Welcome to our weekly data science news section where we discuss news, developments, and research about data science, analytics artificial intelligence & machine learning that has happened in the past week.
Data science is a multidisciplinary field that has its roots in statistics, computer science, and data analytics skills. It not only provides a versatile career but also helps in creating global job opportunities. In addition to that, it helps businesses to make data-driven decisions.
Companies from all domains, Finance, Marketing, Retail, and Banks have a great demand for data scientists. Data Scientists not only have a good depth of knowledge based on the analysis of data but also have a deeper understanding of both structured and unstructured data. The five biggest tech companies – Google, Amazon, Apple, Microsoft, and Facebook are looking for Data Scientists at a larger level. According to a report shared by the U.S. Bureau of Labor Statistics, “data science expertise will drive a 27.9 percent rise in employment in the field by 2026”. Today, not only there is a huge demand, but there is also a noticeable shortage of qualified data scientists.
“If you are passionate about computers and research through data analytics, then pursuing an advanced level data science program is the next step for you”, said Amit Gupta, Director, Narayana Business School. “Data science is used by experts for collecting data via their technology and social science skills. Hence, an expert data scientist is who collects, stores, analyses and manages the data. It helps an organization to make data-driven decisions”, he added.
A research team from Skoltech and FBK (Italy) presented a methodology to derive 4D building models using historical maps and machine learning. The implemented method relies on the geometric, neighborhood, and categorical attributes to predict building heights. The method is useful for understanding urban phenomena and changes contributing to defining the actual shapes of the cities.
In 3D/4D city modeling applications based on historical data. The lack of building heights is a major obstacle for accurate space representation, analysis, visualization, or simulations. “The implemented learning and predictive procedures tested on historical data have proven to be effective and promising for many other applications. Based on a few attributes for the prediction, it expands to diverse real-life contexts with missing elevation data. The resulting models will be a great help in bridging the geospatial knowledge gap in past or remote situations,” Emre Ozdemir, a Skoltech and FBK Trento Ph.D. student, explains.
Machine learning is helping the U.S. Patent and Trademark Office shorten the time it takes to assign patent applications to examiners. For instance, it is helping them as they do not have to redo its entire classification process, according to CIO Jamie Holcombe. USPTO sent its top engineers to Google on the East and West coasts to learn more about ML and TensorFlow application programming interfaces. Now those engineers are using Python with TensorFlow to apply ML to patent classification, search, and quality.
“We immersed them in the culture, and they got Googly,” Holcombe said during an ACT-IAC event Wednesday. “They got certified in TensorFlow, which is the open-source library for a lot of neural network feedback loops.” USPTO has patent examiners use those feedback loops to rate how well ML algorithms are classifying patent applications to the art units and examiners that evaluate them, as well as search for algorithms in the system. Despite having 250 years of historical data to train its algorithms with, USPTO relies primarily on daily feedback from examiners to ensure they’re working. USPTO remains in the early stages of ML use, in part because it’s still cleaning its data. While the agency uses robotic process automation (RPA) for clerical and administrative processes, it’s still getting used to the technology before applying it to patent and trademark workflows, Holcombe said.
Colin G. Johnson, an associate professor at the University of Nottingham, recently developed a deep-learning technique that can learn a so-called “fitness function” from a set of sample solutions to a problem. It was initially trained to solve the Rubik’s cube, the popular 3-D combination puzzle invented by Hungarian sculptor Ernő Rubik.
The technique devised by Johnson is based on two main approaches. One is stepwise learning and the other is the use of a deep neural network. When applied to Rubik’s cube, the technique tries to unscramble it step by step. In other words, it tries to shift its parts to achieve a simpler configuration, repeating this step several times, until it solves the cube.
Johnson evaluated the technique he developed in a series of experiments. In addition to that, he compares it with a previously developed approach based on a class of algorithms called random forest classifiers, with a baseline approach. It was not only based on traditional error-based fitness but also on other existing computational techniques. His deep learning technique compared favorably with all these alternative methods, while also highlighting the advantages of tackling tasks in a step-by-step way.
Cloudera has launched Applied ML Prototypes. In addition to that, it is a complete machine learning project for use cases that give data scientists a running start on development. The AI and ML deployments are well underway. For CXOs the biggest issue will be managing these initiatives and figuring out where the data science team fits in. In addition to that, it will be also about what algorithms to buy versus build.
Cloudera plans on adding dozens of AMP use cases that will accelerate the use of emerging machine learning. Santiago Giraldo, director of product marketing, data engineering, and machine learning at Cloudera, said AMPs goal is to change how machine learning is delivered going forward. “Machine learning remains difficult to get to production. It takes some time to develop the models,” said Giraldo. Giraldo noted that AMPs don’t replace what data scientists do but give them a starting-off point so they can focus on the code, nuances, and iterating for the business use case.
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