In October 2019, Google announced its 53-qubit quantum computer named Sycamore had achieved ‘quantum supremacy.’

That’s when quantum computers can complete tasks exponentially more quickly than their classical counterparts.

In this case, Google said its quantum machine completed a task in 200 seconds that would have taken the world’s most powerful computer 10,000 years to complete. IBM, another major player in quantum computing, took issue with the findings.

Either way, it was a big milestone in quantum computing, and it’s leading to a lot of hype in the field. Here’s how quantum computing works, and how it could change everything from Wall Street to Big Pharma and beyond.

CNBC explores the rise of open source software and how it went from fringe movement to mainstream and core to the every enterprise.

Open-source software powers nearly all the world’s major companies. This software is freely available, and is developed collaboratively, maintained by a broad network that includes everyone from unpaid volunteers to employees at competing tech companies. Here’s how giving away software for free has proven to be a viable business model. 

As millions of Americans hit the roads today for Thanksgiving travel, I wonder how different it would be if self-driving cars were the norm.

CNBC explores the current state of self-driving cars.

More companies are trying to bring self-driving cars to the masses than ever before, but a truly autonomous vehicle still doesn’t exist. It’s not clear if, or when, our driverless future will arrive. Where exactly are we with self-driving cars, and when can we expect them to be a part of our daily lives? 

CNBC got a first look inside Lyft’s level 5 lab, where it builds self-driving cars that are being tested on roads now.

Self-driving rides are also available to select Lyft passengers in Arizona and Las Vegas, where Lyft opened its app to autonomous vehicle companies Waymo and Aptiv.

Lyft says it’s completed more than 75,000 self-driving rides.

Watch the video to see how the program works.

CNBC takes a closer look at what’s going on with its cryptocurrency project, Libra

When Facebook first announced it was getting into the crypto business—with a basically unregulated currency called Libra—the reaction from Wall Street and government bankers was about as expected. Fast-foward a few months, and Libra is in trouble. The social media giant had lined up a long list of corporate backers for the initiative, including major players in the payments space. And in October 2019, several prominent backers began to back out. Here’s how Facebook’s crypto future got into serious trouble.

To say that there’s been a lot of buzz around AI lately would be an understatement and incredibly comedic considering this site is called “Frank’s World of Data Science & AI.” What’s been interesting to see is the evolution of MLaaS, or Machine Learning as a Service, platforms where very little knowledge of computational models are needed to create smarter applications.

Check out this video from GrowthTribe to see why this is a big deal and will impact your career in AI and Data Science.

If 2018 was the year of AI & ML, then 2019 is going to be the year of AI/ML Operationalization. I see this all the time with customers: AI requires a lot of teams to work together that traditionally have not worked together. 

Here’s an interesting article on the five things that all great companies do successfully adopt AI. Listed last, but certainly not least, in the list is having a Data Driven culture. Aside from the fact that it subliminally promotes my podcast, the importance of company culture cannot be overstated.

From the article (emphasis added):

Data-driven Culture Without a strong, data-driven organizational culture, none of the above can ever be successful. Some of the world’s largest companies like Amazon, Google, and Facebook have embraced data as part of their organization’s culture. Here are some things they, and the aforementioned customers, do well:

  • Treating data as an enterprise asset.
  • Creating a central data strategy (a Data Hub) to integrate all types of data
  • Strong data governance & data lineage.
  • ML-enabled data cataloging to find data efficiently.
  • Robust Master/Reference data management.
  • Mixing IT-led and self-service data preparation & wrangling capabilities.
  • Implementing self-service exploration capabilities to visually interact with data.

Data is an asset, potentially a very lucrative one and likely one that will either drive your business into the next decade or drive you out of business before the next decade.