To reduce the time and computational costs of inference of large language models, there has been interest in parameter-efficient low-rank early-exit casting of transformer hidden-representations to final-representations. Such low-rank short-cutting has been shown to outperform identity shortcuts at early model stages while offering parameter-efficiency in shortcut jumps. However, current low-rank methods maintain a separate early-exit shortcut jump to final-representations for each transformer intermediate block-level during inference. In this work, we propose selection of a single One-Jump-Fits-All (OJFA) low-rank shortcut that offers over a 30x reduction in shortcut parameter costs during inference. We show that despite this extreme reduction, our OJFA choice largely matches the performance of maintaining multiple shortcut jumps during inference and offers stable precision from all transformer block-levels for GPT2-XL, Phi3-Mini and Llama2-7B transformer models.

In the shade of a coconut palm, Chandrika tilts her smartphone screen to avoid the sun's glare. It is early morning in Alahalli village in the southern Indian state of Karnataka, but the heat and humidity are rising fast. As Chandrika scrolls, she clicks on several audio clips in succession, demonstrating the simplicity of the app she recently started using. At each tap, the sound of her voice speaking her mother tongue emerges from the phone. Before she started using this app, 30-year-old Chandrika (who, like many South Indians, uses the first letter of her father's name, K., instead of a last name) had just 184 rupees ($2.25) in her bank account. But in return for around six hours of work spread over several days in late April, she received 2,570 rupees ($31.30). That's roughly the same amount she makes in a month of working as a teacher at a distant school, after the cost of the three buses it takes her to get there and back. Just by reading text aloud in her native language of Kannada, spoken by around 60 million people mostly in central and southern India, Chandrika has used this app to earn an hourly wage of about $5, nearly 20 times the Indian minimum. And in a few days, more money will arrive--a 50% bonus, awarded once the voice clips are validated as accurate. Chandrika's voice can fetch this sum because of the boom in artificial intelligence (AI). Right now, cutting edge AIs--for example, large language models like ChatGPT--work best in languages like English, where text and audio data is abundant online.

The concern is so prevalent that new responsible A.I. measures have been floated by federal government, requiring companies to vet for these biases and to run systems past humans to avoid them. Ray Eitel-Porter, managing director and global lead for responsible A.I. at Accenture, outlined during a virtual event hosted by Fortune on Thursday that the tech consulting firm operates around four "pillars" for implementing A.I.: principles and governance, policies and controls, technology and platforms, and culture and training. "The four pillars basically came from our engagement with a number of clients in this area and really recognizing where people are in their journey," he said. "Most of the time now, that's really about how you take your principles and put them into practice." Many companies these days have an A.I. framework.

That's the payments volume running over Visa's global network, a network whose vast global expanse is a tempting playground for cyberthieves. Visa's cybersecurity team, as Chief Information Security Officer Sunil Seshadri told Karen Webster, also logs as many as 8 billion security events every day -- that's billion with a "b." Not all events are intrusions or even attempts, but also include routine security logs and regular everyday network activity. These logs provide deep insight into what is happening in Visa's infrastructure and network on a real-time basis. But finding the signal in this noisy data is a challenge.