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Employees at Google, Amazon and Microsoft Have Threatened to Walk Off the Job Over the Use of AI

There is. Our engagement with AI will transform us. Technology always does, even while we are busy using it to reinvent our world. The introduction of the machine gun by Richard Gatling during America’s Civil War, and its massive role in World War I, obliterated our ideas of military gallantry and chivalry and emblazoned in our minds Wilfred Owen’s imagery of young men who “die as Cattle.” The computer revolution beginning after World War II ushered in a way of understanding and talking about the mind in terms of hardware, wiring and rewiring that still dominates neurology. How will AI change us? How has it changed us already? For example, what does reliance on navigational aids like Waze do to our sense of adventure? What happens to our ability to make everyday practical judgments when so many of these judgments—in areas as diverse as credit worthiness, human resources, sentencing, police force allocation—are outsourced to algorithms? If our ability to make good moral judgments depends on actually making them—on developing, through practice and habit, what Aristotle called “practical wisdom”—what happens when we lose the habit? What becomes of our capacity for patience when more and more of our trivial interests and requests are predicted and immediately met by artificially intelligent assistants like Siri and Alexa? Does a child who interacts imperiously with these assistants take that habit of imperious interaction to other aspects of her life? It’s hard to know how exactly AI will alter us. Our concerns about the fairness and safety of the technology are more concrete and easier to grasp. But the abstract, philosophical question of how AI will impact what it means to be human is more fundamental and cannot be overlooked. The engineers are right to worry. But the stakes are higher than they think.

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On Using Hyperopt: Advanced Machine Learning

In Machine Learning one of the biggest problem faced by the practitioners in the process is choosing the correct set of hyper-parameters. And it takes a lot of time in tuning them accordingly, to stretch the accuracy numbers.

For instance lets take, SVC from well known library Scikit-Learn, class implements the Support Vector Machine algorithm for classification which contains more than 10 hyperparameters, now adjusting all ten to minimize the loss is very difficult just by using hit and trial. Though Scikit-Learn provides Grid Search and Random Search, but the algorithms are brute force and exhaustive, however hyperopt implements distributed asynchronous algorithm for hyperparameter optimization.

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XTPL ultra-precise Nanometric Printer receives Honorable Mention at Display Week 2018 I-Zone

Closing in on molecular manufacturing…


http://xt-pl.com received an honorable mention from I-Zone judges for its innovative product that prints extremely fine film structures using nanomaterials. XTPL’s interdisciplinary team is developing and commercializing an innovative technology that enables ultra-precise printing of electrodes up to several hundred times thinner than a human hair – conducive lines as thin as 100 nm. XTPL is facilitating the production of a new generation of transparent conductive films (TCFs) that are widely used in manufacturing. XTPL’s solution has a potentially disruptive technology in the production of displays, monitors, touchscreens, printed electronics, wearable electronics, smart packaging, automotive, medical devices, photovoltaic cells, biosensors, and anti-counterfeiting. The technology is also applicable to the open-defect repair industry (the repair of broken metallic connections in thin film electronic circuits) and offers cost-effective, non-toxic, flexible industry-adapted solutions.

XTPL’s technology might be the only one in the world offering cost-effective, non-toxic, flexible, industry adapted solution for the market of displays TFT/LCD/OLED, integrated circuits (IC), printed circuit boards (PCB), multichip modules (MCM); photolithographic masks & solar cells market.

XTPL delivers also solutions for research & prototyping including printing head, electronics, software algorithms which are the core of the system driving the electric field and the assembly process of nanoparticles implemented in XTPL’s Nanometric Lab Printer. It is a device that offers necessary functionalities to test, evaluate and use XTPL line-forming technology with nanometric precision and enables positioning of the printing head with micrometric resolution precisely.

Official video explaining XTPL’s technology: https://youtu.be/WMerzxzCXuw

Filmed at the I-Zone demo and prototype area at SID Display Week, the world’s largest and best exhibition for electronic information display technology.

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How artificial intelligence is changing the pharmaceutical industry

But the great potential of artificial intelligence shall become fully clear when considering its possible applications to drug discovery. It seems an era ago since the Human Genome Project was completed in 2003; since then, sequencing capabilities and softwares for data analysis rapidly established themselves as the new paradigm for drug discovery thanks to the increasing availability of IT technologies and the institutional and governmental support to big data analytics’ policies.

The exponential growth of the market

The annual growth rate of the market of artificial intelligence for healthcare applications has been recently estimated by Global Market Insights to be 40% CAGR (Compounded Average Growth Rate) per year up to 2024, starting from a value on $ 750 million in 2016.

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Ion Engine Startup Wants to Change the Economics of Earth Orbit

For as long as she can remember, she’s puzzled over what’s out there. As a kid drifting off to sleep on a trampoline outside her family’s home near Portland, Ore., she would track the International Space Station. She remembers cobbling together a preteen version of the Drake Equation on those nights and realizing that the likelihood of intelligent alien life was something greater than zero. Star Trek marathons with her father catalyzed her cosmic thinking, as did her mother’s unexpected death when Bailey was 8. The house lost some of its order—some of its gravity—which led to more nights gazing skyward on the trampoline.

In college, Bailey got a hard-won paid internship at the now-merged aerospace giant Hamilton Sundstrand and joined a team repairing turbine engines. She hated it. “It was the opposite of pushing the envelope,” she says. “Nothing new ever went into that building. Nothing new ever left that building.”

By the time she set off to get a master’s degree in mechanical engineering at Duke University, the idea of logging 30 years at a place like Boeing Cor NASA had lost all appeal. She tried her hand at finance and later law, and was unlucky enough to excel at both. “I made it pretty far down that path, but then I thought, Wait, if I become a lawyer, then I’m a lawyer and that’s what I do,” she recalls. “What if I don’t want to do that on Tuesdays?”

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