Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 299

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.

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.

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.

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?”

The Digital Poorhouse

About the future death of explainability to understand AI thinking, the writing is on the wall…

These divergent approaches, one regulatory, the other deregulatory, follow the same pattern as antitrust enforcement, which faded in Washington and began flourishing in Brussels during the George W. Bush administration. But there is a convincing case that when it comes to overseeing the use and abuse of algorithms, neither the European nor the American approach has much to offer. Automated decision-making has revolutionized many sectors of the economy and it brings real gains to society. It also threatens privacy, autonomy, democratic practice, and ideals of social equality in ways we are only beginning to appreciate.

At the simplest level, an algorithm is a sequence of steps for solving a problem. The instructions for using a coffeemaker are an algorithm for converting inputs (grounds, filter, water) into an output (coffee). When people say they’re worried about the power of algorithms, however, they’re talking about the application of sophisticated, often opaque, software programs to enormous data sets. These programs employ advanced statistical methods and machine-learning techniques to pick out patterns and correlations, which they use to make predictions. The most advanced among them, including a subclass of machine-learning algorithms called “deep neural networks,” can infer complex, nonlinear relationships that they weren’t specifically programmed to find.

Predictive algorithms are increasingly central to our lives. They determine everything from what ads we see on the Internet, to whether we are flagged for increased security screening at the airport, to our medical diagnoses and credit scores. They lie behind two of the most powerful products of the digital information age: Google Search and Facebook’s Newsfeed. In many respects, machine-learning algorithms are a boon to humanity; they can map epidemics, reduce energy consumption, perform speech recognition, and predict what shows you might like on Netflix. In other respects, they are troubling. Facebook uses AI algorithms to discern the mental and emotional states of its users. While Mark Zuckerberg emphasizes the application of this technique to suicide prevention, opportunities for optimizing advertising may provide the stronger commercial incentive.