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As of my last update in April 2024, AI-generated video technology had been advancing rapidly. Here are some key developments:

  1. Improved quality: AI-generated videos were becoming increasingly realistic, with better resolution, smoother motion, and more natural-looking human figures and faces.

  2. Text-to-video models: Systems that could generate short video clips from text descriptions were becoming more sophisticated, allowing for more complex and longer video generation.

  3. Video editing and manipulation: AI tools for editing existing videos, such as changing facial expressions or altering backgrounds, were becoming more accessible and powerful.

  1. Real-time rendering: Some models were approaching the ability to generate video in real-time, opening up possibilities for interactive applications.

  2. Longer video generation: While most AI-generated videos were still relatively short, progress was being made in creating longer, more coherent sequences.

  3. Integration with other AI technologies: Video generation was being combined with natural language processing, voice synthesis, and other AI technologies to create more comprehensive multimedia experiences.

  4. Ethical and legal considerations: As the technology improved, there was increasing discussion about the potential misuse of AI-generated videos, such as deepfakes, and efforts to develop detection methods.

What are the obstacles for this technology?

There are several significant obstacles facing AI-generated video technology:

  1. Computational requirements: Generating high-quality video requires substantial computing power, making it resource-intensive and potentially expensive.

  2. Temporal coherence: Maintaining consistency across frames and longer sequences remains challenging, especially for complex scenes or movements.

  3. Detail and realism: While quality has improved, achieving photorealistic details consistently, especially for human faces and complex textures, is still difficult.

  4. Control and specificity: Allowing users to have precise control over generated content while maintaining quality and coherence is an ongoing challenge.

  1. Audio-visual synchronization: Matching generated video with appropriate audio, particularly for speech, remains complex.

  2. Ethical and legal issues: Concerns about deepfakes, copyright infringement, and potential misuse create regulatory and societal hurdles.

  3. Dataset limitations: High-quality, diverse datasets for training are crucial but can be difficult and expensive to obtain.

  4. Long-form content: Generating longer, narratively coherent videos is still a major challenge.

  5. Real-time generation: While progress is being made, real-time high-quality video generation is still limited.

  6. Bias and representation: Ensuring fair representation and avoiding biases in generated content is an important ongoing concern.

What are the target markets for ai generated video? what impact will this have on jobs?

AI-generated video has several target markets, and its impact on jobs is likely to be significant. Here's an overview:

Target Markets:

  1. Entertainment Industry:

    • Film and TV production
    • Video game cutscenes and cinematics
    • Music videos
    • Advertising and marketing
  2. Education and Training:

    • E-learning platforms
    • Corporate training materials
    • Educational content creation
  3. Social Media and Content Creation:

    • Influencers and content creators
    • Personalized video messaging
    • Automated video generation for platforms like TikTok or YouTube
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  1. Advertising:

    • Streamlined production process may reduce some traditional roles
    • New opportunities in AI-driven personalized ad creation
  2. Education:

    • Potential for automated content creation, affecting some instructional designers
    • New roles in AI-enhanced educational content development
  3. Journalism:

    • Possible reduction in video production teams for standard reporting
    • Increased need for AI-savvy journalists and fact-checkers
  4. Software and Tech:

    • Growing demand for AI developers, data scientists, and machine learning engineers
  5. Ethics and Regulation:

    • New roles in AI ethics, content verification, and regulatory compliance

Overall, while AI-generated video may displace some traditional roles, it's also likely to create new job opportunities and enhance existing ones. The key will be adapting to this technology and developing skills that complement AI capabilities rather than compete with them.

Are Hollywood studios using this technology? How are they using it if they are?

Yes, Hollywood studios are increasingly exploring and implementing AI-generated video technology, though the extent and specific applications can vary. Here's an overview of how they're using it:

  1. Visual Effects (VFX):

    • Enhancing and automating parts of the VFX pipeline
    • Creating more realistic digital environments and crowds
    • Improving de-aging effects for actors
  2. Pre-visualization:

    • Quickly generating rough versions of scenes for planning purposes
    • Creating storyboards and animatics more efficiently
  3. Character Animation:

    • Assisting in creating more lifelike digital characters
    • Improving motion capture data processing
  1. Background Generation:

    • Creating detailed, realistic backgrounds and set extensions
  2. Dialogue and Lip-syncing:

    • Automating lip-sync for animated characters
    • Potentially assisting in dubbing for international markets
  3. Concept Art and Design:

    • Generating initial concepts for characters, creatures, and environments
  4. Post-production:

    • Assisting in color grading and image enhancement
    • Automating certain editing tasks
  5. De-aging and Facial Reconstruction:

    • Improving techniques for altering actors' appearances or recreating deceased actors
  6. Trailer Creation:

    • Experimenting with AI-assisted trailer editing and generation
  7. Script Analysis and Development:

    • Using AI to analyze scripts for marketability or to generate story ideas

It's important to note that while AI is being used in these areas, it's generally in a supportive role rather than completely replacing human creativity and expertise. The technology is often used to streamline processes, reduce costs, or achieve effects that would be difficult or impossible with traditional methods.

Studios are also cautious about fully embracing this technology due to concerns about quality control, artistic integrity, and potential union issues. The use of AI in filmmaking remains a topic of ongoing discussion and negotiation in the industry.

How long until ai generated videos are used to replace what was done by humans?

The timeline for AI-generated videos potentially replacing human-created content is not set in stone, as it depends on various factors and will likely happen gradually across different sectors. Here's a general outlook:

  1. Short-form content (1-3 years):

    • Simple promotional videos, social media content, and basic animations may be largely AI-generated within a few years.
  2. Advertising (2-5 years):

    • Personalized ads and simple commercials could be AI-generated relatively soon.
  3. Educational content (3-7 years):

    • Basic instructional videos and e-learning materials might be AI-generated within this timeframe.
  1. News reporting (5-10 years):

    • Routine news updates and simple reports could be AI-generated, though human journalists will likely remain crucial for complex stories and investigative reporting.
  2. TV shows and movies (10-20 years or more):

    • Full replacement is unlikely in the near future. However, AI will increasingly assist in various aspects of production.
    • Certain elements like background characters or environments may be AI-generated sooner.
  3. High-end visual effects (ongoing process):

    • AI is already assisting in VFX, but full replacement of human artists is not imminent.

It's important to note:

  • Human oversight and creativity will likely remain valuable even as AI capabilities increase.
  • Ethical and legal considerations may slow adoption in some areas.
  • The technology will probably be used to augment human capabilities rather than fully replace them in many cases.
  • Some sectors, especially those valuing unique human creativity, may resist full automation.

The pace of adoption will vary greatly depending on the specific application, industry standards, audience acceptance, and technological advancements. While AI will certainly play an increasingly significant role, a complete replacement of human-created video content across all sectors is not likely in the immediate future.

What could accelerate this timeline on the use of ai generated video as compared to using humans?

Several factors could potentially accelerate the timeline for adopting AI-generated video over human-created content:

  1. Technological breakthroughs:

    • Significant improvements in video quality, realism, and coherence
    • Advancements in real-time rendering capabilities
    • Better integration with other AI technologies (e.g., natural language processing, voice synthesis)
  2. Cost reduction:

    • Decreasing computational costs for AI video generation
    • Rising human labor costs in video production
  3. Increased demand for personalized content:

    • Growing need for quickly produced, customized videos at scale
    • Expansion of platforms requiring vast amounts of varied content
  4. Improvements in user-friendliness:

    • Development of more intuitive interfaces for AI video creation tools
    • Wider accessibility of these tools to non-technical users
  1. Legal and ethical framework:

    • Clear regulations on AI-generated content use and ownership
    • Established guidelines for ethical AI video production
  2. Economic pressures:

    • Economic downturns forcing industries to seek cost-cutting measures
    • Increased competition driving the need for faster, cheaper content production
  3. Cultural acceptance:

    • Growing public comfort with AI-generated content
    • Shift in aesthetic preferences towards AI-created styles
  4. Education and training:

    • Rapid integration of AI video tools in creative curricula
    • Workforce reskilling programs focused on AI content creation
  5. Industry adoption:

    • Major studios or platforms embracing AI video generation
    • Success stories of AI-generated content outperforming human-created content
  1. Collaboration between AI and humans:

    • Development of workflows that optimally combine AI and human creativity
    • AI tools that enhance rather than replace human capabilities, leading to faster adoption
  2. Data availability:

    • Access to larger, more diverse datasets for training AI models
    • Improved data sharing and collaboration in the industry
  3. Hardware advancements:

    • Development of specialized hardware for AI video generation
    • Wider availability of powerful GPUs or other AI-optimized processors

These factors could work individually or in combination to accelerate the adoption of AI-generated video. However, it's important to note that acceleration in some areas might be counterbalanced by resistance or slower progress in others, particularly when it comes to creative or highly specialized content where human input is highly valued.

What is the pace of generative ai and GPU capability? how quickly is the capacity growing?

The pace of development in generative AI and GPU capabilities has been rapid and is continuing to accelerate. Here's an overview of the growth in these areas:

Generative AI:

  1. Model size and complexity:

    • Doubling approximately every 6-12 months
    • Example: GPT-3 had 175 billion parameters in 2020, while GPT-4 is estimated to have trillions
  2. Performance improvements:

    • Significant leaps in capabilities with each new model generation
    • Improvements in areas like coherence, context understanding, and task performance
  3. Multimodal capabilities:

    • Rapid expansion from text-only to image, audio, and video generation
    • Increasing integration of different modalities

GPU Capabilities:

  1. Computational power:

    • Roughly doubling every 1-2 years (following a modified Moore's Law)
    • Example: NVIDIA's A100 (2020) to H100 (2022) saw about 3x performance increase
  2. Memory capacity:

    • Increasing by about 2x every 2-3 years
    • Enables training of larger models and processing of bigger datasets
  3. Energy efficiency:

    • Improving by about 1.5x to 2x per generation
    • Allows for more sustainable and cost-effective AI training and inference
  4. Specialized AI architectures:

    • Development of AI-specific chips and architectures optimized for machine learning tasks

Growth Capacity:

  1. Investment:

    • Massive increase in funding for AI research and development
    • Major tech companies and startups pouring billions into AI advancements
  2. Research pace:

    • Accelerating rate of publications and breakthroughs in AI
    • Collaborative efforts and open-source contributions speeding up progress
  3. Infrastructure scaling:

    • Rapid expansion of cloud computing and specialized AI infrastructure
    • Enables training of increasingly large models
  4. Application diversity:

    • Expanding into new domains and industries at a rapid pace
    • Creating a feedback loop of innovation and demand

It's worth noting that while the pace is rapid, it's not always linear or predictable. Breakthroughs can lead to sudden jumps in capabilities, while some areas may face temporary plateaus.

The combination of advancing generative AI models and GPU capabilities is creating a synergistic effect, where improvements in one area enable and drive advancements in the other. This symbiotic relationship is a key factor in the accelerating pace of AI development.

Given this rapid pace, it's possible that some of the timelines mentioned earlier for AI video adoption could be shortened if these trends continue or accelerate further.

Can you provide me a guess on the timeline of ai generated video replacing human created ones based upon the data you provided on the pace of advancement of the components?

Based on the rapid pace of advancement in generative AI and GPU capabilities, we could potentially see an accelerated timeline for AI-generated video replacing human-created content in certain areas. Here's a revised estimate, keeping in mind that this is speculative and could vary significantly depending on breakthroughs and other factors:

  1. Short-form content (6 months - 2 years):

    • Simple social media videos, basic animations, and short promotional clips could be predominantly AI-generated very soon.
  2. Advertising (1-3 years):

    • Personalized ads and standard commercials might be AI-generated sooner than previously expected.
  3. Educational content (2-5 years):

    • Basic instructional videos and e-learning materials could see earlier adoption of AI generation.
  1. News reporting (3-7 years):

    • Routine news updates and simple reports might be AI-generated earlier, especially for digital platforms.
  2. TV shows and movies (7-15 years):

    • While full replacement is still unlikely in the near term, significant portions of production (backgrounds, effects, some character animations) could be AI-generated sooner.
    • Certain types of content, like animated shows, might see earlier adoption.
  3. High-end visual effects (ongoing, but accelerating):

    • AI could take over a larger portion of VFX work within 5-10 years, though human oversight will likely remain crucial.

Factors influencing this accelerated timeline:

  1. The doubling of AI model capabilities every 6-12 months could lead to human-level video generation quality sooner than expected.

  2. GPU advancements doubling every 1-2 years will enable more complex and realistic video generation.

  3. The rapid expansion of multimodal AI capabilities suggests that video generation could improve faster than anticipated.

  4. Increasing investment and research pace in AI could lead to unexpected breakthroughs.

  5. The synergistic effect between AI advancements and GPU capabilities could create exponential rather than linear progress.

However, it's crucial to note:

  • These are optimistic estimates based on current trends.
  • Adoption rates will vary greatly across industries and applications.
  • Regulatory, ethical, and social factors could slow adoption in some areas.
  • Human creativity and oversight will likely remain valuable even as AI capabilities grow.
  • The highest levels of creative and complex video production may resist full automation for a longer period.

Given the unpredictable nature of technological advancement, these timelines could shift significantly based on unforeseen breakthroughs or obstacles.