The children were sitting in circle on the floor. "The flat hat has a number and a label that says parrots and battercakes" -- one of the kids screamed Every single child starts laughing. "Nooooo, it was the black cat is under the table and it eats carrots and pancakes" -- another child replied I realized only then that they were playing telephone (or broken telephone as we call it in Argentina). Human communication is complex, mainly because each person expresses themselves differently. We could speak the same language but use different slang, words, or expressions to convey the same message.

Yeelen Knegtering, CEO & Co-founder of Klippa, is passionate about developing digital products that help people to save time on administrative hassle and spend time on the things they love. With a degree in Information Technology at the University of Groningen, he started Klippa with the idea that there had to be a better way to organize and manage receipts. Now, Klippa is a document digitization company with a focus on digitizing and automating document streams for companies.

Companies have high hopes for transformational technologies, especially those that leverage the vast amount of data being collected. Approaches using methods such as artificial intelligence (AI), machine learning (ML) and deep learning are becoming increasingly accessible and gaining significant traction, as they allow for deep insights to be extracted from large and varied data sets. As noted in the 2019 IT Audit survey by Protiviti and ISACA, this transformation has the potential to fuel long-term growth. However, as Protiviti's recent global AI survey reveals, most companies are still at the starting gate when it comes to figuring out the answers to basic questions: What are the possible use cases for AI? Can we measure ROI? What data do we have and how usable is it?

This post was originally sent as our monthly newsletter about trends in machine learning and artificial intelligence. If you'd like these analyses delivered directly to your inbox, subscribe here! Not so long ago, it seemed like all the really impressive applications of deep learning were occurring in computer vision. Other applications, like natural language processing (NLP), appeared to be a few steps behind. This was partly the result of there being more initial interest and research in computer vision and partly just a statement of the complexity of human language.

From climate change to opioid addiction, we are facing serious public health crises that put our research and data management experts to the test. When it comes to scientific evidence, systematic literature reviews--painstaking assessments of all the literature ever produced on a given subject--are often regarded as the gold standard. Though no research method is foolproof, says Vox health correspondent Julia Belluz, "these studies represent the best available syntheses of global evidence about the likely effects of different decisions, therapies and policies." That comprehensiveness comes at high price, though, in terms of time and money. It involves sifting through enormous volumes of literature--sometimes hundreds of thousands of scientific abstracts--stored in academic databases.