🌿 The Quest for Authenticity in an AI-Synthetic WorldPlus: ChatGPT is Getting Lazy with us and Google solves “unsolvable” Math Problems with AI.
Happy Friday, The rapid release of AI tools is increasingly shaping the online world into a realm of inauthenticity, sparking a growing desire for genuine, real-world interactions. ChatGPT is getting lazy and its competitors know it. Also, we have DeepMind’s new stride as well as Tools of the day and where the money is going in this industry.
💪🏽 Important AI News and Trends.
🌐 Web3 and Other Tech news.
The Quest for Authenticity in an AI-Synthetic World
Generative AI tools like Instagram's backdrop, Snapchat's image generation, and chatbots are making it easy for anyone to create fake, AI-generated images, videos, and conversations. Generative AI tools flood social media with realistic fake images, videos, and conversations engineered to manipulate experiences to maximize user engagement, including AI "super-influencers" tailored to each user. The online world is becoming profoundly inauthentic; this rising tide of artificial and fabricated content optimized to addict and exploit is likely to exacerbate existing issues like political polarization, fragmented social bonds, adolescent mental health crises, and the ability of authoritarian regimes to control their populations. In response, calls for increased accountability measures around content authentication, transparency, legal liability, age verification, and more underscore a growing consensus that restoring a shared sense of truth and social cohesion is an urgent priority; meanwhile, people are also likely to increasingly seek out more genuine in-person connections and experiences to counterbalance the artificiality of our tech-mediated lives. The very technologies that threaten to undermine trust and social fabrics may also drive a rising desire for authenticity. 💡 🚀 Turn Your Website Into a Lead Generation Machine
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ChatGPT is Getting Lazy on us.OpenAI has confirmed user observations that ChatGPT's responses have become less effective, a situation labeled as 'laziness'. This acknowledgment came after user reports on platforms like the ChatGPT subreddit, where users noticed diminished quality in ChatGPT's interactions, including incomplete or lackluster responses. OpenAI identified the issue may be linked to the last update on November 11. They are investigating the root cause, noting that changes in model behavior can be subtle and affect only certain prompts. OpenAI needs to find a solution fast because technology is cyclical and companies are recyclable. We have Claude's latest model as well as Mistrals that offer very attractive technology and all other tech behemoths are working hard at taking ChatGPT crown. They should not get complacent when 2024 will be the year of ubiquitous AI. This recent acknowledgment, however, confirms a real change in ChatGPT's performance, though the exact cause remains unspecified. Follow the Money
Google solves “unsolvable” Math Problems with AI
Google DeepMind has achieved a significant milestone by using a large language model, named FunSearch, to solve a renowned unsolved problem in pure mathematics. This success marks the first instance where a large language model has uncovered a solution to a major scientific challenge, producing novel and verifiable information not previously known. FunSearch, an AI tool based on Google's PaLM 2 and fine-tuned on computer code, differs from DeepMind's previous AI systems like AlphaTensor and AlphaDev, which were more limited in application. By generating and then refining millions of suggestions, FunSearch arrived at a correct and previously unknown solution to the "cap set problem," a complex mathematical challenge. Additionally, FunSearch displayed its versatility by addressing the "bin packing problem," offering a faster solution than existing human-devised methods. This breakthrough not only demonstrates the potential of large language models in scientific discovery but also opens new avenues for their application in a variety of problems. Cheat Sheet of the Week
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🤔 What Does that even Mean?Transfer Learning: A method in machine learning where a model developed for one task is reused as the starting point for a model on a second task. It's particularly useful when there's a limited amount of data available for the second task. Example: A company repurposes an AI model trained to identify cars in photos for a new task: recognizing motorcycles. Using transfer learning, they adapt the existing car model with a smaller motorcycle image dataset, speeding up the training process despite limited data. 📰 Publications I am currently reading and recommending:
🧰 AI Tools of the Day3D Models.
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Friday, December 15, 2023
🌿 The Quest for Authenticity in an AI-Synthetic World
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