On December 15, the United States Air Force successfully flew an AI copilot on a U-2 spy plane in California, marking the first time AI has controlled a U.S. military system. Dr. Will Roper, the Assistant Secretary of the Air Force for Acquisition, Technology and Logistics, reveals how he and his team made history: With call sign ARTUu, we trained uZero -- a world-leading computer program that dominates chess, Go, and even video games without prior knowledge of their rules -- to operate a U-2 spy plane. Though lacking those lively beeps and squeaks, ARTUu surpassed its motion picture namesake in one distinctive feature: it was the mission commander, the final decision authority on the human-machine team. And given the high stakes of global AI, surpassing science fiction must become our military norm. Our demo flew a reconnaissance mission during a simulated missile strike at Beale Air Force Base on Tuesday. ARTUu searched for enemy launchers while our pilot searched for threatening aircraft, both sharing the U-2's radar. With no pilot override, ARTUu made final calls on devoting the radar to missile hunting versus self-protection. Luke Skywalker certainly never took such orders from his X-Wing sidekick! The fact ARTUu was in command was less about any particular mission than how completely our military must embrace AI to maintain the battlefield decision advantage. Unlike Han Solo's "never-tell-me-the-odds" snub of C-3PO's asteroid field survival rate (approximately 3,720 to 1), our warfighters need to know the odds in dizzyingly-complex combat scenarios. Teaming with trusted AI across all facets of conflict -- even occasionally putting it in charge -- could tip those odds in our favor. But to trust AI, software design is key. Like a breaker box for code, the U-2 gave ARTUu complete radar control while "switching off" access to other subsystems. Had the scenario been navigating an asteroid field -- or more likely field of enemy radars -- those "on-off" switches could adjust. The design allows operators to choose what AI won't do to accept the operational risk of what it will. Creating this software breaker box -- instead of Pandora's -- has been an Air Force journey of more than a few parsecs...

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Amazon today rolled out Live Translation, a new Alexa feature that aims to assist with conversations between people who speak two different languages by leveraging speech recognition and machine translation technology. Amazon says that Live Translation can interpret between a number of dialects in real time, including English and French, Spanish, Hindi, Brazilian Portuguese, German, or Italian. From a report: The pandemic appears to have supercharged voice app usage, which was already on an upswing. According to a study by NPR and Edison Research, the percentage of voice-enabled device owners who use commands at least once a day rose between the beginning of 2020 and the start of April. Just over a third of smart speaker owners say they listen to more music, entertainment, and news from their devices than they did before, and owners report requesting an average of 10.8 tasks per week from their assistant this year compared with 9.4 different tasks in 2019. And according to a new report from Juniper Research, consumers will interact with voice assistants on 8.4 billion devices by 2024. Launching Live Translation requires asking Alexa on an Amazon Echo device to translate one of the supported languages. The command "Alexa, translate French" will translate between English and French, for example, while "Alexa, stop" will end the translation session. The Echo will beep during the session to indicate when to speak in the other language, and Echo devices with a screen like the Echo Show will display a transcription of the conversation. Users can take pauses between sentences, and Alexa will automatically detect the language in which they're speaking and translate each side of the conversation.

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"Don a headset which places a sensor on the back of your head, and it'll detect your brainwaves which can then be translated into digital actions," writes Engadget. VentureBeat reports that NextMind "has started shipping its real-time brain computer interface Dev Kit for $399." The device translates brain signals into digital commands, allowing you to control computers, AR/VR headsets, and IoT devices (lights, TVs, music, games, and so on) with your visual attention. Paris-based NextMind is part of a growing number of startups building neural interfaces that rely on machine learning algorithms. There are invasive devices like the one from Elon Musk's Neuralink, which in August revealed a prototype showing readings from a pig's brain using a coin-shaped device implanted under the skull. There are also noninvasive devices like the electromyography wristband that translates neuromuscular signals into machine-interpretable commands from Ctrl-labs, which Facebook acquired in September 2019. NextMind is developing a noninvasive device — an electroencephalogram (EEG) worn on the back of your head, where your brain's visual cortex is located. When we spoke with NextMind CEO Sid Kouider last year, he promised the kits would begin shipping in Q2 2020. Then the pandemic hit. "We had about three, four months of delays due to COVID-19, but not more than that in terms of production," Kouider told VentureBeat. The company shipped "hundreds" of Dev Kits in November after producing its first thousand units. Another thousand units are set to be produced next month.

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An anonymous reader shares a report: More recent Nvidia graphics cards have a proprietary feature called Deep Learning Super Sampling (DLSS), and while it's often been touted as a powerful new rendering tool, the results have sometimes been underwhelming. Some of this is down to the oddly mixed-message around how DLSS was rolled-out: it only works on more recent Nvidia cards that are still near the cutting edge of PC graphics hardware⦠but DLSS is designed to render images at lower resolutions but display them as if they were rendered natively at a higher resolution. If you had just gotten a new Nvidia card and were excited to see what kind of framerates and detail levels it could sustain, what DLSS actually did sounded counterintuitive. Even games like Control, whose support of DLSS was especially praised, left me scratching my head about why I would want to use the feature. On my 4K TV, Control looked and ran identically well with and without DLSS, so why wouldn't I just max-out my native graphics settings instead rather than use a fancy upscaler? Intellectually, I understood the DLSS could produce similarly great looking images without taxing my hardware as much, but I neither fully believed it, nor had I seen a game where the performance gain was meaningful. Cyberpunk 2077 converted me. DLSS is a miracle, and without it there's probably no way I would ever have been happy with my graphics settings or the game's performance. I have a pretty powerful video card, an RTX 2080 TI, but my CPU is an old i5 overclocked to about 3.9 GHz and it's a definite bottleneck on a lot of games. Without DLSS, Cyberpunk 2077 was very hard to get running smoothly. The busiest street scenes would look fine if I were in a static position, but a quick pan with my mouse would cause the whole world to stutter. If I was walking around Night City, I would get routine slow-downs. Likewise, sneaking around and picking off guards during encounters was all well and good but the minute the bullets started flying, with grenades exploding everywhere and positions changing rapidly, my framerate would crater to the point where the game verged on unplayable. To handle these peaks of activity, I had to lower my detail settings way below what I wanted, and what my hardware could support for about 80 percent of my time with the game. Without DLSS, I never found a balance I was totally happy with. The game neither looked particularly great, nor did it run very well. DLSS basically solved this problem for me. With it active, I could run Cyberpunk at max settings, with stable framerates in all but the busiest scenes.

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Salesforce today peeled back the curtains on ReceptorNet, a machine learning system researchers at the company developed in partnership with clinicians at the University of Southern California's Lawrence J. Ellison Institute for Transformative Medicine of USC. From a report: The system, which can determine a critical biomarker for oncologists when deciding on the appropriate treatment for breast cancer patients, achieved 92% accuracy in a study published in the journal Nature Communications. Breast cancer affects more than 2 million women each year, with around one in eight women in the U.S. developing the disease over the course of their lifetime. In 2018 in the U.S. alone, there were also 2,550 new cases of breast cancer in men. And rates of breast cancer are increasing in nearly every region around the world. In an effort to address this, Salesforce researchers developed an algorithm -- the aforementioned ReceptorNet -- that can predict hormone-receptor status from inexpensive and ubiquitous images of tissue. Typically, breast cancer cells extracted during a biopsy or surgery are tested to see if they contain proteins that act as estrogen or progesterone receptors. (When the hormones estrogen and progesterone attach to these receptors, they fuel the cancer growth.) But these types of biopsy images are less widely available and require a pathologist to review. In contrast to the immunohistochemistry process favored by clinicians, which requires a microscope and tends to be expensive and not readily available in parts of the world, ReceptorNet determines hormone receptor status via hematoxylin and eosin (H&E) staining, which takes into account the shape, size, and structure of cells. Salesforce researchers trained the system on several thousand H&E image slides from cancer patients in "dozens" of hospitals around the world.

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Apple has moved its self-driving car unit under the leadership of top artificial intelligence executive John Giannandrea, who will oversee the company's continued work on an autonomous system that could eventually be used in its own car, Bloomberg reports. From the report: The project, known as Titan, is run day-to-day by Doug Field. His team of hundreds of engineers have moved to Giannandrea's artificial intelligence and machine-learning group, according to people familiar with the change. Previously, Field reported to Bob Mansfield, Apple's former senior vice president of hardware engineering. Mansfield has now fully retired from Apple, leading to Giannandrea taking over. Giannandrea joined Apple in 2018 as its vice president of AI Strategy and Machine Learning before being promoted to Apple's executive team as a senior vice president later that year. He ran Google's machine-learning and search teams before that. At Apple, in addition to the car project, he is in charge of Siri and machine-learning technologies across Apple's products. Mansfield initially retired from Apple in 2012, only to return for less than a year as its senior vice president in charge of chip technology. Mansfield stepped down from that role in 2013 and then remained as a part-time consultant.

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Google CEO Sundar Pichai apologized Wednesday for the company's handling of the departure of AI ethics researcher Timnit Gebru and said he would investigate the events and work to restore trust, according to an internal memo sent companywide and obtained by Axios. From the report: Gebru's exit has provoked anger and consternation within Google as well as in academic circles, with thousands of people signing an open letter urging Google to reexamine its practices. In the note, Pichai acknowledged the depth of the damage done by the company's actions and said the company would look at all aspects of the situation, but stopped short of saying the company made a mistake in removing Gebru. "I've heard the reaction to Dr. Gebru's departure loud and clear: it seeded doubts and led some in our community to question their place at Google," Pichai said in the memo. " I want to say how sorry I am for that, and I accept the responsibility of working to restore your trust." While Pichai's memo strikes a contrite tone, it's unclear how far it will go to addressing the significant upset within Google's ranks, especially among those concerned with its commitments to diversity and academic freedom.

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Google today launched an experimental app for Android that leverages AI to make communication more accessible for people with speech and motor impairments. Called Look to Speak, it tracks eye movements to let people use their eyes to select prewritten, customizable phrases and have them spoken aloud. From a report: Approximately 18.5 million people in the U.S. have a speech, voice, or language impairment. Eye gaze-tracking devices can provide a semblance of independence, but they're often not portable and tend to be expensive. The entry-level Tobii 4C eye tracker starts at $150, for instance. To address this need, speech and language therapist Richard Cave began collaborating with a small group at Google to develop Look to Speak. The app, which is available for free and compatible with Android 9.0 and above, enables users to glance left, right, or up to select what they wish to say from a phrase list. With Look to Speak, people can personalize the words and sentences on their list and adjust eye gaze sensitivity settings. Google says the app's data remains private and never leaves the phone on which it's installed.

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President Donald Trump on Thursday signed an executive order that aims to guide how federal agencies adopt artificial intelligence (AI) as part of efforts to build public trust in the government using this technology. From a report: The order itself directs federal agencies to be guided by nine principles when designing, developing, acquiring, and using AI. These principles emphasise that AI use by federal agencies be lawful; purposeful and performance-driven; accurate, reliable, and effective; safe, secure, and resilient; understandable; responsible and traceable; regularly monitored; transparent; and accountable. To implement these principles, the order directs the Office of Management and Budget to create a roadmap by the end of May 2021 for how the government will better support the use of AI. This roadmap will include a schedule for engaging with the public and timelines for finalising relevant policy guidance. The order also calls on agencies to continue to use voluntary consensus standards developed with industry participation. "This order recognises the potential for AI to improve government operations, such as by reducing outdated or duplicative regulations, enhancing the security of federal information systems, and streamlining application processes," Trump said in a statement. Federal agencies will also be required to prepare an inventory of AI use cases, as well as review and assess these use cases for consistency. The General Services Administration, meanwhile, has been directed to establish an AI track within the Presidential Innovation Fellows program to attract experts from industry and academia to work within agencies to further the design, development, acquisition, and use of AI in government.

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Timnit Gebru, one of the best-known AI researchers today and co-lead of an AI ethics team at Google, said she was fired Wednesday for sending an email to "non-management employees that is inconsistent with the expectations of a Google manager." VentureBeat reports: She said Google AI employees who report to her were emailed and told that she accepted her resignation when she did not offer her resignation. According to Casey Newton's Platformer, who reportedly obtained a copy, Gebru sent the email in question to the Google Brain Women and Allies listserv. In it, Gebru expresses frustration with the lack of progress in hiring women at Google and lack of accountability for failure to make progress. She also said was told not to publish a piece of research and advised employees to no longer fill out diversity paperwork because it didn't matter. No mention is made of resignation. "There is no way more documents or more conversations will achieve anything. We just had a Black research all hands with such an emotional show of exasperation. Do you know what happened since? Silencing in the most fundamental way possible," the email reads. When asked by VentureBeat for comment, a Google spokesperson provided a link to the Platformer article with a copy of an email sent Thursday by Google AI chief Jeff Dean to company research staff. In it, Dean said a research paper written by Gebru and other researchers was submitted for publication at a conference before completing a review process and addressing feedback. In response, Dean said he received an email from Gebru. "Timnit wrote that if we didn't meet these demands, she would leave Google and work on an end date. We accept and respect her decision to resign from Google," he said. "Given Timnit's role as a respected researcher and a manager in our Ethical AI team, I feel badly that Timnit has gotten to a place where she feels this way about the work we're doing. I also feel badly that hundreds of you received an email just this week from Timnit telling you to stop work on critical DEI programs. Please don't. I understand the frustration about the pace of progress, but we have important work ahead and we need to keep at it."

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Researchers have made a major breakthrough using artificial intelligence that could revolutionize the hunt for new medicines. The scientists have created A.I. software that uses a protein's DNA sequence to predict its three-dimensional structure to within an atomâ(TM)s width of accuracy. weiserfireman shares a report: The achievement, which solves a 50-year-old challenge in molecular biology, was accomplished by a team from DeepMind, the London-based artificial intelligence company that is part of Google parent Alphabet. Until now, DeepMind was best known for creating A.I. that could beat the best human players at the strategy game Go, a major milestone in computer science. DeepMind achieved the protein shape breakthrough in a biennial competition for algorithms that can be used to predict protein structures. The competition asks participants to take a protein's DNA sequence and then use it to determine the protein's three-dimensional shape. Across more than 100 proteins, DeepMind's A.I. software, which it called AlphaFold 2, was able to predict the structure to within about an atom's width of accuracy in two-thirds of cases and was highly accurate in most of the remaining one-third of cases, according to John Moult, a molecular biologist at the University of Maryland who is director of the competition, called the Critical Assessment of Structure Prediction, or CASP. It was far better than any other method in the competition, he said.

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Dyson will invest an additional 2.75bn pound ($3.67 billion) on developing technologies and products over the next five years [Editor's note: the link may be paywalled; alternative source], as the appliances brand pushes deeper into areas such as artificial intelligence, robotics and energy storage. From a report: The company founded by billionaire James Dyson and famous for its vacuum cleaners said it intended to double its portfolio of products by 2025 and enter new fields, taking it "beyond the home" for the first time. Although it did not provide any breakdown of the investments, they will be focused in Singapore, where the group controversially decided to move its headquarters last year, as well as the UK and the Philippines. The announcement comes more than a year after Dyson abandoned its ambitious plans to manufacture an electric vehicle from scratch in the Asian city-state. Sir James had hoped that the EV project would redefine his business but, after spending hundreds of millions of pounds, concluded that it was too expensive to compete against established carmakers.

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In some of the most remote and impoverished corners of the world, where respiratory illnesses abound and trained medical professionals fear to tread, diagnosis is increasingly powered by artificial intelligence and the internet. From a report: In less than a minute, a new app on a phone or a computer can scan an X-ray for signs of tuberculosis, Covid-19 and 27 other conditions. TB, the most deadly infectious disease in the world, claimed nearly 1.4 million lives last year. The app, called qXR, is one of many A.I.-based tools that have emerged over the past few years for screening and diagnosing TB. The tools offer hope of flagging the disease early and cutting the cost of unnecessary lab tests. Used at large scale, they may also spot emerging clusters of disease. "Among all of the applications of A.I., I think digitally interpreting an image using an algorithm instead of a human radiologist is probably furthest along," said Madhukar Pai, the director of the McGill International TB Center in Montreal. Artificial intelligence cannot replace clinicians, Dr. Pai and other experts cautioned. But the combination of A.I. and clinical expertise is proving to be powerful. "The machine plus clinician is better than the clinician, and it's also better than machine alone," said Dr. Eric Topol, the director of the Scripps Research Translational Institute in San Diego and the author of a book on the use of A.I. in medicine. In India, where roughly one-quarter of the world's TB cases occur, an app that can flag the disease in remote locations is urgently needed.

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An anonymous reader shares a report: Last year, electric scooters were booming in big cities across the United States and other countries as urbanites embraced a relatively novel way of getting around town. The rentable, battery-boosted rides also brought a rising number of pedestrian-involved crashes as some riders illegally zipped down sidewalks and darted around traffic before the craze was interrupted by the pandemic. Downtowns became ghost towns when businesses told workers to stay home, and e-scooter business slowed, dropping as much as 70 percent. As people reemerge from shutdowns, wary of congested trains and buses, the micromobility industry may enjoy a post-pandemic renaissance, analysts say. People are buying more of the two-wheelers in some markets. China-based Niu saw sales rise sales 6.3 percent internationally as cities such as Boston, New York and Minneapolis expanded bike lanes to encourage social distancing, setting the framework for a potential e-scooter comeback. By the time the novel coronavirus is in the rearview mirror, riders could be encountering a new type of e-scooter, one that picks up safety tools from modern cars. Last week, micromobility companies Luna and Voi Technology came together to kick off a test fleet of e-scooters with pedestrian detection. The test scooters are deployed in Northampton, England. Luna, a Dublin-based start-up, developed the system of cameras and sensors that it says will enable the scooters to learn and respond to their environments. Voi, a Swedish e-scooter manufacturer, integrated Luna's computer vision system into 50 of its e-scooters. [...] The immediate goal for Voi and Luna is to have the devices detect people and objects in a scooter's path, even if the rider doesn't see them. The idea is to make scooter users and pedestrians feel safe as they navigate busy streets, which is the most significant issue plaguing cities with legalized shared e-scooters, according to Fredrik Hjelm, CEO of Voi.

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An anonymous reader shares a report: Researchers in China say they've created sarcasm detection AI that achieved state-of-the-art performance on a dataset drawn from Twitter. The AI uses multimodal learning that combines text and imagery since both are often needed to understand whether a person is being sarcastic. The researchers argue that sarcasm detection can assist with sentiment analysis and crowdsourced understanding of public attitudes about a particular subject. In a challenge initiated earlier this year, Facebook is using multimodal AI to recognize whether memes violate its terms of service. The researchers' AI focuses on differences between text and imagery and then combines those results to make predictions. It also compares hashtags to tweet text to help assess the sentiment a user is trying to convey. "Particularly, the input tokens will give high attention values to the image regions contradicting them, as incongruity is a key character of sarcasm," the paper reads. "As the incongruity might only appear within the text (e.g., a sarcastic text associated with an unrelated image), it is necessary to consider the intra modality incongruity." On a dataset drawn from Twitter, the model achieved a 2.74% improvement on a sarcasm detection F1 score compared to HFM, a multimodal detection model introduced last year. The new model also achieved an 86% accuracy rate, compared to 83% for HFM. The paper was published jointly by the Chinese Academy of Sciences and the Institute of Information Engineering, both in Beijing, China. The paper was presented this week at the virtual Empirical Methods in Natural Language Processing (EMNLP) conference.

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"Amazon's engineers are tweaking Alexa's algorithm to help the virtual assistant guess users' requests, and offer to resolve them, before the demand is even uttered," reports ZDNet: After being asked, for example, how long a cup of tea should brew for, Alexa will be able to suggest setting a timer for the number of minutes that are recommended. Alexa engineers Anjishnu Kumar and Anand Rathi explained in a blog post that the improvement is the continuation of efforts to make interactions with the virtual assistant as natural as possible. Chatting with Alexa should be as natural as talking to another human being, said the engineers, and enabling the technology to anticipate what's coming next in conversation is key to enable a smooth flow of dialogue. "Now, we're taking another step towards natural interaction with a capability that lets Alexa infer customers' latent goals — goals that are implicit in customer requests but not directly expressed," wrote Kumar and Rathi.... The engineers used a deep-learning model that accounts for various elements in the dialogue with the customer before deciding whether a suggestion should be triggered or not. The algorithm makes an assessment based on factors ranging from the text of the dialogue to the users' previous behaviors towards the virtual assistant, including how often they engage with Alexa's multi-skill suggestions. "We are thrilled about this invention as it aids discovery of Alexa's skills and provides increased utility to our customers," said the Amazon engineers. "Our early experiments showed that not all dialogue contexts are well suited to latent-goal discovery," the engineers point out in their blog post. "When a customer asked for 'recipes for chicken', for instance, one of our initial prototypes would incorrectly follow up by asking, 'Do you want me to play chicken sounds?'"

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An anonymous reader quotes a report from Ars Technica: On Thursday, an Amazon AWS blogpost announced that the company has moved most of the cloud processing for its Alexa personal assistant off of Nvidia GPUs and onto its own Inferentia Application Specific Integrated Circuit (ASIC). Amazon dev Sebastien Stormacq describes the Inferentia's hardware design as follows: "AWS Inferentia is a custom chip, built by AWS, to accelerate machine learning inference workloads and optimize their cost. Each AWS Inferentia chip contains four NeuronCores. Each NeuronCore implements a high-performance systolic array matrix multiply engine, which massively speeds up typical deep learning operations such as convolution and transformers. NeuronCores are also equipped with a large on-chip cache, which helps cut down on external memory accesses, dramatically reducing latency and increasing throughput." When an Amazon customer -- usually someone who owns an Echo or Echo dot -- makes use of the Alexa personal assistant, very little of the processing is done on the device itself. [...] According to Stormacq, shifting this inference workload from Nvidia GPU hardware to Amazon's own Inferentia chip resulted in 30-percent lower cost and 25-percent improvement in end-to-end latency on Alexa's text-to-speech workloads. Amazon isn't the only company using the Inferentia processor -- the chip powers Amazon AWS Inf1 instances, which are available to the general public and compete with Amazon's GPU-powered G4 instances. Amazon's AWS Neuron software development kit allows machine-learning developers to use Inferentia as a target for popular frameworks, including TensorFlow, PyTorch, and MXNet.

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Artificial intelligence is being used to create new songs seemingly performed by Frank Sinatra and other dead stars. 'Deepfakes' are cute tricks -- but they could change pop for ever. From a report: "It's Christmas time! It's hot tub time!" sings Frank Sinatra. At least, it sounds like him. With an easy swing, cheery bonhomie, and understated brass and string flourishes, this could just about pass as some long lost Sinatra demo. Even the voice -- that rich tone once described as "all legato and regrets" -- is eerily familiar, even if it does lurch between keys and, at times, sounds as if it was recorded at the bottom of a swimming pool. The song in question not a genuine track, but a convincing fake created by "research and deployment company" OpenAI, whose Jukebox project uses artificial intelligence to generate music, complete with lyrics, in a variety of genres and artist styles. Along with Sinatra, they've done what are known as "deepfakes" of Katy Perry, Elvis, Simon and Garfunkel, 2Pac, Celine Dion and more. Having trained the model using 1.2m songs scraped from the web, complete with the corresponding lyrics and metadata, it can output raw audio several minutes long based on whatever you feed it. Input, say, Queen or Dolly Parton or Mozart, and you'll get an approximation out the other end. "As a piece of engineering, it's really impressive," says Dr Matthew Yee-King, an electronic musician, researcher and academic at Goldsmiths. (OpenAI declined to be interviewed.) "They break down an audio signal into a set of lexemes of music -- a dictionary if you like -- at three different layers of time, giving you a set of core fragments that is sufficient to reconstruct the music that was fed in. The algorithm can then rearrange these fragments, based on the stimulus you input. So, give it some Ella Fitzgerald for example, and it will find and piece together the relevant bits of the 'dictionary' to create something in her musical space." Admirable as the technical achievement is, there's something horrifying about some of the samples, particularly those of artists who have long since died -- sad ghosts lost in the machine, mumbling banal cliches. "The screams of the damned" reads one comment below that Sinatra sample; "SOUNDS FUCKING DEMONIC" reads another. We're down in the Uncanny Valley. Deepfake music is set to have wide-ranging ramifications for the music industry as more companies apply algorithms to music. Google's Magenta Project -- billed as "exploring machine learning as a tool in the creative process" -- has developed several open source APIs that allow composition using entirely new, machine-generated sounds, or human-AI co-creations. Numerous startups, such as Amper Music, produce custom, AI-generated music for media content, complete with global copyright. Even Spotify is dabbling; its AI research group is led by Francois Pachet, former head of Sony Music's computer science lab.

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Can algorithms identify unusual medication orders or profiles more accurately than humans? Not necessarily. From a report: A study coauthored by researchers at the Universite Laval and CHU Sainte-Justine in Montreal found that one model physicians used to screen patients performed poorly on some orders. The study offers a reminder that unvetted AI and machine learning may negatively impact outcomes in medicine. Pharmacists review lists of active medications -- i.e., pharmacological profiles -- for inpatients under their care. This process aims to identify medications that could be abused, but most medication orders don't show drug-related problems. Publications from over a decade ago illustrate technology's potential to help pharmacists streamline workflows by taking on tasks like reviewing orders. But while more recent research has investigated AI's potential in pharmacology, few studies have demonstrated its efficacy. The coauthors of this latest work looked at a model deployed in a tertiary-care mother-and-child academic hospital between April 2020 and August 2020. The model was trained on a dataset of 2,846,502 medication orders from 2005 to 2018. These had been extracted from a pharmacy database and preprocessed into 1,063,173 profiles. Prior to data collection, the model was retrained every month with 10 years of the most recent data from the database in order to minimize drift, which occurs when a model loses its predictive power.

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For his monthly intention in November, Pope Francis prayed that AI will be beneficial for humanity. From a report: It's up for debate whether the development of automation and AI will ultimately be good for humankind, and it can't hurt to have a little divine intervention on our side. What he's saying: "We pray that the progress of robotics and artificial intelligence may always serve humankind," reads Francis' intention for November, which is published each month by the Pope's Worldwide Prayer Network. Background: This isn't the first time Francis has ventured into the fraught territory of AI ethics and alignment. In February, the Vatican hosted executives from IBM and Microsoft for a summit on "human-centered" ways of designing AI. They formulated the "Rome Call for AI Ethics," which called for AI to be designed with a focus on the good of the environment and "our common and shared home and of its human inhabitants."

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