Machine Learning or ML, could easily feature in the century’s Top 5 Money Spinners list. Just like various disciplines of engineering such as Computer Science, Information Technology, Electronics, Communication Systems, and more… ML too promises a great deal for just about every industry out there. And that must be why Forbes.com pegs ML to become a $30.6 Billion industry by 2024, with a CAGR of 42.8%.
One thing is for sure and that is ML is not a bubble. Both the current technology trends and projections prove it beyond a doubt. Here are a few trends that are making ML the darling amongst technology innovators.
ML is making Cloud-Optimization Easy
Cloud Computing is another technology that cuts across many different industries. The Cloud Computing market is anticipated to touch $623.3 Billion by 2025. So any technology that helps optimize the Cloud is a sure winner – and this is currently one of ML’s most widespread applications. ML is helping several start-ups harness the cloud without having to pay through their nose for certified experts.
AI and IoT Converge
AI will soon be used to detect problems in a machine and deploy other ‘mechanic machines’ to fix it. With advanced learning models deployed at the edge layer, these models will soon have speech and video synthesis capabilities.
ML will enable Machine Managers
It might not be too long before organizations stop hiring managers and start making them! You see…Machine learning algorithms are powerful in pattern recognition and predictive analytics. Apart from making decisions, AI recognizes patterns with ease. This makes them perfect for repetitive junior-managerial tasks, with due stress on ‘junior-managerial’.
Virtual Agents will replace Live Agents
AI-powered Chatbots have proved their merit in the COVID-19 pandemic, by helping large organizations disseminate accurate information to millions of people. We expect chatbots to be adopted by a ton of other organizations and reap the benefits of higher efficiency and lower costs.
ML for CyberSecurity
ML could be used to identify, recognize and respond to a cyberattack without human intervention. In an age where the speed of detection is crucial, ML proves itself to be a boon for Chief Security Officers and their reports.
Even with all these trends going int the favor of ML, some people believe that the field of ML could face yet another AI winter (a period of reduced funding and interest in Artificial Intelligence research, this happened earlier in 1974-1980, 1987-1993 and several smaller episodes). Here are some of the issues due to which some people’s expectations fall short.
Training of Algorithms
Data Scientists need to first train the algorithms, by identifying a large enough sample to help the system determine a cluster. The more items that are identified, the better the mapping. But with a large set of clusters, you could end up needing thousands of samples to properly train a set.
A model could easily assign more importance to a particular factor, primarily because the modeler is at the mercy of the data available. This could lead to incorrect decision making.
Handling Edge Cases
Data models dependent on clustering do not handle edge cases well. Simply put…let’s say we have a routine that classifies photographs as boys and girls. But upon being shown a baby’s photo or an image of a muscular female with a short hair, the data model could easily miscategorize the image.
We humans change with time
Let’s assume you run a food app business, and your IT team trains this app to identify and suggest a customer’s favorite food (say Pizza). It’s highly unlikely that the customer is going to order only Pizza for the rest of his/her lifecycle. Your food app will eventually run out of intelligent suggestions for this customer.
These are some of the factors that makes us think that while ML systems are superior in decision making, they are best kept in an advisory role. The ML system we are born with, inside our heads, is still better when it comes to decisions that are too difficult to quantify in an AI setting.
So…what Pizza would you like to have for dinner tonight?
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