Manasi: After I graduate, I would like to work in industry in the area of machine learning, possibly for healthcare applications.

AI focusses on the creation of intelligent machines that can perform functions like speech recognition, learning & problem solving.

Three decades and hundreds of millions of lines of computer code after graduating from the Milwaukee School of Engineering, NVIDIA's Dwight Diercks returned today to celebrate a donation that will put his alma mater at the forefront of AI undergraduate education. Diercks, who grew up the son of a mailman, working on his family's pig farm in Red Wing, Minnesota, came to NVIDIA as its 22nd employee. Today, he oversees a team of some 5,000 software engineers around the world who ship tens of millions of lines of code each month that help accelerate the world's computing. Diercks' $34 million gift, the largest from an alum in MSOE's 116-year history, is the keystone in the school's efforts to infuse its engineering program with artificial intelligence. Two years ago, MSOE became one of the very few programs, together with Carnegie Mellon, to offer a computer science degree focused on AI.

Do you want to work in the biggest emerging field of technology since the birth of the Internet? Machine learning is impacting EVERYTHING. As a member of the machine learning group you get to apply your neurons to our neural net processing engines. We combine high performance software with cutting edge hardware to run deep neural nets FAST!

You don't need a professional degree to master AI and ML, but you do need boundless passion.

Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

Following the announcement by the Central Board of Secondary Education (CBSE) that it will soon begin to offer Artificial Intelligence (AI) as an elective to students from classes 9 to 12, it has now been revealed that American IT major IBM will be preparing the curriculum for the same, reports The Times of India. Members of IBM India, along with individuals from its global team, will collaborate with subject experts to prepare the curriculum; IBM will launch a pilot project covering 1,000 schools across a number of cities as a trial for the curriculum. As part of the pilot, school principals will firstly be made aware of AI technology, after which foundational lessons regarding the same will given to teachers. The curriculum will consist of a five-stage training process for students, and through it students will participate in research-informed readings, ideation and innovation workshops, as well as hands-on projects. Commenting on the effort, vice-president of India Software Labs, IBM, stated,"Unlike creating a curriculum for professionals, introducing the subject to schoolchildren is different. We have a chance to create a course to give the best effect by using a design-based approach to problem-solving with AI. The programme intends to develop a professional learning community for teachers in partnership with the industry. We will ensure it provides collaborative global project opportunities for students and recognise learning achievements through badging and accreditation."

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.