Machine learning allows computers to function without explicit programming, which means, the algorithms are trained to improve upon the data that is provided to them. Machine learning also gave us the concept of self-driven cars, spam-protected emails, speech recognition, personalized marketing and much more. Today, the technology is used to determine pickup locations, cab arrival times, provide entertainment recommendations, and identifies patterns, finding map routes and much more. Gartner predicts that 10% of all vehicles will possess self-governing driving abilities by 2021. Along with Machine Learning, autonomous driving systems also draw their power from Artificial Intelligence. Machine learning is, in fact, a subset of artificial learning or AI.
When machines are programmed to take decisions by learning and unlearning through newer data sets, it is known as machine learning. Netflix, for example, generates recommendations to its viewers based on the content that is tagged on their platform through which, the algorithm collects user data thus providing a unique facility and keeping the company ahead of its competitors. To implement machine learning-based prediction systems, you do not need big data sets or data, science experts. Leverage cloud computing platforms with economic models that will help you stay budget-friendly. If you are still new to machine learning and artificial intelligence, speak to experts at XCEL Corp for a solution.
Real-world industry applications will begin demonstrating their key value and benefits to the consumers in 2019, augmenting the criticality of Artificial Intelligence and Machine Learning. While it has not been tested in large-scale market applications, researchers and scientists have been ascertaining the importance of AI-based technologies.
Tracking the progress AI and ML trends
According to Machine Learning in 2019: Tracing The Artificial Intelligence Growth Path, investments to the tune of $58 billion will be devoted to Machine Learning, by the end of 2021 and in the latter part of 2022, the ML industry will be worth about $9 million, given its current growth rate at a CAGR of 42%. ML will also drive digital transformation and the neural networks, by 2024, will possess a market worth of $23 billion. In the United States alone, the Deep Learning applications market is predicted to go up to $935 million in 2025 as against $100 million in 2018.
Global businesses are rapidly adopting advanced AI technologies, which will additionally boost the market to $13 trillion in 2030. Here are some of the trends in Machine Learning and Artificial Intelligence that you can look forward to.
As technology advances, hacking techniques are progressing as well, and when devices connect to the internet to create data, the systems are exposed to some unsafe risks. Machine learning, when deployed by cybersecurity firms, can enhance security levels thus acting as a defense against potential security threats.
Improving IT operations
Hardware components, software components, operating systems and server applications generate massive data in the form of status reports, log files and error listings. Intelligent business insights can be produced when MI captures data, and cleans it for usage, creating a proactive IT enterprise.
Automated machine learning is all set to drive data science, where analytics and business intelligence will come together to even minimize the need for expert analysts. Data preparation will become more mature with augmented analytical models, where results will be derived automatically thus reducing the need for top-level executives with strong statistical backgrounds. Disruption and innovation will find direction when organizations begin adopting augmented analytics.
Optimizing cloud platforms
According to Gartner, the worldwide public cloud market will grow even more, to provide a wide number of services. At the same time, it is getting rather complicated for new entrants as the need for certified experts cannot be denied. With several opportunities to innovate, machine learning can improve customer experience when deployed across applications thus optimizing the usage of the cloud.
Digital Data deletion
Not all data is important and necessary. With the reduction in storage costs, large volumes of data can be efficiently preserved using cloud computing. At the same time, storage costs can even increase when massive amounts of data are generated to be stored. Machine learning can help identify the need and importance of a data set and delete the unwanted ones, thus minimizing storage costs, controlling expenditure and removing the hassles of handling unnecessary data.
Reach out to XCEL Corp to deploy intelligent machine learning and artificial intelligence solutions that can save a lot of money and time for your business.