By now it's evident that artificial intelligence (AI) is the singular most definitive technology of this generation, and it's powering broad industrial transformation across critical use cases. Ronald van Loon is a NVIDIA partner and had the opportunity to apply his expertise as an industry analyst to explore the implications of MLPerf benchmarking results on the next generation of AI. Enterprises are facing an unprecedented moment as they strive to leverage AI for competitive advantage. This means optimizing training and inferencing for AI models to gain differentiating benefits, like significantly improved productivity for their data science teams and achieving faster time to market for new products and services. However, AI is advancing incredibly quickly and AI model size is dramatically increasing in such areas as Natural Language Processing (NLP), which has grown 275 times every two years using the Transformer neural network architecture.

Speech and language are central to human intelligence, communication, and cognitive processes. Understanding natural language is often viewed as the greatest AI challenge--one that, if solved, could take machines much closer to human intelligence. In 2019, Microsoft and Alibaba announced that they had built enhancements to a Google technology that beat humans in a natural language processing (NLP) task called reading comprehension. This news was somewhat obscure, but I considered this a major breakthrough because I remembered what had happened four years earlier. In 2015, researchers from Microsoft and Google developed systems based on Geoff Hinton's and Yann Lecun's inventions that beat humans in image recognition. I predicted at the time that computer vision applications would blossom, and my firm made investments in about a dozen companies building computer-vision applications or products.

Speech and language are central to human intelligence, communication, and cognitive processes. Understanding natural language is often viewed as the greatest AI challenge--one that, if solved, could take machines much closer to human intelligence. In 2019, Microsoft and Alibaba announced that they had built enhancements to a Google technology that beat humans in a natural language processing (NLP) task called reading comprehension. This news was somewhat obscure, but I considered this a major breakthrough because I remembered what had happened four years earlier. In 2015, researchers from Microsoft and Google developed systems based on Geoff Hinton's and Yann Lecun's inventions that beat humans in image recognition.