jschveibinz

I love projects. But this reminds me of the model we used in signal and image processing:

- detection: just separating signal from background with confidence

- classification: confidently separating different signals in noise (e.g. PCA)

- identification: requires ground truth (perfect example), but still difficult to do with confidence

If the scientists have ground truth, e.g. "this call means [xyz]", then it’s a signal processing problem to do identification.

If there is no ground truth, then AI would have to have a lot of background data on whale activity at different locations including visual and audio content to try to infer (and that's about it) what the vocalization might mean. And this is where science starts to leave the building.

It's kinda' like inventing the Rosetta Stone using AI: probably not going to be very accurate or repeatable.

I've been thinking a lot about this transition happening, and I hope that everyone is recognizing that this is what the web will probably look like very soon.

URL-->user request--> answer & visualization.

No more static menus, buttons, lists, etc.

There's probably some good work out there helping small businesses adapt to this change? If done right, the real-time token use to generate dynamic pages can be minimized.

Some attempts to benchmark and compare LLM's:

https://artificialanalysis.ai/

https://benchlm.ai/

https://tokenscost.com

https://www.vellum.ai/llm-leaderboard

Are there any projects out there for doing something similar?

This is very, very well done. Thank you.

The implication that biology defies physics is just incorrect on many levels. There is no "truth" to ignore. The claim that life requires information that is otherwise not defined by physical laws is a misinterpretation of what defines information. A tree "bending toward the sun" is an emergent property of underlying physical laws--not a creation or demand for information.

I think it's important to to point out that there's a lot of "slop" to go around. I mean, making bad decisions and/or projections based on flawed data is nothing new.

The entire human scientific community has been shown recently on average to be generating "slop" and calling it science.

My bottom line: humans generate a lot of BS--and LLM's are just averaging it and giving it back to us.

Nic Zentner is a great lecturer on the unique geology of the Pacific Northwest. He has a lot of interesting material on YT. Here is the link to his academic webpage:

https://www.cwu.edu/about/directory/geological-sciences/nico...

For those interested in brain evolution and practical neuroscience/human behavior:

Carl Sagan's books "Broca's Brain" and "The Dragons of Eden" from the 70's-80's are good initial reads on brain composition and evolution. The Sapolsky lectures and books are also great resources.

That's very plausible and a good point!

So here is an excellent example and interesting race condition that is developing in AI applications and venture-backed startups: what is truly AI-resilient enough to invest millions of dollars in a "land grab"--when the land is literally falling away with each passing day.

There is no doubt that AI will be helping to run businesses. But, there is also little doubt that existing businesses will have the wherewithal to develop the proprietary tools they need to do this work--without needing to buy it from others.

With respect to scope, it is not an overstatement to say that developing trust in AI tools enough to allow them free reign over critical business workflows may be a bridge too far. Look at self-driving car technology--that last 10% is really, really hard. So adoption will be slow and methodical.

There are no secrets. The basic model for starting and running a business (or a project) is straightforward. I like many of you have developed my own models (using AI assistance) for doing this and have been testing for months. And I like many of you have 30+ years of small business experience to back up my models.

This says nothing about the R&D happening at every major corporate & professional software services company in the world right now using the same AI tools to build their own solutions--let alone the enterprises who would be customers doing exactly the same thing.

So there is an enormous amount of competition and what I am calling a "race condition" between the crowd trying to disrupt professional services with AI-developed software and the crowd already in professional services software e.g. SAP and business in general that is doing the exact same thing--with more capable tools, more staff and much more R&D funding.

For the big companies, it's a race to survive.

Investors in startups have a tough decision to make on these small startups that have so little moat and so little prior knowledge and experience.

As an angel investor myself, I am skeptical of investing in any kind of software right now--and maybe forever.

On the plus side, every freelancer now has the power (and responsibility) to optimize their own offerings.

It's going to be very, very interesting...

Look, folks. I'm an old man and pretty much out of the workforce at this point in my life. But what I see in these comments day, after day is rather pitiful. As your Uncle John, I'm going to give you a lecture:

From the article:

>I can’t really blame anyone who’s just gone along with things

Whst is this supposed to mean "blame"? Do you "blame" people for driving cars instead of riding horses? Do you "blame" software engineers for eliminating entire industries?

Folks, writing software can be fun and satisfying. Do it for the enjoyment. But suck it up, because the technology wave you rode to a well-paying job was always going to be short-lived for the majority of programmers.

Just like the engineers who built steam engines, or living room radios, or tube TV's, or Internet dial-up modems and long distance phone networks, or whatever other technology that has come and gone: just move on, because you helped to put those people out of work--and they moved on.

You're going to drive yourselves crazy with the constant re-hashing and sobbing over the obvious changes that are happening. Figure out how you go forward and start going forward.

Not much discussion here, but this is a good observation about not just incorporating something like creativity into your life, but how much emphasis we all put on incorporating pleasure into our work lives. That transition happened sometime in the 80's.

I'll step up to the mic and give my opinion:

There was a time when most jobs were physically demanding in one way or another. Working on a factory production line doesn't allow much opportunity for adding creativity into your work. The answer: avocations.

My dad was a machinist--which can be a very creative job--but he always preached to me the need to have activities (creative or otherwise) outside of work. He played music, built projects in the garage, and played around with drawing and painting at home.

We are likely transitioning away (once again) from a majority of jobs that include opportunities for creativity to a majority of jobs that are just plain "work." So it's a good idea to remember that time outside of work can be the opportunity to be creative.

I can hear the comments already about no time outside of work: family, commute, etc. But remember, if you want something bad enough, you will make time for it.

I'm just as likely to neglect this fact as anyone else: we only get one chance to live. If it’s important, then make time for it.

I yield the floor.

Other papers/articles on this topic:

Tierney:

"The Reign of..." https://www.nytimes.com/2015/10/04/opinion/sunday/the-reign-...

Kinnaman:

"Is it Time to..." https://www.vox.com/2016/2/13/10972986/recycling

Kinnaman, Thomas C. "Determining the Socially Optimal Recycling Rate." Resources, Conservation and Recycling, vol. 85, 2014, pp. 5–10, doi:10.1016/j.resconrec.2013.11.002.

I volunteer mentor at the Rady School Sullivan Center at UCSD. It's a very good program that offers no-credit "learn by doing" programs that pairs entrepreneurial-minded students with technology startups. Everybody wins.

The most important lessons for students include: realistic expectations, a new vocabulary, and a real-world understanding of the complexities of building a startup.

I've worked at other universities where startups are treated more like sports to get interest and social media attention: competitions, events, socializing, etc. Those are nice things, but they are superficial in my opinion.

My advice for students would be to find opportunities to get in the trenches and experience the challenges and hardships faced by founders--those are the most important lessons.

A water bladder above the cockpit might help? I'm not a nuclear physicist, but I stayed at a HolInn Express last night...

Lesson: competition applies to spiritual pursuits as much as any other aspect of human existence.

Wow, this is important. This experiment needs to be duplicated (by many different people) and then we will know more. Right now I'm wary of buying SanDisk cards...

Learning is accelerated by writing down notes, etc. Research has shown this brain loop is important.

blacksmiths--gone machinists--mostly gone hand looms--gone wheelwrights--gone newspapers--mostly gone circuit designers--mostly gone draftsmen--gone secretarial workers--gone

Automation (software and mass-production equipment) has transformed our world and our lives.

I'm just some guy watching what's happened over the past 50 years. But this trend is only accelerating. There will be many more industries that are "transformed" by technology.

tl;dr statistical estimates still hold

Funny, but also scary.

Here are just a few of viewpoints on what constitute world problems to solve:

https://80000hours.org/problem-profiles/

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

https://encyclopedia.uia.org/

Interestingly, one list identifies "AI" as a top world problem! One person's problem is another person's solution, I guess--and vice versa, as well.

An extreme example: curing a disease is good for patients but bad for the healthcare industry--which is (in kind) also bad for healthcare workers and everyone in science working on cures.

The problem can be fixed: adjust or realign the business model and incentives around the needs of the stakeholders.

- Students want a credential because business and society demands a credential

- Universities want income to support their missions of research, education and other "motives"

- Professors want to do research, and they don't really want to teach (in general)

- AI is a resource for learning, assessment and project development

- Society needs educated people

What ideas do you have to make everyone satisfied and maximally functional?

Hmmm, that's currently the 46th best post on HN right now?

I read the post. The title is correct, but the article itself doesn't follow, because the major theme is about caring. Caring about something enough to build it out is important. But "caring" does not equal "customer demand".

I'll be presumptuous and suggest a better theme:

"Success begins and ends with the value proposition--if customers want it bad enough, you have a potential path to build something. But...don't build it if you don't care about it."

Great. The problem you are solving is "learning." But I don't think you are effectively solving it yet.

For example, a powerful technique is to take notes on everything you read--by hand, on paper. The motor-visual-processing loop is extremely powerful for learning.

Another: teach someone what you think you learned. If you can do that without your notes, you really understand it.

Check all available learning research and try to incorporate the most powerful techniques into your MVP solution--and then do community sampling again.

Keep going...

Fear is normal. Jump in.

Learn how to learn. Get really good at that. That is the essence of your college experience. Perhaps consider a minor in a life science, hard technology, or other field that has good employment prospects.

Everything is software these days--it's going to be about applying software rather than pure programming skills.

Take a physics class or two. Learn how and why things work.

If your school has co-op work--do it. It raises your employment prospects at graduation.

Expect to have to consider multiple options/tracks in your career ahead. Adaptation and flexibility will be important. The world is changing quickly.

You can't study everything in college, but you will need to be ready to study and adapt in the years ahead. Good luck and do well in school.

One more thing: be social. Your network of friends will be extremely valuable to you.

This is a nice post. I like the message and the shared experience.

It has been my experience that perhaps the most important "tentacle" not included in the post is the social network.

The more communities one belongs to, the more robust are the opportunities for that individual to find work and assistance when it's needed. Join, participate and help others--that is a good safety net.

Serious question: can we define the top 5 parameters that distinguish human-generated non-fiction writing from AI "slop" writing (I don't like the term "slop" because it carries a built-in bias)?

Since AI output is based on the most likely next word/token given the context, let's consider how humans do this, i.e. choose the next word based on context.

Is one of the parameters the degree of "randomization"?

Are these other parameters?

- Specific experience?

- Recent exposure to relevant materials, i.e. paraphrasing?

- Borrowed knowledge from a specific resource?

- Writing style, i.e. choice of words or sentence structure?

I'd like to hear what others think.

Geez, growing up in the 60's and 70's I watched a lot of TV: Little Rascals, Three Stooges, Bugs Bunny--the good stuff. I guess without that time spent watching my brain now would be the size of a large watermelon.