With Alexas, home robots, Google Chromes and curated content on social media becoming commonplace these days, human interaction with machines has reached a whole new level. Everywhere around us, the unseen yet efficient effect of Artificial Intelligence (AI) is continuously functioning, be it in the ridesharing apps such as Uber, Google-powered route predictions, mobile check deposits or Gmail’s smart email categorization[1].
The easiest way to approach this would be to understand that AI is a way of incorporating human intelligence into machines. This is achieved by helping the machines respond to a set of written rules or algorithms in order to emulate the intelligence that is innate to mankind.
Today, new industries are being created from the bottom up, leveraging AI – self driving cars and drones, intelligent, personalized shopping experiences, AI-enabled customer service assistants, assistance in healthcare including surgeries and maintaining health records, automated financial advisors, smart home devices, surveillance, smart logistics, well, the list is endless[2]. In fact, in the case of e-commerce alone, AI-enabled smart logistics and recommendation engines are looked upon as the biggest drivers of Return on Investment (ROI).
As the applications of AI begin to deliver a more human-like interaction, the buzz around terms and concepts such as Machine Learning (ML), Deep Learning (DL) and more, has been on the rise. To those benefiting from the varied applications, the terms seem interchangeable. They are, however, far from that!
In order to understand, let us take the example of Amazon Go (one of the first applications of AI that we know in Amazon’s physical store), claimed as the world’s most advanced shopping technology by Amazon, it boasts of no lines, no checkouts, just grab and go[3]!This pitch is powered by AI, however, that is not all. There are further technologies, considered to be subsets of AI – Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and much more[4] that enable the interaction. Together, they form the science that enables the creation of AI solutions to issues in the real world. Some of the commonly used – and confused – terms under AI include –
- Machine Learning, which gives the machines the ability to learn. It is a way of training machines on the stipulated algorithms.
- Deep Learning, which is the technique used to enable machine learning. It emulates the way our brain processes information, identifies these patterns and then deploys them to enable machine learning.
- Artificial Neural Networks, which are the algorithms that imitate the way human neural networks work and respond to external and internal stimuli.
- Computer Vision, which uses pattern recognition and DL to recognize and understand the contents of a picture or video and interpret surroundings.
- NLP, which is the ability of machines/computers to understand and generate human language both in written and speech form.
With advancements in computing and data storage as well as the interconnectivity of devices and machines, more and more language and image inputs are flowing into our devices. This has enabled the evolution of computer speech and image recognition, further enabling machine learning, and making AI possible.
Using these technologies in business is a given today. Using them efficiently and effectively is a game changer. According to a PwC report[5], AI will contribute US$15.7 trillion to the global economy by 2030. The key sectors that are at the frontline of opportunity for AI-based productivity gains are healthcare, automotive, financial services, retail, manufacturing and technology, communication and entertainment.
The picture is however, not all rosy, with justifiable concerns being raised related to privacy and protection of sensitive business/financial/health data. Cybersecurity would need to be addressed for the adoption of AI to grow.
For individual business, a good starting point would
be to understand the technological trends and competitive developments in their
industry that they need to keep up with to remain competitive, not just now but
also in the future, and to understand how they can leverage AI as a strategy
for their sustained growth.
[1]https://emerj.com/ai-sector-overviews/everyday-examples-of-ai/
[2]https://becominghuman.ai/10-powerful-examples-of-ai-applications-553f7f062d9f
[3]https://www.amazon.com/b?node=16008589011
[4]https://www.sas.com/en_us/insights/articles/big-data/artificial-intelligence-machine-learning-deep-learning-and-beyond.html
[5]https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html
