Artificial Intelligence and Machine Learning are phrases that have become mainstream because they’re embedded in the best of what modern technology has to offer. Although both originate from a common school of thought, they do not mean the same thing. The amount of people who confuse them with each other, or use them interchangeably with conviction, is way too high.
AI (Artificial Intelligence)
Artificial intelligence can be perceived as an umbrella term that includes aspects like human-AI interaction, sensors, computerized vision + audio, planning, understanding,recognition, searching, and learning. However, its scope cannot be limited to the mentioned facets, because it continues to evolve. AI is generally interpreted as the incorporation of human intelligence to machine.
This implies that machines shall be able to carry out tasks that we consider ‘smart’, because they require a thinking process that is primarily a trait of humanity. AI is meant to mimic the human mind, hence it is likely to include more person-like capabilities with further advancement in technology.
ML (Machine Learning)
Machine learning is a branch of AI, or more precisely an approach to achieve AI. ML can exist independently but AI is dependent on it. The amplitude of machine learning is finite as it simply indicates that computers be given the power to learn. The idea is to give machines access to large data sets and let them process it on their own. This allows computers to learn things themselves as opposed to providing explicit programming.
At present, machine learning is contemplated as the core element of AI, because the way it functions is comparable to the way our brains work. It trains algorithms to predict solutions, and this ability improves with time and extension of data sets; more data builds greater accuracy. Thanks to the internet, AI is now exposed to unlimited data which has caused rapid progress in machine learning. Just like our brain reacts in accordance to past experiences or memories, ML operates by following data patterns in the system.
Types of AI
Artificial intelligence can be categorized into:
- General AI
- Applied AI
General AI is rare because it is extremely complicated to design and integrate. It is supposed to handle a variety of tasks like humans through understanding, interpretation and response to stimuli. It requires very high-level expensive technology, thereby constricting widespread application. The famous robot named Sophia is one eligible example, though there is yet much to explore and attain.
Applied AI is a common thing as it is created to focus on a particular or narrow range of functionality. It tends to surpass human intelligence in one capacity, whilst lacking others. It is a part of many modern applications we use everyday.
A neural network is a computer system that is designed and developed to process information in the way our brains do. It enables machines to examine the world like we do, with added value of speed, accuracy and lack of bias. The system works on the concept of probability by shaping statements, predictions or decisions based on the database. They also include a feedback loop which lets them sense when they’re wrong, and allows them to alter output for future reference. Machine learning uses this neural network as a framework for training the computer system, in order to improve performance without human interference.
DL (Deep Learning)
Deep learning is a subset of Machine Learning that combines multiple layers of neural networks. It is what powers AI in the background, and is a core element of the most advanced technologies today, such as autonomous cars. It is superior to typical machine learning techniques, because it is capable of more thorough and profound assessment of data. Thus, one can conclude that DL is to ML what ML is to AI.
Algorithms don’t care about corrections or improvements, they are only programmed to carry out the course. In spite of all, when machine learning gets to a point where it can communicate like a real person with true conviction, it’s safe to say that it has risen to the level of AI. AI and particularly ML have achieved high grounds in recent years. They are part of all major industries, including healthcare, banking, software engineering, e-commerce and automobiles.