Despite embracing technology, the “Blade Runner” and “Alien” director has long incorporated the fear of AI in his stories onscreen and knows better than most about its ramifications. Scott previously told Rolling Stone in November 2023 that when it came to his concerns about artificial intelligence broadly, he said that AI was “dangerous” and akin to a “technical hydrogen bomb.”
The expanding economic impact of AI, highlights a significant gap between AI infrastructure investments and actual revenue generation. Despite easing GPU shortages and increased investments by cloud providers, AI-related revenue, particularly dominated by OpenAI, remains insufficient to justify the massive capital expenditures. The analysis reveals that this gap has grown from $125 billion to $500 billion, posing challenges for the AI industry while emphasizing the need for realistic expectations and sustainable value creation.
OpenAI training and inference costs could reach $7bn for 2024, AI startup set to lose $5bn – report
The power of AI will transform every facet of our society, from the micro changes in our day-to-day lives to the macro changes in global geopolitics. It will challenge our values and assumptions and make us reconsider what it means to be human. It is inevitable that some capital will be wasted getting there. We may even experience a bubble or two. But this is part of the growing pains of advancing humankind. Society, like our individual lives, seldom take the shortest route. As to the argument that we are in a bubble right now, we think it deserves some reconsidering.
The article advocates for employee ownership of companies, using Central States Manufacturing as a model. The company, owned by its employees through an Employee Stock Ownership Plan (ESOP), demonstrates how such structures can create significant wealth for workers across all levels, improve long-term company performance, and reduce wealth inequality. Employee ownership aligns worker and company interests, leading to sustainable growth and better employee benefits. The article highlights the benefits of ESOPs and calls for broader adoption and awareness of such models.
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.
When collecting hdri make sure the data supports basic metadata, such as:
Iso
Aperture
Exposure time or shutter time
Color temperature
Color space Exposure value (what the sensor receives of the sun intensity in lux)
7+ brackets (with 5 or 6 being the perceived balanced exposure)
In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allow a greater dynamic range of luminances (a Photometry measure of the luminous intensity per unit area of light travelling in a given direction. It describes the amount of light that passes through or is emitted from a particular area, and falls within a given solid angle) between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to represent more accurately the wide range of intensity levels found in real scenes ranging from direct sunlight to faint starlight and to the deepest shadows.
The two main sources of HDR imagery are computer renderings and merging of multiple photographs, which in turn are known as low dynamic range (LDR) or standard dynamic range (SDR) images. Tone Mapping (Look-up) techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Photography
In photography, dynamic range is measured in Exposure Values (in photography, exposure value denotes all combinations of camera shutter speed and relative aperture that give the same exposure. The concept was developed in Germany in the 1950s) differences or stops, between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.
The human response to brightness is well approximated by a Steven’s power law, which over a reasonable range is close to logarithmic, as described by the Weber�Fechner law, which is one reason that logarithmic measures of light intensity are often used as well.
HDR is short for High Dynamic Range. It’s a term used to describe an image which contains a greater exposure range than the “black” to “white” that 8 or 16-bit integer formats (JPEG, TIFF, PNG) can describe. Whereas these Low Dynamic Range images (LDR) can hold perhaps 8 to 10 f-stops of image information, HDR images can describe beyond 30 stops and stored in 32 bit images.
You’ve been in the VFX Industry for over a decade. Tell us about your journey.
It all started with my older brother giving me a Commodore64 personal computer as a gift back in the late 80′. I realised then I could create something directly from my imagination using this new digital media format. And, eventually, make a living in the process. That led me to start my professional career in 1990. From live TV to games to animation. All the way to live action VFX in the recent years.
I really never stopped to crave to create art since those early days. And I have been incredibly fortunate to work with really great talent along the way, which made my journey so much more effective.
What inspired you to pursue VFX as a career?
An incredible combination of opportunities, really. The opportunity to express myself as an artist and earn money in the process. The opportunity to learn about how the world around us works and how best solve problems. The opportunity to share my time with other talented people with similar passions. The opportunity to grow and adapt to new challenges. The opportunity to develop something that was never done before. A perfect storm of creativity that fed my continuous curiosity about life and genuinely drove my inspiration.
Tell us about the projects you’ve particularly enjoyed working on in your career
One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.
There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.
I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.
IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.
In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.
By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.
Today HSL and HSV are most commonly used together with the sRGB color space.
One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.
The main drawback on the other hand is that their properties don’t match human perception particularly well.
Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.
With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.
