approach for enabling such capabilities in machines. In this paradigm, the agent adapts to a new task from limited data by leveraging a wealth of experience collected in performing related tasks. For agents that must take actions and collect their own experience, meta-reinforcement learning (meta-RL) holds the promise of enabling fast adaptation to new scenarios.

In case you're wondering, I am now a slave to a chemical clock. Last night, for no special reason, the Evil Dex kept me up until 4:30 and then didn't wake me up until 9:30. That's pretty inconvenient, though it is five hours of sleep, which isn't too bad. I used the time last night to read one of Elizabeth Warren's books since it's increasingly looking like she'll be the 46th president of the United States. For you doubters, and I know you're out there, here are some sample question of the kind that Aristo had to answer: Now, yes, I scored 100 percent on this just like you did.

Artificial Intelligence is spreading its wings almost everywhere. Starting from the businesses to even the agricultural fields, AI is powering the world in many ways than one. There have been various discussions surrounding the fact that AI has the potential to impact the education sector as well. There seems to be various possibilities that Artificial Intelligence is expected to spur innovation in the field of education. Starting from becoming a latest form of technology for the colleges to being adopted in schools, there seems to be a whole lot of things expected from AI entering in the education industry and specifically in the ed-tech sector!

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.

Artificial Intelligence (AI) as an idea seems to have caught the imagination of both industry and academia alike. Although AI-related academic research has been in place since the late nineties but it is recently that products and services inspired by AI have emerged out of labs into our daily routine activities. Whether it is the buzz around autonomic vehicles, drones, speech recognition, various voice response systems like Alexa and Google assistant, every single one of these products has some form of AI at its core. Undoubtedly, ever-increasing processing speeds and storage capacities along with possibilities of machine to machine (M2M) communication have let the cat out of the bag. Today we produce more data in a single day then possibly we did in the entire year in the eighties.

The benefits of teaching a child a foreign language are truly increíble! Studies show that children who are exposed to a second language have increased cognitive ability, greater social flexibility, improved listening skills, higher memory retention, and improved problem-solving skills. There is no doubt that raising a child to be bilingual is muy bien and will have lasting benefits. Pair one of the best tablets for kids with any of our 10 favorite apps and websites to help your niños y niñas learn español. StudyCat teaches Spanish through games.

Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop "Help, Anna!" (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural language-and-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments. We publicly release code and data at https://github.com/khanhptnk/hanna .

Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time, but could only achieve inferior translation accuracy. In this paper, we proposed a novel approach to leveraging the hints from hidden states and word alignments to help the training of NART models. The results achieve significant improvement over previous NART models for the WMT14 En-De and De-En datasets and are even comparable to a strong LSTM-based ART baseline but one order of magnitude faster in inference.