Microsoft is planning to add text predictions to Word in March. From a report: The new feature will work similarly to Google Docs' Smart Compose option, using machine learning to predict what words an author will need to speed up document creation. Microsoft originally announced a beta of text predictions last year, but it's now on the Microsoft 365 roadmap to reach all Word users on Windows next month. Word will highlight grayed-out predictions when users are writing a document, and the suggestions can be accepted using the Tab key or rejected by hitting Escape. Text predictions can also be completely disabled by Word users.

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The first church of artificial intelligence has shut its conceptual doors. From a report: Anthony Levandowski, the former Google engineer who avoided an 18-month prison sentence after receiving a presidential pardon last month, has closed the church he created to understand and accept a godhead based on artificial intelligence. The Way of the Future church, which Levandowski formed in 2015, was officially dissolved at the end of the year, according to state and federal records. However, the process had started months before in June 2020, documents filed with the state of California show. The entirety of the church's funds -- exactly $175,172 -- were donated to the NAACP Legal Defense and Education Fund. The nonprofit corporation's annual tax filings with the Internal Revenue Service show it had $175,172 in its account as far back as 2017. Levandowski told TechCrunch that he had been considering closing the church long before the donation. The Black Lives Matter movement, which gained momentum over the summer following the death of George Floyd while in police custody, influenced Levandowski to finalize what he had been contemplating for a while. He said the time was right to put the funds to work in an area that could have an immediate impact. "I wanted to donate to the NAACP Legal Defense and Education Fund because it's doing really important work in criminal justice reform and I know the money will be put to good use," Levandowski told TechCrunch. Way of the Future sparked interest and controversy -- much like Levandowski himself -- from the moment it became public in a November 2017 article in Wired. It wasn't just the formation of the church or its purpose that caused a stir in Silicon Valley and the broader tech industry. The church's public reveal occurred as Levandowski was steeped in a legal dispute with his former employer Google. He had also become the central figure of a trade secrets lawsuit between Waymo, the former Google self-driving project that is now a business under Alphabet, and Uber. The engineer was one of the founding members in 2009 of the Google self-driving project also known as Project Chauffeur and had been paid about $127 million by the search engine giant for his work, according to court documents. In 2016, Levandowski left Google and started self-driving truck startup Otto with three other Google veterans: Lior Ron, Claire Delaunay and Don Burnette. Uber acquired Otto less than eight months later.

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The Next Web writes: A trio of computer scientists from the Rensselaer Polytechnic Institute in New York recently published research detailing a potential AI intervention for murder: an ethical lockout. The big idea here is to stop mass shootings and other ethically incorrect uses for firearms through the development of an AI that can recognize intent, judge whether it's ethical use, and ultimately render a firearm inert if a user tries to ready it for improper fire... Clearly the contribution here isn't the development of a smart gun, but the creation of an ethically correct AI. If criminals won't put the AI on their guns, or they continue to use dumb weapons, the AI can still be effective when installed in other sensors. It could, hypothetically, be used to perform any number of functions once it determines violent human intent. It could lock doors, stop elevators, alert authorities, change traffic light patterns, text location-based alerts, and any number of other reactionary measures including unlocking law enforcement and security personnel's weapons for defense... Realistically, it takes a leap of faith to assume an ethical AI can be made to understand the difference between situations such as, for example, home invasion and domestic violence, but the groundwork is already there. If you look at driverless cars, we know people have already died because they relied on an AI to protect them. But we also know that the potential to save tens of thousands of lives is too great to ignore in the face of a, so far, relatively small number of accidental fatalities...

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The BBC reports on "the computers rejecting your job application," noting that applicants are now being screened with AI-scored tests that involve counting dots in boxes and matching emotions to facial expressions: The questions, and your answers to them, are designed to evaluate several aspects of a jobseeker's personality and intelligence, such as your risk tolerance and how quickly you respond to situations. Or as Pymetrics puts it, "to fairly and accurately measure cognitive and emotional attributes in only 25 minutes". Its AI software is now used in the initial recruitment processes of a number of multinational companies, such as McDonald's, bank JP Morgan, accountancy firm PWC, and food group Kraft Heinz. An interview with a human recruiter then follows if you pass. "It's about helping firms process a much wider pool [of applicants], and getting signals that someone will be successful in a job," says Pymetrics founder Frida Polli... Another provider of AI recruitment software is Utah-based HireVue. Its AI system records videos of job applicants answering interview questions via their laptop's webcam and microphone. The audio of this is then converted into text, and an AI algorithm analyses it for key words, such as the use of "I" instead of "we" in response to questions about teamwork. The recruiting company can then choose to let HireVue's system reject candidates without having a human double-check, or have the candidate moved on for a video interview with an actual recruiter. HireVue says that by September 2019 it had conducted a total of 12 million interviews, of which 20% were via the AI software. The remaining 80% were with a human interviewer on the other end of a video screen. The overall figure has now risen to 19 million, with the same percentage split. HireVue first started offering the AI interviews in 2016. Its users include travel services firm Sabre. Meanwhile, a report from 2019 said that such is the growth in the use of AI that it will replace 16% of recruitment sector jobs before 2029.

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An anonymous reader quotes a report from TechCrunch: Minneapolis voted Friday to ban the use of facial recognition software for its police department, growing the list of major cities that have implemented local restrictions on the controversial technology. After an ordinance on the ban was approved earlier this week, 13 members of the city council voted in favor of the ban, with no opposition. The new ban will block the Minneapolis Police Department from using any facial recognition technology, including software by Clearview AI. That company sells access to a large database of facial images, many scraped from major social networks, to federal law enforcement agencies, private companies and a number of U.S. police departments. The Minneapolis Police Department is known to have a relationship with Clearview AI, as is the Hennepin County Sheriff's Office, which will not be restricted by the new ban.

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An anonymous reader quotes a report from The Next Web: Scientists from the University of Copenhagen have developed an AI tool that can predict who'll die from COVID-19 with up to 90% accuracy. It also predicted whether someone who's admitted to hospital with COVID-19 will need a respirator with 80% accuracy. The researchers fed the system health data from almost 4,000 COVID-19 patients in Denmark to train it to find patterns in their medical histories. Unsurprisingly, BMI and age were the most decisive indicators. But the study also showed that males and people with high blood pressure or a neurological disease had an elevated risk. The next most influential health factors were having chronic obstructive pulmonary disease (COPD), asthma, diabetes, and heart disease. The study paper has been published in the journal Nature.

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A Slashdot reader shared an anonymous tip about "new consumer-grade artificial intelligence employed to restore 85 year-old Popeye cartoons, using only the available digital copies as sources for the remastering." It's eerie to see vintage cartoons like Popeye the Sailor meets Sindbad the Sailor upgraded to high resolution. It's apparently the work of Cartoon Renewal Studios, a group "Dedicated to the loving and careful preservation of classic off-copyright animation" (according to its web site). There's not much information, but Jim Ames of Cartoon Renewal Studios turned up in an online forum promising "we're restoring ALL the classic cartoons to brilliant 1080 HD so they can be enjoyed forever." I've been dreaming of this project for some time... We will be posting THOUSANDS of off-copyright cartoons digitally remastered and upscaled to 1080 HD. We can process about 50 cartoons a month, at this time... Hoping to scale up to 100 cartoons a month processing capability next month. We could finish 1000 cartoons in 2021... stay tuned...

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An anonymous reader Phys.Org: The Korea Institute of Science and Technology (KIST) announced that the collaborative research team led by Dr. Kwan Hyi Lee from the Biomaterials Research Center and Professor In Gab Jeong from Asan Medical Center developed a technique for diagnosing prostate cancer from urine within only 20 minutes with almost 100% accuracy. The research team developed this technique by introducing a smart AI analysis method to an electrical-signal-based ultrasensitive biosensor. As a noninvasive method, a diagnostic test using urine is convenient for patients and does not need invasive biopsy, thereby diagnosing cancer without side effects. However, as the concentration of cancer factors is low in urine, urine-based biosensors are only used for classifying risk groups rather than for precise diagnosis thus far. Dr. Lee's team at the KIST has been working toward developing a technique for diagnosing disease from urine with an electrical-signal-based ultrasensitive biosensor. An approach using a single cancer factor associated with a cancer diagnosis was limited in increasing the diagnostic accuracy to over 90%. However, to overcome this limitation, the team simultaneously used different kinds of cancer factors instead of using only one to enhance the diagnostic accuracy innovatively. The team developed an ultrasensitive semiconductor sensor system capable of simultaneously measuring trace amounts of four selected cancer factors in urine for diagnosing prostate cancer. They trained AI by using the correlation between the four cancer factors, which were obtained from the developed sensor. The trained AI algorithm was then used to identify those with prostate cancer by analyzing complex patterns of the detected signals. The diagnosis of prostate cancer by utilizing the AI analysis successfully detected 76 urinary samples with almost 100 percent accuracy. The results of the study have been published in the journal ACS Nano.

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An anonymous reader writes: China produces as many artificial intelligence researchers as the US, but it lags in fundamental research. The government hopes to make up ground with a new AI lab in Beijing that brings together top researchers from AI and industry to focus on things like the mathematical foundations of machine learning and neuroscience-inspired AI. But as WIRED reports, it also suggests that even the Chinese government has concerns about the ethical challenges raised by AI. Among the first projects that the government is funding: a Chinese version of GPT-3 for government use. From the article: Noam Yuchtman, a professor at the London School of Economics, has published work that uses evidence from China to suggest that AI benefits uniquely from state intervention, because algorithms are so hungry for data and computer power that governments have access to. But he adds that such a fast-moving and unpredictable technology may also pose problems for governments. "Innovation by its very nature is sort of uncertain, and perhaps nowhere more so than in AI," he says.

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Google artificial intelligence researcher Margaret Mitchell has been locked out of corporate systems, making her the second outspoken critic at the company to be sidelined after colleague Timnit Gebru departed in acrimonious circumstances last month. From a report: The Alphabet unit has an Ethical AI team, led by Mitchell, and a set of principles for developing the technology in a socially responsible manner. Gebru tweeted on Tuesday that Mitchell's "corp access is now locked" and that the researcher had been told she would remain locked out "for at least a few days."

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Dallas, TX-based cloud security firm Trend Micro interviewed 500 IT directors and managers, CIOs and CTOs — and discovered that over two-fifths of them believe they'll be replaced by AI by 2030. ZDNet reports: Only 9% of respondents were confident that AI would definitely not replace their job within the next decade. In fact, nearly a third (32%) said they thought the technology would eventually work to completely automate all cybersecurity, with little need for human intervention. Almost one in five (19%) believe that attackers using AI to enhance their arsenal will be commonplace by 2025. Around a quarter (24%) of IT leaders polled also claimed that by 2030, data access will be tied to biometric or DNA data, making unauthorised access impossible. In the shorter term, respondents also predicted the following outcomes would happen by 2025. They predict that most organisations will have significantly reduced investment in property as remote working becomes the norm (22%) Nationwide 5G will have entirely transformed network and security infrastructure (21%), and security will be self-managing and automated using AI (15%). However, attackers using AI to enhance their arsenal will be commonplace (19%)

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"Behold the cakie: It has the crispiness of a cookie and the, well, 'cakiness' of a cake." So says a triumphant blog post by Google Cloud's developer advocate and an applied AI Engineer for Google's Cloud AI. "We also made breakies, which were more like fluffy cookies, almost the consistency of a muffin" (or bread). Food and Wine explains the project (in an article shared by Slashdot reader John Trumpian): Inspired by the pandemic-spawned spike in searches for baking, the team at Google Cloud "decided to dive a little deeper into the trend and try to understand the science behind what makes cookies crunchy, cake spongy and bread fluffy," according to a post on their blog. Then, once armed with that machine learning knowledge, they attempted to mix these attributes into what they bill as "two completely new baking recipes...." [T]hese Google Cloud employees organized about 700 recipes covering cookies, cakes, and breads — standardizing measurements, isolating the key ingredients, and re-categorizing things like banana breads that aren't really "breads." Then, they fed them into a tool called "AutoML Tables" to create a machine learning model that was able to predict whether a recipe was a cookie, cake, or bread based on its ingredient amounts. ["If you've never tried AutoML Tables, it's a code-free way to build models from the type of data you'd find in a spreadsheet like numbers and categories — no data science background required," explains the blog post.] Of course, recipes don't necessarily fit perfectly into one category. As Sara Robinson, who led the project, explained, a recipe might come back as 97 percent bread, 2 percent cake, and 1 percent cookie. So what if she asked the model to create its own recipe: something that's 50 percent cookie and 50 percent cake? That's how the Cakie was born. And she was happily surprised by the results. "It is yummy," Robinson said. "And it strangely tastes like what I'd imagine would happen if I told a machine to make a cake cookie hybrid." Based on that success, she and colleague Dale Markowitz continued to tweak their model — which resulted in the Breakie. "We should caveat that while our model gave us ingredients, it didn't spit out any baking directions, so we had to improvise those ourselves," the blog post explains. "And, we added chocolate chips and cinnamon for good measure." Robinson also built a prediction-making web app to help quickly experiment with different ingredient ratios. They ultimately identified which ingredients were the biggest "signal" of cake-ness, cookie-ness, and bread-ness, concluding that "In our case, the amount of butter, sugar, yeast and egg in a recipe all seemed to be important indicators..."

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schwit1 shares a report from ScienceAlert: [S]cientists have just delivered their verdict on whether we'd be able to control a high-level computer super-intelligence. The answer? Almost definitely not. The catch is that controlling a super-intelligence far beyond human comprehension would require a simulation of that super-intelligence which we can analyze. But if we're unable to comprehend it, it's impossible to create such a simulation. Rules such as "cause no harm to humans" can't be set if we don't understand the kind of scenarios that an AI is going to come up with, suggest the authors of the new paper. Once a computer system is working on a level above the scope of our programmers, we can no longer set limits. Part of the team's reasoning comes from the halting problem put forward by Alan Turing in 1936. The problem centers on knowing whether or not a computer program will reach a conclusion and answer (so it halts), or simply loop forever trying to find one. As Turing proved through some smart math, while we can know that for some specific programs, it's logically impossible to find a way that will allow us to know that for every potential program that could ever be written. That brings us back to AI, which in a super-intelligent state could feasibly hold every possible computer program in its memory at once. Any program written to stop AI harming humans and destroying the world, for example, may reach a conclusion (and halt) or not -- it's mathematically impossible for us to be absolutely sure either way, which means it's not containable. The alternative to teaching AI some ethics and telling it not to destroy the world -- something which no algorithm can be absolutely certain of doing, the researchers say -- is to limit the capabilities of the super-intelligence. It could be cut off from parts of the internet or from certain networks, for example. The new study rejects this idea too, suggesting that it would limit the reach of the artificial intelligence -- the argument goes that if we're not going to use it to solve problems beyond the scope of humans, then why create it at all? If we are going to push ahead with artificial intelligence, we might not even know when a super-intelligence beyond our control arrives, such is its incomprehensibility. That means we need to start asking some serious questions about the directions we're going in.

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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.

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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).

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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."

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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.

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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.

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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."

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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."

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