HIGHLY TECHNICAL is for curious leaders and data enthusiasts. Get jargon-free insights, industry trends, relatable case studies, and smart tips to stay ahead of the curve. Perfect for anyone looking to turn data into action—plus, a little nerdy humor along the way!
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HITECH: How many tokens to solve a Millennium Prize Problem?? and Dolly Data
It was quite the eye-opener to have TWO different people I've known for years tell me they didn't know Contemporary Analysis does "insert the project they were struggling with."
Last year I was catching up with an old friend (who also happened to be a client of CAN in the early days.) He was sharing about his organization's efforts (ie struggles) to build and implement AI.
I asked why he hadn't call me.
He stared at me. "I didn't know you do that."
Yup. We do that. We do a lot more than we did 4 years ago (let alone what we did in 2008 when we started...)
Not knowing how we can help your organization with Data, AI implementation, and Cybersecurity isn't your fault.
Many of our clients have tech-debt...last year we realized we had marketing-debt.
And I'll admit, it's been a fun problem to solve.
Our team spend the summer working with our CMO and an expert brand strategist to completely comb through and redefine who we are, who we help, and how we serve.
You'll begin to see slight changes soon. The newsletter is getting an upgrade. The website is getting an upgrade. And we're focused on how we can show up for our greater community even more.
In the meantime, just know this:
If your problem is data, AI, or cybersecurity related, we're your people.
And the best time to call us? Before you begin (or even decide on) your next project.
When everyone is spending on AI, and few are seeing ROI (or even tracking for outcomes), we will tell you which projects are worth doing (and which project are not worth doing), help you build the ones that are, and train your people to run it without us.
300 billion output tokens, 88 hours and 10,000 concurrent agents.
That is the resource load OpenAI deployed to solve a Millennium Prize Problem before the competition could.
After allegedly hearing rumors on X that a couple unnamed researchers were close to solving a Millennium Prize Problem, OpenAI got inspired to tackle the unsolved problems as well.
The Millennium Prize Problems are 7 mathematically important problems selected by the The Clay Mathematics Institute of Cambridge in 2000. There is a $1Million prize waiting for the solver of each problem, the oldest of which has gone unsolved for more than 160 years.
On September 8, OpenAI announced they were "sharing a solution to the Navier–Stokes existence and smoothness problem."
And while this solution is impressive (and undoubtedly impressed investors), the mathematics community is peeved.
"Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, [who's research on this project was independent from Anthropic] and Tristan Buckmaster, a math professor at NYU. After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement...
We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models."
Sounds like damage control to me.
Turns out, even before the official announcement "allegations of scooping, spying, and flagrant violations of long-standing academic norms" began flying from Academia.
Robert Hart at the Verge reports:
Buckmaster said he contacted OpenAI after learning the company had become aware of their progress, to ask when it began working on the problem and what data its model had been trained on.
The conversation, he said, quickly turned sour, with an OpenAI researcher asking him, “Why would you ruin your career?” when he said he would go public with what happened. When he asked why going public would ruin his career, Buckmaster said he received the following reply: “If you don’t want me to be nice, then I don’t have to be nice.” OpenAI urged Buckmaster to instead publish the work and credit OpenAI’s internal model, dropping Alpöge as coauthor.
Or, if you want to keep your data out of their next training model, we have a tool and solution for you. Please call us before you lose your intellectual property to these big ever-hungry models.
Dolly, Data, and Daily Streaming Charts
Matt Bailey is back with an answer to the question we are all asking...
"Am I the only one still listening (mourning and celebrating) Dolly Parton?"
Before streaming, we could measure adoration for a passed artist based on record sales. But, how long did those albums stay in rotation once they were home? Hard to say.
When Tina Turner and Jimmy Buffet passed, their streams spiked for one and four days, respectively.
How does Dolly compare? Two weeks after her passing, "Jolene" and "9 to 5" remain in the Spotify 200.
Source: Graphs about Songs
"Overall, 15 Dolly Parton classics reached the Spotify 200 the day after her death, Fans collectively played those songs almost 12 million times—that’s 13% of all streams in the Spotify 200 for the day. That’s the highest of any artist’s death in the streaming era."
Come join us at The Tipsy Pig on September 30th for an evening of drinks, conversation, and networking! We'll have the outdoor patio reserved, so feel free to bring a colleague, friend, or anyone interested in the local data or IT community. As we all know, there has been a lot of change across the local market recently. With that in mind, we wanted this month's event to focus less on presentations and more on building connections, sharing perspectives, and helping one another navigate what's next. Whether you're actively hiring, exploring new opportunities, working through a transformation initiative, or simply looking to expand your network, this is a great chance to connect with fellow professionals in data, analytics, AI, and technology.
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HIGHLY TECHNICAL is for curious leaders and data enthusiasts. Get jargon-free insights, industry trends, relatable case studies, and smart tips to stay ahead of the curve. Perfect for anyone looking to turn data into action—plus, a little nerdy humor along the way!
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