Today, AI has turned into reality what used to be the stuff of sci-fi novels. For decades, scholars from diverse disciplines have been predicting how AI and robotics are about to change the way we think, work and live. Although, not everyone is on the same page when it comes to AI, there is no denying that it is already demonstrating its positive potential in many industries. One area where AI is expected to play a huge role is education. However, in India, the education sector is still seeking ways to respond to the advent of this technology.

Want to build skills in artificial intelligence (A.I.) and deep learning? Udacity and Google are launching a free introductory course on the subject, which naturally leans into TensorFlow, the open-source library for deep learning software developed by Google. "Intro to TensorFlow for Deep Learning" is a two-month course, and now open to enrollment. Its goal is to help developers build A.I. applications that can scale (using TensorFlow, of course). It's the second TensorFlow-based collaboration between the two firms; in 2016, Udacity and Google launched a TesnorFlow course that taught students the basics of the platform.

Google and online learning hub Udacity have launched a free course designed to make it simpler for software developers to grasp the fundamentals of machine learning. The "Intro to TensorFlow for Deep Learning" course is designed to be more accessible to developers than previous machine-learning courses offered by Udacity. "Our goal is to get you building state-of-the-art AI applications as fast as possible, without requiring a background in math," says Mat Leonard, head of the School of AI at Udacity. "If you can code, you can build AI with TensorFlow. You'll get hands-on experience using TensorFlow to implement state-of-the-art image classifiers and other deep learning models. You'll also learn how to deploy your models to various environments including browsers, phones, and the cloud."

We consider a problem of stochastic online learning with general probabilistic graph feedback. Two cases are covered. (a) The one-step case where for each edge $(i,j)$ with probability $p_{ij}$ in the probabilistic feedback graph. After playing arm $i$ the learner observes a sample reward feedback of arm $j$ with independent probability $p_{ij}$. (b) The cascade case where after playing arm $i$ the learner observes feedback of all arms $j$ in a probabilistic cascade starting from $i$ -- for each $(i,j)$ with probability $p_{ij}$, if arm $i$ is played or observed, then a reward sample of arm $j$ would be observed with independent probability $p_{ij}$. Previous works mainly focus on deterministic graphs which corresponds to one-step case with $p_{ij} \in \{0,1\}$, an adversarial sequence of graphs with certain topology guarantees or a specific type of random graphs. We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability.

The Data Incubator recently teamed up with MRINetwork to increase its access to hiring partnerships worldwide. MRINetwork is comprised of over 1,500 search professionals who specialize in hundreds of industries, many of whom came from the industries in which they now recruit. The addition of MRINetwork, and its network of existing clients, will add thousands of hiring partners on top of TDI's existing 300 hiring partnerships. As the need for data scientists has increased exponentially over the past few years, MRI provides TDI students with immediate access to new data science positions in geographies worldwide, as well as greater access to companies with a fundamental need for the data science talent required to harness the power of their data.

Six titans of industry stood onstage at MIT's Kresge Auditorium yesterday, assembled to speak on a panel about artificial intelligence (AI), including David H. Koch Institute professor Robert Langer; Helen Greiner, cofounder of iRobot, the Bedford-based company perhaps best known for its line of autonomous vacuum cleaners; Xiao'ou Tang, founder of computer vision startup SenseTime, which last year raised $1.2 billion in venture capital at a valuation of more than $4.5 billion; and Eric Schmidt, former executive chairman of Google. The discussion capped off a three-day celebration of MIT's new Stephen A. Schwarzman College of Computing, which will offer its first classes in physics, economics, biology, economics, machine learning, and related disciplines this fall. The panelists shared thoughts on a range of topics, but one they repeatedly touched on was entrepreneurship. Entrepreneurs, Schmidt argued in his opening remarks, drive the economy -- they're spigots for ideas that form the basis of industries. "[Founders are] people who are filled with a vision -- something they care about -- and they personalize it, they believe in it, and they convince others to follow them," he said. But, he said, they're in "need [of] more juice."

There are loads of Data Science courses at Udemy, not just the ones listed above. If none of these take your fancy, have a look around and I'm sure you'll find others that might just hit the spot. I also recommend taking a look at courses in Statistics, Artificial Intelligence, Machine Learning and Deep Learning too. Udemy's list changes every 30 days, so I will update this post regularly to reflect these changes. Final word - when you've done any of these courses, please return and leave some feedback and a review in the comments below.

AI For Everyone will launch on February 28th! This non-technical course will teach you the language of AI, how to plan and execute successful AI projects, and how to drive AI adoption in your company. This course is taught by deeplearning.ai

Mark Cahill, managing director for the ManpowerGroup, UK, commented that companies were deploying a myriad of approaches to upskill their existing workforce and build talent further, with many employers turning to long-term training courses. Online learning management systems are a popular channel for organizations to use, providing mass content which is especially useful for onboarding, compliance and cybersecurity training. Companies need to promote a culture of learning, provide career guidance, and offer short, focused upskilling opportunities. People need to know how to prepare for high growth roles of the future and that their employer supports their learning. As well as providing internal in-person and online training, companies can tap into external resources by partnering with organizations such as schools, universities and industry bodies to build communities of talent." The report also found that demand for IT skills is growing significantly: 16% of employers expect to increase headcount in IT, five times more than those expecting a decrease. The vast majority of employers in the U.S plan to increase or maintain headcount as a result of automation. Upskilling is on the rise, with 76% of companies planning to upskill their workforce by 2020, up from 28% in 2011. In the UK, 95% of employers are planning to increase or maintain headcount as a result of automation, according to the report. The research found that companies that are digitalizing are growing and this growth is producing more and new kinds of jobs. Cahill argued that the narrative around automation and AI "stealing our jobs" couldn't be further from the truth. As robots enter the workforce, they are transforming jobs but equally creating more employment opportunities as well. Every industry needs to accept this revolution is here to stay. Employers need to work out how to manage the shift and get humans to collaborate with machines."

We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design of such adaptive algorithms is to calibrate a so-called step-size or learning rate hyperparameter depending on variance, gradient norms, etc. A recent technique promises to overcome this difficulty by maintaining multiple learning rates in parallel. This technique has been applied in the MetaGrad algorithm for online convex optimization and the Squint algorithm for prediction with expert advice. However, in both cases the user still has to provide in advance a Lipschitz hyperparameter that bounds the norm of the gradients. Although this hyperparameter is typically not available in advance, tuning it correctly is crucial: if it is set too small, the methods may fail completely; but if it is taken too large, performance deteriorates significantly. In the present work we remove this Lipschitz hyperparameter by designing new versions of MetaGrad and Squint that adapt to its optimal value automatically. We achieve this by dynamically updating the set of active learning rates. For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.