{"id":7736,"date":"2022-11-11T11:56:33","date_gmt":"2022-11-11T10:56:33","guid":{"rendered":"https:\/\/serengetitech.com\/?p=7736"},"modified":"2025-10-28T10:43:42","modified_gmt":"2025-10-28T09:43:42","slug":"7-artificial-intelligence-and-machine-learning-trends","status":"publish","type":"post","link":"https:\/\/serengetitech.com\/de\/business\/7-artificial-intelligence-and-machine-learning-trends\/","title":{"rendered":"7 Artificial Intelligence and Machine Learning Trends"},"content":{"rendered":"

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Artificial intelligence and machine learning have become very popular. This technology provides companies with insight into trends in customer behavior and company business patterns, as well as assistance in developing new products.<\/p>\n\n\n\n

Some of the largest companies, such as Google and Facebook, use machine learning in the basics of their operations.<\/p>\n\n\n\n

The COVID-19 pandemic has prompted new applications and technological improvements in the industry. It has accelerated AI adoption in fields such as healthcare, where AI-based technology and applications are widely used.<\/p>\n\n\n

Facts & Figures<\/h2>\n\n\n

By 2025, the AI market will be worth $190 billion<\/a>.<\/p>\n\n\n\n

By 2025, AI will be used in 90%<\/a> of commercial enterprise apps.<\/p>\n\n\n\n

By 2023, there will be 8 billion<\/a> voice assistants. <\/p>\n\n\n\n

During the COVID-19 pandemic, adoption increased by 37% in banking, by 27% in retail and by 20% in IT<\/a>.<\/p>\n\n\n\n

Between 2015 and 2019, the number of businesses adopting AI in business increased by 270%<\/a>.<\/p>\n\n\n\n

83%<\/a> of companies consider AI to be a strategic priority.<\/p>\n\n\n\n

According to 54%<\/a> of corporate leaders, AI technologies applied in their companies have already enhanced productivity.<\/p>\n\n\n\n

72%<\/a> of businesses that use AI believe it will simplify their work.<\/p>\n\n\n\n

20%<\/a> of leading manufacturers will use embedded intelligence (AI, IoT and blockchain technologies) to automate operations and reduce execution times by up to 25%.<\/p>\n\n\n\n

By 2025<\/a>, banking will be one of the two businesses spending the most on AI solutions.<\/p>\n\n\n\n

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Low-Code & No-Code Machine LearningIn order to survive and remain competitive, companies must keep up with the rapid changes taking place in the world of technology.<\/gwmw><\/gwmw><\/gwmw><\/h2>\n\n\n

In order to survive and remain competitive, companies must keep up with the rapid changes taking place in the world of technology.<\/p>\n\n\n\n

Applications based on artificial intelligence and machine learning are considered to be a technology that brings disruption, significant changes and benefits in business across various industries. However, this technology represents a narrow niche of software development that requires experts, which is why it is associated with advanced software development skills. Simply put, to make software based on artificial intelligence, you need experts with skills and extensive development experience.<\/p>\n\n\n\n

Low-Code and No-Code platforms allow users to create their own programs and applications in a simple way. Thanks to the drag-and-drop system, the requirements for writing code are minimal or non-existent. Such platforms save time and money as they require less technical skills and less code writing.<\/p>\n\n\n

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The potential of developing AI applications using low code\/no-code platforms is significant. This design drastically simplifies standard application development tasks and brings software development closer to users who do not have much experience in programming, which makes it accessible to a large number of companies.<\/p>\n\n\n\n

Low-code and no-code machine learning platforms enable organizations to implement machine learning without significant domain expertise or training. These technologies enable citizen programmers\u2014individuals without formal software development expertise, who use no-code and low-code platforms to build machine learning applications, to create machine learning applications and reduce the burden on data scientists.<\/p>\n\n\n\n

In addition to enabling people who are not AI experts to create AI applications, thanks to predefined components, these platforms enable faster time to market, better product scalability and lower development costs.<\/p>\n\n\n

TinyML<\/gwmw><\/gwmw><\/h2>\n\n\n

TinyML is a new approach to creating ML and AI models that operate on hardware-constrained devices such as microcontrollers. TinyML is a better option since it allows for faster algorithm processing because data does not have to go back and forth from the server. This is especially important for larger servers since it speeds up the entire process.<\/p>\n\n\n\n

The agriculture and healthcare sectors are two areas that benefit from technological advancement. They often employ IoT devices with TinyML algorithms to monitor and forecast data collected.<\/p>\n\n\n

RPA<\/h2>\n\n\n

RPA is a type of Business Process Automation in which the user defines a set of instructions that the software robot will do on the computer rather than the user. It is a collection of many technologies combined into a single instrument for automation applications.<\/p>\n\n\n\n

Software robots can imitate most human-computer interactions and do numerous error-free manual mouse and\/or keyboard motions in a short amount of time. Their key benefits are that they don't get tired, they always perform their tasks in the same way, they can operate 24\/7, and they can be scaled based on the demands of the business.<\/p>\n\n\n

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