May 7, 2023
What are neural networks? Neural networks are interconnected data processing systems that can learn to recognize patterns and make predictions.
In biology, data processors in the central processing unit (CPU) or brain are called neurons. In computer science, data processors in CPUs are simply referred to as processors. Neural networks are composed of processors that transform input into output by switching on or off (firing off or not like neurons). These processors can learn to recognize patterns in data and store in memory the associated connections. Based on recognized patterns and learned associations, the neural processors can run simulations projecting future results (i.e. predictions) based on sample input.
Processing - Patterning - Predicting are the hallmarks of intelligence. At Creatix, we call them "The three Ps" of intelligence. Due to their ability to process data, identify patterns, and make predictions, neural networks are the foundation of the clever AI applications that are beginning to become omnipresent in state-of-the-art digital technology and that are beginning to change human society.
Understanding, or at least conceptualizing, neural networking is essential to beginning to grasp how modern artificial intelligence (AI) works, and how it will change everything. Since the 1950s, the stated goal of the AI field has been to develop human-made (i.e. "artificial") machines that can mimic human biological intelligence. Due to the fact that human intelligence is based on neural networks (interconnected neurons), computerized neural networks are fundamental also in AI.
Understanding neural networks will not happen overnight. At Creatix, we are studying the concept with hopes of being able to explain it in simple terms. Computerized neural networks are heavy in math and dense in computer science. Biological neural networks in our brains are more complicated and only partly understood in 2023. There are many neural processes in our brains that are not fully understood yet. The more we learn, the more complicated it gets. Nonetheless, as with almost every field of study, we can start with baby steps--or even crawling--and enjoy the journey into complexity. Moreover, as with most complex applications, understanding of internal mechanics is not necessary to deriving beneficial utility. That is, you don't have to be a neuro scientist to be able to think. You don't have to be a computer scientist to benefit from computers, the internet, AI, and neural networking technology.
Luckily, many of us--if not all of us--find revolving joy in learning. We enjoy figuring out how things work. One of the most fascinating areas of study these days is artificial intelligence (AI). Interestingly, the more we learn about AI, the more we learn about our own biological brains. This is not a coincidence because the stated goal of AI is to develop computerized systems that can mimic or replicate human intelligence. This is the best time to jump into the AI train. Become an AI enthusiast, student, and entrepreneur today.
Bookmark these words. The more you learn about AI, the better off you will be in the future. Even though the first formal AI workshop was held about 77 years ago, we are still at the early stages of AI development. AI will impact every single aspect of human life in modern and future societies. Just like computers and the internet changed everything, AI will continue change everything. This is easy to predict and almost guarantee because AI is a nothing other than a continuation of the computer and internet revolution. AI marks a new milestone or station on the journey and along the way to full computerization of human life. This is the time to purchase a ticket, and hop into the AI train.
The mathematics behind AI neural networks are not magic. Put simply, and oversimplifying, neural networking is simply data processing (turning input into output by application of programmed rules); identifying and storing memories of patterns in input and output; and processing forward (making predictions) based on similar patterns of input and output. We're all in this together, and you can influence the train direction.
In future posts, we will look into the clever mathematics behind computerized neural networks. There is a mechanical way of processing input, mining data for pattern recognition, and simulating input for future output prediction.
Humanity has made tremendous progress in AI technology. All industries (all) will continue implementing AI solutions into their production platforms. AI applications will deploy into all (all) facets of economic activity on Earth. Whether you are excited about the future or anxious about it, you owe it to yourself and your company to think about AI and incorporating it into your productive environment and execution strategy.
Stay tuned to Creatix.one. The best is yet to come.
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