7 Machine Learning Trends to Define 2023

● Brands such as Netflix have already saved over $1 Billion through content

personalization powered by ML.

● When it comes to the stock market, ML algorithms have a reputation of getting 62% of its

predictions correct.

● ML market will experience a CAGR of 42.08% from 2018 to 2024.

● Advanced ML and AI, which is undergoing aggressive R&D at the moment, is expected

to increase the global GDP by 14% in the next 8 years.

Here are some of the latest trends in this exciting field:

Machine Learning Trends to Define 2023

● Increased use of artificial neural networks:

The Artificial Neural Networks market, which was at $160.8 Million in 2021, will reach a value of

$296 Million by 2024 according to Emergen Research. Neural networks are a type of machine

learning algorithm that are inspired by the way the brain works. They are used to identify

patterns in data, and they have been shown to be very effective at tasks like image recognition

and natural language processing.

● More data, more problems:

As machine learning algorithms become more sophisticated, they require more data to learn

from. This is a challenge for companies who want to use machine learning but don’t have

access to large data sets.

● Increased use of reinforcement learning:

Reinforcement learning is a type of machine learning that involves taking actions in an

environment in order to maximize a reward. This approach is well suited to problems like

robotics and game playing, where an agent can learn from its mistakes and get better over time.

The global reinforcement learning market share is at $21.17 Billion today. By 2029, after

experiencing a CAGR of 38.8% in the forecast period, the market will reach a value of $209.9 Billion.

● More focus on explainability:

As machine learning algorithms become more powerful, there is a growing need for them to be explainable. This means that when they make a decision, we need to be able to understand why they made that decision.

● More use of transfer learning:

Transfer learning is a type of machine learning where knowledge learned in one task is applied to another task. This can be very helpful when data is scarce, as it allows us to leverage knowledge from other tasks.

● More use of unsupervised learning:

Unsupervised learning is a type of machine learning where the algorithms learn from data without being given any labels. Orion eSolutions finds this useful for tasks like clustering, where we want the algorithm to group data points together without knowing beforehand what the groups should be.

● More use of deep learning:

Deep learning is a type of machine learning that uses deep neural networks. These are networks with many layers, and they are able to learn complex patterns in data. Deep learning has been used for tasks like image recognition and natural language processing, and it is one of the most promising areas of machine learning.

Summing Up

The world of machine learning is always evolving. With tech disruptions, large scale investment, and the market leaders jumping on the wagon, this field is all set to define 2023 in an unprecedented manner.

Stay tuned for more such tech-centric discussions. If you wish to add to this conversation,

comment below, or simply drop a message at [email protected].