We're talking about different things here. I'm talking about advancing AI to automate technician knowledge as related to profit-interfacing business processes. E.g., for customer SLAs and network-performance metrics.
The example - without AI - was for a specific, IBM network-outsourcing business model. I'm certain the cost-benefits were fully in place. Fact is, the knowledge can be automated at a high rate and at acceptable costs. Those workers lost their technical positions, which they occupied in those business processes. This was an example showcasing the state of knowledge capturing/disruption in the industry in 1996. I believe some industry giants have been working on "industry on a chip" approaches. Bot support services are growing. Given the rate of knowledge intermediation by AI, this sea change is heading towards a constant. Soon, quantum processors would kick it into high gear. W.r.t. your metrics, if we were to conduct a knowledge-automation project now, the data you mentioned could probably be calculated. This data in the example has been matured as applied knowledge. It already exists in an optimal (compressed) state. Any performance impact could be managed via capacity planning. On Thu, Jul 11, 2024, 07:26 Matt Mahoney <mattmahone...@gmail.com> wrote: > > On Wed, Jul 10, 2024, 6:23 PM Quan Tesla <quantes...@gmail.com> wrote: > >> In 1996, I contracted to IBM network management outsourcing to help >> automate back-office jobs. We automated 2 customer-facing business >> processes to IBM world and ISO stabdards, with complete organizational >> transformation to BPM level 3, within 8 months. >> >> Point being, it takes less than you think to capture process-related >> knowledge, to replace technical, people workers. >> > > How many bits were encoded in your project? (The compressed size of your > source code). How much did it cost? Normal software productivity is 10 > lines = 160 bits per day. IBM has 300K employees at $200K revenue each, > which comes to $100 per line or $6 per bit. > >> >> >> Last, when enabled by quantum processors, this tacit-knowledge-enginering >> process becomes possible in near-real time. The bastions of traditional >> knowledge-based control is fast nearing an event horizon, to be replaced by >> near-total control infrastructure. >> > > Quantum isn't magic. It does not speed up neural networks because they > perform time irreversible operations like writing to memory. The brain is > not quantum. It's intelligent because it has 600T parameters and 10 > petaflops throughput, running on 300M lines of code equivalent in our DNA, > which was programmed by an algorithm performing 10^50 transcription > operations on 10^37 DNA bases consuming 500 TW of solar power for 3 billion > years. > > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + > delivery options <https://agi.topicbox.com/groups/agi/subscription> > Permalink > <https://agi.topicbox.com/groups/agi/T59fe5c237460bf34-Mf6d62e01990dbef2b10d7817> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T59fe5c237460bf34-Ma0b08e72d692e706507671f9 Delivery options: https://agi.topicbox.com/groups/agi/subscription
