To say that artificial intelligence is trending would be an understatement. After the launch of ChatGPT, interest in artificial intelligence was 525% higher than at the beginning of 2022.
This growing interest in the field of artificial intelligence has led to the proliferation of a number of technical terms, often used interchangeably. But to truly recognize the potential that AI has to change the world, you’ll need to understand that artificial intelligence, machine learning (ML), and deep learning (DL) are all distinct terms and concepts. In this blog, we’ll set the record straight by breaking down these three concepts and speculating on what’s next in the field of AI.
What Is Artificial Intelligence?
AI is a broad field that encompasses the development of intelligent systems that can perform tasks that typically require human-like intelligence. This can include tasks such as natural language processing, speech recognition, pattern recognition, and decision making, and visual perception.
Data scientists achieve artificial intelligence through a variety of methods, including rule-based systems and supervised learning.
Artificial intelligence is already becoming widespread, with many consumers being familiar with interactive voice assistants like Siri or advanced chatbots like ChatGPT.
Overall, AI is the overarching field that encompasses various methods for developing intelligent systems by training algorithms on data. Those methods include machine learning and deep learning.
What Is Machine Learning?
Machine learning is a method of training computer systems that are able to learn and adapt without following explicit instructions. Machine learning algorithms are able to learn from the data they are provided and improve their performance over time.
In order to learn and improve their accuracy, machine learning algorithms require human intervention and large quantities of structured data. Depending on the level of human involvement, machine learning activities can be categorized as either supervised or unsupervised learning.
Large language models like ChatGPT are examples of technologies trained through machine learning. Developers use machine learning to train the models on almost all human knowledge and teach them to understand written language.
What Is Deep Learning?
Deep learning is a type of machine learning that involves the use of neural networks with many layers to learn and make decisions. (Hence the term “deep.”) Deep learning algorithms are able to learn complex patterns and can be used for tasks such as image and speech recognition.
Self-driving cars are an example of deep learning in action. Automotive manufacturers are using deep learning to train cars to recognize the people and objects they’ll encounter in the field. Think pedestrians, streetlights, stop signs, crosswalks, and other vehicles. While autonomous vehicles are still a work in progress, deep learning will be pivotal to training these machines to safely navigate the world.
What’s Next for AI?
AI is already producing technical marvels of the likes from science fiction. But this revolution in AI is only the beginning. Over the coming decades, engineers and data scientists will continue to push the limits of artificial intelligence by developing more sophisticated training methods. So what’s next in the field of AI?
One area of research that is gaining a lot of attention is explainable AI, systems that are able to explain their decisions and actions in a way that is understandable to humans.
This is becoming increasingly important as AI systems are being used in more critical applications, such as healthcare and finance, where it’s necessary to understand the reasoning behind the AI’s decisions.
ChatGPT is technically an example of a black-box or unexplained AI. But what’s interesting is that if you ask ChatGPT to describe the reasoning behind a response, it will attempt to provide an explanation. While it might be too soon to consider the chatbot explainable AI, this functionality does serve as an example of what interactions could look like between humans and explainable AI.
Another area of research that’s gaining traction is the development of AI systems that are able to learn and adapt over time, even in changing environments. This is known as continual learning or lifelong learning, and it’s an important step toward the development of AI systems that are able to function in the real world. With continual learning, neural networks can remember learned knowledge, even if its environment has changed or the training data for that knowledge no longer exists.
Machine Learning and Cybersecurity
The proliferation of advanced AI will inevitably lead to a rise in machine-learning-based cyberattacks – and a number of cybersecurity innovations to stop them. Bad actors are already using machine learning-based threats like autonomous malware and CAPTCHA bypass to target companies and consumers. At the same time, cybersecurity engineers will turn to machine learning to improve response times and the ability of security systems to respond to difficult-to-detect threats.
Democratized Machine Learning
Engineers and data scientists are already using AI to create transformative technologies. But what will happen when everyone – regardless of technical skills – has the ability to leverage machine learning?
Democratizing machine learning will provide unprecedented access to the algorithm training abilities of machine learning to consumers and professionals of every skill level. With potential applications including personalization, document processing, fraud detection, and supply chain optimization, the potential impact of a democratizing learning methodology is truly limitless.
This writer can’t help but speculate that there will likely be a democratized AI platform that makes machine learning accessible in the same way that ChatGPT brought large language models to the masses.