This article was written by Swami Chandrasekaran. Click on picture to zoom in. When I wrote my blog post Becoming a Data Scientist-- Curriculum via Metromap, little did I know that it will receive a rousing feedback. Over years a lot of people reached out to me with very kind words and how they use it as a guide in their data scientist journey. Also, many who sought permission to use the Metromap picture in their presentations as well as a few universities that also reached out to use it as part of their syllabus.

A red flag when it comes to models appears if the system in play uses data that is inappropriately matched to the problem the marketing team is trying to solve. Successful AI approaches start with the business use case and what information a human needs to analyze the problem under consideration. A machine will most likely need the same data. However, AI companies often use the data to which they have access, whatever that might be, and not the best data for solving the problem. For marketing teams assessing a solution, it's essential to consider if there are public data sets to leverage or if it's possible to purchase relevant data sets to help solve the problem.

Aerospace Xelerated recently hosted a panel discussing one of our upcoming programme themes. In the'Demystifying AI' panel, we welcomed some exciting guests from the field: The diverse range of panellists, hosted by Programme Manager Ksenia Kurileva, discussed the importance and challenges that Artificial Intelligence poses. We learned how panelists got into the AI space and their experiences with artificial intelligence, including the projects that they are currently working on. John McKenna shared how Artificial Intelligence is assisting in aviation regulation in relation to drone company Sees.ai. He flagged the issue of capturing data and its importance to AI: data is required to train AI models, but the initial data needs to be obtained before that can happen.

According to a study by McKinsey Global Institute, AI is estimated to create an additional 13 trillion US dollars of value annually by the year 2030. Even today the artificial intelligence technologies are generating a tremendous amount of revenue, but it is mostly in the software field. However, by 2030, the revenue that will be generated will be outside the software industry, especially in sectors such as retail, travel, transportation, automotive, materials, manufacturing, and so on. There are several areas where AI will be highly impactful, but there is a lot of unnecessary hype surrounding it as well. Goldilocks Rule of AI states that one should not be optimistic or too pessimistic about AI technology.

Natalie Lao was set on becoming an electrical engineer, like her parents, until she stumbled on course 6.S192 (Making Mobile Apps), taught by Professor Hal Abelson. Here was a blueprint for turning a smartphone into a tool for finding clean drinking water, or sorting pictures of faces, or doing just about anything. "I thought, I wish people knew building tech could be like this," she said on a recent afternoon, taking a break from writing her dissertation. After shifting her focus as an MIT undergraduate to computer science, Lao joined Abelson's lab, which was busy spreading its App Inventor platform and do-it-yourself philosophy to high school students around the world. App Inventor set Lao on her path to making it easy for anyone, from farmers to factory workers, to understand AI, and use it to improve their lives.

As per the report by International Data Corporation, almost 50 percent of the participating global organizations perceived Artificial Intelligence as a top priority. While 25 percent of these businesses had successfully implemented a company-wide AI strategy, almost 60 percent had modified their business model to accommodate AI-driven functionalities. However, one-fourth of the respondents reported that up to 50 percent of their AI projects couldn't provide the desired results. Overall, many businesses experience issues when trying to incorporate Artificial Intelligence (also known as AI) into their operations. There are various reasons for this, such as the higher costs involved, the requirement of specialist skills, and reliance on the collection of comprehensive data.

Artificial Intelligence (AI) is top of mind for leading corporations these days – 96.4% of top executives reported earlier this year that AI was the number one disruptive technology that they were investing in, up from 68.9% just two years ago. In addition, 80% of these executives identified AI as the most impactful disruptive technology, up from 46.6% two years earlier. Yet, for many organizations, Artificial Intelligence remains a mystery. For specialists, AI implies a very specific connotation in terms of intelligence demonstrated by machines, in contrast to the more common usage of AI which encompasses all varieties of machine assisted learning, most notably machine learning, deep learning, and natural language. For the sake of this discussion, we will assume the broadest definition of AI.

When we're asked, "What is artificial intelligence?" Perhaps it's a sassy-talking technology like Siri from Apple, or helpful humanoid counterparts like those depicted in The Jetsons. Some might even imagine sophisticated robots threatening to extinguish the human race. Nowadays, there are as many definitions of AI as there are companies trying to pitch AI solutions. So, how do law firms know how to incorporate artificial intelligence? When is the right time to use legal AI? Think of AI as computers performing tasks previously thought to require human intelligence.

Instead, she went on, "we call it GPS." And it's a significant truth because it means the world of AI stays mired in the average person's mind as something of a science fiction-type character -- a Terminator programmed to kill, a Matrix hero designed to liberate, a Star Wars robot set to serve. But AI is not one and the same as a robot. Simply put, AI is everywhere. And people ought to know.

The rush to exponential technologies in new business models has placed artificial intelligence (AI) in the forefront of boardroom priorities for 2018. This report provides a framework on how to realize the path to full AI, design for infinite ambient orchestration, build any AI-driven smart service and identify when to automate with AI. Scientists have long aspired to recreate humans in machine form. While early attempts focused on the physical aspects of humanity, today's computer scientists have aspired to recreate the intelligence aspects in computer form. From vision to hearing, speech, touch and smell, scientists have come closer to replicating our five senses in the pursuit of artificial intelligence (AI).