In a paper published in the journal Nature Physics, DeepMind researchers describe an AI system that can predict the movement of glass molecules as they transition between liquid and solid states. The techniques and trained models, which have been made available in open source, could be used to predict other qualities of interest in glass, DeepMind says.

Beyond glass, the researchers assert the work yields insights into general substance and biological transitions, and that it could lead to advances in industries like manufacturing and medicine. Machine learning is well placed to investigate the nature of fundamental problems in a range of fields, a DeepMind spokesperson told VentureBeat. We will apply some of the learnings and techniques proven and developed through modeling glassy dynamics to other central questions in science, with the aim of revealing new things about the world around us.

Glass is produced by cooling a mixture of high-temperature melted sand and minerals. It acts like a solid once cooled past its crystallization point, resisting tension from pulling or stretching. But the molecules structurally resemble that of an amorphous liquid at the microscopic level.

Solving glass physical mysteries motivated an annual conference by the Simons Foundation, which last year hosted a group of 92 researchers from the U.S., Europe, Japan, Brazil, and India in New York. In the three years since the inaugural meeting, theyve managed breakthroughs like supercooled liquid simulation algorithms, but theyve yet to develop a complete description of the glass transition and predictive theory of glass dynamics.

Thats because there are countless unknowns about the nature of the glass formation process, like whether it corresponds to a structural phase transition (akin to water freezing) and why viscosity during cooling increases by a factor of a trillion. Its well-understood that modeling the glass transition is a worthwhile pursuit the physics behind it underlie behavior modeling, drug delivery methods, materials science, and food processing. But the complexities involved make it a hard nut to crack.

Fortunately, there exist structural markers that help identify and classify phase transitions of matter, and glasses are relatively easy to simulate and input into particle-based models. As it happens, glasses can be modeled as particles interacting via a short-range repulsive potential, and this potential is relational (because only pairs of particles interact) and local (because only nearby particles interact with each other).

The DeepMind team leveraged this to train a graph neural network a type of AI model that directly operates on a graph, a non-linear data structure consisting of nodes (vertices) and edges (lines or arcs that connect any two nodes) to predict glassy dynamics. They first created an input graph where the nodes and edges represented particles and interactions between particles, respectively, such that a particle was connected to its neighboring particles within a certain radius. Two encoder models then embedded the labels (i.e., translated them to mathematical objects the AI system could understand). Next, the edge embeddings were iteratively updated, at first based on their previous embeddings and the embeddings of the two nodes to which they were connected.

After all of the graphs edges were updated in parallel using the same model, another model refreshed the nodes based on the sum of their neighboring edge embeddings and their previous embeddings. This process repeated several times to allow local information to propagate through the graph, after which a decoder model extracted mobilities measures of how much a particle typically moves for each particle from the final embeddings of the corresponding node.

The team validated their model by constructing several data sets corresponding to mobilities predictions on different time horizons for different temperatures. After applying graph networks to the simulated 3D glasses, they found that the system strongly outperformed both existing physics-inspired baselines and state-of-the-art AI models.

They say that network was extremely good on short times and remained well matched up to the relaxation time of the glass (which would be up to thousands of years for actual glass), achieving a 96% correlation with the ground truth for short times and a 64% correlation for relaxation time of the glass. In the latter case, thats an improvement of 40% compared with the previous state of the art.

In a separate experiment, to better understand the graph model, the team explored which factors were important to its success. They measured the sensitivity of the prediction for the central particle when another particle was modified, enabling them to judge how large of an area the network used to extract its prediction. This provided an estimate of the distance over which particles influenced each other in the system.

They report theres compelling evidence that growing spatial correlations are present upon approaching the glass transition, and that the network learned to extract them. These findings are consistent with a physical picture where a correlation length grows upon approaching the glass transition, wrote DeepMind in a blog post. The definition and study of correlation lengths is a cornerstone of the study of phase transition in physics.

DeepMind claims the insights gleaned could be useful in predicting the other qualities of glass; as alluded to earlier, the glass transition phenomenon manifests in more than window (silica) glasses. The related jamming transition can be found in ice cream (acolloidal suspension), piles of sand (granular materials), and cell migration during embryonic development, as well as social behaviors such as traffic jams.

Glasses are archetypal of these kinds of complex systems, which operate under constraints where the position of elements inhibits the motion of others. Its believed that a better understanding of them will have implications across many research areas. For instance, imagine a new type of stable yet dissolvable glass structure that could be used for drug delivery and building renewable polymers.

Graph networks may not only help us make better predictions for a range of systems, wrote DeepMind, but indicate what physical correlates are important for modeling them that machine learning systems might be able to eventually assist researchers in deriving fundamental physical theories, ultimately helping to augment, rather than replace, human understanding.

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Misaligned incentives can encourage incomprehensibility.

The precept of the people, by the people, for the people demonstrates not only that citizens must choose leaders through accountability processes and institutions, but also that each citizens decision-making must be as informed as possible. This vision can only be accomplished through the availability of valid and relevant information that must be understandable to all audiences.

Over the last two decades, policy experts have hoped that novel technologies would be used to make information more meaningful, but many of these expectations are still unfulfilled. In her book with Will Walker, Incomprehensible!, Wendy Wagner demonstrates that various legal programs are built on the foundational assumption that more information is better, ignoring the imperative of usable and meaningful communication.

The design of many legal programs favors the production or reporting of undigested information, which is in turn passed along to an unequipped, disadvantaged audience. Wagner argues that although there are numerous procedural steps required for Congress to pass laws, there are no institutional controls that require a bill to be comprehensible to other members of Congress. This suggests that even today there remains an endemic, fundamental problem of unintelligibility.

The principle of governmental transparency is only fulfilled when information is relevant and understandable to a general audience. Unintelligible information or the mere release of unprocessed data does not fulfill the principle of transparency. On the contrary, it opens the doors wide for parties with technical expertise to profit from their strategic advantages over the less empowered. This concern is particularly relevant in the face of modern challenges, such as misinformation and the lack of actors that process information on behalf of citizens.

Automating government processes through machine learning would have uncertain implications in this regard, especially when the inner workings of those processes are unintelligible and might not benefit the average citizen, as Wagner argues.

Scholars have argued that machine learning can meet the laws demands for transparency and does not contravene the principles of nondelegation doctrine, due process, equal protection, and reason giving. It also can enhance efficacy, efficiency, and legitimacy in government. Principles of fair algorithmic governance, however, go beyond mere disclosure and understandability of the technical aspects of machine learning resources, such as the source code, data, objective function, parameters, and training data sets. Algorithmic governance is rooted in the very ecosystem over which those technical resources are applied and operate.

Thus, even if these technical resources are put into the open, they will introduce even more confusion if they are applied to a convoluted law that can only be understood by selected partieswith a narrow exception for machine learning that is applied to make laws more meaningful to a wider audience. Applying algorithm-based decisions to an ecosystem of unintelligible laws or regulations that favor a few knowledgeable stakeholders will compound any endemic problem, particularly if these very stakeholders further their agenda through their knowledge of machine learning. This situation would worsen the already fragile ecosystem to which Wagner refers.

The future of machine learning in government is therefore uncertain, because the technology is applied where processing help is needed, but also where it is convenient for stakeholders with great knowledge and agendas that might not be aligned with the average citizen.

According to Cary Coglianese, algorithms will likely be applied more often to assist, rather than to supplant human judgment. Indeed, the judiciary in the United States has been cautious and rather slow to utilize algorithms, mainly applying them to areas of risk assessment and dispute resolution. The majority of these tools are based on statistical approaches or conventional supervised learning logistic regression models, rather than unsupervised learning models.

Administrative agencies, on the other hand, seem to be way ahead of the judiciary. They have already employed full-fledged machine learning tools for various regulatory taskssuch as identifying cases of possible fraud or regulatory violations, forecasting the likelihood that certain chemicals are toxic, identifying people by facial-recognition when they arrive in the United States, prioritizing locations for police patrols, and more. As in the criminal justice system, none of this artificial intelligence has fully replaced human decision-making, with the exception of processes like the automation and optimization of traffic light and congestion avoidance, which has relegated humans to a supervisory control role, common of the automatic control field.

The application of machine learning to a government process is one of the last stages of a continuum in which algorithms become increasingly complex. This continuum starts with the processing of data which can offer meaningful visualizations, proceeds with the utilization of statistical approaches that can provide even more insights, and continues with the utilization of full-fledged machine learning approaches. The use of machine learning in governmental settings has not escaped controversy, particularly on the issues of bias, prejudice, and privacy that can arise from imperfect data. In addition to the fundamental issues Wagner addresses, various aspects of machine learning do not seem to be proper in early stages of this continuum, bringing a certain degree of pessimism about the application of machine learning in such an imperfect context.

My concerns are not unfounded. One example of the possible application of machine learning to an imperfect context is model legislation, also referred to as model bills. Unsuspecting lawmakers across the United States have been introducing these bills designed and written by private organizations with selfish agendas. For lawmakers, copying model legislation is an easy way to put their names on fully formed bills, while building relationships with lobbyists and other potential campaign donors. Model legislation gets copied in one state capitol after another, quietly advancing hidden agendas of powerful stakeholders. A study carried out by USA TODAY, The Arizona Republic, and The Center for Public Integrity found that more than 2,100 bills that became law in the last eight years had been almost entirely copied from model legislation.

Although the process of adopting model legislationor algorithmic objects, as I call them, because they could be re-utilizedcould be perfectly appropriate for bills with a proper purpose, the model bills passed into law often pursue the goals of powerful groups. Some of these bills introduced barriers for injured consumers to sue corporations, limited access to abortion, and restricted the rights of protesters, among others.

According to the study, model legislation disguises its authors true intent through deceptive titles and descriptions. The Asbestos Transparency Act, for example, did not help victims exposed to asbestos as its title implied; it was written by corporations who wanted to erect more obstacles for victims seeking compensation. The HOPE Act made it more difficult for people to get food stamps and was written by a conservative advocacy group. In all, these copycat bills amount tothe nations largest, unreported special-interest campaign, driving agendas in every statehouse and touching nearly every area of public policy, note two reporters involved with the Center for Public Integrity in its recent study.

Open Government Data, a technical and policy stance favoring publicly available government data which will facilitate the upcoming adoption of machine learning, is another area of concern. Very expensive initiatives and data portals in the United States have raised expectations but have failed to change agency resistance to openness or invigorate public participation. On the contrary, these initiatives have created barriers to access by favoring individuals and organizations with highly technical skills.

The problem of unintelligibility is not limited to the United States. An assessment of international government portals indicates that data-oriented technologies are not being used to make things more understandable, signaling to the myopic work of influential international organizations that have pushed for expensive technical implementation while leaving aside the needs of disadvantaged audiences in spite of the explicit warnings a decade earlier.

These are a few challenges the regulatory community must address to be ready for the eventual application of machine learning. Wagner is right to highlight these challenges, and her book pinpoints suggestions for addressing them at a fundamental level.

Martin J. Murillo is a member of the Institute of Electrical and Electronics Engineers and works as a control systems engineer, political scientist, and data scientist on the application of machine learning to government and politics.

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Machine Learning Could Make Government More Incomprehensible - The Regulatory Review

Google has rolled out a new technology to improve audio quality in Duo calls when the service cant maintain a steady connection called WaveNetEQ. Its based on technology from Googles DeepMind division that aims to replace audio jitter with artificial noise that sounds just like human speech, generated using machine learning.

If youve ever made a call over the internet, chances are youve experienced audio jitter. It happens when packets of audio data sent as part of the call get lost along the way or otherwise arrive late or in the wrong order. Google says that 99 percent of Duo calls experience packet loss: 20 percent of these lose over 3 percent of their audio, and 10 percent lose over 8 percent. Thats a lot of audio to replace.

Every calling app has to deal with this packet loss somehow, but Google says that these packet loss concealment (PLC) processes can struggle to fill gaps of 60ms or more without sounding robotic or repetitive. WaveNetEQs solution is based on DeepMinds neural network technology, and it has been trained on data from over 100 speakers in 48 different languages.

Here are a few audio samples from Google comparing WaveNetEQ against NetEQ, a commonly used PLC technology. Heres how it sounds when its trying to replace 60ms of packet loss:

Heres a comparison when a call is experiencing packet loss of 120ms:

Theres a limit to how much audio the system can replace, though. Googles tech is designed to replace short sounds, rather than whole words. So after 120ms, it fades out and produces silence. Google says it evaluated the system to make sure it wasnt introducing any significant new sounds. Plus, all of the processing also needs to happen on-device since Google Duo calls are end-to-end encrypted by default. Once the calls real audio resumes, WaveNetEQ will seamlessly fade back to reality.

Its a neat little bit of technology that should make calls that much bit easier to understand when the internet fails them. The technology is already available for Duo calls made on Pixel 4 phones, thanks to the handsets December feature drop, and Google says its in the process of rolling it out to other unnamed handsets.

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Google is using machine learning to improve the quality of Duo calls - The Verge

Policymakers often draw on the work of social scientists to predict how specific policies might affect social outcomes such as the employment or crime rates. The idea is that if they can understand how different factors might change the trajectory of someones life, they can propose interventions to promote the best outcomes.

In recent years, though, they have increasingly relied upon machine learning, which promises to produce far more precise predictions by crunching far greater amounts of data. Such models are now used to predict the likelihood that a defendant might be arrested for a second crime, or that a kid is at risk for abuse and neglect at home. The assumption is that an algorithm fed with enough data about a given situation will make more accurate predictions than a human or a more basic statistical analysis.

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Now a new study published in the Proceedings of the National Academy of Sciences casts doubt on how effective this approach really is. Three sociologists at Princeton University asked hundreds of researchers to predict six life outcomes for children, parents, and households using nearly 13,000 data points on over 4,000 families. None of the researchers got even close to a reasonable level of accuracy, regardless of whether they used simple statistics or cutting-edge machine learning.

The study really highlights this idea that at the end of the day, machine-learning tools are not magic, says Alice Xiang, the head of fairness and accountability research at the nonprofit Partnership on AI.

The researchers used data from a 15-year-long sociology study called the Fragile Families and Child Wellbeing Study, led by Sara McLanahan, a professor of sociology and public affairs at Princeton and one of the lead authors of the new paper. The original study sought to understand how the lives of children born to unmarried parents might turn out over time. Families were randomly selected from children born in hospitals in large US cities during the year 2000. They were followed up for data collection when the children were 1, 3, 5, 9, and 15 years old.

McLanahan and her colleagues Matthew Salganik and Ian Lundberg then designed a challenge to crowdsource predictions on six outcomes in the final phase that they deemed sociologically important. These included the childrens grade point average at school; their level of grit, or self-reported perseverance in school; and the overall level of poverty in their household. Challenge participants from various universities were given only part of the data to train their algorithms, while the organizers held some back for final evaluations. Over the course of five months, hundreds of researchers, including computer scientists, statisticians, and computational sociologists, then submitted their best techniques for prediction.

The fact that no submission was able to achieve high accuracy on any of the outcomes confirmed that the results werent a fluke. You can't explain it away based on the failure of any particular researcher or of any particular machine-learning or AI techniques, says Salganik, a professor of sociology. The most complicated machine-learning techniques also werent much more accurate than far simpler methods.

For experts who study the use of AI in society, the results are not all that surprising. Even the most accurate risk assessment algorithms in the criminal justice system, for example, max out at 60% or 70%, says Xiang. Maybe in the abstract that sounds somewhat good, she adds, but reoffending rates can be lower than 40% anyway. That means predicting no reoffenses will already get you an accuracy rate of more than 60%.

Likewise, research has repeatedly shown that within contexts where an algorithm is assessing risk or choosing where to direct resources, simple, explainable algorithms often have close to the same prediction power as black-box techniques like deep learning. The added benefit of the black-box techniques, then, is not worth the big costs in interpretability.

The results do not necessarily mean that predictive algorithms, whether based on machine learning or not, will never be useful tools in the policy world. Some researchers point out, for example, that data collected for the purposes of sociology research is different from the data typically analyzed in policymaking.

Rashida Richardson, policy director at the AI Now institute, which studies the social impact of AI, also notes concerns in the way the prediction problem was framed. Whether a child has grit, for example, is an inherently subjective judgment that research has shown to be a racist construct for measuring success and performance, she says. The detail immediately tipped her off to thinking, Oh theres no way this is going to work.

Salganik also acknowledges the limitations of the study. But he emphasizes that it shows why policymakers should be more careful about evaluating the accuracy of algorithmic tools in a transparent way. Having a large amount of data and having complicated machine learning does not guarantee accurate prediction, he adds. Policymakers who don't have as much experience working with machine learning may have unrealistic expectations about that.

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AI cant predict how a childs life will turn out even with a ton of data - MIT Technology Review

Over the past few years, many companies have embraced artificial intelligence (A.I.) and machine learning as the way of the future. Thats been good news for those technologists whove mastered the tools and concepts related to A.I. and machine learning; those with the right combination of experience and skills can easily earn six-figure salaries (with accompanying perks and benefits).

As A.I. and machine learning mature as sub-industries, its inevitable that more certifications proving technologists skills will emerge. For example, Google recently launched aTensorFlow Developer Certificate, whichjust like it says on the tinconfirms that a developer has mastered the basics of TensorFlow, the open-source library for deep learning software developed by Google.

This certificate in TensorFlow development is intended as a foundational certificate for students, developers, and data scientists who want to demonstrate practical machine learning skills through building and training of basic models using TensorFlow,read a note on the TensorFlow Blog. This level one certificate exam tests a developers foundational knowledge of integrating machine learning into tools and applications.

Those who pass the exam will receive aa certificate and a badge. In addition, those certified developers will also be invited to join ourcredential networkfor recruiters seeking entry-level TensorFlow developers, the blog posting added. This is only the beginning; as this program scales, we are eager to add certificate programs for more advanced and specialized TensorFlow practitioners.

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

Google and TensorFlow arent the only entities in the A.I. certification arena.IBM offers an A.I. Engineering Professional Certificate, which focuses on machine learning and deep learning. Microsoft also has a number of A.I.-related certificates,including an Azure A.I. Engineer Associatecertificate. And last year, Amazon launchedAWS Certified Machine Learning.

Meanwhile, if youre interested in learning how to use TensorFlow, Udacity and Google areoffering a two-month course (just updated in February 2020) designed to help developers utilize TensorFlow to build A.I. applications that scale. Thecourse is part of Udacitys School of A.I., a cluster of free courses to help those relatively new to A.I. andmachine learninglearn the fundamentals.

As the COVID-19 pandemic forces many companies to radically adjust their products, workflows, and internal tech stacks,interest in A.I. and machine learning may accelerate; managers are certainly interested in tools and platforms that will allow them to automate work. Even before the virus emerged, Burning Glass, which collects and analyzes millions of job postings from across the country, estimated that jobs involving A.I. would grow 40 percent over the next decadea number that might only increase under the current circumstances.

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PALO ALTO, Calif., March 31, 2020 /PRNewswire/ -- RapidPlan PT is the first clinical application of machine learning in proton treatment planning

RT Peer Review is designed to streamline and accelerate the radiation therapy peer review process

Eclipse v16 has received CE mark and is 510(k) pending

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Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.

Previously only available for photon-based radiotherapy treatment planning, RapidPlan is knowledge-based treatment planning software that enables clinicians to leverage knowledge and data from similar prior treatment plans to quickly develop high-quality personalized plans for patients. This knowledge-based planning software is now available for proton treatment planning with RapidPlan PT. The software also allows dose prediction with machine learning models that can be used as a decision support tool to determine which patients would be appropriate for proton or photon therapy. Varian is the first vendor in the industry to offer machine learning capability in both proton and photon treatment planning.

"With the number of operational proton treatment rooms continuing to increase, there is a need for experienced proton therapy clinicians," said Kolleen Kennedy, chief growth officer, president, Proton Solutions, Varian. "RapidPlan PT helps bridge the learning curve, allowing established centers to share their models and clinical experience. The machine learning in RapidPlan PT has the potential to reduce proton treatment plan optimization from a one to eight hour process, as reported by clinical proton centers, to less than 10 minutes, while also potentially improving plan quality."

In many radiotherapy departments, radiation therapy peer review meetings have been routinely integrated into the clinical QA process for safer healthcare delivery for the patient. Although the relevant patient information is manually retrievable from the clinical database, there is currently no efficient and effective platform to support these peer reviews. The RT Peer Review feature in Eclipse v16 is designed for the oncology community to seamlessly integrate this review process into their normal clinical workflow by automatically presenting the necessary information that is required for peer review.

About VarianAt Varian, we envision a world without fear of cancer. For more than 70 years, we have developed, built and delivered innovative cancer care technologies and solutions for our clinical partners around the globe to help them treat millions of patients each year. With an Intelligent Cancer Care approach, we are harnessing advanced technologies like artificial intelligence, machine learning and data analytics to enhance cancer treatment and expand access to care. Our 10,000 employees across 70 locations keep the patient and our clinical partners at the center of our thinking as we power new victories in cancer care. Because, for cancer patients everywhere, their fight is our fight.

SOURCE Varian

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Artificial Intelligence plays an important role in the pharmaceutical industry and the coming years there is simply no sign of the adoption of this cutting-edge technology slowing down. From making healthcare process automated to help in drug discovery, AI with machine learning can bring revolution in this industry. The key customer-oriented areas where AI is being implemented within the sector are the following:

Through natural language processing, audio and video files are transcribed from voice to text. These files shall be obtained from video-recordings from patients and customers speaking providing their opinion about a particular product or service. The dataset must be considerably large more than 300 audio-video files in order to assure accuracy. The larger the amount of datapoints, the better results that will be obtained.Within that process, an intelligent platform performs a sentiment analysis, which means the platform mines for a series of keywords or statements, as well as the demographics of the speaker (including gender and, possibly, age).Post-transcription, that data is categorized and classified, ready for analysis based on the chosen parameters.

Machine Learning uses diverse approaches to the creation of autonomous and supervised Neural Network-based speech recognition and translation systems. The two vanguard approaches in this period are Long Short-Term Memoryand CNN. The LTSM network or RNN has an 82 per cent accuracy score, while the vision-based Convolutional Neural Network scores 95 per cent accuracy.

Every Machine Learning algorithm takes a dataset as input and learns from this data. The algorithm goes through the data and identifies patterns in the data. For instance, suppose we wish to identify whose face is present in a given image, there are multiple things we can look at as a pattern:

Clearly, there is a pattern here different faces have different dimensions like the ones above. Similar faces have similar dimensions. The challenging part is to convert a particular face into numbers Machine Learning algorithms only understand numbers. This numerical representation of a face (or an element in the training set) is termed as afeature vector. A feature vector comprises of various numbers in a specific order.

As a simple example, we can map a face into a feature vector which can comprise various features such as:

Essentially, given an image, we can map out various features and convert it into a feature vector as:

So, our image is now a vector that could be represented as (23.1, 15.8, 255, 224, 189, 5.2, 4.4). Of course there could be countless other features that could be derived from the image (for instance, hair colour, facial hair, spectacles, etc). However, for the example, let us consider just these 5 simple features.

Machine Learning can help us here with 2 key elements:

What is the relationship between machine learning and optimization? On the one hand, mathematical optimization is used in machine learning during model training, when we are trying to minimize the cost of errors between our model and our data points. On the other hand, what happens when machine learning is used to solve optimization problems?

In simple terms, we can use the power of machine learning to forecast travel times between each two locations and use the genetic algorithm to find the best travel itinerary for our delivery truck. The following parameters need to be followed:

Here, you can predict who, and when an employee will terminate the service. Employee churn is expensive, and incremental improvements will give significant results. It will help us in designing better retention plans and improving employee satisfaction. This will be measured through the following attributes:

We will build a model that automatically suggests the right product prices. We are provided of the following information:

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The Future of Healthcare And AI Impact - Analytics India Magazine

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In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection. The variations of the electromagnetic fields caused by changes of the fluid distribution are measured in a wide range of distances from the well. The data are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a machine-learning (ML) platform. The complete paper clarifies the details of the ML work flow applied to electrical resistivity tomography (ERT) models using an example based on synthetic data.

An important question in well completions is how one may acquire data with sufficient accuracy for detecting the movements of the fluids in a wide range of distances in the space around the production well. One method that is applied in various Earth disciplines is time-lapse electrical resistivity. The operational effectiveness of ERT allows frequent acquisition of independent surveys and inversion of the data in a relatively short time. The final goal is to create dynamic models of the reservoir supporting important decisions in near-real-time regarding production and management operations. ML algorithms can support this decision-making process.

In a time-lapse ERT survey [often referred to as a direct-current (DC) time-lapse survey], electrodes are installed at fixed locations during monitoring. First, a base resistivity data set is collected. The inversion of this initial data set produces a base resistivity model to be used as a reference model. Then, one or more monitor surveys are repeated during monitoring. The same acquisition parameters applied in the base survey must be used for each monitor survey. The objective is to detect any small change in resistivity, from one survey to another, inside the investigated medium.

As a first approach, the eventual variations in resistivity can be retrieved through direct comparison between the different inverted resistivity models. A different approach is called difference inversion. Instead of inverting the base and monitor data sets separately, in difference inversion, the difference between the monitor and base data sets is inverted. In this way, all the coherent inversion artifacts may be canceled in the difference images resulting from this type of inversion.

Repeating the measurements many times (through multiple monitor surveys) in the same area and inverting the differences between consecutive data sets results in deep insight about relevant variations of physical properties linked with variations of the electric resistivity.

The Eni reservoir electromagnetic monitoring and fluid mapping system consists of an array of electrodes and coils (Fig. 1) installed along the production casing/liner. The electrodes are coupled electrically with the geological formations. A typical acquisition layout can include several hundred electrodes densely spaced (for instance, every 510m) and deployed on many wells for long distances along the liner. This type of acquisition configuration allows characterization, after data inversion, of the resistivity space between the wells with relatively high resolution and in a wide range of distances. The electrodes work alternately as sources of electric currents (Electrodes A and B in Fig. 1) and as receivers of electric potentials (Electrodes M and N). The value of the measured electric potentials depends on the resistivity distribution of the medium investigated by the electric currents. Consequently, the inversion of the measured potentials allows retrieval of a multidimensional resistivity model in the space around the electrode array. This model is complementary to the other resistivity model retrieved through ERT tomography. Finally, the resistivity models are transformed into fluid-saturation models to obtain a real-time map of fluid distribution in the reservoir.

The described system includes coils that generate and measure a controlled electromagnetic field in a wide range of frequencies.

The geoelectric method has proved to be an effective approach for mapping fluid variations, using both surface and borehole measurements, because of its high sensitivity to the electrical resistivity changes associated with the different types of fluids (fresh water, brine, hydrocarbons). In the specific test described in the complete paper, the authors simulated a time-lapse DC tomography experiment addressed to hydrocarbon reservoir monitoring during production.

A significant change in conductivity was simulated in the reservoir zone and below it because of the water table approaching four horizontal wells. A DC cross-hole acquisition survey using a borehole layout deployed in four parallel horizontal wells located at a mutual constant distance of 250 m was simulated. Each horizontal well is a constant depth of 2340 m below the surface. In each well, 15 electrodes with a constant spacing of 25 m were deployed.

The modeling grid is formed by irregular rectangular cells with size dependent on the spacing between the electrodes. The maximum expected spatial resolution of the inverted model parameter (resistivity, in this case) corresponds to the minimum half-spacing between the electrodes.

For this simulation, the authors used a PUNQ-S3 reservoir model representing a small industrial reservoir scenario of 19285 gridblocks. A South and East fault system bounds the modeled hydrocarbon field. Furthermore, an aquifer bounds the reservoir to the North and West. The porosity and saturation distributions were transformed into the corresponding resistivity distribution. Simulations were performed on the resulting resistivity model. This model consists of five levels (with a thickness of 10 m each) with variable resistivity.

The acquisition was simulated in both scenarios before and after the movement of waterthat is, corresponding with both the base and the monitor models. A mixed-dipole gradient array, with a cycle time of 1.2 s, was used, acquiring 2,145 electric potentials. This is a variant of the dipole/dipole array with all four electrodes (A, B, M, and N) usually deployed on a straight line.

The authors added 5% of random noise in the synthetic data. Consequently, because of the presence of noisy data, a robust inversion approach more suited to presence of outliers was applied.

After the simulated response was recorded in the two scenarios (base and monitor models), the difference data vector was created and inverted for retrieving the difference conductivity model (that is, the 3D model of the spatial variations of the conductivity distribution). One of the main benefits of DC tomography is the rapidity by which data can be acquired and inverted. This intrinsic methodological effectiveness allows acquisition of several surveys per day in multiple wells, permitting a quasi-real-time reservoir-monitoring approach.

Good convergence is reached after only five iterations, although the experiment started from a uniform resistivity initial model, assuming null prior knowledge.

In another test, the DC response measured in two different scenarios was studied. A single-well acquisition scheme was considered, including both a vertical and a horizontal segment. The installation of electrodes in both parts was simulated, with an average spacing of 10 m. A water table approaching the well from below was simulated, with the effect of changing the resistivity distribution significantly. The synthetic response was inverted at both stages of the water movement. After each inversion, the water table was interpreted in terms of absolute changes of resistivity.

The technology is aimed at performing real-time reservoir fluid mapping through time-lapse electric/electromagnetic tomography. To estimate the resolution capability of the approach and its theoretical range of investigation, a full sensitivity analysis was performed through 3D forward modeling and time-lapse 3D inversion of synthetic data simulated in realistic production scenarios. The approach works optimally when sources and receivers are installed in multiple wells. Time-lapse ERT tests show that significant conductivity variations caused by waterfront movements up to 100150 m from the borehole electrode layouts can be detected. Time-lapse ERT models were integrated into a complete framework aimed at analyzing the continuous information acquired at each ERT survey. Using a suite of ML algorithms, a quasi-real-time space/time prediction about the probabilistic distributions of invasion of undesired fluids into the production wells can be made.

The rest is here:
Well-Completion System Supported by Machine Learning Maximizes Asset Value - Journal of Petroleum Technology

(Dusit/Shutterstock)

Artificial intelligence is becoming more ubiquitous and necessary these days. From preventing fraud, real-time anomaly detection to predicting customer churn, enterprise customers are finding new applications of machine learning (ML) every day. What lies under the hood of ML, how does this technology make predictions and which secret ingredient makes the AI magic work?

In the data science community, the focus is typically on algorithm selection and model training, and indeed those are important, but the most critical piece in the AI/ML workflow is not how we select or tune algorithms but what we input to AI/ML, i.e., feature engineering.

Feature engineering is the holy grail of data science and the most critical step that determines the quality of AI/ML outcomes. Irrespective of the algorithm used, feature engineering drives model performance, governs the ability of machine learning to generate meaningful insights, and ultimately solve business problems.

Feature engineering is the process of applying domain knowledge to extract analytical representations from raw data, making it ready for machine learning. It is the first step in developing a machine learning model for prediction.

Feature engineering involves the application of business knowledge, mathematics, and statistics to transform data into a format that can be directly consumed by machine learning models. It starts from many tables spread across disparate databases that are then joined, aggregated, and combined into a single flat table using statistical transformations and/or relational operations.

(NicoElNino/Shutterstock)

For example, predicting customers likely to churn in any given quarter implies having to identify potential customers who have the highest probability of no longer doing business with the company. How do you go about making such a prediction? We make predictions about the churn rate by looking at the underlying causes. The process is based on analyzing customer behavior and then creating hypotheses. For example, customer A contacted customer support five times in the last month implying customer A has complaints and is likely to churn. In another scenario, customer As product usage might have dropped by 30% in the previous two months, again, implying that customer A has a high probability of churning. Looking at the historical behavior, extracting some hypothesis patterns, testing those hypotheses is the process of feature engineering.

Feature engineering is about extracting the business hypothesis from historical data. A business problem that involves predictions such as customer churn is a classification problem.

There are several ML algorithms that you can use, such as classical logistic regression, decision tree, support vector machine, boosting, neural network. Although all these algorithms require a single flat matrix as their inputs, raw business data is stored in disparate tables (e.g., transactional, temporal, geo-locational, etc.) with complex relationships.

(Semisatch/Shutterstock)

We may join two tables first and perform temporal aggregation on the joined table to extract temporal user behavior patterns. Practical FE is far more complicated than simple transformation exercises such as One-Hot Encoding (transform categorical values into binary indicators so that ML algorithms can utilize). To implement FE, we are writing hundreds or even thousands of SQL-like queries, performing a lot of data manipulation, as well as a multitude of statistical transformations.

In the machine learning context, if we know the historical pattern, we can create a hypothesis. Based on the hypothesis, we can predict the likely outcome like which customers are likely to churn in a given time period. And FE is all about finding the optimal combination of hypotheses.

Feature Engineering is critical because if we provide wrong hypotheses as an input, ML cannot make accurate predictions. The quality of any provided hypothesis is vital for the success of an ML model. Quality of feature is critically important from accuracy and interpretability.

Feature engineering is the most iterative, time-consuming, and resource-intensive process, involving interdisciplinary expertise. It requires technical knowledge but, more importantly, domain knowledge.

The data science team builds features by working with domain experts, testing hypotheses, building and evaluating ML models, and repeating the process until the results become acceptable for businesses. Because in-depth domain knowledge is required to generate high-quality features, feature engineering is widely considered the black-arts of experts, and not possible to automate even when a team often spends 80% of their effort on developing a high-quality feature table from raw business data.

Feature engineering automation has vast potential to change the traditional data science process. It significantly lowers skill barriers beyond ML automation alone, eliminating hundreds or even thousands of manually-crafted SQL queries, and ramps up the speed of the data science project even without a full light of domain knowledge. It also augments our data insights and delivers unknown- unknowns based on the ability to explore millions of feature hypotheses just in hours.

Recently, ML automation (a.k.a. AutoML) has received large attention. AutoML is tackling one of the critical challenges that organizations struggle with: the sheer length of the AI and ML project, which usually takes months to complete, and the incredible lack of qualified talent available to handle it.

While current AutoML products have undoubtedly made significant inroads in accelerating the AI and machine learning process, they fail to address the most significant step, the process to prepare the input of machine learning from raw business data, in other words, feature engineering.

To create a genuine shift in how modern organizations leverage AI and machine learning, the full cycle of data science development must involve automation. If the problems at the heart of data science automation are due to lack of data scientists, poor understanding of ML from business users, and difficulties in migrating to production environments, then these are the challenges that AutoML must also resolve.

AutoML 2.0, which automates the data and feature engineering, is emerging streamlining FE automation and ML automation as a single pipeline and one-stop-shop. With AutoML 2.0, the full-cycle from raw data through data and feature engineering through ML model development takes days, not months, and a team can deliver 10x more projects.

Feature engineering helps reveal the hidden patterns in the data and powers the predictive analytics based on machine learning. Algorithms need high-quality input data containing relevant business hypotheses and historical patterns and feature engineering provides this data. However, it is the most human-dependent and time-consuming part of AI/ML workflow.

AutoML 2.0, streamlines feature engineering automation and ML automation, is a new technology breakthrough to accelerate and simplify AI/ML for enterprises. It enables more people, such as BI engineers or data engineers to execute AI/ML projects and makes enterprise AI/ML more scalable and agile.

About the author: Ryohei Fujimaki, Ph.D., is the founder and CEO of dotData. Prior to founding dotData, he was the youngest research fellow ever in NEC Corporations 119-year history, the title was honored for only six individuals among 1000+ researchers. During his tenure at NEC, Ryohei was heavily involved in developing many cutting-edge data science solutions with NECs global business clients, and was instrumental in the successful delivery of several high-profile analytical solutions that are now widely used in industry. Ryohei received his Ph.D. degree from the University of Tokyo in the field of machine learning and artificial intelligence.

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Read more from the original source:
What is Feature Engineering and Why Does It Need To Be Automated? - Datanami

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Artificial Intelligence and Machine Learning Market 2020 Industry Share, Size, Technology, Application, Revenue, Top Companies Analysis and 2025...