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The Differences of and Similarities Between Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning are trending terms in the tech sphere that are often – and incorrectly – used interchangeably.

At the base level, artificial intelligence (AI) stems directly from the study of computer science whereas machine learning (ML) is a subset of artificial intelligence, thus making it twice removed from the parent field of computer science.

If we take a closer look at a few tangible uses of AI, it’s easier to understand how some applications of artificial intelligence overlap with machine learning, and simpler to distinguish differences between the two.

From SMART boards to intelligent tutoring assistant bots for children with learning disabilities or for those who simply need additional help, AI is acting as a positive force in keeping students on pace for success.

Whether they’re used for customer service and answering basic FAQs about a product or service or to provide purchase recommendations on an e-commerce website, you can’t go more than a day without encountering one.

Ten years later, he completed development on a computer checkers-playing program – the first to independently learn how to play a game by using a machine learning algorithm called alpha-beta pruning.

The point of rote learning is that the more a person (or in this case, a machine learning program) studies and memorizes something, the higher likelihood that the individual (or program) will remember what it has learned.

Because machine learning is based on algorithms that make predictions on next steps, Samuel used this to train the ML program to remember the positions it had seen on the checkerboard as well as the value of certain positions (e.g.

ML algorithms use neural networks, computer vision, and AI to recognize the type of road they are driving on, what certain street signs mean, if there is a streetlight, if there are pedestrians or other cars on the road, and any other random obstructions the self-driving vehicle might come into contact with.

Because ML algorithms are trained to recognize objects and patterns, facial recognition pulls from computer vision and ML to help systems recognize physical characteristics in order to authenticate that the person trying to access a device is actually the person who owns it.

AI marketing uses machine learning algorithms to track patterns in your online habits (as well as those of others) and make assumptions about your purchase patterns, who you are, and how to best target advertisements to you.

Jumping Into Artificial Intelligence: Five Considerations For Creating An AI Company

My journey in AI and machine learning has spanned over a decade, in which time I’ve built 100-plus machine learning models and 50-plus models in production that have been deployed in actual products.

More specifically, the full engineering team should understand data science and data engineering, and most will need to be able to build simple models.

This means that they will need to learn to query data and look at spreadsheets to recommend features or measure the accuracy of their models in conjunction with data engineers and data scientists.

Typically, most product managers don’t have these kinds of data skills, but the availability of products like Data Studio, Presto and Big Query have made it easier for product managers to query and analyze their domain-specific data.

Teams need to ensure, however, that they avoid “data lake hype.” I don't believe that building massive data lakes is the proper approach for optimally utilizing AI.

For example, if you’re researching a person’s background, a Google search can return 100 useful insights about a person beyond just their resume.

In 2018, Gartner predicted that 'through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them.'These failures may happen if you don't have the right data or the appropriate integration to take action and have a real impact.

But as you jump in, make sure you are considering the right building blocks -- from your team’s skill set to your data to dealing with privacy concerns.

What's the difference between BI and machine learning with Rob Craft (Google)

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