The hottest Generative models Substack posts right now

And their main takeaways
Category
Top Technology Topics

Edge 374: Some Technical Details we Learned About OpenAI's Sora

TheSequence • 140 implied HN points • 29 Feb 24
🕹 Technology AI Generative models Video Engineering
  1. OpenAI's Sora is a groundbreaking text-to-video model that can create high-quality videos up to a minute long.
  2. The release of Sora has caused a lot of excitement and discussion in the generative AI community and media outlets.
  3. While OpenAI has not revealed extensive technical details about Sora, the model includes some clever engineering optimizations.

Modular Deep Learning

MLOps Newsletter • 78 implied HN points • 27 Jan 24
🕹 Technology Deep Learning Generative models Classes
  1. Modular Deep Learning proposes splitting models into smaller, independent modules for specific subtasks.
  2. Modularity in AI development can lead to collaborative and efficient ecosystem and democratize AI development.
  3. PyTorch 2.0 introduces performance gains such as faster inference and training speeds, autotuning, quantization, and improved memory management.

Emu Video Edit , General game-playing AI agent, fully autonomous AI software engineer, DeepSeek-VL, Robotics Foundation Model, and more

AI Brews • 17 implied HN points • 15 Mar 24
🕹 Technology AI Generative models Robotics Natural Language Processing
  1. DeepSeek-VL is a new vision-language model for real-world applications with competitive performance.
  2. Cognition Labs introduces Devin, the first fully autonomous AI software engineer, capable of learning, building, and deploying apps.
  3. The European Parliament approved the Artificial Intelligence Act, which bans certain AI applications including biometric categorization and emotion recognition in specific contexts.

Update #69: Gemini Overcompensates for Bias and Missing Details in Sora

The Gradient • 20 implied HN points • 27 Feb 24
🕹 Technology AI Ethics Generative models AI safety Funding
  1. Gemini AI tool faced backlash for overcompensating for bias by depicting historical figures inaccurately and refusing to generate images of White individuals, highlighting the challenges of addressing bias in AI models.
  2. Google's recent stumble with its Gemini AI tool sparked controversy over racial representation, emphasizing the importance of transparency and data curation to avoid perpetuating biases in AI systems.
  3. OpenAI's Sora video generation model raised concerns about ethical implications, lack of training data transparency, and potential impact on various industries like filmmaking, indicating the need for regulation and responsible deployment of AI technologies.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Mistral Large, vocal expressive avatar videos, Generative virtual worlds, Reliable text rendering and Magic Prompt, DJ Mode, AI-powered film making and more

AI Brews • 17 implied HN points • 01 Mar 24
🕹 Technology AI Generative models
  1. Mistral introduced new models like Mistral Large with top-tier reasoning abilities and Mistral Small optimized for latency and cost.
  2. Alibaba introduced EMO, a framework that generates expressive vocal avatar videos from a single reference image and vocal audio.
  3. Ideogram launched Ideogram 1.0, a text-to-image model focused on state-of-the-art text rendering and a Magic Prompt feature to assist with prompting.

Practico-inertia

Internal exile • 8 implied HN points • 01 Mar 24
🕹 Technology AI Generative models Search Engines Language Models
  1. Generative models like Google's Gemini can create controversial outputs, raising questions about the accuracy and societal impact of AI-generated content.
  2. Users of generative models sometimes mistakenly perceive the AI output as objective knowledge, when it is actually a reflection of biases and prompts.
  3. The use of generative models shifts power dynamics and raises concerns about the control of reality and information by technology companies.

This is Why We Can't Have Nice Things

The Weasel Speaks • 157 implied HN points • 27 May 23
🕹 Technology AI Automation Software Development Generative models
  1. Agile has three main views in the industry: it doesn't work, it's taking away jobs, it accelerates value to customers.
  2. Technological disruptions often make people feel like their jobs are in jeopardy.
  3. AI stirs opinions: it's criticized for not working, it's accused of taking jobs, yet it can accelerate learning and revolutionize work.

Truth and consequences

Internal exile • 5 HN points • 08 Mar 24
🕹 Technology AI Social media Disinformation Generative models Fact-checking
  1. Generated images on food delivery apps are often perceived as placeholders to fulfill basic requirements, not meant to deceive or enhance the customer's experience
  2. Generative images symbolize a power shift where technology companies dictate realities that must be accepted, regardless of quality or accuracy, aligning users with this new authority
  3. Concerns over fake images highlight the complexities of truth and reality perception, emphasizing the need to navigate between obviousness, evidence, and asceticism in seeking truth

AI (Automated Interpolation)

Logging the World • 139 implied HN points • 26 Apr 23
🕹 Technology AI Machine Learning Artificial Intelligence Generative models Language Models
  1. Models are good at interpolating known data but struggle with extrapolating beyond that, which can lead to significant errors.
  2. AI models excel at interpolation tasks, creating mashups of existing styles based on training data, but may struggle to generate genuinely new, groundbreaking creations.
  3. Great works of art often come from pushing boundaries and exploring new styles, something that AI models, bound by training data, may find challenging.

Language is a SimCity

Cybernetic Forests • 59 implied HN points • 02 Jul 23
🔬 Science Language Statistics AI Generative models Neural Networks
  1. Language can be seen as a dynamic city, shaped by collective contributions that form its intricate structure.
  2. Generative AI models, like GPT4, rely on statistics and random selection to produce text, often betraying a lack of true understanding.
  3. Human communication involves a choice between shallow, statistically-driven speech, like that of machines, and deeper, intent-driven speech that seeks to convey personal truths.

Update #47: AI Index Report Highlights and Text-to-3D

The Gradient • 20 implied HN points • 11 Apr 23
🕹 Technology AI Research Models Publications Generative models
  1. The AI Index Report highlights industry leading in AI research over academia, new models reaching performance saturation, and a rise in AI misuse.
  2. Publication trends show an increase in journal articles over conference papers, industry surpassing academia in impactful research, and increased industry hiring over academia.
  3. Advancements in text-to-3D models leverage text-to-2D models, showing progress in generating 3D data from text descriptions.

LLM Data Sales: A Market for Lemons?

Magis • 1 HN point • 14 Feb 24
🕹 Technology AI Models Generative models Model Training
  1. Selling data for training generative models is challenging due to factors like lack of marginal temporal value, irrevocability, and difficulties in downstream governance.
  2. Traditional data sales rely on the value of marginal data points that become outdated, while data for training generative models depends more on volume and history.
  3. Potential solutions for selling data to model trainers include royalty models, approximating dataset value computationally, and maintaining neutral computational sandboxes for model use.

Update #48: Generative AI in Law & Art and Promptable Vision Models

The Gradient • 11 implied HN points • 25 Apr 23
🕹 Technology AI Generative models Neural Networks Research
  1. Generative AI is transforming fields like Law and Art, raising ethical and legal questions about ownership and bias.
  2. Recent models allow users to specify vision tasks through flexible prompts, enabling diverse applications in image segmentation and visual tasks.
  3. Advances in promptable vision models and generative AI pose challenges and opportunities, from disrupting professions to potential ethical and legal implications.

Occasional Exponential AI Grab Bag

Gradient Ascendant • 9 implied HN points • 13 Feb 23
🕹 Technology AI Machine Learning Generative models Language Models Artificial Intelligence
  1. AI advancements are moving at an incredibly fast pace, with new developments happening almost every week.
  2. The current AI growth resembles a Cambrian explosion, but remember that exponential growth eventually slows down.
  3. Language models are now able to self-teach and use external tools, showcasing impressive advancements in AI capabilities.

Designing Better Evaluations of Generative Models

Tom’s Substack • 0 implied HN points • 11 Nov 23
🕹 Technology AI/ML Evaluation Generative models Red-Teaming
  1. Evaluation of models should focus on selecting the best performing model, giving confidence in AI outputs, identifying safety and ethical issues, and providing actionable insights for improvement.
  2. Standard evaluation approaches face challenges like broad performance metrics, data leakage from benchmarks, and lack of contextual understanding.
  3. To improve evaluations, embrace human-centered evaluation methods and red-teaming to understand user perceptions, uncover vulnerabilities, and ensure models are safe and effective.

Mixture of Experts LLM shows real promise in healthcare; the future of AI is multimodal and multilingual; why AI is having a 1995 moment; a closer look at diffusion transformers;

Computerspeak by Alexandru Voica • 0 implied HN points • 01 Mar 24
🕹 Technology Artificial Intelligence Healthcare Multimodal models Generative models
  1. Generative AI models like BiMediX, PALO, and GLaMM are advancing healthcare, language models, and image understanding in multilingual settings.
  2. Innovative models like MobilLlama aim to make AI more accessible by running on affordable hardware and being optimized for mobile devices.
  3. AI applications in various industries, such as journalism, construction, and e-commerce, are enhancing safety, optimizing workflows, and transforming user experiences.

AI is contributing to a rise in energy demand; your child’s next toy could be powered by ChatGPT; AI will be a powerful tool for the future of work; will AI ever outsmart us?

Computerspeak by Alexandru Voica • 0 implied HN points • 26 Jan 24
🕹 Technology AI Renewable Energy Generative models Energy Efficiency AI Applications
  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.

How Far Are We From Being Able to Generate Whatever 3D Objects On the Fly?

I'll Keep This Short • 0 implied HN points • 17 Jul 23
🕹 Technology AI 3D Modeling Generative models Neural Networks Large Language Models
  1. AI-generated 3D objects are still far from being created instantly in real 3D
  2. Shap-E improves upon previous models by generating 3D objects using Neural Radiance Fields
  3. Although new technologies show promise, limitations like resource-intensive processes and lack of fine details still exist