Understanding Artificial Intelligence: An Basic Introduction

Artificial Intelligence (AI) represents a revolutionary branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. AI is not a singular technology but a collection of technologies and methods, each with its unique capabilities and applications.

Key Areas of AI Application (but not limited to)

  1. Image Detection: One of the most profound uses of AI is in image detection and analysis. AI systems can identify and classify objects within images with remarkable accuracy, thanks to technologies like neural networks and machine learning. This capability is extensively used in various fields ranging from medical diagnostics, where AI helps in identifying diseases through image analysis, to security systems, where AI aids in surveillance and threat detection.
  2. Content Generation: AI’s role in content generation has been transformative. By understanding and processing language, AI can create written content, compose music, or generate realistic images and videos. This aspect of AI, often driven by machine learning models like GANs (Generative Adversarial Networks), is widely used in entertainment, marketing, and educational content creation. These AI models learn from a vast amount of data to generate new, original content that can be both creative and contextually relevant.
  3. Chatbots: AI-powered chatbots are another significant application. These are software applications that simulate human conversation, either through text or voice interactions. By leveraging natural language processing (NLP) and machine learning, chatbots can understand and respond to user queries, automate customer service, provide personalized recommendations, and even handle complex tasks like booking appointments or processing orders. Their ability to learn from interactions enables them to improve over time, making them increasingly efficient in various service industries.

The Role of NVIDIA in AI

NVIDIA, a leader in AI technology, provides both hardware and software solutions that are at the forefront of AI research and application. NVIDIA’s GPUs (Graphics Processing Units) are particularly renowned for their high performance in AI computations, essential for training complex machine learning models. The company also offers a comprehensive suite of AI software tools that assist developers in creating, training, and deploying AI models efficiently.

NVIDIA: Building a Hardware and GPU Empire for AI

NVIDIA Corporation, founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, began as a graphics chip company, revolutionizing the gaming industry with their GPU (Graphics Processing Unit) innovations. However, over the years, NVIDIA has evolved beyond gaming, establishing itself as a dominant force in the realm of artificial intelligence (AI).

The Shift to AI

The pivotal shift for NVIDIA came with the realization that GPUs, originally designed for rendering graphics, were exceptionally well-suited for AI computations. The parallel processing capabilities of GPUs made them ideal for handling the complex and data-intensive tasks required in training AI models, which involve processing large datasets and performing millions of mathematical operations.

Advancements in GPU Technology

NVIDIA’s GPUs are known for their high performance and efficiency, attributes critical in the AI field. The company has continually innovated, producing a range of GPUs that cater to different needs – from personal AI projects to large-scale enterprise applications. Their CUDA (Compute Unified Device Architecture) technology, a parallel computing platform and API model, became a game-changer, allowing developers to use C++ programming language to write software that could perform computations on the GPU.

AI-Specific Hardware Development

Recognizing the specific demands of AI, NVIDIA introduced GPUs specifically designed for deep learning and AI applications. The Tesla series, for instance, was a significant step in providing specialized hardware for AI computations. Following this, the introduction of the NVIDIA DGX systems provided an integrated hardware and software system specifically for deep learning.

Software Ecosystem and Developer Support

NVIDIA’s strength lies not just in its hardware but also in its comprehensive software ecosystem. The company offers a suite of tools and software, like the NVIDIA Deep Learning AI, which enables developers to easily create and deploy AI models. This ecosystem is supported by a robust community of developers, researchers, and enterprises, making NVIDIA’s platforms a go-to choice for AI development.

Impact on AI Research and Applications

NVIDIA’s hardware and software innovations have had a profound impact on AI research and applications. Their GPUs are used in a variety of AI applications, from autonomous vehicles to healthcare diagnostics. By providing the computational power necessary for large-scale AI model training and inference, NVIDIA has played a pivotal role in advancing the field of AI.

In summary, NVIDIA has successfully transitioned from a graphics-centric company to a leader in AI technology. Through its continuous innovation in GPU technology and a strong software ecosystem, NVIDIA has built an empire that is central to the training and running of AI models, driving forward the AI revolution.

NVIDIA Resources and Software

Understanding the AI landscape requires understanding the business of NVIDIA.

NVIDIA GPU’s are powering the AI revolution. The GPU’s are used to run ChatGPT, as well as the majority of all Graphics processing in home and work PCs for running Games, Architecture Visualisation applications, basically anything which renders images to screens.

The move to AI has not been a sudden move for NVIDIA as the GPU architectures have always run complex algorithms at high speeds. It is only since modern break throws with Large Language Models and reduction in cost of development which has exploded the AI industry building towards an AI capable of general intelligence.

Below we highlight resources by NVIDIA for your AI journey.

NVIDIA Omniverse: An Overview

NVIDIA Omniverse is a platform designed to facilitate collaboration and simulation in 3D workflows, particularly targeting professionals in fields like design, animation, and engineering. It represents a significant leap in creating and operating shared virtual spaces for real-time collaboration and photorealistic simulation.

Core Features of NVIDIA Omniverse

  1. Collaborative Environment: One of the key features of Omniverse is its ability to enable collaboration among various users in real-time, regardless of their physical location. This is particularly beneficial for teams working on complex 3D projects where multiple contributors need to work simultaneously and interactively.
  2. Universal Scene Description (USD): At the heart of Omniverse is the use of Universal Scene Description (USD), a framework developed by Pixar. USD serves as a common language for defining, packaging, and sharing 3D scenes, allowing interoperability among different software applications. This means that professionals can use their preferred 3D tools (like Autodesk Maya, Adobe Photoshop, or Unreal Engine) and seamlessly integrate their work into Omniverse.
  3. Real-Time Photorealistic Rendering: Leveraging NVIDIA’s advanced GPU technology, Omniverse provides real-time photorealistic rendering capabilities. This feature is crucial for fields like architectural visualization, where seeing accurate lighting, materials, and physics can make a significant difference in the design process.
  4. Simulation and AI Integration: Omniverse is not just about visualization; it also integrates simulation and AI technologies. This allows for the creation of environments where physical laws are accurately simulated, which is essential for testing designs in virtual spaces. Moreover, the integration of AI can lead to more intelligent and efficient design processes, like automated layout generation or performance optimization.
  5. Extensibility and Customization: NVIDIA designed Omniverse to be highly extensible, allowing developers to build custom tools and applications on top of the platform. This flexibility ensures that Omniverse can cater to a wide range of industry-specific needs and workflows.

Applications of NVIDIA Omniverse

The potential applications of NVIDIA Omniverse are vast and varied. It’s being used in fields such as:

  • Architecture, Engineering, and Construction (AEC): For collaborative design, visualization, and simulation of buildings and infrastructure.
  • Media and Entertainment: For real-time collaboration in animation and visual effects production.
  • Manufacturing: For visualizing and simulating product designs and factory layouts.
  • Automotive: For designing, simulating, and visualizing vehicles in realistic environments.


NVIDIA Omniverse represents a significant advancement in the way professionals can collaborate, create, and simulate in 3D spaces. Its use of standard formats like USD, combined with NVIDIA’s GPU technology, makes it a powerful platform for a variety of industries seeking to harness the power of advanced visualization and simulation.

NVIDIA NGC: An Overview

NVIDIA NGC (NVIDIA GPU Cloud) is a comprehensive cloud-based platform that provides a wide array of GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC). It’s designed to give developers and data scientists access to a rich repository of software and tools that can accelerate AI, ML, and HPC workflows.

Key Components and Resources of NVIDIA NGC

  1. Container Registry: One of the central features of NGC is its container registry, which offers a range of GPU-optimized containers for AI, ML, and HPC applications. These containers come pre-configured with all the necessary dependencies, frameworks, and libraries, which simplifies deployment and reduces compatibility issues. Key frameworks available include TensorFlow, PyTorch, MXNet, and more.
  2. Pre-trained Models and Model Scripts: NGC provides access to a vast collection of pre-trained models and model training scripts, particularly useful in deep learning applications. These models cover a wide range of applications, from image and speech recognition to natural language processing. By using these pre-trained models, developers can save significant time and resources in the model development process.
  3. Helm Charts for Kubernetes: NGC offers Helm charts for Kubernetes, making it easier to deploy complex software stacks on Kubernetes clusters. These charts are optimized for NVIDIA GPUs, ensuring efficient utilization of hardware resources in distributed computing environments.
  4. SDKs and APIs: NVIDIA provides various SDKs (Software Development Kits) and APIs (Application Programming Interfaces) to support different aspects of AI, ML, and HPC development. These tools assist in optimizing applications for NVIDIA hardware, integrating AI capabilities, and enhancing computational efficiency.
  5. Domain-specific Workflow Support: NGC supports domain-specific workflows in areas like healthcare, robotics, automotive, and financial services. This includes specialized models and software tools tailored to the unique requirements of these industries.
  6. Integration with Major Cloud Providers: NGC is integrated with major cloud providers like AWS, Azure, and Google Cloud Platform. This integration allows users to deploy NGC resources on their preferred cloud infrastructure, leveraging the scalability and flexibility of cloud computing.
  7. Community and Support: NGC also hosts a community forum where users can share insights, ask questions, and collaborate. NVIDIA provides support and documentation to help users navigate and utilize the platform effectively.

Benefits of NVIDIA NGC

  • Accelerated Workflow: By providing access to GPU-optimized software and tools, NGC significantly accelerates AI, ML, and HPC workflows.
  • Ease of Use: The use of containers and pre-configured setups reduces the complexity of deploying and managing AI and computing applications.
  • Versatility and Scalability: NGC’s compatibility with various cloud platforms and its wide range of tools make it a versatile choice for different scales of operations, from individual developers to large enterprises.


NVIDIA NGC stands as a robust and comprehensive platform, crucial for anyone involved in AI, machine learning, or high-performance computing. Its rich set of resources, ease of deployment, and GPU optimization make it a go-to solution for accelerating and simplifying complex computational tasks.

NVIDIA NGC Catalog: A Comprehensive Resource Hub

The NVIDIA NGC Catalog is a centralized, cloud-based repository that provides an extensive range of GPU-accelerated software for various applications in artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC). It is a part of the NVIDIA NGC (NVIDIA GPU Cloud) platform and serves as a key resource for developers, data scientists, and researchers.

Key Components of the NGC Catalog

  1. GPU-Optimized Containers: The NGC Catalog offers a wide array of containers that are optimized for NVIDIA GPUs. These containers include necessary libraries, frameworks, and dependencies, ensuring compatibility and performance. They cover popular AI and ML frameworks like TensorFlow, PyTorch, and MXNet, as well as applications for data science, deep learning, and HPC.
  2. Pre-Trained Models: NGC Catalog provides access to a variety of pre-trained models, particularly useful in deep learning applications. These models, trained on extensive datasets, can be used for tasks like image and speech recognition, natural language processing, and more. They enable developers to jumpstart their AI projects by reducing the time and resources needed for training models from scratch.
  3. Model Scripts and Training Tools: Alongside pre-trained models, the catalog includes model scripts and training tools that help in customizing and optimizing models for specific applications. These resources are valuable for fine-tuning models to achieve higher accuracy and efficiency.
  4. Helm Charts for Kubernetes Deployments: For applications that require orchestration and management at scale, NGC Catalog provides Helm charts optimized for Kubernetes. These charts facilitate the deployment of complex software stacks on Kubernetes clusters, leveraging NVIDIA GPUs for computational tasks.
  5. Software Development Kits (SDKs): The catalog includes various NVIDIA SDKs, such as the NVIDIA Deep Learning SDK, NVIDIA HPC SDK, and others. These SDKs offer libraries and tools that support the development and optimization of applications on NVIDIA’s hardware platforms.
  6. Domain-Specific Solutions: Recognizing the diverse needs of different industries, NGC Catalog provides domain-specific solutions for areas like healthcare, autonomous vehicles, robotics, and financial analytics. These tailored solutions help in addressing industry-specific challenges and leveraging AI and ML effectively.

Advantages of the NGC Catalog

  • Streamlined Deployment: The use of containers and pre-configured environments in the NGC Catalog simplifies the deployment process, making it easier for users to get their applications up and running quickly.
  • Enhanced Performance: With GPU-optimized software, users can leverage the full power of NVIDIA GPUs, leading to significant improvements in computational speed and efficiency.
  • Resource Efficiency: The availability of pre-trained models and training tools reduces the need for extensive computational resources, saving time and energy in the model development phase.
  • Scalability and Flexibility: The integration with cloud platforms and Kubernetes ensures that solutions from the NGC Catalog can scale according to the needs of the project, providing flexibility across different computing environments.

Pre-trained AI Models can be found at the following links.

Generative AI, Language, Vision, Speech, Biology, Recommenders.



NVIDIA Inception Program: An Overview

The NVIDIA Inception Program is a comprehensive initiative designed to nurture and accelerate the growth of startups working in artificial intelligence (AI) and data sciences. This program provides unique tools, resources, and opportunities to startup companies that are innovating and reshaping industries with advances in AI and data science. NVIDIA, being a leading force in AI technology, leverages its vast experience and resources to support these emerging businesses.

Key Features of the NVIDIA Inception Program

  1. Customized Technical Support: Startups in the program have access to NVIDIA’s deep learning experts and a wealth of technical resources. This support ranges from guidance on optimizing AI models to advice on best practices in leveraging NVIDIA’s GPU technology for various applications.
  2. Access to NVIDIA’s Technology: Members of the Inception Program get preferential access to NVIDIA’s cutting-edge technology, including GPUs, software, and SDKs. This access is crucial for startups that rely on high computational power and efficiency for AI and ML tasks.
  3. Marketing and Networking Opportunities: NVIDIA provides marketing support to startups in the Inception Program. This includes exposure through NVIDIA’s marketing channels and opportunities to participate in events and webinars. Such visibility is invaluable for young companies looking to establish themselves in competitive markets.
  4. Training and Educational Resources: NVIDIA offers comprehensive training in AI and ML through its Deep Learning Institute (DLI). Startups in the program can benefit from these educational resources to improve their team’s skills and understanding of AI and ML technologies.
  5. Co-Marketing Opportunities: Inception members often get opportunities to collaborate with NVIDIA on joint marketing efforts. This can include featuring in NVIDIA’s blogs, case studies, and social media content, which can significantly boost a startup’s visibility.
  6. Cloud Credits and Discounts: NVIDIA partners with leading cloud service providers to offer cloud credits and discounts to Inception members. This support helps startups in reducing operational costs associated with running high-intensity computing tasks in the cloud.
  7. Business Development Support: The program also assists in business aspects by providing opportunities for networking with potential investors, customers, and partners. This aspect is crucial for startups looking to scale their operations and expand their market reach.

Benefits for Startups

  • Technology Access and Optimization: Startups get to leverage NVIDIA’s advanced GPU technology, which is critical for AI and ML operations.
  • Expert Guidance and Support: The technical support from NVIDIA’s experts helps startups in overcoming challenges and optimizing their AI solutions.
  • Visibility and Growth Opportunities: Marketing support and networking opportunities provided by the program can significantly aid in a startup’s growth and recognition in the industry.


The NVIDIA Inception Program is a valuable initiative for startups specializing in AI and data sciences. By providing technical support, access to advanced technology, and various growth opportunities, NVIDIA plays a crucial role in fostering innovation and supporting the next generation of AI and data science companies. For startups looking to make a mark in the rapidly evolving field of AI, being a part of the NVIDIA Inception Program can be a significant advantage.

Scenegraph Studios is proud to say they have been accepted onto the NVIDIA Inception Program utilising NVIDIA AI models for our projects, and clients.

What is AI Training?

Training in AI: A Fundamental Process

Training is a fundamental concept in the field of artificial intelligence (AI), particularly in machine learning and deep learning. It refers to the process by which an AI model learns to perform a specific task, whether it’s recognizing patterns, making decisions, or generating responses.

Understanding the Training Process

  1. Data Collection: The first step in training an AI model is gathering a dataset. This dataset should be representative of the real-world scenarios where the AI will operate. For instance, if you’re training a model to recognize dogs in images, you need a diverse set of dog images.
  2. Data Preparation: Once collected, data often needs to be cleaned and formatted. This might involve normalizing values, dealing with missing data, or converting data into a format suitable for the AI model (like transforming images into pixels).
  3. Model Selection: Selecting an appropriate model is crucial. This can be a simple linear regression model for basic tasks or more complex neural networks for tasks like image recognition or natural language processing.
  4. Feature Selection and Engineering: Features are the variables the model uses to make predictions. In many cases, selecting the right features or engineering new ones from raw data can significantly improve a model’s performance.
  5. Algorithm Training: The chosen model is then ‘trained’ using the prepared dataset. Training involves feeding the data into the model and allowing it to adjust its internal parameters (like weights in a neural network) to make accurate predictions or decisions. This process is often iterative, requiring multiple passes over the data.
  6. Validation and Testing: Throughout the training process, the model’s performance is evaluated using validation and testing datasets. These datasets are separate from the training data and provide an unbiased evaluation of the model’s effectiveness.
  7. Tuning and Optimization: Based on performance metrics, the model may undergo tuning, where parameters are adjusted, or the model is re-trained with different settings to improve accuracy or efficiency.

Types of Training

  • Supervised Learning: The most common training type, where the model is trained on labeled data (data that already contains the correct answer).
  • Unsupervised Learning: Here, the model is trained on unlabeled data and must find patterns and relationships on its own.
  • Semi-Supervised and Self-Supervised Learning: These are hybrid approaches that use a mix of labeled and unlabeled data.
  • Reinforcement Learning: The model learns by receiving feedback in the form of rewards or penalties for its actions in a dynamic environment.

Importance in AI

Training is critical in AI as it determines the effectiveness and accuracy of the AI model. A well-trained model can accurately make predictions or decisions, leading to better outcomes in various applications like healthcare diagnostics, autonomous vehicles, or personalized recommendations. Conversely, a poorly trained model can result in inaccurate predictions, leading to suboptimal or even harmful outcomes.

In conclusion, training is the backbone of AI model development. It involves not just feeding data into an algorithm, but a series of steps designed to ensure that the model can generalize its learning to new, unseen data, thus performing its intended function accurately and reliably.

Hardware to Train AI

Training AI models, especially those involving complex tasks or large datasets, requires robust and efficient hardware. The choice of hardware can significantly impact the speed and effectiveness of the training process. Here’s an overview of the key types of hardware commonly used in AI training:

Central Processing Units (CPUs)

  • Role: CPUs, the general-purpose processors in computers, are capable of performing a wide variety of tasks. While they are not as fast as GPUs in parallel processing, they are essential for tasks that require sequential processing.
  • Usage: CPUs are often used for AI tasks that don’t require intensive parallel computations, like certain types of data pre-processing or smaller-scale machine learning models.

Graphics Processing Units (GPUs)

  • Role: GPUs, initially designed for rendering graphics in video games, excel at performing multiple operations simultaneously (parallel processing). This capability makes them ideal for training AI models, particularly deep learning neural networks, which involve processing vast amounts of data simultaneously.
  • Manufacturers: Leading manufacturers include NVIDIA and AMD. NVIDIA’s GPUs, in particular, are widely recognized for their efficiency in AI training, supported by a robust ecosystem of AI development tools like CUDA.

Tensor Processing Units (TPUs)

  • Role: Developed by Google, TPUs are custom-designed chips specifically for AI workloads. They are optimized for the operations commonly performed in neural network calculations, offering high throughput and efficiency.
  • Usage: TPUs are particularly effective for training and running large-scale deep learning models and are a key component of Google’s cloud computing AI services.

Field-Programmable Gate Arrays (FPGAs)

  • Role: FPGAs are integrated circuits that can be configured by a customer or designer after manufacturing – hence “field-programmable”. They offer flexibility and can be optimized for specific tasks.
  • Usage: In AI, FPGAs are often used for custom, specialized processing tasks. Their adaptability makes them suitable for specific types of neural networks or AI algorithms.

High-Performance Computing (HPC) Clusters

  • Role: HPC clusters are networks of computers that combine their processing power to tackle complex computations. They often use a mix of CPUs and GPUs to maximize performance.
  • Usage: For training extremely large and complex AI models (like those used in climate modeling or genomic research), HPC clusters are essential due to their vast computational capabilities.

Quantum Computers

  • Emerging Role: Quantum computers, which use quantum bits (qubits) for processing, are an emerging field in AI hardware. They have the potential to process certain types of computations exponentially faster than classical computers.
  • Current State: As of now, quantum computing is still in its nascent stages and not yet widely used for AI training, but it holds promising potential for the future.

Cloud-Based Services

  • Role: Cloud services from providers like AWS, Google Cloud, and Microsoft Azure offer access to powerful computing resources, including GPUs and TPUs, without the need for local hardware.
  • Usage: Cloud-based AI training is popular among startups and organizations that require flexibility and scalability without the overhead of maintaining physical hardware.


The choice of hardware for AI training depends on the specific requirements of the task, such as the size and complexity of the model, the budget, and the desired training speed. GPUs are currently the most popular choice for a wide range of AI training tasks, but TPUs, FPGAs, and cloud-based solutions are also important parts of the ecosystem, each offering unique advantages for different scenarios.

Hardware to Run AI

AI can be run on a large number of devices and is becoming a more common feature in mobile devices, cloud systems, and dedicated high end machines.

In short, AI requires special hardware designed specifically for AI processing.

NVIDIA GPU’s utilise CUDA Cores. Similar to a CPU core, but a lot simpler. You may think ‘why is it a simple core’, well unlike a CPU core which needs to be smart and run many algorithms at once over multiple programs, a GPU Cuda Core is simple so is built to do 1 task very well. With being simpler, it is also a lot smaller meaning more Cores can be put onto the chip. For example, a CPU on average has 4 to 12 cores (average home computers). A GPU normally has thousands of cores.

Let Scenegraph help you on your AI journey

At Scenegraph, our workstations utilise NVIDIA A6000 GPUs having 10,752 cores. This allows Scenegraph to develop advanced 3D scenes, train basic AI models in a short period of time, and run many AI models together. Reach out to us to discuss your needs with AI deployment.

Scoping Session: How it works

On the scoping call, we will quickly understand your needs and help plan a path to help fix the problem you are having.

To make sure we scope your project as fast as possible, we will send you a quick form to fill out so understand your needs before joining the call.

Book in with Dr David Tully by clicking the date and time you prefer.

Scoping Session: How it works

On the scoping call, we will quickly understand your needs and help plan a path to help fix the problem you are having.

To make sure we scope your project as fast as possible, we will send you a quick form to fill out so understand your needs before joining the call.

Book in with Dr David Tully by clicking the date and time you prefer.