If there is one thing we learned from the COVID-19 pandemic, it's that when humans are sent home, machines keep working. This doesn't mean that robots will take over the world. It does, however, mean that our technical landscape is changing. Human history has a long and favorable track record of technological advancements, particularly when it comes to ideas that seem ludicrous at the time (Wright brothers, anyone?). The printing press, assembly line and personal computer have all helped move civilization forward by leaps and bounds over the last few centuries.

As it goes every year, one hot feature sets a trend in technology, and suddenly every company boasts some variation of which that is uniquely theirs. This year, that feature is AI. Hot on the heels of Alexa's and Google Assistant's holiday successes, Artificial Intelligence on phones has become the de facto must-have feature – whether consumers know it or not. In any case, manufacturers seem not to realize that AI doesn't mean "Anything Intuitive" – that's just how operating systems are supposed to be. Yet it seems that OEM's are eager to label nearly any vaguely intuitive feature as AI.

MR. DEAN: Aaron, where is AI making a difference right now for your business? LEVIE: Box helps companies manage and share, and collaborate around their information. If you think about all of the unstructured data in the enterprise--every document, every media asset, every email, every proposal, every contract--all of this data you work on for one second and then it goes into an archive or repository, and you never get or extract value from it in the future. At Box, we have tens of billions of files stored in the platform, and some customers have billions of files in their own instance. We want to be able to help customers make more sense of their information, and hopefully that begins to change the very business processes they run.

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.

Machine Learning is the crux of Artificial Intelligence. With increasing developments in AI, IoT and other smart technologies, machine learning jobs are gaining higher exposure and demand in the technology market. If you are currently an IT professional, you might be interested in a career switch because of the exciting opportunities the industry offers to its aspirants. Or, you might have an interest that you have wanted to pursue long. However, not knowing exactly how to start a career in machine learning can lead an aspirant in the wrong way. There should be a proper agenda on how to identify the right opportunity and approach it in a systematic way. In this article, let us see some of the essential steps that one can take towards their machine learning journey.