Machine learning solutions continue to incorporate changes into businesses’ core processes and are becoming more prevalent in our daily lives. The global machine learning market is predicted to grow from $8.43 billion in 2019 to $117.19 billion by 2027.
Despite being a trending topic, the term ‘machine learning’ is often used interchangeably with the concept of artificial intelligence. In fact, machine learning is a subfield of artificial intelligence based on algorithms that can learn from data and make decisions with minimal or no human intervention.
Many companies have already begun using machine learning algorithms due to their potential to make more accurate predictions and business decisions. In 2020, $3.1 billion in funding was raised for machine learning companies. Machine learning has the power to bring transformative changes across industries.
With machine learning being so prominent in our lives today, it’s hard to imagine a future without it. Here are our predictions for the development of machine learning in 2021 and beyond.
Quantum computing is one advancement that has the potential to boost machine learning capabilities. Quantum computing allows the performance of simultaneous multi-state operations, enabling faster data processing. In 2019, Google’s quantum processor performed a task in 200 seconds that would take the world's best supercomputer 10,000 years to complete.
Quantum machine learning can improve the analysis of data and obtain more profound insights. Such increased performance can help businesses to get better results than via more traditional machine learning methods.
So far, there is no commercially ready quantum computer available. However, a handful of big tech companies are investing in technology, and the rise of quantum machine learning is not that far off.
Automated machine learning or AutoML is automating the process of applying machine learning algorithms to complete real-life tasks. AutoML simplifies the process so that a person or business can apply complex machine learning models and techniques without being an expert in machine learning.
AutoML enables a wider audience to use machine learning, which indicates its potential to change the technology landscape. A data scientist can use AutoML to their benefit, for instance, to quickly find the algorithms they can use or whether any algorithms have been missed. Here are some stages of development and deployment of a machine learning model that AutoML can automate:
The healthcare industry generates massive amounts of data. Applying machine learning techniques can greatly contribute to better predictions and treatments.
Manufacturers are only in the early stages of adopting machine learning. In 2020, only 9% of survey respondents were leveraging artificial intelligence in their business processes.
Machine learning tools can be used for various manufacturing purposes, including monitoring equipment performance and condition, predicting product quality and forecasting energy consumption. With the ongoing advancements in the field of machine learning, we can expect more robots in manufacturing premises in the near future.
Among many other benefits, using machine learning in manufacturing can reduce costs, enhance quality control and improve supply chain management.
Tesla, Waymo and Honda are among the car development companies exploring the possibility of deploying self-driving cars. And while manufacturers have already presented cars with partial automation, fully autonomous vehicles are still under development. Machine learning is one of the main technologies that can help to turn these dreams into reality.
Deep learning, a class of machine learning algorithms, can help improve perception and navigation in autonomous vehicle manufacturing, including path planning, scene classification and obstacle and pedestrian detection.
As new technologies continue to unfold, machine learning algorithms can be used more productively. The future of machine learning will open multiple opportunities for businesses. Make sure that your business is ready to make the most of what is coming.
Looking for some advice to help you learn more about machine learning? Get in touch!
Originally published at the ELEKS blog.