What You Need to Know to Sustain a 10-Minute Conversation About Artificial Intelligence and Machine Learning

Don’t be ignorant. Know the definitions, or lack thereof.

Everyone’s manager in the modern workplace.

So what is Artificial Intelligence?

“AI involves machines that can perform tasks that are characteristic of human intelligence” -John McCarthy, 1956

Artificial Intelligence is neither artificial nor intelligent.

Artificial Intelligence can be divided into two general fields: General AI and Narrow AI

So what are some examples of technologies that fall under the field of Narrow AI?

So what do I need to know about Machine Learning to not make an ass of myself in a high level conversation about the subject?

“A field of computer science that uses statistical techniques to give computer systems the ability to “learn” with data without being explicitly programmed.” -Arthur Samuel

Spam filters for email with traditional programming vs. …
All math. Different types of math.

Supervised vs. Unsupervised machine learning

What is supervised machine learning?

What is unsupervised machine learning?

“Unsupervised machine learning is the machine learning task of inferring a function that describes the structure of “unlabeled” data (i.e. data that has not been classified or categorized). Since the examples given to the learning algorithm are unlabeled, there is no straightforward way to evaluate the accuracy of the structure that is produced by the algorithm.”

Given a goal (learn to walk) an agent discovers how to do just that using unsupervised learning.

A quick deviation: how can my data reveal patterns, trends and predictions to me that I might have not known otherwise through manual/traditional methods of analysis?

Start off with a defined goal or answer you are trying to achieve.

You start off with a goal in mind for building a machine learning suite of tools. Machine learning by nature forces you to become more intimate with your data and most importantly explore it. In starting that process of exploring your data, you suddenly might come upon insights you had never seen before… and suddenly, you have another goal to add onto you list of goals where machine learning can help answer your questions.

Okay, so how do I actually do the thing you call training a model so magic comes out at the end?

The six steps of machine learning

1. Define Objectives.

2. Collect data.

Garbage data in. Garbage models out. Garbage predictions. Useless machine learning.

3. Understand and prepare the data.

Understanding and preparing data for machine learning training is 80% of the battle. It will take the longest, and if done wrong, will completely set you up for complete failure.

4. Create and evaluate the model.

The most important thing to know is that you should go into this phase and process knowing you will fail, and knowing that you will have to try a multitude of different models to determine where you get the best initial accuracy.

5. Refine the model.

6. Serve and monitor the model.

A common misconception is that once the model training is done, the hard work is over. In fact, launching the model into production against your high velocity real time datasets is where everything… breaks. Then you have to go back, re-evaluate how your training data compares to data seen in production… Think of it as launching a brand new software product into production for the first time. It will break. Badly.

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