Machine learning is gaining popularity across scientific and technical fields, but it’s often not clear to researchers, especially young scientists, how they can apply these methods in their work.

Machine learning (ML), loosely defined as the “ability of computers to learn from data without being explicitly programmed,” has become tremendously popular in technical disciplines over the past decade or so, with applications including complex game playing and image recognition carried out with superhuman capabilities. The Earth and space sciences (ESS) community has also increasingly adopted ML approaches to help tackle pressing questions and unwieldy data sets. From 2009 to 2019, for example, the number of studies involving ML published in AGU journals approximately doubled.

In many ways, ESS present ideal use cases for ML applications because the problems being addressed—like climate change, weather forecasting, and natural hazards assessment—are globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so.

The Tools of the Trade

An ML algorithm can be thought of broadly as a mathematical function containing many free parameters (thousands or even millions) that takes inputs (features) and maps those features into one or more outputs (targets). The process of “training” an ML algorithm involves optimizing the free parameters to map the features to the targets accurately.

There are two broad categories of ML algorithms relevant in most ESS applications: supervised and unsupervised learning (a third category, reinforcement learning, is used infrequently in ESS). Supervised learning, which involves presenting an ML algorithm with many examples of input-output pairs (called the “training set”), can be further divided, according to the type of target that is being learned, as either categorical (classification; e.g., does a given image show a star cluster or not?) or continuous (regression; e.g., what is the temperature at a given location on Earth?). In unsupervised learning, algorithms are not given a particular target to predict; rather, an algorithm’s task is to learn the natural structure in a data set without being told what that structure is.

Supervised learning is more commonly used in ESS, although it has the disadvantage that it requires labeled data sets (in which each training input sample must be tagged, or labeled, with a corresponding output target), which are not always available. Unsupervised learning, on the other hand, may find multiple structures in a data set, which can reveal unanticipated patterns and relationships, but it may not always be clear which structures or patterns are “correct” (i.e., which represent genuine physical phenomena).

Applications in Earth and Space Sciences

Books and classes about ML often present a range of algorithms that fall into one of the above categories but leave people to imagine specific applications of these algorithms on their own. However, in practice, it is usually not obvious how such approaches (some seemingly simple) may be applied in a rich variety of ways, which can create an imposing obstacle for scientists new to ML. Below we briefly describe various themes and ways in which ML is currently applied to ESS data sets (Figure 1), with the hope that this list—necessarily incomplete and biased by our personal experience—inspires readers to apply ML in their research and catalyzes new and creative use cases.

1. Pattern Identification and Clustering

One of the simplest and most powerful applications of ML algorithms is pattern identification, which works particularly well with very large data sets that cannot be traversed manually and in which signals of interest are faint or highly dimensional. Researchers, for example, applied ML in this way to detect signatures of Earth-sized exoplanets in noisy data making up millions of light curves observed by the Kepler space telescope. Detected signals can be further split into groups through clustering, an unsupervised form of ML, to identify natural structure in a data set.

Conversely, atypical signals may be teased out of data by first identifying and excluding typical signals, a process called anomaly or outlier detection. This technique is useful, for example, in searching for signatures of new physics in particle collider experiments.

2. Time Series and Spatiotemporal Prediction

An important and widespread application of supervised ML is the prediction of time series data from instruments or from an index (or average value) that is intended to encapsulate the behavior of a large-scale system. Approaches to this application often involve using past data in the time series itself to predict future values; they also commonly involve additional inputs that act as drivers of the quantities measured in the time series. A typical example of ML applied to time series in ESS is its use in local weather prediction, with which trends in observed air temperature and pressure data, along with other quantities, can be predicted.

In many instances, however, predicting a single time series of data is insufficient, and knowledge of the temporal evolution of a physical system over regional (or global) spatial scales is required. This spatiotemporal approach is used, for example, in attempts to predict weather across the entire globe as a function of time and 3D space in high-capacity models such as deep neural networks.

3. Emulators and Surrogates

Traditional, physics-based simulations (e.g., global climate models) are often used to model complex systems, but such models can take days or weeks to run on even the most powerful computers, limiting their utility in practice. An alternate solution is to train ML models to act as emulators for physics-based models or to replicate computationally intensive portions within such models. For example, global climate models that run on a coarse grid (e.g., 50- to 100-kilometer resolution) can include subgrid processes, like convection, modeled using ML-based parameterizations. Results with these approaches are often indistinguishable from those produced by the original model alone but can run millions or billions of times faster.

4. Boundary or Driving Conditions

Many physics-based simulations proceed by integrating a set of partial differential equations (PDEs) that rely on time-varying boundary conditions and other conditions that drive interior parts of the simulation. The physics-based model then propagates information from these boundary and driver conditions into the simulation space—imagine, for example, a 3D cube being heated at its boundary faces with time-varying heating rates or with thermal conductivity that varies spatiotemporally within the cube. ML models can be trained to reflect the time-varying parameterizations both within and along the simulation boundaries of a physical model, which again may be computationally cheaper and faster.

5. Interpretability and Knowledge Discovery

If a spatiotemporal ML model of a physical system can be trained to produce accurate results under a variety of input conditions, then the implication is that the model implicitly accounts for all the physical processes that drive that system, and thus, it can be probed to gain insights into how the system works. Certain algorithms (e.g., random forests) can automatically provide a ranking of “feature importance,” giving the user a sense of which input parameters affect the output most and hence an intuition about how the system works.

More sophisticated techniques, such as layerwise relevance propagation, can provide deeper insights into how different features interact to produce a given output at a particular location and time. For example, a neural network trained to predict the evolution of the El Niño–Southern Oscillation (ENSO), which is predominantly associated with changes in sea surface temperature in the equatorial Pacific Ocean, revealed that precursor conditions for ENSO events occur in the South Pacific and Indian Oceans.

6. Accelerating Inversions

A ubiquitous challenge in ESS is to invert observations of a physical entity or process into fundamental information about the entity or the causes of the process (e.g., interpreting seismic data to determine rock properties). Historically, inverse problems are solved in a Bayesian framework requiring multiple runs of a forward model, which can be computationally expensive and often inaccurate. ML offers alternative methods to approach inverse problems, either by using emulators to speed up forward models or by using physics-informed machine learning to discover hidden physical quantities directly. ML models trained on prerun physics-based model outputs can be used for rapid inversion.

7. Creating High-Resolution Global Data Sets

Satellite observations often provide global, albeit low-resolution and sometimes indirect (i.e., proxy-based), measurements of quantities of interest, whereas local measurements provide more accurate and direct observations of those quantities at smaller scales. A popular and powerful use for ML models is to estimate the relationship between global proxy satellite observations and local accurate observations, which enables the creation of estimated global observations on the basis of localized measurements. This approach often includes the use of ML to create superresolution images and other data products.

8. Uncertainty Quantification

Typically, uncertainty in model outputs is quantified using a single metric such as the root-mean-square of the residual (the difference between model predictions and observations). ML models can be trained to explicitly predict the confidence interval, or inherent uncertainty, of this residual value, which not only serves to indicate conditions under which model predictions are trustworthy (or dubious) but can also be used to generate insights about model performance. For instance, if there is a large error at a certain location in a model output under specific conditions, it could suggest that a particular physical process is not being properly represented in the simulation.

9. Physics-Informed Neural Networks

Domain experts analyzing data from a given system, even in relatively small quantities, are often able to extrapolate the behavior of the system—at least conceptually—because of their understanding of and trained intuition about the system based on physical principles. In a similar way, laws and relationships that govern physical processes and conserved quantities can be explicitly encoded into neural network algorithms, resulting in more accurate and physically meaningful models that require less training data.

10. Finding and Solving Governing Equations

In certain applications, the values of terms or coefficients in PDEs that drive a system—and thus that should be represented in a model—are not known. Various ML algorithms were developed recently that automatically determine PDEs that are consistent with the available physical observations, affording a new and powerful discovery tool.

In still newer work, ML methods are being developed to directly solve PDEs. These methods offer accuracy comparable to traditional numerical integrators but can be dramatically faster, potentially allowing large-scale simulations of complex sets of PDEs that have otherwise been unattainable.

Addressing Urgent Challenges

The Earth and space sciences are poised for a revolution centered around the application of existing and rapidly emerging ML techniques to large and complex ESS data sets being collected. These techniques have great potential to help scientists address some of the most urgent challenges and questions about the natural world facing us today. We hope the above list sparks creative and valuable new applications of ML, particularly among students and young scientists, and that it becomes a community resource to which the ESS community can add more ideas.

Acknowledgments

We thank the AGU Nonlinear Geophysics section for promoting interdisciplinary, data-driven research, for supporting the idea of writing this article, and for suggesting Eos as the ideal venue for dissemination. The authors gratefully acknowledge the following sources of support: J.B. from subgrant 1559841 to the University of California, Los Angeles, from the University of Colorado Boulder under NASA Prime Grant agreement 80NSSC20K1580, the Defense Advanced Research Projects Agency under U.S. Department of the Interior award D19AC00009, and NASA/SWO2R grant 80NSSC19K0239 and E.C. from NASA grants 80NSSC20K1580 and 80NSSC20K1275. Some of the ideas discussed in this paper originated during the 2019 Machine Learning in Heliophysics conference.

The post Ten Ways to Apply Machine Learning in Earth and Space Sciences appeared first on Artificial Intelligence.

Artificial intelligence has been making waves in recent years, enabling us to solve problems faster than traditional computing could ever allow. Recently, for example, Google’s artificial intelligence subsidiary DeepMind developed AlphaFold2, a program which solved the protein-folding problem. This is a problem which has had baffled scientists for 50 years.

Advances in AI have allowed us to make progress in all kinds of disciplines—and these are not limited to applications on this planet. From designing missions to clearing Earth’s orbit of junk, here are a few ways artificial intelligence can help us venture further in space.

Astronaut assistants

Do you remember Tars and Case, the assistant robots from the film Interstellar? While these robots don’t exist yet for real space missions, researchers are working towards something similar, creating intelligent assistants to help astronauts. These AI-based assistants, even though they may not look as fancy as those in the movies, could be incredibly useful to space exploration.

A recently developed virtual assistant can potentially detect any dangers in lengthy space missions such as changes in the spacecraft atmosphere—for example increased carbon dioxide—or a sensor malfunction that could be potentially harmful. It would then alert the crew with suggestions for inspection.

An AI assistant called Cimon was flown to the international space station (ISS) in December 2019, where it is being tested for three years. Eventually, Cimon will be used to reduce astronauts’ stress by performing tasks they ask it to do. NASA is also developing a companion for astronauts aboard the ISS, called Robonaut, which will work alongside the astronauts or take on tasks that are too risky for them.

Mission design and planning

Planning a mission to Mars is not an easy task, but artificial intelligence can make it easier. New space missions traditionally rely on knowledge gathered by previous studies. However, this information can often be limited or not fully accessible.

This means the technical information flow is constrained by who can access and share it among other mission design engineers. But what if all the information from practically all previous space missions were available to anyone with authority in just a few clicks. One day there may be a smarter system—similar to Wikipedia, but with artificial intelligence that can answer complex queries with reliable and relevant information—to help with early design and planning of new space missions.

Researchers are working on the idea of a design engineering assistant to reduce the time required for initial mission design which otherwise takes many human work hours. “Daphne” is another example of an intelligent assistant for designing Earth observation satellite systems. Daphne is used by systems engineers in satellite design teams. It makes their job easier by providing access to relevant information including feedback as well as answers to specific queries.

Satellite data processing

Earth observation satellites generate tremendous amounts of data. This is received by ground stations in chunks over a large period of time, and has to be pieced together before it can be analyzed. While there have been some crowdsourcing projects to do basic satellite imagery analysis on a very small scale, artificial intelligence can come to our rescue for detailed satellite data analysis.

For the sheer volume of data received, AI has been very effective in processing it smartly. It’s been used to estimate heat storage in urban areas and to combine meteorological data with satellite imagery for wind speed estimation. AI has also helped with solar radiation estimation using geostationary satellite data, among many other applications.

AI for data processing can also be used for the satellites themselves. In recent research, scientists tested various AI techniques for a remote satellite health monitoring system. This is capable of analyzing data received from satellites to detect any problems, predict satellite health performance and present a visualization for informed decision making.

Space debris

One of the biggest space challenges of the 21st century is how to tackle space debris. According to ESA, there are nearly 34,000 objects bigger than 10cm which pose serious threats to existing space infrastructure. There are some innovative approaches to deal with the menace, such as designing satellites to re-enter Earth’s atmosphere if they are deployed within the low Earth orbit region making them disintegrate completely in a controlled way.

Another approach is to avoid any possible collisions in space, preventing the creation of any debris. In a recent study, researchers developed a method to design collision avoidance maneuvers using machine-learning (ML) techniques.

Another novel approach is to use the enormous computing power available on Earth to train ML models, transmit those models to the spacecraft already in orbit or on their way, and use them on board for various decisions. One way to ensure safety of space flights has recently been proposed using already trained networks on board the spacecraft. This allows more flexibility in satellite design while keeping the danger of in orbit collision at a minimum.

Navigation systems

On Earth, we are used to tools such as Google Maps which use GPS or other navigation systems. But there is no such a system for other extraterrestrial bodies, for now.

We do not have any navigation satellites around the Moon or Mars but we could use the millions of images we have from observation satellites such as the Lunar Reconnaissance Orbiter (LRO). In 2018, a team of researchers from NASA in collaboration with Intel developed an intelligent navigation system using AI to explore the planets. They trained the model on the millions of photographs available from various missions and created a virtual Moon map.

As we carry on to explore the universe, we will continue to plan ambitious missions to satisfy our inherent curiosity as well as to improve the human lives on Earth. In our endeavors, artificial intelligence will help us both on Earth and in space make this exploration possible.

The post Five ways artificial intelligence can help space exploration appeared first on Artificial Intelligence.

The reforms in the Indian space sector announced by Union Finance Minister Nirmala Sitharaman will bring space technology from research and development labs to the commercial arena, said a senior space industry official on Monday.

According to him, the reforms proposed are only the beginning and technology should come out of labs to generate employment and wealth.

On Saturday, Sitharaman announced that Indian private sector will be a co-traveller in India’s space sector journey and a level-playing field will be provided for them in satellites, launches, and space-based services.

She also said a predictable policy and regulatory environment will be provided to private players.

According to her, the private sector will be allowed to use the facilities of Indian Space Research Organisation (ISRO) and other relevant assets to improve their capacities.

Sitharaman said future projects for planetary exploration, outer space travel and others are to be opened up for the private sector, adding there will be a liberal geo-spatial data policy for providing remote-sensing data to tech-entrepreneurs subject to various checks.

“Of the present global space economy of $360 billion, 1.7 per cent revenue comes from the launch segment, 5.3 per cent from satellite manufacturing, 35 per cent from satellite services or payloads and another 34.7 per cent from ground services,” Tapan Misra, Senior Adviser, ISRO, told IANS.

Welcoming Sitharaman’s proposal to revamp the space/data policy, Misra said: “If we target even five per cent of the global space economy, we are looking at a business potential of more than Rs 125,000 crore while ISRO’s budget is hovering around Rs 10,000 crore. We have a huge potential in the space industry which we could not harness in a commercial sense.”

“Space commerce is expected to grow quickly once Covid-19-related lockdowns are relaxed. These reforms will help spur the growth of Indian private space companies and increase India’s share in the global space market. It will be a real opportunity for Indian private sector considering the way defence-space is also growing now,” S. Rakesh, Chairman-cum-Managing Director, Antrix Corporation Ltd, told IANS.

According to Misra, if India aspires to become a reasonable player in the global space economy, then the thrust areas for innovation and production should be payloads and ground hardware.

Taking forward the proposed reforms measures, Misra said the space business should be bifurcated into two segments viz innovation and operation and production.

“The kind of manpower needs in these two areas is different. The former needs inspirational leadership, different and high quality manpower and a stimulating work environment,” Misra said.

On the other hand, production and operation requires different managerial leadership that is strong but a comparatively lesser number of techno-management specialists to man shop floor management and business operation and a workforce of comparatively high skilled manpower.

“Also, the work environment has to be target-oriented and hence comparatively more regimented,” he said.

Given the contradictory requirements, Misra said, it is prudent to separate them but with a strong interaction mechanism.

“First part can be led by ISRO and the government to ensure stability, risk taking in technology and maintaining a certain relaxed environment for innovation,” he said.

According to him, ISRO should play henceforth a catalyst in triggering innovations by co opting start-ups and research and development (R&D) of private sectors.

This synergy will trigger innovations, designed towards not only producing quality technology but a cheaper and competitive cost advantage and taking certain elements of those innovations to day to day commercial, mass production technology, he said.

Second part can be handed over to private players, for employment generation and scaling up production and turnover, he said.

According to Misra, the Indian space agency, start-ups and industries targeting space R&D should concentrate on innovation on technology, future technology prediction and indigenisation.

Some of the suitable areas are: Satellite payloads like optical cameras, radar sensors, communication and navigation payloads and planetary payloads; New algorithms, data processing concepts, data mining and assimilation; New satellite configuration and innovation in satellite control technology elements and software, satellite and planetary navigation; New rocket engines, hybrid platforms, new fuels and materials, aerodynamic and navigation research; Space stations and others.

As fundamental space science research needs a wide variety of expertise, the university system should be made to pick up the challenge.

Misra also said the satellite application and data research should be opened up for innovative small industries and start-ups to tap into the imagination potential of Indian technical and scientific brains.

The remote sensing date should be made available to the public with the click of a mouse.

Though some say the major deterrent for the private sector to get into the space industry is the investment, Misra is of the view that suitable skilled manpower and savvy management expertise and production engineers are the issues.

“So, modalities and incentives of migration and sharing of ISRO expertise to private space industries are to be worked out,” he remarked.

As for Sitharaman’s announcement that the private sector will be allowed to use ISRO facilities Misra said it would enable faster take-off by the private players.

“This is where many tricky things are to be traversed and certain policy initiatives in costing, guarantee requirements, time and space sharing are to be worked out,” he said.

The post ‘Reforms will bring space tech from labs to commercial arena’ appeared first on Artificial Intelligence.

Quetzal-1, the first Guatemalan satellite, developed by students, teachers, researchers, and graduates of the Universidad del Valle de Guatemala (UVG), will be launched into orbit next Tuesday, April 28, at 09:00 a.m. m. (Guatemala time). The CubeSat 1U satellite will be released from the Kibo module of the Japan Aerospace Exploration Agency (JAXA) and communication with it is expected to be achieved from the Control Center, located at UVG, at the time it orbits near Guatemala.

UVG will broadcast the preview of the satellite’s release on their social networks with interviews with team members. During the interviews, the lessons learned, achievements, challenges and stories that these six years of work have left will be discussed. The release can be seen in a live broadcast on JAXA’s official YouTube channel.

This project is one of the efforts of the Universidad del Valle de Guatemala to put science at the service of society. The competencies developed and put into practice by UVG students, graduates and researchers promote the local development of technologies, research and ventures with a view to creating a positive impact in the country.

“Personally, the most enriching aspect of this project is human capital because we are developing the next generation of engineers in the country and this will change history,” says Dr. Luis Zea, co-director of the project.

More than 100 people collaborated in this project, including students, teachers, engineers, and graduates of mechanical engineering, industrial mechanics, mechatronics, electronics, computing, and physics. The project seeks to train in Guatemala the human resources trained to develop and operate this type of satellites. The complexity of its development lies in the fact that more than 70% of the modules were developed at UVG by students and graduates.

A learning opportunity

This project is for educational, research and capacity building purposes for students of all levels. Since 2016, the project has been part of UVG’s agenda of taking knowledge outside the University with talks, lessons and presentations on the subject to students at the secondary and primary levels.

This event and the theme of the space is part of the lessons that UVG offers to all educational centres to work during this period of suspension of classroom classes. Thus, UVG ​​also seeks to motivate more children and young people to study science and engineering programs.

Transfer to space

The first Guatemalan satellite was transferred from Earth to the International Space Station (ISS) in the Dragon-powered capsule by the SpaceX-based Falcon 9 rocket on March 6, 2020, and was stored until programming of the launch.

This is the second satellite that, thanks to the support of the Japan Aerospace Exploration Agency (JAXA) and the United Nations Office for Outer Space Affairs (UNOOSA), will be launched into space through the KiboCube program.

The post The first Guatemalan satellite will be released into space appeared first on Artificial Intelligence.

After a momentous 2019, ending with unexpected international travel lasting up to two days before Christmas, we were thankful to finally use the holiday break to sink into our cozy family room couch with our dogs Lilly and Oliver, browse our OTT app of choice, and catch up with the latest entertainment. We stumbled onto the second season of Lost in Space, which we binge- for the next couple of days.

Without giving away any spoilers, the first episode, Shipwrecked, finds the Robinson family trying to leave a toxic planet to return to the mothership, the Resolute. Yes, you may say that we are suckers for a good sci-fi show, but what was so interesting in the plot was how Maureen Robinson used first-hand information generated by the systems installed in the Jupiter (their ship) to guide the family off the treacherous planet. While she did do some number crunching by hand, the data she relied on was misleading and incomplete, sending the journey off track, almost sinking the Jupiter in the acid sea hole and killing the entire family. Dr. Smith included.

Most certainly, your company is not the Jupiter and, hopefully, your team is not trying to get back to the Resolute. However, this opening episode made me think of the relevance of data and its pivotal role in transforming the operation management of any fashion company trying to, if not scale up, survive — certainly a goal for many out there.

When it comes to the way product data is assembled, collected, stored, and shared, how would you rate your company? While you may think of your PLM application as fundamental to all production and design efforts, just as Maureen Robinson was relying on the information of the Jupiter, you’d be shortchanged to think that you have all you need to make key decisions that may usher you into business success. The truth is that misinformation can make you or break you, and relying on unilateral data to set up important business governance would be a huge miscalculation. Don’t get me wrong, a PDM is a must-have for establishing a strong foundation for any business managing a sizeable style portfolio, but by no means is it the Holy Grail.

Maybe the writer behind Maureen Robinson didn’t have to enhance the story by correlating the information available to her with other variables to improve the quality of the data. That would have had a different outcome, for sure. We’ve seen myriad clients struggle with how to manage their sizeable internal data sets and make decisions based on the outputs of a siloed enterprise system to turn customer data into intelligent and actionable insights—insights that come available to be shared real-time with key stakeholders, enabling informed decision-making which impacts the course of their operations. When clusters of different pieces of information come together to conform data, it becomes very powerful in decision-making. This clustered information that is combined purposefully makes a difference between what is plain information and actionable data. This is what many have coined as big data, a term that is getting some attention in the fashion runways. Well, maybe not the runways, but the alleyways of the folks in charge of making it all come together seamlessly with systems that inform multiple workstreams that it takes to put a piece of clothing on the market.

And while imagination can fuel the way technology ecosystems come together, just as it created the complex systems of the Jupiter, the real concern is that if data doesn’t come together to inform decision-making, it will certainly sink your business. Once you have a system in place, like a PLM, it is relatively easy to manage data if the information is reliable, nomenclatures are consistent, and upkeep protocols are followed according to plan. But for instance, imagine for a moment if all other enterprise systems in place are able to comfortably “talk” to each other and “mingle” happily together in a normalized environment. Suddenly, you have opened the valuable data contained in your PLM that is arrested to some and generated clusters of information accessible to a bigger pool of employees whose own tasks and responsibilities depend on the foundation of product data contained in the PLM. As Mark Harrop would call it, a “data lake.” I like to think of it as the democratization of data. And it is a real thing, by the way.

Big data is a term used to describe a collection of data that is huge in size, often systematically conformed from multiple sources. It is the process of democratizing the data that makes it big data. A problem with big data is that it grows and changes constantly, and organizations struggle to leverage the opportunities to capture actionable insights by the aggregation of other variables supplied by siloed systems. Corporations already spend huge amounts of money furnishing capabilities that serve vertical teams: a PLM is a system used by production and design, an ERP is used by finance and merchandising, a DAM is used by creative teams, etc. One could argue that these are useful and considered staples in any company ecosystem, but what happens when a key executive would like to see a wider view of how a specific category of products has performed? For example, all the types of support that a skew had, what worked and what didn’t, and key metrics of all of the tactics in the market – not after the fact, but right now. It may also be relevant to examine specific efforts that went into support, from advertising all the way down to social, PR, CRM, and credit obtained from sending samples to specific outlets, including wholesale. This is the essence of a true 360-degree approach. Do you run your team down to the ground for days to get reports that are full of holes and errors and only provide insight into a static window of time? What if the meeting is postponed? Do you have to go back and recreate all that work for the next meeting to reflect updates? It happens more often than you think – and if it happens to you, well, you know how crippling it is.

But, “Danger, Will Robinson!” When senior leadership plays no role in enforcing this data aggregation nirvana, things can go wrong very quickly. The democratization of data is a companywide commitment, and while it resides with functional teams, with the support of information leadership, it really begins with strong support and vision from the top. This is a reality behind some of our most successful implementations. Governance is key here, and reports and planning flow generated with big data should be a requirement embedded in the exiting processes of the organization for reporting and planning. Teams should get hands-on and be comfortable with new capabilities while being encouraged to explore the possibilities that aggregated data provides. In our experience with data aggregation implementations, we’ve seen that after the initial shock is overcome, teams become very excited when they see their time cut in half to generate things that used to take days, if not weeks. Also, the visibility they now have, usually starting from the PLM, has been a game-changer, as they can now better realize the importance of their contribution and the value behind having real-time visibility in the variables that can turn things around in the performance of seasonal efforts. Lastly, teams also realize the importance of collaboration as a key factor for success. When interactions in the company are now fueled by information, more intelligent conversations will start to occur, and folks will come together with a different perspective of what their roles are to influence the bottom line.

If we live in an online world, it only makes sense that the systems that help us do our job are available to enable us to fish information from the data pond upon request and without a stumble. In a right-here, right-now economy, it is important that your systems are knit together to give you an edge with the consumer demand and the competition. Data aggregation is a must-have. Today’s society is on-demand, and it thrives on information. It is big data that will boost you forward, just as it helped the Robinsons get out of the acid planet at the end.

The post Lost in Space; When the Lack of Big Data Fails to Lead the Way appeared first on Artificial Intelligence.

University of North Carolina philosophy and religion prof Diana Pasulka tracks the growth of belief in alien intelligences:

As for why humans might be more willing to embrace this new “religion” of alien belief now than in the past, Pasulka says advances in space exploration play a role — but she also thinks an increasing belief that our planet is headed toward a crisis factors into the equation.

“A lot of people see disaster on the horizon, and there’s a deep fear that we won’t be able to save ourselves,” she told Vox. “So what will save us? Well, for some, it will be these advanced beings who come to us and tell us what we can do or how we can escape.”

KRISTIN HOUSER, “PROFESSOR: BELIEF IN ALIENS COULD REPLACE TRADITIONAL RELIGION ” AT FUTURISM

It’s the topic of her new book, American Cosmic: UFOs, Religion, Technology, whose blurb reads, “More than half of American adults and more than seventy-five percent of young Americans believe in intelligent extraterrestrial life. This level of belief rivals that of belief in God.”

According to Pasulka, some high-profile people believe:

But there’s something different about the UFO narrative. Here we have people who are actual scientists, like Ellen Stofan, the former chief scientist at NASA, who are willing to go on TV and basically make announcements like, “We are going to find extraterrestrial life.” Now, she’s not exactly talking about intelligent extraterrestrial life, but that’s not how many people interpret her.

She says we’re going to find life, we’re going to find habitable planets and things like that. So that gives this type of religiosity a far more powerful bite than the traditional religions, which are based on faith in things unseen and unprovable.

Pasulka provided Vox with an account of how she and a colleague were led blindfold by a space shuttle scientist to a place near Roswell, New Mexico—famed setting of space alien encounters of the commercial entertainment kind: “I never conclude whether it’s true or not… Do aliens actually exist? I don’t know. But my book is more about this new form of religiosity and how it’s becoming more influential among scientists and people in Silicon Valley and Americans more generally.”

Illing asks an interesting question: “Do you see the obsession with alien life as a byproduct of our worship of technology?” She tells him, Technology defines our world and culture; it’s our new god…”

There’s the idea that technology like artificial intelligence is going to kill us, but then there’s this idea that technology will be our savior, which is a very religious idea.

These discussions go on despite the fact that we have not found a single life form, simple or complex, let alone any intelligence, other than on Earth. And there are serious reasons to doubt that AI can ever rival human intelligence, let alone “kill us.”

Pasulka’s findings about the meaning popular culture attaches to ET echo philosopher George Gilder’s warning about the religious aspirations of Silicon Valley achievers: “They believe that AI will achieve what my friend Ray Kurzweil calls the Singularity when it will attain capabilities far beyond human minds and thus be able to reproduce itself. And project itself into the universe and seed the universe with a cascade of ever more intelligent machines, thus kind of propagating human life throughout the universe.”

In that case, of course, the tech moguls themselves control the ETs (or, in the case of projects to upload one’s brain in order to escape death), they might become the ETs themselves.

Popular culture is looking for high-tech ETs to be its saviors and Silicon Valley aspires to become those ETs. What could possibly go wrong?

See also: AI as an emergent religion Science philosopher Mike Keas’s new book discusses how AI and ET are merging, to create a religion of futurist magic

Silicon Valley’s strange, apocalyptic cult Key Valley figures hope to beat death the transhumanist way. Oh, by the way, YOU are doomed

and

Tales of an Invented God The most important characteristic of an AI cult is that its gods (Godbots?) will be created by the AI developers and not the other way around. They will mesh with ET, an eternal cyborg who is always Out There.

The post WILL SPACE ALIENS BECOME A NEW MAJORITY RELIGION? appeared first on Artificial Intelligence.

Star Sound Waves

Using artificial intelligence (AI) and sound waves, researchers have found a possible means of looking inside stars.

It’s based on the fact that stars aren’t solid objects — far from it, in fact. They’re intense, vibrating balls of plasma held together by their own gravity and with wildly energetic nuclear reactions at their core. Now, researchers say that they’re beginning to find ways to discern the internal state of a star by looking at the vibrations that propagate from its core to the surface.

Ringing Like A Bell

Much like bubbles boil to the surface of a pot of water as it’s heated, sound waves resonate throughout a star, and smaller stars producer a higher pitch than larger stars, just like a smaller bell would produce a higher pitch than a larger bell. They are akin to seismic waves that travel through Earth when earthquakes occur. And by studying a star’s sound waves, researchers can tell how old a star is, how big it is, what it’s made of and more.

“Stellar sound waves are very similar to the symphonies in our concert halls here on Earth,” Radboud University researcher and study co-author Luc Hendriks said in an email. “These sound waves are caused by starquakes. These quakes create sound with specific frequencies, just as flutes or guitars or pianos have specific “tones” and “overtones” (or harmonics). So from the tones, we deduce how big the star is, as the sound probes the size of the “concert hall”. So for us, a star is a gigantic 3D musical instrument, and its sound waves probe the physical conditions in its interior.”

Most recently, researchers have studied stellar sound waves using NASA’s Kepler space telescope and NASA’s Transiting Exoplanet Survey Satellite (TESS). These instruments are able to observe and measure stellar sound waves by studying the brightness of the stars. Stellar vibrations reveal themselves visibly as brightening and dimming, so instruments like Kepler and TESS have been able to observe stellar sound waves by watching the stars twinkle. In its lifetime, Kepler observed the sound waves of tens of thousands of stars and TESS is expected to observe the sound waves of up to one million red giants.

Using sophisticated computer models, Hendriks and Katholieke Universiteit Leuven astronomer Conny Aerts think they’ve found a brand new way to use these vibrations to see what’s going on inside stars.

Stellar AI

Hendriks and Aerts fed simulations of star activity, created using computer models that collect and synthesize information about stars, to an AI network. The network absorbed this information and found relationships between internal variables like stellar mass, age, what elements the stars contain, and the vibration patterns visible on their surfaces.

The AI can then take real-life stellar sound wave data and compare it to the simulations to discern some of the internal characteristics of a star, providing a new tool for researchers studying stars through their sound waves. It is even possible that the AI might be able to analyze raw stellar sound wave data quicker than a human.

But this star-analyzing AI network is still very new, and hard results are still to come. The researchers’ paper on the technology is posted on the arXiv pre-print server, and has been accepted to the technical journal of the Astronomical Society of the Pacific.

The post How to look inside a star with artificial intelligence and sound waves appeared first on Artificial Intelligence.