A House Democrat is moving to block the Trump administration from being able to use military-grade drones to surveil protests in the U.S. Rep. Jimmy Gomez, D-Calif., introduced the bill in response to the Department of Homeland Security (DHS) reportedly using MQ-9 Reaper drones to monitor the protests in Los Angeles earlier this year. "The U.S. government should never use military drones to spy on its own people. Not under anyone," Gomez told Fox News Digital in a statement. "This bill would stop Trump's abuse of power and get these combat drones out of our neighborhoods." An MQ-9 Reaper flies by on a training mission at Creech Air Force Base in Indian Springs, Nevada.

In the spring of 2017, in a room on the second floor of Google's Building 1965, a college intern named Aidan Gomez stretched out, exhausted. It was three in the morning, and Gomez and Ashish Vaswani, a scientist focussed on natural language processing, were working on their team's contribution to the Neural Information Processing Systems conference, the biggest annual meeting in the field of artificial intelligence. Along with the rest of their eight-person group at Google, they had been pushing flat out for twelve weeks, sometimes sleeping in the office, on couches by a curtain that had a neuron-like pattern. They were nearing the finish line, but Gomez didn't have the energy to go out to a bar and celebrate. He couldn't have even if he'd wanted to: he was only twenty, too young to drink in the United States.

Focusing on doomsday scenarios in artificial intelligence is a distraction that plays down immediate risks such as the large-scale generation of misinformation, according to a senior industry figure attending this week's AI safety summit. Aidan Gomez, co-author of a research paper that helped create the technology behind chatbots, said long-term risks such as existential threats to humanity from AI should be "studied and pursued", but that they could divert politicians from dealing with immediate potential harms. "I think in terms of existential risk and public policy, it isn't a productive conversation to be had," he said. "As far as public policy and where we should have the public-sector focus – or trying to mitigate the risk to the civilian population – I think it forms a distraction, away from risks that are much more tangible and immediate." Gomez is attending the two-day summit, which starts on Wednesday, as chief executive of Cohere, a North American company that makes AI tools for businesses including chatbots.

Check out all the on-demand sessions from the Intelligent Security Summit here. Aidan Gomez, cofounder and CEO of Cohere AI, admits that the company, which offers developers and businesses access to natural language processing (NLP) powered by large language models (LLMs), is "crazy under the radar." Given the quality of the company's foundation models, which many say are competitive with the best from Google, OpenAI and others, that shouldn't be the case, he told VentureBeat. But Cohere, he emphasizes, has been "squarely focused on the enterprise and how we can add value there." In any case, the Toronto-based Cohere, founded in 2019 by Gomez, Ivan Zhang and Nick Frosst, may not remain unnoticed for long.

As a patient recovers from a wound, a doctor may watch over them, monitoring the healing process and prescribing treatments based on the body's responses. But a wide variety of factors including diet, age, or diseases such as diabetes all play a role in healing, and monitoring these variables on a daily basis can be difficult for doctors, especially for chronic conditions. In the face of these challenges, researchers at UC Santa Cruz are envisioning systems that could constantly monitor these complex factors as well as the ongoing responses of the body, and by way of a machine learning algorithm, suggest or even administer treatment. This continuous response to metrics or tests is a form of feedback control--an engineering concept for comparing a variable to a target value. Control theory is commonly used in the engineering of dynamic systems, but much less so by doctors.

If you want to ride the next big wave in AI, grab a transformer. A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other. First described in a 2017 paper from Google, transformers are among the newest and one of the most powerful classes of models invented to date. They're driving a wave of advances in machine learning some have dubbed transformer AI.

Cohere Inc., an AI startup founded by University of Toronto alumni that uses natural language processing to improve human-machine interactions, has raised US$125 million as it looks to open a new office in Silicon Valley, the Globe and Mail reports. The latest financing round, led by New York-based Tiger Global Management, comes only five months after Cohere secured $US40 million in venture capital financing, according to the Globe. Cohere's software platform helps companies infuse natural language processing capabilities into their business using tools like chatbots, without requiring AI expertise of their own. The company originated in a 2017 paper co-authored by CEO Aidan Gomez, who interned at the Google Brain lab of deep learning pioneer and University Professor Emeritus Geoffrey Hinton, a Cohere investor. Cohere's other co-founders are alumnus Nick Frosst, who also worked with Hinton at Google, and Ivan Zhang, a former U of T computer science student.

Cohere, a startup creating large language models to rival those from OpenAI and AI2Labs, today announced the general availability of its commercial platform for app and service development. Through an API, customers can access models fine-tuned for a range of natural language applications, in some cases at a fraction of the cost of rival offerings. The pandemic has accelerated the world's digital transformation, pushing businesses to become more reliant on software to streamline their processes. As a result, the demand for natural language technology is now higher than ever -- particularly in the enterprise. According to a 2021 survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their natural language processing (NLP) budgets grew by at least 10% compared to 2020, while a third -- 33% -- said that their spending climbed by more than 30%.

The ability of computers to understand and generate language took a huge leap forward in 2017 when researchers at Google developed new natural -anguage AI models called Transformers. Some of the experts who built and trained those seminal models have since branched out on their own by founding the Toronto-based startup Cohere, which today announced a new $40 million Series A funding round. The technology that undergirds Cohere's natural-language processing models was originally developed by the Toronto-based Google Brain team. Two of that team's members, Aidan Gomez and Nick Frosst (along with a third cofounder, Ivan Zhang), started Cohere two years ago to further develop and commercialize the models, which are delivered to customers through an API. Cohere is backed by neural network pioneer and Turing Award winner Geoffrey Hinton, who led the Toronto Google Brain team, as well as some other big names in the AI world like Stanford computer science professor Fei-Fei Li. "Very large language models are now giving computers a much better understanding of human communication," Hinton said in a statement to Fast Company.

Physics calculations may work perfectly well in theory. On a blackboard, academic science is pretty predictable (outside of the quantum realm, perhaps). Yet, nothing is manufactured in a complete vacuum, is it? When it comes to real-world settings, millions of factors could impact the state of a physical object -- material, friction, temperature, pressure, altitude, wear… the list goes on. With so many tangible conditions increasing the likelihood of deviation, it can be difficult to reproduce a digital twin that accurately represents real-world conditions. This is, in part, why some believe the next generation of digital twins will be more driven by artificial intelligence (AI).