Artificial intelligence refers to the simulation of human intelligence processes by machines including robots and computer systems. Most of the specific applications of AI include natural language processing, speech recognition, machine vision, and expert systems.
Yes, they certainly can. In fact, modern AI is based on Machine Learning. The latter is a category of computer algorithms that can automatically learn from data, instead of depending on humans to program each step. ML is a subset of artificial intelligence.
While AI involves all developments of technologies created to simulate human behavior, ML is a huge part of all this
Machine learning algorithms can be broadly categorized into 3 types:
01. Supervised Learning:-
It happens when we provide the machine with a ton of information about a case and its outcome. The machine does all the work once supervised.
02. Unsupervised Learning:-
This one is absolutely opposite than the supervised one. There is no help from AI engineers and the machine has to learn every operation on its own. This learning is extremely useful in finding anomalies, clustering problems, and recognizing patterns in data.
03. Reinforcement Learning:-
In this type of learning, the algorithm learns consistently from its environment by interacting with it.
AI projects can take anywhere from a few months to a year to go live, depending on their scope and complexity. It is advised not to underestimate the time it takes to prepare the data before a data science engineer builds an AI algorithm
In AI, the biggest challenges are power efficiency, learning from less data, learning from unlabeled data (unsupervised learning), and generalizing across multiple tasks. In addition to making AI unbiased and explainable, the industry also aims to quantify confidence levels and understand how it works. It would be interesting to understand how AI used for autonomous driving decides how to drive safely on a road in a variety of environments and weather conditions.
