There are multiple ways to learn data science, machine learning and deep learning concepts. You can watch videos, read articles, enroll in courses, attend meetups, among other things. I have personally learned a LOT from interacting with data science experts and industry thought leaders. Their experience in managing end-to-end machine learning and deep learning projects, their thinking when building a data science team from scratch, how they managed tough projects and overcame hurdles, etc. – we simply cannot learn all of these in any course. So, I am thrilled to present an exclusive interview with one such data science expert and industry thought leader – Dr. Sunil Kumar Vuppala!

This isn't a movie script or a futuristic scenario – this is all happening right now thanks to the power of Natural Language Processing (NLP)! I honestly feel the number of breakthroughs happening in this field is unparalleled. The past two years have been a blur – the Transformer architecture, introduced in 2017, has truly transformed the NLP space. From the super-efficient ULMFiT framework to Google's BERT, NLP is truly in the midst of a golden era. Are you ready to be part of this revolution?

Abstract: The deployment of AI is truly transformational when it impacts the core business tasks and processes of the enterprise. For this reason, most organizations should only undertake AI initiatives with strategic impact potential. Experience shows that AI transformation programs achieve better results if organized in successive iterations of projects that implement high-value or even disruptive use cases. If managed properly, each project will create momentum and elements that will accumulate until critical mass is achieved. This iterative bottom-up approach is the most effective and realistic way of facing the daunting task of achieving the required AI proficiency within a reasonable time and cost.

Over the last decade, though, the tech industry has downsized ultrasound scanners into devices resembling TV remotes. It has also created digital stethoscopes that can be paired with smartphones to create moving pictures and readouts. Proponents say these devices are nearly as easy to use as stethoscopes and allow doctors to watch the body in motion and actually see things such as leaky valves. "There's no reason you would listen to sounds when you can see everything," Topol said. At many medical schools, it's the newer devices that really get students' hearts pumping.

Recently Bangladesh Institute of Peace and Security Studies (BIPSS) published a commentary titled'Artificial Intelligence: The Future of Warfare' authored by Ms. Faria Ulfath Leera. The commentary briefly discusses different aspects of AI in warfare and how it will change the nature of warfare in the days to come.

Thanks to today's "Internet of Things" (IoT), there is an "automation" for almost every aspect of our lives. From such mundane if not downright silly things as kitchen faucets that activate on voice command, to the impressive -- massive shipping warehouses run by robotics -- many aspects of life today go beyond that imagined decades ago in science fiction. While we still are waiting for flying cars depicted in the Jetsons television show of the 1960s, or space hotels as portrayed in the sci-fi epic 2001, the array of technologically driven devices available to the average citizen is indeed impressive. Yet, while automation and artificial intelligence simplifies or altogether eliminates many of the activities of day-to-day life, the technology complicates others. For example, how do you program a self-driving car in an emergency situation to choose between the life of a pedestrian or that of its "driver?"

Virtus Health has announced in partnership with Harrison-AI and Vitrolife that it will commence randomised controlled trials of its artificial intelligence (AI) technology, called Ivy, by the end of the year. Speaking at The Future of Health event in Sydney this week, Virtus Health group CEO Sue Channon explained the tests will be used to further validate the use of AI when it comes to in-vitro fertilisation (IVF). She explained how for the last 12 months, embryologists have been using Ivy as a supporting tool to increase the potential success of pregnancy through IVF. "At this stage Ivy is still a supporting tool, we're not letting Ivy make the decision on its own," she said, explaining how one patient got pregnant during the cycle that Ivy was used after five unsuccessful IVF cycles. "We are seeing an improvement of pregnancy outcome as a result of Ivy."

With the industry 4.0 revolution around, Research N Reports presents a detailed analysis of Cognitive Computing market that offers latest insights for business professionals. Using BI tools such as Factiva and Hoover, the report offers a comprehensive analysis and is a mix of market intelligence studies and industry insights. Prepared by a panel of highly experienced market analysts and consultants, the report is spread across 137 pages offering chapter wise detailed market analysis that enables the clients with multiple data points and encourages them to have a 360 degree overview of the market performance. Clients can ask for sample of this report that gives a detailed overview of the market conditions, driving and restraining factors, segments, trends and opportunities. Covering the latest information about the market, the samples can give a basic understanding upon the report contents and its format.

This article describes the machine learning services provided in SQL Server 2017, which support in-database use of the Python and R languages. The integration of SQL Server with open source languages popular for machine learning makes it easier to use the appropriate tool--SQL, Python, or R--for data exploration and modeling. R and Python scripts can also be used in T-SQL scripts or Integration Services packages, expanding the capabilities of ETL and database scripting. What has this to do with stone soup, you ask? It's a metaphor, of course, but one that captures the essence of why SQL Server works so well with Python and R. To illustrate the point, I'll provide a simple walkthrough of data exploration and modeling combining SQL and Python, using a food and nutrition analysis dataset from the US Department of Agriculture. You might have heard that data science is more of a craft than a science. Many ingredients have to come together efficiently, to process intake data and generate models and predictions that can be consumed by business users and end customers. However, what works well at the level of "craftsmanship" often has to change at commercial scale. Much like the home cook who has ventured out of the kitchen into a restaurant or food factory, big changes are required in the roles, ingredients, and processes. Moreover, cooking can no longer be a "one-man show;" you need the help of professionals with different specializations and their own tools to create a successful product or make the process more efficient. These specialists include data scientists, data developers and taxonomists, SQL developers, DBAS, application developers, and the domain specialists or end users who consume the results. Any kitchen would soon be chaos if the tools used by each professional were incompatible with each other, or if processes had to be duplicated and slightly changed at each step. What restaurant would survive if carrots chopped up at one station were unusable at the next?

Machine learning and artificial intelligence are by no means perfect, and it takes human intervention to constantly tweak algorithms. Those applications are essentially based on math problems and may never bee 100% accurate, so companies and software developers should think carefully before going down that road. At a recent conference, TWIMLcon: AI Platforms, panelists spoke about the ethics of artificial intelligence and the need for its human developers to take painstaking actions to ensure these applications work for everybody. Any one group or central team should not be the only to write code and fix fairness or the whole company. To do this, companies must have a diverse group of people working on these applications.