“There are many good reasons to be concerned about the rise of generative AI(…). Unfortunately, there are also many good reasons to be concerned about copyright’s growing prevalence in the policy discourse around AI’s regulation. Insisting that copyright protects an exclusive right to use materials for text and data mining practices (whether for informational analysis or machine learning to train generative AI models) is likely to do more harm than good. As many others have explained, imposing copyright constraints will certainly limit competition in the AI industry, creating cost-prohibitive barriers to quality data and ensuring that only the most powerful players have the means to build the best AI tools (provoking all of the usual monopoly concerns that accompany this kind of market reality but arguably on a greater scale than ever before). It will not, however, prevent the continued development and widespread use of generative AI.”
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“(…) As Michal Shur-Ofry has explained, the technical traits of generative AI already mean that its outputs will tend towards the dominant, likely reflecting ‘a relatively narrow, mainstream view, prioritizing the popular and conventional over diverse contents and narratives.’ Perhaps, then, if the political goal is to push for equality, participation, and representation in the AI age, critics’ demands should focus not on exclusivity but inclusivity. If we want to encourage the development of ethical and responsible AI, maybe we should be asking what kind of material and training data must be included in the inputs and outputs of AI to advance that goal. Certainly, relying on copyright and the market to dictate what is in and what is out is unlikely to advance a public interest or equality-oriented agenda.”
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“If copyright is not the solution, however, it might reasonably be asked: what is? The first step to answering that question—to producing a purposively sound prescription and evidence-based prognosis, is to correctly diagnose the problem. If, as I have argued, the problem is not that AI models are being trained on copyright works without their owners’ consent, then requiring copyright owners’ consent and/or compensation for the use of their work in AI-training datasets is not the appropriate solution. (…)If the only real copyright problem is that the outputs of generative AI may be substantially similar to specific human-authored and copyright-protected works, then copyright law as we know it already provides the solution.”
Narrative voice via Artlistai, News Reporter PlayAI, All other voices are V2V in Elevenlabs. Powered by (in order of amount) ‘HailuoAI’, ‘KlingAI’ and of course some of our special source. Performance capture by ‘Runway’s Act-One’. Edited and color graded in ‘DaVinci Resolve’. Composited with ‘After Effects’.
In this film, the ‘Newton’s Cradle’ isn’t just a symbolic object—it represents the fragile balance between control and freedom in a world where time itself is being manipulated. The oscillation of the cradle reflects the constant push and pull of power in this dystopian society. By the end of the film, we discover that this seemingly innocuous object holds the potential to disrupt the system, offering a glimmer of hope that time can be reset and balance restored.
Depth of field is the range within which focusing is resolved in a photo.
Aperture has a huge affect on to the depth of field.
Changing the f-stops (f/#) of a lens will change aperture and as such the DOF.
f-stops are a just certain number which is telling you the size of the aperture. That’s how f-stop is related to aperture (and DOF).
If you increase f-stops, it will increase DOF, the area in focus (and decrease the aperture). On the other hand, decreasing the f-stop it will decrease DOF (and increase the aperture).
The red cone in the figure is an angular representation of the resolution of the system. Versus the dotted lines, which indicate the aperture coverage. Where the lines of the two cones intersect defines the total range of the depth of field.
This image explains why the longer the depth of field, the greater the range of clarity.
Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.
“Unless you have all the relevant spectral measurements, a colour rendition chart should not be used to perform colour-correction of camera imagery but only for white balancing and relative exposure adjustments.”
“Using a colour rendition chart for colour-correction might dramatically increase error if the scene light source spectrum is different from the illuminant used to compute the colour rendition chart’s reference values.”
“other factors make using a colour rendition chart unsuitable for camera calibration:
– Uncontrolled geometry of the colour rendition chart with the incident illumination and the camera.
– Unknown sample reflectances and ageing as the colour of the samples vary with time.
– Low samples count.
– Camera noise and flare.
– Etc…
“Those issues are well understood in the VFX industry, and when receiving plates, we almost exclusively use colour rendition charts to white balance and perform relative exposure adjustments, i.e. plate neutralisation.”
