larryfreeman

My 3 favorite non-fiction books are:

* Mindset by Carol S. Dweck

* Innovating: A Doer's Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong by Luis Perez-Breva

* Fall In Love with the Problem, Not the Solution: A Handbook for Entrepeneurs by Uri Levine

These three books really changed my viewpoint and I've been rereading them every year.

Each time I have been laid off, the opportunity afterwards has been significantly better. Perhaps, I have just been lucky.

After my first lay off, I got a job at Sun Microsystems in 1999. I was able to buy a house. After my second lay off from Sun in 2007, I was able to receive a significant promotion as a director. After my third lay off in 2017, I was able to find a great opportunity at Walmart where I no longer have management responsibilities.

If I hadn't lined up my next job so quickly, I definitely would have started my own company or consulting business. The most important thing is to believe in yourself, stay current, and prepare to ride the next wave in technology. :-)

I think that we need a better definition of creativity. I suspect that ChatGPT is merely derivative (like a person who reviews all the ideas out there and attempt to pick the best ones) as opposed to original (breaking the conventions typically by a person with a unique viewpoint). This begs the question of what is the definition of creativity and how can we be sure that human creativity is not also derivative. I am scratching my head on this one. How can we define creativity so that it clear that human beings can be original without being derivative?

If you are asking me. Of course, I've spent many hours on it. I have not seen anything that I would consider original. I am very impressed by the quality of responses and how well context impacts the content. It's seems most amazing at summarization and categorization.

I am very surprised by the results of "Let's play Dungeon and Dragons where you are the Dungeon Master" or Write new song lyrics or Complete the following poem or even write the following essay or chapter of a book.

I am surprised by the quality of the response in terms of flow. It sounds very much like a college undergraduate to my eyes. At the same time, it sounds like a well-read, undergraduate who doesn't fully understanding topics beyond the fundamentals. On the fundamentals, ChatGPT is surprisingly strong.

That's a summary of response based on my many hours of using it.

Thanks! I appreciate the explanation. I think that you put your finger on the major assumption of the argument.

I'm not sure why you disagree with the Chinese Room argument. I would be interested. I agree that Searle was solely a philosopher who did not take an engineering viewpoint.

Searle's main point is that if I have a book that tells me how to respond and I never learn Chinese, then I do not understand Chinese. If you see a flaw in this reasoning, I am very interested.

My point is just that LLM models are a compression of the content available on the internet equivalent to a rule book. It is definitely fascinating how powerful LLMs are as far as summarization and forming coherent responses to input.

I am a big fan of Kahneman and agree with you that it is will be very interesting to ask GPT-4 the questions in that book.

I agree. The model is where the intelligence is which is the compressed intelligence latent in the training data.

I am arguing similar to John Searle that the processing is not intelligent. The model is a Searlean rulebook.

https://en.wikipedia.org/wiki/Chinese_room

Agreed. LLMs process information on their own.

I thought you were saying it was "obvious" that the processing demonstrated intelligence.

My point was the level of intelligence shown is relative the quality and quantity of the data used for training. The data is where the intelligence is and the model is a compression of that latent intelligence.

Let us disagree on what is "obvious". Given an input and an output, you believe that the complexity of the output proves that intelligence takes place.

I agree that ChatGPT is more than a proxy. Unlike Clever Hans, it is processing the content of the question asked. But it is like Clever Hans in that the query is processed by looking for a signal in the content of the data used to train ChatGPT.

The real question is where this intelligent behavior comes from? Why does statistical processing lead to these insights?

I believe that the processing is not intelligent primarily because I see that holes in the data available leads to holes in reasoning. The processing is only as good as the dynamics of the content that it being processed. This is the part that I believe will become obvious over time.

I suspect that there is something else going on than intelligence which will become obvious over the next few years.

There was a horse, "Clever Hans" who appeared to have the ability to answer surprisingly complicated mathematical questions. Did "Clever Hans" have mathematical intelligence. Not at all. He was responding to a cue unknowingly being given by his trainer.

I suspect the same thing is happening with ChatGPT. What if all that is happening is that the text is being formulated to very complicated cues that are implicit in the very complicated, statistical analysis?

https://en.wikipedia.org/wiki/Clever_Hans

The best summer intern that I ever hired did not have any programming experience. He was a Berkeley student who had recently decided to change his major but hadn't started taking any programming classes.

When we interviewed him, it was clear that he was a serious student, he was very smart, and that he would work very hard if we gave him the internship. As the hiring manager, I turned him down because of the lack of programming experience.

I was overruled by our CTO. Apparently, the candidate was a family friend of the CTO and the CTO had strong confidence that he would learn programming very quickly.

The Berkeley student was given the paid internship. In an 8 week period over summer, the candidate learned ios programming, identified a problem that was impacting customers, proposed a fix, and was able to release the fix to customers. When he demonstrated the issue and his fix to the engineering team, it was clear that he had spent long hours working with the app.

My lesson from this is that the best candidate may not be the one who appears the best on paper. More important is a very smart candidate who is willing to work very hard.

If anyone is interested, I posted details on my approach here: https://bitcointalk.org/index.php?topic=5455261.0

Thanks for the tip. Bitcoin Talk looks very interesting. I'll take a look at the different boards!

To be clear, I was refering to this statement:

> What I am trying to say is, it doesn't matter if the subject is actually conscious. All that matter is we (I), feel/think that it is conscious, or deserving of care and respect.

My point was that it does matter if the subject is actually conscious. Human beings are easily fooled and it does matter if people mistakenly think it is conscious when it is not.

Not really clear what you are unclear about. My main point is that human beings have sentience and can reason about cobtext in terms of how an action affects others. Computers are following a statistical algorithm without any sentience or any understanding beyond the statistical thresholds. Ignoring the complexity and brilliant mathematics, it is at the core no different than a key word matcher like the classic application "eliza". Its performance is amazing but it is really the same algorithm at its core.

I apologize for not being more clear. I find it very challenging to distinguish between details about LLP and "consciousness". My key point is that "human consciousness" is very different than ChatGPT. ChatGPT is statistically processing content already created and "appearing" to be conscious. Human beings have characteristics that ChatGPT does not share (sentience and context). We are often mistaking the what is needed to generate content (human consciousness) with what is capable of processing that content in very interesting ways (ChatGPT).

I do not believe that I am confusing free will and consciousness. See my comment above. Determinism versus free will is independent of knowledge available. Consider a paralyzed person incapable of any action. That person if the senses are all working still has awareness and context. A statistical engine only appears to. A LLM model is basing all actions on a complex matrices of thresholds. It is surprising and amazing how well that works. Given stimulus that takes advantage of those minute differences in thresholds, a wrong response will be returned. Human are not fooled in this way. Minute differences are typically missed or even skipped. Human beings can be fooled by optical illusions and by contradicting context (a statement like pick the "red" circle written in green ink and the person mistakenly picks the "green" circle. LLM models do not make these types of mistakes.

Sentience and knowledge is what I am talking about. Free will is what you do with that knowledge.

I am baffled when smart people say something like this. Without consciousness, without a stake in the game, the behavior is pure statistics. Statistics is limited by probabilities and logical gates. Nothing else. Consciousness is about being aware which means insights about context and harm. People are limited in ways that a statistical engine is not and that makes all the difference in the world.

I am not saying that the left does not get outraged. I am saying that the mainstream media has not been able to capitalize on that rage for profit. That seems to me to be more the issue than an over-saturation of the liberal perspective or a lack of over-saturation of the conservative perspective.

Talk radio and Fox news are highly popular. As I understand it, before he was fired, Tucker Carlson was the most watched news-related show.

I agree that there are popular progressive and left-leaning shows that focus on outrage (John Oliver, Jon Stewart, etc.) but as I understand it, they are not as successful or popular as Fox News and conservative talk radio.

My point was not that conservatives are more outraged than liberals (I suspect that each side sincerely believes that the other sides shows greater outrage). I just meant from a business perspective, it appears to me that the establishment cable news services are struggling to maintain audience loyalty which tells me that they are not as good as conservative media at taking advantage of audience outrage.

I see the problem a different way.

Murdoch was successful in many markets before starting Fox in the US. His formula was to go after the tabloid market with higher quality content (plenty of stories on scandals and beautiful/famous people, some conspiracy theories, and also quality content). Conservative media had been dominating talk radio and there was plenty of conservative media (Heritage Foundation, National Review) for conservative viewpoints before Fox. CNN (via Ted Turner) had shown that cable news could be profitable and Fox/Talk Radio have shown that rage media creates a loyal audience.

It's not that mainstream media is liberal media as often claimed (I find much of establishment media: CNN, MSNBC, USA Today, People, etc. too superficial to be liberal or conservative -- it's just a business that tries to attract the establishment audience which tends to skew liberal). The real issue in my mind is that mainstream media has not been very good at rage media while Fox and Talk Radio have been virtuoso at it. Trump temporarily made it easy for CNN and MSNBC to thrive at rage media (all liberals could agree on their shock about Trump's actions), but without Trump, liberals naturally fall into infighting between progressives and establishment viewpoints (for example, Bernie Sanders versus Hillary Clinton or AOC versus James Carville)

I keep hearing this about abstract mathematics "never" having an impact because it is too abstract and relates to pure mathematics. It's not true. Mathematics is a formal system that provides insights on surprising patterns. Surprising patterns can almost always be applied outside their intended area. And not surprisingly, non-Euclidean geometry and even the inability for mathematics to find certain proofs related to primes has resulted in breakthroughs in other areas. Surprising patterns take time to have effect mostly because they are not generally known until some genius is able to apply them outside their intended area.

I am 99.999% certain that you are wrong to say that "this kind of abstract mathematics will 'never' have any meaningful impact on day-to-day software engineering." I would be less certain if you replaced "never" with "will probably not have a significant impact in the short term".

I believe that it is straight forward to show how Searle's Chinese Room argument can be applied to ChatGPT.

The idea is not the "process" for building the rules but the "process" where the rules are applied. True sentience requires a "true" understanding of the information that is being presented (otherwise, it is not reflective of sentience but rather trickery).

The Chinese Room applies because ChatGPT or any other computer is generating text purely through logic, that is, logical rules.

To make this point clear, let's assume just for argument's sake that at some quantum level, the electrical activity underlying a computer is partially sentient (I am not making this claim -- just using it as an example). Even if this were true, this would not prove that the sentience is involved with the text being generated by the same computer.

This underlying sentience would be like the "person" in the Chinese room. The text generated are the Chinese words which would be completely independent from the "sentience" following the rules.

Generating text at sufficient complexity to fool the average person does not prove that "sentience" is occurring. Generating text consistently in a way that is only explainable through sentience is what is required. ChatGPT, because it is easily shown to be flawed, does not rise to this level of evidence.

In the Chinese Room example, the complexity of what could be displayed by the rulebooks would have a limited complexity (though, the surprise of ChatGPT is that this complexity is greater than many of us would have supposed). Just like the story of Clever Hans the Horse (see https://en.wikipedia.org/wiki/Clever_Hans), once the limitation of the process is realized, it becomes clear that there is not "true" understanding of what is generated which means that the rules, in themselves, do not reflect "true" sentience.

We use Teams, Slack, and Zoom.

Zoom is much easier to use than Teams for conference calls. When I use Teams for conference calls, I find the default never works for establishing a connection (I have found that connecting through the browser works fine -- but this is not the default way to connect -- it still takes some effort to pick the path that works).

I have also found the user interface is non-intuitive. I had little trouble getting up to speed on zoom (from webex). I might have less problems with Team once I get used to its interface. At present, if anyone accidentally schedules a meeting with Team (which sometimes is the default conference call setting in our Outlook), they usually create a zoom conference call link which is used instead (usually, the notification for the zoom link goes out through slack -- for some reason, folks don't even chat over team to let folks know that the meeting has been changed to zoom).

As far as the application itself, I find that Slack is much easier to use and has capabilities that have been very helpful. The Slack user interface was intuitive and I quickly got up to speed. For Teams, I use it when I need to (not all times at our company have slack) but everyone I know prefers slack. The slack ui is more intuitive and has features such as slacking to yourself which is useful for notetaking. Again, my opinion of Teams might change as I better learn the user interface.