"We're a global managed hosting provider. Our core customer set is a U.S.-based customer that is looking to go global," explained Adam Rogers, Managing Director at ANEXIA, in this SYS-CON.tv All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA.

TLDR; Virtual Reality can be immersive and fun, but add natural deep Artificial Intelligence and you quite literally get a new world which -- beyond the computer generated world around you -- may not actually be so virtual.

When a May 2016 crash killed the person operating a Tesla Model S driving in Autopilot mode, advocates of autonomous vehicles feared a slowdown in development of self-driving cars. Instead the opposite has occurred. In August, Ford publicly committed to field self-driving cars by 2021. In September, Uber began picking up passengers with self-driving cars in Pittsburgh, albeit with safety drivers ready to take over. October saw Tesla itself undeterred by the fatality.

Disruptive technologies are now dictating our future, as new innovations increasingly blur the lines between physical, digital and biological realms. Robots are already in our operating rooms and fast-food restaurants; we can now use 3D imaging and stem-cell extraction to grow human bones from a patient's own cells; and 3D printing is creating a circular economy in which we can use and then reuse raw materials. This tsunami of technological innovation will continue to change profoundly how we live and work, and how our societies operate. In what is now called the fourth Industrial Revolution, technologies that are coming of age--including robotics, nanotechnology, virtual reality, 3D printing, the Internet of Things, artificial intelligence, and advanced biology--will converge. And as these technologies continue to be developed and widely adopted, they will bring about radical shifts in all disciplines, industries and economies, and in the way that individuals, companies and societies produce, distribute, consume and dispose of goods and services.

For decades, scientists have tried to make robots more like human beings by designing computer algorithms that allow them to learn and become smarter. In fact, similar kinds of biological algorithms might exist in people and govern not only how we learn and act but also how our species evolved. That is the firm belief of Professor Leslie Valiant, whose ground-breaking research has been fundamental to the development of machine learning, artificial intelligence and the broader field of computer science. "You think of an algorithm as something running on your computer, but it could just as easily run on a biological organism," said the 67-year-old in an interview in January (2016) with Quanta Magazine, which reports on developments in mathematics and the physical and life sciences. "If one has a more high-level computational explanation of how the brain works, one would get closer to having an explanation of human behaviour that matches our mechanistic understanding of other physical systems."

A survey by Personetics shows that the financial services industry is getting a closer to supporting conversational commerce, supporting projects that use chatbots to improve the overall customer experience. Powered by chatbots, conversational commerce (Voice-First Banking) allows organizations to interact with customers over digital and messaging platforms, providing answers to questions, advice and offers in real-time. A survey conducted by Personetics shows that over three quarters of financial institution respondents view chatbots as a viable commercial solution now or within the next 1-2 years, and almost half of the companies already have active chatbot projects in place. A majority of the respondents see a substantial share of customer conversations handled by bots within 3-5 years. Chatbots are becoming more useful on a daily basis and are able to serve millions of customers 24/7--a perfect fit for companies that want to deliver instant customer service while cutting costs. Bots can run on popular messaging platforms, with over 33,000 bots running in Facebook Messenger already.

Add write pop music hits to the list of things that Artificial Intelligence (AI) can now do. As well as write poetry and novels, AI is now being used to create music by a Grammy-nominee producer, who collaborated with IBM's Watson cognitive computing platform on his newest release. Alex Da Kid used Watson to analyze the composition of five years' worth of Billboard songs, as well as cultural artefacts such as newspaper articles, film scripts and social media commentary. The idea was to understand the "emotional temperature" of the time period, and use this to inform Alex's creative process. I asked him to explain how the insights from the data contributed to the finished work, and he told me "Watson scraped millions of conversations, newspaper headlines and speeches – all of which showed me how emotionally volatile we as humans are and have been, particularly over the last five years."

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

This week, data is becoming knowledge as all streams of data are converging and leading me to the conclusion that every business is in need of the same types of data, tools, and results. From payment processing to media to rentals to retail to finance to big pharma, the same problems are coming up of "How do I ingest all kinds of data (variety), constantly changing (agile), often broken (flexible, schemaless or schema flexible), do some transformations in stream and land it in my big data environment (Hadoop with some flavors of NoSQL or data warehouse (SAP HANA or SQL Server or Oracle X) on the side)?" Oh and it's got to be fast, scalable, easy to use, and have a UI that can be used by my intern/data engineers. Some of the data is coming from IoT devices, cameras, beacons, web logs, Twitter, Facebook, 3rd party paid feeds, free feeds from NOAA, government and partner data sources, and legacy systems. So what can support text files, JSON, JMS, MQTT, REST, XML, MongoDB, S3 and a host of sources and formats?

Lecture 20, which focuses on the AI business, is not available in MIT Lecture Videos due to unknown reasons..