Remote work isn't the future. It's a current reality, with nearly 75% of U.S. workers working remotely at least some of the time, according to Owl Labs' State of Remote Work 2019 Report. Data scientists and other data professionals are no exception to the rule and are able to bring their work home with them if and when the need, or desire, arises. However, a switch to remote work isn't as straightforward as simply taking a work laptop home.

Graphcore, a UK-based startup that designs processors specifically for artificial intelligence software (AI) applications, has just raised $150 million, bringing the company's valuation to just shy of $2 billion.

As Kara Kennedy, market director at G2, noted over two years ago:

"Edge computing is evolving around the developing need to move much of the processing nearer to IoT sensors themselves to decrease that latency and improve efficiency."

As we’ve mentioned previously (see: Databricks), whether you are looking to build or buy AI, open- source solutions can be attractive.

AWS’ flagship conference re:Invent did not fail to impress us here at G2, not to mention the consumers at the conference and beyond.

As we’ve discussed previously (see The Data Toolbox: The Expanding Domain of AI & Analytics), data does not magically become insights. Rather, data experts must use dedicated tools to convert raw data into meaningful insights.

We all know the story. Data is accumulating rapidly and serves as the fuel that keeps the well-oiled engine of artificial intelligence and advanced analytics humming. The other story around data is the data rights one, where the public is pushing for their personal data to remain private and personal.

Last week, Databricks raised $400 million in a Series F funding round, bringing the company's total funding to almost $1 billion (making Databricks an almost-minotaur).

Data is everywhere, and if we want to be able to do anything with it, we must find it first. Google has built a multibillion-dollar business on this premise by indexing the world’s information and allowing you to find it with a painfully simple interface.

At work, we are using an increasingly large number of applications (over 80 on average) and with this great proliferation comes great frustration. We often forget (at least I do!) where to find a particular file, who we sent a message to, and more.

Killer robots. Threatening humanoids. Robo-apocalypses and evil robots taking over the world. (Just kidding.)

Movies like Blade Runner, I, Robot, and Ex Machina and sensationalized articles in the press about artificial intelligence (AI) paint AI with a scary and sinister brush. Unfortunately, AI is often summed up as something scary and negative that will steal all of our jobs, or perhaps even worse, completely and totally overpower us.

We’ve heard it all before: “We need to buy some AI.”

Companies realize that powering their products and operations with artificial intelligence (AI) can help them make sense of their data and give them a competitive advantage.

Alteryx’s recent acquisition of Feature Labs, a startup focused on feature engineering for machine learning and AI, is but the latest example of how acquisitions are driving innovation and competition in the data and analytics space.

When you think of McDonald’s, the first thing you think of is probably not technology, such as AI-powered personalization and voice tech-enabled ordering. However, given the company's recent acquisition of Israel-based Dynamic Yield and last week’s acquisition of Apprente, perhaps that is precisely what we should be thinking about.

Google, following in the footsteps of other tech giants, has published its open-source differential privacy library on GitHub.