AI companies are at pains to emphasise the care they take to prevent their models from making things up. Yet some errors are tricky to prevent https://t.co/RUyzmIpcyd ...

If you're a major artificial intelligence company worth $183 billion, it might seem like bad business to reveal that in testing your AI models resorted to blackmail. Why does Enthropic consider disclosures like that so essential. Because you could end up in the world of like the cigarette companies or the opioid companies where they knew there were dangers and they they didn't talk about them and certainly did not prevent them. ...

We’re open-sourcing an evaluation used to test Claude for political bias.In the post below, we describe the ideal behavior we want Claude to have in political discussions, and test a selection of AI models for even-handedness:https://t.co/IzP0aSLtvp ...

Understanding neural networks through sparse circuits:OpenAI (@OpenAI):We’ve developed a new way to train small AI models with internal mechanisms that are easier for humans to understand.Language models like the ones behind ChatGPT have complex, sometimes surprising structures, and we don’t yet fully understand how they work.This approach ...

Industry Oversight - The industry lacks sufficient oversight regarding AI models [1] - There is more public knowledge about Oreos than about AI models [1] Concerns - The author @parmy explains the lack of oversight [1]

AI Development - Indian users are influencing the development of popular AI models [1] - Indian firms excel at designing AI services for diverse audiences [1]

Core Insights - Jrog's shares surged after reporting earnings that exceeded estimates, leading to an increase in full-year guidance [1][3] - The company is recognized as a leading provider of DevOps and software release automation tools [2] Financial Performance - Revenue grew by 26% year-over-year to $136.9 million, surpassing estimates, with cloud revenue increasing by 50% year-over-year, now representing 46% of total revenue [3][4] - Adjusted EPS was reported at 22 cents per share, beating estimates and showing improvement from the previous year [4] - Operating cash flow reached $30.2 million, while free cash flow was $28.8 million [4] - Cash and cash investments increased by 39% year-over-year to $651.1 million [5] Customer Metrics - The number of customers with annual recurring revenue over $1 million rose by 54% year-over-year to 71 [5][6] - Customers generating over $100,000 in annual revenue increased to 1,121, up from 966 last year [6] Stock Performance and Analyst Reactions - Jrog's stock has appreciated approximately 100% year-to-date, reflecting strong market performance [7] - Following the earnings report, several analysts raised their price targets, with 18 out of 19 analysts rating the stock as a buy [9][10] - Oppenheimer upgraded the stock to outperform with a price target of $75, citing the company's ability to expand its customer base into emerging opportunities like AI [10][11]

The Gentic AI startup Sierra is hosting its first customer conference today in San Francisco. And joining us right now to talk all things AI, Brett Taylor. He's the co-founder and CEO of Sierra. He's also the chair of Open AI's board of directors and so many other things.Created Google Maps, the like button uh on Facebook. I mean, I don't I don't know what else what the resume is long. Brett, congratulations on today.Tell us about what you're announcing in terms of this new Agentic uh agent. Well, uh, first ...

Even when new AI models bring clear improvements in capabilities, deprecating the older generations comes with downsides.An update on how we’re thinking about these costs, and some of the early steps we’re taking to mitigate them: https://t.co/VCTMW0d2e8 ...

Connecting AI models to different apps usually means writing custom code for each one.For instance, if you want to use a model in a Slack bot or in a dashboard, you'd typically need to write separate integration code for each app.Let's learn how to simplify this via MCPs.We’ll use @LightningAI's LitServe, a popular open-source serving engine for AI models built on FastAPI.It integrates MCP via a dedicated /mcp endpoint.This means that any AI model, RAG, or agent can be deployed as an MCP server, accessible ...