Ivy’s Data Science Weekly 23032021

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Data Science Weekly

Welcome to our weekly news section where we discuss news, developments, and researches about data science, artificial intelligence, machine learning that has happened in the past week.

Data Science aims to make cars safer

The automobile industry is on the leading edge of technology, transforming the way people travel. The key difference between the automobile industry a decade ago and the industry now is data-driven innovation, not just manufacturing. Not only is Data science making transportation easily accessible, but also is providing an experience of the ease of traveling. In addition to that, it is also done without having to deal with the high costs of ownership. 
Since the automotive industry is a profitable industry, there’s more scope for customer-centric innovation with data. Automotive data scientists aim at delivering only high-quality vehicles. Data scientists can analyze all the parts, suppliers, and test data. In this sense, they not only closely examine the financial performance of suppliers, but also predict their availability to deliver the parts on time. In addition to that, they use econometrics regressions to check the economic viability of the supplier’s location.
 Autonomous vehicles are a hot topic in the automobile industry. This relies on deep learning models and sensor fusion algorithms. Data science translates IoT indicators like battery change monitors into actionable insights. When the vehicle is put to use, it’s not enough if the system detects a pedestrian, the sensors must be able to identify which way they are going.
Coinciding with International Women’s Day on March 8, Lawrence Livermore National Laboratory’s fourth Women in Data Science (WiDS) regional event brought women together to discuss successes, opportunities, and challenges of being female in such a field.
The Lab’s first-ever virtual WiDS gathering attracted dozens of LLNL data scientists. In addition to that, there were people outside the Lab and featured speakers, a career panel, and breakout sessions where women could network and discuss mentoring and career advancement. Kupresanin, who is heavily involved with academia through her role with the Predictive Science Academic Alliance Program, and with the University of California through her work as a Data Science Institute (DSI) council member, said the broader adoption of ML is creating greater demand for statisticians and data scientists, positions that could be filled by women currently seeking their degrees.
Also, she focuses on the uncertainty quantification of predictive models for the weapons program. Advances in machine learning (ML) and artificial intelligence (AI) are helping accelerate scientific discovery, and CASC researchers work with the scientific community to build a firm theoretical foundation for ML and AI, with a particular emphasis on the Lab’s broad mission space, she said.
Apple is hiring a machine learning scientist. The job involves collaborating with software engineers and clinicians to implement data collection strategies, among other duties. Moreover, they are set to develop “cutting-edge” algorithms for the early detection of certain diseases or disease conditions, according to a job posting published on the company’s website earlier this month.  
In addition to that, it will also involve creating tools to cleanse and efficiently annotate data. Moreover, it will involve analyzing large-scale data sets to detect features that capture the relationship between observation and outcome.  
There is a technique that speeds up calculations of drug molecules’ binding affinity to proteins. Since, drugs can only work if they stick to their target proteins in the body, assessing that stickiness is a key hurdle in the drug discovery and screening process. The new research combining chemistry and machine learning could lower that hurdle. The new technique, dubbed DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous state-of-the-art methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.
In addition to that the researchers plan to improve DeepBAR’s ability to run calculations for large proteins. Theref0re, it is a feasible task by recent advances in computer science. “This research is an example of combining traditional computational chemistry methods, developed over decades, with the latest developments in machine learning,” says Ding. “So, we achieved something that would have been impossible before now”. It is partly fund by the National Institutes of Health.
OctoML helps businesses accelerate AI model inference and training and relies on the open-source Apache TVM machine learning compiler framework. Moreover, companies like Amazon, AMD, Arm, Facebook, Intel, Microsoft, and Qualcomm use TVM.
According to the studies of 2020 State of AI report from McKinsey, businesses capable of deploying multiple AI models are usually high performers. However, a survey of business leaders included in the report found fewer than 20% have taken deep learning projects beyond the pilot stage. In the latest news for such a company, OctoML today raised a $28 million Series B funding round.

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