#ai

The Commonwealth Bank of Australia’s use of generative AI tools has halved customer losses from scams and reduced fraud by 30%.

Text-generative Transformers include an embedding component that converts text tokens into numerical vectors to capture semantic meaning.

Here’s a Bash script that uses a TDD loop with an LLM to iteratively write Python code to pass tests.

Researchers have developed a portable, wireless EEG acquisition system called BrainGPT capable of converting thoughts into text without the need for an fMRI machine.

Jobs most negatively impacted by AI since ChatGPT’s release: writing jobs (-33%), translation jobs (-19%), and customer service jobs (-16%).

Economic history shows an expansion in the variety of tasks performed by humans, with new jobs continually emerging despite automation.

Meta has developed TestGen-LLM, a tool using LLMs to enhance human-written tests, ensuring test suite improvements by passing certain filters.

Klarna has launched an AI assistant powered by OpenAI, which is active across 23 markets and offers support in over 35 languages.

Business owners are increasingly finding AI like ChatGPT valuable for tasks beyond basic applications, such as data visualisation and financial reporting, saving time on manual data processing.

The paper compares Large Language Models (LLMs) with Junior Lawyers and Legal Process Outsourcers (LPOs) in terms of contract review performance.

Researchers have developed a system called Mobile ALOHA for mobile manipulation tasks that require both hands and the whole body, aiming to improve robotic mobility and dexterity beyond simple table-top manipulation.

Clinicians developed a deep learning model that predicts reported sex from retinal fundus photographs without needing to code.

Apple has launched a new machine learning framework specifically for Apple Silicon.

These researchers suggest each occupation has an inflection point after which AI improvements harm human workers’ prospects.

Here’s a GPT trained on knowledge from 17th-century texts. So, it answers in historical style, including outdated scientific concepts.

The author has been at OpenAI for a year and observed that generative models closely approximate their training datasets.

Recently, the Biden administration announced its framework to manage the deployment of AI by executive order. The order throws a bone to both sides of the AI argument: in some regards, the administration is embracing AI; in others, it’s hampering it. Overall, I’m disappointed.

This team taught GPT to navigate iOS and Android by sending it screenshots and giving it instructions.

President Biden released an executive order on AI development, drawing parallels to the early fears and regulatory considerations during the dawn of the microprocessor and internet, highlighting that past technological advancements were less hindered by government intervention.

I don’t want to single out jrincayc because these arguments are common from those concerned about AGI, and the author does acknowledge many arguments against their recommended approach. Sharing nonetheless, because this post clearly demonstrates several of the problems with the anti-AI movement within tech.

Researchers from UC Berkeley, Shanghai, and Osaka University have trained a computer to analyse brain activity while listening to music and recreate the song, including a recognisable version of Another Brick in the Wall.

Some analysts predict AI could enable a 30% annual growth rate in the US economy, but Tyler Cowen argues for more moderate estimates, expecting a boost of ¼ to ½ of a percentage point.

Large organisations often function with a single directive mind, usually a CEO or equivalent figure, supported by middle managers.

Researchers have developed a deep learning model, with 95% accuracy, that can extract data from keyboard keystrokes recorded by a microphone and 93% when recorded on Zoom.

The study presents an AI model that can predict how viral variants affect protein–protein binding.