Shameless self promotion but this is exactly how I ended up writing a book on strength training for climbing, just pursuing every rabbit hole I could.
I was ready to self publish but found a publisher who was interested. I had to make some changes to make it more readable, but you might have luck approaching publishers yourself
To justify your self promotion, can you tell me the name of your book? I've been climbing for over 30 years and seen the entire progression of training practices and would love to read a comprehensive book on the subject.
The Physiology of Climbing. I can mail you a free copy once I get back home this weekend - I’d love any feedback!
To be clear, I aimed to avoid prescribing certain routines through most of the book. I wanted to basically provide a knowledge foundation for readers to evaluate routines or create their own. So instead of saying eg you should campus board, I try to explain that power has to be trained separately from max strength if you care about increasing power
The title seems a bit hyperbolic compared to the article. It does briefly mention cardiovascular risk, but I was able to immediately find a meta-analysis showing no correlation.
If the gripe is with processed foods containing protein, then sure maybe there's a risk compensation argument, but personally speaking I buy Halo Top when I'm craving ice cream, not as a way to avoid eating chicken.
I also imagine that the target audience for these products are people who are relatively active and in that case the ideal protein consumption numbers are generally accepted to be significantly higher than the 0.8g/kg cited in the article.
Really appreciate you sharing your perspective. I recently wrote a book as a passion project and have been sitting anxiously on a contract. I'm not concerned about the money (I don't think my book will be a huge thing). My main motivation for going trad is the credibility as you somewhat alluded to. Do you think this is misguided on my part? Basically just so I can point at it in the future and say "a professional in the industry thought my book was worth printing with their name on it"
First, congratulations on finishing your book and getting a contract! That is a huge achievement.
I do not think your reasoning is misguided at all. If you think a traditional publisher affords you more credibility and a sense of satisfaction, that is reason enough to go with trad - _especially_ since as you say you're not concerned about the money, so there is no reason to worry about a traditional publisher's royalty rates compared to other options.
I believe your reason for wanting to go with a publisher is perfectly valid.
I have a question for you both (drakonka and turkeygizzard): Would you ever sell all or a portion of the rights to future earnings for your already published books to a third party? We've seen in the music industry PE firms basically acquiring known catalogues for the residuals and I'm wondering why that doesn't seem to happen in the publishing industry.
It happens quite a bit! I mentioned it in another comment here, but one thing that publishers can be very useful for is audiobook rights and translations. These are very costly to produce and it sometimes makes more sense to offload that part to a publisher. That is definitely something I'd consider doing if the opportunity came along.
That's a good point. I'm already in the process of using voice synthesis to narrate one of my books. It is still a huge time outlay to get to the quality bar I want, but much cheaper than paying for a narrator.
One thing working in favor of human narrators is the fans. Audiobook listeners can get very attached to certain voices, to the point where they'll read _anything_ that narrator works on regardless of the book's author or genre. If I had the budget for it, I'd definitely favor a well-known human narrator over AI for the visibility aspect of working with that person. But most authors don't have the budget to hire popular narrators, which is where less popular or entry-level narrators may find themselves losing work to AI alternatives. The narration quality is still higher with competent humans at this time as well, but that'll change.
For translations, I don't think I'll ever trust AI entirely (just like I don't trust myself as a human writer entirely!) I'd still be hiring a native-speaking human editor and proofreader if generating AI translations. Or more likely, I'd be hiring a human translator who is able to charge competitively by using AI in their workflows (and is also able to handle the quality checks etc for me).
Interesting! Do you have an email or way to get in touch by chance? I'd love to connect and ask more as I'm both writing a book and considering trying to build some stuff in this space. Alternatively, I'm at jb2956 at georgetown.edu!
Audiobook natation isn't that expensive - the same narrators being used by publishing houses can do it for $200 an hour with it being 10-12k words per hour. Audiobook production is a few thousand for most books under the current system.
For most self-published authors, a few thousand dollars is a lot to drop into a project that may never pay out. And in many cases if they do have the money, it makes more business sense to spend that budget on editors and cover designers across multiple books.
But there are definitely people who fund their own audiobook production. And narrator royalty share options exist too, which some use (I would personally not). It's just not the default option or choice for many.
Wow, I've only read the abstract so far but this basically goes against all conventional research on this topic AFAIK. I read probably ~50 papers on this topic (maximal protein dosing) and emailed a few researchers about it as I was writing a book. I believe the saturation point for a relative dose for a single meal is generally held to be ~0.25g per kg of bodyweight. Of course, there are some obvious observable issues with this belief given that there are people who practice intermittent fasting and have no issues building muscle, so it'll be really interesting to see if this study replicates
EDIT: After skimming the paper, I don't see anything immediately wrong with it. But there are some important nuances to note: the subjects were all fasted and given milk protein (casein in milk protein is known to take longer to absorb than pure whey protein which is a popular choice for these studies), and the measurements were made after an hour of exercise. This would skew the results towards more protein sensitivity than in normal settings where a person is pretty much always somewhat well-fed and not always eating after exercise. This is still encouraging because the results for post-exercise protein metabolism have still indicated a much lower limit than a 100g dose. Their report that oxidation rates didn't increase significantly is also notable since the belief has generally been that excess protein is oxidized and burned for energy instead of being incorporated into muscle. However, it would've been nice for them to include a 50g group as well to see if the dose-response relationship was really still linear between 25-50-100
Ultimately, this result seems encouraging for increasing post-exercise protein consumption for muscle gain, but we shouldn't discount the fact that the subjects were fasted before exercise. It would be interesting to see this study prolonged over the course of a day with further protein ingestions to see if the area-under-the-curve of muscle protein synthesis would eventually equalize in both groups, or if the larger immediately-post-exercise dose made a lasting difference. Existing research seems to not indicate such an "anabolic window". I might speculate that there is a daily limit for protein ingestion, but it doesn't matter if you hit that limit in one meal or five. That said, I have previously come across a paper that found medium-sized, spaced out doses to be more effective than infrequent large doses and overly frequent small doses, so there's still more to discover here
Yeah, I think the least controversial takeaway is that you can afford to have a large protein shake immediately after workout. I personally don't think you need to. But this study and some others indicate that muscle responds best to protein consumption when it's in some post-exercise state (but it's unclear if that state is 30 minutes or 3 hours or longer etc). I'd say it's more important to focus on your overall daily consumption rather than the timing of it
A summary of my own looking into it is “don’t worry too much about it”.
For the average gym goer the most important thing is working out consistently to failure and periodically reevaluating your training so that it’s safe and optimal based on the latest research. How much protein you can shove in your face is rarely a concern, westerners get enough. Getting a bigger chest or shoulders when what you are doing isn’t working requires looking at your body mechanics and how to activate those muscle groups. As an example, I couldn’t squat for shit until I learned that I require a different stance than most people, due to how my hip sockets are angled (my feet naturally come to rest when standing at about a 80 degree angle, extremely wide compared to most)
Everything I’ve read seems to indicate that it’s probably ideal to eat something within a few hours after a heavy workout, but you don’t need to immediately rush down 2 scoops of protein shake or waste away. Excess protein doesn’t really hurt you either. Protein eaten is gradually absorbed throughout a surprisingly long time as it travels through the gut (the time of which varies greatly between people, I digest extremely slowly), which in practice can act like a “store” of amino acids despite the body not really having something dedicated for that purpose like fatty acids and carbohydrates. I haven’t seen studies on this, but I personally also suspect some amount protein is “stored” and released via gut bacteria, much like what happens with soil bacteria and nitrogen fertilizer. And that’s ignoring all the recycling of amino acids the body does anyways.
Likewise, muscle repair is a progressive demand over days, not an instant demand. Photos of muscle cells over time after a workout are illustrative of this.
Basically you don’t need to treat protein like a diabetic treats blood sugar.
For those people at an advanced enough level where they need to find optimizations to obtain more results, then yes, individual amino acids have metabolic effects that can likely be exploited. In fact, some amino acids (like leucine) can likely be a contributor of not just muscle gain, but also obesity because of how they work. Some people can benefit from restricting “protein” in order to lose weight, because they start burning body fat for fuel again. Likewise, fat isn’t just fat, but each fatty acid has its own complex waterfall of metabolic impacts (beware omega-6’s, which are very high in western raised pork and chicken fat).
Food can be thought of as a load of chemical signals for your body. So the above study isn’t surprising.
For anyone reading this in the future, please be aware the working out "to failure" is this posters' opinion. From my research, I'd tend to say it's actually not favoured in the current state of the art.
From what I understand, achieving (example) 80% of failure can achieve most of the same level of gains while avoiding a disproprionately significant amount of fatigue resulting from achieving full failure.
Not published yet sadly. I'm close to signing a contract with a publisher that would see it get released in the summer. I've contemplated self-publishing though so it can see the light of day sooner
That's not what it means. This is a style of writing common in some academic fields where when they measure multiple qualities, they'll present them in the abstract next to each other in parentheses rather than repeat full sentences.
You should read "All else being equal, those highest on cognitive ability experience a 22% (53.2%) increase in the probability of realism (pessimism)" as as a 22% increase in realism and a 53.2% increase in pessimism
Yes and a fellow English scholar openly laughed in the face of our PhD highly credentialed Professor because certain attempts to overly sophisticate or garnish banality isn’t a “style of writing” it’s a foul and wrong and do not do it.
She read the sentence including “…and the data crystallized to show..” and she couldn’t finish he was almost in the floor.
As an academic, I also don't write like that, but laughing at it sounds like the reaction of a jerk. Writing like that can come from cultural differences (in some cultures they really seem to love flourish) or from being a non-native English speaker and translating common idioms literally, it doesn't necessarily mean that the writer is clueless.
I haven't read the paper beyond this one section - but I plugged this question into GPT-4 and got a similar response. However, if I used military time (replacing noon with 12:00 as well), then GPT does get it right. Granted, it still hedges much more than any normal person would. But basically I wonder if it's struggling especially with the 12-hour clock concept
I'm a backend engineer with about 5-6 years of experience. I've been programming since I was about 13. Recent side projects have focused on using GPT to automate fuzzy things like scraping Twitter and dealing with customer service bot menus. Over the last year or so, I also read a few hundred papers related to strength training and turned that research into a book on climbing training, which I'm currently shopping around to publishers
Embarrassing / dumb q, but how do you actually get to talking with them in a meaningful way? I can imagine with repeats, it's easier to build up rapport, but I don't think I've ever hit it off so strongly with someone at a coffee shop that we exchange info after one interaction (mine here aren't super social fwiw)
You didn't ask me and I'm definitely not suggesting you exclusively go out to bars but if you're in a nicer neighborhood, especially with patrons that skew older, you can basically flat out ask them for advice like you did here and you'll usually get a dump truck of it and their personal stories in exchange for your name and buying a few beers. You might even get invited to a backyard cookout that same weekend.
Alcohol is a double-edged sword because it breaks down barriers. It's your call what you do with the information and experiences it provides. It isn't too bad if you're careful.
You can take all that as a way to level up and be more sociable in sober settings like coffee shops. It translates to everywhere. People aren't different when they're sober apart from being slightly more defensive due to stress. That's actually a pretty good conversation starter if done in a non-confrontational way. The main skill is just learning to be genuinely interested in other people. It's easy to mimic this and return the favor when you've seen enough of this kind of attention given to you.
That's too many words and also implies you have that much of a choice over who you talk to when you're new. Some places are meant to be more social than others. Emphasis is on "nice neighborhood bar", i.e. not a dive full of shady sour-faced dudes who whine about drink prices, don't tip, and are looking to "score" or whatever. Doesn't have to be fancy, just not the college crowd.
The default move for pretty much anyone new is to find an open seat at the bar near people who are also there alone (you know because they're sitting apart). Order a drink and try to guess simple questions or comments they might also have in mind. Try to keep it lighthearted. In fact the more restrained you are the more some of those people are going to be weirded out by you. You're expected to be comfortable. They want you to say something. The best way to avoid the extra words is to say things when you're also doing something else. If you are ordering a drink next to them, they're already listening and watching you. If you turn to them to say anything in that moment you didn't need to break any ice.
Everyone likes answering questions about themselves because they're there to be seen as much as you. Notice something and be nice. 90% of the time even just "nice shoes..." will get a smile and an in. "Where did you get them?" Next part depends on the shoes: "What do you do? Do you run? Do you hike? Did you just get out of work (non-slip/uniform)? Did you just come back from a wedding? Do you make balloon animals? (kidding)" "What are you drinking? Nice choice. Have you visited that brewery down the road? You watch football? Have you had the food here? Why don't they ever put enough salt? Can you pass the salt? Hey what's the bartender's name? Melissa! Can I get some silverware and salt? Thanks so much, oh and a shot for my friend." etc.
The hardest part is the first friends, but they also determine what other people you'll meet. Don't overthink it if it seems everyone's the same. Just pick the person you were most interested in first because if you don't then people will still assume that's why you're near them instead.
EDIT: I can't believe I glossed over your question, but...
> What’s in it for them for telling you.
What's in it for them is the same as what's in it for you. You want to relax somewhere and meet new people. For some people there's not much in it for them other than being a friend to someone who needs it. For yet still others, they simply don't ask such questions because they think the mindset of needing to always make a return sounds like work, and they're not there to work.
Thank you for articulating this. I remember similar problems and arguments arising after RNNs and CNNs became massively successful. People argued that training larger models would be infeasible for several reasons that all were made moot by Attention Is All You Need. Somebody seems to always figure out a new approach
First of all, the article argues that you need a major breakthrough, arguably attention was such a breakthrough?
That said, this doesn't really seem all that comparable. The article points out very fundamental properties of all the diverse current approaches: They are tightly data constrained. You either need to cheap simulation or massive real world data. That's not an arcane technical point.
I'm not sure I understand it well enough to say but watching a video on it [0] I think there were a few key points:
* "Attention is all you need" introduced positional encoding which allows you to keep context of the word, allowing for more complex translation (and thus generative/chatgpt like tasks?) because words now have context relative to each other. Contrast this with "bag of words" models that only tells you whether the word is present or not.
* I don't quite understand why but transformers (which "AiaYN" introduced) can be made fully parallel, compared with the RNN/LSTM networks which has to be serial per token. Fully parallel allows for GPU optimization, which means you can take advantage of Moore's law for training.
I'm always a bit suspicious when people claim a breakthrough of this sort. There's no doubt that better algorithms give better results but how much is due to just faster computers, cheaper compute, memory, etc.
To add another anecdote to your question: the transformer became a part of the first context aware embedding model GPT-1. Not to say it couldn’t be done with another tool but it was first done with a transformer. Previous embedding models like word2vec, GloVe and fasttext were not contextually embedding and didn’t give you a language graph that would then go on to support a language model capable of “understanding” what you were saying or asking for.
Attention is all you need paper just proposed an AR model that didn’t have to be trained step by step. The scaling happened later in BERT and GPT and OpenAI’s scaling work
What are the mechanics of doing that? How do you tell it what page to open, how far to scroll, what exactly you want to hear about, and how do you plug that into the model and get a result?
- scraping is relatively dumb and straightforward. I use playwright to login and just scroll my timeline for the first 100 tweets. I run the thing every 3 hours right now, but definitely could tweak the number of tweets vs frequency
- I only care about AI tweets really on my timeline, so filtering to that is pretty straightforward just passing it to GPT
- I included the prompts in the link. Definitely far from perfect, but it works well enough. It does surprisingly suck sometimes, like I've noticed that it doesn't always pick up on tweets about LangchainAI despite AI being in the name etc
Sure thing - but just FYI I really only care about AI tweets in my timeline, so YMMV if you want it to summarize your timeline in general. I tried a generic "summarize my timeline" approach before and it didn't work well. Filtering to within a certain topic seems to really help
I was ready to self publish but found a publisher who was interested. I had to make some changes to make it more readable, but you might have luck approaching publishers yourself