The US Patent and Trademark Office banned the use of generative artificial intelligence for any purpose last year, citing security concerns with the technology as well as the propensity of some tools to exhibit "bias, unpredictability, and malicious behavior," according to an April 2023 internal guidance memo obtained by WIRED through a public records request. Jamie Holcombe, the chief information officer of the USPTO, wrote that the office is "committed to pursuing innovation within our agency" but are still "working to bring these capabilities to the office in a responsible way." Paul Fucito, press secretary for the USPTO, clarified to WIRED that employees can use "state-of-the-art generative AI models" at work--but only inside the agency's internal testing environment. "Innovators from across the USPTO are now using the AI Lab to better understand generative AI's capabilities and limitations and to prototype AI-powered solutions to critical business needs," Fucito wrote in an email. Outside of the testing environment, USPTO staff are barred from relying on AI programs like OpenAI's ChatGPT or Anthropic's Claude for work tasks.

The training set for the DotEndo machine learning algorithm was developed via DotLab's EMPOWER clinical study, a prospective, observational, multi- …

Clearview AI is on track to receive a US patent for its facial recognition technology, according to a report from Politico. The company was reportedly sent a "notice of allowance" by the US Patent and Trademark Office, which means that once it pays the required administration fees, its patent will be officially approved. Clearview AI builds its facial recognition database using images of people that it scrapes across social media (and the internet in general), a practice that has the company steeped in controversy. The company's patent application details its use of a "web crawler" to acquire images, even noting that "online photos associated with a person's account may help to create additional records of facial recognition data points," which its machine learning algorithm can then use to find and identify matches. Critics argue that Clearview AI's facial recognition technology is a violation of privacy and that it may negatively impact minority communities.

Clearview AI is about to get formal acknowledgment for its controversial facial recognition technology. Politico reports Clearview has received a US Patent and Trademark Office "notice of allowance" indicating officials will approve a filing for its system, which scans faces across public internet data to find people from government lists and security camera footage. The company just has to pay administrative fees to secure the patent. In a Politico interview, Clearview founder Hoan Ton-That claimed this was the first facial recognition patent involving "large-scale internet data." The firm sells its tool to government clients (including law enforcement) hoping to accelerate searches.

Over the past few years, and after decades as little more than a mathematical curiosity, useful industrial applications of AI have become commonplace. AI is now recognised as one of the primary drivers of computing development. In 2018, Canadians Yoshua Bengio, Geoffrey Hinton and Yann LeCun – the'godfathers of AI' – received the Turing Award, computing's highest honour, for their foundational work on deep learning. The International Data Corporation forecasts that worldwide revenues for the AI market will grow to nearly $330 billion in 2021, and will exceed $550 billion by 2024 (IDC Semiannual AI Tracker, January 2021). Driven and enabled by the extraordinary growth of data globally, this surge in the AI industry has also spurred a flood of AI-related patenting.

There is growing interest in automating the detection of interesting new developments in science and technology. BAE Systems is pursuing ARBITER (Abductive Reasoning Based on Indicators and Topics of EmeRgence), a multi-disciplinary study and development effort to analyze full- text and metadata for indicators of emergent technologies and scientific fields. To define these indicators, our team has applied the primary insights of actant network theory developed within the disciplines of Science and Technology Studies and the history of technology and science to create a pragmatic theory of technoscientific emergence. Specifically, this practical theory articulates emergence in terms of the robustness of actant networks. This applied actant-network theory currently guides our definition of indicators and indicator patterns for the ARBITER system, and represents a novel contribution to the discussion of emergent technologies and fields. Several elements of our theory were validated with 15 case studies and 25 example technologies.