To be a good engineer you had to be stubborn in the early days of your career, you had to have tenacity and persistence to understand and solve the problems you saw. Unfortunately that filter selects for people who are prone to going too far into the detail, who get a satisfaction from conquering complexity rather than avoiding it. Given the lack of big picture thinking, they readily fall into herd-like behavior. Given the ego payoff for the complexity, it is very difficult for them to accept criticisms about their process or lack of perception.
Skill loss works both ways. You might miss out on forming early skills in using llms effectively, and end up playing catch up in 3-5 years from now if LLMs mark all the skills you hold to be void.
It is also likely LLMs will change programming languages, we will probably move to more formal, type safe languages that the LLM can work with better. You might be good at your language but find the world shifts to a new one that everyone has to use LLMs to be effective for.
Is there really that much skill involved in using LLMs effectively, most of the criticisms I see end up being countered with something along the lines of "you're not using the right model". That implies that much of the skill people talk about is less important than picking the correct model to use (which tend to be the more expensive ones).
And in your LLM future, who will maintain all of the legacy systems that are written in languages the LLMs don't end up assimilating? It's reasonably safe to assume there will be plenty of work left there.
Ontological frameworks have been around for many decades. They have had limited success because it is very difficult to represent knowledge in an object oriented approach. An example is Ologs:
The main difference is in Olog, each entity is represented in memory and then a mathematical set of rules are used to make computations on these entities to yield desired result(s). Also, each of these entities have to be manually written and saved. In my approach, the representation of entities includes the connections, attributes, etc., all of which can be automatically learned by the imosyn system, much like how learning is done in ML and its sister algorithms.
Also, I believe my approach would lead to adoption because there is no need to know set theory, you simply write code as you normally would, we have abstracted away all the complexity; making it more intuitive.
In the end, you can never tell whether or not people will use what you are working on.
It is logical to assume self driving cars start off in low risk areas, prove their concept, and gradually take on more and more use cases. Things like retirement villages and so on, where this incremental approach has already had success. Skepticism is easy when thinking about the grand scale but this incrementalism will surely win out in the end.
Given the complexity of the setting, there is far more possible causations than the implied by the finders of this correlation.
For example, perhaps better funded hospitals have more females on staff, and that drives the clinical outcomes.
Or maybe this relationship was one of thousands of possible relationships, as it is clear they have examined as much, and it's just appeared statistically significant by chance.
Hornsdale power reserve derives most of it's revenue from selling ancillary services, it does not provide energy. Hornsdale's success is not foreshadowing a battery revolution unfortunately.
The reason nuclear costs so much is because we've stopped building plants. There's no industry anymore, let alone economies of scale. It's a fallacious argument to say it costs too much. If we went all in on nuclear to tackle climate change the costs would quickly collapse. After all, they were built in the 70s and dramatically decarbonized the industry at the time.
For example, EnergyAustralia just announced that they're pulling forward the retirement of Yallourn coal powered station by four years [1], and building a 350MW utility scale energy system to absorb and discharge renewable power to offset the coal plant being retired:
"Under the agreement, EnergyAustralia will retire Yallourn in mid-2028 and build new storage capacity through a 350 MW, four-hour, utility-scale battery project that will be completed by 2026. This ensures energy storage is built to firm increased renewable energy in Victoria, before Yallourn exits the system.
EnergyAustralia’s goal is to be carbon neutral by 2050. Yallourn’s retirement will reduce the company’s emissions profile by 60 per cent, accelerating the pathway towards achieving this ambition."
As we speak (mid day local Victoria time), renewables are providing 48% of total electrical demand in Victoria [2].
"But what is clear is that there is a massive shift happening here. Coal is on the way out, as is “baseload” gas, and wind and solar and storage facilities, particularly battery storage, are on the way in. And many of the biggest batteries are being planned at the sites of coal and gas generators already closed or expected to close in coming years or decades."
"The sizes it is mooting are up to 500MW (and maybe two hours storage) for Liddell, 250MW and four hours storage at Torrens (1,000MWh), and 200MW and four hours storage (800MWh) at Loy Yang A. The final call on storage duration will be determined by the sort of market opportunities it sees for the batteries in the different states – longer for storing excess wind and solar, shorter for grid security services."
No, the enterprise value of the company doesn't change. It was owned by 4b of debt before and is now owned by 5b of equity, and has 1b in cash. It also has an intact business.
Under your logic no company would ever raise equity to pay off debt.
