I have a friend at the gym who just turned eighty. He still works and works out every day. He's also keenly interested in AI. I love his energy and I'm always happy to answer his AI questions. Most of his questions revolve around how AI can help him do his job better. While I can't answer all of those questions—because a lot of that will be trial and error—I can show him the tools, and then he will need to see what works and what is better when the human does it. But the rest of his questions revolve around the fundamentals: "Define AI and machine learning for me."

AI and Machine Learning Defined

AI refers to computer programs that can perform cognitive tasks typically associated with human intelligence. Machine learning is a subset of AI focused on developing programs that can analyze data to make decisions or predictions.

AI Assistance in the Workplace

I always explain my use of AI to people as "my intern," since it helps draft my articles after I come up with a concept. Although there are times when I don't yet have a concept for an article, my "intern" and I brainstorm together.

AI-Driven Tools

AI-driven tools are perfect for such "mundane" tasks, allowing all of us to focus on creative and strategic endeavors. (I hesitate to use the word "mundane" because AI is capable of so much more and it is evolving continually in scope and capability.) These machine learning algorithms learn and adapt. My "intern" has learned my writing style so it can help with rewrites of my articles when I struggle to word a difficult concept. My intern often helps me work out the conclusion for my pieces. I know what I want to say, but I need more fluidity in how I am saying it. Working with my AI intern gives me the same power as if I were collaborating with another writer.

But how does all of this work? How was my "intern" created?

AI Development Techniques

There are two main techniques used to design AI programs:

* Rule-Based Techniques: These involve creating AI programs that follow predefined rules to make decisions. For example, a spam filter using rule-based techniques would block emails containing specific keywords using its predefined logic.

* Machine Learning Techniques: These involve creating AI programs that can analyze and learn from data patterns to make independent decisions. In the case of a spam filter, machine learning could flag potential spam for the recipient to review. Once the recipient marks these emails as being from a trusted source, the filter learns and adapts to include similar emails from that sender in the future. Machine learning excels in environments requiring flexibility and adaptability.

AI tools can use either rule-based or machine learning techniques, or a combination of both. Rule-based techniques are suited for tasks requiring rigidity, like blocking clearly suspicious messages. Machine learning techniques are better for tasks needing flexibility, like recognizing that messages from trusted senders with typos are not spam.

Approaches to Training ML Programs

So, where does all this "learning" come from in machine learning? There are three types of learning: Supervised, Unsupervised, and Reinforcement Learning.

Supervised Learning

In supervised learning, the ML program learns from a labeled training set—data that includes labels or tags providing context and meaning. For example, an email spam filter trained with supervised learning uses a set of emails labeled as “spam” or “not spam.” This approach is used when there's a specific output in mind, ensuring precision and accuracy in results. For instance, when I think of supervised learning, I think of autonomous vehicles. In order for this technology to work, large teams of people had to label massive amounts of data. Using video and images, pedestrians, cyclists, other vehicles, road signs, and lane markings were labeled. By learning from these labeled examples, the car can accurately identify and respond to various objects in real-time.

Unsupervised Learning

Unsupervised learning involves the machine learning program learning from an unlabeled training set—data without labels or tags. For example, ML might analyze a dataset of unsorted email messages to find patterns in topics, keywords, or contacts. Unsupervised learning is used to identify patterns without a specific output in mind, uncovering hidden structures within data.

Reinforcement Learning

Reinforcement learning involves the ML program using trial and error to learn which actions lead to the best outcome, receiving rewards for making good choices. This approach is commonly used by conversational AI tools, which improve their responses over time based on user feedback. It's like teaching a dog new tricks, rewarding it for good behavior. (I laugh every time my dog sneezes because she is so dramatic about it. She's made a positive association with sneezing and now does it when she wants something. "Oh, that food looks good." [hugely dramatic puppy sneeze])

Final Thoughts

I love that more and more people are getting curious about AI and machine learning. This growing curiosity is the fuel that drives innovation and progress. By understanding the basics and how these technologies can be applied, we can harness their power to transform our work and lives. Whether it's an AI-driven intern assisting with writing or a generative AI creating personalized experiences, these technologies offer endless possibilities. AI offers us a world where mundane tasks are automated, freeing up time for creativity and strategic thinking. I'm an optimist when it comes to AI, with a healthy dose of caution. Thanks to this rapid pace of AI, we are currently living through one of the most exciting tech eras in modern times. Those of you who are curious enough to read a daily AI blog are on the early end of adapting to a technology that will ultimately change all of our lives.

Additional Resources for Inquisitive Minds

The Mechanics of Machine Learning: Memorization vs Generalization. Diana Wolf Torres. Deep Learning Daily.

Beyond the Black Box: Understanding AI's Recommendations. Diana Wolf Torres. Deep Learning Daily.

Video: Artificial Intelligence in about 90 Seconds. Deep Learning Daily.

#ArtificialIntelligence, #MachineLearning, #GenerativeAI, #SupervisedLearning, #UnsupervisedLearning, #ReinforcementLearning, #FutureOfWork, #AITrends

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