As AI continues to automate routine and analytical tasks, many roles will evolve or disappear. This raises an important question about which careers can offer long-term security, meaningful work, and strong earning potential in an AI-driven world
https://runwayml.com/research/theturingreel
I can vividly remember teaching my AP English class in 1999 when I first heard of “Turnitin.com”; my first thought was “how am I going to scan all of these pages into that thing?” Back then I graded papers on a first pass with my trusty No. 2 Dixon Ticonderoga pencil. Now what was I going to do? For years I used my pencil as a key aid in the writing process with my students. It was collaborative because we worked together – I would suggest ideas an reframe sentences and thoughts to model writing in line with whatever rubric my assignment called for. Often times students adopted my suggestions whole-cloth, other times we would workshop different stylistic choices. My students and I shared in the rhetorical process. If they chose to use my margin note “try something like this,” are they not able to claim ownership because the original words were mine and not theirs? I was the human intelligence that helped guide my students. They took my advice and incorporated it often. Other times they vehemently opposed my suggestions. I was their personal ChatGPT and I enjoyed that work immensely. But it was often brief and temporal, because I only had so much time to visit individually with 75 students. Can we really now castigate a tool that students can have beside them during every moment of their learning journey? The ethical dilemma is this: students could accept, reject, argue with, or ignore me. Today, institutions now assume AI outputs are automatically suspect while often students see them as automatically authoritative. Agency is the key issue. When I suggested phrasing, students exercised their agency to decide whether to adopt or reject my suggestions. My authority was negotiable and if they accepted my suggestions, even verbatim, authorship was never in question. Students are struggling today with teachers making them think AI is a “forbidden oracle,” whereas teachers are also short-sighted in thinking Turnitin is an infallible detector. The problem is in both cases human judgment is being “outsourced.” In 1999, I trusted my students negotiate my (human) guidance; now we pretend that same negotiation between students and AI itself is the problem. What mattered was not that I was always right; but that my authority was provisional. Fast forward almost 30 years and now we not only have a tool for students to generate a decent five-paragraph essay, but a second tool that claims it can detect the use of the first. And that tool is the same one I struggled to understand in 1999: Turnitin. Although this time Turnitin is losing the battle against this newer tool, and students all over academia are suffering from that loss. Academia now is forced to embrace a structure that rewards certainty over caution. Boom: you get the AI-cheating accusation era. We’re living in a time where a student can be treated like they robbed a bank because a dashboard lit up yellow. Is this how math teachers felt about calculators when they first entered the scene? Can you today imagine any high-level mathematics course that didn’t somehow incorporate this tool? Is ChatGPT the “writing calculator” that in decades will sit beside every student in an English class along with that No. 2 Dixon Ticonderoga? Or will pencils continue to suffer a slow extinction? I’m not writing this because I think academic dishonesty is cute. Students absolutely can use AI to outsource thinking, and pretending otherwise is naïve. I’m writing this because the process of accusing students is an ethical problem now. It’s not just “Are people cheating?” It’s “What evidence counts, who bears the burden, and how much harm are we willing to cause to catch some portion of cases?” When a school leans on AI detectors as objective arbiters, the ethics get ugly fast: false positives, biased outcomes, coerced confessions, and a general atmosphere of suspicion that corrodes learning. I believe it is ethically wrong to treat AI-detection scores as dispositive evidence of misconduct; accusations should require due process and corroborating evidence. current detectors are error-prone and easy to game, and the harms of false accusations are severe. If institutions want integrity, they should design integrity—through assessment design, and clear AI-use policies, not outsource judgment to probabilistic software and call it “accountability.” MIT’s teaching-and-learning guidance says this bluntly: AI detection has high error rates and can lead to false accusations; educators should focus on policy clarity and assessment design instead of policing with detectors. (MIT Sloan Teaching & Learning Technologies). Tony J. D'Orazio Liberty University MA in Composition--AI Integrated Writing Expected 2027
Speaking at the World Economic Forum in Davos, Switzerland, Huang described AI as a five-layer cake consisting of energy, chips, cloud infrastructure, models and application. He said AI’s application–how the technology is used in a specific industry–is the most critical layer of that cake as it is where the economic benefits lie.
Using AI for advice or other personal reasons is linked to depression and anxiety.[1] Apple is turning Siri into an AI bot that’s more like ChatGPT.[2] Amazon One Medical introduces agentic Health AI assistant for simpler, personalized, and more actionable health care.[3] Todoist’s app now lets you add tasks to your to-do list by speaking to its AI.[4] Sources: [1] https://www.nbcnews.com/health/mental-health/ai-chatbots-personal-support-linked-depression-anxiety-study-rcna255036 [2] https://www.theverge.com/news/865172/apple-siri-ai-chatbot-chatgpt [3] https://www.aboutamazon.com/news/retail/one-medical-ai-health-assistant [4] https://techcrunch.com/2026/01/21/todoists-app-now-lets-you-add-tasks-to-your-to-do-list-by-speaking-to-its-ai/
Did Apple make the right choice in partnering with Google for Siri's AI features?
i installed qwen3 coder 30b locally and i am running it as an agent using my own llm controller,and i am running gemini 3 from google antigravity. i asked both to complete a set of tasks. 1-create a game of tic tac toe 2-create a game website as a prop 3-create a blue background with a rotating cube. 4-Write an HTML file with CSS that creates a fully responsive three-column layout. It must collapse to a single column on screens under 600px. Do not use any frameworks. 5-Write an HTML file that generates a procedural, animated starfield background using the
"Microsoft has introduced a new artificial intelligence model aimed at pushing robots beyond controlled factory environments. The system, called Rho-alpha, targets one of robotics’ long-standing limitations: the inability to adapt to unpredictable, real-world settings. Developed by Microsoft Research, Rho-alpha is the company’s first robotics-focused model derived from its Phi vision-language AI family. Microsoft describes it as part of a broader shift toward physical AI, where intelligent agents interact directly with the physical world rather than operating only in digital spaces. Unlike traditional industrial robots, Rho-alpha does not rely on rigid task scripts. The model translates natural language instructions into control signals for robots performing complex two-handed manipulation tasks."
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Latest updates from the BBC's specialists in fact-checking, verifying video and tackling disinformation.
Apple, Siri, and Google Gemini AI are at the forefront as Apple rebuilds its assistant, betting on AI model commoditization and flexibility.
With the “gym,” Insilico is now targeting other biotech and pharmaceutical companies, offering to train new AI models for them.
The Economic Times picked the word "Kafkaesque" as its "Word of the Day" -- but spelled it "Kafkaesliue" in an AI-generated graphic.
Taylor said the free market will ultimately determine where the value is and which AI players have the best products.
What we learned from three iterations of a performance engineering take-home that Claude keeps beating.
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This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and …
Civic institutions—the rule of law, universities, and a free press—are the backbone of democratic life. They are the mechanisms through which complex societies encourage cooperation and stability, while also adapting to changing circumstances. The real superpower of institutions is their ability to evolve and adapt within a hierarchy
A comprehensive guide to 113 battle-tested agentic patterns for building production AI agents.
eBay bans AI “buy for me” agents & LLM scrapers, updates arbitration & dispute resolution rules in User Agreement update effective Feb. 20, 2026.
162 games analyzed: AI deception is strategic, not intrinsic. Watch Gemini 3 create fake 'alliance banks' to betray GPT and Kimi, but cooperate perfectly with copies of itself.
ANCHORAGE WASHINGTON The Pentagon has issued prepare-to-deploy orders to roughly 1 500 active-duty soldiers from the 11th Airborne Division setting off a wave of debate
The famed convention's organizers have banned AI from the art show.
Keep track of the most polluted cities in the world with our air quality index (AQI) ranking.
: Craig Guildford banned Israeli fans based on Microsoft's match report, told MPs 'we don't use AI,' then discovers... they did
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Tech leaders have taken the stage this week at the World Economic Forum in Davos, Switzerland, to discuss how AI will impact ...
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OpenAI coming out with a bunch of huge news lately, and it all means something if you take it together. Sarah Friar ...
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Microsoft CEO, Satya Nadella says we need to find a use for AI or the bubble will burst, taking the world economy with it, WTF!
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Missed Part 1? Watch the original "Crazy Beds" here: https://www.youtube.com/watch?v=GrgJ30vZgyI Subscribe now to unlock ...
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Live everyday at https://www.twitch.tv/hasanabi Edited by: https://twitter.com/Archb98 Other Links TikTok: ...
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In today's episode of The Infographics Show, artificial intelligence made work easier, but what if it took every job instead?
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This is a weird and difficult topic. Curious to hear your thoughts. Follow me! Instagram: https://bit.ly/2WoR7W1 Twitter: ...
5:39
OpenAI CFO Sarah Friar joins 'Squawk Box' to discuss the state of the AI race, news of Apple picking Google's Gemini to run ...
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1. https://youtu.be/-9bo8HlSxwQ?si=xmAhyB6jOqhnw9eU 2.
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In this video, I break down the five AI fundamentals that actually matter if you want real results, not wasted months chasing shiny ...
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GLM-4.7-Flash is a 30B-A3B MoE model, offering strong performance in the 30B class for efficient, lightweight deployment. It excels in benchmarks like AIME, GPQA, and SWE-bench, making it suitable for tasks requiring advanced reasoning and coding capabilities.
TranslateGemma-4b-it is a lightweight, open translation model supporting 55 languages, capable of translating text or extracting text from images. It's designed for resource-constrained environments, enabling state-of-the-art translation on local infrastructure.
PersonaPlex-7B-v1 is a real-time, full-duplex speech-to-speech conversational model that jointly performs streaming speech understanding and generation. It enables natural conversational dynamics like interruptions and overlaps by concurrently processing user audio and generating its own spoken responses, conditioned on voice and text prompts for persona control.
GLM-Image is a text-to-image model with a hybrid autoregressive + diffusion decoder architecture, excelling in text rendering and knowledge-intensive generation. It supports both text-to-image and image-to-image tasks including editing and style transfer.
FLUX.2-klein-4B is a fast, 4B parameter rectified flow transformer for unified image generation and editing. It delivers state-of-the-art quality with sub-second inference on consumer GPUs, supporting text-to-image and multi-reference image editing for interactive and latency-critical applications.
FLUX.2-klein-9B is a fast, 9B parameter image generation and editing model delivering state-of-the-art quality with sub-second inference for real-time applications. It supports text-to-image and multi-reference image-to-image editing.
Pocket TTS is a lightweight, CPU-efficient text-to-speech model (100M parameters) offering low-latency audio generation (~200ms) and voice cloning capabilities. It's ideal for applications requiring fast, on-device speech synthesis without GPU dependencies, supporting Python API and CLI integration.
TranslateGemma-27B-IT is a lightweight, open translation model supporting 55 languages, capable of translating text and extracting/translating text from images. It's designed for efficient deployment on resource-constrained environments, enabling state-of-the-art translation for diverse applications.
LTX-2 is a DiT-based audio-video foundation model capable of generating synchronized video and audio from various inputs including images, text, and audio. It supports local execution and offers distilled and upscaler checkpoints for practical applications.
GLM-4.7-Flash is a 30B-A3B MoE model offering a balance of performance and efficiency for lightweight deployment. It excels in benchmarks like AIME and GPQA, supporting local inference with frameworks such as vLLM and SGLang for text generation and tool-calling use cases.
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RebuttalAgent is a multi-agent framework that reframes rebuttal generation as an evidence-centric planning task, improving coverage, faithfulness, and strategic coherence in academic peer review.
Agent Lightning is a flexible RL framework for training LLMs in various agents, using a hierarchical RL algorithm and decoupling execution from training to handle complex interactions.
A suite of open-source music foundation models is introduced, featuring components for audio-text alignment, lyric recognition, music coding, and large language model-based song generation with controllable attributes and scalable parameterization.
MemOS, a memory operating system for Large Language Models, addresses memory management challenges by unifying plaintext, activation-based, and parameter-level memories, enabling efficient storage, retrieval, and continual learning.
ShapeR generates high-fidelity 3D shapes from casual image sequences using visual-inertial SLAM, 3D detection, and vision-language models with rectified flow transformer conditioning.
Conditional memory via Engram module enhances Transformer models by enabling efficient knowledge lookup and improving reasoning capabilities through optimized sparsity allocation.
To support reliable long-term interaction in complex environments, LLM agents require memory systems that efficiently manage historical experiences. Existing approaches either retain full interaction histories via passive context extension, leading to substantial redundancy, or rely on iterative reasoning to filter noise, incurring high token costs. To address this challenge, we introduce SimpleMem, an efficient memory framework based on semantic lossless compression. We propose a three-stage pipeline designed to maximize information density and token utilization: (1) Semantic Structured Compression, which applies entropy-aware filtering to distill unstructured interactions into compact, multi-view indexed memory units; (2) Recursive Memory Consolidation, an asynchronous process that integrates related units into higher-level abstract representations to reduce redundancy; and (3) Adaptive Query-Aware Retrieval, which dynamically adjusts retrieval scope based on query complexity to construct precise context efficiently. Experiments on benchmark datasets show that our method consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost, achieving an average F1 improvement of 26.4% while reducing inference-time token consumption by up to 30-fold, demonstrating a superior balance between performance and efficiency. Code is available at https://github.com/aiming-lab/SimpleMem.
Audio Language Models (ALM) have emerged as the dominant paradigm for speech
and music generation by representing audio as sequences of discrete tokens.
Yet, unlike text tokens, which are invertible, audio tokens are extracted from
lossy codecs with a limited bitrate. As a consequence, increasing audio quality
requires generating more tokens, which imposes a trade-off between fidelity and
computational cost. We address this issue by studying Continuous Audio Language
Models (CALM). These models instantiate a large Transformer backbone that
produces a contextual embedding at every timestep. This sequential information
then conditions an MLP that generates the next continuous frame of an audio VAE
through consistency modeling. By avoiding lossy compression, CALM achieves
higher quality at lower computational cost than their discrete counterpart.
Experiments on speech and music demonstrate improved efficiency and fidelity
over state-of-the-art discrete audio language models, facilitating lightweight,
high-quality audio generation. Samples are available at
https://continuous-audio-language-models.github.io
VibeVoice synthesizes long-form multi-speaker speech using next-token diffusion and a highly efficient continuous speech tokenizer, achieving superior performance and fidelity.
SmolDocling is a compact vision-language model that performs end-to-end document conversion with robust performance across various document types using 256M parameters and a new markup format.
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🎬 火宝短剧 - 基于AI的一站式短剧生成平台 《一句话生成完整短剧，从剧本到成片全自动化》 Huobao Drama - An AI-Powered End-to-End Short Drama Generator "One Sentence to Complete Drama: Fully Automated from Script to Final Video"
Marketing skills for Claude Code and AI agents. CRO, copywriting, SEO, analytics, and growth engineering.
Stop juggling AI accounts. Quotio is a beautiful native macOS menu bar app that unifies your Claude, Gemini, OpenAI, Qwen, and Antigravity subscriptions – with real-time quota tracking and smart auto-failover for AI coding tools like Claude Code, OpenCode, and Droid.
OpenSource Claude Cowork. A desktop AI assistant that helps you with programming, file management, and any task you can describe.
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