Artificial Intelligence and Machine Learning are two buzz words that have become the talk of the town, especially when the conversation is around Technology Innovation in Enterprise. As discussions progress, it is quite easy to get confused between both the terms. Most of us are still unaware of the fact that Artificial Intelligence and Machine Learning are two separate terms which come under the same umbrella.
Artificial Intelligence or AI is an umbrella concept which talks about machines replicating human behaviour. It is the ability of a machine to think, learn, understand and perform actions like the human brain. In other words, it can be termed as the intelligence of a machine.
Google’s self-driving cars and Tesla’s “autopilot” feature are two such examples which have captured much attention lately. Both these concepts are based on Artificial Intelligence. Some common examples of AI in daily life are the digital voice assistants such as Siri, Google Now and Cortana. With the introduction of AI, a new era of automation has begun. Well, who wouldn’t love the idea of a helping hand to ease some of our day-to-day tasks?
On the other hand, Machine Learning is an algorithm which helps machines to replicate human behaviour. With the help of this algorithm, a machine can understand the repetitive patterns found and program its functions accordingly. Have you ever wondered how Google automatically completes your search query or corrects it before you get a chance to do so? This is a common example of Google’s Machine Learning algorithm through which it automatically corrects or completes a user query.
Artificial Intelligence has its foundation back in 1940’s and 50’s when scientists initially put forward the concept of ‘an artificial brain’ on the table. We have heard myths of scientists designing ‘mechanical men’ to mimic human activities. This trails back to the time when efforts were made to design computers which could perform basic arithmetic and logical operations independently. Earlier the concept of AI was to create super machines which could possess the same characteristics as the human brain. This was a more generalized approach and could not be pulled off easily. A practical implementation of AI refers to the situation when machines can perform tasks better without any human input. It can be best described as an augmented intelligence tool.
Machine Learning basically includes the algorithms to detect patterns in existing data, identify similar patterns in future data and make data driven predictions. These algorithms evolve behaviours based on empirical data, so they can accomplish more than coding software routines with specific instructions, by adapting to new circumstances. Thus, we can say that the machines simply learn from past experiences and gets trained to perform tasks.
AI and Machine Learning have found their ways into the present life from sci-fi movies and books. AI is an umbrella term for various models and algorithms, which combined with the large volume of data results in certain characteristics in the machines. Machine Learning is one such algorithm helping to understand the repetitive patterns and help machines in intelligent decision making.
AI has become an integral part of our lives through gadgets and electronic goods used in daily life. Both home and office are witnessing the application of AI landscaping the future trends. It comes with the promise of automating tasks and transforming the existing world. While AI talks about automating tasks performed by humans, it cannot permanently replace humans. AI simplifies the human work and transforms the scope of a human’s role, without replacing anybody from their existing one.
For enterprises to start this journey with AI or Machine Learning, its important to identify and collaborate with the right technology innovation partner. At Ignitho, our engagement in many instances start with us partnering with enterprises to nurture business ideas on limited budgets, through Rapid Prototyping , for the web and mobile, using new technologies like AI, Machine Learning & Augmented Reality where relevant. In most cases, enterprises leverage these prototypes to build Scalable Solutions using our outcome-based delivery model. We have delivered early success in this area and continue to develop frameworks and prototypes.
The scope of AI and Machine Learning in the future is beyond our imagination. If human brain takes roughly 13 milliseconds to see and respond to an image, considering the pace of improvement these intelligent machines would take even less time to process it. With high performing, intelligent machines, we are not too far from a decade when machines would be sharing our work space. AI and Machine Learning will transform the world in ways we only dreamed of!