At the G7 meeting in Montreal last year, Justin Trudeau told WIRED he would look into why more than 100 African artificial intelligence researchers had been barred from visiting that city to attend their field's most important annual event, the Neural Information Processing Systems conference, or NeurIPS. Now the same thing has happened again. More than a dozen AI researchers from African countries have been refused visas to attend this year's NeurIPS, to be held next month in Vancouver. This means an event that shapes the course of a technology with huge economic and social importance will have little input from a major portion of the world. The conference brings together thousands of researchers from top academic institutions and companies, for hundreds of talks, workshops, and side meetings at which new ideas and theories are hashed out. Tejumade Afonja, a master's student from Nigeria who is studying at Saarland University in Germany, posted her rejection letter to Twitter.

The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered." In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.

With my concept of The Matrix Conspiracy I put myself in the risk of being accused of being a paranoid conspiracy theorist. This is not the case. I m just making aware of that there exists a conspiracy theory which is called The Matrix Conspiracy, and that this conspiracy in fact is a global spreading ideology. My critique is in that way ideology critique, or cultural critique. The concept of the Matrix comes from mathematics, but is more popular known from the movie the Matrix, which asks the question whether we might live in a computer simulation. In The Matrix though, there is also an evil demon, or evil demons, namely the machines which keep the humans in tanks linked to black cable wires that stimulates the virtual reality of the Matrix. Doing this the machines can use the human bodies as batteries that supply the machines with energy. It is the fascination of the virtual reality that deceives the humans. The philosophy behind the movie comes from especially two philosophers: Rene Descartes and George Berkeley. Descartes was very dubious concerning how much we can trust our senses. Therefore he took up the question Is life a dream? However, his intention with this was in his Meditations to develop a confident cognition-argument. In his Meditations Descartes presents the problem approximately like this: I frequently dream during the night, and while I dream, I am convinced, that what I dream is real. But then it always happens, that I wake up and realize, that everything I dreamt was not real, but only an illusion. And then is it I think: is it possible, that what I now, while I am awake, believe is real, also is something, which only is being dreamt by me right now? If it is not the case, how shall I then determinate it? Precisely because Descartes not even in dreams can doubt, that 2 plus 3 is 5, he leaves the dream-argument in his Meditations and goes in tackle with the question, whether he could be cheated by an evil demon concerning all cognition, also the mathematics. This radical skepticism leads him forward to the cogito-argument: Cogito ergo Sum (I think, therefore I exist). But he didn t deny the existence of the external world. The external world he described in a way that resembles what would later be known as modern natural sciences. In the view of nature in natural science, nature is reduced to atomic particles, empty space, fields, electromagnetic waves and particles etc., etc. I have called this the instrumental view of nature. Berkeley is famous for the sentence Esse est percipi, which means that being, or reality, consists in being percepted (to be is to be experienced). The absurdity in Berkeley s assertion is swiftly seen: If a thing, or a human being for that matter, is not being perceived by the senses, then it does not exist. In accordance with Berkeley there therefore does not exist any sense-independent world. He ends in solipsism, the consequence that only I, and my perceptions, can be said to exist.

Gazing up at the night sky in a rural area, you'll probably see the shining moon surrounded by stars. If you're lucky, you might spot the furthest thing visible with the naked eye – the Andromeda galaxy. When the Department of Energy's (DOE) Legacy Survey of Space and Time (LSST) Camera at the National Science Foundation's Vera Rubin Observatory turns on in 2022, it will take photos of 37 billion galaxies and stars over the course of a decade. The output from this huge telescope will swamp researchers with data. In those 10 years, the LSST Camera will take 2,000 photos for each patch of the Southern Sky it covers.

Is artificial intelligence getting too smart (and intrusive) for its own good? A growing number of nations have concluded that it's time to take a close look at AI's impact on an array of critical issues, including privacy, security, human rights, crime, and finance. A proposal for an international oversight panel, the Global Partnership on AI, already has the support of six members of The Group of Seven (G7), an international organization comprised of nations with the largest and most advanced economies. The G7's dominant member, the United States, remains the only holdout, claiming that regulation could hamper the development of AI technologies and hurt US businesses. The Global Partnership on AI and OECD's G20 AI principles represent a good first step toward building a worldwide AI regulatory structure, noted Robert L. Foehl, an executive-in-residence for business law and ethics at Ohio University.

This is just an image representation. Let's talk about this topic in detail... The immense capabilities artificial intelligence is bringing to the world would have been inconceivable to past generations. But even as we marvel at the incredible power these new technologies afford, we're faced with complex and urgent questions about the balance of benefit and harm. When most people ponder whether AI is good or evil, what they're essentially trying to grasp is whether AI is a tool or a weapon.

Bacteria are evolving resistance to antibiotics much faster than new drugs can be developed, potentially leading us to a dangerous future where infections are more likely to be deadly. Now, an artificial intelligence model has identified a powerful new antibiotic called halicin, which cleared infections of most superbugs in mouse tests. Ever since antibiotics were invented in the early 20th century, we've been locked in an arms race with bacteria. Antibiotics work for a while, but eventually the bugs evolve resistance to those in wide use. Scientists develop new ones, so bacteria continue to evolve, and so on.

The UK government is launching an investigation to determine the levels of bias in algorithms that could affect people's lives. A browse through our'ethics' category here on AI News will highlight the serious problem of bias in today's algorithms. With AIs being increasingly used for decision-making, parts of society could be left behind. Conducted by the Centre for Data Ethics and Innovation (CDEI), the investigation will focus on areas where AI has tremendous potential – such as policing, recruitment, and financial services – but would have a serious negative impact on lives if not implemented correctly. "Technology is a force for good which has improved people's lives but we must make sure it is developed in a safe and secure way. Our Centre for Data Ethics and Innovation has been set up to help us achieve this aim and keep Britain at the forefront of technological development. I'm pleased its team of experts is undertaking an investigation into the potential for bias in algorithmic decision-making in areas including crime, justice and financial services. I look forward to seeing the Centre's recommendations to Government on any action we need to take to help make sure we maximise the benefits of these powerful technologies for society."

AWNBench is a Stanford University project designed to allow different deep learning methods to be compared by running a number of competitions. Two parts of the Dawnbench competition attracted our attention, the CIFAR 10 and Imagenet competitions. Their goal was simply to deliver the fastest image classifier as well as the cheapest one to achieve a certain accuracy (93% for Imagenet, 94% for CIFAR 10). In the CIFAR 10 competition our entries won both training sections: fastest, and cheapest. In this post we'll discuss our approach to each competition.

Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations are performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. Outputs comprise concentration profiles of the reactants and products. The inputs and outputs of these simulations are concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) emulators to approximate system behavior. The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models. The ML emulators are specifically trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay, and degree of mixing. Linear classifiers and regressors fail to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP accurately classify the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predict the QoIs. At run time, trained ML emulators are $\approx10^5$ times faster than the high-fidelity numerical simulations. Speed and accuracy of the ensemble and MLP models facilitate uncertainty quantification, which usually requires 1,000s of model run, to estimate the uncertainty bounds on the QoIs.