TJBot is open source and we'd love to see what you can make with it. It comes with an initial set of recipes (step-by-step instructions) to help you connect your TJBot to Watson cognitive services and bring him to life. The recipes are based on Raspberry Pi. Use one of the starter recipes or create your own. If you have created your own recipe, we would love to include it as a featured recipe!

Obviously, it's artificial intelligence, but it is an intelligence we can use to help us determine what's useful in all of the big data being collected through the Internet of Things (IoT). And we can teach it to tell us what's important to investigate there, too. AI won't replace humans, but it will give us a huge boost. Paul Muller, vice president of strategic marketing at Hewlett Packard Enterprise, has a conversation with technology analyst Theo Priestley about AI, including what it is, how to use it, fears about it, and how it can help us long term. They talk about the myths around AI--such as it's a human replacement vehicle rather than a way to augment our work, or it's just software rather than hardware and software--and how artificial intelligence doesn't mean it's not intelligent and doesn't practice self-preservation.

The reality will lie between these two extremes. Based on research gathered from surveying Asian business leaders and human resources and AI professionals, this report argues that AI's future will cleave much more closely to the positive outcome. Moreover, this future appears to be approaching quickly: advances in deep learning and the rapid expansion of process automation in such diverse sectors as manufacturing, transportation, and financial services mean that AI's impact is growing exponentially with each passing year. Decision makers in all organizations must now begin to understand how AI will alter their own operational processes and those of suppliers, partners, and customers. Asia's business landscape is poised not only to benefit greatly from AI's rise, but also to define it.

Soaring over the world's highest buildings and racing a solar-powered car across the desert may not be on your vacation itinerary this summer, but you can still see what such experiences might be like, thanks to the spectacular new IMAX film, "Dream Big: Engineering Our World." The movie takes audiences from Dubai to Australia, while sharing stirring stories about the impact engineers have on society. Narrated by Academy Award winner Jeff Bridges, it celebrates the inspiring work and people behind these engineering feats. Featured footage includes the Great Wall of China and the Golden Gate Bridge, not to mention an underwater robot developed by a team of students from an underprivileged high school in Phoenix. Made up primarily of Mexican immigrants, the robotics team entered its creation in a competition and succeeded against all odds.

You are parent and wish to teach your 8 year old boy how to play violin? But does this have anything to do with artificial intelligence (AI)? Recent scientific experiments have shown that very young babies -- as young as 9-month old -- that learn music can significantly improve many of their cognitive functions, such as their future language acquisition. Children who learn how to play music young get both better verbal and language learning skills than the ones who don't, because they gain enhanced sound representation abilities and modify their brain connectivity. For adults, learning language is a great example.

In a setting befitting the opening scene of a sci-fi thriller at the recently opened Leverhulme Center for the Future of Intelligence at Cambridge University, Professor Stephen Hawking cautions that the future of artificial intelligence could potentially be "either the best, or worst thing to ever happen to humanity." Reiterating his 2014 statement to the BBC, Hawking urges that if done wrong, "the development of full AI could spell the end of the human race" –– think Terminator, I, Robot, or WestWorld. But on the other hand, Hawking believes that amplifying our minds through utilization of artificial intelligence can transform every aspect of our lives. The overarching concern surrounding AI is machine morality and whether it is safe for society. If people do not have proper ethical guidelines or fully comprehend the risks AI could play on mankind, is the expansion of functionalities and the powering of complex self-evolving capabilities – as Hawking would put it – the'worst thing to happen to humanity?'

The answer is a resounding no! Fortunately, there's still time left to prepare for one of the biggest digital transformations of all time. Reports suggest that a collection of the world's leading tech companies, including Microsoft, Apple, Samsung, Huawei, LG, Acer, Intel, Meta, Magic Leap, and ODG are all planning to double down on mixed reality (MR) this year by bringing affordable devices to the masses. In particular, excitement continues to grow over speculation that Apple may be ready to unveil its MR device in the coming months to coincide with the 10-year anniversary of the iPhone. Tim Cook has recently made bold statements about augmented reality (AR), indicating his company will have a major horse in the race. Many analysts believe that horse will be the dominant front-runner.

You probably haven't heard of YouTube-8M, but it's a big deal for anyone working in the field of machine learning. In short, it's a large database of labeled video content that programmers can use to test out their algorithms. Today, Google announced that YouTube-8M is getting a major update, with even more labels across more its videos, as well as audio elements. And the company is also aiming to make the dataset even better with a Kaggle competition, which will offer big bucks from a $100,000 prize pool to teams who build the best algorithms for tagging around 700,000 new videos (using the 8M dataset for training). "The dataset was created from over 7 million YouTube videos (450,000 hours of video) and includes video labels from a vocabulary of 4716 classes (3.4 labels/video on average," Google wrote on the competition page.

Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.

How many samples are sufficient to guarantee that the eigenvectors and eigenvalues of the sample covariance matrix are close to those of the actual covariance matrix? For a wide family of distributions, including distributions with finite second moment and distributions supported in a centered Euclidean ball, we prove that the inner product between eigenvectors of the sample and actual covariance matrices decreases proportionally to the respective eigenvalue distance. Our findings imply non-asymptotic concentration bounds for eigenvectors, eigenspaces, and eigenvalues. They also provide conditions for distinguishing principal components based on a constant number of samples.