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.

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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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Google TensorFlow Cert Suggests AI, ML Certifications on the Rise - Dice Insights

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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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.

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Well-Completion System Supported by Machine Learning Maximizes Asset Value - Journal of Petroleum Technology

Nature Machine Intelligence published a joint paper from researchers at Intel Labs and Cornell University demonstrating the ability of Intels neuromorphic test chip, Loihi, to learn and recognize 10 hazardous chemicals, even in the presence of significant noise and occlusion. The work demonstrates how neuromorphic computing could be used to detect smells that are precursors to explosives, narcotics and more.

Loihi learned each new odor from a single example without disrupting the previously learned smells, requiring up to 3000x fewer training samples per class compared to a deep learning solution and demonstrating superior recognition accuracy. The research shows how the self-learning, low-power, and brain-like properties of neuromorphic chips combined with algorithms derived from neuroscience could be the answer to creating electronic nose systems that recognize odors under real-world conditions more effectively than conventional solutions.

We are developing neural algorithms on Loihi that mimic what happens in your brain when you smell something, said Nabil Imam, senior research scientist in Intels Neuromorphic Computing Lab. This work is a prime example of contemporary research at the crossroads of neuroscience and artificial intelligence and demonstrates Loihis potential to provide important sensing capabilities that could benefit various industries.

Intel Labs is driving computer-science research that contributes to a third generation of AI. Key focus areas include neuromorphic computing, which is concerned with emulating the neural structure and operation of the human brain, as well as probabilistic computing, which createsalgorithmic approaches to dealing with the uncertainty, ambiguity, and contradiction in the natural world.

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Intel + Cornell Pioneering Work in the Science of Smell - insideBIGDATA