In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type. The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion. Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Hundreds of millions of years of evolution have blessed our planet with a wide variety of lifeforms, each intelligent in its own fashion. Each species has evolved to develop innate skills, learning capacities, and a physical form that ensure its survival in its environment. But despite being inspired by nature and evolution, the field of artificial intelligence has largely focused on creating the elements of intelligence separately and fusing them together after development. While this approach has yielded great results, it has also limited the flexibility of AI agents in some of the basic skills found in even the simplest lifeforms.

The Air Force Research Laboratory, in partnership with United Kingdom's Defence Science and Technology Laboratory (Dstl), have demonstrated for the first time the ability for the U.S. and the U.K. to jointly develop, select, train and deploy state-of-the-art machine learning algorithms in support of the armed forces of each of the two nations. This research is designed to support adjacent, collaborating U.S. and U.K. brigades with enduring wide-area situational awareness, which aims to improve decision-making, increase operational tempo, reduce risk to life and reduce manpower burden. The in-person, virtual demonstration was hosted jointly at AFRL's Information Directorate in Rome and Dstl at its site near Salisbury, U.K., Oct. 18. The demonstration highlighted integrated AI technologies across the two nations, showcasing the ability to share data and algorithms through a common development and deployment platform to enable the rapid selection, testing and deployment of AI capabilities. The event was made possible by a U.K. and U.S. partnership agreement concerning autonomy and AI collaboration established in December 2020.

Every time a human or machine learns how to get better at a task, a trail of evidence is left behind. A sequence of physical changes--to cells in a brain or to numerical values in an algorithm--underlie the improved performance. But figuring out exactly what changes to make is no small feat. It's called the credit assignment problem, in which a brain or artificial intelligence system must pinpoint which pieces in its pipeline are responsible for errors and then make the necessary changes. Put more simply: It's a blame game to find who's at fault.

By enabling computers to perform each task intelligently, machine learning systems can carry out complex processes by learning from data. Recent years have seen exciting advances in machine learning, which have raised its capabilities across a suite of applications. For years, Google has been using machine learning for several tasks, including Autocorrecting misspelled words or showing useful results. Whats's more, they also created an individual's virtual assistant called Google Assistant. Just Say'OK Google,' and your assistant is ready to help you perform various tasks.

This year may go down as the point that regulators started to try to get a handle on the use of AI and ML in medical devices. Over the past 10 months, FDA has issued an AI/ML action plan for regulating the technology in medical devices, the European Commission has released contentious plans for the entire AI field and the U.K. has proposed an overhaul of how it regulates AI as a medical device. Now, the U.S. and U.K. have begun working together on a global initiative. Working with their peers at Health Canada, officials at FDA and the U.K.'s MHRA have laid out the following guiding principles: Collectively, the principles cover concerns about the possible biases of algorithms, their applicability to clinical practice and the potential for them to evolve as they are used in the real world. FDA and its collaborators have expanded on each of the principles, explaining, for example, that developers need to have "appropriate controls in place to manage risks of overfitting, unintended bias or degradation of the model" when their systems are "periodically or continually trained after deployment."

In rural areas, basic health care can be out of reach. Keller Rinaudo founded Zipline, a delivery company that uses drones to deliver necessary medical supplies within hours, even minutes. Keller Rinaudo is the CEO and co-founder of Zipline, a drone delivery company that delivers life-saving medicine to remote places. The company began by focusing on delivering blood for urgent medical situations. Previously, Rinaudo was also the CEO and a co-founder of Romotive, a former company established in 2011 that made inexpensive small robots that use mobile phones as their computing system, machine vision system, and wireless communication system.

Said Vladimir Puzyrev of Curtin Universitys Oil and Gas Innovation Centre and the School of Earth and Planetary Sciences, "This project is an important step towards adding value to existing digital geochemical datasets." Researchers at Australia's Curtin University and the Geological Survey of Western Australia are using deep learning to analyze geochemical data as part of an effort to expand mineral exploration in the region. The Western Australia Mineral Exploration (WAMEX) database contains more than 50 million samples, making manual analysis cost prohibitive and time consuming. Curtin's Vladimir Puzyrev said, "The ultimate aim of this research project is to help identify new mineral deposits in Western Australia by analyzing big geochemical data using deep learning methods."