Why We Need to Gear Up for the Challenges of the Future
At the IIT E-Summit 2018, there were many seminars discussing cutting edge technology. One such seminar conducted was on the future of Artificial Intelligence and how we need to gear up to meet the challenges of the future.
Artificial Intelligence: What is That?
To the uninitiated, artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. AI was coined by John McCarthy, an American computer scientist, in 1956 at The Dartmouth Conference. Though in the true sense, artificial intelligence research kicked off only in the last four decades. Owing to the prolific reach of social media, artificial intelligence has come down from the hallowed walls of tech research labs to the common household.
What Are The Different Types of AI
Reactive Machines are the AI systems that are purely reactive and have neither memories nor cognitive capabilities to use past experiences to make current decisions. Game simulations are examples of reactive machines.
Limited memory is AI systems with machines that can look into the past. Self-driving cars do some of this already. Using the learning of past experiences, the machine can take steps, although these learnings are only transient, and cannot become a part of the machine’s repository of learning.
Theory of mind is a very advanced class of machine systems. Theory of mind AI is still in its nascent stages of development. However, AI researchers believe that the way to make progress is to start by designing robots that can carry out some of the things kids can do early in the developmental process.
Self-aware AI is the kind of stuff Hollywood flicks are made of. These are an advanced type of artificial intelligence that involves machines that have consciousness. Scientists haven’t been able to build something that displays this type of AI yet.
Examples of AI Technology in Everyday Life
1.Machine learning: The science of getting a computer to act without programming. If you have heard the term deep learning, you should know that deep learning is a subset of machine learning that, in a limited sense, is the automation of predictive analytics.
2.Machine vision: The science of making computers ‘see.’ Machine vision captures and analyzes visual information using a camera, analogue-to-digital conversion and digital signal processing. For example, when Google Photos recognizes people in the pictures, and the places you have been tobased on the pictures, you must know that it is Machine vision that is working in the background. Pinterest is a lesser-known social giant, that is rising high in the machine vision space.
3.Natural Language Processing (NLP): Synthesis of human language by a computer program. NLP is integrated into the Google Search Engine, Swift Key, a popular app for predictive typing. Grammar and spell checking on your smartphones are also powered by NLP.
4.Robotics: a field of engineering focused on the design and manufacturing of robots. Robotic process automation, for example, can be programmed to perform high-volume, repeatable tasks normally performed by humans. Robotics is a dynamic, fast-growing field. We have a sudden spurt in automation in most processes. Use of high tech robotics for complicated surgeries are now the latest developments in Robotics.
Do We See Artificial Intelligence Being Used in Everyday Life?
Do you use Google maps on your phone? Well, that’s powered by AI. Are you on Facebook? The ads, sponsored posts, and videos that you see are curated because of AI. Love to shop on Amazon? Artificial intelligence is tracking your every purchase, product view, and your preferences.
Big Tech companies such as Google, Microsoft, and Amazon bolster their core businesses of targeting ads or anticipating your next purchase. Google and Amazon, in particular, are working hard to make their virtual assistants and smart speakers more powerful. Amazon, for example, has devices with cameras to look at their owners and the world around them.
