We introduce the probably approximately correct (PAC) version of the problem of {Battling-bandits} with the Plackett-Luce (PL) model -- an online learning framework where in each trial, the learner chooses a subset of $k \le n$ arms from a pool of fixed set of $n$ arms, and subsequently observes a stochastic feedback indicating preference information over the items in the chosen subset; e.g., the most preferred item or ranking of the top $m$ most preferred items etc. The objective is to recover an `approximate-best' item of the underlying PL model with high probability. This framework is motivated by practical settings such as recommendation systems and information retrieval, where it is easier and more efficient to collect relative feedback for multiple arms at once. Our framework can be seen as a generalization of the well-studied PAC-{Dueling-Bandit} problem over set of $n$ arms. We propose two different feedback models: just the winner information (WI), and ranking of top-$m$ items (TR), for any $2\le m \le k$. We show that with just the winner information (WI), one cannot recover the `approximate-best' item with sample complexity lesser than $\Omega\bigg( \frac{n}{\epsilon^2} \ln \frac{1}{\delta}\bigg)$, which is independent of $k$, and same as the one required for standard dueling bandit setting ($k=2$). However with top-$m$ ranking (TR) feedback, our lower analysis proves an improved sample complexity guarantee of $\Omega\bigg( \frac{n}{m\epsilon^2} \ln \frac{1}{\delta}\bigg)$, which shows a relative improvement of $\frac{1}{m}$ factor compared to WI feedback, rightfully justifying the additional information gain due to the knowledge of ranking of topmost $m$ items. We also provide algorithms for each of the above feedback models, our theoretical analyses proves the {optimality} of their sample complexities which matches the derived lower bounds (upto logarithmic factors).

Andrew Yan-Tak Ng, regarded as one of the world's foremost experts on Artificial Intelligence (AI), firmly believes that despite the widespread mistrust of AI, it is good for governments, companies and individuals. Currently co-chairman and co-founder of the online learning platform Coursera and an adjunct professor at Stanford University's computer science department, Ng served as chief scientist and vice-president at Chinese tech company Baidu and was founding lead of the Google Brain team. In a phone interview from the Coursera headquarters in Mountain View, California, Ng spoke about the need for the Indian government to invest in education. He also shared his perspective on the potential of AI and the fears surrounding it. We would like you to propose one big idea to mark India's Independence Day.

In the last few years, machine learning applications have quietly entered every aspect of life: social media to speech recognition, radiology to retail, warfare to writing articles, coding to customer service, robotics to route optimization. During the 40 year information age, we told computers what to do. With advances in artificial intelligence, particularly machine learning, and faster processing chips we can feed computers giant data sets and they can (in narrow slivers) draw some inferences on their own. As we reported in Ask About AI, the rise of code that learns marks the beginning of a new era of augmented intelligence. It's a great opportunity for us to expand access to a great education and for young people to make a big contribution.

Here's how it's related to artificial intelligence, how it works and why it matters. Deep learning is a subset of machine learning, which itself falls within the field of artificial intelligence. Artificial intelligence is the study of how to build machines capable of carrying out tasks that would typically require human intelligence. That rather loose definition means that AI encompasses many fields of research, from genetic algorithms to expert systems, and provides scope for arguments over what constitutes AI. Within the field of AI research, machine learning has enjoyed remarkable success in recent years -- allowing computers to surpass or come close to matching human performance in areas ranging from facial recognition to speech and language recognition. Machine learning is the process of teaching a computer to carry out a task, rather than programming it how to carry that task out step by step. At the end of training, a machine-learning system will be able to make accurate predictions when given data.

Many companies are experimenting with AI on a small scale, and a few have made a commitment that their organizations will be "AI first" or "AI-driven." But what does this mean? What is AI doing or leading, and, in particular, what is the role of leadership in making organizations AI-driven? We see a lot of confusion around opportunity and action. In the 2018 Deloitte Global Human Capital Trends survey and report of business and HR leaders, 72% indicated that AI, robots, and automation are important -- but only 31% felt their organizations were prepared to address strategy to implement these technologies.

Education has always been a hot topic among intellectuals and reformers. It has seen quite a change in the last decade or so, but not significant enough to get noticed. The new era of learning is still focused on keeping students in the classroom in the hopes that they will bring a better future to themselves and to society as a whole. The current education system has always been focused on a batch study where individual growth is never focused on. With the expansion of the internet, things have changed drastically, as now, anyone can do self-study using YouTube, Udacity, or TED.

In recent years, the use of Artificial Intelligence (AI) has been widely changed many aspects of our lives. For example, retailers understand consumer behaviour by analysing customer data through AI, and video game companies create immersive games with AI to enhance gaming experience. Education too has great potential to utilise AI for enhancing the quality of education by streamlining learning and teaching procedures. What Is Artificial Intelligence (AI)? Artificial Intelligence is the intelligence demonstrated by machines, in contrast to the human intelligence.

Keeko is just 45 centimeters tall, or one-foot seven inches, and weighs only 45 kilograms, roughly 99 pounds. Gliding across the room to the amazement of starry-eyed five-year-olds, it rolls its head and tells the transfixed children "remember to wash your hands before you eat." They all giggle and rush to cuddle the diminutive AI robot with the cutesy, cartoon character voice, and stare with utter bewilderment. In nursery schools across China, Keeko models are being brought in as teaching aids to engage and stimulate young and impressionable minds. With the assistance of her "little helper," Yang Huizhen showed her class the importance of recycling.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

An expert recently advised parents to play computer games along with their children - but have they any hope of winning? According to William Shakespeare: "Cowards die many times before their deaths." If he had been alive today, he could have added that the middle-aged computer gamer dies with even greater regularity. Every day, as I try to recapture the skills of my youth, I suffer the ignominy of being repeatedly blasted out of virtual existence, probably by a primary school pupil. Then, as I grudgingly hand over the console controller to my 12-year-old son, I watch as he makes it all look so easy.