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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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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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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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.

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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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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

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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.

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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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What is Feature Engineering and Why Does It Need To Be Automated? - Datanami