Voice-controlled appliances are nothing new. What might be new, however, is [Moonshine AI] running it all locally on a Raspberry Pi Pico 2 W!
The voice interface is roughly divided into three parts: voice activity detection, SpellingCNN speech-to-text and a neural text to speech. The speech to text supports up to 50 tokens, and can be re-trained to support any specific words you want. It runs a simple loop: detect voice activity, listen for (command) tokens, process them in C++, use the TTS to reply, and repeat.
Now, to be fair, it is a bit of a squeeze: 3.6 MiB of the available 4 MiB FLASH and 468 KiB SRAM on a stock Pi Pico 2 board. It leaves you with just about enough space to write a small amount of extra software, but it’ll be a challenge to fit anything substantial. Still, fitting three different types of AI model needed to make this possible in such a space is quite impressive.
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Nearly 20 years ago (!), in 2007, I published my first and only book: Microsoft 2.0. It focused on changes I expected at the company in the “Post-Gates” era. What would remain the same and what likely would be different once co-founder and CEO Bill Gates had left the building?
CEO Satya Nadella has not exited the company (yet). But there’s no question that Microsoft and its mission have morphed considerably in the past year or two. I’m not quite ready to christen this the Microsoft 3.0 era, even though Nadella handed the reins of Microsoft’s dominant commercial business to Judson Althoff nearly a year ago.
That decision resulted in Nadella moving into more of a “founder mode” role, allowing him to focus less on the day-to-day work of running the business. (Microsoft historians may recall that Gates made a somewhat similar move back in 2000 when he became Microsoft’s chief software architect.)
While it might not yet be time for Microsoft 3.0, we arguably could be in the “Microsoft 2.5” era. Windows and Office are still around and still play a big role. Microsoft still builds and sells developer tools and databases. But there’s no question that the cloud and all things AI are at the top of the pecking order now.
I’m embarking on a series here at GeekWire that will focus on what matters to Microsoft and, by extension, to its customers, partners, investors, and employees these days. Who are some of the people shaping and leading the company? What are their opportunities and challenges right now?
Over the next few weeks, I will be profiling various Microsoft execs working on plans for Microsoft’s ongoing evolution. Some are company veterans; some are newcomers. I’ll be talking with top execs from Microsoft’s Security, Copilot, Windows + Devices, Xbox, GitHub, and more.
I’m interested in their strategies for Microsoft’s key products and technologies and how they plan to try to turn Microsoft’s ambitious vision into reality. What are their teams building? What do they see as their biggest challenges and opportunities? And where do they see the technologies in their respective areas heading?
I feel like many of us who’ve been keeping track of the biggest tech companies (myself included) have fallen into the trap of blaming or attributing everything a company does to AI. Layoffs? AI is the culprit. Price increases? It’s all thanks to AI. Changing sales strategies? Chalk it up to AI …
But upon further reflection, I believe Microsoft’s strategy is more nuanced than “AI or bust.” There’s no question that Microsoft’s AI ambitions are shaping its goals and tactics. But Microsoft, as a heavily enterprise-focused entity, can’t simply stop supporting products that aren’t built from the ground up with AI (as much as it might like to do so). Nor can it just leave behind customers who aren’t 100% onboard with its AI moves.
Couple those enterprise hurdles with some not-so-popular consumer decisions, like axing 3,200 people in the gaming unit, and Microsoft’s approach to turning the ship looks a lot trickier.
Our Microsoft 2.5 series kicks off Thursday. Stay tuned.
L-R: Mantra CEO John Patrick Mullin, Docugami CEO Jean Paoli, and Inveniam CEO Patrick O’Meara. The companies are partnering to make DGML a standard for AI, with Docugami turning documents into data, Inveniam verifying it on a blockchain, and Mantra providing the chain.
Jean Paoli has spent his career making documents readable by machines — first as a co-creator of XML, then helping build the file formats behind Microsoft Office. Now his Kirkland, Wash.-based startup, Docugami, is open-sourcing the technology at the heart of its business, betting it can become a standard way to turn documents into data that people and AI agents can trust.
The company is releasing its technology, called DGML (short for Document Graph Markup Language), under Apache 2.0, a widely used open-source license, so other developers and companies can adopt it.
The idea is to turn it into a shared standard that no single company owns, much as XML became a common foundation across the tech industry.
The move reflects a shift in where the value is created in AI. Docugami until now has made its money selling software that turns unstructured documents into usable data. It’s betting now that there’s more value in proving that data is trustworthy instead.
How it works: Docugami is teaming up with Inveniam, a Detroit company whose software helps big investors keep tabs on the mountains of paperwork behind real estate and other hard-to-value assets. Inveniam will record a kind of digital fingerprint of each piece of DGML data on NVNM Chain, its blockchain built with Mantra, a crypto firm that Inveniam is acquiring.
That means, for example, that a single fact buried in a 200-page lease — such as the rental rate, a renewal option, or a default clause — can be verified on its own, without exposing the whole document. An investor, auditor, or AI agent can trace it to the page it came from.
To work with documents, AI systems usually convert them into a simpler format first. DGML enters a growing field of contenders in that regard, competing with the popular Markdown format and DocLang, a new open standard for AI-ready documents backed by IBM, Nvidia and Red Hat.
The business model: This is a big move for a company of Docugami’s size, taking the 30-person startup in a new direction. Paoli is handing the industry the technology his team spent years building, and pinning the company’s future on a larger idea.
The plan is to make money not from the format itself but from the value of the trusted data. Once a company converts its leases or loans into DGML and anchors the key numbers on the blockchain, investors, lenders and auditors can pay to draw on that verified data.
Docugami will share in the revenue through its partnership with Inveniam. The company also stands to collect a small fee each time a piece of data is recorded on the chain.
The company is giving away the DGML format and a working version of the software, but not everything. Paoli said the company is keeping some of its own technology private, including AI models it has fine-tuned to read documents, and could sell those or other tools to enterprises.
“The business model of everybody is changing. And if you know any company where it’s not true, you need to tell me, because I haven’t met them yet,” Paoli said in an interview.
Docugami has raised about $13 million to date, including a $10 million seed round in 2020 that drew the first investment in Grammarly’s history.
The partnership: Paoli met Patrick O’Meara, Inveniam’s CEO, a few months ago, through a former Microsoft colleague who had become one of O’Meara’s advisers. They quickly realized they had been working toward the same idea from different directions.
Inveniam, founded in 2017, helps big investors keep track of assets that are hard to value, like office towers, private loans and infrastructure. It monitors the documents behind those assets and flags changes as they happen, and its clients include some of the world’s largest sovereign wealth funds, according to O’Meara.
What it lacked was a consistent way to break those documents into verifiable pieces. That is what Docugami provides.
“We’re not putting the data itself on-chain, just a fingerprint of the document. Change one bit, one byte, one pixel, and the hash won’t match,” O’Meara said.
Paoli said the project uses the underlying blockchain, not the token.
“Crypto as an industry has gone through a lot of changes in the last 18 to 24 months, and it’s growing up in a lot of ways. This is a real use case with fundamental value, not just pure speculation,” Mantra’s Mullin said in an interview.
The result is a division of labor: Docugami turns documents into data, Inveniam verifies it and brings the customers, and Mantra provides the chain where the proof is recorded.
The DGML specification, sample documents and reference code are at dgml.io and on GitHub.
Editor’s note: This story was updated after publication to correct the name of a competing document format, DocLang, and to note that Inveniam’s blockchain is called NVNM Chain.
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