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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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What is a Gamut or Color Space and why do I need to know about CIE
http://www.xdcam-user.com/2014/05/what-is-a-gamut-or-color-space-and-why-do-i-need-to-know-about-it/
In video terms gamut is normally related to as the full range of colours and brightness that can be either captured or displayed.
Generally speaking all color gamuts recommendations are trying to define a reasonable level of color representation based on available technology and hardware. REC-601 represents the old TVs. REC-709 is currently the most distributed solution. P3 is mainly available in movie theaters and is now being adopted in some of the best new 4K HDR TVs. Rec2020 (a wider space than P3 that improves on visibke color representation) and ACES (the full coverage of visible color) are other common standards which see major hardware development these days.
To compare and visualize different solution (across video and printing solutions), most developers use the CIE color model chart as a reference.
The CIE color model is a color space model created by the International Commission on Illumination known as the Commission Internationale de l’Elcairage (CIE) in 1931. It is also known as the CIE XYZ color space or the CIE 1931 XYZ color space.
This chart represents the first defined quantitative link between distributions of wavelengths in the electromagnetic visible spectrum, and physiologically perceived colors in human color vision. Or basically, the range of color a typical human eye can perceive through visible light.Note that while the human perception is quite wide, and generally speaking biased towards greens (we are apes after all), the amount of colors available through nature, generated through light reflection, tend to be a much smaller section. This is defined by the Pointer’s Chart.
In short. Color gamut is a representation of color coverage, used to describe data stored in images against available hardware and viewer technologies.
Camera color encoding from
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applicationsCIE 1976
http://bernardsmith.eu/computatrum/scan_and_restore_archive_and_print/scanning/
https://store.yujiintl.com/blogs/high-cri-led/understanding-cie1931-and-cie-1976
The CIE 1931 standard has been replaced by a CIE 1976 standard. Below we can see the significance of this.
People have observed that the biggest issue with CIE 1931 is the lack of uniformity with chromaticity, the three dimension color space in rectangular coordinates is not visually uniformed.
The CIE 1976 (also called CIELUV) was created by the CIE in 1976. It was put forward in an attempt to provide a more uniform color spacing than CIE 1931 for colors at approximately the same luminance
The CIE 1976 standard colour space is more linear and variations in perceived colour between different people has also been reduced. The disproportionately large green-turquoise area in CIE 1931, which cannot be generated with existing computer screens, has been reduced.
If we move from CIE 1931 to the CIE 1976 standard colour space we can see that the improvements made in the gamut for the “new” iPad screen (as compared to the “old” iPad 2) are more evident in the CIE 1976 colour space than in the CIE 1931 colour space, particularly in the blues from aqua to deep blue.
https://dot-color.com/2012/08/14/color-space-confusion/
Despite its age, CIE 1931, named for the year of its adoption, remains a well-worn and familiar shorthand throughout the display industry. CIE 1931 is the primary language of customers. When a customer says that their current display “can do 72% of NTSC,” they implicitly mean 72% of NTSC 1953 color gamut as mapped against CIE 1931.
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AI and the Law – Netflix : Using Generative AI in Content Production
https://www.cartoonbrew.com/business/netflix-generative-ai-use-guidelines-253300.html
- Temporary Use: AI-generated material can be used for ideation, visualization, and exploration—but is currently considered temporary and not part of final deliverables.
- Ownership & Rights: All outputs must be carefully reviewed to ensure rights, copyright, and usage are properly cleared before integrating into production.
- Transparency: Productions are expected to document and disclose how generative AI is used.
- Human Oversight: AI tools are meant to support creative teams, not replace them—final decision-making rests with human creators.
- Security & Compliance: Any use of AI tools must align with Netflix’s security protocols and protect confidential production material.