“What is AI?”. For starters let’s assume AI is just a tool to benefit humankind. But humankind is not perfect and humans do mistakes. So if AI aka artificial intelligence continues to mimic human intelligence then will it be free to do the same silly mistakes as the humans do? Actually, this thought coins another term, the “rationality”. Rationality means to do the right thing given what it knows. So this makes two types of AI. One to mimic human capabilities as they are and another one to make only the right choices ignoring human flaws. The second approach is more popular than the first as in our day to day life we all try to make as fewer mistakes as possible. But from time to time the definition of intelligent system changes and after reaching a certain milestone we try to achieve much higher picks. For example, a program named Eliza, emulates a Rogerian psychotherapist. And Eliza is programmed with a preset of texts and some common text patterns.Then we all know about the story of Garry Kasparov and deep blue. World chess grandmaster Garry Kasparov was first ever been defeated by an intellectual decision making AI on chess board.
Thereafter in 1997, New York City human was defeated by a mere computer program. And that was a pretty big event which made the world believe that machines can be intelligent too if programmed accordingly.
After these consecutive events, AI had to split into specialized subfields. These fields focus on specific parts of intelligence and its inception. Some fields focus on human consciousness, some on human intelligent behaviour development, social or mob decision making and so on. Among them a broad and well-studied field is Machine Learning, focusing on how dumb systems can learn alike human and make decisions accordingly. Both educational institutions and industries of different verticals have embraced this field cordially and currently every sector is adapting to machine learning to get their task process automated.
Machine learning in different industry thus gave birth to few professions and put emphasis on data science (which works with data to extract and use as many information as possible). Now let’s look into few professions that has been created for machine learning,
- Data engineer: Are those working for structuring and managing data. He is responsible to maintain and design a data warehouse. But generally, he is not responsible for getting insights from data. Managing distributed data source and their accessibility is another important duty for him. Generally, companies that have massive historical data start with these professionals. They actually make way for data analysis as it is almost impossible to make sense of data from massive unstructured sources.
- Data analyst: Data analyst analyse data and finds insights out of them. From time to time they use well-established machine learning algorithms and help to make important and business critical decisions. Finding out revenue points and cost-cutting places of a department are some of his jobs. Also finding the right machine learning algorithm for prediction is one of his mission-critical jobs.
- Data scientist: Data scientists have the most critical job comparing to others. He is the one responsible for creating new machine learning algorithms time to time. He analyzes the situations and tries to reach a generic point where he can propose an algorithm t1o solve it for all those cases. His work is a long term asset for an organization.
But this hierarchy is important for large enterprises. Machine learning is more of a culture for an organization’s who are tech savvy. And to start this culture it’s required to start using the existing services first. Then extending those with a coherent team and make new breakthroughs with the extensions.
References
- Artificial Intelligence a Modern Approach. Peter Norvig, Stuart Russell
- Eliza: http://psych.fullerton.edu/mbirnbaum/psych101/Eliza.htm
- Posted originally : https://github.com/gitrifatjahanazad/introduction-to-ai