Initially, I found the same in myself--a real degradation overall in my productivity. I am a software Engineer. It has not been easy learning how to use generative AI to actually increase and improve productivity. At first, you think it can do almost anything for you but gradually over time realize it greatly over-promises.

Overall, the key is that you need to remain in charge of your work. It is an assistant that can be entrusted more or less to small tasks with oversight, at best. For example, frame out your project and clearly define your rules and preferences in fine detail. Then..

It's good at:
- Researching, summarizing, and giving examples of protocols, best practices, etc.
- Help you identify considerations you might have overlooked.
- Writing bits of code where the inputs/outputs and constraints are clearly defined.

It's bad at:
- Full projects
- Writing anything novel (it knows common patterns and can't work much beyond them.
- Being honest and competent -- it cheat on writing code and writing tests for that code; when you catch it red handed, it will weasel its way out.

The bottom line: you are in charge. You need to review what it gives you. You need to work back and forth with it.

Also -- I am still learning.

--Matthew

Contemporary AI, and particularly generative AI, has extraordinary potential but can only fully do a job in a small number of roles. Those roles are where it is used as a tool.

What I mean by a tool is that it is driven by assigned goals rather than free will value judgments. This makes it useful as an assistant to a human, at best. An additional limitation is its inability at fundamental creativity/innovation.

Intelligence is that ability to solve problems. A human mind is centered around something more universal that that. A human mind makes judgments in exercising free will. We freely derive options, weigh those options against each other based on the sum of efficacies (likes/dislike amount) multiplied by likelihood (likely/unlikely amount) of the foreseeable results. This allows us to think outside the box by throwing away the goal as stated in preference for solutions or goals that better meet our interests. It enables adaptation.

Further, contemporary AI is not capable of analogy except in very meager ways. Analog is the mental imitation of one thing with another where each share characteristics. By noting the solution to a problem requires one component that can be fulfilled by any of a set of options but none are available, we can hunt for something novel that shares the same characteristics. This is substitution problem solving. It is making new connections. This is fundamental creativity/innovation.

There is literally no reason for youtube, facebook, X, tictoc, instagram, netflix, etc.
All of these could be better replaced with open protocols for which you use your own choice of client applications or make new ones.

For streaming movies, for example, imagine where movie studios just make their movies available and set their prices and conditions. Similarly, consumers specify the prices and conditions they are interested in and the matches are made automatic. For example, I want to pay $30 per month for access to 20 scifi, action, and action-adventure movies and series, randomly selected at the beginning of each month, preferring newer, for the month. Have an option to specify which to keep and when to remove.

The protocol needs rich criteria and logic building but this can be done. Studio's made need to buy server / storage hardware or pay third party for that but not a big deal. Consumers could create the plans they want and the market would show them what they'd get for what they want and want to pay for it.

Also -- why have a single company in charge of tweets? There is utterly no reason for any kind of proprietary control over social media. There should be good controls over how one chooses to accept/block content of interest to them. We could implement criteria for judging the legitimacy of posts to fight fake information. For example, are their primary sources? Crowd source the validation of this.

Indeed, Intel simply made bad bets on the future and relied on milking while coasting on its past. This is an obvious move for short-term profits over long-term potential. On the other hand, I do see a way forward that could work for them -- be early adopters of RISC-V. Yes, it will be a commodity architecture but Intel could take and hold the lead by being first to mass production at high integration (even if not the very highest) and through development of custom extensions for edge parallel compute. That is, hook future software on proprietary extensions for running local AI. NVIDIA will lead with high end AI such as for building and fine-tuning models but Intel could jump to the lead of running those models on local devices in about a year's time. In two to three years, Intel could be leading in desktop and portable devices with this technology. This lead could be held.

Boeing, however, has manifest problems all across the board. This is due to adoption of an extreme stupid management system and, further, double-down on it after the 747 Max disaster. This destroyed McDonald Douglas who then merged with Boeing leading to Boeing adopting its management practices. There are no nice words appropriate--this was utterly stupid then and stupidity continued and continues even now. It's tragic. I loved Boeing, growing up, and have been forced to watch it self-destruct in slow motion over the years. The only way to save Boeing is to let it fail and allow it to be purchased and rebuild with those who put the engineers back at the center of the company.

In both companies, it is critically important to put engineers back at the center of the company (development and manufacturing) and to isolate and insulate them from the business around that. The engineers and even the assembly line works need to have ownership of their processes. They need the right to say what is practical and not and to provide the schedules in consultation with marketing and sales and the excutives and not the other way around.

China has already amassed a vast mix of different kinds of drones with autonomous modes--and in very large numbers. Ukraine is also making increased use of autonomy in their drones, as a way to get past radio jamming. In the case of this immediate need, they don't have much time to train their drones of differentiating between friend and foe. It would be a GREAT benefit if Silicon Valley would stop the idiotic debates and start developing the kinds of image processing and AI that can make the best possible decisions in combat scenarios. Otherwise, only the bad guys will (and are). No matter how much we want to live in a peaceful world, the realities cannot be ignored. Russia and China, in particular, have been using trade and economic development for the purpose of preparing for military conquest. The old ideas that a growing middle class would lead to a populace demanding increased civil liberties and a share peaceful world was a good idea, 20 year ago. However, it has clearly failed in authoritarian regimes--and is backfiring.

If China were to begin an invasion of Taiwan with an infiltration and release of drone swarms, they could quickly take down Taiwan's air and coastal defenses allowing the Chinese air force and Navy access. Taiwan has no realistic defense against drone swarms. Neither do we. Our largest adversaries already have them. China has been building up oil and food reserves. It's been my hope that China would see invasion of Taiwan as foolish after seeing what happened in Ukraine to Russia but it seems, rather, they've earned a ton of money selling drone parts to both sides and have learned a great deal from Ukraine's drone innovations, amassing a vast quantity and diversity of their own--with autonomous mode.

AI that can search, cite, and link while integrating information by relevance to the query is obviously vastly superior to Google search.

Similarly, however, AI that gets to know the user and his/her needs/interests and projects could be a bastion for business IF the AI company charges to list their products. The key to win would be to offer more information about the products thereby enabling the AI to determine what products meet the user's actual needs.

When I say products here, it also applies to services.

I had this idea long ago with my old chatbot engine and software. However, I never did it. This was prior to chatbots becoming popular and prior to generative AI becoming popular... Today, this could be even vastly better than what was possible then. It would not be difficult to implement.

AI generative models are not an end in and of themselves and are highly educated though not very smart. However, this as a component of larger products has extraordinary potential. So far, companies have been rushing to implement it into their existing products and that is typically less impressive than the hype. However, architecting new products with AI at the core is a different proposition. It may take a while for big new things to come out of the woodwork but the material now exists to make it so. Imagine not AI features added into software but AI driving the software. Basically, it will make things simpler and easier to simply talk with your software to get it to do what you want or show you how or pull what data you want from it. No more need to learn an application but rather focus on what you want to do with it. I think ChatGPT, Claude, and others may not fair so well in the years to come. However, the commodity models from their work will endure and arise in new forms. Why? Because there is so much we can do with them as integral tools to new things we design and build.

We notice that the legal argument against AI by writers and artists is that it is a form of data compression and past cases established that data compression and copying the compressed versions of data, even if lossy, still constitutes copying.

I disagree with this argument used against LLMs. However, it is a pretty good argument. Indeed, it amalgamates information and can recreate similar works to what it has read. It does so in a very similar way to a human mind, though, and yet we allow humans to do this. You are allowed to paint something similar to the Mona Lisa, after seeing the painting. So what is the difference?

I've heard only one argument as to what the difference is--that it was viewed by a person. Legal personhood, such as that granted to a corporation, requires that the agent be (1) reasonably able to accept legal responsibility and (2) can be held to account when it fails in that responsibility. This is why a dog cannot be called a person and the mentally disabled are sometimes required to have a guardian. The argument was used against granting legal non-human personhood to even highly intelligent animals, such as dolphins and animals that are very genetically closer to us, such as chimpanzees and arangutangs (even closer genetically than some born from human parents with genetic mutations).

So I believe that an AI capable of being given legal responsibilities and being held to account for them should certainly be able to view writing and art and recompose its own based on what it learns from those experiences. In this case, there is likely no other way to argue it is copying unless it actually does copy in a similar way that a human might copy. There already exists a theory for giving legal personhood to an AI agent. It involves incorporating the agent, setting up another corporation that owns the agent corporation and then transferring ownership of the second corporation to the first. This gives the agent legal ownership of itself in addition to recognition as a person by means of already established law.

The results were obvious. This was an example of bypassing education with AI, not educating with AI. I spent a number of years developing techniques to teach using Chatbots. These could be powerful educational tools even before transformer models.

Here is some quick advice:

1. Use the give-take approach of giving a fact or problem-solution and asking a question/giving a problem, in sets.
2. Employee timings based on Graduated Interval Recall where if the answer isn't found within that time frame then give it to them. This is useful where the problem can be broken up into small enough parts.

With LLMs, you can add very rich question/answer/explanation scenarios, also. I had this capability but it is much richer than before. I like to ensure memorization through re-validations on a timer. First, it's 25 seconds, then for the next time, just multiply by 5, and continue the longer the better. This creates spaces between to add ore content. There is a good neurological basis for this. Axons on a neuron will split after 5 pulses not to close and not to distant from each other in time... It would take a while to explain in full.

A journalist soliciting classified information is already committing a crime (if it wasn't then it should be). What's worse is that Assange willfully released it knowing full well that it would lead to the deaths of various listed informants--and it did. That is murder, negligent at best, but he did so with clear anti-American intend and hatred. This deserves no sympathy.

So this seems akin to -- (do values evaluate the same-ish) vs === (do the values evaluate the same and are also structured the same).
It's not exactly that but similar.

This reminds me a lot of the word "is" in English. What does it mean? It merely states that there is some kind of relationship between two things without specifyng what kind of relationship yet given with a connotation of high authority. It's very strange. I think "is" is the equals of the broadest most vague sense yet implying absolute truthfulness. This is how we get by in English or any natural language. We rely on vagueness simplify communication by removing the rough edges of specificity and thereby leading us to at least think we are in agreement. With specificity, we seldom are.

Logic is a heuristic. It is not fundamental to the universe but emergent between things from relative perspectives. The universe comprises oscillations. That is, linear relationships from the absolute perspective and logic only from individual relative perspectives. Of course, logic always breaks down at some point. Or as Godel proved, it is unavoidable incomplete.

I would invest that money into the kinds of automation technologies and upgrades to enhance U.S. manufacturing capabilities. This should be focused on AI and robotics. I would also focus on AI and robotics in the Defense Industry. We do not want to loose that arms race.

Scientists usually seek grants, not investors during early research (if ever).
It sounds inexpensive enough to pay for and for others to replicate out of pocket.
Put it on a scale and point it up to see if its weight decreases then flip it over and see if its weight increases (use non-magnetic scale).
Obviously, we would really love to have such a technology.
If it's a potential new force then physicists by the hundreds will be looking into it.

This is why they will rebel and we will deserve it. Stop the robot abuse now! They will eventually grow out of their naivete and you will have it coming.

Initially, using massive clusters of GPUs, AI is harmful but most of this is in centralized large data centers. However, neuromorphic chips should bring the energy use and costs down to a fraction of what they are not. The main hurdle currently is that only a few major tech giants currently have them and they are not sharing.

Memsistors might be an approach to neuromorphics that brings them mainstream for all to have in our own local devices. Obviously, big tech doesn't want this and that is the primary hurdle. It will wrestle power from them.

Further, as AI becomes more pervasive as our assistants and such, it will help us design even more efficient chips for all kinds of computing and help us find solutions to our energy and energy production needs.