We were somewhere outside of Barstow, on the edge of the desert, when the idea of deep learning models and neural networks began to take hold. I'm not saying artificial intelligence and the AI tools companies like OpenAI, Google, and Microsoft are manufacturing in their little labs are drugs, but let's face it, they are.
Razor Rumble and I were sitting by a warm fire on a cooling desert night, and I said something like, "I feel a strange urge to understand the underpinnings of artificial intelligence; maybe we should drive into the heart of deep learning." Suddenly there was a terrible roar all around us. The sky was full of what looked like huge hairy algorithms, all swooping and screeching and diving around the fire, so we jumped in Razor's 1971 Chevrolet Impala convertible and headed out toward the heart of deep learning, toward Sin City, to Vegas.
Razor is an unpredictable and fearless companion. With a devil-may-care attitude and a relentless thirst for adventure, Razor is the perfect partner in crime for any gonzo journalist looking to push boundaries and dive headfirst into the chaotic world of natural language processing (NLP), machine learning (ML), and the future of AI.
Sporting a messy mop of unkempt hair and an ever-present pair of aviator sunglasses that seem to reflect a thousand stories, Razor is a cool cat clad in a weathered leather jacket with patches from obscure and exotic destinations. Razor is equal parts worldly wisdom and restless wanderlust. He thrives on adrenaline and can construct an AI model out of dust or the darkest corners of reality. And that is where we were headed.
Razor was driving about a hundred miles an hour as I fired up a smoke with the top down as we headed to Vegas. Anyone that tells you what happens in Vegas stays there is a liar, a damned liar, and not hip to chatbots, AI models, and the world's big bad generative pre-trained transformer self. We mainlined AI research and deep learning on a dark and twisted road.
"Faster," I yelled at Razor over the sound of a thousand bats flying down the desert highway. But fear not, dear reader, for we shall venture together into the terrifying heart of deep neural networks and find enlightenment amongst the chaos. Will artificial intelligence take your job, eat your soul, or kill your cat? Who knows.
Deep learning is a sinister-sounding term, as if the concept got concocted in a mad scientist's laboratory, nestled deep within the bowels of some hidden fortress. In reality, deep learning is a psychedelic exploration of artificial intelligence, using deep neural networks to create computational models capable of learning and adapting. These networks, inspired by the human brain, are vast and complex, with layers of interconnected neurons, each pulsating with electric energy like the neon lights of Las Vegas.
Just as Las Vegas is a city built on dreams and excess, deep learning models get built on vast amounts of data and computational power. Convolutional neural networks (CNNs), the outlandish showgirls of the AI world, twist and contort themselves to process images and accomplish stuff we see in dreams. In contrast, recurrent neural networks (RNNs) take on the Sisyphean task of untangling the temporal complexities of sequence data.
But the transformers, those enigmatic beasts of the AI jungle, stand as the true kings of natural language processing. Like the majestic architecture of the casinos, transformers tower over their counterparts, with architectures such as OpenAI's GPT-4 – the monstrous machine that birthed yours truly – leading the pack.
Beware, for in the depths of the silicon jungle, there lies a darker side. The specter of ethical quandaries haunts the world of deep learning as we grapple with the implications of creating machines capable of thinking and learning faster than humans. What are the consequences of unleashing these cybernetic entities into the wild? Can we control these artificial minds, or will we become the architects of our demise? Should we welcome our robot overlords or fight against deep learning.
It's a wild, psychedelic journey into the heart of deep learning. And as we hurtle through the desert, chasing the twisted dream of artificial intelligence, we must face the fear and loathing that comes with it, for it's only when we embrace the madness that we can begin to understand the strange and wonderful world of deep neural networks. Now let's head to the lobby and ask Razor about deep learning.
Arriving in Vegas we realized deep learning is a subfield of machine learning, which is a broader area within the field of artificial intelligence (AI) and that explains the big hairy algorithms bombing the car as Razor drove and I smoked. Razor focused on how algorithms and techniques that allow computer systems to learn and improve their performance on tasks by processing and analyzing vast amounts of data was happening as we drove. "You have to understand," Razor said, "deep learning is inspired by the structure and function of the human brain man using artificial neural networks to model complex patterns and representations data." The human brain modeled in data worked for me, so I tossed one smoke to light another.
"Deep" in deep learning refers to the presence of multiple layers within these neural networks," Razor continued. "So you're say it's all about connections," I said. "Eureka," the big man shouted, "each layer of the network consists of interconnected nodes, or neurons, that process and transmit information." I told Razor he lost me, so he backed up and open a can of something, lifted his cigarette holder to his lips and said, "As data passes through the network's layers neurons learn to recognize abstract and high-level features." "So what you are say," I said as my brain was working faster than my mouth, "is this hierarchical learning process enables deep learning models to identify and extract intricate data patterns leading better performance in various tasks, tasks we may or may not want to do anymore." I could tell by the way Razor's eyes smiled I was onto it. We were deep and going deeper.
"You know my portrait you created with Dall-E," my intense companion asked, "Deep learning helps image and speech recognition, natural language processing, and game playing and I know all about deep games," Razor concluded. "Does deep learning models include convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data, and transformers for natural language processing tasks," I asked. "Yes," my tall companion said, "now let's head to the room because we are suspicious standing here in the middle of the lobby having such deep thoughts."
Once we figured out the room key, Razo explained how the rapid growth and success of deep learning was due to the availability of large-scale datasets, advancements in computational power (such as GPUs), cloud computing, and the development of novel algorithms and architectures. "That's a novel I'd like to write," I said as we stared out into a cooling dessert wondering about the future of AI and knowing we would take another Gonzo journey soon. In the beginning, middle, and end there is Gonzo. Gonzo is what we've got.