Also, terms like data science, analytics, statistics, databases etc are ones that most people associate with the professional world exclusively. However, all these and many of their associated ones aren't meant for data science professionals only. In reality, most people experience the role of data science in their day-to-day lives and in almost every situation. From new friend suggestions by Facebook to Google's help to complete a search phrase to television shows predicted by Netflix in accordance to your preferences, and many more -- data science is being used by common people in almost every situation. We often praise the high-tech device or the high-end technology once we leverage its abilities, failing to acknowledge the role of data science in making them happen.
Artificial intelligence, machine learning, and deep learning technologies have entered the mainstream; they are being adopted by enterprises all over the world. While these technologies certainly hold the potential to vastly improve the quality of operations in the corporate sector, they also stand to disrupt many existing markets. AI can easily be extended, adapted, and applied to different business operations. When considering that AI is just a computer program, we can begin to see the potential scope of the technology. The reason that AI is being adopted on such a large scale is due to its capacity to bring intelligence to tasks that previously did not have it. This, coupled with the technology's ability to automate repetitive processes with intelligence, makes it a highly disruptive power in various sectors. Keeping this in mind, we explored some of the industries that are most likely to be impacted by the widespread adoption of AI technology. Let us see why companies are so eager to adopt artificial intelligence.
In a way, AI is about understanding, and then mimicking how we think, learn and process information. The science and applications of AI have evolved since the early years: 1950's 1980's 2010's Generation 1 (From 1950's): Rule Based Systems (No Learning) In the early days, most applications of AI were rule-based computer programs (commonly known as Expert Systems) designed to solve problems that human brains performed easily. Such AI programs required experts to develop rules and combine with programs to solve problems. It required a programmer to write a program to capture the knowledge of a subject matter expert. The program then asked a series of questions to a user (usually not an expert in that subject) and then based on the answers/input provided, the computer would suggest a "solution" to the problem.
Self-driving vehicles will be widely adopted by 2020, and it won't just be cars -- driverless delivery trucks, autonomous delivery drones, and personal robots will also be commonplace. Low-cost 3D sensors like Microsoft's Kinect will speed the development of perceptual technology, while advances in speech comprehension will enhance robots' interactions with humans. Computer-based learning won't replace the classroom, but online tools will help students learn at their own pace using techniques that work for them. Online teaching will increasingly widen educational access, making learning lifelong, enabling people to retrain, and increasing access to top-quality education in developing countries.
How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, technical fellow and a managing director at Microsoft Research. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.