Data

Uh, this is not Riker's story.

This is primarily a Picard and Data story.

Mm-hmm.

Uh, so he's, you know, very much relegated to the B plot along with other members of the crew.

[laughs] Yeah.

He's trying to make sure Zefram Cochrane wakes up on time to, to do his, uh, test flight.

But you're right that this is very much a Picard and Data-focused story.

In fact, it... for the elevator pitch I was gonna give for the movie was The Last Temptation of Data-

And I pledge that I shall do so, Charlie.

Okay, today we're joined by Jenica Pounds, also known as Data Republican, open source platform that gave Americans searchable access to billions of dollars in federal grant spending.

Her findings were explosive.

uh recently there was a nigerian chief who billed medicare for over 30 million dollars in fraud suspected alleged fraud allegedly using the funds to build a palace and her targets are now these days are angry because she is messing with their money follow the money everybody she's been doxxed and harassed and now the immediate the media is diverting attention she's written a new book It is called Unelected.

Pre-order now.

Also, you can follow her on X.

It is Data Republican.

And then we'll be followed with Larry Elder.

This movement started in places like St. Charles, Missouri, Johnson City, Tennessee, Pike County, Georgia, Putnam County, Indiana.

Regular citizens standing up at city council meetings, demanding answers and transparency, and using every tool they could find to resist data centers.

communities are being sold a well-worn corporate marketing pitch about how data centers will bring jobs and revenue, and they're not buying it.

Just last week, Trump, with his usual nuance and compassion for the little guy, jumped on this bandwagon, posting on Truth Social, the only reason that communities throughout the USA should not want data centers is if they want to end up being backwards and poor.

Classy Trump.

He went on to say, if we kill the golden goose, you will only have yourselves to blame.

And that's a big if.

Here's Robert Reich explaining just how significant these concerns are and where all of this opposition is coming from.

The desire is really to speed the decision-making process.

So today we're gonna explore how autonomous reasoning, distributed data, and mission-tailored artificial intelligence are transforming both the enterprise networks and also the realities of operating at the tactical edge.

So as we frame the operational shift, exercises and recent real world operations remind us that enterprise assumptions while they're reliable in terms of connectivity and centralized data and cloud-based models, when you operate in a degraded contested environment, sometimes all bets are off.

And warfighters need adaptable tools and networks that are going to function even when the bandwidth is scarce and when they have intermittent communications.

So closing the gap between enterprise capability and tactical need remains one of the Army's biggest challenges.

So from my perspective, technology isn't the limiting factor.

We've made incredible strides with implementing AI models.

The biggest challenge with integrating them in the operational environment where connectivity is constrained, data distributed across multiple endpoints, systems, EUD devices, and the tactical formations don't always have or own the infrastructure with that.

The thing about Clarify, according to its co-founders, it is very executive specific in terms of how it works and what it keeps track of.

And it's very specific to you and your data graph.

And so because it has narrower but deeper context about you, it provides you with better answers than you can get from a generalized LLM kind of a tool.

And this echoes for me with an interview that I did with Pinar Ormagee, the CEO of Lexful, a company we've talked about on the show before.

They're building sort of a documentation solution of the future.

That goes beyond that, basically, to create a very complete data graph for an MSP that helps them utilize AI more effectively.

And when I interviewed her at ChannelCon just a few weeks ago, she was kind of talking about an experience that she personally had with Claude, where it gave her some bad information.

And her diagnosis of what had happened essentially is that Claude has too much context.

We've had this tipping point where three things have just come together.

Massive amounts of data, incredible computing resources to analyze that data, and sophisticated algorithms that can run on these computers to analyze that data.

And Data came along, but nothing was happening.

Fast computing came along, and more interesting things happened.

And then you throw in things like deep learning or reinforcement learning or the transformer, and all of a sudden, things just take off.

当相机试。

是啦，当它相机这样试，看看那个，即是操控啊，或者那个出来的效果，他们收不收货啰。这个，这个可能都是要稍后一段时间才会知道。不过呢，就是，呃，在 Keynote 里面有个很有趣的一点，你刚刚讲的 computational [吸气] photography 啦，就是，呃，或者现在在讲的可能 AI 修改、修图啊这样。那么它在这个 Keynote 里面呢，都提到一个很有趣的，就是一个的，呃，新的功能啦，我想是，就是，呃，这一代的，呃，电话开始提供到这个功能，就是因为它是-它是-是 based on 个 sensor 那里去做的，就是它就说是做一个，就是好像，好像菲林这样的效果，一个，呃，就是它 sign 了那个 data，就是说 sign 了那个，呃，就是摄影的那个，那个 sensor 的 data，去放进去那幅照片里，确保那幅照片是你部相机，是你部手机是拍下去的。

就是不是，嗯-

是啦，不是-

就是数码，数码签名是。 是啦，那所以，呃，有趣的，就是这个可以，就是，在现在这个，就是-

有图无真相的时代。

是啦，什么都是 AI 可以 gen 出来的时候，呃，现在苹果又推一个这样的功能，就说，啊，我可以 sign 了你那幅相去储回那个 data，在你那幅相那里，让你可以拿这幅相出来证明，你真的是，真是影，真是你影的，这样这件事，哈，真是，呃，是啦，真是用 iPhone 影的这样，可能都是一个 somehow 可以-

它说那个是一个 pixel level 的 sign，呃-

And then Morningstar continued this until pretty recently, actually.

But nowadays, by the way, all data is owned by somebody.

And it's basically, I need licenses and so forth.

So actually, I was giving him a much more complicated project.

I sold it to Morningstar.

Morningstar eventually bought Crisp.

And Crisp is the source of the the data here.

In the end, we have rights to basically produce the data for the book and for other purposes.