AI and ML
In a recent talk that I delivered, the audience raised this question; what is the difference between AI and ML? While they might look very similar sounding technologies, they are, in fact, different yet complementing. The difference lies in the basic definition of Learning and Intelligence.
Learning is the act of acquiring new or modifying and reinforcing existing knowledge, behaviours, skills, values, or preferences which may lead to a potential change in synthesizing information, depth of the knowledge, attitude or behaviour relative to the type and range of experience. (ref: Wikipedia)
Intelligence has been defined in many different ways including as one’s capacity for logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity, and problem solving. It can be more generally described as the ability or inclination to perceive or deduce information, and to retain it as knowledge to be applied towards adaptive behaviours within an environment or context. (ref: Wikipedia)
Observe, learning is part of the definition of Intelligence. And that is the difference. Machine Learning feeds into Artificial Intelligence. The basic principle of life; learning leads to intelligence.
Microsoft Cognitive Services
The concept of Artificial Intelligence has been around for a while. But the reason why it never became a mainstream technology is because learning needed huge amount of data processing. And not everyone could afford Mainframes and Data Centers. And hence it was limited to only a few set of institutions. But the Cloud changed all that. Microsoft Azure provided that affordable infrastructure to build massive Machine Learning capabilities, leading into Artificial Intelligence. And Microsoft Azure Cognitive Services were born.
Microsoft Cognitive Services were launched with the concept that the service will learn from a global pool of data. As more and more people use the service, the learning algorithms keep improving. Using this methodology of learning, Microsoft launched it’s Cognitive Services APIs that provides basic Vision, Text and Speech recognition services.
Recently, Microsoft released a preview version of the Custom Vision Service that allows developers to build their Custom Vision models for their specific needs. This can be a game changer for AI based applications. Most AI applications will need custom vision if they need to recognize objects that pertain to their business. Imagine building a robot that can recognize specific type of nuts, bolts or tools and take actions accordingly. Imagine an ecological survey application that allows citizens to upload pictures of birds they spot around the city and automatically recognizes the species of those birds.
That, in my mind, is a true Artificial Intelligence Framework. One that allows you to customize the learning required for your specific application.
More custom versions of APIs are being released as we speak. Refer to the Microsoft Azure Cognitive Services page for a complete list.
Intelligent Machines are round the corner
An even bigger wave of AI applications are around the corner. As Microsoft Azure IoT Edge take wings. And while the name suggests IoT, it will very easily extend into Artificially Intelligent Sensors, Devices and Robots.
Microsoft Azure IoT edge allows you to build complex learning algorithms in the cloud and then, using Docker containers, move them to the edge with a minimal reference data.
This ability will truly revolutionize AI. Never before did developers have access to elastic compute and storage to train complex algorithms that could eventually be run in small devices. This suddenly opens up a whole world of possibilities where intelligent machines can now actually be intelligent without any dependency on the cloud. Or, for most part at least. They can most of the processing locally and go to the cloud only for the parts that needs more data/compute.
So, where is the Innovation in AI
I think all the required technologies to build an AI based solution are arriving fast. What is now required is for Startups and Enterprises to build innovative solutions using these AI frameworks. Setting up a Chatbot or a Recognizing faces is a few minutes of work of setting up Rest calls to these cognitive services. The real innovation will be to tie these services to complex backend systems that provide deep insights or actually solve complex problems that is otherwise not humanly possible.