Let’s start with an AI use case before moving into some more news.
Axon has launched Draft One, an AI-powered software program that automates police report writing. The software uses GPT-4 to draft high-quality police report narratives by auto-transcribing audio from police body cameras. While Axon claims the software has safeguards and requires human officer review, critics raise concerns about potential misuse and the software’s ability to generate inaccuracies. The company states that trials have shown a significant decrease in report writing time.
Cleanlab, an AI startup from MIT, has developed a tool called the Trustworthy Language Model that assignsa reliability score to output generated by large language models. This tool aims to help users determine which responses from chatbots are trustworthy and which should be discarded. By providing a score between 0 and 1, businesses can make more informed decisions when using these models. Companies like Berkeley Research Group have already adopted the Trustworthy Language Model, reducing the workload and improving efficiency. Cleanlab hopes that this tool will address concerns about the accuracy and reliability of large language models, making them more attractive to businesses.
TD Synnex has launched an AI industry ecosystem, bringing together independent software vendors, partners, and vendors to explore the immediate benefits of artificial intelligence (AI) and create new solutions. The group aims to address how specific sectors and organizations can harness the power of AI and collaborate to develop real-world use cases.
From Axios: A top economics researcher argues that generative AI can benefit workers if businesses use it as a flexible tool to complement their tasks rather than replace them. The researcher highlights the potential for AI to improve productivity and avoid the negative labor market outcomes seen in past automation waves. However, the researcher warns that the bias towards automating tasks and the lack of transparency in AI tools are roadblocks to achieving this “pro-worker” phenomenon.
Why do we care?
AI excels at summarization tasks, and so we continue to see use cases in that space. The concerns raised about potential inaccuracies and misuse highlight the critical need for robust safeguards and human oversight. These concerns are especially pertinent given the high stakes of law enforcement documentation and the potential consequences of errors. Axon’s approach, which includes human officer review of the AI-generated reports, suggests an awareness of these issues, though the effectiveness of these safeguards will likely be a key area of scrutiny as the technology is adopted more widely.
And thus why tools like the Trustworthy Language Model intrigue. For businesses, this could mean enhanced decision-making capabilities and reduced risks associated with using AI in operations.
