ROUND TABLE
POST
The Banshees of Inisherin, Cinelab All change please
SIMPSON: It all depends on how good AI really becomes. When I look at ChatGPT, OpenAi, DALL-E and Midjourney, they all hold such promise. The last time most of us felt like this about technology was probably when smart phones were first introduced.
MITCHELL: The problem with how we work today is that it’s an incredibly labour-intensive, manual process and involves a lot of top-level orchestration, whereas we mere humans can introduce error. We also seem to have a labour shortage. These amazing Machine Learning and AI tools have the potential to revolutionise how we work, from optimisation to building completely new tools and services. They were made possible due to the technology only being found in the cloud and being able to process huge data sets on custom silicone. The current travesty here is that in film and TV, workflow is predominantly not in the cloud and is therefore, for the most part, unable to leverage this.
I doubt most people aspire to filling out spreadsheets or copying data from a FileMaker Pro database into their vfx tool or watching render bars. We leave aspiring talent tied up for two to three years doing donkey work, almost as a rite of passage. Imagine if we can free these people up and have them doing what they are good at, doing what they really want to be doing. We would get people trained up faster and our talent pool would open up.
The film industry is ripe for a serious upgrade in how we are working, with new tools, tech, optimisation and AI/ML I would argue that we are about to see a bigger transition over the next 5 years than that of the impact we had when we moved from film to digital.
Next Gen
SIMPSON: In a traditional post facility model, there are staff waiting for people to die or move on. They’ve been sometimes working for ten years to get to the job they want. With media creation becoming more democratised - a lot of new technologies are already on our phones. People will be able to create higher quality images more easily. And in turn all these lower-level skills become increasingly redundant to a point where nobody will ever need them. Amateur filmmakers will not need them and will be coming in at a higher level and finessing, rather than grappling with the process.
Where does ML, MA & AI have a role?
PALMER: Seagate uses AI a lot and it’s the low-level functions that we’ve automated, In our production processes, we pump out millions and millions of hard drives every day. We start off by digging raw materials out of the ground and then a year later - and 26,000 processes later - we produce the hard drive.
ML I would argue that we are about to see a bigger transition over the next 5 years than
that of the impact we had when we moved from film to digital.”
TOM MITCHELL MISSION
“With new tools, tech, optimisation and AI/
And every stage of that process is photographed. Using AI we compare it to a golden view of what hard drives should be. That way we can detect nuances within the design and when we make our ASIC devices, we can compare the actual product to what it should be. By doing so we can see when things are moving away from the high quality product.
This way we can intervene much sooner and before it
goes into the field. It’s all the low-level stuff that a human couldn’t do fast enough that we use AI for.
CHADFIELD: When you look at younger adults, many of them are already using the skills that we have all learned over the years, so those skills are transferrable. In a way, maybe our job then is to figure out and transfer their way of operating into what we see as relevant, and our way of operating.
WOODALL: What you’re describing is subjective. The whole point about speeding things up is that it’s binary, it’s either right or it’s wrong. The whole process that we intervene and interfere with is subjective. It’s creative. We like to have
Spring 2023
televisual.com 105
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148
