Generative AI

Generative AI, or Gen AI, stands as a fascinating realm within artificial intelligence, focusing on the creation of new data, spanning images, text, and music.
These models undergo training on extensive datasets, learning the intricacies of existing information to then craft entirely novel data that echoes the patterns they absorbed during training.

In essence, Gen AI is an innovative facet of artificial intelligence with the capability to conjure diverse outputs, from images and text to musical compositions. Leveraging its understanding of patterns, Gen AI unfolds as a tool mirroring human creativity, holding immense value across industries like gaming, entertainment, and product design.

Neural Networks

Neural networks, known as artificial neural networks (ANNs), are a method that teaches computers how to process data. They are a subset of machine learning and act as a series of machine learning algorithms that seek relations in data sets.

Neural networks essentially mimic the way the brain works. They resemble the structures of interconnected neurons, which are nerve cells that send messages throughout the body. This extreme interconnectedness and rapid communication is what makes them so effective in processing information and learning to solve problems.

Artificial neural networks function as building blocks in the same way neurons do for the brain and nervous system. They transmit and process information in interconnected units called artificial neurons. Every neuron processes data using a simple mathematical operation, similar to how biological neurons receive and send electrical signals.

How Neural Networks Work

The number of inner or hidden layers in a neural network varies depending on the complexity of a problem it needs to solve. Solving a simple addition problem would require only a few layers, while a series of complex math problems would require more than one hidden layer. Neural networks use a feedforward process in which data passes from the input layer, like the top layer of a sandwich, to the output layer, or the other side of a sandwich, to make predictions or classify data.4

Every neuron takes the sum of its inputs and then applies an activation layer to produce an output that gets processed to the next layer. Weighted connections represent the strength of the links between neurons. When training an algorithm to optimize network performance, you adjust those weights and reduce the differences between its predictions and the target values.

Non-linearity refers to non-linear activation functions introduced to the individual nodes of a linear network.5 Activation functions determine the output of a neuron based on the weighted sum of its inputs. They allow the modelling of complex relationships within data. Examples of activation functions include:

Sigmoid function, which maps inputs to a range between zero and one in traditional neural networks.
Rectified linear units (ReLU), which are used in deep learning to return the input for positive values or zero for negative values.
Hyperbolic tangent (tanh) functions, which map inputs to a range between negative one and one in a neural network

Generative Adversarial Networks (GAN’s)

Enter GANs, the mischievous duo of neural networks engaged in a duel. The generator, akin to a skilled illusionist, crafts realistic data. Its opponent, the discriminator, plays detective, discerning between reality and illusion. Together, they dance until the generator weaves illusions indistinguishable from reality.

Transformer Models

Behold the transformer models, linguists of the digital realm. Masters of language nuances, they grasp the relationships between words, spinning grammatically sound and semantically rich text. Beyond words, they compose symphonies of music and elegant lines of code. Imagine transformer models as the brainy architects of language understanding in computers. They’re like supercharged tools that ace various language tasks—translating languages, summarizing text, and answering questions.

These models made a grand entrance in 2017 through a paper titled “Attention is All You Need” by Vaswani and team. Since then, they’ve been the go-to wizards for many language-related jobs and have even proven their skills in areas like computer vision and speech recognition. Transformers are the multitaskers of autoregressive models. They not only generate text but also translate languages and summarize text. They’re the all-in-one tool for language-related tasks, predicting what comes next based on the entire context. In a nutshell, autoregressive models are the architects of sequential data, bringing order and predictability to the creative process.

Diffusion Models

A particular training set can be used to produce new data instances using generative models such as diffusion models. Diffusion models operate by progressively adding noise to the training data, which may later be recovered by learning to reverse the process. Once the model is trained, it can be used to generate new data by simply passing randomly sampled noise through the learned denoising process.

Diffusion models have several advantages over other generative models, such as GANs. Diffusion models are easier to train and less prone to instability. Additionally, diffusion models can learn a latent space representation of the data that is disentangled, meaning that different factors of variation in the data are represented by different dimensions of the latent space. This makes diffusion models well-suited for tasks such as data visualization and data manipulation.

Recurrent Neural Network Language Model (RNN-LM)

Think of RNN-LM as a language wizard. It reads tons of text and learns to predict the next word. It’s like having a friend who can finish your sentences because they know you so well.

GPU Mining

Graphics processing units (GPUs), a type of technology that originated in the gaming world, have quickly evolved to help power the artificial intelligence (AI) revolution. These electronic circuits were developed to help create the highest quality visuals in modern gaming, but they also have many applications in AI.¹

Parallel processing is one of the superpowers of GPUs that helps them excel at performing several complex tasks simultaneously. This is essential for rendering high-quality graphics and also for boosting AI workloads.

Microchips & Semiconductors

AI in semiconductor manufacturing is transforming one of the world’s most intricate and aggressive sectors, constantly evolving in terms of innovation, quality, input costs and revenue generation. The sector faces numerous challenges from design issues and demand shifts to geopolitical tensions and supply-demand imbalances.

Using a variety of inputs, Generative and Agentic AI helps users create new and innovative content fast. These models are designed to be versatile and adapt to different types of data, enabling them to generate a wide range of outputs.

Nvidia’s Blackwell AI ‘superchip

Nvidia has unveiled a “superchip” for training artificial intelligence models, the most powerful it has ever produced. The US computing firm, which has recently rocketed in value to become the world’s third-largest company, has yet to reveal the cost of its new chips, but observers expect a high price tag that will make them accessible to only a few organisations.

“Blackwell is just going to be an amazing system for generative AI,” said Jensen Huang. “And in the future, data centres are going to be thought of as AI factories.”

Nvidia claims its Blackwell chips can deliver 30 times performance improvement when running generative AI services based on large language models such as OpenAI’s GPT-4 compared with Hopper GPUs, all while using 25 times less energy.

Advanced Micro Devices (AMD)

While AMD is one of the competitors behind Nvidia, the GPU market is growing so quickly that there are still plenty of opportunities. The company said it expects the market for AI accelerators in data centres could top $150 billion by 2027.

AMD’s data centre segment has been a bright spot due to rising demand for the company’s MI300 AI GPU, which has been fastest-ramping product in AMD history. Although it’s a distant second place to Nvidia, AMD has the ability to capitalize on GPU demand to accelerate AI workloads.

Medical Magic

GAN’s help create pretend medical images, like MRIs and CT scans. This means less waiting for real medical images and better training for computers to spot diseases.

Encoders & Decoder’s

Training these encoder and decoder buddies happens together, like a dynamic duo, using various loss functions. Picture it as a game where they try to reduce two types of errors: the difference between the original and reborn image (reconstruction error) and the divergence between the secret code’s distribution and a standard normal distribution (Kullback-Leibler divergence). For example, the following loss function can be used to train a VAE to generate images: Loss = Reconstruction error + Kullback-Leibler divergence. The reconstruction error is the difference between the input image and the output image. Once this VAE duo graduates from training school, the decoder becomes a magician. It can whip up new images by playing with the secret code, using tricks like Gaussian or uniform sampling.

Vector Quantized Variational Autoencoder (VQ-VAE)

Recurrent neural networks (RNNs) can predict fundamental frequency (F0) for statistical parametric speech synthesis systems, given linguistic features as input. However, these models assume conditional independence between consecutive F0 values, given the RNN state. In a previous study, we proposed autoregressive (AR) neural F0 models to capture the causal dependency of successive F0 values. In subjective evaluations, a deep AR model (DAR) outperformed an RNN. Here, we propose a Vector Quantized Variational Autoencoder (VQ-VAE) neural F0 model that is both more efficient and more interpretable than the DAR.

This model has two stages: one uses the VQ-VAE framework to learn a latent code for the F0 contour of each linguistic unit, and other learns to map from linguistic features to latent codes. In contrast to the DAR and RNN, which process the input linguistic features frame-by-frame, the new model converts one linguistic feature vector into one latent code for each linguistic unit. The new model achieves better objective scores than the DAR, has a smaller memory footprint and is computationally faster. Visualization of the latent codes for phones and moras reveals that each latent code represents an F0 shape for a linguistic unit.

Stochastic Differential Equation

Stochastic Differential Equation (SDE)-Based Diffusion Models: Imagine a recipe where the ingredients can change at random: SDEs capture how things evolve with a dash of unpredictable spice! (Accurate, plagiarism-free, and human-like). In other words, SDE-based models take complexity up a notch, solving equations to craft images. Training might be a challenge, but the reward is stunning—high-quality, realistic images that leave an impression.

Deep Generative Models

Deep generative models are like creative apprentices in the world of machines. Their job? To whip up entirely new data based on what they’ve learned from massive datasets. It’s like a chef learning to create new dishes by understanding the flavours of countless recipes.Now, imagine one of the star performers in this AI orchestra: the Generative Adversarial Network (GAN). GANs are like actors in a play, where one creates a scene (the generator), and the other critiques it (the discriminator). It’s a friendly competition that results in the generation of new, lifelike data—be it images, text, music, or even video. But that’s not all! Another key player is the Variational Autoencoder (VAE). This one is like a curious explorer in the data landscape. VAEs learn the hidden patterns in the training data and use that knowledge to conjure up new data. It’s like a tour guide through the data wilderness.

Prompt Engineering

Prompt engineering is designing and crafting prompts to guide a large language model (LLM) to generate desired outputs. It is a critical component of using LLMs effectively, as the quality and specificity of the prompt can significantly impact the quality of the output. LLMs are trained on massive datasets of text and code, and they can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, LLMs are not perfect, and they can sometimes generate outputs that are inaccurate, irrelevant, or nonsensical. Prompt engineering can help mitigate these problems by providing the LLM with additional information and context about the desired output. For example, a prompt engineer might provide the LLM with a specific topic, style, or format for the output. They might also provide the LLM with examples of desired outputs. Prompt engineering is a complex and challenging task, but it is essential for using LLMs effectively. By carefully crafting prompts, prompt engineers can help LLMs generate accurate, informative, and creative outputs.

What is an AI Chatbot?

Artificial intelligence chatbots are chatbots trained to have human-like responses and conversations. This is done using a process known as natural language processing (NLP). With NLP, the AI chatbot is able to interpret human language model as it is written, which enables them to operate more or less on their own. In other words, AI chatbot software can understand language outside of pre-programmed commands and provide a real-time, AI generated response based on existing data. This allows site visitors to lead the conversation, voicing their intent in their own words. What’s more, AI chatbots are constantly learning from their conversations, so, over time, they can adapt their responses to different patterns and new situations. This means they can be applied to a wide range of uses, such as customer service analysing a customer’s feelings or making predictions about what a site visitor is looking for on your website.

How Generative AI Chatbots are Developed

First, developers start with a set of labelled data. Then, they select a machine learning model to analyse the data and make predictions or identify patterns. Next, human software developers train the model by updating the data, adjusting the model parameters, or reinforcing the algorithm until it consistently produces the desired outputs. During this process, the algorithm within the model continuously updates itself.. In some cases, the training may use other methods that do not rely on direct human intervention, such as pattern recognition or programmed incentives (Brown, 2021). After training, the developers validate the model by inputting new data and testing if it can perform reliably. Finally, the developers may create different software applications that apply the AI model in a more usable way. The development of AI chatbots, which are powered by sophisticated LLMs, usually requires substantial investment from large organizations or companies. Often, these companies release AI chatbots for free to engage users in a beta test where user data is gathered to further enhance the model.