Namaste Yogis. Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered, mostly correct! Here no question is too mundane. Doris from Puerto Rico asks if data science, artificial intelligence, and machine learning are identical terms with different names?
Doris, you came to the right place. Let’s take a stroll as we talk. Without doubt, the birth of computing has resulted in a data explosion. Since the creation of social media, the acceleration in data volume has grown 44 times, according to IDC, Azeem Azhar. But data, like oil, is useless until it is “processed”. In 2006, British mathematician Clive Humby famously said, “data is the new oil”. What did Homby mean? Humby meant raw data, like oil, isn’t useful but when it processed/refined it is essential. In other words, data must be processed into information to unleash its potential. Stop and consider social media. Social media companies sell advertisement based on customer data. As with oil, there is a constant battle over data ownership. The processing of data into information is a new discipline, called data science. Essentially, data science is an umbrella term that encompasses data gathering, analytics, mining, and artificial intelligence, and machine learning. Doris it’s now time to talk artificial intelligence.
Humans are extremely clever. We invent solutions whether the “problem” is nature made or of our own doing. With mountains of data and an avalanche of money at stake, we have developed a new tool, artificial intelligence, (AI) to deal with data overload problems. AI is essentially, teaching machines to learn from data and derive a variety of useful insights. AI is commonly defined as a simulation of human intelligence in machines that are programmed to think and learn like humans. The key is the A in AI; it is artificial intelligence. AI includes a wide range of methods that enable computers to perform tasks that would ordinarily require a human. Basically, AI enables computers to mimic human intelligence using logic. For example, with AI, problem solving is possible, as is language understanding, pattern recognition, and even decision-making, or at least recommended decisions. With limited time, we need to move onto machine learning.
Machine Learning (ML) is the capability of artificial intelligence systems to learn by extracting patterns from data. ML provides systems the ability to automatically learn and improve from the experience without being explicitly programmed. The key is not explicitly programmed. ML develops computer programs that teach themselves to grow and change when exposed to new data. ML uses the data in a dataset to detect patterns and adjust actions accordingly. And ML automates analytical model building using statistical and machine learning algorithms. With ML, machines “learn” thereby leading to artificial intelligence.
Doris, thank you for the question. Hopefully, I explained the differences well.
Yogi Nelson
Blockchain & AI Forum 