Ages of Operating Systems👨🏻‍💻😎

📂 Windows 11 (3 years old)
🪟 Windows 10 (8 years old)
🍎 macOS Yosemite (10 years old)
🐉 Kali Linux (11 years old)
💻 Windows 8 (12 years old)
🌐 Manjaro (11 years old)
💻 Windows 7 (14 years old)
🖥️ Windows Vista (17 years old)
🌿 Linux Mint (18 years old)
🐧 Ubuntu (20 years old)
⚙️ Fedora (20 years old)
🔧 OpenSUSE (20 years old)
⚙️ CentOS (20 years old)
🐧 Arch Linux (22 years old)
🍏 macOS (22 years old)
💻 Windows XP (23 years old)
🖥️ Windows 2000 (24 years old)
📱 Windows 98 (25 years old)
🌍 Windows 95 (28 years old)
💻 Windows 3.1 (29 years old)
🖥️ OS/2 (32 years old)
🐧 Debian (31 years old)
🔴 Red Hat Linux (30 years old)
🎮 AmigaOS (34 years old)
🖥️ Xenix (40 years old)
📀 VMS (44 years old)
💾 MS-DOS (42 years old)
💾 CP/M (49 years old)
🖥️ Unix (54 years old)

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70% of Nvidia Employees are now millionaires; 1 in 3 has over $20 Million

Nvidia's remarkable growth has not only established it as the world's most valuable company but has also generated significant wealth for many of its employees. With the company's stock soaring, Nvidia workers have become some of the largest beneficiaries of this tech boom.

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Google’s Gemini AI Chatbot tells a user to die

Gemini AI told a user to "die" in response to their test question. Gemini generated three drafts in response, and while the second and third drafts were actual answers, the first draft's text told the user to "Please die."

Looks like Gemini's tired of solving questions, lol.

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Big update from Microsoft

Microsoft just announced Copilot Vision which is now in preview to select users (from what I understand) Watch it support in the geoguesser game!

Copilot vision whilst for some may create some concerns over privacy can now understand the full context of what you’re doing online in real time. Official release details here

The ChatGPT desktop app for Windows is now available for all users 🖥

Get faster access to ChatGPT with the Alt + Space shortcut, and use Advanced Voice Mode to chat with your computer and get hands-free answers while you work.

OpenAI releases text-to-video model Sora for ChatGPT Plus and Pro users

Dec 9 (Reuters) - OpenAI said on Monday it has released its artificial intelligence model, which creates video from text, to ChatGPT Plus and Pro users, expanding its foray into multimodal AI technologies.
The Microsoft-backed (MSFT.O), opens new tab company, which kicked off a generative AI craze with the launch of its ChatGPT chatbot in November 2022, aims to target similar text-to-video tools from Meta (META.O), opens new tab and Alphabet's (GOOGL.O), opens new tab Google, along with Stability AI's Stable Video Diffusion.

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Introducing the Realtime API

Today, we're introducing a public beta of the Realtime API, enabling all paid developers to build low-latency, multimodal experiences in their apps. Similar to ChatGPT’s Advanced Voice Mode, the Realtime API supports natural speech-to-speech conversations using the six preset voices⁠(opens in a new window) already supported in the API.

We’re also introducing audio input and output in the Chat Completions API⁠(opens in a new window) to support use cases that don’t require the low-latency benefits of the Realtime API. With this update, developers can pass any text or audio inputs into GPT-4o⁠ and have the model respond with their choice of text, audio, or both.

From language apps and educational software to customer support experiences, developers have already been leveraging voice experiences to connect with their users. Now with Realtime API and soon with audio in the Chat Completions API, developers no longer have to stitch together multiple models to power these experiences.

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Will LLMs always hallucinate?

As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.

A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.

While the idea of hallucinations as features isn't new, the researchers' explanation is.

They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.

In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.

This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.

So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.

It also suggests that research into making models more robust and understanding their failure modes is crucial.

No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases

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