People are talking about artificial intelligence (AI) a lot these days, and it can be hard to keep up with all the new trends. Some companies and students are worried about which AI trends are important and will still be useful in five years. They want to know if training their data scientists in machine learning will make a difference to their business. They also want to know which other businesses are using this technology and if it’s working for them.
To help out, in this article, we will talk about 5 AI trends that we think people should follow in 2024 and beyond.
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Natural Language Processing: Natural Language Processing (NLP) is a branch of Artificial Intelligence that uses machine learning to teach computers how to understand written and spoken language. NLP has many applications, making it one of the most valuable areas of AI. It’s especially useful in voice interface technology, such as Google Home or Amazon Alexa. Instead of typing on a screen, we can talk to our devices, and they’ll understand our casual language. NLP has two sub-applications: natural language understanding, where a computer interprets the meaning of the text, and natural language generation, where a system generates a logical response to text or input.
On-demand video streaming services companies use NLP technology for many of their features. One example that most people are familiar with is auto-generated captions. This technology has been available on streaming services companies for decades and has been improved over time, with translations available in multiple languages.
Robotic Process Automation: Robotic Process Automation (RPA) is a technology that can help with repetitive tasks on the computer, like invoicing a client. If you have a task that you do many times a day, like opening an email attachment, copying data, and sending emails, RPA can take over that task. RPA is like a software robot that can do these tasks automatically. RPA is also used for tasks like invoicing, billing, payroll processing, and data extraction. If it needs human help, it will let you know. This frees people to do more interesting work, which is a big trend for companies.
Open-source AI frameworks: The world of programming relies heavily on frameworks and libraries that streamline the coding process by removing redundancies. JavaScript libraries such as React and Angular, for instance, help developers create websites faster and with fewer errors by providing common components. Similarly, open-source AI programming frameworks have enabled the rapid expansion of AI technology. By making these AI tools available to programmers, data scientists, and technical teams of all skill levels, AI research is no longer the exclusive domain of Silicon Valley professionals or Ph.D. candidates.
Thanks to the libraries and platforms that support AI functionality, highly complex artificial intelligence algorithms, models, pipelines, and training procedures are now within reach of those with an interest in the technology.
Reinforcement learning: Reinforcement learning is a way for AI to learn by trying different actions and receiving rewards for good decisions. It’s like training a pet: when the pet does something good, it gets a treat, and when it does something bad, it gets scolded. Similarly, an AI agent is trained by rewarding it for good actions and penalizing it for bad actions.
Reinforcement learning can be used in personalized recommendations, advertising, and content optimization. One example is e-commerce websites. These websites use reinforcement learning to optimize their online advertising campaign. Which gave a return on investment of 180% to 240% without increasing the advertising budget.
Edge computing: Processing this vast amount of data can be challenging, often requiring the transmission of information to cloud computing servers located miles away. This can cause issues when there is a loss of Wi-Fi connectivity.
Edge computing solves this problem by bringing the servers and data storage closer to the devices, enabling real-time data processing that results in quicker computing responses and reduces network latency. While cloud computing can be compared to big data, edge computing can be thought of as immediate data. In addition to device-level edge computing, edge computing can also be performed on nodes, which are mini-servers located in proximity to local telecommunication providers.
Conclusion: It’s clear that AI has continued to rapidly advance and shape our world. The five trends outlined earlier in the year have proven to be not only important but transformative in every industry. Looking ahead, we can expect AI to continue to make significant contributions to our lives and societies in ways we can only imagine. As technology becomes more accessible and democratized, we can anticipate even more innovation and breakthroughs in the coming years.
It’s clear that AI is no longer just an experiment, but a mainstream technology that businesses cannot ignore. Staying competitive in the fast-paced technological landscape of the future can be aided by investing in online courses, such as LeanTube to learn about AI for both individuals and businesses. LearnTube is a safe and secure platform that offers free access to a wealth of resources. It uses a range of teaching techniques, such as the LearnTube app and a WhatsApp bot, to deliver engaging and interactive education to students. Check out LearnTube courses now.
