The hottest AI Applications Substack posts right now

And their main takeaways
Category
Top Technology Topics
The Asianometry Newsletter 3553 implied HN points 07 Mar 24
  1. The trillion-dollar investment in AI chips does raise skepticism, with questions about its sustainability and impact on the semiconductor industry.
  2. The concept of scaling laws, driving investments, presents interesting parallels to Moore's Law in the semiconductor industry, suggesting potential future impact on AI.
  3. Competition in AI chips, particularly against Nvidia, is heating up as tech giants aim for vertical integration, potentially shifting the landscape of AI chip design and market dynamics.
Liberty’s Highlights 1041 implied HN points 17 Jan 24
  1. Opportunity cost is often invisible but significant, so consider it in decision-making.
  2. Relative valuation in investing can be misleading, so always dig deeper.
  3. Mixing children of different ages in schools could offer benefits in learning and social development.
The Fintech Blueprint 452 implied HN points 06 Feb 24
  1. Annual card fraud exceeds $33B, with digital wallets, credit, and debit cards projected to handle 86% of global POS payments by 2026.
  2. Mastercard introduced a new AI model, Digital Intelligence Pro, to improve fraud detection by using a proprietary recurrent neural network.
  3. Digital Intelligence Pro aims to reduce false positive fraud flags, leading to better fraud detection rates, potential savings of $90B yearly for merchants, and improved customer experiences.
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Implications, by Scott Belsky 471 implied HN points 19 Dec 23
  1. Society evolves as wild concepts become mainstream, like connected appliances and AI-powered persona designers.
  2. The future of entertainment will focus on shared, authentic, non-scalable experiences over high-tech extravagance.
  3. Scarcity and authenticity will be essential in the next wave of digital experiences, emphasizing uniqueness and community connections.
Artificial Ignorance 67 implied HN points 21 Feb 24
  1. Adding 10x capacity to a system unlocks new capabilities and prevents breaking, leading to fundamental changes.
  2. Gemini 1.5's 10x larger context window enables tasks like analyzing entire codebases, filtering massive datasets, and potentially building AI with better memory.
  3. Groq's custom AI chips achieve lightning-fast token generation, paving the way for real-time AI conversations, enhanced data handling, and possible use in finance, medicine, and robotics.
Artificial Ignorance 42 implied HN points 01 Mar 24
  1. User-generated content companies are capitalizing on the value of their posts and comments by offering them through licensing regimes.
  2. Tech giants like Google, Apple, Microsoft, and others are heavily investing in AI initiatives and tools to advance various industries.
  3. AI advancements are causing concerns regarding bias, safety, and potential misuse in areas like diverse data deals, model releases, and deepfakes.
Artificial Ignorance 54 implied HN points 09 Feb 24
  1. Google rebrands AI product line Bard to Gemini, introducing multiple tiers including a paid plan powered by Gemini Ultra.
  2. Initiatives like the C2PA aim to address deepfake content issues, with companies like Google and OpenAI joining efforts for content authentication.
  3. FCC deems robocalls with fake AI-created voices as illegal, showcasing government agencies taking action on AI regulation independently.
Last Week in AI 176 implied HN points 04 Dec 23
  1. ChatGPT has made a significant impact in the past year with its interactive and conversational dialogue capabilities
  2. Amazon's new AI chatbot Q for companies has faced reliability issues, including hallucinations and data exposure during its public preview
  3. Generative AI, like image generation, consumes significant energy, equivalent to charging a smartphone, prompting a need to consider the environmental impact of AI technologies
Public Experiments 154 HN points 27 Jun 23
  1. Natural language interfaces for AI are challenging due to the vast degree of freedom in text input.
  2. Prompt engineering is crucial for effectively utilizing large language models to ensure correct and meaningful responses.
  3. For most users, interacting with AI systems through buttons and defined interfaces can lead to more efficient and seamless experiences compared to using natural language prompts.
Sudo Apps 121 HN points 06 May 23
  1. Training Large Language Models (LLMs) with new data constantly is impractical due to the vast amount of information and privacy concerns.
  2. OpenAI's focus on improving LLMs in other ways instead of just increasing model size indicates the end of giant model era.
  3. Using tokens, embeddings, vector storage, and prompting can help provide LLMs with large amounts of data for better interpretation and understanding.
The Strategy Deck 39 implied HN points 17 Jul 23
  1. Data labeling is crucial for improving the quality of ML models by adding meaningful labels.
  2. Data labeling tools offer features like support for various data types, collaboration between annotators, and data versioning.
  3. ML platforms for data labeling include multi-modal, general purpose tools for manual labeling and programmatic tools focusing on specific data types and niches.
Public Experiments 2 HN points 16 Feb 24
  1. Many people have yet to experience the impact of AI in their daily lives, indicating that the anticipated AI-driven future is not fully realized yet.
  2. AI tools like ChatGPT and Copilot are currently used by individuals but haven't proliferated widely, with some potential hurdles being the need for broader education and the slow pace of product innovation.
  3. The future of AI products may unfold slowly over the next 5-10 years, with challenges like technical limitations, business viability, and the need for transformative breakthroughs still to be addressed.
Addition 19 implied HN points 07 Jun 23
  1. Brand safety in AI is not a one-size-fits-all concept; it varies based on the specific use case and how AI is implemented.
  2. Design decisions play a crucial role in aligning the level of risk in an AI system with what the organization is willing to accept.
  3. Addressing brand safety creatively involves different approaches like incorporating safety checks, narrow use cases, and extensive testing to mitigate risks.
Machine Economy Press 3 implied HN points 04 May 23
  1. Mojo Programming Language combines Python syntax with the speed of C, making it ideal for AI development.
  2. Mojo is about 35,000 times faster than Python, offering exceptional AI hardware programmability and model extensibility.
  3. Mojo allows writing portable code faster than C, seamlessly inter-operating with the Python ecosystem, and includes features like a unified inference engine and zero-cost abstractions.
DecafQuest's Newsletter 0 implied HN points 01 Jun 23
  1. The author shares personal experiences with coding and how it led to meeting others in the digital landscape.
  2. Louis Pereira's journey demonstrates how entrepreneurship in the digital world can thrive even without a technical background.
  3. Using no-code platforms like Bubble, individuals can create digital assets that require minimal maintenance and can evolve into successful ventures.
Kiernan 0 implied HN points 09 Sep 23
  1. Embedding vectors provide numerical representations for different types of content, allowing for easy comparison and search based on similarity.
  2. Starting with the answer in search means casting a broad net by providing an example of what you're looking for, rather than specific keywords.
  3. By utilizing embedding vectors, search results can be tailored to match concepts or sentiments, making searches more efficient and effective.
Computerspeak by Alexandru Voica 0 implied HN points 26 Jan 24
  1. AI is contributing to a rise in energy demand, leading to challenges like increased electricity consumption and the unexpected need to delay closing coal-fired power plants in some areas.
  2. Investments in renewable energy are on the rise, with more funds now going into clean energy projects compared to traditional fossil fuels, showcasing a positive shift towards sustainability.
  3. Researchers are exploring spiking neural networks inspired by the brain's efficiency to reduce the energy footprint of AI, potentially opening doors to new applications like long-range search and rescue, prosthetics, and edge computing.
Computerspeak by Alexandru Voica 0 implied HN points 19 Jan 24
  1. Artificial intelligence was a dominant topic at the World Economic Forum in Davos, with a focus on safety, responsible adoption, and regulation.
  2. Expectations surrounding generative AI are being tempered as practical real-world applications begin to emerge, following typical cycles of emerging technologies.
  3. AI advancements include DeepMind solving high-school geometry problems, AI-powered functionalities integrated into Samsung phones, and increased focus on regulating generative AI in APAC.