Aron Chase is a 17-year-old student in London.Aron Chase Some artificial intelligence researchers at companies like Google and Facebook are now earning more money than investment bankers at Goldman Sachs and J.P. Morgan. These researchers also have the privilege of working in a field of technology that's poised to have a major impact on the world we live in. But, for many people, it's not clear how to go about getting a job in AI. This week, 17-year-old Londoner Aron Chase asked Shane Legg -- the chief scientist and cofounder of DeepMind, an AI lab acquired by DeepMind for a reported £400 million -- for five pieces of advice for an AI enthusiast like himself. "Hey Shane I'm currently 17 from London England and am very passionate about AI, also learning about in-depth human needs. What would be the 5 pieces of advice and tips you would give to a young person like me?" Chase wrote on Twitter.

I cover artificial intelligence and Google DeepMind. In previous roles I've written about tech policy, European startups, the gig economy, and venture capital. Previously I was a senior te...

Education is slow to change. For example, the classroom model hasn't changed much in centuries. Still, when it comes to technology, educators are quite willing to adopt new things. Many schools have already embraced emerging technologies such as virtual classrooms. Students are also accessing devices rather than opening textbooks at an increasing rate. Of course, it would be nearly impossible to discuss technology and education without mentioning AI.

Aron Chase is a 17-year-old student in London.Aron Chase Some artificial intelligence researchers at companies like Google and Facebook are now earning more money than investment bankers at Goldman Sachs and J.P. Morgan. These researchers also have the privilege of working in a field of technology that's poised to have a major impact on the world we live in. But, for many people, it's not clear how to go about getting a job in AI. This week, 17-year-old Londoner Aron Chase asked Shane Legg -- the cofounder of DeepMind, an AI lab acquired by DeepMind for a reported £400 million -- for five pieces of advice for an AI enthusiast like himself. "Hey Shane I'm currently 17 from London England and am very passionate about AI, also learning about in-depth human needs. What would be the 5 pieces of advice and tips you would give to a young person like me?" Chase wrote on Twitter.

Python is a high-level language created by Guido van Rossum and first released in 1991. It is named after the greatest comedy act of all time, Monty Python. Python can be used to create pretty much any type of application. Python has been popular for many years and it's popularity shows no signs of stopping anytime soon. Being an in-demand language, knowing Python is beneficial for your career as a software developer. Having working knowledge of high-level programming languages is something any software developer should have. Here's a list of some of the best Python tutorials, Python courses, and Python books in 2018 to help you learn Python. Get the Ultimate Python Development kit. Get everything you need to code Python. Top package includes Egghead.io at the lowest price ever!

Right now, Jeremy Howard – the co-founder of fast.ai Why? His own students are beating him. And their names can now be found across the tops of leaderboards all over Kaggle. So what are these secrets that are allowing novices to implement world-class algorithms in mere weeks, leaving behind experienced deep learning practitioners in their GPU-powered wake? Allow me to tell you in ten simple steps.

Elon Musk, the founders of DeepMind, and other AI luminaries have signed a letter that guarantees they won't develop "lethal autonomous weapons." It's the latest effort to draw attention to the moral risks raised by AI weapons, but prohibiting the technology may ultimately prove challenging. Big shots: The letter was signed by Musk; DeepMind's Demis Hassabis, Shane Legg, and Mustafa Suleyman; Skype founder Jaan Tallinn; and the well-known AI researchers Stuart Russell, Yoshua Bengio, and Jürgen Schmidhuber. Peace movement: Tech companies are being forced to examine military uses of their technology. Employee outrage recently prompted Google to promise that it wouldn't let its AI be used to make weapons.

Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences. In this paper, we present a joint problem formalization of these interrelated issues, and propose generative methods that address these questions simultaneously. Specifically, we leverage the latent space obtained by training a deep generative model---the Variational Autoencoder (VAE)---via a loss function that incorporates both rating performance and item reconstruction terms. We then apply a greedy search algorithm that utilizes this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing. An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items. As case studies on real-world data, we applied our method on the MART abstract art and Movielens Tag Genome dataset, which resulted in promising results: small and diverse sets of novel items.

Action recognition has attracted increasing attention from RGB input in computer vision partially due to potential applications on somatic simulation and statistics of sport such as virtual tennis game and tennis techniques and tactics analysis by video. Recently, deep learning based methods have achieved promising performance for action recognition. In this paper, we propose weighted Long Short-Term Memory adopted with convolutional neural network representations for three dimensional tennis shots recognition. First, the local two-dimensional convolutional neural network spatial representations are extracted from each video frame individually using a pre-trained Inception network. Then, a weighted Long Short-Term Memory decoder is introduced to take the output state at time t and the historical embedding feature at time t-1 to generate feature vector using a score weighting scheme. Finally, we use the adopted CNN and weighted LSTM to map the original visual features into a vector space to generate the spatial-temporal semantical description of visual sequences and classify the action video content. Experiments on the benchmark demonstrate that our method using only simple raw RGB video can achieve better performance than the state-of-the-art baselines for tennis shot recognition.

We investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder. We compared an ideal system that uses manually corrected linguistic features including phoneme and prosodic information in training and test sets against a few other systems that use corrupted linguistic features. Both subjective and objective results demonstrate that corrupted linguistic features, especially those in the test set, affected the ideal system's performance significantly in a statistical sense due to a mismatched condition between the training and test sets. Interestingly, while an utterance-level Turing test showed that listeners had a difficult time differentiating synthetic speech from natural speech, it further indicated that adding noise to the linguistic features in the training set can partially reduce the effect of the mismatch, regularize the model, and help the system perform better when linguistic features of the test set are noisy.