The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing. Through self-paced labs and, partnering with Scan to deliver instructor-led workshops, DLI teaches the latest techniques for designing, training, and deploying neural networks across a variety of application domains. As well as self-paced labs, Scan can also come to your place of work meaning you don't have to leave the office to be able to solve your deep learning and accelerated computing problems. You can also take part in self pace online DLI courses via the NVIDIA DLI website.

Artificial Intelligence is moving at the speed of light, with multiple companies creating software, products and services in not just a vertical way – more of a horizontal disruption. Form Healthcare to Security, from Real Estate to Telecom, here is a look into 28 companies powering the disruption with AI – 1st Edition. Sherpa.ai was founded in 2012 after deep research into Artificial Intelligence, with the conviction of creating a personal assistant that would be not just useful, but indispensable for users. In order to do this, Sherpa brought together a team of experts in Artificial Intelligence who, coupled with a fantastic design, have been able to create the next generation of Digital Assistants which will help users make their life not just more exciting, but also more enjoyable. WellSaid Labs has developed state of the art text-to-speech technology that creates life-like synthetic voice, from the voices of real people.

Jill Watson has been a teaching assistant (TA) at the Georgia Institute of Technology for five years now, helping students day and night with all manner of course-related inquiries. But for all the hard work she has done, she still can't qualify for outstanding TA of the year. That's because Jill Watson, contrary to many students' belief, is not actually human. This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. Created back in 2015 by Ashok Goel, professor of computer science and cognitive science at the Institute, Jill Watson is an artificial system based on IBM's Watson artificial intelligence software.

Perhaps we can expect an iPhone-like symphonic progression in models here? Many companies are throwing their hat into the translation technology ring. Web translation software is being surpassed by portable, state-of-the-art technology in the form of earpieces, hand-held devices and apps, all of which are enabling users to quickly navigate our multilingual world on-the-go. Most recently, American Airlines announced it is testing interpreter mode for Google Assistant to help communication between their employees and travellers who speak a different language. In recent years, artificial intelligence (AI) has drastically enhanced the accuracy and quality of foreign language translations – allowing machines to help break down language barriers for customer service teams and tourists alike.

If you were planning to enroll in college next fall - don't.

In this paper, we propose a multi-timescale replay (MTR) buffer for improving continual learning in RL agents faced with environments that are changing continuously over time at timescales that are unknown to the agent. The basic MTR buffer comprises a cascade of sub-buffers that accumulate experiences at different timescales, enabling the agent to improve the tradeoff between adaptation to new data and retention of old knowledge. We also combine the MTR framework with invariant risk minimization [Arjovsky et al., 2019] with the idea of encouraging the agent to learn a policy that is robust across the various environments it encounters over time. The MTR methods are evaluated in three different continual learning settings on two continuous control tasks and, in many cases, show improvement over the baselines.

There are dozens of articles written comparing the relative merits of Python and R for data science, and this isn't one of them. Instead, this an article about the divergence of data analysts and machine learning engineers, and their differing needs in a programming language. The simple version is that machine learning engineers are, fundamentally, software engineers, and they use programming languages designed for software engineering--not statistics. This may sound fairly obvious, but it represents a change in the machine learning ecosystem, one that is worth diving into further. Comparisons of R and Python often highlight perceived advantages of either language that are, at best, marginal and subjective.

Coursera has introduced a new tool that helps universities identify courses on the company's online learning platform that most closely match their on-campus offerings. The CourseMatch solution uses machine learning and natural language processing to "automate the matching and minimize the need for human curation," according to a company blog post. CourseMatch can "ingest" on-campus course catalogs in more than 100 languages and map them to the most relevant Coursera courses in any of the languages available on the platform. It then returns up to five "matches" along with a relevance score, with higher scores given for stronger matches. Some 1,800 schools globally have already used the tool to match more than 2.6 million on-campus courses with Coursera equivalents, according to the blog post.

We have seen rapid advancements in Artificial Intelligence and related technologies in recent times. Use of AI applications for - Automated customer support systems, chatbots, and personalized shopping experience with product recommendations are common examples. With the growth of Artificial Intelligence industry, we have also seen increase in demand of professionals who are skilled in this technology. The demand for AI professionals are more in - product companies for services like - chatbots, AI_powered visual search, and recommendation engines; companies that offer either offshore, recruitment or training services. Business have realised the potential of AI to devise new products and process to gain a competitive advantages by saving costs and time.

Since we launched the Coronavirus Response Initiative on March 12, more than 2,600 colleges and universities around the world have activated Coursera for Campus programs to take learning online and minimize student disruption. We're humbled by the global response and are working hard to be even more useful to universities who need to move online quickly. As universities go live using our offering, they urgently need an easy solution to help identify courses on Coursera that most closely match each course in their on-campus catalogues. Manual curation is too slow when it's to be done across thousands of universities and millions of on-campus courses, especially when faculty and staff are already stretched thin. Two weeks ago, the Data Science team at Coursera started developing a natural language processing solution to automate the matching and minimize the need for human curation.