weekend ai reads for 2025-03-21

Peskoe said state regulators can face political pressure to approve big economic investments already touted by elected officials for their economic impacts. He added that utilities have a long history of “exploiting their monopolies” to attract competitive lines of business.

Our analysis reveals specific failure modes that could destabilize liberal institutions. Enhanced state capacity through AGI could enable unprecedented surveillance and control, potentially entrenching authoritarian practices. Conversely, rapid diffusion of AGI capabilities to non-state actors could undermine state legitimacy and governability. We examine how these risks manifest differently at the micro level of individual bureaucratic decisions, the meso level of organizational structure, and the macro level of democratic processes.

AI will become very good at manipulating emotions. I think we’re on the verge of that. At the moment we’re just thinking of AI crunching data or something. But very soon, AI will be able to figure out how you create certain kinds of emotions in people – anger, sadness, laughter.

If you want something deterministic, you should use a database.

The real value, though, lies in what happens around the model. For example, LLMs became significantly more useful when they gained the ability to fact-check themselves using real-time web data—and cite their sources.

Overall, I found Manus to be a highly intuitive tool suitable for users with or without coding backgrounds. On two of the three tasks, it provided better results than ChatGPT DeepResearch, though it took significantly longer to complete them.

We used self-hosted open-source AI software to securely transcribe and help classify the material, which enabled reporters to match up related files and to reconstruct the day’s events, showing in painstaking detail how law enforcement’s lack of preparation contributed to delays in confronting the shooter.

Barnett finds that 34 percent of tasks can be performed remotely, but only 13 percent of occupations have, as their top five most important subtasks, things that can all be done remotely. Thirteen percent can then serve as an (admittedly very rough) estimate of the share of jobs that could, in principle, be fully automated by a sufficiently advanced cognitive AI.

When we freely license our work, we do so in service of those goals: free and open access to knowledge and education. But when trillion dollar companies exploit that openness while giving nothing back, or when our work enables harmful or exploitative uses, it can feel like we’ve been naïve. The natural response is to try to regain control.

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

MCP (Model Context Protocol) is a standard way to connect AI models to data sources and tools. It allows AI to access information and capabilities beyond what they were originally trained on.

Some essential elements for deploying AI tend to be fully or partially centralized (Exhibit 1). For risk and compliance, as well as data governance, organizations often use a fully centralized model such as a center of excellence. For tech talent and adoption of AI solutions, on the other hand, respondents most often report using a hybrid or partially centralized model, with some resources handled centrally and others distributed across functions or business units—though respondents at organizations with less than $500 million in annual revenues are more likely than others to report fully centralizing these elements

While it may sound obvious that leaders need to gain greater fluency in tech and regulations, time and day-to-day fire drills can limit leadership teams from making progress unless they intentionally commit through formal initiatives.

The researchers note that developers, too, can be proactive in how they approach new projects. As they begin to meet with domain experts and gather information, they ought to suss out the nature of the project at hand.

Starting this fall semester, primary and secondary schools in Beijing will offer at least eight hours of AI classes every academic year—with students as young as six years old being taught how to use chatbots and other tools, general background on the technology, and AI ethics.

The AI model was designed to generate “warmup” exercises that help students activate prior knowledge. In user evaluations, these AI-generated exercises were rated better than human-created ones in terms of accessibility, alignment with learning objectives, and teacher preference.

The highest-rated approach fed the model an additional dataset of original curriculum materials and used complex and nuanced prompts informed by an expert educator.

“All these AI scientist papers are using peer-reviewed venues as their human evals, but no one consented to providing this free labor,” wrote Prithviraj Ammanabrolu, an assistant computer science professor at UC San Diego

1. Use AI to Generate Data

2. Use AI to Improve Data

3. Use AI for Tasks That Don't Need Your Data

4. Use AI for Tasks That Work Well with Messy Data

5. Use AI to Build a Knowledge Foundation

Yet Llama has achieved widespread success since launching in 2023. Companies including Spotify, AT&T, and DoorDash use Llama models in production today.

Instead, while traffic from the latest evolution of chatbots “show 8 percent higher engagement,” such visitors are also “9 percent less likely to convert compared to other sources of traffic” as of Feb. 2025, Adobe said, citing its analysis of “1 trillion” U.S. retail site visits.

With design, and perhaps user research especially, there's a danger though on relying too much on AI. It's so much faster and efficient, but much about user research is catching these brief moments of... humanity.