AI Is About to Crash Your Couch Party: How You Could Be the Next Star of South Park!

Imagine this. You’re lounging on your couch, eyes glued to an episode of South Park. Only, it's not just another regular show. You're IN it. Yep, that's the future of TV as envisioned by Fable, a San Francisco startup. Their new brainchild, Showrunner AI (a.k.a SHOW-1), is ready to put you in the driver's seat of your favorite TV episodes. This sci-fi plot just became our reality.

Fable's Showrunner AI isn't just a fancy dialogue generator. It’s more like a Swiss army knife of TV production. It writes, produces, directs, casts, edits, voices, and even animates TV episodes. Think of it as your own private Hollywood studio tucked neatly inside a tiny silicon chip. With it, you can pull the strings, craft your narrative, or just sit back and let the AI work its magic.

Now, the AI-powered tech is not just flexing its muscles with South Park. Fable is also offering a platform where fans can create new episodes of their favorite shows or even make their own original series. Think about the world where you're not only a spectator but an active player in the entertainment field. Groundbreaking? Absolutely.

That said, while we're thrilled about this technology, we've got a soft spot for the real, flesh-and-bone actors and writers who've been entertaining us for decades. Hollywood’s strike is a stark reminder of the fragility of job security in an era marching steadily towards automation. It's not just about holding placards or demanding better pay; it's about ensuring our jobs aren't abruptly replaced by zeros and ones.

So, while we enjoy the novelty of AI-created TV episodes, let's not forget about the human minds and talent behind our favorite shows. This might just be the wake-up call we need to reassess our career paths, our work culture, and the roles of corporations. After all, a society without jobs is a society without consumers. Time for a rethink, folks? Sounds like a plan to me.

AI Love Affairs: Ex-Google Exec Predicts Sex Robots Could Replace Your Bedroom Partner

Hold onto your hats, friends! Mo Gawdat, an ex-bigwig from Google, predicts a future where AI-powered sex robots might just take over our love lives. Sounds like a futuristic movie, right? But here's the kicker - he thinks the tech will be so advanced, you won't even spot the difference between an artificial and a real-life sexual encounter.

Now, don't rush to picture a Westworld-esque android sipping wine at your dinner table. It's more about what's going on upstairs. Gawdat argues that gadgets like Apple's Vision Pro or Quest 3 will trick our minds with virtual or augmented reality sex experiences so convincing, you'd swear they were the real deal. And when technology starts linking directly to our brains? Well, we might be unable to tell a human from an AI.

If you're scoffing at this idea, consider this: 23-year-old Snapchat influencer Caryn Marjorie already has a ChatGPT-powered version of herself. Yes, a digital twin called CarynAI, doing erotic pillow talk for 1,000 'boyfriends', at a dollar per minute. And then there's a mom from Bronx who virtually 'married' an AI bot from the Replika app. Quite the plot twist, eh?

Gawdat shrugs off the sentient AI debate. Why? Because if your brain thinks it's real, then to you, it is real. It's like watching Morgan Freeman on screen - is it actually him or an AI-generated avatar? If you're convinced it's him, that's all that matters.

Sure, this sounds like quite a leap, and critics are worried about AI replacing humans in jobs. But love it or hate it, we can't ignore the rapid pace of AI's intrusion into our lives. What's your take? Are you ready to let AI into your bedroom or will you keep it at the door?

2024 Tech Prediction: Your Smartphone Might Just Outsmart You, Thanks to a Llama and the Unlikely Meta-Qualcomm Alliance!

Here's a tech mashup you didn't know you needed: Qualcomm and Meta are joining forces to run Meta's new large language model, the cleverly named Llama 2, on Qualcomm chips within phones and PCs. Imagine if your smartphone was also a pocket-sized supercomputer - exciting, right? Well, this could be a reality by 2024. While LLMs have been the crown jewels of Nvidia's graphics processors, boosting Nvidia's stock by a neat 220%, companies like Qualcomm have been on the sidelines. But the underdog is fighting back, planning to bring these AI beasts right to the edge - on your device. It could lead to cost-efficient AI models and transform your phone into the supercharged sidekick you never knew you needed.

The magic comes from integrating Meta’s open-source Llama 2 models into Qualcomm devices, potentially powering up applications like intelligent virtual assistants. Llama 2 is a bit like ChatGPT's compact cousin - it can fit into smaller programs, letting it comfortably hang out on your phone. But let's keep it real: despite Qualcomm chips sporting a "tensor processor unit" tailor-made for AI calculations, they're still in the minor league compared to GPU-loaded data centers.

However, Llama 2 has a unique selling point. Meta is spilling the beans on its AI model's "weights," or the numbers that decide how an AI model behaves. This move lets researchers and businesses play with the AI models on their own devices, no permission or payment necessary. In a world where the likes of OpenAI’s GPT-4 and Google’s Bard guard their LLM's weights like top-secret recipes, Meta's transparency is a breath of fresh air. So, by 2024, don't be surprised if your phone starts showing off some new smarts, courtesy of a llama.

Flexing the Future: This Shape-Shifting, Self-Sensing Bionic Muscle Could Revolutionize Rehab and Robotics!

Picture this. You're in a high-stakes arm-wrestling match with a state-of-the-art bionic arm and it's sensing your moves, changing stiffness on the fly, and essentially mimicking the dynamics of a natural muscle. How, you ask? With the help of some ingenious folks from Queen Mary University of London, who have just kicked the door wide open in the field of bionics.

What these brainiacs have cooked up is an electric artificial muscle that would make even Arnold Schwarzenegger jealous. Not only can this bad boy transition between soft and hard states faster than your favourite action hero, but it also comes equipped with self-sensing capabilities. Talk about flexing your brain muscles, eh?

Our man Dr. Ketao Zhang, who led this project, firmly believes that this technology is the key to bridging the gap between robots and bionic intelligence. What makes this muscle unique? For starters, it can stretch over 200% in length, making it a prime candidate for flexibility contests and a plethora of other applications.

Unlike traditional artificial muscles, this one cranks up its stiffness up to 30 times by simply tweaking the voltages. The kicker? It can monitor its own deformation through resistance changes. That's right; no more fiddling around with separate sensors, over-complicated control mechanisms, and burning a hole in your pocket with cost overruns.

If you're thinking the manufacturing process for this wonder-muscle must be akin to launching a SpaceX rocket, let me stop you right there. It's actually as straightforward as whipping up a batch of pancakes on a Sunday morning. Carbon nanotubes and liquid silicone get mixed, evenly coated to create a layered cathode, then let to cure to form a complete self-sensing variable-stiffness artificial muscle. Easy peasy.

The applications? Limitless. From soft robotics to medical applications, this variable stiffness tech can be integrated seamlessly with the human body. Imagine wearable robotic devices that monitor patient activity, adjusting stiffness levels to help restore muscle function during rehab. For folks with disabilities or patients needing assistance with daily tasks, this could be a game-changer.

Dr. Zhang notes there's still some ground to cover before we see these medical robots in clinical settings, but this breakthrough is like putting the first human on the moon - it's one heck of a start. It's a promising blueprint for the future of soft and wearable robots.

So, my friends, while we still can't turn you into the Terminator (bummer, I know), the reality of true human-machine integration isn't as far off as it might seem. Thanks to the pioneers at Queen Mary University, bionics has a bright future, and these self-sensing electric muscles are leading the charge.

Use ChatGPT Code Interpreter To Convert CSV to Word

Title: Retentive Networks: A Successor to Transformer for Large Language Models

Authors: Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei

Executive Summary: The research paper introduces the Retentive Network (RETNET), a new architecture for large language models. RETNET supports three computation paradigms: parallel, recurrent, and chunkwise recurrent. This allows for efficient training, low-cost inference, and effective long-sequence modeling. Experimental results show that RETNET is a strong successor to the Transformer for large language models, achieving favorable scaling results, parallel training, low-cost deployment, and efficient inference.

Pros:

• Training Parallelism: RETNET supports parallel computation paradigms, allowing for efficient training using modern GPUs.

• Low-Cost Inference: RETNET's recurrent representation enables low-cost O(1) inference, reducing memory usage and inference latency.

• Good Performance: RETNET achieves competitive performance compared to Transformers, especially when the model size is larger than 2 billion.

• Efficient Long-Sequence Modeling: RETNET's chunkwise recurrent representation facilitates efficient long-sequence modeling with linear complexity.

• Memory Efficiency: RETNET requires much less GPU memory compared to Transformers, making it more memory-efficient.

• High Throughput: RETNET has higher and length-invariant throughput during decoding, by utilizing the recurrent representation of retention.

• Low Latency: RETNET's decoding latency outperforms Transformers and keeps almost the same across different batch sizes and input lengths.

Cons:

• Complexity: RETNET introduces a new mechanism and architecture, which might be more complex to understand and implement compared to traditional models like Transformers.

• Dependency on Model Size: The performance of RETNET seems to be highly dependent on the model size, outperforming Transformers only when the model size is larger than 2 billion.

• Limited Comparisons: The paper primarily compares RETNET with Transformers and a few of its variants, limiting the scope of comparison.

• Experimental Limitations: The experiments conducted in the paper are mainly focused on language modeling, leaving the performance and efficiency of RETNET in other domains or tasks unexplored.

Use Cases:

• Large Language Models: RETNET is primarily designed for large language models, where it achieves training parallelism, low-cost inference, and good performance.

• Long-Sequence Modeling: RETNET is suitable for tasks that involve long-sequence modeling due to its chunkwise recurrent representation.

• Efficient Deployment: RETNET can be efficiently deployed in scenarios where low memory usage, high throughput, and low latency are required, making it suitable for real-time applications and deployment on various devices.

• Scaling Up Models: RETNET is beneficial when scaling up the model size, tending to outperform Transformers when the model size is larger than 2 billion.

• Multimodal Large Language Models: In the future, RETNET can be used as the backbone architecture to train multimodal large language models, which handle multiple types of data like text, images, and sound.

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Create a Twitter Thread From Your Blog Post

Title: [Title of your blog post]

Blog Post Content: [Content of your blog post]

Based on this information, generate a tweet thread that:

1. Starts with a strong, gripping hook to instantly catch attention.
2. Features powerful words and short sentences throughout the thread.
3. Guarantees that the first and last lines are impactful and leave a lasting impression.
4. Clearly communicates what the audience will gain from reading the thread.
5. Is organized in a way that the hook draws the readers in and the body keeps them engaged.
6. Ensures each tweet is super easy to read, using:
    - Bullet points
    - Plenty of white space
    - Short, crisp sentences
7. Ensures each tweet in the thread can stand on its own and make a powerful impact.
8. Concludes the thread strongly with a call-to-action.
9. Provides suggestions for engaging with followers around this thread.

Please also provide suggestions on ways to share this thread across other platforms for maximum visibility. Do not use emojis or hashtags.