I think what is going to happen is that there are going to be many startups that start the business ground-up from minimum number of humans. So their culture would be completely different from the existing businesses and they can promote efficiency/cost reduction as the selling point to compete with existing industries. And if these startups succeed, then others might adapt their approach. Most likely, this is where we will start seeing disruptions when automated vs non-automated companies go head-to-head in the future.

I would be very comfortable if there are layers of safety in play such that I am not getting an opinion on just one a single machine. For example, multiple independent AIs that come to the same conclusion would be reassuring and can be done readily. A reflective module that checks these answers can be useful as well. Once you add multiple layers of protection and this system is proven to be very safe, then I no longer need a human doctor.

Basically, I feel like if you are going to give LLMs much more capabilities through utilizing 3rd party plugins, then you should probably use a weaker version of the LLM to save computational power. The amount of computation involved in answering a single prompt is much higher for LLMs with larger number of parameters compared to that of smaller number. However, you are seemingly getting better/more accurate answers as a result of using GPT-4 vs say GPT-3. But if the 3rd party apps can compensate for the LLMs in thousands of different ways, it would be prudent to use GPT-3 with TaskMatrix.ai as opposed to GPT-4. At least that is how I see it.

Imo, the emphasis on education should be less on details on more on grasping the big picture. Right now, the system is such that students put a lot of emphasis on knowing all the details in college and then building upon that knowledge to grasp the big picture when they are employed for at least 5-10 years in the same industry. Given that the AI will handle a lot of these details, the current education system that emphasizes gaining knowledge at this refined level is obsolete and useless. And if you de-emphasize the details, then you can spend a lot more time, looking more at the big pictures and as such accelerate the student's understanding and progression towards essentially managerial roles.

The algorithm behind GPT is based largely on accurately guessing for the next word given a sentence. This procedure is simple enough such that if you have a large amount of text data, you can write a simple script that can automatically retrieve the answer and you will get these solutions really fast with 100% accuracy.

This is also the reason why in some other industries, "training" procedure is much more cumbersome and expensive. Any field which requires experimental data (e.g. lifetime of a battery) is just not seeing as rapid progress with ML compared to other fields because there just isn't much experimental data and it is not easy to rapidly accumulate/conduct experiments. So training is difficult there in the sense that gathering big data is a huge challenge in itself.

The training via broad sensory inputs will probably come in the multimodal LLMs. So essentially, the next generation LLMs will be able to look at an image and either be able to answer questions regarding that particular image (GPT-4 probably has this capability) or just treat the image itself as the input and say something about the image unprompted (GPT-4 probably does not have this capability). I think the latter ability will make the LLM seem more AGI like given that the current LLMs only respond to the inquiry of the users. But if the AGI can respond to an image and if you put this inside a robot, then presumably, the robot can respond naturally to the ever-changing image that is seen from its sensors and talk about it accordingly.

I think once this happens, then the LLM will seem less like a tool and more like a being. This probably does not solve the symbolic logic part of building up knowledge from simple set of rules, but that is probably a separate task on its own that will not be solve by multimodality but by layering the current LLM with another deep learning model (or via APIs/plugins with third party apps).

Gary Marcus is wrong on this. There have been already papers published that trains simple machine learning models on publications made before date X and demonstrating that the algorithm can find concepts found in publications after date X. These were not even using LLM but simple Word2Vec abstractions where each of the words in the publications were mapped to vectors and the ML model learned the relationships between the numerical vectors for all papers published before date X.

I think there would still be tangential benefits but many of these benefits are quickly taken for granted. For example, let's say that the LLM can eventually do all the time-consuming tasks (e.g. ordering food, finding hotels, talking to customer service) that you had to do yourself previously. It is a clear benefit, right? But after a while, we just take this for granted and won't even see it as a clear benefit anymore. Is that us being spoiled or just the psychology of human beings?

It might be the case that US based companies cannot compete with other non-US companies that do not have this restriction. I suspect that labor costs are significantly high enough in the total budget to make a difference. Moreover, I just think US as a whole would decline significantly if you have millions of people who are essentially working as some sort of an unnecessary prop just so that the system remains in tact. It might be better than the alternative of massive unemployment but just doesn't seem like a satisfactory solution.

Seems quite inefficient though. Can't GPT just access the HTML or other type of codes associated with the website and just access the websites via the text as opposed to image?

Wouldn't it be more like the tech support is constantly monitoring your computer screen so you don't even have to print screen?

I think for this to be truly effective, the LLM would need to take in huge amounts of computer screen images in its training set, and I am not sure if that was done for the pre-trained model for GPT-4. But once this is done for all possible computer screen image combinations that one can think of, then it would probably be akin to the self-driving car type of algorithm where you can navigate accordingly based on the images.

But this type of multi-modality would be useful if you have the person actually sitting in front of the computer working side-by-side with the AI, right? Because if you want to eliminate the human from the loop, then I am not sure if this is an efficient way of training the LLM since these type of computer screen images are what helps a human navigate the computer, and not necessarily optimal for the LLM.