When looking back at the role artificial intelligence (AI) has played in revolutionizing different industries that would typically require human intelligence, it is important to consider the next steps in this journey and how it is starting to evolve. With the growth of the industry, the volume and complexity of data are becoming unmanageable for pre-existing AI models.
This has resulted in the need for large AI model training, which requires substantial computational power, particularly from Graphics Processing Units (GPUs) that are specifically designed to handle the high level of parallel processing involved in training these models. In this blog, we will explore the importance of large AI model training for the growing industry, plus explain how it works, and outline the challenges and future predictions.
The history of artificial intelligence
Dating back to the 1990s, Margaret A. Boden described artificial intelligence (AI) as āthe study of how to build or program computers to enable them to do what minds can do.ā When looking at more recent studies, the depth of the concept increases with the terminology of āthird-generation artificial intelligenceā being used. According to a study from Science China, this is a combination of the āknowledge-driven methods of the first generation and the data-driven methods of the second generation, using the four elements of knowledge: data, algorithms, and computing power.ā
Some believe that AI stems from pre-20th century in ancient Greek mythology, but the term was not coined until the 1950s whereby the study of AI began. Over the past 70 years, we have seen a range of milestones in the AI field, from Deep Blue in the 1990s all the way to Siri in 2011, and GPT-1 in 2018.
Source: Data Science Live Demo Class - The History of AI
Joey de Villa spoke about the topic of AI during a Navigate North America 2024 session, where he looked into the history of AI, current trends, and the ethical considerations we must navigate as this technology evolves. Watch the full session here:
āAI has been simmering for a long time. In fact, it has been around since pretty much the beginning of electronic computers. In fact, the first description of an electronic computer, if you asked anybody in the 1950s and 60s, was that it was an electronic brain, and it was all about trying to mimic human thinking and reasoning capability."
What is large AI model training?
The process of training AI models based on a high amount of data can be defined as large-scale AI model training. With the amount of data progressively growing, we are starting to see more large-scale AI models that are made up of complex architectures and high-powered computational resources.
Using potentially trillions of data pieces, there is no true benchmark to define what can be classified as a large-scale model. A prime example of this is GPT-1 which was considered a ālarge-scaleā model with 117 million parameters and 600 billion tokens. Then, in 2023, the latest model GPT-4 was released which had roughly 1.7 trillion parameters and 13 trillion tokens. This makes it apparent how the vast size of the dataset required for models to be classified as a large-scale model is growing exponentially.
Epoch AI conducted a study with 81 large-scale models across 18 countries to demonstrate the timeline of growth with models such as AlphaGo to Gemini:
Source: Epoch AI - Tracking Large-Scale AI Models
How does large AI model training work?
Now that we have an understanding of what large AI model training is, letās take a look at how it all works. In the below image, we have outlined the essential steps required for AI model training to work:
Training large-scale AI models involves a series of structured steps that differ from the standard AI training process due to the massive amount of data and computational resources required. These resources include powerful GPUs, which are essential for efficiently processing and training models at such a large scale. Below is an overview of the key aspects involved in large-scale AI model training:
| Step | Description |
|---|---|
| Problem definition | Begin by clearly identifying the specific issue or area where AI can provide a solution or enhancement. |
| Data collection | Acquire the necessary data that is relevant to addressing the identified problem. |
| Data preparation | Process and organize the data, including cleaning and transforming it, to make it ready for analysis. |
| Model development | Apply machine learning techniques to create a model using the prepared data. |
| Model training | Instruct the model to recognize patterns and relationships within the data through training. |
| Model evaluation | Assess the modelās effectiveness by testing its performance on separate data. |
| Model refining | Make adjustments to the modelās parameters and retrain it to enhance its accuracy and performance. |
| Deployment | Implement the finalized model into a live environment where it can be utilized for its intended purpose. |
| Maintenance | Continuously observe the modelās performance and make updates as necessary to maintain its effectiveness. |
The challenges of large AI model training
With the sheer amount of data that is involved in large AI model training, it is more important than ever to acknowledge and address the challenges faced by those utilizing AI models. These challenges include a range of issues, including data management, the complexity of managing vast datasets, and the energy consumption required. Being able to navigate these challenges successfully is essential for advancing AI in a responsible and sustainable manner.
| Challenge | Description |
|---|---|
| Security | Governance frameworks will be needed to manage risks around bias, security vulnerabilities, and lack of transparency. This will come through action both internally at organizations and by governments themselves. A line will need to be walked between empowering innovation, whilst ensuring the safe and responsible adoption of this technology. |
| Data management | The effectiveness of large AI models hinges on access to vast amounts of high-quality data. Gathering, curating, and maintaining such extensive datasets is a daunting task. Ensuring data diversity and avoiding biases are crucial to prevent models from making inaccurate or unfair predictions. |
| Training time | Training large AI models can take weeks or even months, depending on the model's size and the computational resources available. The availability of high-performance GPUs is crucial in reducing training time and enabling more frequent iterations, which are key to refining these models. This extended training period slows down the pace of innovation and iteration, making it difficult to quickly test and implement new ideas. |
| Energy consumption | The energy consumption of large AI models is a growing concern. Training these models can have a substantial carbon footprint, raising questions about the environmental impact of AI research and development. |
| Maintenance | Once trained, deploying large AI models in real-world applications presents its own set of challenges. These models often require significant computational resources even for inference, making it difficult to integrate them into resource-constrained environments. |
| Talent gaps | The field of large AI model training is rapidly evolving, and there is a growing need for specialized expertise. However, there is a shortage of researchers and engineers with the necessary skills to tackle the challenges associated with training and deploying large AI models. |
| Computational resources | Taking into account the vast size of large-scale models, they require immense computational resources for training, including hardware such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). While these can be costly additions for organizations, they can also consume substantial amounts of energy. |
Alongside the team at Pieces, we hosted an online meetup that looked into the complexities associated with Large Language Models (LLMs) and how to manage them. This meetup focused primarily on deploying and managing large AI models, particularly on Kubernetes, while covering emerging trends such as dynamic resource allocation and environmental considerations. Watch the full recording here:
The future of large AI model training
While the evolution of AI is ongoing, people are beginning to speculate about what we can expect from the future of large AI model training. The core belief is that we will begin to move towards more automation, increased collaboration between industry and academia, greater reliance on open-source models, and a balanced approach that leverages both AI capabilities and human expertise.
One of the major points we can consider surrounding the future of large AI model training is the evolution of augmenting developer tasks with tools such as GitHub Copilot to make more automated processes where tools are able to understand context, pull feature branches, and submit code for review without any human intervention. Some of the other predictions that can be made for the future include:
- The collaboration between industry and academia
- Human-AI collaboration to ensure accuracy and innovation
- The potential for novel applications which could involve combining different models for various purposes
During Navigate North America 2024 in Tampa, Florida, we spoke with industry experts Josh Mesout, James Gress, Brandon Dey, and Cate Gutowski, about their predictions surrounding the future of AI and machine learning. Watch the full recording here:
Over the years, we have hosted other panel discussions that touch upon similar topics - for more on these, check out the links below:
- Europe 2023: What does the future of AI and ML look like in 2024
- North America 2023: The future of machine learning and AI
What are we doing at Civo?
At Civo, weāre transforming how businesses approach machine learning, scientific computing, and generative AI with our cloud GPU-powered compute and Kubernetes offerings. By leveraging industry-leading NVIDIA GPUs, we provide high-performance computing solutions that are scalable, cost-effective, and easy to integrate into your existing infrastructure.
Whether you're working on AI training, high-performance computing, or graphics-intensive tasks, Civo's GPU solutions offer the power, flexibility, and sustainability you need to succeed.
Summary
The development of artificial intelligence necessitates the training of increasingly large and complex models to handle vast data sets, which in turn requires powerful GPUs to manage the intensive computational demands. From the early days of Deep Blue to modern giants like GPT-4, AI's evolution reflects its growing capability and complexity. With the future of the industry unclear, it promises greater automation, enhanced collaboration between academia and industry, and innovative applications, all while balancing AI's capabilities with human expertise.
If you want to learn more about what we are doing at Civo, click here.
