AI advancement poses a significant threat to the adtech industry, with its ability to filter out ads, potentially reducing the $1 trillion in annual revenue that companies like Google, Meta, and TikTok currently enjoy. This series explores how AI disrupts the crucial ad inventory by appealing to consumers' desire for ad-free content and questions the stability of Big Tech's business models. The analysis delves into AI's effects on ad consumption, which has implications for major players like OpenAI, Microsoft, Apple, Meta, and Alphabet.
Elon Musk's lawsuit highlighted OpenAI's departure from its original open-source ethos to a more closed, profit-driven model that contradicts its founding principles. An email between OpenAI cofounder Ilya Sutskever and Musk from 2015 suggests that OpenAI knew early on that it would deviate from its stated mission. Criticism mounts as OpenAI faces accusations of failing to correct public misperceptions, enabling potentially harmful AI outputs and veering away from its nonprofit origins.
The release of Claude 3 has been quite popular, but the prompting style for these models is slightly different. Anthropic has collected a set of user prompts that work well for a wide variety of tasks and topics.
Apple researchers have developed methods for training large language models on both text and images, leading to state-of-the-art performance in multimodal AI tasks.
Built on top of Stable Video, Stable Video 3D can generate 3D models from a single image. It outperforms Stable Zero 123 and other methods. Weights are available for research purposes and commercial use with a Stability AI membership.
Next token prediction is a simple objective that leads to complex behaviors. This work found that a single self attention layer trained with gradient descent broke the problem down into hard retrieval and soft composition, which enabled in-context learning and strong overall performance.
A weird fact of modern language modeling is that we train a tokenizer first before training the model. The second weird fact is that vocab size doesn't seem to matter too much at large scales.
SPEAKERS: Dr. Ali Arsanjani, Ph.D., Director of Engineering @ Google; Alex Watson, Co-Founder and Chief Product Officer @ Gretel; Maarten Van Segbroeck, Ph.D., Principal Applied Science @ Gretel
WHAT: This workshop will showcase an end-to-end machine learning approach [10] to automatically anonymize sensitive customer data in financial documents, including:
This project introduces a new method for identifying objects in images taken from various spectrums like RGB, near-infrared, and thermal imaging, focusing on object-centric information to overcome background noise and improve recognition accuracy.
A new companion website, More Useful Things, offers a trove of free AI and machine learning resources, underlining the quirky and effective ways AI-generated prompts, like imaginative scenarios, can outperform human-crafted ones in tasks such as mathematical problem-solving. The experiment underscores the importance of adding context, few-shot learning, and chain-of-thought techniques for more consistent prompting results. As AI models advance, prompting as a skill may become less critical, with AI improving at inferring user intent, but currently, structured prompting remains an evolving art with significant potential benefits.
Recent studies suggest human prompt engineers may become obsolete as AI and machine learning models increasingly optimize their own prompts. Algorithmically generated prompts can be bizarre yet effective, outperforming human-crafted ones and reducing optimization time dramatically. While automatically tuned prompts show promise, experts believe that the demand for prompt-related jobs will evolve rather than disappear, potentially under new job titles like LLMOps (Large Language Model Operations).
Copilot Pro users can still opt for the older model and they also have access to Copilot GPT Builder for creating custom chatbots without programming expertise.
KAIST researchers have developed the C-Transformer, an ultra-low-power AI chip for large language models that boasts significant energy efficiency improvements over Nvidia's A100 GPU.
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xAI has released the weights and architecture of its 314 billion parameter Mixture-of-Experts model, Grok-1. It is written in JAX and uses a modern Transformer architecture with GeGLU, ROPE, sandwich Norm, and other niceties.
OpenAI appears to have accidentally published a blog post that was indexed by Bing and DuckDuckGo before it was quickly taken down. The post was an announcement for GPT-4.5 Turbo, a new model that surpasses GPT-4 Turbo in speed, accuracy, and scalability. The cached description mentions a knowledge cutoff date of June 2024 and a 256k context window. OpenAI has not commented on the leak.
Anthropic's Claude 3 Haiku is its fastest and most cost-effective AI model. It features advanced vision capabilities and excels in benchmarks. Claude 3 Haiku is designed for enterprises, with a focus on speed and affordability.
We can find some interesting trends by looking at GitHub stars for popular AI models. Most open-source AI tooling seems to be focused on infrastructure and applications.
Cappy is a small model trained to take instructions and a candidate completion and return a score for how well the completion fulfills the instructions. It outperforms much larger models at this task, suggesting it may be used as a feedback mechanism for training and generation.
ScoreHMR introduces a novel method for 3D human pose and shape reconstruction by leveraging the power of diffusion models. This approach, which bypasses traditional optimization techniques, uses score guidance to align a human body model with image observations.
Exploring the "speed of light" theoretical limit for transformer-based language model inference, this article highlights the importance of memory bandwidth over computational power, demonstrating that inference speed is primarily constrained by the ability to read data from memory rather than perform calculations, a key factor for optimizing and understanding AI performance.
The author of this article proposes differentiating AI discussions by using terms like "harmful," "helpful," "capable," and "incapable" rather than the vague good/bad spectrum. They personally view AI as capable but potentially harmful due to unresolved issues such as copyright infringement and bias exaggeration. The author invites readers to clarify their own positions on AI using these more specific terms.
This article reflects on the historical pattern of technological innovations impacting jobs, with the rise of AI being the latest example. It compares past shifts, like the advent of writing, printing, and typewriters, to the current AI revolution, pondering whether AI will ultimately enhance or diminish human creativity. The author calls for resistance and adaptation to preserve the uniquely human element in creative endeavors amidst the growing influence of AI-generated content.
kapa.ai offers insights on structuring documentation for LLMs, advocating for clear hierarchy, segmentation by sub-products, inclusion of troubleshooting FAQs, self-contained code snippets, and the establishment of community forums.
Research by Incogni [4] reveals that imposter calls (impersonating a person, agency, or company) were a major problem in 2023. AI makes it easier for bad actors to aggregate user information for robocalls, imposter calls, and other types of fraud [4]. Deep fake technology is bound to make this worse.
OpenAI has announced partnerships with Le Monde and Prisa Media involving the integration of their content into ChatGPT to provide users with interactive and insightful access to news and to aid in model training.
The European Parliament has approved the world's first comprehensive AI regulatory framework, setting global standards for artificial intelligence, categorizing risks, and aiming for innovation alongside fundamental rights protection, with staggered implementation starting in 2025.
Figure has implemented OpenAI's technology to allow its robots to have full conversations with people. OpenAI's models provide high-level visual and language intelligence while Figure's neural networks deliver fast, low-level dexterous robot actions. This X post contains a video showing a person talking to a Figure robot and getting it to perform tasks, explain the reasoning behind the tasks, and self-assess on how the tasks went.
Evaluating language models trained to code is a challenging task. Most folks use HumanEval from OpenAI. However, some open models seem to overfit to this benchmark. LiveCodeBench is a way to measure coding performance while mitigating contamination concerns.
Researchers in a recent study used a virtual team called 'Evil Geniuses' to test the safety of LLMs. They found that these AI agents are less robust against malicious attacks, provide more complex responses, and make it harder to detect inappropriate replies.