Science fiction has always been a medium for futuristic imagination and while different colored aliens and intergalactic travel are yet to be discovered, there is an array of technologies that are no longer figments of the imagination thanks to the world of science fiction. Some of the creative inventions that have appeared in family-favorite movies like "Back to the Future" and "Total Recall," are now at the forefront of modern technology. Here are a few of our favorite technologies that went from science fiction to reality. This article is brought to you by All About Space. All About Space magazine takes you on an awe-inspiring journey through our solar system and beyond, from the amazing technology and spacecraft that enables humanity to venture into orbit, to the complexities of space science.

"We build models and machines in AI that are more complicated than we can understand" Only 13% of finance organizations are using artificial intelligence, analytics, and automation to transform multiple processes across their enterprises; when it comes to meeting business expectations to generate actionable insights from company data, not a single survey respondent states that their function can do so significantly; none of the executives say they have a structured way to generate predictive insights to meet businesses' changing and varied demands; no one is using AI, analytics, and automation to fundamentally reimagine their finance function [Genpact survey of 500 CFOs and senior finance executives] The Indiana Donor Network can immediately spot anomalies in its donor network and boost contributions. The machine learning-based system from Sisense Inc. can spot, for example, when a hospital that regularly produces organ donations doesn't deliver any in a particular week. After an anomaly is spotted, the organization can arrange a meeting the next day with the hospital to review protocols. Before, a problem in the system would have come to light 30 to 45 days after the fact. I want to emphasize, though, that we haven't solved language understanding yet in any satisfying way"--Sam Bowman "This is a new period in the world's history--we build models and machines in AI that are more complicated than we can understand"--Jason Yosinski, co-founder of Uber AI Labs

Carnegie Mellon University (CMU) spinoff company RoadBotics has announced a ground-breaking option to complement its interactive, online pavement assessment platform, that identifies common distresses such as potholes, surface deterioration, and fatigue cracking. Spun out of CMU's Robotics Institute in 2016, RoadBotics has raised US$11.4m in venture capital investment to date to help fund its vision of applying technology to not only improve the world's roads, but also the critical infrastructure that impacts people's lives on a daily basis. RoadBotics empowers towns and cities to make objective, data-driven decisions about their roads and infrastructure by automating inspections and generating actionable information about road networks. The company's detailed maps, unbiased ratings, and practical tools save time and money for hundreds of communities across the USA and around the world. Historically, most local governments and highways agencies have manually inspected their roads for surface distresses, allowing subjective data to creep into the decision process when making plans for repairs.

Robots could be trained to disinfect surfaces, take temperatures, collecting swabs and provide social support for quarantined patients to help combat coronavirus. The'dull, dirty and dangerous jobs' could be automated, but we would need to add many new functionalities to machines first, roboticists argued in a journal editorial. Many of these necessary capabilities, however, are neither being funded or developed at present, the experts cautioned. Robots could be trained to disinfect surfaces, take temperatures, collecting swabs and provide social support for quarantined patients to help combat coronavirus. Among the signatories of the editorial are roboticist Howie Choset of the Carnegie Mellon University in Pittsburgh and the president of the National Academy of Science, Marcia McNutt.

As we have announced recently, Appsilon Data Science's AI for Good initiative is working together with biodiversity conservationists at the National Parks Agency in Gabon and in collaboration with experts from the University of Stirling. Part of our role in the project is to develop an image classification algorithm capable of classifying wildlife seen in images taken by camera traps located in the forests of Gabon. The project has received support from the Google for Education fund which allowed us to embark on this journey with the immense power of the latest computational resources at hand. Below are some interesting findings we made so far. Stay tuned for more news on the progress! We have recently participated (and taken the 5th place out of 811 participants) in the Hakuna Ma-data competition, in which we classified images of wildlife from the savannahs of Serengeti.

Many governments worldwide are looking at using Artificial Intelligence (AI) and other cognitive technologies as part of making their operations more efficient, better serving their citizens, and increasing the range of ways they can meet their missions. It's no surprise then that the US Government and forward thinking leadership is making investments into AI technologies. Additionally, some agencies such as the US Department of Health & Human Services (HHS) are also seeing how other emerging technologies such as blockchain can help. Jose Arrieta, the CIO at the US Department of Health & Human Services (HHS), interviewed on a recent AI Today podcast episode while he was the associate Deputy Assistant Secretary for Acquisition at HHS is one such leader who sees how AI and blockchain can have a big impact at the agency. He became a program manager to build IT systems to use machine learning to analyze biographical information about people.

In a blog post and accompanying paper, researchers at Google detail an AI system -- MetNet -- that can predict precipitation up to eight hours into the future. They say that it outperforms the current state-of-the-art physics model in use by the U.S. National Oceanic and Atmospheric Administration (NOAA) and that it makes a prediction over the entire U.S. in seconds as opposed to an hour. It builds on previous work from Google, which entailed an AI system that ingested satellite images to produce forecasts with a roughly one-kilometer resolution and a latency of only 5-10 minutes. And while it's early days, it could the runway for a forecasting tool that could help businesses, residents, and local governments better prepare for inclement weather. MetNet takes a data-driven and physics-free approach to weather modeling, meaning it learns to approximate atmospheric physics from examples and not by incorporating prior knowledge.

Researchers have used artificial intelligence to detect Vietnam War-era bomb craters in Cambodia from satellite images – with the hope that it can help find unexploded bombs. The new method increased true bomb crater detection by more than 160 percent over standard methods. The model, combined with declassified U.S. military records, suggests that 44 to 50 percent of the bombs in the area studied may remain unexploded. As of now, attempts to find and safely remove unexploded bombs and landmines – called demining – has not been as effective as needed in Cambodia, said Erin Lin, assistant professor of political science at The Ohio State University. She cites a recent UN-commissioned report that has criticized the Cambodian national clearance agency for presenting a picture of rapid progress by focusing on areas at minimal or no risk of having unexploded mines.

Researchers at the Medical College of Georgia have been working to develop an AI-based framework using a Phone-based survey to reduce the time to identification of a person under investigation (PUI) for the COVID-19 infection as well as the rapid isolation of this individual. The World Health Organization's situational data indicates that there were more than 70 thousand confirmed cases with more than 2000 death rates. The researchers are trying to mitigate such issues by quick detection through the phone-based app without spending much of the available medical resources. They are using machine learning algorithms in a mobile phone-based web survey to improve possible case identifications of COVID-19 in a faster manner to bring timely interventions. The researchers hope that this app will help reduce the spread of the pandemic in the susceptible population.

The companies already partner on charging system operations managed services. The new deal, which is already being implemented, includes network operations center services and field services in radio, core and transmission technology. AI-driven intelligent and data driven operations are part of the deal. AI-related efficiency benefits, automation and data analytics will enable MTN Benin and Ericsson to run predictive operations to enable a shift from reactive to proactive IT and network management – boosting customers' experiences, as well as network quality and performance. Stephen Blewett, Chief Executive Officer, MTN Benin, says: "Network managed operations play a significant role in improving MTN customers' satisfaction and enhancing customer experience as well as enabling revenue growth and cost efficiency. We expect advanced technologies like AI, automation and analytics to further accelerate operational transformation through this new managed services partnership."