2020 Sees Major AI Advancements in Training Efficiency and Language Models
Despite global challenges, 2020 brings significant AI breakthroughs including drastically reduced training costs and the impressive GPT-3 language model. These advancements point toward a future of more accessible and capable artificial intelligence.
Artificial intelligence experiences significant breakthroughs in 2020 despite global disruptions, with OpenAI researchers demonstrating a 44x decrease in the computing power required to train image classifiers since 2012. This exponential growth in training efficiency surpasses the traditional pace of Moore's Law, indicating that AI systems become significantly less time-consuming and resource-intensive to develop.
The release of OpenAI's Generative Pre-trained Transformer 3 (GPT-3) in June stands out as one of the most remarkable achievements of the year. Costing over $4.5 million to develop and growing by a factor of 10 from its predecessor, this natural language processing algorithm produces highly human-like text and solves unfamiliar language tasks, opening new possibilities for automated writing and communication.
Additionally, Unity makes strides in July by advancing the use of synthetic data for training AI models. This approach proves hugely important because it allows developers to generate artificial datasets rather than relying solely on expensive, time-consuming real-world data collection, ultimately accelerating the pace of future machine learning innovations.