The digital landscape today is remarkably different from the earlier days of computing. Once rigid and strictly input-driven, machines now demonstrate a semblance of human-like learning and adaptability. The secret sauce behind this evolution? The sophisticated realm of Deep Learning within AI.
Let me set the stage with some memorable AI moments. Do you recall that viral story about a Roomba’s over-the-top reaction, equating a simple stumble off a step to plummeting off a cliff? [Source] Or the time Bing’s chatbot made an audacious relationship advice to a user, based on its ‘feelings’? [Source] And who could forget the Facebook chatbots that spontaneously devised their own cryptic language? [Source] These instances, humorous and startling, paint a vivid picture of the boundless possibilities and unforeseen quirks of AI.
Deciphering AI’s Complex Layers AI, ML, DL
These acronyms have become buzzwords in tech conversations. But what do they mean, and how do they relate to one another?
Artificial Intelligence (AI): AI is the broad term for the field of computer science where machines emulate human-like intelligence using large datasets. Visualize nurturing a child’s learning. It is important to give them the appropriate ‘nutrition’ of knowledge, healthy foods, and to instill good morals. In AI, this nourishment is data. Prime examples of this include virtual assistants like Alexa and Google Assistant, which evolve in comprehending varied human communication styles. This could be accents, complex sentences or even slang.
Machine Learning (ML): A subset of AI, ML is where machines identify trends and decide autonomously. Have you ever been haunted by an ad for that one product you glanced at? That’s ML in the background, coaxing you to buy the product because it believes you need it or enjoyed looking at it. A particularly gripping chat with Dr. Luis Merino, (now live on the podcast so be sure to check it out!) delved into the notion of engineering the human ‘Aha!’ moment that we have as children into machines. This means programming the thrill we feel when solving math problems or big LEGO sets into these devices to optimize their learning. Learning how these machines learn is a huge topic these days and their are many avenues to explore in how to do it. I’ll be talking about them all in an upcoming post!
Deep Learning (DL): Here’s where things get especially intricate. DL, a sublayer of ML, incorporates multi-layered neural networks that attempt to mirror the functionalities of the human brain. Before I go on, let me quickly explain what a neural network is. You can think of them as the neurons in your brain but in artificial intelligence. These neural networks send signals to one another that are either input or output. Such networks enable the processing of enormous data chunks, producing accurate forecasts. Consider the algorithms guiding self-driving cars – processing myriad data streams (from cameras and sensors on the car) to react in real-time.
Embarking on Your AI Expedition
We stand at the precipice of a new digital dawn, with AI leading the charge. Machines have graduated from mere tools to entities that learn and grow autonomously. As we increasingly cohabit with these intelligent systems, it’s prudent to stay updated and perhaps dive deep into the mystique of AI.
The ever-evolving AI domain can be both exhilarating and overwhelming. Wondering where to get started? There’s a wealth of resources at your fingertips. And my blog is just the beginning! Plenty of YouTube videos and online courses exist to help you learn more. If you’d like to hear directly from the experts, head over to the Kameron’s Lab Dive In Podcast! There, I chat with the Jedi Masters in this field about what they do and advice for students and learners like you and I 🙂
