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AI Jobs and Career

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• Full Stack Engineer [$150K-$220K]
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• More AI Jobs Opportunitieshere

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Software Engineer - Tooling & AI Workflows (Contract)	Contract	$90 / hour
DevOps Engineer (India)	Full-time	$20K - $50K / year
Senior Full-Stack Engineer	Full-time	$2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India	Contract	$20 - $30 / hour
Senior Software Engineer	Contract	$100 - $200 / hour
Senior Software Engineer	Pre-qualified, Full-time	$150K - $300K / year
Senior Full-Stack Engineer: Latin America	Full-time	$1.6K - $2.1K / week
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Generalist Video Annotators	Contract	$45 / hour
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What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.

https://youtu.be/0RQZJ83aaLA

Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖

In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.

Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.

Join the conversation about the future of AI and share your thoughts on how models like Q* are shaping the landscape. Don’t forget to like, share, and subscribe for more deep dives into the fascinating world of artificial intelligence! #OpenAIQStar #AStarAlgorithms #DeepQLearning #ArtificialIntelligence #MachineLearningInnovation”

🚀 Whether you’re a tech enthusiast, a professional in the field, or simply curious about artificial intelligence, this podcast is your go-to source for all things AI. Subscribe for weekly updates and deep dives into artificial intelligence innovations.

✅ Don’t forget to Like, Comment, and Share this video to support our content.

📌 Check out our playlist for more AI insights

📖 Read along with the podcast:

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.

Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.

To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.

Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.

On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.

Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.

One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.

Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.

To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.

In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!

Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.

How the Rumors about Q* Started

There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.

It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.

The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.

Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.

A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.

Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.

It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.

OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.

In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.

In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.

The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.

Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.

This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.

If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.

AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.

By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.

Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.

In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

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Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

The Future of Generative AI: From Art to Reality Shaping

Improving Q* (SoftMax with Hierarchical Curiosity)

Combining efficiency in handling large action spaces with curiosity-driven exploration.

Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity

Softmaxwithhierarchicalcuriosity

Adaptive Softmax with Hierarchical Curiosity

This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.

Adaptive Softmax

Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.

Hierarchical Curiosity

Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.

Combining Adaptive Softmax and Hierarchical Curiosity

By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.

Here’s the proposed algorithm:

1. Initialize the Q-values for all actions in all states.

2. At each time step:

   a. Observe the current state s.

   b. Select an action a according to an exploration policy that balances exploration and exploitation.

   c. Execute action a and observe the resulting state s’ and reward r.

   d. Update the Q-value for action a in state s:

   Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))

   where α is the learning rate and γ is the discount factor.

   e. Update the curiosity bonus for state s:

   curio(s) = β * |r – Q(s, a)|

   where β is the curiosity parameter.

   f. Update the probability distribution over actions:

   p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))

3. Repeat steps 2a-2f until the termination criterion is met.

The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.

• 
  How far have ~30B open models actually come? Qwen3.8 vs Qwen3.6 vs Gemma 4
  by /u/MaySaki2 (Artificial Intelligence) on August 15, 2026 at 3:55 pm

  With Qwen3.8-27B out, I compared it with Qwen3.6-27B and Gemma 4 31B. They’re unusually good models to compare because they’re all around the same size: Qwen3.8: 27B, 262K context Qwen3.6: 27B, 262K context Gemma 4: 31B, 256K context What’s interesting is where the gains are going. Qwen3.8 pulls ahead particularly on coding and agentic benchmarks, while Gemma 4 is still very competitive on general reasoning. Comparing 3.8 directly with 3.6 also shows how much performance has moved in a single generation without increasing the parameter count. And these aren’t datacenter-sized models. Quantized, this is roughly the class of AI you can run on a high-end consumer GPU. The gap between “local model” and genuinely useful AI is getting pretty small. Full benchmark + hardware comparisons: https://canitrun.dev/models/qwen3.8-27b/ https://canitrun.dev/models/compare/qwen3.8-27b-vs-qwen3.6-27b/ https://canitrun.dev/models/compare/qwen3.8-27b-vs-gemma-4-31b/ submitted by /u/MaySaki2 [link] [comments]

• 
  We can optimize the Wetware
  by /u/Ordinary_Variable (Artificial Intelligence) on August 15, 2026 at 3:16 pm

  I think there are huge areas we can optimize prompt engineering on the human-interface side. We can learn how to prompt better if we had more feedback, more verbose metrics. 1: Show me the amount of compute I use for every question. And if its possible, show me the amount of compute per word or per sentence. I bet that if people saw that info they could refine their questions to get answers with a tiny fraction of the compute. And people would start to add things to their prompts that greatly reduce the compute. I like adding "Be succinct" or "In 100 words or less.", but I'm sure there are even better things you could do. 2: Put in a translator layer that converts your prompt into a hyper efficient one. You can toggle it off, or have it give you both replies, the one you would get if what you typed was sent directly, and the answer to the question after it was optimized. It will reword things you type to use less tokens but still get the core answer you were looking for. Sometimes all I really need is a 3 word answer but I forget to tell it "x words or less". Right now the AI goes on and on and its wasting its own compute and my time. 3: Give a direct one line reply at the top of the output and if that's what you want, you can hit the "Stop" button and skip all the compute. I think I've seen some AI implement this already, but everyone needs to do it. Or maybe have a "short reply" button that uses the same model, but adds the hidden prompt "In 2 sentences or less." (The user interface needs to keep that one or two sentence reply on screen and not scroll past it when more text loads in, that way the user can actually read the whole thing.) These ideas won't just help the big companies save a ton of electricity, but offline AIs are so slow and wasteful they also need optimizations. Humans getting better at using these tools is the future of AI. AI is going to hit a limit. 1,000 IQ may never be possible, but if we can speedrun our AI use we will save not just compute, but human time. Right now human attention needs to be optimized too. You can only read so fast. submitted by /u/Ordinary_Variable [link] [comments]

• 
  Bernie Sanders’ AI letter: A bankrupt appeal to the oligarchs
  by /u/DryDeer775 (Artificial Intelligence) on August 15, 2026 at 2:51 pm

  On August 10, Senator Bernie Sanders sent a letter to Sam Altman of OpenAI, Dario Amodei of Anthropic and Mark Zuckerberg of Meta demanding that they “pause AI development” and “stop building machines that humans cannot control,” promoting it with an accompanying video. The letter is characteristic of Sanders’ politics. It is full of demagogy and empty rhetoric that educates no one about anything. It is calculated to stoke fear, while covering up every basic question the technology poses: Who owns it? Who controls it? In whose interests is it being developed? Sanders’ first example of the supposed dangers of AI exemplifies his method. “AI has been used for the first time ever to create new viruses,” he writes, warning that “this type of development, in the wrong hands, could lead to new bioweapons that result in the deaths of tens of millions of people.” From Sanders’ letter, one would assume that individuals are concocting in laboratories deadly new pathogens, and Sanders uses rhetoric designed to appeal to the promoters of the anti-Chinese conspiracy theories of the origins of COVID-19. What actually happened was in fact a major scientific breakthrough. On August 6, Science published work from Brian Hie’s laboratory at Stanford and the Arc Institute reporting the first viral genomes designed by a generative model. The viruses are bacteriophages, which infect bacteria, not human cells, and the research is directed toward developing therapies against antibiotic-resistant infections, which kill more than 1 million people every year. The achievement is an indication of the enormous progressive potential of the technology. In Sanders’ telling, however, a step toward curing the incurable becomes the herald of a plague. Sanders attempts to conflate the medical breakthrough with real dangers posed by AI models carrying out cyberattacks. submitted by /u/DryDeer775 [link] [comments]

• 
  How China Is Winning the AI Race From Second Place
  by /u/Robert-Nogacki (Artificial Intelligence) on August 15, 2026 at 2:37 pm

  Every frontier model since 2023 has been American and Chinese labs trail by about seven months (Epoch). Meanwhile Chinese open models reached 41 percent of Hugging Face downloads, Qwen passed 700 million downloads with more derivatives than Google and Meta combined, and inference cost at fixed capability fell about 280 times in two years. The argument: capability is a leak rate, not a stock, because a model can be copied through its own API, and in that world second place given away free beats first place behind a meter. submitted by /u/Robert-Nogacki [link] [comments]

• 
  Crack?
  by /u/KeizerSauze (Artificial Intelligence) on August 15, 2026 at 2:35 pm

  It’s fascinating to see that, as is often the case, though perhaps not to the same extent today, tech players are “selling” us on the revolution, which in this case is AI… They’ve had a hard time admitting that this AI is really just a conversational chatbot, with a few exceptions like Y. Lecun. But they’re good at it, I have to admit, their marketing makes us believe in it, we’ve believed in it, and we want to believe in it. Still, for example, how can we accept being told that if the result isn’t what we expected, it’s because our prompts are bad? Worse yet, theYouTube channels, the LinkedIn posts with “answer this and I’ll give you my document… miracle.” I work in strategy, in-house after an external firm (i.e., a Tier 1 strategy consulting firm), and I’ve seen my fair share of nonsense, like how SAP S/4 Hana delivers “quantifiable added value…” But this takes the cake,I have to tip my hat to them! Well, of course there are things that work, like bug hunting, for example. I use it for my personal administrative tasks; it’s a huge time-saver. When will the crack come that we’ll all have to pay for? submitted by /u/KeizerSauze [link] [comments]

• 
  Which is more generous Z.ai vs Kimi vs Qwen vs Cursor vs Opencode subscription plans
  by /u/hapless_pants (Artificial Intelligence) on August 15, 2026 at 2:28 pm

  Hi, student here, am working on projects where i am building, benchmarking, testing, deploying, creating scripts for auto deployment and testing etc. Been using Chatgpt Plus+ (cant afford higher tier subscription). Opus didn't work out for me cause of worst usage limits. So presently am on a cycle of building one application in a week, than wait for next reset, to deploy/test/bench whereas i want to work on multiple stuff. I tried using cheaper models like luna as well as deepseek v4 flash and they just fall apart on this kind of work. so therefore am looking at Cursor, Qwen Token Plan (Qwen3.8-Max), GLM Coding Plan (GLM-5.3), Kimi Code (Kimi K3), or OpenCode Go,all around $20/mo. Tried GLM and Kimi myself, both decent, GLM looking promising. Qwen3.8-Max is average but works when i provide enough context on what to look for and how to do stuff. So my Main hurdle is figuring out which of these subscription plan provide generous usage of their frontier model. If anyone has experience with all these subscriptions would love your input on this. Or is what I'm asking for even realistic on a $20/mo plan, or is a higher tier subscription just the only real answer here? Also another thought would it make more sense to self-host something like Qwen 3.8B/27B run it in loops in a sandboxed test environment. working on all the issues or testing deployment scripts etc. And on success call a SOTA model to evaluate the work done (i can even use deepseek v4 flash from opencode hence the mention of this plan) TLDR: which of Qwen/GLM/Kimi/OpenCode gives the most frontier usage per $ for working on deployment/testing/benchmarking etc, and is self-hosting a small model/use opencode deepseek v4 flash + SOTA verification loop a better viable move? submitted by /u/hapless_pants [link] [comments]

• 
  Apple trains own China LLM with Alibaba, cleared by Beijing
  by /u/Justgototheeffinmoon (Artificial Intelligence) on August 15, 2026 at 1:08 pm

  Beijing quietly did something it has not done for any other Western tech company: it cleared a US firm to ship its own AI model inside mainland China. According to a [MacRumors write-up of Reuters' reporting](https://www.macrumors.com/2026/08/14/apple-trained-own-ai-model-for-china/), Apple has trained a China-specific large language model with development support from Alibaba, and is now described as "the first foreign company approved by the Chinese government to offer a proprietary AI model in the country." Rollout is expected in the coming months. The setup is a departure from Apple's earlier plan. Under the original arrangement, Apple Intelligence in China would piggyback on Alibaba's Qwen model, much the way it uses ChatGPT elsewhere. Now the reporting describes a "dual-track" approach: Apple ships its own trained-for-China LLM alongside the existing Alibaba integration. A brief moment of self-spoilage helped confirm the direction, when Apple published a Chinese-language support guide on August 10 explaining how Mac users could connect Qwen to Siri and Writing Tools, then pulled the page within a day. The interesting part is not the model itself but the permission. US tech firms have spent the last few years being told, in effect, that a non-Chinese generative model would not be allowed to reach mainland consumer users at scale. Apple is now the exception, and the price of that exception appears to be co-development with a Chinese national champion. That is a template as much as it is a product launch. It sits alongside our recent coverage of [Apple's push for CXMT memory in its next-gen devices](https://aiweekly.co/alerts/apples-push-for-cxmt-memory-meets-skeptical-us-officials) and belongs to a much wider China-AI beat we have been [tracking with hundreds of alerts this quarter](https://aiweekly.co/ai-news-today/china-ai-news). --- Our coverage: https://aiweekly.co/alerts/apple-trains-own-china-llm-with-alibaba-cleared-by-beijing submitted by /u/Justgototheeffinmoon [link] [comments]

• 
  If everyone gets access to the same AI, where does the human advantage move?
  by /u/Powerful_Creme2224 (Artificial Intelligence) on August 15, 2026 at 12:49 pm

  I've been thinking about this a lot lately. Being good at AI tools is obviously an advantage right now. But I don't think the information gap lasts forever. Models get better. Interfaces get easier. Good workflows spread. Things that took an expert months to learn eventually become a button. So what is left on the human side? The best analogy I have is a strange one: Imagine the numbers 1, 2, 3, and your job is to find another integer somewhere between them. Obviously, there isn't one. That's the point. Maybe the advantage isn't getting better and better at choosing between 1, 2 and 3. Maybe it's noticing that there is another variable that the original frame didn't contain. In investing, that might be finding a strange rule, structural edge, or entering before everyone else sees it. For a creator, it might be communicating some tiny human detail that technically isn't necessary, but somehow touches people. In business, it might be realizing that the process everyone is trying to automate faster isn't actually the bottleneck. The common part is that the useful variable wasn't obvious inside the original problem. And I think this may actually get harder as AI gets smarter. Bad AI shows you its cracks. It gives weird answers. It contradicts itself. You can see where the frame is broken. Very capable AI is different. Its explanations become smoother. Its reasoning sounds increasingly complete. The choices it gives you all make sense. And that may make it harder for a human to notice: Maybe the problem isn't which answer is best. Maybe something is missing from the question itself. That's the part I'm increasingly interested in. If AI becomes extremely good at reasoning inside a frame, perhaps one of the remaining human advantages is the ability to notice when the frame itself should be broken. Or maybe AI eventually becomes better at that too. I'm not sure. But I suspect that "using AI well" and "seeing the variable AI didn't give you" are going to become very different skills. submitted by /u/Powerful_Creme2224 [link] [comments]

• 
  Japanese tech company SoftBank Group sees profit drop despite AI investments
  by /u/Traditional_Blood799 (Artificial Intelligence) on August 15, 2026 at 12:17 pm

  submitted by /u/Traditional_Blood799 [link] [comments]

• 
  Generative AI, Credit/Recognition, Anthropocentrism, Egoism
  by /u/Hot-Organization-737 (Artificial Intelligence) on August 15, 2026 at 12:17 pm

  This is just a shower thought tier idea rather than profound philosophical analysis. Why do many of the anti-genAI arguments and stances revolve around recognition and consent? Everything in this universe is a collaboration, we all contribute to everything in some fashion, yet I do not thank you and you do not thank me for our day to day lives. Take "Ai art is disgusting and immortal because someone/something else is profiting off the work of others" or a similar idea "AI art is disgusting because there is no acknowledgement or credit given to the person whose training data contributed significantly to the output of the AI" What I'm about to say next isn't original, but I haven't engaged with others in this thought. How much individual credit does one deserve for a produced work? Suppose I locked you in a room all by yourself and there were no other people in that room, but I supplied you with tools and media, and left you to fully compose a piece of media "by yourself". You finish your media then slap "by [name]" on the front or back of it. I feel like that's how a lot of work is done today essentially. You compose a piece, you do research. You write a love letter, and if no one else contributed to your work, you just slap your name on it and call it yours. There is no credit given to those who made the tools, there's no credit for the media which supplied your inspiration, even though it's obviously true that we wouldn't have Dragonball without superman, or that there would be no playboy without the camera. I don't really see the creators of Superman on the cover of a dbz manga though, I just see Akira toriyama. You get my point. When it comes to AI art, there is severe dissatisfaction with the morality of how to credit others work in the final result of an AI image. I don't understand why these arguments can't be flipped onto the works of artists who compose their work "independently" As a side tangent I think nobody is trying to put responsibility on the machine, but rather people who use genAI. Many antagonists desire more that users of gen AI involve something like consent or credit or permission. There is an overwhelming amount of similar rhetoric in these spaces, but if I simply drew an astounding piece that showcased Goku and Superman engaging in extremely homosexual frolicking, would you use similar narratives and feel similar emotions and demand that I acknowledge the creators of those IPs. In my drawing, or am I allowed to post that on my social pages with my name signature on it? I will admit, although I haven't been trying to be obscure in the first place, I haven't delved deeply into this type of rhetoric that antis use to push their "anti" agenda. I simply noticed that is very popular, despite seeming easy to deflect. I suppose it caught on because it's easy to load on emotionally or with a sense of superior morality? -------- How credit does humanity itself deserve for human art? Should we not give thanks or credit to the particles that compose our world? Do the fish not deserve credit for the genetic data and skeletal scaffolding that composes us? Sure, they didnt have any intentions of human art, but when an AI piece of media is generated, how much intention did the proposed abscent people who should receive credit have in the production of the AI image? Thanks for reading ^.^ submitted by /u/Hot-Organization-737 [link] [comments]