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#aiagents

7 posts7 participants1 post today
Public
☮ ♥ ♬ 🧑‍💻

“Right now, I’d wager the majority of #KnowledgeWorkers and #OfficeWorkers wouldn’t be comfortable working with #Rovo, meaning the context they’re likely to experience #AIAgents in is as an imposition from somewhere else in the #business.

Valliani said the goal would be to integrate AI tools into each team member’s everyday flow, and that the likes of #Atlassian was primed to do that thanks to the graph that gives its software so much #insight about how people #work.” <archive.md/udlfS> / <smh.com.au/technology/ai-won-t>

VS

“#Atlassian chief executive Mike Cannon-Brookes has #failed to convince #investors of his plan to lure #customers to its #CloudPlatform by making its #ArtificialIntelligence assistant #free, with an adverse reaction to the company’s quarterly earnings guidance shaving billions of dollars off his wealth.”
<archive.md/mKa8d> / <afr.com/technology/atlassian-w>

When you have to play to both sides of the market, users who pay, investors who buy.

A reminder, #AI costs #money to operate. I couldn’t see IBM mainframe time handed out for free. That’s the lesson. #ArtificialIntelligence

Public
Miguel Afonso Caetano

"So this is why you keep invoking AI by accident, and why the AI that is so easy to invoke is so hard to dispel. Like a demon, a chatbot is much easier to summon than it is to rid yourself of.

Google is an especially grievous offender here. Familiar buttons in Gmail, Gdocs, and the Android message apps have been replaced with AI-summoning fatfinger traps. Android is filled with these pitfalls – for example, the bottom-of-screen swipe gesture used to switch between open apps now summons an AI, while ridding yourself of that AI takes multiple clicks.

This is an entirely material phenomenon. Google doesn't necessarily believe that you will ever want to use AI, but they must convince investors that their AI offerings are "getting traction." Google – like other tech companies – gets to invent metrics to prove this proposition, like "how many times did a user click on the AI button" and "how long did the user spend with the AI after clicking?" The fact that your entire "AI use" consisted of hunting for a way to get rid of the AI doesn't matter – at least, not for the purposes of maintaining Google's growth story.

Goodhart's Law holds that "When a measure becomes a target, it ceases to be a good measure." For Google and other AI narrative-pushers, every measure is designed to be a target, a line that can be made to go up, as managers and product teams align to sell the company's growth story, lest we all sell off the company's shares."

pluralistic.net/2025/05/02/kpi

pluralistic.netPluralistic: AI and the fatfinger economy (02 May 2025) – Pluralistic: Daily links from Cory Doctorow
#AI#GenerativeAI#BigTech
Public
Pietro395 :proton: 🇮🇹

🤖 In questi giorni ho sperimentato la creazione di Agenti che possano compierei dei task autonomamente, la prima cosa che sto provando è quella di avere ogni mattina le ultime 5 notizie tech riassunte in forma testuale e in audio stile podcast da ascoltare in auto.

Tramite API ottengo le notizie, GPT 4.1 visita e fa il riassunto di ogni articolo, GPT 4o-mini sistema il testo e un API self-hosted crea un audio inviando il tutto su Telegram

Molto interessante 🚀

#ai#aiagents#tecnologia
Public *
Miguel Afonso Caetano

"We are releasing a taxonomy of failure modes in AI agents to help security professionals and machine learning engineers think through how AI systems can fail and design them with safety and security in mind.
(...)
While identifying and categorizing the different failure modes, we broke them down across two pillars, safety and security.

- Security failures are those that result in core security impacts, namely a loss of confidentiality, availability, or integrity of the agentic AI system; for example, such a failure allowing a threat actor to alter the intent of the system.

- Safety failure modes are those that affect the responsible implementation of AI, often resulting in harm to the users or society at large; for example, a failure that causes the system to provide differing quality of service to different users without explicit instructions to do so.

We then mapped the failures along two axes—novel and existing.

- Novel failure modes are unique to agentic AI and have not been observed in non-agentic generative AI systems, such as failures that occur in the communication flow between agents within a multiagent system.

- Existing failure modes have been observed in other AI systems, such as bias or hallucinations, but gain in importance in agentic AI systems due to their impact or likelihood.

As well as identifying the failure modes, we have also identified the effects these failures could have on the systems they appear in and the users of them. Additionally we identified key practices and controls that those building agentic AI systems should consider to mitigate the risks posed by these failure modes, including architectural approaches, technical controls, and user design approaches that build upon Microsoft’s experience in securing software as well as generative AI systems."

#AI#GenerativeAI#AIAgents
Public
Miguel Afonso Caetano

"Inherent security flaws are raising questions about the safety of AI systems built on the Model Context Protocol (MCP).

Developed by Anthropic, MCP is an open source specification for connecting large language model-based AI agents with external data sources — called MCP servers.

As the first proposed industry standard for agent-to-API communication, interest in MCP has surged in recent months, leading to an explosion in MCP servers.

In recent weeks, developers have sounded the alarm that MCP lacks default authentication and isn’t secure out of the box — some say it’s a security nightmare.

Recent research from Invariant Labs shows that MCP servers are vulnerable to tool poisoning attacks, in which untrusted servers embed hidden instructions in tool descriptions.

Anthropic, OpenAI, Cursor, Zapier, and other MCP clients are susceptible to this type of attack..."

thenewstack.io/building-with-m

The New Stack · Building With MCP? Mind the Security GapsA recent exploit raises concerns about the Model Context Protocol, AI's new integration layer.
#AI#GenerativeAI#AIAgents
Public
Miguel Afonso Caetano

"To test this out, the Carnegie Mellon researchers instructed artificial intelligence models from Google, OpenAI, Anthropic, and Meta to complete tasks a real employee might carry out in fields such as finance, administration, and software engineering. In one, the AI had to navigate through several files to analyze a coffee shop chain's databases. In another, it was asked to collect feedback on a 36-year-old engineer and write a performance review. Some tasks challenged the models' visual capabilities: One required the models to watch video tours of prospective new office spaces and pick the one with the best health facilities.

The results weren't great: The top-performing model, Anthropic's Claude 3.5 Sonnet, finished a little less than one-quarter of all tasks. The rest, including Google's Gemini 2.0 Flash and the one that powers ChatGPT, completed about 10% of the assignments. There wasn't a single category in which the AI agents accomplished the majority of the tasks, says Graham Neubig, a computer science professor at CMU and one of the study's authors. The findings, along with other emerging research about AI agents, complicate the idea that an AI agent workforce is just around the corner — there's a lot of work they simply aren't good at. But the research does offer a glimpse into the specific ways AI agents could revolutionize the workplace."

tech.yahoo.com/ai/articles/nex

Yahoo Tech · Carnegie Mellon staffed a fake company with AI agents. It was a total disaster.By Shubham Agarwal
#AI#GenerativeAI#AIAgents
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