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Continue to Enhance Your Knowledge of Neural Networks for Deep Learning Applications



New Perspectives on This Critical AI Methodology


As you develop a software project fueled with AI-based solutions, you’ll need a good understanding of how AI uses artificial neural networks to enable useful applications, such as object recognition and anomaly detection.


To further your understanding, keep reading to gain fresh insights on neural networks, including a brief history of this methodology, the impact of neural networks on businesses today, and more.


A Brief History of Neural Networks


Although neural networks currently enjoy the spotlight in popular science magazines and even in major media, this was not always the case. According to MIT News, the concept of neural nets was developed in the 1940s, but scientists have wavered on the value of this approach, until more recently, as new technologies, such as graphic processing units (GPUs) and neural processing units (NPUs) have demonstrated their usefulness for neural net computing.


In fact, an Open Data Science interview with Jack McCauley, Berkeley professor and co-founder of Oculus VR, cites the gaming industry’s breakthroughs at the turn of the century as the primary catalyst for the use of neural networks for applications outside of entertainment. Without gaming GPUs, says McCauley, the ability to describe the dimensions of an image, known as tensor algebra, would not exist.


How Are Neural Networks Impacting Major Industries Today?

Soon, advanced AI applications like diagnostic computer vision will be as commonplace in our lives as gaming. Of course, there are already many practical uses of deep neural networks like the video, audio, and text recognition tools we’re all familiar with:


  • Search engines, like Google.

  • Speech to text conversion.

  • ChatGPT4.

  • Customer service chatbots.

  • And many more.


Neural networks, whether convolutional, recurrent, or another form of data flow, also provide beneficial applications for many industries outside of consumer use:


  • Medical facilities utilize image recognition for AI-driven diagnosis.

  • Marketing companies learn their target audiences with AI-backed software that analyzes online behavior.

  • Computer vision and deep learning-based advanced driver assistance systems (ADAS) provide safety for drivers on the road with sensors and cameras.

  • Finance, meteorology, and the energy sector all depend on predictive models to benefit customers or inform internal decision-making.


MIT Sloan School of Management notes that 50% of companies with more than 5,000 employees are harnessing the power of AI. Industries commonly employing AI include manufacturing, healthcare, agriculture, and professional services. This data suggests that neural networks are making a crucial impact on businesses, not to mention the 100 million ChatGPT users or the tens of millions of consumers who own the latest iPhone.


Although AI seems to be experiencing widespread adoption, many businesses are still making adjustments to prepare for this tech transformation, but the revolution is growing. According to the Bipartisan Policy Center, half of U.S. small businesses simply don’t have the tools to leverage AI yet. However, they are largely interested in using the U.S. Small Business Association’s AI loan program to invest in this technology.


There will be more and more potential in the near future to reach both large and small markets with AI technology. Therefore, companies that wish to capitalize on the growth of AI may find it useful to seek assistance from a seasoned AI engineering team.


How Your Team Can Leverage Neural Network Technology


If you have a concept for a project that employs AI technology, including convolutional neural networks, recurrent neural networks and LLMs, you may need the assistance of an AI development firm.


Although some software developers and tech-forward companies have in-house talent, finding truly exceptional, experienced engineers with significant knowledge of artificial neural networks can be challenging.


For superior engineering support, consulting firms exist to provide comprehensive, dedicated support for your project. However, AI is a continually evolving field that is rapidly growing before our eyes. Blink once and a host of innovations have been made.


To navigate these changing waters and the challenges that come with revolutionary ideas and thinking requires a partner that not only understands AI, but has experience in these key areas:


  1. The full lifecycle of an AI model, from development to marketplace deployment.

  2. Employing technologies that have made current AI use cases possible, including edge computing, system-on-chip engineering, graphic processing units, and neural processing units.


Consulting firms with these key facets of AI under their belt understand the technologies that allow AI to thrive and what is needed for the next wave of success in this field. A storied history of software development for the innovations that have led to deep learning technology is an advantage for today’s AI boom and a quality that should be thoughtfully considered when developing AI use cases.

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