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.

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.

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 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. 

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.

In this special guest feature, Ian Richardson, CEO & Co-Founder, theICEway, discusses how as the world continues to open for travel, cruise industry leaders are looking to leverage the next wave of travel technology to improve the passenger experience.

Robocorp, a top provider of Gen2 robotic process automation (RPA), announced the results of their State of RPA survey, which was designed to understand the challenges users face with current RPA solutions. The results will help usher in the next generation of enterprise automation – Gen2 RPA.

In this contributed article, editorial consultant Jelani Harper highlights how Natural Language Generation (NLG) is arguably the nexus point of natural language technologies. It utilizes Natural Language Processing (NLP), is a prerequisite for conversational AI, and largely requires Natural Language Understanding (NLU) for meaningful responses to interrogatives or commands.

Domino Data Lab, a leading Enterprise MLOps platform trusted by over 20 percent of the Fortune 100, announced its new Nexus hybrid Enterprise MLOps architecture that will allow companies to rapidly scale, control and orchestrate data science work across different compute clusters — in different geographic regions, on premises, and even across multiple clouds.

Dataiku announced Dataiku 11, a pivotal update of the company’s data science and AI platform that helps organizations to deliver on the promise of Everyday AI. This packed release provides new capabilities for expert teams to deliver more value at scale, enables tech-savvy workers to take on more expansive challenges, helps non-technical workers more easily engage with AI, and provides strengthened AI Governance to ensure projects are robust, transparent, and ready for success at scale.

In this contributed by Adam Glaser from Appian believes that in a fast-changing, digital-driven world, having access to the right data at the right time is crucial. That is why, as data evolves, it must be brought together in a reliable and efficient way that creates a powerful asset, not a compliance challenge. 

In this special guest feature, Yashar Behzadi, Ph.D., CEO and Founder of Synthesis AI, discusses on the importance of a community like OpenSynthetics in developing more capable AI models. OpenSynthetics, an open community for creating and using synthetic data in AI/ML and computer vision, is open to practitioners, researchers, academics, and the wider industry.

Jyo is also a speaker and she recently spoke at women in tech global conference where she highlighted the difference between customer-focusedand customer-centric and why organizations should be customer-centricrather than customer-focused.Jyo is based in Sydney, Australia. She has worked in tech forthe past ten years of her life and she has been obsessed with the life. She loves reading (besides the beach while she sips margaritas)and is an author of a book.As a customer success manager, Jyo ensures that both her company andcustomers are successful. In her role, she seeks to understand why people purchase technology and how technology helps meet their goals.Obsession (Being passionate) is a vital key to unlocking the possibilities inyour career. While Jyo was in college, she recognized that coding was not her forte and she did fail at some "coding". On graduation, she was scared while on the first job, because no matter how hard she tried, coding did not come easy to her.As she was on the job she had some honest conversations and had the opportunityto gradually build her career as the customer leader that she is now.

In this regular column, we take a look at highlights for important research topics of the day for big data, data science, machine learning, AI and deep learning. It’s important to keep connected with the research arm of the field in order to see where we’re headed. We drill down into topical areas: data science, machine learning, AI, and deep learning. Enjoy!

Great Expectations, a leading open-source platform for data quality, announced the results of a survey highlighting top pain points and consequences of poor data quality within organizations. Insights from 500 data practitioners (engineers, analysts, and scientists) showed that 77% have data quality issues and 91% said it’s impacting their company’s performance.