Legal challenges in AI

Summary of weekly AI news featuring Google Cloud's achievements, legislative updates, and technological innovations across the industry.

Last Week in AI: Episode 27

Welcome to another edition of Last Week in AI. From groundbreaking updates in AI capabilities at Google Cloud to new legislative proposals aimed at transparency in AI model training, the field is buzzing with activity. Let’s dive in!

Google Cloud AI Hits $36 Billion Revenue Milestone

Google Cloud has announced significant updates to its AI capabilities at the Google Cloud Next 2024 event, amidst reaching a $36 billion annual revenue run rate, a substantial increase from five years prior.

Key Takeaways:

  • Impressive Growth: Google Cloud’s revenue has quintupled over the past five years, largely driven by its deep investments in AI.
  • Gemini 1.5 Pro Launch: The new AI model, now in public preview, offers enhanced performance and superior long-context understanding.
  • Expanded Model Access: Google has broadened access to its Gemma model on the Vertex AI platform, aiding in code generation and assistance.
  • Vertex AI Enhancements: The platform now supports model augmentation using Google Search and enterprise data.
  • TPU v5p AI Accelerator: The latest in Google’s TPU series offers four times the compute power of its predecessor.
  • AI-Driven Workspace Tools: New Gemini-powered features in Google Workspace assist with writing, video creation, and security.
  • Client Innovation: Key clients like Mercedes-Benz and Uber are leveraging Google’s generative AI for diverse applications, from customer service to bolstering cybersecurity.

Why It Matters

With its expanding suite of AI tools and powerful new hardware, Google Cloud is poised to lead the next wave of enterprise AI applications.

New U.S. Bill Targets AI Copyright Transparency

A proposed U.S. law aims to enhance transparency in how AI companies use copyrighted content to train their models.

Key Takeaways:

  • Bill Overview: The “Generative AI Copyright Disclosure Act” requires AI firms to report their use of copyrighted materials to the Copyright Office 30 days before launching new AI systems.
  • Focus on Legal Use: The bill mandates disclosure to address potential illegal usage in AI training datasets.
  • Support from the Arts: Entertainment industry groups and unions back the bill, stressing the protection of human-created content utilized in AI outputs.
  • Debate on Fair Use: Companies like OpenAI defend their practices under fair use. This could reshape copyright law and affect both artists and AI developers.

Why It Matters

This legislation could greatly impact generative AI development, ensuring artists’ rights and potentially reshaping AI companies’ operational frameworks.

Meta Set to Launch Llama 3 AI Model Next Month

Meta is gearing up to release Llama 3, a more advanced version of its large language model. Aiming for greater accuracy and broader topical coverage.

Key Takeaways:

  • Advanced Capabilities: Llama 3 will feature around 140 billion parameters, doubling the capacity of Llama 2.
  • Open-Source Strategy: Meta is making Llama models open-source to attract more developers.
  • Careful Progress: While advancing in text-based AI, Meta remains cautious with other AI tools like the unreleased image generator Emu.
  • Future AI Directions: Despite Meta’s upcoming launch, Chief AI Scientist Yann LeCun envisions AI’s future in different technologies like Joint Embedding Predicting Architecture (JEPA).

Why It Matters

Meta’s Llama 3 launch shows its drive to stay competitive in AI, challenging giants like OpenAI and exploring open-source models.

Adobe Buys Creator Videos to Train its Text-to-Video AI Model

Adobe is purchasing video content from creators to train its text-to-video AI model, aiming to compete in the fast-evolving AI video generation market.

Key Takeaways:

  • Acquiring Content: Adobe is actively buying videos that capture everyday activities, paying creators $3-$7 per minute.
  • Legal Compliance: The company is ensuring that its AI training materials are legally and commercially safe, avoiding the use of scraped YouTube content.
  • AI Content Creation: Adobe’s move highlights the rapid growth of AI in creating diverse content types, including images, music, and now videos.
  • The Role of Creativity: Despite the accessibility of advanced AI tools, individual creativity remains crucial, as they become universally accessible.

Why It Matters

Adobe’s strategy highlights its commitment to AI advancement and stresses the importance of ethical development in the field.

MagicTime Innovates with Metamorphic Time-Lapse Video AI

MagicTime is pioneering a new AI model that creates dynamic time-lapse videos by learning from real-world physics.

Key Takeaways:

  • MagicAdapter Scheme: This technique separates spatial and temporal training. Thus, allowing the model to absorb more physical knowledge and enhance pre-trained time-to-video (T2V) models .
  • Dynamic Frames Extraction: Adapts to the broad variations found in metamorphic time-lapse videos, effectively capturing dramatic transformations.
  • Magic Text-Encoder: Enhances the AI’s ability to comprehend and respond to textual prompts for metamorphic videos.
  • ChronoMagic Dataset: A specially curated time-lapse video-text dataset, designed to advance the AI’s capability in generating metamorphic videos.

Why It Matters

MagicTime’s advanced approach in generating time-lapse videos that accurately reflect physical changes showcases significant progress towards developing AI that can simulate real-world physics in videos.

OpenAI Trained GPT-4 Using Over a Million Hours of YouTube Videos

Major AI companies like OpenAI and Meta are encountering hurdles in sourcing high-quality data for training their advanced models, pushing them to explore controversial methods.

Key Takeaways:

  • Copyright Challenges: OpenAI has used over a million hours of YouTube videos for training GPT-4, potentially breaching YouTube’s terms of service.
  • Google’s Strategy: Google claims its data collection complies with agreements made with YouTube creators, unlike its competitors.
  • Meta’s Approach: Meta has also been implicated in using copyrighted texts without permissions, trying to keep pace with rivals.
  • Ethical Concerns: These practices raise questions about the limits of fair use and copyright law in AI development.
  • Content Dilemma: There’s concern that AI’s demand for data may soon outstrip the creation of new content.

Why It Matters

The drive for comprehensive training data is leading some of the biggest names in AI into ethically and legally ambiguous territories, highlighting a critical challenge in AI development: balancing innovation with respect for intellectual property rights.

Elon Musk Predicts AI to Surpass Human Intelligence by Next Year

Elon Musk predicts that artificial general intelligence (AGI) could surpass human intelligence as early as next year, reflecting rapid AI advancements.

Key Takeaways:

  • AGI Development Timeline: Musk estimates that AGI, smarter than the smartest human, could be achieved as soon as next year or by 2026
  • Challenges in AI Development: Current limitations include a shortage of advanced chips, impacting the training of Grok’s newer models.
  • Future Requirements: The upcoming Grok 3 model will need an estimated 100,000 Nvidia H100 GPUs.
  • Energy Constraints: Beyond hardware, Musk emphasized that electricity availability will become a critical factor for AI development in the near future.

Why It Matters

Elon Musk’s predictions emphasize the fast pace of AI technology and highlight infrastructural challenges that could shape future AI capabilities and deployment.

Udio, an AI-Powered Music Creation App

Udio, developed by ex-Google DeepMind researchers, allows anyone to create professional-quality music.

Key Takeaways:

  • User-Friendly Creation: Udio enables users to generate fully mastered music tracks in seconds with a prompt.
  • Innovative Features: It offers editing tools and a “vary” feature to fine-tune the music, enhancing user control over the final product.
  • Copyright Safeguards: Udio includes automated filters to ensure that all music produced is original and copyright-compliant.
  • Industry Impact: Backed by investors like Andreessen Horowitz, Udio aims to democratize music production, potentially providing new artists with affordable means to produce music.

Why It Matters

Udio could reshape the music industry landscape by empowering more creators with accessible, high-quality music production tools.

Final Thoughts

As we wrap up this week’s insights into the AI world, it’s clear that the pace of innovation is not slowing down. These developments show the rapid progress in AI technology. Let’s stay tuned to see how these initiatives unfold and impact the future of AI.

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Future Dubai AI Police

Last week in ai

Launch on a riveting journey in this week’s “Last Week in AI” as we unravel the velocity narrative of GPT-4, the monumental strides of DALL·E 3 in AI imagery, the mirage of Arrakis in the quest for efficiency, the legal crescendo in AI’s copyright conundrum, transparency tally unveiling AI’s report card, and Dubai’s futuristic AI guard patrolling residential precincts. This narrative is a kaleidoscope, reflecting the multi-faceted developments in the AI sphere. Through this lens, readers will traverse through the technical, ethical, and legal avenues, understanding the impact and significance each holds in the contemporary digital era.

GPT-4’s Velocity

Latency Chronicles

GPT-4 is on a speed spree, narrowing the latency divide with GPT-3.5. It’s a narrative of steady progression, with GPT-4’s latency witnessing a consistent dip over recent months. A high token count, contrary to popular belief, doesn’t translate to a lag in response. The median request latencies showcase a remarkable consistency across both models, firmly standing under 1 ms per token. Yet, the show-stealer is the 99th percentile, displaying more than a 50% cut in latencies in a mere trimester. It’s a leap, not a step, towards real-time AI interactivity. 🚀

Delving into Latency Dynamics

  • Round Trip Time: A significant slice of the latency pie, impacting the request’s round-trip voyage.
  • Queuing Time: The silent influencer, dictating the wait before the process ignition.
  • Processing Time: The heart of the matter, where GPT-4 is trimming the fat, honing its speed.

Real-World Relevance

The latency topic isn’t just a tech jargon; it’s a crucial pivot steering user satisfaction in real-time applications. This narrative isn’t merely a gaze into GPT-4’s speed evolution; it’s a blueprint of how this speed saga significantly molds user experience in the fiercely competitive digital arena. Through the latency lens, we’re not just observing an AI’s speed metamorphosis; we’re getting a front-row seat to the unfolding of a new AI era.

DALL·E 3: A Giant Leap in AI Imagery

Unprecedented Imagery Capabilities

OpenAI rolls out DALL·E 3 in ChatGPT Plus and Enterprise, marking a significant stride in AI imagery. This powerhouse can craft unique visuals from plain conversations, enabling users to fine-tune the images further. The prowess of DALL·E 3 surpasses its predecessor, offering visually captivating and sharper images. Its knack for rendering intricate details like text, hands, and faces is noteworthy, especially when fed with detailed prompts.

Safety and Ethical Measures 🛡️

OpenAI isn’t blind to potential misuse. A robust multi-tiered safety system curtails DALL·E 3’s ability to churn out harmful imagery—be it violent, adult, or hateful content. They’ve also dialed down on generating imagery mimicking living artists, public figures, and have amped up demographic representation in the visuals.

  • Feedback Loop: Users can flag unsafe or inaccurate outputs, aiding OpenAI’s continuous effort to refine DALL·E 3’s safety net.

What’s in Store for Readers?

Dive into the mechanics of DALL·E 3, explore its enhanced image generation capabilities, and understand the ethical guardrails OpenAI has installed to prevent misuse. This narrative shines a light on the evolution of AI in creating visually compelling content while ensuring a safer digital space for all.

Arrakis: A Mirage in the AI Desert

The Quest for Efficiency

OpenAI’s venture, Arrakis, inspired by the desolate planet from “Dune,” aimed to fuel AI applications like ChatGPT affordably. However, efficiency expectations led to its early retirement this year. Models powering ChatGPT are costly, demanding hefty compute power from giants like Microsoft, Amazon, and Google.

Financial Horizon 📈

OpenAI’s CEO, Sam Altman, eyes a meteoric rise to $1.3 billion in annual revenue, a leap from $28 million in 2022.

  • Competitive Landscape: Google’s impending Gemini model and an upcoming AI safety summit pose fresh challenges for OpenAI.


This narrative not only explores the technical escapade but also the business dynamics and emerging competitive arena in the AI landscape.

Litigation Beats: AI in the Copyright Crossfire

The Legal Crescendo

Universal Music Group, among others, has hit Anthropic with a lawsuit over Claude 2’s distribution of copyrighted lyrics. The AI, when prompted, belts out lyrics strikingly similar to chart-toppers like Katy Perry’s “Roar” and others. The plaintiffs cry foul, alleging unauthorized distribution and training on copyrighted tunes.

The Copyright Conundrum 🎶

The suit underscores the tension between generative AI and copyright norms. It alleges Anthropic could curtail such distributions, pointing to Claude 2’s selective response to certain prompts.

  • AI’s Copyright Dance: Anthropic’s saga highlights the broader challenge of navigating copyright waters in AI’s musical endeavors.

Engrossing Insights Await

This narrative tunes into the legal, ethical, and technological notes that compose the ongoing copyright symphony in the AI arena.

Transparency Tally: AI’s Report Card Unveiled

The Transparency Index

Eminent researchers from Stanford, MIT, and Princeton unfurled the 2023 Foundation Model Transparency Index. It’s a meticulously crafted ledger with 100 indicators dissecting transparency across three domains: upstream, model, and downstream.

Open vs Closed: The Transparency Spectrum 📊

Open models, with freely accessible weights, are the vanguards of transparency. Among them, Meta’s Llama 2 and Hugging Face’s BLOOMZ outshine, rivaling even the best closed model.

  • Transparency Deficit: Closed developers lag, chiefly due to veiled upstream elements like data, labor, and compute resources.

Navigating Through the Index

This exposition unveils the transparency landscape of foundation models. Grasp the variance between open and closed models, and understand the concerted push towards more transparent, equitable AI realms. This is a deep dive into the heart of AI transparency, illuminating the path towards a more open AI ecosystem.

The Future Patrol: Dubai’s AI Guard

Robo-Patrol Unveiled

Dubai Police debut a self-driving patrol vehicle, marrying eco-friendliness with advanced surveillance tech in residential precincts. The electric sentinel boasts 15-hour battery life, cruising at 5 to 7 kilometers per hour.

Technological Vanguard 🤖

Embedded with 360-degree cameras and facial recognition, this patrol vehicle isn’t just a roving eye but a smart safety net.

  • Real-Time Vigilance: Links with the Command Center, recognizing faces, decoding license plates, and detecting potential criminal acts.

Aerial Ally

A drone partner extends the patrol’s reach, wirelessly tethered for coordinated surveillance, covering ground and air.

Insightful Expedition

Dive into Dubai’s innovative stride towards automated neighborhood watch, exploring the fusion of AI and autonomous mobility in law enforcement. Unravel how this tech-marriage reshapes community safety, setting a precedent in modern policing.


  1. What significant leap has GPT-4 made in the realm of latency? GPT-4 has been on a remarkable speed spree, narrowing the latency divide with GPT-3.5, exhibiting more than a 50% reduction in the 99th percentile of latencies, a stride towards real-time AI interactivity. 🚀
  2. How has DALL·E 3 enhanced image generation? DALL·E 3, now rolled out in ChatGPT Plus and Enterprise, marks a significant upgrade in AI imagery, crafting unique visuals from plain conversations and rendering intricate details with much higher precision.
  3. Why was OpenAI’s Arrakis project shelved? The quest for a more affordable engine for AI applications like ChatGPT hit a roadblock with Arrakis, due to unmet efficiency expectations, necessitating its early retirement.
  4. What’s the essence of the lawsuit against Anthropic regarding copyrighted lyrics? Anthropic faces legal heat for its AI model, Claude 2, allegedly distributing copyrighted lyrics and possibly using them for training, reflecting the broader challenge of navigating copyright norms in AI’s musical endeavors. 🎶
  5. What insights does the 2023 Foundation Model Transparency Index provide? The index, crafted by eminent researchers, dissects the transparency landscape of foundation models, spotlighting the transparency vanguard role of open models like Meta’s Llama 2 and Hugging Face’s BLOOMZ.
  6. How does Dubai’s self-driving patrol vehicle contribute to community safety? This eco-friendly sentinel, equipped with advanced surveillance tech, introduces a new era of automated neighborhood watch, enhancing safety through real-time vigilance and coordinated aerial surveillance. 🤖


We’ve navigated through diverse AI narratives—from GPT-4’s speed strides to DALL·E 3’s visual mastery, the legal riddle around Anthropic, transparency in AI, to Dubai’s tech-infused patrol. Each tale sheds light on the profound impact and the ethical, legal, and technical intricacies enveloping AI.

Moreover, the discourse around copyright norms and transparency underscores AI’s complex societal interplay. As we forge ahead, delving deeper into these discussions is crucial for harnessing AI responsibly.

Curious about leveraging these AI advancements for your enterprise? Discover what Vease can do for your business in this evolving digital realm. For more AI insights, visit our blog. Missed last week’s updates? Catch up here.

Craving even more insight? Don’t miss the YouTube video below by Matt Wolfe, the founder of FutureTools.

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