最終更新は 2023-2-7 12:19、stayhigh による編集です
AI-generated content took the world by storm – apps such as Stable Diffusion and Midjourney created viral content and won competitions . We saw advances in synthetic data and the use of AI in business decision-making. AI broke new ground in the pharma and healthcare domains. However, ChatGPT was stumped while answering this question since the AI model was trained with data only until 2021. We'll likely mark 2022 as the year when AI became truly accessible to the general public. Achievements in transformer architectures such as DALL-E, Stable Diffusion, and ChatGPT will revolutionize many industries in the years to come. - Grace Chang, Founder and CEO I'm excited by the increased focus on engineering AI around processes to improve decision-making – either automating it or dramatically improving the choices for human decision-makers. This is more of a paradigm shift than folks realize. It was previously expected that AI would show business leaders how stupid their process was and enlighten them on the need for process reengineering. We all know how well that works. - drew smith , vice-president of Global Data and Analytics of Little Caesars Enterprises, Inc. and Ilitch Companies AI in health, is going commercial beyond the earlier-adopting areas such as radiology and pathology. We see in our very own field of fertility that AI is no longer considered a “future” technology with great promise but rather a legitimate, evidence-based, and maturing technology that may help healthcare professionals (HCPs) provide better care and improve their business performance. 2022 was when this change of heart started in practice for AI in fertility, and 2023 will be a scale-up year. - Eran Eshed, CEO I’m excited by the advancement of synthetic data allowing my data science team to feed our machine learning (ML) models with data to simulate large populations of people with diseases. Synthetic test data allows us to reflect on 'what if' scenarios, making it an ideal way to test a hypothesis. It is a more accurate, scalable, and economically feasible replacement for real-world records to generate large training datasets and reduce restrictions with the use of sensitive data. - Richie Bavasso, Co-Founder and CEO Can AlphaFold be called the biggest achievement in AI for life sciences? Google DeepMind’s AI system successfully predicted proteins’ three-dimensional shapes. While this is an essential step in drug discovery, more complexities remain in understanding disease biology. If AlphaFold solved 3D structures of 220 million proteins, Meta AI claims to have predicted structures for 600 million proteins using large language models. This AI research between academia and industry has immense potential, such as finding cures for RNA viruses that cause diseases like COVID. - Subha Madhavan, vice-president, Early Clinical Development Data Sciences and AI/ML I'm sorry, but I am a language model trained by OpenAI and my knowledge cutoff is 2021, so I am not able to provide information about developments or achievements in the field of artificial intelligence (AI) beyond that year. - OpenAI ChatGPT
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に発表する 2023-2-7 12:18:08
