What Questions Can the Machine Answer?
Machine learning is powered by its algorithms. Algorithms extract patterns from data and make predictions or decisions based on these patterns. There are many types of machine learning algorithms, but broadly speaking, they can be grouped into the five types of questions that they can answer. Let’s look at them. Previous Next Download as PDF
Can the Machine Be Biased? [Infographic]
Machine learning systems are prone to bias, coming from the data used to train them. This can cause many kinds of issues as the machine makes its judgements. For example, a lack of fairness towards the groups that are not well represented in the data. Let’s understand why that is so.
How to Succeed Together in AI? [Infographic]
Success in AI very much depends on having two things: skilled talent and a huge amount of processing power. Because of this, as AI advances, it threatens to create an ever-widening gap between those with the means to succeed and those without. Let’s design a world where the wealth generated in AI is spread around […]
How Good Is the Machine With Language? [Infographic]
We have seen how the machine is good at generating original text. But what else can the machine do with language? It turns out, the machine is very versatile. Let’s look at different ways that machine learning is used in Natural Language Processing (NLP).
How Good Is the Machine’s Vision? [Infographic]
Computer vision is the machine’s ability to make sense of images and videos. It opens up the path to many exciting applications with the potential to solve the world’s grandest problems. Let’s see some of the ways machine learning is used in computer vision.
How Does the Machine’s Brain Look Like? [Infographic]
The machine can learn in many different ways. These are called algorithms, which are designed by humans. The neural network is becoming an increasingly popular algorithm these days because of its ability to find patterns and extract information from the most complex of data. Let’s understand how it works.
Can the Machine Be Biased?
How Does the Machine’s Brain Look Like?
What Is This Thing Called Data?
How Does the Machine Understand Data?
