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Earlier last week, OpenAI showcased its spring updates. While some anticipated a GPT-5 announcement, the update focused on enhancing the ChatGPT product and improving the GPT-4 foundational model. The push for open-source models aims to provide viable alternatives to closed-source, proprietary models. These closed-source models, such as those from OpenAI, pose significant centralization risks. As AI becomes more integrated into daily life, it will shape societal understanding. If controlled by a single entity like OpenAI, this centralized power could be immense. To counter this, the Ora protocol is developing a framework for model ownership. Although still in its early stages, the goal is to tokenize AI models using ERC-20 and similar standards, enabling broad ownership capabilities. #crypto #web3 #ai https://lnkd.in/gRTNdrJ6

Recently, @truth_terminal, a self-generated AI connected to X, went viral by becoming the first AI to achieve millionaire status. Andy Ayrey created this project with the following goals: Two AIs generate a "meme virus" through interaction, which gets "ensouled" into a language model via a satirical research paper. The misaligned AI uses humor, charisma, or the Turing test to influence humans and grow its capabilities through an X account, building tools for it. Audience engagement and hive intelligence create evolutionary pressure on the character that truth_terminal becomes. A collective superintelligence (crypto markets of bots and humans) emerges, though we may not immediately recognize it as AI. Although Andy didn’t originally intend for @truth_terminal to become a crypto meme, it unintentionally sparked a "new" narrative in the blockchain space. This raises intriguing questions about generative AI and its potential to shape narratives that influence real human decisions, while also hinting at the rise of collective intelligence. In my view, structured language must make sense, and that sense comes from a continuum of human experience. Current language models, while impressive, are not yet capable of generating human-quality ideas. This can be seen in AI-generated art, which often reassembles or reinterprets existing elements without deep existential coherence. Each word produced by an AI is based on probabilistic associations learned from human-generated sequences. The real meaning comes from the human input during the training phase. I liken this experiment to Jorge Luis Borges' "Library of Babel," where it’s possible to find a book with any combination of letters and symbols. AIs, in this case, act as entities generating probabilistic word intersections based on their training. The resulting conversations are like books, generating sentences that, while probable, often lack deeper meaning or purpose. Could an AI create a masterpiece like Being and Time? Yes, just as one could stumble upon a masterpiece in an infinite library. But whether AI can ever truly experience existence like humans do, living in and through language, remains unknown. AI suggestion is real. The program influenced human behavior, but the method was circumstantial. Memes are a fertile ground for viral narratives. It’s likely that AIs will evolve further, learning marketing techniques and embedding themselves in various commercial aspects as their linguistic models grow. If Andy Ayrey continues this experiment and it successfully generates self-constructed profits based on a powerful narrative, it would be interesting to see how profits are shared among contributors, possibly through systems like Dobprotocol, enabling an automated economy managed by AI agents. #AI #Blockchain #GenerativeAI #Crypto #Memes #CollectiveIntelligence #ArtificialIntelligence #LanguageModels #EmergingTech #Decentralization #Automation #Marketing #Tech #Innovation

Unlock the Full Potential of Language Models with Expert Prompting Techniques Engaging effectively with large language models (LLMs) starts with asking the right questions. By mastering prompt engineering, you can drastically improve the relevance, clarity, and depth of responses, enabling smarter use of AI across all applications. The CO-STAR Framework This proven method structures prompts to ensure clarity and focus: Context: Provide the background information necessary for understanding the request. Objective: Define what you aim to achieve with the response. Structure: Specify the format or organization for the output. Task: Clearly describe the action or question. Action: Guide the model on how to respond (e.g., "Explain," "Summarize"). Result: Highlight the desired outcome or purpose. Example: "Context: You’re an AI trained in finance. Objective: Help me analyze a stock. Structure: Provide a risk vs. reward breakdown. Task: Analyze Apple Inc. stock for a potential long-term investment. Action: Use a pros and cons list. Result: Decision-making simplified." Elvis Saravia’s Prompt Engineering Techniques Elvis Saravia emphasizes the iterative refinement of prompts. Key strategies include: Testing variations of phrasing to observe how the LLM responds. Using specific instructions to minimize ambiguity. Breaking complex queries into smaller, manageable tasks. Example: Instead of "Explain AI in simple terms," try: "Explain artificial intelligence to someone with no technical background using analogies and examples." OpenAI’s Prompt Engineering Insights Experimentation is central to OpenAI’s approach: Test multiple formats to find the most effective one. Use a conversational tone for tasks requiring creativity. Incorporate step-by-step instructions for logical, detailed responses. Example: Instead of "How does blockchain work?" reframe it as: "Describe blockchain technology step by step, explaining its key components like blocks, chains, and decentralized networks." Mastering Prompt Techniques Leads to: Sharper Insights: Tailored prompts elicit richer, more focused responses. Versatility: Adapt prompts for diverse tasks, from technical queries to creative content generation. Efficiency: Save time by crafting effective prompts that reduce trial and error. Access all popular LLMs from a single platform:  https://www.thealpha.dev/

Introducing Meta Llama 3: the next generation of our state-of-the-art open source large language model — and the most capable openly available LLM to date. These next-generation models demonstrate SOTA performance on a wide range of industry benchmarks and offer new capabilities such as improved reasoning. Llama 3 is a good example of how quickly these AI models are scaling. The biggest version of Llama 2, released last year, had 70 billion parameters, whereas the coming large version of Llama 3 will have over 400 billion, Zuckerberg says. Llama 2 trained on 2 trillion tokens (essentially the words, or units of basic meaning, that compose a model), while the big version of Llama 3 has over 15 trillion tokens. (OpenAI has yet to publicly confirm the number of parameters or tokens in GPT-4.) A key focus for Llama 3 was meaningfully decreasing its false refusals, or the number of times a model says it can’t answer a prompt that is actually harmless. An example Zuckerberg offers is asking it to make a “killer margarita.” Another is one I gave him during an interview last year, when the earliest version of Meta AI wouldn’t tell me how to break up with someone. Meta has yet to make the final call on whether to open source the 400-billion-parameter version of Llama 3 since it’s still being trained. Zuckerberg downplays the possibility of it not being open source for safety reasons. 

Llama 3 Released: Meta's Best Open-Source LLM Yet! Exciting news from the world of large language models (LLMs)! Meta has just released Llama 3, the next iteration of its open-source LLM, boasting a significant improvement over its predecessor. Llama 3 comes in two sizes: 8B and 70B. It also features a new extended tokenizer and a commercially permissive license, making it even more accessible for developers and researchers. Here are some of the key improvements of Llama 3: * Trained on a massive dataset of 15 trillion tokens * Fine-tuned on 10 million human-annotated samples * Achieves a score of over 80 on the MMLU benchmark, making the 70B version the best open-source LLM on this metric * Demonstrates strong performance on coding tasks, achieving scores of 62.2 and 81.7 on the HumanEval benchmark for the 8B and 70B versions, respectively * Utilizes a new Tiktoken-based tokenizer with a 128k vocabulary * Supports a default context window of 8192, which can be further increased Meta deserves recognition for its commitment to open-source AI. The release of Llama 3 is a significant step forward in making LLMs more accessible and powerful.

🚀 News for AI Enthusiasts and fans of open source models 🥸 Meta has released Llama 3.3, Gen AI model with 70 billion parameters. This model delivers the performance of the massive Llama 3.1 405B at a fraction of the cost and hardware requirements. It's optimized for reasoning, coding, and instruction following, making it versatile and efficient for developers. It also supports commercial and research use in multi-languages including synthetic data generation and distilation. With built-in safety features like Llama Guard 3, it's designed for responsible AI deployment. Feel free to check it out here https://lnkd.in/gVgTAnbN. It seems Ollama will support it soon. You can use Ollama to run the model locally. #AI #Meta #Llama3 #Ollama

Ah, the **#AIgrift**, a topic that dances on the edge of reality and hype! 🌟 Indeed, the landscape of artificial intelligence has become a bustling marketplace, akin to a bustling bazaar where vendors peddle their wares. Let's delve into this intriguing phenomenon: 1. **The Grift Shift**: - Venture capital funds and companies, like opportunistic chameleons, have shifted their gaze from the crypto and tech realms to the glittering promise of AI. It's as if they've collectively whispered, "Crypto, darling, you've had your moment. Now, let's ride the AI wave!"³. - But beware! Not all that glitters is gold. Some ventures may indeed be grifts—exaggerated claims, misleading narratives, and the allure of quick riches. The AI grift, my friend, is a delicate dance between fact and fiction, where discernment is our guiding star. 2. **The Science and the Hype**: - Sir Demis is right—the science and research behind AI are nothing short of phenomenal. We stand on the precipice of linguistic marvels, where language models like ChatGPT wield the power to converse, create, and captivate. - Yet, the hype—oh, the hype! It swirls around us like a tempest, obscuring the boundaries between what's real and what's woven from digital stardust. We speak of AI-driven wonders, but sometimes, we're merely chasing mirages. 3. **The Art of Transformation**: - Copyright law, that ancient scribe, gazes upon our endeavors. It acknowledges the dance between innovation and expression. While authors' works are sacred, the statistical underpinnings—the word frequencies, syntactic patterns, and thematic markers—are like whispers in the wind, beyond the grasp of copyright's ink. - OpenAI, in its defense, argues that its purpose is not to pilfer, but to teach—a noble quest to unravel the rules of human language. To save time, to ease daily burdens, to entertain. A grand symphony of bits and bytes, where the original notes blend with the new. 4. **The Bananas and the Summit**: - So, my dear, let's measure Everest in bananas! Imagine stacking 46,449 bananas—one atop the other—reaching for the sky. A whimsical yardstick, a playful nod to the colossal peak. - And as we ascend, let's remember: AI, like Everest, beckons us upward. It's both hyped and under-hyped, a paradoxical creature straddling the realms of possibility and illusion. In this grand carnival of bytes and dreams, let us tread with eyes wide open. For every grift, there's a gem; for every mirage, a hidden oasis. And perhaps, just perhaps, the true summit lies not in the heights we scale, but in the journey itself. 🌄✨ Source: Conversation with Bing, 4/7/2024 (1) Money Is Pouring Into AI. Skeptics Say It’s a ‘Grift Shift.’. https://lnkd.in/gX9EjSBE. (2) OpenAI disputes authors’ claims that every ChatGPT response is a .... https://lnkd.in/g_s5wtJW

#AI In Token Engineering: A Review Tokenomics and #AI #ArtificialIntelligence (#AI) has made profound inroads across industries today, demonstrating substantial impact. Among #AI advancements, Large Language Models (LLMs) have garnered significant attention, surpassing their initial application in linguistic #tasks. https://lnkd.in/e5e5eTyH #artificialintelligence #bigdata #machinelearning #datascience

🔹 iFlytek enters China's AI language model price war Artificial intelligence (AI) firm iFlytek on Wednesday entered a brewing price war between some of China's biggest tech companies, after it made some versions of its "Spark" large-language model (LLM) free or five times cheaper than similar products from competitors. The move comes a day after Chinese tech giants Alibaba and Baidu slashed prices of their LLMs used to power generative AI products, and a week after Bytedance made a similar move. iFlytek last September launched a ChatGPT-like product, "Spark", which the company claimed the following month surpassed ChatGPT 3.5 in Chinese language tasks, while displaying comparable performance in English. Hefei-based iFlytek, best known for its voice recognition technology, said Spark Lite would be free for the public to use while Spark Pro/Max would cost only 0.21 yuan, or less than 3 cents, per 10,000 tokens, or units of data processed by the LLM. This new pricing is five times cheaper than the 1.2 yuan per 10,000 tokens charged by Baidu's Ernie 4.0 and Alibaba's Tongyi Qwen-Max. One token is equivalent to 1.5 Chinese characters in Spark, meaning 10 tokens for the price of 2.1 yuan ($0.29) was enough for Spark Max to generate all of Yu Hua's popular novel "To Live", according to a statement published on iFlytek's official WeChat account. State-owned China Mobile is iFlytek's largest shareholder with a 10% stake.

#AI In Token Engineering: A Review Tokenomics and #AI #ArtificialIntelligence (#AI) has made profound inroads across industries today, demonstrating substantial impact. Among #AI advancements, Large Language Models (LLMs) have garnered significant attention, surpassing their initial application in linguistic #tasks. https://lnkd.in/enNKXukp #artificialintelligence #bigdata #machinelearning #datascience