Knowledge and accuracy: LaMDA can access the internet for the latest information, while both GPT-3 and even GPT-4 have knowledge cut-off dates of September 2021. If asked about more up-to-date events, these models could generate fictional responses. Training data: LaMDA's training dataset comprised primarily of dialog, while GPT-3 used everything from Wikipedia entries to traditional books. That makes GPT-3 more general-purpose and adaptable for applications like ChatGPT. Human training: In the previous section, we talked about how Google hired human workers to fine-tune its model for safety and quality.

The role of machine learning and artificial intelligence (AI) in the health care sector is rapidly expanding. This expansion may be accelerated by the global spread of COVID-19, which has provided new opportunities for AI prediction, screening, and image processing capabilities.1 Applications of AI can be as straightforward as using natural language processing to turn clinical notes into electronic data points or as complex as a deep learning neural network performing image analysis for diagnostic support. The goal of these tools is not to replace health care professionals, but to enable better patient experience and better inform the clinical decision-making process to improve the safety and reliability of clinicians. Clinicians and health systems using these new tools should be aware of some of the key issues related to safety and quality in the development and use of machine learning and AI. The performance of a chatbot on a shopping website poses little harm to users, but AI used in health care, particularly clinical decision supports or diagnostic tools, can have significant impact on a patient's treatment.

Many industry experts have made predictions about the future of artificial intelligence (AI), machine learning, and automation in the workplace--but little research exists on how skilled and unskilled employees view the coming technology disruptions. Gartner surveyed more than 2,700 employees in the US and the UK across multiple industries and skill levels, including unskilled workers, skilled manual workers, clerical workers, and professionals, to determine how different groups view AI in the workplace. The majority of employees (52%) said they would prefer AI to be deployed as an on-demand helper--essentially, acting as their own employee--rather than as their manager (9%), coworker (11%), or proactive assistant (32%), the report found. In this role, AI could help by limiting their routine work tasks, as well as reducing mistakes, respondents said. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Workers differ significantly in their opinions about how AI will impact jobs, the report found.

Many industry experts have made predictions about the future of artificial intelligence (AI), machine learning, and automation in the workplace--but little research exists on how skilled and unskilled employees view the coming technology disruptions. Gartner surveyed more than 2,700 employees in the US and the UK across multiple industries and skill levels, including unskilled workers, skilled manual workers, clerical workers, and professionals, to determine how different groups view AI in the workplace. The majority of employees (52%) said they would prefer AI to be deployed as an on-demand helper--essentially, acting as their own employee--rather than as their manager (9%), coworker (11%), or proactive assistant (32%), the report found. In this role, AI could help by limiting their routine work tasks, as well as reducing mistakes, respondents said. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) Workers differ significantly in their opinions about how AI will impact jobs, the report found.