GOOGLE’S NEW SENSOR DENOISNG ALGORITHM brings yet another game changer for LOW LIGHT PHOTOGRAPHY. Within a handful of years, this will be added to other factors coming down the pipe, giving further impetus to a revolution in night vision. The video below speaks for itself. In effect, the system takes a series of images from different angles, exposures, and so on, then accurately reconstructs what is missing:
Google co-founder Sergey Brin has taken a rather similar stance as Tesla CEO Elon Musk on artificial intelligence, emphasizing AI dangers in a recent investor communication. According to the Russian-born billionaire, the present day is an era of possibilities, but it is also a time when responsibility has to be practiced, particularly when it comes to emerging technologies.
“We’re in an era of great inspiration and possibility, but with this opportunity comes the need for tremendous thoughtfulness and responsibility as technology is deeply and irrevocably interwoven into our societies,” he wrote.
Brin’s statements were outlined in Alphabet’s recent Founders’ Letter, where the 44-year-old billionaire described how Google is utilizing bleeding-edge technology for its ventures. While AI as a discipline is still an emerging field, Brin noted that there are already a lot of everyday applications for the technology. Among these are the algorithms utilized by Waymo’s self-driving cars, the smart cooling units of Google’s data centers, and of course, Google Translate and YouTube’s automatic captions.
Apple Inc. recently added audiobook narration to the growing list of occupations where algorithms are poised to replace humans alongside graphic designers, college essayists and limerick writers. Luckily, the fine art of newslettering remains (ahem) far beyond the capabilities of even the most sophisticated artificial intelligence software. Still, hope is at hand for those not fortunate enough to toil in the newsletter mines but still seeking gainful employment that won’t disappear as robots take control.
To remain employed in an AI-dominated workplace, train as an artisan.
In mathematical physics, a closed timelike curve (CTC) is a world line in a Lorentzian manifold, of a material particle in spacetime, that is “closed”, returning to its starting point. This possibility was first discovered by Willem Jacob van Stockum in 1937[1] and later confirmed by Kurt Gödel in 1949,[2] who discovered a solution to the equations of general relativity (GR) allowing CTCs known as the Gödel metric; and since then other GR solutions containing CTCs have been found, such as the Tipler cylinder and traversable wormholes.
Artificial Intelligence is the buzzword of the year with many big giants in almost every industry trying to explore this cutting-edge technology. Right from self-checkout cash registers to AI-based applications to analyse large data in real-time to advanced security check-ins at the airport, AI is just about everywhere.
Currently, the logistics industry is bloated with a number of challenges related to cost, efficiency, security, bureaucracy, and reliability. So, according to the experts, new age technologies like AI, machine learning, the blockchain, and big data are the only fix for the logistics sector which can improve the supply chain ecosystem right from purchase to internal exchanges like storage, auditing, and delivery.
AI is an underlying technology which can enhance the supplier selection, boost supplier relationship management, and more. When combined with big data analytics AI also helps in analysing the supplier related data such as on-time delivery performance, credit scoring, audits, evaluations etc. This helps in making valuable decisions based on actionable real-time insights.
What is the Drake Equation? We are talking about The Odds of ALIEN LIFE. Is there life out there in the Universe? How are the chances to find Extraterrestrial life?
We don’t know the answers to a lot of questions, for example: How many alien societies exist, and are detectable? Even though we don’t know how to answer such a question, we can at least try to figure it out with a little help from our beloved…Math. First, we have to have a pretty good idea about how the universe works, and of course about the star and planetary formation, as well as conditions for life as we know it. This means we have to study and collect a lot of data. Luckily for us, we – humans — aren’t so bad. Physics, astronomy, chemistry, biology and all-natural sciences offer us the hints for the mathematical set of parameters that will give us an equation to calculate the number of alien societies that exist and are detectable. Second, one has to sit down and think about which parameters should appear in the equation, and which not. Do you think it’s difficult? I think so. But luckily for us, in 1961 scientists Drake came up with a famous equation, that estimated the number of transmitting societies in the Milky Way Galaxy…
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As science fiction would have you believe, you can’t really go to “another dimension.” Dimensions are more about how we see the world. But some things point to not just one, but two dimensions of time, according to one expert. If it were true, the theory could fix the biggest problem in physics, which is that quantum mechanics and general relativity don’t agree with each other.
Itzhak Bars from the University of Southern California in Los Angeles says that’s the case. Up, down, left, right, forward, back, and space-time are the normal three dimensions. In Bars’s theory, time is not a straight line. Instead, it is a curved 2D plane that is woven into all of these dimensions and more.
Dr. Bars has been working on “two-time physics” for more than ten years. All of this started when he started to wonder what time has to do with gravity and other forces. Even though the idea of more dimensions sounds strange, more and more physicists are thinking about it because it could help create the “theory of everything” or “unified theory of physics” that everyone wants. This would put all of the basic forces of the universe into a single, simple math equation.
H umans are at the center of most discussions about both the environment and technology. One goal of sustainability is to ensure that future generations of humans have opportunities to thrive on planet Earth. Debates about the ethics of technology often focus on how to protect human rights and promote human autonomy.
At the same time, some conversations about the environment and technology are now taking humans out of the equation. As Adam Kirsch points out in a new book, “The Revolt Against Humanity: Imagining a Future Without Us,” people in two very different schools of thought are coming to a similar conclusion: that the world might not have people much longer and might be better off as a result.
Kirsch takes readers on a guided tour of the discussions in these two camps. “Antihumanists” are obsessed with our having sown the seeds of our demise and bringing environmental apocalypse upon ourselves — possibly even deserving to go extinct. “Transhumanists” are obsessed with maintaining control and envision a future in which we use technology to become something greater than homo sapiens and even cheat death itself.
Irina Rish is a world-renowned professor of computer science and operations research at the Université de Montréal and a core member of the prestigious Mila organisation. She is a Canada CIFAR AI Chair and the Canadian Excellence Research Chair in Autonomous AI. Irina holds an MSc and PhD in AI from the University of California, Irvine as well as an MSc in Applied Mathematics from the Moscow Gubkin Institute. Her research focuses on machine learning, neural data analysis, and neuroscience-inspired AI. In particular, she is exploring continual lifelong learning, optimization algorithms for deep neural networks, sparse modelling and probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis. Prof. Rish holds 64 patents, has published over 80 research papers, several book chapters, three edited books, and a monograph on Sparse Modelling. She has served as a Senior Area Chair for NeurIPS and ICML. Irina’s research is focussed on taking us closer to the holy grail of Artificial General Intelligence. She continues to push the boundaries of machine learning, continually striving to make advancements in neuroscience-inspired AI.
In a conversation about artificial intelligence (AI), Irina and Tim discussed the idea of transhumanism and the potential for AI to improve human flourishing. Irina suggested that instead of looking at AI as something to be controlled and regulated, people should view it as a tool to augment human capabilities. She argued that attempting to create an AI that is smarter than humans is not the best approach, and that a hybrid of human and AI intelligence is much more beneficial. As an example, she mentioned how technology can be used as an extension of the human mind, to track mental states and improve self-understanding. Ultimately, Irina concluded that transhumanism is about having a symbiotic relationship with technology, which can have a positive effect on both parties.
Tim then discussed the contrasting types of intelligence and how this could lead to something interesting emerging from the combination. He brought up the Trolley Problem and how difficult moral quandaries could be programmed into an AI. Irina then referenced The Garden of Forking Paths, a story which explores the idea of how different paths in life can be taken and how decisions from the past can have an effect on the present.
To better understand AI and intelligence, Irina suggested looking at it from multiple perspectives and understanding the importance of complex systems science in programming and understanding dynamical systems. She discussed the work of Michael Levin, who is looking into reprogramming biological computers with chemical interventions, and Tim mentioned Alex Mordvinsev, who is looking into the self-healing and repair of these systems. Ultimately, Irina argued that the key to understanding AI and intelligence is to recognize the complexity of the systems and to create hybrid models of human and AI intelligence.
While “protein” often evokes pictures of chicken breasts, these molecules are more similar to an intricate Lego puzzle. Building a protein starts with a string of amino acids—think a myriad of Christmas lights on a string— which then fold into 3D structures (like rumpling them up for storage).
DeepMind and Baker both made waves when they each developed algorithms to predict the structure of any protein based on their amino acid sequence. It was no simple endeavor; the predictions were mapped at the atomic level.
Designing new proteins raises the complexity to another level. This year Baker’s lab took a stab at it, with one effort using good old screening techniques and another relying on deep learning hallucinations. Both algorithms are extremely powerful for demystifying natural proteins and generating new ones, but they were hard to scale up.
