Artificial Intelligence and Machine learning are two catchphrases at the moment, and often seem to be used interchangeably. However, both the terms are different from each other, not just in approach, but also in algorithms and logical thinking.
Machine learning is a set of approaches and technologies that offers various means by which computer systems can easily encode learning from data and later apply information to that learning to conclude. Moreover, ML also affects the future of cybersecurity. The internet plays a crucial role in our everyday lives. However, it is still unsafe where hackers can steal sensitive information from users. Both AI and ML here acts as a double-edged sword. Thus, use a VPN to protect your data and stay protected.
In more precise words, machine learning is a requirement for AI. In the same way, not all ML systems are operating in the direction of where we’re going to achieve with AI. MIT Professor Luis Perez-Breva expresses his views in this regard. He argues that complicated training and data-intensive learning systems surely possess ML capabilities, but this doesn’t make them AI capable. He explains most of what is being branded as AI in the market is not AI, but a different version of ML. In this version, the systems are being trained to perform a narrow and specific task, utilizing various approaches to ML, among which deep learning is the most popular one.
He also maintains that if you’re trying to get a system to identify an image, by feeding enough data, and with the magic of math, neural nets, and statistics, you’ll obtain the desired results. In reality, you’re using the human’s understanding of the image. The opinion is created by large data sets that can be mathematically matched against the inputs to verify what humans can understand.
Both AI and ML are not the same thing, but the perception at times often leads to considerable confusion. To aid our readers in this regard, I have come up with a piece of writing to explain the difference between the two of them.
Artificial Intelligence is a subcategory of computer science that mainly focuses on solving tasks that humans can do, but computer systems can’t, for example, image recognition. AI can be approached in various ways. Like for instance, I am writing a computer program that enforces a set of rules devised by domain experts.
Initially, the field of Machine Learning was considered as a subcategory of AI, which was concerned with the development of algorithms so computers can automatically learn and predict models from data.
For instance, if we want to create a program that can recognize handwritten digits from images. One would look at these images and comes up with a set of if-this-then-that rules to tell which image is displayed in a particular model (for example, by looking at the relative locations of pixels).
Another approach will be of using a machine learning algorithm. The algorithm can easily fit into a predictive model depending on thousands of labeled image samples that have been gathered in a database. Now, this is deep learning, which is a subfield of machine learning.
Both machine and deep learning help in developing AI; however, AI does not necessarily have to be developed by using machine learning.
In simple words, artificial intelligence is based on machine learning. Machine learning is an integral part of data science that draws features from algorithms and statistics to work on data collected and produced from multiple resources. Hence, it can be said that data science combines a bunch of algorithms from machine learning to develop a solution. During this entire process, various ideas from traditional domain experts, mathematics, and statistics are borrowed.
This also means that data science stands for complete terms includes all aspects of ML for functionality. Whereas, machine learning is also an element of artificial intelligence, where a comprehensive set of purposes are achieved on a whole new level.
To clear the confusion of the concept that ML is another version of AI- let’s briefly discuss the differences between the two absolute terms.
Machine learning is a way of thinking that enables an algorithm to evolve. Here learning also means feeding the algorithm with a massive amount of data so it can adjust itself and can improve gradually. Moreover, machine learning also allows computers to recognize patterns in an enormous database and later act on them. ML is exemplified by the capability of computers to identify images.
However, AI is different from this- it is surrounded by mimicking human decision-making processes and performing complex tasks in a human-like way than ever before. This includes machines that can perform specific functions of human intelligence. These tasks include planning, problem-solving, recognizing images and voices, or any other job which can be regarded as smart.
As Artificial intelligence is a broad term, it is divided into two main categories, i.e., General AI and Weak AI.
- General AI: It is the intelligence of machines that can successfully perform any intellectual task that a human being can perform. Systems and devices that are dependent on general AI can also perform any tasks that don’t exist in our reality but will take time before creation.
- Weak AI: It is also known as narrow AI that is restricted to a specific area. Weak AI stimulates human thoughts and benefits humanity by automating tedious tasks by analyzing data in ways that humans fail to do.
Although differences lie between AI and ML- but talking about the business perspective, both work together to benefit any business. Now, many organizations are switching towards this cutting-edge innovation. Both ML and AI provides security to the enterprise in the form of biometrics and also protects them from getting attacked by hackers or fraudsters.
To sum up all, it can be said that AI is a continuous journey of developing modern machinery by using human intellect. It is a far-realized approach to be able to colonize the social mindset to perform regular operations. The programmatic enforcement for AI can take some time. But as far as machine learning is a concern, you can start working on small sets of data for initial tasks of adoption and screening.
Machine learning is a subtype of AI, and it will require some time to develop and deliver fully. But in no case, it is another version of AI.