High AI training costs have been a major barrier to AI adoption, stopping many corporations from implementing AI technology. In keeping with a 2017 Forrester Consulting Report, 48% of corporations highlighted high technology costs as certainly one of the first reasons for not implementing AI-driven solutions.

Nonetheless, recent developments have shown that AI training costs are rapidly declining, and this trend is predicted to proceed in the long run. In keeping with the ARK Invest Big Ideas 2023 report, training costs of a giant language model just like GPT-3 level performance have plummeted from $4.6 million in 2020 to $450,000 in 2022, a decline of 70% per 12 months.

Let’s explore this trend of declining AI training costs further and discuss the aspects contributing to this decline.

How Have AI Training Costs Modified Over Time?

In keeping with the recent ARK Invest 2020 research, the fee of coaching deep learning models is improving 50 times faster than Moore’s Law. Actually, the expense related to running an AI inference system has drastically reduced to almost negligible levels for varied use cases.

Furthermore, training costs have decreased ten times yearly over the past few years. As an illustration, in 2017, training a picture classifier like ResNet-50 on a public cloud cost around $1,000, but by 2019, the fee had decreased significantly to roughly $10.

These findings align with a 2020 report by OpenAI, which found that the quantity of computing power needed to coach an AI model to perform the identical task has been decreasing by an element of two every 16 months since 2012.

Moreover, the ARK report highlights the declining AI training costs. The report forecasts that by 2030 the training cost of a GPT-3 level model will come all the way down to $30, in comparison with $450,000 in 2022.

Aspects That Contribute to Declining AI Training Costs

Training AI models grow to be cheaper and easier as AI technologies proceed to enhance, making them more accessible to a wider range of companies. Several aspects, including hardware and software costs and cloud-based AI, have contributed to declining AI training costs.

Let’s explore these aspects below.

1. Hardware

AI requires specialized high-end costly hardware to process high volumes of information and computations. Organizations like NVIDIA, IBM, and Google provide GPUs and TPUs to execute high-performance computing (HPC) workloads. High hardware costs make it difficult to democratize AI on a big scale.

Nonetheless, as technology advances, hardware costs are decreasing. In keeping with the ARK Invest 2023 report, Wright’s Law predicts that AI-relative compute unit (RCU) production costs, i.e., AI training hardware costs, should decrease by 57% annually, resulting in a 70% reduction in AI training costs by 2030, as shown within the graph below.

AI training hardware cost – ARK Invest Big Ideas 2023

2. Software

AI software training costs might be lowered by 47% annually through increased efficiency and scalability. Software frameworks like TensorFlow and PyTorch enable developers to coach complex deep learning models on distributed systems with high performance, saving time and resources.

Moreover, large pre-trained models like Inceptionv3 or ResNet and transfer learning techniques also help reduce costs by allowing developers to fine-tune existing models somewhat than training them from scratch.

AI software training cost – ARK Invest Big Ideas 2023

3. Cloud-Based Artificial Intelligence

Cloud-based AI training reduces costs by providing scalable computing resources on demand. With the pay-as-you-go model, businesses only pay for his or her computing resources. Also, cloud providers offer pre-built AI services that speed up AI training.

As an illustration, Azure Machine Learning is a cloud-based service for predictive analytics that enables rapid model development and implementation. It offers flexible computing resources and memory. Users can scale as much as hundreds of GPUs quickly to extend their computing performance. It allows users to work through their web browsers on pre-configured AI environments, eliminating setup and installation overhead.

The Impact of Declining AI Training Costs

The decreasing costs of AI training have significant implications for various industries and fields, leading to improved innovation and competitiveness.

Let’s discuss just a few of them below.

1. Mass Adoption of Sophisticated AI Chatbots

AI chatbots are on the rise resulting from declining AI costs. Especially after the event of OpenAI’s ChatGPT and GPT-4 (Generative Pre-trained Transformer), there was a noticeable surge within the variety of corporations seeking to develop AI chatbots with similar or higher capabilities.

As an illustration, five days after its release in November 2022, ChatGPT amassed 1 million users. Although today, the fee to run the model at scale is roughly $.01 per query, Wright’s Law predicts that by 2030, chatbot applications just like ChatGPT will probably be deployable on an enormous scale less expensive (estimated $650 to run a billion queries), with the potential to process 8.5 billion searches per day, akin to Google Search.

Cost to execute AI inferences per billion queries – ARK Invest Big Ideas 2023

2. Increased Use of Generative AI

The declining costs of AI training have led to a surge in the event and implementation of generative AI technologies. In 2022, there was a major increase in using generative AI, driven by the introduction of revolutionary generative AI tools, reminiscent of DALL-E 2, Meta Make-A-Video, and Stable Diffusion. In 2023, now we have already witnessed a ground-breaking model in the shape of GPT-4.

Aside from image and text generation, generative AI helps developers write code. Programs like GitHub Copilot may help complete a coding task in half the time.

Time to finish coding tasks – ARK Invest Big Ideas 2023

3. Higher Usage of Training Data

Reduced AI training costs are expected to permit higher utilization of machine learning training data. As an illustration, ARK Invest 2023 report suggests that by 2030, the fee of coaching a model with 57 times more parameters and 720 times more tokens than GPT-3 (175B parameters) is projected to diminish from $17 billion to $600,000.

Data availability and quality will probably be the first limiting factor for developing advanced machine learning models on this low-cost computing world. Nonetheless, training models would develop the capability to process an estimated 162 trillion words or 216 trillion tokens.

The longer term of AI looks very promising. To learn more in regards to the latest trends and research in the sector of artificial intelligence, visit Unite.ai.