01-14-19 | Blog Post

AI vs Machine Learning: Key Differences

Blog Posts

Everyone has been talking about artificial intelligence (AI) right now and the potential uses it has for civilization. Or, is everyone buzzing about machine learning? Are these ideas the same thing? If not, what’s the difference? In this post, we’ll explore just that.

Artificial Intelligence (AI)

AI has roots going back to the 1950s, when John McCarthy (widely regarded as the father of AI) and other researchers first came up with a definition for the concept. They defined AI as any program or machine that could complete a task that a human would have to apply “intelligence” to in order to complete that same task. This of course, is a pretty broad definition of AI. After all, even “intelligent conversation” has different meanings to everyone. As AI grew and scientists refined their definitions, the term took two different paths: Narrow AI, and general AI.

Let’s start with narrow AI. Narrow AI is a specific intelligence that is applied to a program or machine. This is the AI that’s typically used in our everyday lives today, from surgical robots to voice assistants like Siri or Alexa. It may not be dynamically changing–a computer that can translate a document into French for you could be considered a form of AI, but it takes from a certain set of words that have already been programmed in and doesn’t recognize new words that may come from your document. And, sadly, the machine can’t make pancakes for you. In other words, the intelligence the machine is applying is very specific in scope.

General AI, on the other hand, is much broader in scope. It’s what people consider machines like the Terminator or HAL, the scary or wonderful (depending on your opinion) nearly-human creatures that can evolve and adapt to anything the world throws at them, much like a human.

Machine Learning (ML)

Machine Learning is a specific type of AI where computers take data and “teach” themselves through algorithms and statistical analysis to be more accurate in their analysis of that data and any pattern predictions that arise as a result. In other words, all machine learning is a form of AI, but not all AI is machine learning. A specific example is search engine recommendations. Based on specific patterns (your search history and the results you clicked on or products you bought), search engines have taught themselves to find similar products or stories that they think would be relevant and interesting to you, solely based on the data you entered. Some call it creepy, while others just call it handy.

Of course, both of these concepts have huge impacts on people personally and within the enterprise. While we’ve personally turned to Alexa and Siri to tell us the difference between a teaspoon and a tablespoon, businesses can use AI to better automate processes and transactions for themselves and their customers. They can use AI for customer support in off-hours with chat robots. They can use machine learning to better tailor product recommendations or make more informed decisions about a hospital patient’s health needs. The list goes on and on. The impact AI already has, and the potential impact it could have in the next few years, is huge.

AI and its subset machine learning (and further subset Deep Learning, which we’ll cover in a later post) will only continue to evolve and grow to provide humans with more efficient, productive and potentially healthier lives. As the field has evolved, so have  the uses and examples of AI we can apply to everyday life. It’s present in everything from driverless cars, computers that play Jeopardy!, and programs that do your taxes for you. What will come next? Personally, I’m waiting for the Pancake O-Meter robot that cooks fresh pancakes on demand.

Curious to learn more? Check out our blog post about machine learning!

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