In this special guest feature, Sanjay Vyas, CTO at Planful, discusses how tech departments can interpret data to steer company trajectory. Business leaders will need to encourage their teams to think like data scientists to not only collect more data at higher levels of granularity, but also find streamlined ways to glean better insights to make more frequent and informed strategic decisions.
Changing Lives: Accelerating the Process for Women To Enter Cybersecurity Careers What will it take to get more women into cybersecurity roles? Despite the incredible growth in open jobs and individuals employed in cybersecurity, women continue to be left behind and are under-represented at every level and every category of cybersecurity related work. International Information
NeuroBlade is on a mission to empower the next wave of digital transformation by developing the new standard for analytics acceleration for large data. It’s increasingly complex, expensive, and time-consuming for organizations to analyze large data volumes. As a result, business and technology leaders cannot rely on price performance when querying and analyzing hundreds of terabytes of data. This creates an analysis gap where less than two-thirds of the data is analyzed.
In this contributed article, Jonathan Serfaty, Head of Data Science, Telstra Ventures, discusses how Venture Capital (VC) is one of the most powerful mechanisms in modern society for catalyzing innovation – particularly in technology – however, ironically, the VC industry itself is not particularly innovative. The truth is that the VC’s have been slow to adopt the AI technologies they help fund.
Candidates are lying and cheating to get hired more than ever. INSIDER recently cited a study, “The Future of Candidate Evaluation,” by our friends over at Glider AI that found candidate fraud has nearly doubled since before the pandemic. As INSIDER reported, more companies believe AI is the solution.
In this special guest feature, Amir Atai, Ph.D. is Co-Founder and CEO of Sway AI, examines how AI is changing healthcare by improving the efficiency and quality of care on many fronts, starting with administration. The article also does a deep dive into the challenges of using AI in healthcare and how no-code AI and machine learning are automating mundane tasks and adding new diagnostic and treatment solutions to make doctors and nurses more efficient.
In this contributed article, Or Lenchner, CEO, Bright Data, examines how public web data collection is essential to ESG efforts in 2022. The recent International Panel on Climate Change report warned that we aren’t doing enough to avoid the dire impacts of climate change – businesses must use every tool at their disposal to support the fight against climate change, especially quality data.
Today, MLCommons®, an open engineering consortium, released new results from MLPerf™ Training v2.0, which measures the performance of training machine learning models. Training models empowers researchers to unlock new capabilities faster such as diagnosing tumors, automatic speech recognition or improving movie recommendations. The latest MLPerf Training results demonstrate broad industry participation and up to 1.8X greater performance ultimately paving the way for more capable intelligent systems to benefit society at large.
As a drilling engineer with so many years and interesting internationalexperiences, Jacqueline is one who seeks a new adventure in data science.Her transition into informatics and data science came after her maternityleave while working at Schlumberger. Trying to fit the experience into her “new”the world did not come easy and in search for an alternative led her to informaticsand data science.In her experience of working in the oil and gas sector, a field dominated by men,she mentioned that while it was a male-dominated field, she never felt suppressed ina negative way that made her feel she was not enough. She also said that the use ofpieces of machinery meant that many of the muscle works were automated and easier.About how she got attracted to data science. Jacqueline believes her interest indata science has the common denominator with her previous job as a drilling engineer, which was the use of data for analysis and description.
In this contributed article, Kyle Kirwan, CEO and co-founder of Bigeye, discusses Data Reliability Engineering (DRE), the work done to keep data pipelines delivering fresh and high-quality input data to the users and applications that depend on them. The goal of DRE is to allow for iteration on data infrastructure, the logical data model, etc. as quickly as possible, while—and this is the key part! —still guaranteeing that the data is usable for the applications that depend on it.