Some people claim they are interchangeable but others disagree. So how similar are ML (machine learning) & AI (artificial intelligence)? In the grand scheme of things, both terms overlap each other but the former is a subset of the latter.
Back in the 1940s, Alan Turing tried training a machine to learn chess moves on a realtime basis. This was the first instance of AI in machines. As technology progressed, programmers started using ML techniques.
ML remains a complex subset of AI, employing unique techniques to train machines. So can one consider them as separate from each other? Read on to know. This article discusses the differences & similarities between both.
AI Includes Much More
AI has been for quite a long time as compared to Machine Learning. Moreover, the former is already a more ‘hip’ term in today’s media. This is because there are plenty of nuances & terms attached to it. The majority of them involve automation. Businesses use AI to refer to innovations. This involves personalized ads, marketing automation, sales forecasting & much more.
Intelligence in machines makes them mimic human behavior. The intention is to make machines greater enablers for people. It involves the use of Natural Language Processeing, Self-awareness, Deep Learning & more. The aim is to make machines smarter & capable of making better decisions. The more unfamiliar situations they come across, the better they will be able to perform with AI.
ML Remains The Subset Of AI
ML involves training computers to solve problems on their own too. It’s concerned with algorithms that are able to absorb new patterns from a data set. This helps them train the machine to make better decisions.
An example would be of showcasing popular products on a website. Let’s say that someone is looking for a stylish pair of glasses as Wiley X Omega. A website using ML techniques would show the most popular eyewear first. Retailers can display those products first which are generating the most sales.
Hence, data is essential when it comes to ML. Developers strive to make algorithms generic so they can apply them to a diverse set of data. It’s essential for algorithms to need the least amount of revisions as possible. Fewer adjustments mean better results through abstraction.
Exposing ML algorithms to fresh data is imperative. This enables them to work without the need for adjustments. The main aim of ML is to enable machines to adapt to new situations with the least amount of readjustments. Yet, it remains of the most effective tools to allow computers to do more.
The Bottom Line?
If AI were a huge lawnmower to cut grass then ML would be the small scissors in the analogy. For fine adjustments, using advanced ML techniques would be fine. But if you were to train a machine with utmost effectiveness, then you’d have to use other techniques in AI. This is because AI is an umbrella term for various other techniques to power machines.