Google this week unveiled a major redesign of Google Pay for Android and iOS. The app is meant to take on not just Apple Pay and Samsung Pay, but also PayPal, Venmo, and Mint all in one. Google also announced partnerships with 11 U.S. banks and credit unions to launch a mobile-first bank account service called Plex next year. Just like with health care, tech companies are becoming increasingly interested in banking. More competition in an industry that still hasn't embraced the internet, let alone the latest tech like artificial intelligence, is exciting.

Machine learning needs some sort of input data to train on. In most cases, that data first needs to be labeled by humans. Photos are a prime example.

Elon Musk-backed Neuralink shared a progress update late last week on its brain-computer interface. The implantable hardware platform was shown working in a pig named Gertrude. When Gertrude touched an object with her snout, neurons captured by Neuralink's technology (which had been embedded in Gertrude's brain two months prior) fired in a visualization on a television monitor. Musk asserted that someday the company would be able to embed a Neuralink device in a human brain, in under an hour, without using general anesthesia. "You open a piece of skull," Musk said.

Microsoft this week launched an Office app that replaces Word, Excel, and PowerPoint on Android and iOS. Merging three apps into one, while adding more features, is quite the achievement. The new Office app is not just for consuming content and maybe a little light editing on the side, but actually creating content on the go. Most interestingly, a lot of these features fundamentally require AI and machine learning to achieve this new mobile productivity paradigm. Microsoft has been adding AI-driven features to its once most profitable product line for years now -- we did a recap of just a handful last year.

On Sunday, the Financial Times published an op-ed penned by Sundar Pichai titled "Why Google thinks we need to regulate AI." Whether he wrote it himself or merely signed off on it, Pichai clearly wants the world to know that as the CEO of Alphabet and Google, he believes AI is too important not to be regulated. He has concerns about the potential negative consequences of AI, and like any technology, he believes there needs to be some ground rules. I simply don't believe that's the full story. "Companies such as ours cannot just build promising new technology and let market forces decide how it will be used," writes Pichai. "It is equally incumbent on us to make sure that technology is harnessed for good and available to everyone. Now there is no question in my mind that artificial intelligence needs to be regulated. It is too important not to. The only question is how to approach it."

While funding news isn't as critical to our coverage here at VentureBeat as it once was, we do still follow the money for transformative tech. In 2019, AI startups raised more money than in 2018, and in fewer rounds, according to data from Crunchbase. According to Pitchbook, though, both deal size and the number of deals decreased from 2018 to 2019. AI -- artificial intelligence -- means different things to different people. It's therefore important to define it when you're going to be making comparisons over time. The AI category that Crunchbase tracks includes startups tagged as intelligent systems, machine learning, natural language processing, and predictive analytics.

A U.S. government study released this week found that 189 facial recognition algorithms from 99 developers "falsely identified African-American and Asian faces 10 to 100 times more often than Caucasian faces." This should be the last such study. We are long overdue for federal governments to regulate or outright ban facial recognition. This year, the NYPD ran a picture of actor Woody Harrelson through a facial recognition system because officers thought the suspect seen in drug store camera footage resembled the actor. This year China used facial recognition to track its Uighur Muslim population.

The room was packed at the annual Machine Learning and the Market for Intelligence conference in Toronto last week. Now in its fifth year, the lengthy name of the event matches the depth of the discussions. But one speaker and her talk stood out to me in particular: Marzyeh Ghassemi, who also happens to be a veteran of Alphabet's Verily, presented "Machine Learning From Our Mistakes." Ghassemi, an assistant professor at the University of Toronto, talked about the importance of predicting actionable insights in health care, the regulation of algorithms, and practice data versus knowledge data. But at the very end, saving the best for last, she emphasized the importance of treating health data as a resource.

Google's Pixel phones are the company's preferred way of showcasing its AI chops to consumers. Pixel phones consistently set the phone camera bar thanks to Google's AI prowess. But many of the AI features have nothing to do with the camera. The Pixel 4 and Pixel 4 XL unveiled this week at the Made by Google hardware event in New York City continue this tradition. Camera improvements aside, the Pixel 4 makes a play for a new arena that Google clearly wants to rule: offline natural language processing.

Depending on who you ask, quantum computing is here, not here, and both. A couple things this week reminded me that it doesn't really matter whether you believe quantum-mechanical phenomena is going to change everything. The mere research into the field is already impacting technology across the board. Binary digits (bits) are the basic units of information in classical computing, while quantum bits (qubits) make up quantum computing. Bits are always in a state of 0 or 1, while qubits can be in a state of 0, 1, or a superposition of the two.