It’s hard to believe that it’s only been a year since the beta version of DALL-E, OpenAI’s text-to-image image generator, was set loose onto the internet. Since then, there’s been an explosion of AI-generated visual content, with people creating an average of 34 million images per day. That’s upwards of 15 billion images created using text-to-image algorithms last year alone. According to Everypixel Journal, it took photographers 150 years, from the first photograph taken in 1826 until 1975, to reach the 15 billion mark.
With new AI text-to-image generators launching at such a rapid pace, it’s tough to keep track of what’s out there, and which produces the best results. We’re here to break down the best AI image-making tools for generating high-quality images from simple descriptions or keywords, or for creating accurate image prompts based on uploaded reference images. – Read More
Daily Archives: January 23, 2024
Cops Used DNA to Predict a Suspect’s Face—and Tried to Run Facial Recognition on It
In 2017, detectives working a cold case at the East Bay Regional Park District Police Department got an idea, one that might help them finally get a lead on the murder of Maria Jane Weidhofer. Officers had found Weidhofer, dead and sexually assaulted, at Berkeley, California’s Tilden Regional Park in 1990. Nearly 30 years later, the department sent genetic information collected at the crime scene to Parabon NanoLabs—a company that says it can turn DNA into a face.
Parabon NanoLabs ran the suspect’s DNA through its proprietary machine learning model. Soon, it provided the police department with something the detectives had never seen before: the face of a potential suspect, generated using only crime scene evidence. – Read More
Beyond AI Exposure:Which Tasks are Cost-Effective to Automate withComputer Vision?
The faster AI automation spreads through the economy, the more profound its potential impacts, both positive (improved productivity) and negative (worker displacement). The previous literature on “AI Exposure” cannot predict this pace of automation since it attempts to measure an overall potential for AI to affect an area, not the technical feasibility and economic attractiveness of building such systems. In this article, we present a new type of AI task automation model that is end-to-end, estimating: the level of technical performance needed to do a task, the characteristics of an AI system capable of that performance, and the economic choice of whether to build and deploy such a system. The result is a first estimate of which tasks are technically feasible and economically attractive to automate – and which are not. We focus on computer vision, where cost modeling is more developed. We find that at today’s costs U.S. businesses would choose not to automate most vision tasks that have “AI Exposure,” and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs falls rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. >Overall, our findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts. – Read More
#strategy
Rick's Cafe AI 