Data engineers ensure raw data is accurately collected, securely and reliably stored, and structured for easy use by data scientists and analysts. And this is no small task. Data engineers are able to speak the language of data science and analytics so they can define project requirements. They must also be adept architects and coders comfortable working with the latest cloud technologies.
Our last month’s edition of the Dataspace newsletter looked at the growing applications of AI for cybersecurity, and explored how some of the major players in tech are incorporating AI based security tools into the suite of products they offer to business clients. However, it is also important to note that hacking can present a […]
What do you do in instances of communications hijacking? How can you ensure that you are actually communicating with the person you think you are communicating with?
This month’s Dataspace newsletter looks at the applications of AI for cybersecurity, and some potential limitations.
We’ve had to be more flexible and understanding with resume anomalies this past year, but there are some things that still don’t fly.
Image alteration is all around us nowadays. This post looks at a new generation of tools that “predict” if an image has been altered.
Beyond the tech specs of how these algorithms actually work, the growing capabilities of predictive text beg a multitude of questions regarding the potential implications for humans.
Join us this Wednesday, January 6th for a virtual presentation about Golden Record at the January Meetup of Detroit New Tech!
With such an abundance of Christmas movies out there, it is no surprise that there is also a substantial amount of data available about Christmas movies. And where there is data, there are data scientists playing with it.
We’ve done a lot of pondering about the future of AI in the workplace, and there are a plethora of theories out there on what this future will look like.
