"What we are witnessing right now is the start of the first phase of a multi-year technology transformation cycle"
This is from the post quarterly result analyst conference call from a leading Indian IT Company.
It is obvious to every observer that the technology innovation that started sometime in 2010 has now entered the expansion phase. We are already seeing next generation technologies that are changing the way world does business. We are entering into the first of a multi-phase technology bull market.
Even though it sounds cliche, the fact is tat technology is going to fundamentally change the way the world does business. Those who understand technology and use it effectively will standout and will lead the world 5-10-15 years from now.
Given that reality, it will be interesting and fun to look at some of the Technologies, themes and ideas that will shape the coming decade. Here in this article, I have listed down 21 ideas. I will elaborate each of them in a separate post in the coming days.
So here goes:
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Deep Learning (DL)
- Robotic Process Automation (RPA)
- Industrial Internet of Things (IIoT)
- Cloud Computing
- Blockchain
- Scalability / Multi-tenancy
- Multi-device / Multi-channel capability
- Microservices
- Security and Compliance
- Industry 4.0
- Customer Experience (CX) and User Experience (UX)
- Self Learning
- Telemetry and Self-healing
- Big Data and Analytics and the associated data strategy
- Digital Control Tower
- Design Thinking
- Voice-first
- Augmented Reality (AR)
- Virtual Reality (VR)
What is Artificial Intelligence?
As per Britannica.com, "Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment."
As per McKinsey, "Artificial Intelligence (AI) is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity."
Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.
What are the different aspects of AI?
Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language. Two aspects of learning are Rote learning - learning something, storing in memory and retrieving it when required and generalization - learning the basics and applying the learning across different situations. For example, in rote learning we may know the past tense of Jump as Jumped but flounder when asked the past tense of Pump. In generalized learning we learn that the way to create past tense is by adding 'ED' to the end of the verb.
There are two kinds of reasoning, Deductive and Inductive. In deductive reasoning, the truth of the premise guarantees the truth of the conclusion (Ram must be in the kitchen or in the library. He is not in the kitchen, so he is in the library), whereas in Inductive reasoning the truth of the premise lends support to the conclusion (She has a history of stealing. Something is missing here. She has stolen it)
Problem solving is a systematic search through a range of possible actions to reach a goal. Problem solving methods are divided into general purpose and special purpose. Special purpose solutions are tailored for a problem and use the specific features of a situation to solve the problem. General purpose method is applicable to a variety of situations. One of the general purpose methods used in AI is 'Means - end analysis', an incremental reduction of the difference between current state and the goal.
In perception, the scene is evaluated based on different sensory organs and the scene is divided into separate objects in different spatial relationship.
A language is a system of signs having meaning by convention. The linguistic meaning may be different from natural meaning ("It is cloudy means it is going to rain"). An important characteristic of human language is Productivity - You can create unlimited variety of sentences. While it is easy to make computers learn to use languages, the more difficult it is to get them to understand language..
Overview of AI
The diagram below shows the linkages between various aspects of AI
Two approaches to AI
Three goals of AI research
- Strong AI (also known as AGI - Artificial General Intelligence) aims to produce a machine whose overall intellectual ability is indistinguishable from that of a human being.
- Applied AI also known as 'Weak AI', 'Narrow AI', or 'advanced information systems' aims to produce commercially viable smart systems - medical diagnostic systems or stock trading systems for example. Siri, Cortana and Google Assistant are other examples.
- Cognitive simulation uses computers to test theories about how human mind works.
Can AI be dangerous?
There are two scenarios where AI can be dangerous.
- It is programmed to create destruction. This is self-evident.
- It creates destruction in its attempt to help humans. You instruct an autonomous car to take you to the Airport 'As Quickly As Possible' and it violates all the traffic rules to comply with your instruction.
Why AI now?
The three factors that propelled AI are:
- Algorithmic advancements: The last sixty years has seen steady advancements in both machine learning and deep learning algorithms. In the last decade itself we had IBM Watson winning Jeopardy! in 2011 and in 2016 Google DeepMind's Alpha Go defeating Lee Sedol, the reigning nine time Go champion. One year later Google created Alpha Zero, which defeated Alpha Go !
- Data expansion: Starting from the 1990s world has seen explosion of data like it has never seen before. In the last decade itself number of Facebook users exceeded One Billion and electronic device users generate 2.5 Quintillion bites of data per day.
- Exponential increase in computing power and storage.
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