In simplest words, Machine Learning (ML) allows computers the ability to learn from examples/data. ML has turned out to be a key to transfer knowledge into computers. And this is important because insight knowledge is what enables intelligence.
If Internet is the fuel for Information Technology development then Machine Learning (ML) is the energy for powering up Artificial Intelligence (AI). ML is constantly being researched & developed such that the technology allows computers to learn and understand knowledge from data, then make a loop of executions & self-improvement, towards the goal of truly intelligence.
Putting knowledge into computers has been a challenge for previous approaches (e.g., rule-based, hand-crafted learning) to Artificial Intelligence (AI). Why?
There are many things which we know intuitively so we cannot communicate verbally. We do not have conscious access to that intuitively knowledge. How can we program computers without knowledge? What is the solution? The solution is for machines to learn that knowledge by themselves, just as we do. ML is going to turn out our dream of truly AI that allows machines ability to adaptively learn and improve by themselves to automate decision-making and facilitate various kinds of human needs.
ML is poised to start an equally large transformation on many industries.
Let’s take a look at a cycle of product development: "Product → Users → Data → Product". During times, products gains more and more users, the more users joining the more data we are having, and with modern ML, the product becomes better by self-learning and improving.
Beside ML, there are the fields of computer vision, robotics, natural language processing (NLP), etc., which are key building blocks towards enabling machine intelligence.
It can be seen that there are lots of industry sectors that would greatly benefit from the development of ML in specific or AI in general, such as: retail, IoT, finance, healthcare, manufacturing, security, space, etc.
In overall, ML is expected to be an indispensable component in any AI systems that help machines make better sense of context and meaning of data. The future of these AI systems remains not only in enabling automation, but also supports of making complex decisions.
Although nowadays, AI can automate lots of tasks instead of human, it is still extremely limited compared to human intelligence. There are various kinds of tasks that a typical human can do within a second of thought, which is also ambitiously expected to be automated by AI in the future.
Ho Chi Minh City, July 25 2019