Anon84

  Location: NYC
  Remote: Yes
  Willing to relocate: No
  Technologies: Machine Learning, Python, LLMs
  Résumé/CV: https://bgoncalves.com/wp-content/uploads/2024/10/resume_goncalves_d4s.pdf
  Website: https://www.data4sci.com
  GitHub: https://github.com/DataForScience/
  Substack: http://data4sci.substack.com/
  Email: bgoncalves@gmail.com

Hi, I'm Bruno, a Data Scientist with a background in Physics (PhD) and Computer Science (M.Sc.) and 15+ years of experience in Data Science and Machine Learning. I am an Expert Python programmer with a focus on Machine Learning applications since 2005 and I'm currently looking for the next big project where I can have an impact. I am interested in full-time and/or contract opportunities.

Both Hopefield Networks and Boltzmann machines trace their origins and motivations to Statistical Mechanics and Spin Glasses.

Coincidently, Giorgio Parisi got the 2021 Nobel prize for his work on spin glasses

  Location: NYC
  Remote: Yes
  Willing to relocate: No
  Technologies: Machine Learning, Python, LLMs
  Résumé/CV: https://bgoncalves.com/wp-content/uploads/2024/10/resume_goncalves_d4s.pdf
  Website: https://www.data4sci.com
  GitHub: https://github.com/DataForScience/
  Substack: http://data4sci.substack.com/
  Email: bgoncalves@gmail.com

Hi, I'm Bruno, a Data Scientist with a background in Physics (PhD) and Computer Science (M.Sc.) and 15+ years of experience in Data Science and Machine Learning. I am an Expert Python programmer with a focus on Machine Learning applications since 2005 and I'm currently looking for the next big project where I can have an impact.

I am interested in full-time and/or contract opportunities.

As one of my Physics Professors once said, "Math is Physics without a purpose."

(I was once a reasonably successful Physicist, so I might be biased :D)

That’s an interesting question, I had never really considered….

There’s probably no definitle 100% safe way, but one possibility might be to exploit some quick of human perception (kind of like what optional illusions do), but of course there’s no guarantees that a sufficiently advanced future AI won’t be able to read it (on enslave humans to translate it).

It's usually a consequence of working in the microcanonical ensemble, where you're forcing the energy of the system to be fixed while exploring its various states. The most common (and intuitive) scenario is the Macrocanonical ensemble where the temperature is fixed the energy is allowed to vary. In this case, of course, there is no negative temperature.

Cool. I actually worked on this back in the early days of my PhD (https://arxiv.org/pdf/0705.2385). Never expected to see it on HN

I read somewhere (don't recall exactly where) that your sense of the passage of time is directly related to how much things change because that impacts how many memories you form. Essentially, when every day is the same, not many new memories are created, so you don't notice time passing (New Years was just a few weeks ago, how can it be July already!), but when things are constantly changing, time passes much more slowly. When you're a child and non-stop learning and exploring, 3 months seems like an eternity.

I wonder if technology development has a similar effect. When the world essentially remains unchanged for your entire life, things must seem much faster with days blending together.

People drown in bathtubs all the time...

There are only two obvious ways to minimize risk: teach people to swim or prevent people from swimming. Guess which one is more cost-effective?

Here is GPT-4o with a "Please explain your thinking step by step"

`The text describes a sequence of events involving two characters, Joe and Sue. Here’s a step-by-step analysis of the text:

First Sentence: "Joe drove Sue to university."

Action: Joe is driving. Destination: University. Passenger: Sue. Second Sentence: "Afterwards he drove home again and drank a tea with her in the kitchen."

Action: Joe drives home. Time Reference: "Afterwards" indicates this action happens after driving Sue to university. Activity: Joe drinks tea. Location: In the kitchen. Companion: Sue ("with her" refers to Sue). Logical Flow and Possible Inconsistencies Driving Sequence:

Joe drives Sue to university. Joe drives home again. This implies Joe drove to university, dropped off Sue, and then returned home. Tea Drinking:

Joe drinks tea with Sue at home in the kitchen. Potential Issues and Interpretation: Continuity Issue: If Sue was dropped off at the university, she shouldn't be home with Joe unless she somehow returned home quickly or didn't actually stay at the university. Timeframe: The text lacks details on the timeframe between dropping off Sue and drinking tea, leading to ambiguity. If there’s a significant time gap, it would make more sense. Otherwise, it’s confusing. Possible Corrections or Clarifications: Correction for Continuity:

"Joe drove Sue to university. Afterwards, he picked her up, and they drove home together and drank tea in the kitchen." This makes it clear that Sue is back home with Joe. Additional Information:

Providing more context about the events or timeframe could help clarify the situation. In conclusion, while the text describes a sequence of actions, it has a logical inconsistency that could confuse readers. Clarifying the timeline or actions would make the text more coherent.`

I was pleasantly surprised, to say the least.

Excellent deep dive (and surprisingly not self-referential). I read the dead tree version (https://amzn.to/4cs5EDA) when it first came out.

He also creates the first (at least that I could find) instance of a auto-regressive (markovian) language model as a clarifying example in the first 10 pages :)

Trying to put the finishing touches on a Python package [1] for epidemic modeling using Compartmental models. This grew out of a series of blog posts I started writing during the pandemic [2] based on my professional experience in epidemic modeling in a previous life where I was the lead developer for a state of the art global epidemic model [3].

[1] http://github.com/DataForScience/epidemik

[2] https://github.com/DataForScience/Epidemiology101

[3] https://www.gleamviz.org/explore.html

Time for a career change?