100% agree. Time series forecasting is really bad and there are some theorems that say it is *impossible for large class of series to be forecasted by more than a small number of steps..turning into a straight-line, meanwhile if you have say a handful of important day indicators of the same signal that are proxies, you can recover pretty much exact curve. Better to spend compute in my opinion or variable learning approximate Xt -> yt mapping than the X_t-4..n -> X_t mapping...
I think the thing is.. sorry Grant, but "motivated understanding" won't come from videos, but writing. Solving things too, but a video can only go so far.
""But I also promised several other things to EPSRC: firstly, that I would be making pull requests to Lean’s mathematics library, adding fundamental objects from modern number theory; this is ongoing. And secondly, and perhaps most importantly, that I would be creating a dynamic document enabling humans to explore the modern proof. My guess is that it is unlikely that Anthropic are going to do this; they will feel that their job is done with the formalization (and they did not formalize the modern proof anyway)."" lol if you say so buddy...
The algorithm's novelty and recommendation accuracy is so far beyond what other competitors like Google, Snapchat, and Meta have that this seems like a coordinated effort by the private sector to push forth their mediocre products and centralize social media service which I absolutely DETEST. Mark Zuckerberg has publicly announced how far behind Tiktok Meta was. Many years later they are still playing catchup.
Though, the saddest thing is that it seems like the U.S citizens, (i.e ANY of tiktoks 160 MILLION US users) have absolutely no say in the operations,a yet we actually interact with the app not these old people in Congress. The fact that Biden so swiftly signed the bill too makes me frustrated as I want to vote for him, but he keeps doing or okaying things that are counter to my values.
The algorithm's novelty and recommendation accuracy is so far beyond what other competitors like Google, Snapchat, and Meta have that this seems like a coordinated effort by the private sector to push forth their mediocre products and centralize social media service which I absolutely DETEST. The saddest thing is that it seems like the U.S citizens, (i.e ANY of tiktoks 160 MILLION US users) have absolutely no say in the operations, yet we actually interact with the app not these old people in Congress. The fact that Biden so swiftly signed the bill too makes me frustrated as I want to vote for him, but he keeps doing or okaying things that are counter to my values.
This is why no matter how many interesting ads or tiktoks, I will never do these genetic testing kits. I wouldn't be surprised military is working on CRISPR like infections that target your specific DNA when sprayed in the air.
How did it mess up the "make every fifth line bold" prompt?
Also, to follow up on the original comment, AI demos are nice, but being a student of history there are still fundamental challenges with these systems. My skepticism is in how much prompting is really required and how can it understand higher level semantics like code refactoring, reproducible examples, large scale design patterns etc.
This synthesis of sequential symbolic processes and probabilistic neural generation is really exciting though.
When the amount of human code edits and tweaking for complex programs goes down from hours to seconds then that's when I'll be impressed and scared.
This is an interesting business model. One problem I foresee though is that frameworks can get outdated in a few years, and that might affect the text generation abilities.
Measuring Coding Challenge Competence With APPS
Abstract:
While programming is one of the most broadly applicable skills in modern society,
modern machine learning models still cannot code solutions to basic problems. It
can be difficult to accurately assess code generation performance, and there has
been surprisingly little work on evaluating code generation in a way that is both
flexible and rigorous. To meet this challenge, we introduce APPS, a benchmark for
code generation. Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and
generate Python code fulfilling this specification. Similar to how companies assess
candidate software developers, we then evaluate models by checking their generated
code on test cases. Our benchmark includes 10,000 problems, which range from
having simple one-line solutions to being substantial algorithmic challenges. We
fine-tune large language models on both GitHub and our training set, and we find
that the prevalence of syntax errors is decreasing exponentially. Recent models such
as GPT-Neo can pass approximately 15% of the test cases of introductory problems,
so we find that machine learning models are beginning to learn how to code. As the
social significance of automatic code generation increases over the coming years,
our benchmark can provide an important measure for tracking advancements.
No offense, but why was this question even asked? What are the people's needs in the first place? I saw a similar study that suggested the best way to improve mental health and economic consequences among the poor is to *drumroll give them money. Then they will create sustainable interventions that don't require source of money to improve the lives.
https://twitter.com/samswey/status/1371481061620781061
Blacks are not only more likely to be killed by the police but also are more likely to be unarmed. Blacks are likely not committing 50% of violent crimes, but are rather convicted for 48% of the crimes which if you've looked into studies on community targeting seem to have to do more with police officer bias.
Totally agree with the small factors take: For me, it would be diet, exercise, amount of sleep, sleep time, behavioral activation, translating unhelpful to helpful thoughts, social life, life circumstances, minimizing tempting distractions and having bluetooth music stations available at all times helps tremendously.
I see the same thing in Applied Machine Learning, where psychologist X writes paper on "My mathematical model for predicting Intimate Partner Violence scores." Where the deep learning model is a basic multilayered perceptron implemented with sklearn. Something any CS Freshman could cook up in 10 minutes.
I use HuggingFace for most of the NLP modeling work, it seems like Spacy 3.0 provides a framework for many different libraries including HuggingFace, StreamLit, Ray and WnB, but if you know how to use these individually, what does Spacy add to this?