Read the full ABC article and watch the video interview to learn more about Tanmay and his work in the field of AI. The Australian Broadcasting Corporation (ABC) recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five, launched his first app at age nine, and has been working with IBM's AI and cognitive APIs for a couple of years now. Tanmay is in a different league from the average pre-teen. In 2013, at age nine, he built "tTables," an app to help kids learn multiplication which Apple's App Store accepted after rejecting it three times. An incredible achievement for a child who loves to code but is largely self-taught.
Posted by Bradley Jiang, Software Engineer. Many people think designing deep learning models and training neural networks is complex and time-consuming, taking days or even weeks of work. But it doesn't have to be. There are a number of tools you can use right now to help you quickly develop and iterate on machine learning models. One such tool is Cloud Datalab.
I have a pretty awesome backlog of blog posts from Udacity Self-Driving Car students, partly because they're doing awesome things and partly because I fell behind on reviewing them for a bit. Here are five that look pretty neat. This is a great blog post if you're looking to get started with point cloud files. The most popular laptop among Silicon Valley software developers is the Macbook Pro. The current version of the Macbook Pro, however, does not include an NVIDIA GPU, which restricts its ability to use CUDA and cuDNN, NVIDIA's tools for accelerating deep learning.
Ontario is increasing support for students in the science, technology, engineering and mathematics (STEM) disciplines, including artificial intelligence, to continue to build a highly skilled workforce and support job creation and economic growth. Leading businesses from around the world choose Ontario because of its talented workforce, strong public education system and commitment to universal health care. These same qualities help to support an ecosystem that enables locally owned companies to succeed and grow. To bolster provincial competitiveness, the government plans to increase the number of postsecondary students graduating in the STEM disciplines by 25 per cent over the next five years. This initiative will boost the number of STEM graduates from 40,000 to 50,000 per year and position Ontario as the number one producer of postsecondary STEM graduates per capita in North America.
One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.
One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. A DIFFERENT & MORE EFFECTIVE APPROACH TO LEARNING DATA SCIENCE: In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. WE'LL BUILD SUPERVISED MACHINE LEARNING ALGORITHMS TOGETHER: I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. LEARN BOTH THE THEORY & APPLICATION OF MACHINE LEARNING: The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.
I'm a little embarrassed to admit this, but I've been seeing a virtual therapist. It's called Woebot, and it's a Facebook chatbot developed by Stanford University researchers that offers interactive cognitive behavioral therapy. And Andrew Ng, a prominent figure who previously led efforts to develop and apply the latest AI technologies at Google and Baidu, is now lending his backing to the project by joining the board of directors of the company offering its services. "If you look at the societal need, as well as the ability of AI to help, I think that digital mental-health care checks all the boxes," Ng says. "If we can take a little bit of the insight and empathy [of a real therapist] and deliver that, at scale, in a chatbot, we could help millions of people."
The history of Artificial Intelligence isn't a long one, around 60-70 years, but the advances in recent years has been huge. The Modern Artificial Intelligence Infographic shows how technology coupled with studies of the human brain have aided in making AI a reality, and a reality we can use everyday. Machines are already intelligent, but we fail to recognise it. When a machine demonstrates intelligence we counter it by saying'it's not real intelligence'. Therefore Al becomes whatever has not been accomplished so far by a machine.
Google's AutoML system recently produced a series of machine-learning codes with higher rates of efficiency than those made by the researchers themselves. In this latest blow to human superiority the robot student has become the self-replicating master. AutoML was developed as a solution to the lack of top-notch talent in AI programming. There aren't enough cutting edge developers to keep up with demand, so the team came up with a machine learning software that can create self-learning code. The system runs thousands of simulations to determine which areas of the code can be improved, makes the changes, and continues the process ad infinitum, or until its goal is reached.
Back in May, Google revealed its AutoML project; artificial intelligence (AI) designed to help them create other AIs. Now, Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that's more efficient and powerful than the best human-designed systems. An AutoML system recently broke a record for categorising images by their content, scoring 82 percent. While that's a relatively simple task, AutoML also beat the human-built system at a more complex task integral to autonomous robots and augmented reality: marking the location of multiple objects in an image. For that task, AutoML scored 43 percent versus the human-built system's 39 percent.