AI coding assistants boost developer productivity and code quality, but they can also introduce legal landmines, such as inadvertently incorporating open-source code with incompatible licenses. CIOs and IT leaders must proactively govern AI-generated code to mitigate IP risks and ensure responsible adoption throughout the software development lifecycle.
In the AI gold rush, all that glitters is not “open.” Confusing open-weight models with open-source ones can lead to compliance missteps and missed innovation. CIOs must understand this difference to better align their IT strategy or risk steering their organization off course.
Organizations are increasingly adopting large language models (LLMs) to enhance operations and decision-making. While deploying these models locally offers significant advantages in terms of data sovereignty and control, it also presents unique security challenges that cannot be overlooked. IT executives who have, or are planning, a local LLM deployment should make sure it is implemented securely, ethically, and effectively to avoid data breaches and operational risks.
Stanford University's Tutor CoPilot has improved students’ mathematics skills by up to 9% over two months. AI’s benefits also extend to language learning courses in educational institutions. IT leaders in education institutions can use open-source tools to create applications to save on costs and protect student and staff data.
AI models facilitate the quick generation of images for websites, social media, applications, and more. AI-generated images save money compared to hiring a graphic designer who could charge US $60/hour. SMEs may be unable to hire a prompt engineer, but becoming adept in image generation only takes practice. IT leaders and marketing professionals in SMEs can look to AI image generation as a cost-effective strategy for marketing images.
AI is becoming a necessary software feature for vendors to stay relevant and ahead of their competition. One major issue with AI in software is the trust that your business data is private and protected. Without this trust, your data could be used by your software vendor or third parties to train their AI models. This article discusses how to manage software with AI to protect your data.
ChatGPT Edu aims to bring responsible AI use to educational institutions. Despite its security and privacy features, there are still concerns with ChatGPT Edu and other similar AI products. CIOs and education technologists can read this article to learn about ChatGPT Edu’s strengths and shortcomings.
AI benefits healthcare by improving the speed of patient diagnosis. Hallucinations are one concern in this process because they can lead to incorrect treatment. Chain-of-thought (CoT) prompting solves this by instructing an LLM to use advanced reasoning to find the best possible answer. Healthcare professionals who use AI can consider using CoT prompting to improve diagnosis speed and accuracy.
Natural disasters are devastating forces that cripple infrastructure and result in deaths if the affected region is unprepared. Traditional weather prediction models are computationally expensive. AI weather prediction models provide faster, higher-quality predictions. Government officials tasked with emergency management can use AI weather prediction models to improve their preparedness and response to natural disasters.
Retailers are facing a surge in shoplifting–highlighting a need for advanced techniques to deter theft effectively. AI tools offer promising results to deter shoplifters and allow retailers to not only reduce theft but also improve overall operational efficiency and customer experience. Tech leaders must learn about the latest AI methods that will enhance their store’s security.