There are 5 modules of 2.5 hours duration each; and include live demos, quiz and hands-on assignments. Each session will be introduced by an opening core-note by leading data scientists and AI influencers. Season 2 of Microsoft'Week of AI' will focus on skilling with deep technical sessions on data science and conversational AI. The content is designed to prepare the attendees for industry recognized certifications. Season 1 of'Week of AI' sessions covered the basics of AI and introduction to data science and machine learning.

While the majority of us are'wow'ing the early applications of machine learning, it continues to evolve at quite a promising pace, introducing us to more advanced algorithms like Deep Learning. This branch, by the way, is attracting even more attention than all other ML-algorithms combined. Of course, I don't have to declare it. It is simply great in terms of accuracy when trained with a huge amount of data. Also, it plays a significant role to fill the gap when a scenario is challenging for the human brain.

Deep learning has proved its supremacy in the world of supervised learning, where we clearly define the tasks that need to be accomplished. But, when it comes to unsupervised learning, research using deep learning has either stalled or not even gotten off the ground! There are a few areas of intelligence which our brain executes flawlessly, but we still do not understand how it does so. Because we don't have an answer to the "how", we have not made a lot of progress in these areas. If you liked my previous article on the functioning of the human brain to create machine learning algorithms that solve complex real world problems, you will enjoy this introductory article on Hierarchical Temporal Memory (HTM). I believe this is the closest we have reached to replicating the underlying principles of the human brain. In this article, we will first look at the areas where deep learning is yet to penetrate.

Chatbots for B2B are a very hot topic, but so far there are few live examples to share. In this pacesetter case study, see a successful B2B chatbot in action. Like many organizations, TSIA member Citrix, a leader in employee productivity tools, was challenged with increasing call deflection while simultaneously improving customer experience. With their customers ranging from small privately owned businesses to large enterprise organizations, they needed a solution that would meet the specific needs of each buyer. While this appeared to be an impossible feat, Citrix was able to accomplish this goal by implementing Bold360, LogMeIn's AI chatbot and live-agent software.

These days it seems like nearly every tech startup is touting the use of AI in their products or business processes. They release press and marketing materials advertising smart, new features that "look" like artificial intelligence, all under the guise that this rebrand will serve the end user better. "We're doing it to save you money …. Unfortunately, recent data shows that companies are less than honest about their use of artificial intelligence, advertising AI product features that are really just basic automation technology features. There is value -- cash value -- associated with a company's ability to appear "tech-savvy". The UK investment firm MMC Ventures says that startups with some type of AI component can attract as much as 50 percent more funding than other software companies. Nevermind that the Wall Street Journal suspects 40 percent or more of those companies don't use any form of real AI at all. "Artificial Intelligence" is the ultimate marketing buzzword.

"The machines are coming for our jobs!" How many times have you heard someone say some version of this? "We're all going to be replaced with automation!" Or, "It's just a matter of time before they give my job to a robot or a machine." While it's true that AI is developing rapidly, maybe, just maybe, our fears about the irrelevance of humanity are just a bit to premature? On this episode, we heard from Stephen Fioretti, VP, CX ISV Strategic Alliances at Oracle, about AI. What we talked about: The future of AI and customer service.

It is only a matter of time before driverless cars take us to work and our children to school, according to James Peng, CEO and co-founder of Pony.ai, a California-based self-driving car start-up. "If I have to give a number, I'll say probably in five years," Peng told CNBC's Deirdre Bosa at the East Tech West conference in the Nansha district of Guangzhou, China. "We'll definitely see a wide adoption of autonomous driving vehicles -- fully autonomous driving vehicles -- on the open roads." That could happen in any part of the world, but Pony.ai has been focusing on the U.S. and China, where the start-up has been testing autonomous vehicles. It recently partnered with Hyundai to introduce an on-demand vehicle service for residents in Irvine, California, where passengers can share autonomous cabs using an app.

Our mission is to revolutionize the future of transportation by building the safest and most reliable technology for autonomous vehicles. Armed with the latest breakthroughs in artificial intelligence, we aim to deliver our technology at a global scale. We believe our work has the potential to transform lives and industries for the better. When it comes to our technology, quality and reliability are hallmark attributes; we don't believe in taking shortcuts. Our emphasis on craftsmanship enables us to deliver an autonomous driving solution that is highly sophisticated and best-in-class.

In this post we are going to learn about Venus, my deep learning computer, and how I built it. Along the way, I will explain at a high-level what each hardware component of a computer does and how I navigated the landscape of selecting parts for a functional build. I'll also describe how I installed relevant software for the machine and include some benchmarks showing the superior performance of a GPU system over a pure CPU system. WARNING: this is a pretty long post that functions as a complete tutorial for building a deep learning computer literally from scratch, no assumptions made. But…since it's long I highly encourage you to peruse and skip any sections depending on your interest. While there are numerous build descriptions out there showing how people constructed their own deep learning rigs, as I went about consulting some of them, I often felt there was some crucial component missing. As you start on your build journey, it's easy to get mired in the weeds of hardware terminology. Should I pick an M.2 SSD or will SATA suffice? Can I get away with HDD? How many PCIe x16 slots do I need? Should I pick DDR4-3000 or DDR4-2400 memory? All this lingo can be very overwhelming especially for newcomers to hardware. But before we start shamelessly name-dropping so that we sound smart, let's go back to the fundamentals.

Companies have historically used focus groups and surveys to understand how people felt. Now, emotional AI technology can help businesses capture the emotional reactions of both employees and consumers in real time -- by decoding facial expressions, analyzing voice patterns, monitoring eye movements, and measuring neurological immersion levels, for example. The ultimate outcome is a much better understanding both of workers and customers. But, because of the subjective nature of emotions, emotional AI is especially prone to bias. AI is often also not sophisticated enough to understand cultural differences in expressing and reading emotions, making it harder to draw accurate conclusions.