Artificial Intelligence, also known as AI is a fast-developing field that will revolutionize the way we deal with technology. AI encompasses the development of intelligent software and machines that are able to learn and adapt to a new environment. The foundation of AI lies in three main fields of study: science, mathematics, and technology. In this article, we’ll see how these three fields have joined to produce the AI technologies we can experience today.
Science plays a major role in the development of AI. AI researchers and developers use scientific methods to gather data, make observations, and develop hypotheses on how intelligent machines can solve problems. The scientific framework serves as a foundation of AI development.
A good illustration of how science is used in AI is computer vision. It’s a field of AI that makes the interpretation of visual information possible. Thanks to the principles of physics, optics, and biology it’s possible to develop algorithms that allow machines to identify and interpret visual information. A fascinating example of that in action is that deep neural networks can identify specific objects in imaging by processing data through its multiple layers of interconnected nodes, much like the human brain does.
Another scientific domain that is crucial in AI is natural language processing (NLP). It’s a field of AI that concentrates on the ability to understand human language. With the use of linguistics, cognitive psychology, and computer science scientists can develop algorithms that allow intelligent machines to interpret, understand, and generate natural language. The many chatbots that are popular today serve as a perfectly illustrative example of this ability.
There’s no doubt that mathematics is the language of AI, as it uses mathematical projects and concepts like calculus, linear algebra, and statistics to create and improve algorithms that allow it to process and analyze huge amounts of data.
As mathematics lays the foundation for the creation and constant development of AI, machine learning is one of its most crucial aspects. It ensures the development of algorithms that enable AI to learn from data. Thanks to mathematical models and statistical techniques, machine learning algorithms can identify patterns in any kind of data and make predictions based on them. For example, intelligent machines are trained to recognize images by seeing a labeled object. The supervisor performing the task can later adjust the algorithm, so the difference between predicted and actual labels is minimized to a negligible degree.
Another crucial area of mathematics in AI is optimization. It’s the process of finding the best solution to a specific problem with a given set of limitations. Why is it so important? Well, the ability to improve the performance of algorithms and models is what makes the minimization of error from the above-mentioned example possible. Thanks to that “skill”, deep neural networks can be trained to adjust the weights and biases of the nodes to make the whole thing possible.
The development of technology is what has made AI if not possible, then more recent. Such advances as cloud computing, big data, and the internet of things have allowed AI to drastically speed up its growth and become available to businesses and then consumers. Thanks to technology we have the necessary infrastructure to build, use, and develop the intelligent system further.
So what areas of technology are especially important for AI development? One of them is big data, meaning the enormous amounts of data we as a society generate every day. AI relies on it to improve its algorithms and models. It’s a good playground to identify patterns and make predictions in. Of course, all of that would not have been possible without the storage and processing power needed to handle all of that, which is again provided by technology.
The second crucial area of technology is the development of hardware. It needs to be very specific in order to handle AI algorithms. The improvement of graphic processing units (GPUs) has greatly sped up the training of deep neural networks.
As AI combines the methods and principles of science, mathematics, and technology, it can be used in various ways throughout different industries. Let’s check all the possible present and possible future applications of AI.
AI is transforming the healthcare industry by providing more accurate diagnoses and coming up with new treatments that improve patient outcomes. Thanks to its analytical capabilities, AI can check X-rays, MRIs, and CT scans for any tumors, lesions, and other abnormalities. The use of AI can also extend to predicting the likelihood of disease comeback and identifying patients who are likely to develop certain conditions.
Another great way of AI application is the development of personalized treatment plans for patients. By analyzing patient data like medical history and lifestyle, AI can suggest a more effective treatment plan with fewer side effects. For example, AI can check the genetic mutation of a patient’s tumor and develop improved cancer treatment plans.
AI’s ability to analyze huge amounts of data is also revolutionizing the finance industry. More accurate predictions, reduced risk, and better customer experience have all become possible thanks to the use of AI. It can analyze financial data to make predictions about stock prices, interest rates, and other financial aspects. As for the reduced risk, AI algorithms can identify potential fraud to greatly reduce financial loss risk.
Regarding the customer experience, AI chatbots are much faster at the basic stuff and don’t require sleep, unlike humans, so it’s a good tool to free up some time for more important tasks. Going even further, if the bot has access to customer data, then it can make highly personalized suggestions and give informed advice in general.
Another useful and not-so-obvious way to use AI in manufacturing is that it can establish a more efficient production process, reduce waste, and improve quality control. How can it do all that? It can predict demand, so you focus on producing the popular item more, which lowers your inventory levels. AI can easily monitor the production process as well and discover potential issues before they develop into problems.
Lastly, AI can use data from the sensors, cameras, and other sources a manufacturer can provide to see whether an item meets quality standards and doesn’t have any defects.
Last but not least, AI can develop more efficient and safer transportation systems. We’re not talking about self-driving cars. Rather that AI can analyze data from the available sensors to identify traffic patterns and adjust traffic lights and other control systems to optimize traffic flow. The same ability can be used for optimizing logistics and route planning which will reduce the time and cost of transportation.
Safety-wise, by analyzing data from cameras, sensors, and other sources, AI can predict accidents before they even happen! Like seeing dangerous driving behavior and alerting drivers in real time.
As we’ve illustrated above, the unique combination of science, mathematics, and technology is what gives all the necessary tools for AI researchers and developers to improve their work even further. With each day it seems that the process becomes quicker and quicker and the uses of AI seem to multiply exponentially. We can all agree that AI’s ability to analyze vast amounts of data and improve its algorithms is fascinating to witness in action. It has all the capabilities to serve a wider range of industries in different and maybe yet unimaginable ways. However, it is important to think about the ethical implication of such use and make sure that this technology is used and developed responsibly.