BREAKING NEWS
LATEST POSTS
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Andrew Perfors – The work of creation in the age of AI
Meaning, authenticity, and the creative process – and why they matter
https://perfors.net/blog/creation-ai/
AI changes the landscape of creation, focusing on the alienation of the creator from their creation and the challenges in maintaining meaning. The author presents two significant problems:
- Loss of Connection with Creation:
- AI-assisted creation diminishes the creator’s role in the decision-making process.
- The resulting creation lacks the personal, intentional choices that contribute to meaningful expression.
- AI is considered a tool that, when misused, turns creation into automated button-pushing, stripping away the purpose of human expression.
- Difficulty in Assessing Authenticity:
- It becomes challenging to distinguish between human and AI contributions within a creation.
- AI-generated content lacks transparency regarding the intent behind specific choices or expressions.
- The author asserts that AI-generated content often falls short in providing the depth and authenticity required for meaningful communication.
- Loss of Connection with Creation:
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Fouad Khan – Confirmed! We Live in a Simulation
https://www.scientificamerican.com/article/confirmed-we-live-in-a-simulation/
Ever since the philosopher Nick Bostrom proposed in the Philosophical Quarterly that the universe and everything in it might be a simulation, there has been intense public speculation and debate about the nature of reality.
Yet there have been skeptics. Physicist Frank Wilczek has argued that there’s too much wasted complexity in our universe for it to be simulated. Building complexity requires energy and time.
To understand if we live in a simulation we need to start by looking at the fact that we already have computers running all kinds of simulations for lower level “intelligences” or algorithms.
All computing hardware leaves an artifact of its existence within the world of the simulation it is running. This artifact is the processor speed.
No matter how complete the simulation is, the processor speed would intervene in the operations of the simulation.If we live in a simulation, then our universe should also have such an artifact. We can now begin to articulate some properties of this artifact that would help us in our search for such an artifact in our universe.
The artifact presents itself in the simulated world as an upper limit.Now that we have some defining features of the artifact, of course it becomes clear what the artifact manifests itself as within our universe. The artifact is manifested as the speed of light.
This maximum speed is the speed of light. We don’t know what hardware is running the simulation of our universe or what properties it has, but one thing we can say now is that the memory container size for the variable space would be about 300,000 kilometers if the processor performed one operation per second.We can see now that the speed of light meets all the criteria of a hardware artifact identified in our observation of our own computer builds. It remains the same irrespective of observer (simulated) speed, it is observed as a maximum limit, it is unexplainable by the physics of the universe, and it is absolute. The speed of light is a hardware artifact showing we live in a simulated universe.
Consciousness is an integrated (combining five senses) subjective interface between the self and the rest of the universe. The only reasonable explanation for its existence is that it is there to be an “experience”.
So here we are generating this product called consciousness that we apparently don’t have a use for, that is an experience and hence must serve as an experience. The only logical next step is to surmise that this product serves someone else.
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AI and the Law – Why The New York Times might win its copyright lawsuit against OpenAI
Daniel Jeffries wrote:
“Trying to get everyone to license training data is not going to work because that’s not what copyright is about,” Jeffries wrote. “Copyright law is about preventing people from producing exact copies or near exact copies of content and posting it for commercial gain. Period. Anyone who tells you otherwise is lying or simply does not understand how copyright works.”
The AI community is full of people who understand how models work and what they’re capable of, and who are working to improve their systems so that the outputs aren’t full of regurgitated inputs. Google won the Google Books case because it could explain both of these persuasively to judges. But the history of technology law is littered with the remains of companies that were less successful in getting judges to see things their way.
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M.T. Fletcher – WHY AGENCIES ARE OBSESSED WITH PITCHING ON PROCESS INSTEAD OF TALENT
“Every presentation featured a proprietary process designed by the agency. A custom approach to identify targets, develop campaigns and optimize impact—with every step of the process powered by AI, naturally.”
“The key to these one-of-a-kind models is apparently finding the perfect combination of circles, squares, diamonds and triangles…Arrows abounded and ellipses are replacing circles as the unifying shape of choice among the more fashionable strategists.”
“The only problem is that it’s all bullshit.”
“A blind man could see the creative ideas were not developed via the agency’s so-called process, and anyone who’s ever worked at an agency knows that creativity comes from collaboration, not an assembly line.”
“And since most clients can’t differentiate between creative ideas without validation from testing, data has become the collective crutch for an industry governed by fear.”
“If a proprietary process really produced foolproof creativity, then every formulaic movie would be a blockbuster, every potboiler novel published by risk-averse editors would become a bestseller and every clichéd pickup line would work in any bar in the world.”
FEATURED POSTS
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VFX pipeline – Render Wall management topics
1: Introduction Title: Managing a VFX Facility’s Render Wall
- Briefly introduce the importance of managing a VFX facility’s render wall.
- Highlight how efficient management contributes to project timelines and overall productivity.
2: Daily Overview Title: Daily Management Routine
- Monitor Queues: Begin each day by reviewing render queues to assess workload and priorities.
- Resource Allocation: Allocate resources based on project demands and available hardware.
- Job Prioritization: Set rendering priorities according to project deadlines and importance.
- Queue Optimization: Adjust queue settings to maximize rendering efficiency.
3: Resource Allocation Title: Efficient Resource Management
- Hardware Utilization: Distribute rendering tasks across available machines for optimal resource usage.
- Balance Workloads: Avoid overloading specific machines while others remain underutilized.
- Consider Off-Peak Times: Schedule resource-intensive tasks during off-peak hours to enhance overall performance.
4: Job Prioritization Title: Prioritizing Rendering Tasks
- Deadline Sensitivity: Give higher priority to tasks with imminent deadlines to ensure timely delivery.
- Critical Shots: Identify shots crucial to the project’s narrative or visual impact for prioritization.
- Dependent Shots: Sequence shots that depend on others should be prioritized together.
5: Queue Optimization and Reporting Title: Streamlining Render Queues
- Dependency Management: Set up dependencies to ensure shots are rendered in the correct order.
- Error Handling: Implement automated error detection and requeueing mechanisms.
- Progress Tracking: Regularly monitor rendering progress and update stakeholders.
- Data Management: Archive completed renders and remove redundant data to free up storage.
- Reporting: Provide daily reports on rendering status, resource usage, and potential bottlenecks.
6: Conclusion Title: Enhancing VFX Workflow
- Effective management of a VFX facility’s render wall is essential for project success.
- Daily monitoring, resource allocation, job prioritization, queue optimization, and reporting are key components.
- A well-managed render wall ensures efficient production, timely delivery, and overall project success.
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AI Data Laundering: How Academic and Nonprofit Researchers Shield Tech Companies from Accountability
“Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.”
“In addition to the Shutterstock clips, Meta also used 10 million video clips from this 100M video dataset from Microsoft Research Asia. It’s not mentioned on their GitHub, but if you dig into the paper, you learn that every clip came from over 3 million YouTube videos.”
“It’s become standard practice for technology companies working with AI to commercially use datasets and models collected and trained by non-commercial research entities like universities or non-profits.”
“Like with the artists, photographers, and other creators found in the 2.3 billion images that trained Stable Diffusion, I can’t help but wonder how the creators of those 3 million YouTube videos feel about Meta using their work to train their new model.”
