Beyond ChatGPT: A Guided Tour of the Expansive Neural Networks Shaping Our Future!

?? Did you know there's way more to AI than ChatGPT and cute pictures and videos? Today I'm going to give you a street deep dive into neural networks. Sure, ChatGPT is the MVP right now, but let's shine a spotlight on the unsung heroes that power our AI-driven lives. Neural networks are like different parts of a versatile toolkit, each tool refined for a specific job. Ready for the grand tour? ?????

At their core, neural networks are a type of AI that consists of layers of interconnected nodes, or "neurons," that process and learn from data. These artificial neurons are loosely modeled after the way our brain cells work, allowing neural networks to recognize patterns and make decisions based on the input they receive.

Simplified: Imagine you're baking a cake with a bunch of friends. You all pass around the ingredient (data in AI) & each of you adds a little something or mixes it up in your special way. You, who first add sugar can be one layer, your friend who mixes it, is another layer. Each of you performing a task represents a 'neuron' in each layer. This is the working chain of a neural network - handling data one layer (your friend) at a time by adding, processing, or changing it.

Now, say you're a team that loves making chocolate cakes. You've made them so much that when given cocoa, sugar, and flour, you instantly know it's for a chocolate cake. That's how a neural network 'learns' to identify patterns (in this case, cake ingredients), when it's processed the same (or similar) information many times! Different teams (types of networks) prefer making different things - carrot cakes, pies, etc., and are 'structured' or 'trained' to excel in those.

The various types of neural networks handle data uniquely, utilizing their structural advantages to excel in diverse tasks.

?? My day-to-day? I use various AI neural networks to create tools to peer into the lush canopies of cannabis grow houses, where understanding plant health is key. I'm also creating AI dope security tools, giving small businesses eyes that miss nothing... None of this is using the AI technology powering ChatGPT. And for my community, I'm giving voice to AI Best Friends that lend an ear to kids suffering from depression, suicide, bullying, and even homework. This is a huge AI world, and I want to show you the different pieces of it and how I Combine tech, creativity, and empathy - to create dope, viable (and profitable products). ????

So, what's in this neural network lineup? Each type packs a punch for the tasks they're crafted for.

??? FNNs (Feedforward Neural Networks): These are the basic, straight-arrow networks where information zips from the input layer to the hidden layers and out through the output layer, with no loops or detours — it's one-way traffic. Hidden layers are the intermediate layers between the input and output layers in an FNN. They are called "hidden" because their inputs and outputs are not directly observable from the outside. These layers perform complex computations on the input data, allowing the network to learn and recognize patterns. A familiar example could be a basic search engine that retrieves results based on keyword matching, without any deeper understanding.

?? CNNs (Convolutional Neural Networks): These are the maestros of image analysis with their specialized layers that break down an image into smaller, overlapping tiles and analyze each tile individually — essentially fine-tuning their focus on specific areas of the image to capture details. This process allows CNNs to identify unique features in an image, such as edges, shapes, and textures, which helps them recognize and classify objects with incredible accuracy. A classic example is facial recognition in social media apps. But for me, it's about catching subtle signs of sickness in cannabis plants. It's groundbreaking for ensuring top-notch crop health.

?? RNNs (Recurrent Neural Networks): These networks have a unique feature — a memory of sorts that accounts for previous information in the sequence, making them ideal for time-series data or language. Voice-to-text functionality is a perfect example of RNNs at work, like those used in virtual assistants on smartphones like Siri.

? LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units): These are advanced RNNs, with special gates that regulate the flow of information, helping the network to remember for longer or forget the unnecessary stuff. Predictive text input while chatting or composing emails is a place where LSTMs can shine. If you add emotionally intelligent dolls that converse with children into the mix, (which is what I am currently doing) this tech truly goes from functional to meaningful.

?? Autoencoders: These are self-educated networks that learn to compress data into a shorter, denser representation and then reconstruct it back with minimum loss. They underpin technologies like anomaly detection in machinery, where a slight deviation from the norm can signal trouble. Think of it as the AI quietly keeping things running smoothly behind the scenes. This is also behind some of the technology I am using in grow houses to detect minute differences in the way the plants are developing.

??? GANs (Generative Adversarial Networks): GANs consist of two neural networks — a generator and a discriminator — that work together in a competitive way. The generator tries to create new content, like images or text, while the discriminator tries to distinguish between the generated content and real-world examples. Through this adversarial training process, the generator learns to create increasingly realistic content that can fool the discriminator. Social media apps that turn your selfie into a classical painting? That's all GAN wizardry. Midjourney, Dall-E, Stable Diffusion... that's all GAN too.

?? Transformers: Unlike other neural networks that process data sequentially, Transformers use "attention mechanisms" to consider the entire input sequence at once. This allows them to understand the context and relationships between different parts of the input, making them particularly well-suited for natural language processing tasks. Attention mechanisms work by allowing the network to focus on the most relevant parts of the input for each output, enabling it to, well, transform the field of natural language processing. They've revolutionized machine translation services, making global communication seamless. And yes, they're the same tech that power ChatGPT, Gemini, and Perplexity plus countless others.

??? RBFNs (Radial Basis Function Networks): These networks home in on specific points and adjust their radius of 'attention' to provide precise predictions. For example, imagine you're trying to predict the price of a house based on its size and location. An RBFN would consider the prices of nearby houses within a certain radius, giving more weight to the closer ones. By adjusting this radius, the network can fine-tune its predictions based on the most relevant data points. If you've ever used a stock prediction tool, odds are there was an RBFN sifting through the noise to forecast the ebb and flow of the market.

?? Siamese Neural Networks: These pairs of twin networks compare inputs, focusing on relationship and difference, especially useful in verification tasks. Think of the way some dating apps verify user photos to ensure profiles are genuine. In security contexts, they're crucial for matching individuals to their ID photos, ensuring the person is who they claim to be.

All these neural networks are the unsung heroes behind the scenes - they're not just AI chatboxes. They're our personal assistants, our security guards, and sometimes even therapists in child bestie form. We're building a world that doesn't just chat back, but one that actively watches over your safety, nurtures your plants, and lends an ear when the world gets too loud. Let's push past the chat and delve into the real AI muscle-powering change. #AIExploration #BeyondChatGPT

#InnovationInTech ??????? This is just a snapshot of the constellation that is AI's neural networks. As I continue to develop and harness these tools, the potential for growth, understanding, and connection within our world expands. The journey of AI is perpetual, and we're just getting started.

To wrap, the world of AI and neural networks is vast and forever changing. From improving crop health to providing emotional support, the applications of these technologies are limitless. As we continue to explore and innovate in this space, it's essential to remember that AI is not just about chatbots or digital assistants — it's a powerful tool that can be harnessed for the betterment of society.

By understanding the unique capabilities of different neural networks and combining them in creative ways, we can unlock new possibilities and tackle some of the world's most pressing challenges. So let's keep learning, experimenting, and pushing the boundaries of what's possible with AI. The future is ours to shape, and it all starts with a curious mind and a willingness to explore the unknown.

I am Micah Berkley, an AI Solutions Architect and Implementation expert who distills AI in ways that can be easily understood. I work with organizations globally, developing solutions and identifying areas where AI could be integrated into their processes, workflows, and campaigns. My company is AI Success Partners, and I am one of the amazing founders at a Miami organization called Biscayne AI. I look forward to being an asset in any capacity to your team in the future.