Lifeboat Foundation

Hey all! Curious about what a Moon base will look like? Here is a video on what the first moon base will look like. If you enjoy the video, please subscribe to my channel for more futurist content!

The coming two decades are scheduled to be very interesting decades for human space exploration and colonization. One of the things on America’s agenda for space exploration is creating humanity’s first moon base, which will most likely happen in the 2030s. If you are interested in what this Moon base will look like, please take a look at this video!

Continue reading “Surviving on the Moon | What Humanity’s First Moon Base Will Look Like” »

This week: Be The Match BioTherapies® (MN, USA) and NantKwest (CA, USA) collaborate to help progress a potential cell therapy for COVID-19-associated ARDS and a new platform from WuXi Advanced Therapies (PA, USA) may help accelerate cell and gene therapy development.

ECommunity, Future Science Group

To many developers, quantum computing may still feel like a futuristic technology shrouded in mystery and surrounded by hype. It’s some mystic dance of 1s and 0s that will enable some calculations in mere hours that today would take the lifetime of the universe to compute. It’s somehow related to a cat that may or may not be dead in a box.

The question we hear most often from developers is how do you make sense of what’s real and get started?

Over the last year, we’ve been working with you, the pioneering community of quantum developers, to understand what all developers will need on the path to scalable quantum computing. You’ve told us that you want to learn more about where quantum could impact your business today, to have easier ways to start writing quantum code, and to run applications against a range of quantum and classical hardware.

This raises the question of whether AI — defined as algorithms that mimic human intelligence — can deliver on its potential, and when. The answer is crucial because AI could become the ultimate industry disrupter, threatening tens of millions of jobs in Asia as business processes are automated. In addition, AI is the subject of intense rivalry between the US and China.

Unicorns abound but enthusiasm has dimmed. Will AI fulfil its potential?

This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter’s Five Forces Analysis. Neural Network Software market report all-inclusively estimates general market conditions, the growth prospects in the market, possible restrictions, significant industry trends, market size, market share, sales volume and future trends. The report starts by an introduction about the company profiling and a comprehensive review about the future events, sales strategies, Investments, business marketing strategy, future products, new geographical markets, customer actions or behaviors with the help of 100+ market data Tables, Pie Charts, Graphs & Figures spread through Pages for easy understanding. Neural Network Software market report has been designed by keeping in mind the customer requirements which assist them in increasing their return on investment (ROI and this research also provides a deep insight into the activities of key players such as Starmind, NeuralWare, Slagkryssaren AB, AND Corporation, Slashdot Media, XENON Systems Pty Ltd, Xilinx Inc and others. and others.

Get Full PDF Sample Copy of Report (Including Full TOC, List of Tables & Figures, Chart) at @ https://www.databridgemarketresearch.com/request-a-sample/?d…are-market

Global neural network software market is set to witness a healthy CAGR of 35.70% in the forecast period of 2019 to 2026.

Even as dramatic social change has been imposed by COVID-19, the kinds of fraud attacks companies experience and the biometric authentication technologies they use to prevent them have remained basically the same. What has changed is that online volumes of traffic, transactions and authentications have reached levels they were expected to years in the future, BehavioSec VP of Products Jordan Blake told Biometric Update in an interview.

As a result, he says, “timelines are getting advanced.”

Demand is coming from new verticals, according to Blake, as numerous people begin using the online channel to interact with many organizations they never have dealt with that way before.

“In one sense, universities have become victims of their own success at teaching online, but some academics are concerned that continued closures could hurt poorer students without access to computers or study space, while others mourn the loss of face-to-face connection while teaching.” Universities have become bloated cliques. Has Covid shown we don’t need mini-towns and fat fees? Poorer students might welcome online courses at 10% of the cost surely and shorter completion time, surely?

Governments are prioritising reopening schools and businesses over campuses. But some academics fear the impact on disadvantaged students – and on their teaching.

Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that we’re using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. This unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that we’re enabling machines to do the thing that our brains seem to do so easily: identify what we’re perceiving in the real world around us.

The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest. The recognition pattern however is broader than just image recognition In fact, we can use machine learning to recognize and understand images, sound, handwriting, items, face, and gestures. The objective of this pattern is to have machines recognize and understand unstructured data. This pattern of AI is such a huge component of AI solutions because of its wide variety of applications.

The difference between structured and unstructured data is that structured data is already labelled and easy to interpret. However unstructured data is where most entities struggle. Up to 90% of an organization’s data is unstructured data. It becomes necessary for businesses to be able to understand and interpret this data and that’s where AI steps in. Whereas we can use existing query technology and informatics systems to gather analytic value from structured data, it is almost impossible to use those approaches with unstructured data. This is what makes machine learning such a potent tool when applied to these classes of problems.

BERLIN: The European Space Agency said Friday that human urine could one day become a useful ingredient in making concrete to build on the moon.

The agency said researchers in a recent study it sponsored found that urea, the main organic compound in urine, would make the mixture for a “lunar concrete” more malleable before it hardens into its sturdy final form.