Navigating the Complexities of Machine Learning and Bias Mitigation

Unlock the secrets behind the AI curtain and learn how to effectively communicate with the technology shaping our future. 

Have you ever pondered the hidden mechanics of AI, or how it's possible for machines to learn and make decisions? 

Let's embark on a journey to illuminate the opaque inner workings of AI, akin to understanding the precise artistry behind your finest cup of coffee. We delve into the critical importance of data quality, the risks of bias, and how inclusion in AI training can steer us toward more equitable tech solutions. 

As your AI Whisperer, I'll guide you through the nuances of machine learning and the necessity for transparency in systems that increasingly influence our lives.

Further into our exploration, we shift focus to the language of AI—prompt engineering and prompt chaining. 

Think of it as the way you'd train a clever yet mischievous puppy: with patience, context, and clear communication. 

I'll share insights on how to craft prompts that make AI more useful and responsive, ensuring your digital conversations yield the results you seek. 

Join us and be part of the conversation; suggest future topics that spark your curiosity and let's collectively sip on the rich, robust insights AI has to offer. 
Together, we'll navigate the complex web of artificial intelligence – one engrossing episode at a time.
Let me know what you want to know about AI- if you have any questions let me know

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Transcript

Speaker 1: 0:14

Hello and welcome back to AI Cafe Conversations, where we serve complex AI topics with the comfort of your favorite coffee house. I am, sahar, ready to guide you through the mysterious inner workings of artificial intelligence. What goes on inside the so-called black box? Quotation marks in the air, just like trying to decode the secret blend in your mystery flavored coffee. Understanding how ai makes decisions can seem daunting. Today, we dive deep into what it means when developers talk about ai systems being trained and how the system learn over time. I'm sipping here. I'm waiting here for a sip of my coffee. Give me a second, yummy. Let's start with a basic question what is this black box in AI? It refers to systems where the decision-making process is not visible to the user, you and me. It's like putting your coffee order into a machine and getting a perfect cup out without seeing any of the steps in between. And the question is do we really need to know the process or we just care about a beautiful cup of coffee?

Speaker 1: 1:39

The core of how AI works revolves around data and algorithms. And a reminder here I'm not a techie, I don't get into all that because basically I don't get it. But think of data as the coffee beans, the essential raw material. The quality of your data affects the output, just as the quality of your beans affects your coffee's taste. Ai is trained using vast amount of this data. Training an AI is like teaching it a recipe. I love cooking, I hope you do too. You give it guidelines and examples, much like teaching a barista to make the perfect espresso Over time. Through a method called machine learning, ai analyzes data to detect patterns and it refines its algorithms as it goes on own recipe book, so to speak.

Speaker 1: 2:49

Machine learning is fascinating. It's a type of AI that learns from data without being explicitly programmed. Wow, imagine if your coffee machine learned over time exactly when you wake up and how you like your coffee and had it ready right when you needed it. Won't you love that? That's machine learning Always adjusting, always improving.

Speaker 1: 3:23

But what about when things go wrong? What if the data isn't good? And I know there are few skepticals out there. Well, just as bad beans can ruin a coffee, bad data can lead AI to make poor or biased decisions, and you might say ai is biased absolutely. It's one of the cons of ai that we need to watch for, because it's fed by you and me and a lot of human beings and we all carry our own biases and we feed it to ai. That's why we have to be careful sometimes. That's why the data used to train ai needs to be as diverse and and biased as possible, and for someone like me that basically is a dei, diversity, equity and inclusion professional, this is something that I would love to see. It's hard again, like we all human beings, we are biased is what we do with our bias, but I keep talking about that so we can create self-awareness, because AI is the future and we want to make sure that is as unbiased as possible. We will never eliminate bias ever but we can mitigate it or minimize it.

Speaker 1: 4:57

There is also the ethical side of ai to consider. Transparency is crucial, especially in eye applications that impact our lives directly, like in healthcare, like in banking. It's important for us to understand how ai systems make decisions to ensure they are fair and equitable. To ensure they are fair and equitable. A sip of coffee again, so I can keep continuing. Okay, as we wrap up today's session and podcast, remember, the world of AI is like a complex brew of the finest coffee it's rich, it's intriguing and a bit mysterious. By learning how AI works, we can better understand its benefits and its challenges.

Speaker 1: 6:04

Ai is here. It doesn't help that we bury our head in the sand like an ostrich, and I keep saying this. We need to be aware that garbage in, garbage out. Every day I see people claiming that they are experts in AI and when I look at their process I kind of roll my eyes internally. They think that entering a couple of words into the prompt, they will get the right answers. They do get answers, because AI will always response, has responses or response is it the right response? Is it what will help them grow? And 90 of the time it's no garbage in, garbage out. Ai will respond to the prompt you're answering. That is why prompt engineering today is becoming a science. You need to understand what prompt engineering is.

Speaker 1: 7:08

Prompt engineering is building the right request, remembering that ai has no background, has no context, has no feelings, has no emotions, cannot predict the future and cannot understand the past. It can only put patterns together to predict what you wanted to answer. So always remember that when you enter a prompt, make sure to build a story that tells the background, the context, the perspective, quid pro quo. You want this, so you can get that. It's very important that you train your AI on how you want it to respond, especially now that chat GPT, one of the AI platforms, has a memory of who you are and how you want it to be done. So always remember that. Like I always say as a firm mom, there are no bad dogs, there are only bad owners. So you need to train your AI so it can help you and make your life easier.

Speaker 1: 8:19

There is prompt engineering and there is prompt chaining. Prompt chaining is when you take the answers that AI gives you and you start getting the layers out, peeling the layers out, peeling the layers out like an onion, one by one. You keep asking it to give you an example, to clarify, to simplify. You can ask it for the tone that you want.

Speaker 1: 8:48

I will have one of the episodes just on prompt engineering and prompt chaining, so you can understand how to use it the proper way. I'm just trying to unfold the basics of the science of AI in a very simple way for people that are like me, that are not techie. I am your AI whisperer, after all. So keep those questions brewing and let your curiosity lead you to explore more about AI and, of course, keep tuning into AI conversations for your fix of tech insights, served with a dash of coffee charm. Let me know in the comments what would you like me to talk about, and I will make sure to make an episode or a podcast about it. I'm signing out. This is Sahar, your AI Whisperer. Until we meet the next time.