We wrote you read. From identifying critical information in the news to AI trends to our own data science experiments, we covered a lot here in 2019. And as the end of year tradition would have, we take a look back at the five AI blog posts of 2019 that garnered the most attention.
Bitvore has a complex and exhaustive system for identifying critical information in news. We look at 1M or more individual news stories per day and analyze them with our battery of AI models, filters, and natural language processing to identify only the most important business news for our clients. We identify when something important happened in our system through what is called a 'signal.' Read More...
Bitvore looks at which industries are adopting AI, how many projects are being worked on, and how well they are performing.
During the space race of the 50s, 60s, and 70s, there was a reason so many monkeys and chimpanzees were shot up into space. Getting into space is relatively easy. Getting back safely is the hard part. Read More...
Bitvore looks at virtually all of the world’s English news content every single day. In order to understand it, we oftentimes break it down into smaller slices aligned with certain issues. For example, we wanted to take a look at executive titles in technology-related industries. We grabbed 60,000 corporate precision news items over the course of a couple of months in that sector, and extracted the titles of the people quoted or discussed in the articles. We extracted 9,045 unique titles. The frequency of results wasn’t much of a surprise. Read More...
Recently, a friend added to a well-known phrase by saying that there's nothing certain in this world except death, taxes... and data. The intersection of taxes and big data is a topic we take deathly serious at Bitvore. Sometimes, years or months before a municipality declares bankruptcy, a fiscal emergency, a budget shortfall, or cuts to city services, and the first action always involves taxes. Read More...
Recently, a group of machine learning researchers trained and documented a state-of-the-art natural language generation system that performed so well on many language modeling benchmarks, that they were called the ‘deepfakes’ of text generation. Read More...