Amazon.com is offering other companies the ability to use the building blocks of the Alexa digital assistant for their own automated versions, the latest effort to embed the company's voice software into other devices. From a report: Fiat Chrysler Automobiles NV will be the first to use Alexa Custom Assistant, relying on Amazon-built speech recognition and other software to power the automaker's in-car tools, Amazon said Friday in a statement. The retail and technology giant also invited other companies to customize the underlying Alexa system with their own wake word, voice and unique capabilities. Alexa is most closely associated with Echo smart speakers, but Amazon has been working to extend the software's reach, and fend off rivals like Apple and Alphabet's Google, by adding utility for tasks like home automation and the potentially lucrative and fiercely contested market for in-car software. Amazon, which lacks the massive base of captive smartphone users of its main rivals, has suggested voice assistants should be able to talk to one another. The company, like competitors, already offers for rent elements of the technology that powers its digital assistant, but Alexa Custom Assistant represents a more complete set of tools, Amazon said.

Read more of this story at Slashdot.

Researchers have created a machine learning system that they claim can determine a person's political party, with reasonable accuracy, based only on their face. TechCrunch reports: The study, which appeared this week in the Nature journal Scientific Reports, was conducted by Stanford University's Michal Kosinski. Kosinski made headlines in 2017 with work that found that a person's sexual preference could be predicted from facial data. [...] The algorithm itself is not some hyper-advanced technology. Kosinski's paper describes a fairly ordinary process of feeding a machine learning system images of more than a million faces, collected from dating sites in the U.S., Canada and the U.K., as well as American Facebook users. The people whose faces were used identified as politically conservative or liberal as part of the site's questionnaire. The algorithm was based on open-source facial recognition software, and after basic processing to crop to just the face (that way no background items creep in as factors), the faces are reduced to 2,048 scores representing various features -- as with other face recognition algorithms, these aren't necessary intuitive things like "eyebrow color" and "nose type" but more computer-native concepts. The system was given political affiliation data sourced from the people themselves, and with this it diligently began to study the differences between the facial stats of people identifying as conservatives and those identifying as liberal. Because it turns out, there are differences. Of course it's not as simple as "conservatives have bushier eyebrows" or "liberals frown more." Nor does it come down to demographics, which would make things too easy and simple. After all, if political party identification correlates with both age and skin color, that makes for a simple prediction algorithm right there. But although the software mechanisms used by Kosinski are quite standard, he was careful to cover his bases in order that this study, like the last one, can't be dismissed as pseudoscience. The most obvious way of addressing this is by having the system make guesses as to the political party of people of the same age, gender and ethnicity. The test involved being presented with two faces, one of each party, and guessing which was which. Obviously chance accuracy is 50%. Humans aren't very good at this task, performing only slightly above chance, about 55% accurate. The algorithm managed to reach as high as 71% accurate when predicting political party between two like individuals, and 73% presented with two individuals of any age, ethnicity or gender (but still guaranteed to be one conservative, one liberal).

Read more of this story at Slashdot.

An anonymous reader quotes a report from VentureBeat: Google researchers developed and benchmarked techniques they claim enabled them to train a language model containing more than a trillion parameters. They say their 1.6-trillion-parameter model, which appears to be the largest of its size to date, achieved an up to 4 times speedup over the previously largest Google-developed language model (T5-XXL). As the researchers note in a paper detailing their work, large-scale training is an effective path toward powerful models. Simple architectures, backed by large datasets and parameter counts, surpass far more complicated algorithms. But effective, large-scale training is extremely computationally intensive. That's why the researchers pursued what they call the Switch Transformer, a "sparsely activated" technique that uses only a subset of a model's weights, or the parameters that transform input data within the model. In an experiment, the researchers pretrained several different Switch Transformer models using 32 TPU cores on the Colossal Clean Crawled Corpus, a 750GB-sized dataset of text scraped from Reddit, Wikipedia, and other web sources. They tasked the models with predicting missing words in passages where 15% of the words had been masked out, as well as other challenges, like retrieving text to answer a list of increasingly difficult questions. The researchers claim their 1.6-trillion-parameter model with 2,048 experts (Switch-C) exhibited "no training instability at all," in contrast to a smaller model (Switch-XXL) containing 395 billion parameters and 64 experts. However, on one benchmark -- the Sanford Question Answering Dataset (SQuAD) -- Switch-C scored lower (87.7) versus Switch-XXL (89.6), which the researchers attribute to the opaque relationship between fine-tuning quality, computational requirements, and the number of parameters. This being the case, the Switch Transformer led to gains in a number of downstream tasks. For example, it enabled an over 7 times pretraining speedup while using the same amount of computational resources, according to the researchers, who demonstrated that the large sparse models could be used to create smaller, dense models fine-tuned on tasks with 30% of the quality gains of the larger model. In one test where a Switch Transformer model was trained to translate between over 100 different languages, the researchers observed "a universal improvement" across 101 languages, with 91% of the languages benefitting from an over 4 times speedup compared with a baseline model. "Though this work has focused on extremely large models, we also find that models with as few as two experts improve performance while easily fitting within memory constraints of commonly available GPUs or TPUs," the researchers wrote in the paper. "We cannot fully preserve the model quality, but compression rates of 10 to 100 times are achievable by distilling our sparse models into dense models while achieving ~30% of the quality gain of the expert model."

Read more of this story at Slashdot.

The maker of a defunct cloud photo storage app that pivoted to selling facial recognition services has been ordered to delete user data and any algorithms trained on it, under the terms of an FTC settlement. TechCrunch reports: The regulator investigated complaints the Ever app -- which gained earlier notoriety for using dark patterns to spam users' contacts -- had applied facial recognition to users' photographs without properly informing them what it was doing with their selfies. Under the proposed settlement, Ever must delete photos and videos of users who deactivated their accounts and also delete all face embeddings (i.e. data related to facial features which can be used for facial recognition purposes) that it derived from photos of users who did not give express consent to such a use. Moreover, it must delete any facial recognition models or algorithms developed with users' photos or videos. This full suite of deletion requirements -- not just data but anything derived from it and trained off of it -- is causing great excitement in legal and tech policy circles, with experts suggesting it could have implications for other facial recognition software trained on data that wasn't lawfully processed. Or, to put it another way, tech giants that surreptitiously harvest data to train AIs could find their algorithms in hot water with the US regulator.

Read more of this story at Slashdot.

Amazon, Walmart, and other companies are using artificial intelligence "to decide whether it makes economic sense to process a return," reports the Wall Street Journal: For inexpensive items or large ones that would incur hefty shipping fees, it is often cheaper to refund the purchase price and let customers keep the products. The relatively new approach, popularized by Amazon and a few other chains, is being adopted more broadly during the Covid-19 pandemic, as a surge in online shopping forces companies to rethink how they handle returns. "We are getting so many inquiries about this that you will see it take off in coming months," said Amit Sharma, chief executive of Narvar Inc., which processes returns for retailers... A Target Corp. spokeswoman said the retailer gives customers refunds and encourages them to donate or keep the item in a small number of cases in which the company deems that option is easier than returning the purchase. A Walmart spokeswoman said the "keep it" option is designed for merchandise it doesn't plan to resell and is determined by customers' purchase history, the value of the products and the cost of processing the returns... Processing online returns can cost $10 to $20, excluding freight, depending on the item, said Rick Faulk, chief executive of Locus Robotics, which uses robots to help automate returns.

Read more of this story at Slashdot.

The author of the book Online Afterlives describes the unusual projects of people like Eugenia Kuyda, co-founder of Luka, an AI-powered chat simulator that books restaurant reservations and makes recommendations. Kuyda worked with computer scientists to convert several thousand text messages between deceased tech entrepreneur Roman Mazurenko and his friends and relatives into a chatbot simulation: "How are you there?" asks a friend. "I'm OK. A little down. I hope you aren't doing anything interesting without me," Roman responds. His friend replies that they all miss him. Another acquaintance asks him if God and the soul exist. Having probably indicated his atheism in chats while he was alive, he says no. "Only sadness." Not content with Luka, Eugenia also designed a chatbot called Replika. A cross between a diary and a personal assistant, Replika asks users a series of questions, eventually learning to mimic their personalities. The goal is to get closer to creating a digital avatar that would be able to reproduce us and replace us once we're dead, but also one that is able to create "friendships" with humans. Since the second half of 2017, over two million people have downloaded Replika onto their mobile devices... Luka and Replika are not the only inventions designed to give a voice to the digital ghosts of the deceased. A few years ago, James Vlahos, an American journalist who has been an AI enthusiast since childhood, created what he calls a "Dadbot." It all started on April 24, 2016, when his father John was diagnosed with lung cancer. Upon learning of his father's illness, James began recording all of their conversations with the idea of writing a commemorative book after his father's death. After 12 sessions, each an hour and a half, he found himself with 91,970 words. The printed transcripts filled around 203 pages... He decided to use the recordings of his father to create something other than a commemorative book. He remembered writing an article that discussed PullString (previously known as ToyTalk), a program designed to create conversations with fictional characters... James used PullString to reorganize the MP3 recordings of his father. He also used it to create his Dadbot, software that works on his smartphone and simulates a written conversation with John, based on the processing of almost 100,000 recorded words... The tone of the conversations reflects the personality of the deceased: "Where are you now?" asks James. "As a bot I suppose I exist somewhere on a computer server in San Francisco. "And also, I suppose, in the minds of people who chat with me."

Read more of this story at Slashdot.

Slashdot reader the_newsbeagle shares an article from IEEE Spectrum: Many associate XPrize with a $10-million award offered in 1996 to motivate a breakthrough in private space flight. But the organization has since held other competitions related to exploration, ecology, and education. And in November, they launched the Pandemic Response Challenge, which will culminate in a $500,000 award to be split between two teams that not only best predict the continuing global spread of COVID-19, but also prescribe policies to curtail it... For Phase 1, teams had to submit prediction models by 22 December... Up to 50 teams will make it to Phase 2, where they must submit a prescription model... The top two teams will split half a million dollars. The competition may not end there. Amir Banifatemi, XPrize's chief innovation and growth officer, says a third phase might test models on vaccine deployment prescriptions. And beyond the contest, some cities or countries might put some of the Phase 2 or 3 models into practice, if Banifatemi can find adventurous takers. The organizers expect a wide variety of solutions. Banifatemi says the field includes teams from AI strongholds such as Stanford, Microsoft, MIT, Oxford, and Quebec's Mila, but one team consists of three women in Tunisia. In all, 104 teams from 28 countries have registered. "We're hoping that this competition can be a springboard for developing solutions for other really big problems as well," Miikkulainen says. Those problems include pandemics, global warming, and challenges in business, education, and healthcare. In this scenario, "humans are still in charge," he emphasizes. "They still decide what they want, and AI gives them the best alternatives from which the decision-makers choose." But Miikkulainen hopes that data science can help humanity find its way. "Maybe in the future, it's considered irresponsible not to use AI for making these policies," he says. For the Covid-19 competition, Banifatemi emphasized that one goal was "to make the resulting insights available freely to everyone, in an open-source manner — especially for all those communities that may not have access to data and epidemiology divisions, statisticians, or data scientists."

Read more of this story at Slashdot.

The machine learning company OpenAI is developing models that improve computer vision and can produce original images from a text prompt. From a report: The new models are the latest steps in ongoing efforts to create machine learning systems that exhibit elements of general intelligence, while performing tasks that are actually useful in the real world -- without breaking the bank on computing power. OpenAI this week is announcing two new systems that attempt to do for images what its landmark GPT-3 model did last year for text generation. DALL-E is a neural network that can "take any text and make an image out of it," says Ilya Sutskever, OpenAI co-founder and chief scientist. That includes concepts it would never have encountered in training, like the drawing of an anthropomorphic daikon radish walking a dog. DALL-E operates somewhat similarly to GPT-3, the huge transformer model that can generate original passages of text based on a short prompt. CLIP, the other new neural network, "can take any set of visual categories and instantly create very strong and reliable visually classifiable text descriptions," says Sutskever, improving on existing computer vision techniques with less training and expensive computational power.

Read more of this story at Slashdot.

Farming equipment like combine harvesters "beam back reams of data to its manufacturer," reports Forbes: GPS records the combine's precise path through the field as it moves. Sensors tally the number of crops gathered per acre and the spacing between them. On a sister machine called a planter, algorithms adjust the distribution of seeds based on which parts of the soil have in past years performed best. Another machine, a sprayer, uses algorithms to scan for weeds and zap them with pesticides. Meanwhile sensors record the wear and tear on the machines, so that when the farmer who operates them heads to the local distributor to look for a replacement part, it has already been ordered and is waiting for them. Farming may be an earthy industry, but much of it now takes place in the cloud. Leading farm machine makers like Chicago-based John Deere or Georgia's AGCO collect data from all around the world thanks to the ability of their bulky machines to extract a huge variety of metrics from farmers' fields and store it online... The amassing of all that data in the hands of the few major companies that sell farm equipment across the country or worldwide has opened up big opportunities for the "smart farming" industry, even as many in the farming community are reluctant to part with information about the fields they plow.... Equipment makers with sufficient sales of machines around the country may in theory actually be able to predict, at least to some small but meaningful extent, the prices of various crops by analyzing the data its machines are sending in — such as "yields" of crops per acre, the amount of fertilizer used, or the average number of seeds of a given crop planted in various regions. Were the company then to sell that data to a commodities trader, say, it could likely reap a windfall: normally, the markets must wait for highly-anticipated government surveys to run their course.

Read more of this story at Slashdot.

Rohit Prasad, Vice President and Head Scientist of Alexa at Amazon, writes: While Turing's original vision continues to be inspiring, interpreting his test as the ultimate mark of AI's progress is limited by the era when it was introduced. For one, the Turing Test all but discounts AI's machine-like attributes of fast computation and information lookup, features that are some of modern AI's most effective. The emphasis on tricking humans means that for an AI to pass Turing's test, it has to inject pauses in responses to questions like, "do you know what is the cube root of 3434756?" or, "how far is Seattle from Boston?" In reality, AI knows these answers instantaneously, and pausing to make its answers sound more human isn't the best use of its skills. Moreover, the Turing Test doesn't take into account AI's increasing ability to use sensors to hear, see, and feel the outside world. Instead, it's limited simply to text. To make AI more useful today, these systems need to accomplish our everyday tasks efficiently. If you're asking your AI assistant to turn off your garage lights, you aren't looking to have a dialogue. Instead, you'd want it to fulfill that request and notify you with a simple acknowledgment, "ok" or "done." Even when you engage in an extensive dialogue with an AI assistant on a trending topic or have a story read to your child, you'd still like to know it is an AI and not a human. In fact, "fooling" users by pretending to be human poses a real risk. Imagine the dystopian possibilities, as we've already begun to see with bots seeding misinformation and the emergence of deep fakes. Instead of obsessing about making AIs indistinguishable from humans, our ambition should be building AIs that augment human intelligence and improve our daily lives in a way that is equitable and inclusive. A worthy underlying goal is for AIs to exhibit human-like attributes of intelligence -- including common sense, self-supervision, and language proficiency -- and combine machine-like efficiency such as fast searches, memory recall, and accomplishing tasks on your behalf. The end result is learning and completing a variety of tasks and adapting to novel situations, far beyond what a regular person can do.

Read more of this story at Slashdot.

schwit1 quotes Intelligent Living: Plenty is an ag-tech startup in San Francisco, co-founded by Nate Storey, that is reinventing farms and farming. Storey, who is also the company's chief science officer, says the future of farms is vertical and indoors because that way, the food can grow anywhere in the world, year-round; and the future of farms employ robots and AI to continually improve the quality of growth for fruits, vegetables, and herbs. Plenty does all these things and uses 95% less water and 99% less land because of it. Plenty's climate-controlled indoor farm has rows of plants growing vertically, hung from the ceiling. There are sun-mimicking LED lights shining on them, robots that move them around, and artificial intelligence (AI) managing all the variables of water, temperature, and light, and continually learning and optimizing how to grow bigger, faster, better crops. These futuristic features ensure every plant grows perfectly year-round. The conditions are so good that the farm produces 400 times more food per acre than an outdoor flat farm. Another perk of vertical farming is locally produced food. The fruits and vegetables aren't grown 1,000 miles away or more from a city; instead, at a warehouse nearby. Meaning, many transportation miles are eliminated, which is useful for reducing millions of tons of yearly CO2 emissions and prices for consumers. Imported fruits and vegetables are more expensive, so society's most impoverished are at an extreme nutritional disadvantage. Vertical farms could solve this problem.

Read more of this story at Slashdot.

DeepMind's latest AI program can attain "superhuman performance" in tasks without needing to be given the rules. From a report: Like the research hub's earlier artificial intelligence agents, MuZero achieved mastery in dozens of old Atari video games, chess, and the Asian board games of Go and Shogi. But unlike its predecessors, it had to work out their rules for itself. It is already being put to practical use to find a new way to encode videos, which could slash YouTube's costs. [...] MuZero could soon be put to practical use too. Dr Silver said DeepMind was already using it to try to invent a new kind of video compression. "If you look at data traffic on the internet, the majority of it is video, so if you can compress video more effectively you can make massive savings," he explained. "And initial experiments with MuZero show you can actually make quite significant gains, which we're quite excited about." He declined to be drawn on when or how Google might put this to use beyond saying more details would be released in the new year. However, as Google owns the world's biggest video-sharing platform -- YouTube -- it has the potential to be a big money-saver. DeepMind is not the first to try and create an agent that both models the dynamics of the environment it is placed in and carries out tree searches -- deciding how to proceed by looking several steps ahead to determine the best outcome. However, previous attempts have struggled to deal with the complexity of "visually rich" challenges, such as those posed by old video games like Ms Pac-Man.

Read more of this story at Slashdot.

Alphabet's Google this year moved to tighten control over its scientists' papers by launching a "sensitive topics" review, and in at least three cases requested authors refrain from casting its technology in a negative light, Reuters reported Wednesday, citing internal communications and interviews with researchers involved in the work. From a report: Google's new review procedure asks that researchers consult with legal, policy and public relations teams before pursuing topics such as face and sentiment analysis and categorizations of race, gender or political affiliation, according to internal webpages explaining the policy. "Advances in technology and the growing complexity of our external environment are increasingly leading to situations where seemingly inoffensive projects raise ethical, reputational, regulatory or legal issues," one of the pages for research staff stated. Reuters could not determine the date of the post, though three current employees said the policy began in June. The "sensitive topics" process adds a round of scrutiny to Google's standard review of papers for pitfalls such as disclosing of trade secrets, eight current and former employees said. For some projects, Google officials have intervened in later stages. A senior Google manager reviewing a study on content recommendation technology shortly before publication this summer told authors to "take great care to strike a positive tone," according to internal correspondence read to Reuters.

Read more of this story at Slashdot.

An anonymous reader quotes a report from Phys.Org: A team of scientists at Freie Universitat Berlin has developed an artificial intelligence (AI) method for calculating the ground state of the Schrodinger equation in quantum chemistry. The goal of quantum chemistry is to predict chemical and physical properties of molecules based solely on the arrangement of their atoms in space, avoiding the need for resource-intensive and time-consuming laboratory experiments. In principle, this can be achieved by solving the Schrodinger equation, but in practice this is extremely difficult. Up to now, it has been impossible to find an exact solution for arbitrary molecules that can be efficiently computed. But the team at Freie Universitat has developed a deep learning method that can achieve an unprecedented combination of accuracy and computational efficiency. The deep neural network designed by [the] team is a new way of representing the wave functions of electrons. "Instead of the standard approach of composing the wave function from relatively simple mathematical components, we designed an artificial neural network capable of learning the complex patterns of how electrons are located around the nuclei," [Professor Frank Noe, who led the team effort] explains. "One peculiar feature of electronic wave functions is their antisymmetry. When two electrons are exchanged, the wave function must change its sign. We had to build this property into the neural network architecture for the approach to work," adds [Dr. Jan Hermann of Freie Universitat Berlin, who designed the key features of the method in the study]. This feature, known as 'Pauli's exclusion principle,' is why the authors called their method 'PauliNet.' Besides the Pauli exclusion principle, electronic wave functions also have other fundamental physical properties, and much of the innovative success of PauliNet is that it integrates these properties into the deep neural network, rather than letting deep learning figure them out by just observing the data. "Building the fundamental physics into the AI is essential for its ability to make meaningful predictions in the field," says Noe. "This is really where scientists can make a substantial contribution to AI, and exactly what my group is focused on." The results were published in the journal Nature Chemistry.

Read more of this story at Slashdot.

Microsoft says in a blog post that PepsiCo is using their Project Bonsai "machine teaching" service to "help ensure its Cheetos cheese-puff snacks all have the same texture, crunch and shape," reports The Wall Street Journal. From the blog post: PepsiCo built a computer vision system that continually monitors Cheeto attributes. Data about qualities such as density and length are fed to the Project Bonsai solution, which makes adjustments to bring the product within spec. This approach reduces the time it takes to correct inconsistencies and allows operators to focus on parts of the line that require human expertise. PepsiCo is preparing to use the solution in a production plant and exploring how to use the solution with other products, including the tortilla chip manufacturing process. An out-of-spec product can't be sold, which leads to wasted resources, time, and money. Greater consistency helps PepsiCo maintain high quality products while maximizing throughput. To make an ideal Cheeto, the solution needed examples of what wasn't ideal -- and needed to know what to do in those cases. The extruder line is self-contained and well-suited for developing and testing an autonomous system solution. Operators had been running it manually, which gave developers the opportunity to build the solution from scratch, instead of on top of other software. The AI solution has a recommendation mode and a closed loop control mode. In both modes, a computer vision system continuously measures the quality of the Cheetos. In recommendation mode, the AI will alert an operator if the product drifts out of spec, displaying on an instrument panel the attributes that are not ideal as well as a recommendation to correct it. The operator can push a button to make any or all recommended adjustments. In control mode, the only difference is that the AI solution skips the recommendation step and adjusts the extruder line specifications independently. The company expects that running this intelligent control system will return product to acceptable attributes faster. In the current extruder line, operators measure product attributes manually at defined intervals. If the Cheetos are out of spec, the operator makes adjustments based on guidelines or experience to return the product to acceptable quality. The problem: Infrequent sampling meant that the line could be producing out-of-spec Cheetos for a longer period of time without anyone realizing. The Project Bonsai solution will monitor the product almost continuously, using sensors to oversee characteristics such as length and bulk density. That way, it knows as soon as the product strays outside a defined range.

Read more of this story at Slashdot.

In Huntsville, Alabama, there is a room with green walls and a green ceiling. Dangling down the center is a fishing line attached to a motor mounted to the ceiling, which moves a procession of guns tied to the translucent line. From a report: The staff at Arcarithm bought each of the 10 best-selling firearm models in the U.S.: Rugers, Glocks, Sig Sauers. Pistols and long guns are dangled from the line. The motor rotates them around the room, helping a camera mounted to a mobile platform photograph them from multiple angles. "It's just like a movie set," said Arcarithm president and CEO Randy E. Riley. This process creates about 5,000 images of each gun floating ethereally. Arcarithm's computer programmers then replace the green backdrop with different environments, like fields, forests, and city streets. They add rain or snow or fog or sun. A program then randomly distorts the images. The result is 30,000 to 50,000 images of the same gun, from multiple angles, in different synthetic settings and of varying degrees of visibility. The point of creating this vast portfolio of digital gun art is to feed an algorithm made to detect a firearm as soon as a security camera catches it being drawn by synthetically creating tens of thousands of ways each gun may appear. Arcarithm is one of several companies developing automated active shooter detection technology in the hopes of selling it to schools, hotels, entertainment venues and the owners of any location that could be the site of one of America's 15,000 annual gun murders and 29,000 gun injuries. Among the other sellers are Omnilert, a longtime vendor of safety notification software, and newcomers ZeroEyes, Defendry, and Athena Securities. Some cities employ a surveillance system of acoustic sensors to instantly detect gunshots. These companies promise to do one better and save precious minutes by alerting police or security personnel before the first shot is fired.

Read more of this story at Slashdot.

Alphabet's Google waived a 1.1 billion-pound loan ($1.5 billion) to DeepMind in 2019 after the U.K.-based artificial intelligence lab continued to ramp up the scale of its research and development. From a report: Revenue jumped 158% in 2019, DeepMind said in a financial filing this week. Sales were 265.5 million pounds, up from 102.8 million pounds a year earlier. Its losses also widened, increasing 1.4% to 476.6 million pounds. DeepMind's parent has agreed to continue funding the company for at least a year after the report's approval. Alphabet's Google Ireland unit waived repayments and interest from the loan to help cover DeepMind's losses. Google acquired DeepMind in 2014 in a 400 million-pound acquisition that gave the Silicon Valley search giant access to cutting edge AI research. DeepMind Chief Executive Officer Demis Hassabis's goal is to produce general-purpose intelligence that can solve an array of problems. It develops products used by its parent company -- like its system for making data centers more energy efficient and a program to improve the accuracy of travel times on Google Maps -- as well as AI with broader applications.

Read more of this story at Slashdot.

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...

Read more of this story at Slashdot.

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.

Read more of this story at Slashdot.

"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.

Read more of this story at Slashdot.