The recent arrival of ChatGPT has generated a lot of buzz across the industry sectors related to cloud computing and artificial intelligence (AI). Tech giants such as Microsoft, Google, and Baidu have all built products and services derived from ‘generative AI’ technologies. This new wave of interest is expected to bring benefits to the participants across the supply chain for GPUs and AI chips. These participants include NVIDIA, TSMC, Unimicron, and AIChip.
However, there are challenges pertaining to the adoption and function-related optimisation of products and services that are powered by generative AI. Furthermore, user experience is at the core of an AI-based technology and involves the protection of personal information and the accuracy of the responses to content requests. Therefore, regulatory issues will likely emerge as generative AI moves to the next phase of its development.
TrendForce says generative AI represents an integration of different types of algorithms, pre-trained models, and multimodal machine learning. Notable ones include Generative Adversarial Network (GAN), Contrast Language-Image Pre-Training (CLIP), Transformer, and Diffusion. Generative AI searches for patterns in the existing data or batches of information and efficiently outputs content that is suitable for scenarios such as data collection and analysis, social interactions, copywriting, etc. There are already many apps powered by generative AI in the market right now, and the most common kinds of output from them include texts, images, music, and software codes.
Data, computing power, and algorithms are the three indispensable factors that drive the development of generative AI. Also, while AI-based products and services are relatively easy to build, optimising them is much more difficult. In this respect, the major cloud companies are in a more advantageous position since they possess huge amounts of the essential resources.
From the perspective of the developers of these products, the existing chat robots such as ChatGPT are able to not only converse with users in natural language but also somewhat meet the demand for ‘comprehending’ users’ input. Hence, having a better capability to understand what users need or desire can, in turn, provide further suggestions to users’ enquiries and responses. And since using an internet search engine is pretty much a habit for the majority of people worldwide, the most urgent task of the major cloud companies is to keep optimising their own search engines.
TrendForce’s latest investigation finds that Google remains the absolute leader in the global market for internet search engines, with a market share of more than 90%. Microsoft, with its Bing, now has a market share of just 3% and will unlikely pose a significant threat in the short term. However, Bing is gaining more users that can contribute to its data feedback and model optimisation cycle. Therefore, Google needs to be on guard against the chance of Microsoft creating differentiation in search-related services and perhaps capture certain kinds of opportunities in the area of online advertising.
Generative AI requires a huge amount of data for training, so deploying a large number of high-performance GPUs helps shorten the training time. In the case of the Generative Pre-Trained Transformer (GPT) that underlays ChatGPT, the number of training parameters used in the development of this autoregressive language model rose from around 120 million in 2018 to almost 180 billion in 2020. According to TrendForce’s estimation, the number of GPUs that the GPT model needed to process training data in 2020 came to around 20 000. Going forward, the number of GPUs that will be needed for the commercialisation of the GPT model (or ChatGPT) is projected to reach above 30 000.
Hence, with generative AI becoming a trend, demand is expected to rise significantly for GPUs, and thereby benefit the participants in the related supply chain. NVIDIA, for instance, will probably gain the most from the development of generative AI. Its DGX A100, which is a universal system for AI-related workloads, delivers 5 petaFLOPS and has nearly become the top choice for big data analysis and AI acceleration.
© Technews Publishing (Pty) Ltd | All Rights Reserved
printer friendly version