lnofeisone

No, they didn't, and I'm not quite sure why you'd pretend these are the same thing. On a practical level, ML uses historic data (usually entire dataset, let's ignore train-test-split for a second) to make generalizable predictions. Statistics draw inferences from a sample. To make statistics ML, just up your sample size to make it population size ;). I can walk you through the same path to show you how AI is basically the same thing (starting with perception). I'd love for you to explain what is it that makes you think I'm pretending? Generally, AI/ML fields borrow heavily from statistics. Sure they are different if you take the purist approach (e.g., ML predicts based on passive observations and AI implies agent interaction with the environment to maximize chances of goal achievement). Sure, other fields are contributing (EE, CS, etc.) and some of the latest algorithms don't come from the field of statistics but at the core, these are all statistical methods (see the assumption with any algorithm that is available today). The reality of things is that what changed the field are three things: 1) availability for computing power (AWS, GCP, etc.) and 2) data, lots of it 3) fusion of different methods (e.g., TensorFlow). Feel free to let me know what insurance-specific algorithm(s) Lemonade has that is not rooted in statistics that are not available to Progressive, Geico, etc. I say insurance-specific because I'm sure Lemonade, by virtue of being new (i.e., no cultural or digital transformations necessary), can rapidly deploy a bag of algorithms to help with processing (robotics process automation), translation (nlp/nlg), etc. So is the premise that they are an efficient back office?

I don't know the company well, but, having worked as a tech person in an insurance-related company, insurers often don't seem to have the culture to adopt market-changing technological solutions. I agree that something like this that seems to be the obvious strategy for tradition insurers. But it isn't necessarily something that those traditional insurers can actually execute. Traditional insurances had AI/ML for decades. They called it statistics. I haven't seen anything revolutionary (e.g., Tesla was the only EV for a while) out of Lemonade and they aren't price competitive if I have a car + rent/own. Throw in challenges with renters in big cities, at minimum there will be turbulence in the next few quarters as older policies start to roll off. Just my 2 cents and I was wrong on LMND stock before.

I'm not up to date on my SPACs. What's the deal with PTICU?

Added 1/3 short position in LMND after seeing my small short be very much worthless. Not sure what Motley Fool sees in this that is so transcending of AI/ML that Geico or Progressive don't already have or can't buy.

sold ORI (thanks Spek), JBGS (thanks Pupil), AM. Reduced KMI, KNOP, and CLMT.

Bought back my nat gas short and bought some INTC bear spreads.

I think what SpaceX has done is exceptional. I also think BA and LMT can just sit back and wait to see how it's done and then step in and replicate with economies of scale without sinking few $Bs.

I bought some December bear verticals here too. Not a lot but just in case the market decides to sell off.

Few BABA shares and a small CLMT lot. The latter has been trading rather strong in the last few weeks after some news of them exploring lubricant unit sale.

If you use thinkorswim, you can connect and export for free. If you want another API, I've been using Quandl at work for some time now. It has stocks, mutual funds, currencies though they are packaging them as different data sets so if cost is an issue this is somethign to consider. Overall, pretty easy Python set up. For personal use for stocks it is about $50/mo. It gives a lot of other metrics too (e.g., revenue). EDIT: Forgot to include the link for ToS-Excel connect - https://www.lockeinyoursuccess.com/wp-content/uploads/2016/04/Connecting-ThinkOrSwim-to-Excel.pdf

Sure. There are things that FB/Twitter/GoogleNews/Netflix/AnyMediaOrganization do that CoBF does not. You can look at these things and judge them on positive or negative: 1. Notifications. Positive: if you are waiting for friend to show up and they are late, you want to be notified that they messaged you. Negative: constant attention hog. (BTW, CoBF has notifications too, just less invasive perhaps). Solution: you can set notifications to whatever you want, including turning them off. 2. Recommendations. Positive: You just read about Model 3, you want to read more about Model 3 Autopilot. You just watched "Sleepless in Seattle", you want to watch "You've Got Mail" or "Big". Negative: Rabbit hole, splurging, etc. Solutions: I'm not sure you can turn off recommendations in most places. Maybe there are knobs in some apps. I'd argue that recommendations is not a huge issue, but maybe they are for some people. 3. Ads. Personalized ads. Positive: get relevant ads. Negative: privacy, pushing you to buy crap. Solution: just fricking adblock everything. Harder on phones... Overall ads are crap. If you ever looked at "personalized" ads, you'd despair on how bad ad targeting is and not about how powerful AI is. 4. Curated timelines/posts. Positive: you see what you want to see. Negative: you see what company thinks you want to see. Echo chambers. Crappy feeds. Maybe addictive feeds. Solution: Personally I think the curated feeds are just crap, so just don't use them. It's not that they are evil-great and addictive. It's that they are just crappy selection. So don't use them. Pretty much every platform allows you to avoid curated feeds. It's possible to have a discussion about all these issues. But not if documentary authors turn the documentary into echo chamber that they themselves condemn. Basically zero opposing or even moderate opinions. And the fake "real story about teenager who got radicalized and missed on love and foodball practice due to evil AI" is just a mind manipulation porn. I think what the documentary authors tried to convey is that when there is a direct and proportional exchange of $ for your attention, the companies will do everything in their power to shift individuals to the "negative" while making it seem innocuous. It's easy to make it innocuous because we, humans, are really easy to trick. It's even easier to trick us when some of these services get so deeply entrenched into the fabric of our lives (e.g., Whatsapp/WeChat for communication with loved ones). The ease with which we get tricked is well captured by Kahneman's "Thinking fast and slow." Nonwithstanding the cheesy side story (or as you call it, manipulation porn), the authors did a pretty good job highlighting how our fallibility got weaponized against us. The "Solution" that you are bringing forward, that's the slow, rational brain. Sure, it makes sense, but we just don't slow down enough to think it through.

DNNGY and ORI starters.

Can you provide some examples of what you viewed as gross exaggerations and unproven claims all over the place?

Corning fits this mold too.

Starter positions in PGRE and JBGS.

Covered GME calls. Not feelign easy with all the outside involvement.

Sold some GME 5 and 10 calls going out to Jan 22.

Closed UNG short. Bought NVAX 50 DEC/Apr and LMND 40 Dec/Mar calendars.

Thanks for sharing. Hard to tell if this was an issue of taking algo out of lab and going against live data vs. truly shoddy data science work. This is a truly hard problem with so many variables and technological complexities (e.g., some of the tests are free form) so I'm leaning on the latter. I'm skeptical that they had the time to really do the proper evaluation. Without IB releasing info, I doubt anyone will successfully reverse engineer their algo. But, it would be nice if all IB participants banded together and provided their scores to do some bias analysis.

Netflix knows when you stop watching a video too. But Netflix isn't a good example to use. In addition to the different content length, Netflix has an issue where their catalog is shrinking as third party content makers pull their libraries to start competing services (https://www.businessinsider.com/netflix-movie-catalog-size-has-gone-down-since-2010-2018-2). Your dissatisfaction with their Recommendations / Personalization may have to do more with how little they have to recommend, versus say Youtube, Instagram, Spotify, which all have millions of content creators. True, but you need to watch a good portion of a movie before you know if you are going to like it or not. And once you are an hour in, you might as well watch the rest and hope for a good ending. You then watched the entire thing and hated it. How does Netflix know? I haven't thumbed up or down anything on Netflix for years. I just never think to do it. So all it has is what I've watched, but it has no idea if I thought the movies I've watched were great, so-so, or horrible. Microsegmentation (user profile building), external data, and and recommendation engines is how Netflix can infer how much of the movie a person watched and if they liked it. It would look like this: There are few questions here. 1) Have you watched the whole move? 2) Did you like it? easy cases: 1) Lnofeisone(ln(e) = 1) is a rater and he has recently watched an episode of Kobra Kai and rated it "like." 2) Lnofeisone is a rater and he has recently watched an episode of Kobra Kai and rated it "dilike." harder case: Lnofeisone watched Cobra Kai recently and didn't rate it. Also, our message "Are you still watching" popped up and was on the screen for 10 hours (and this was validated by IP traffic etc.). Lnofeisone probably didn't watch few episodes of Cobra Kai. It was 2am at this location so Lnofeisone probably fell asleep. Do this for millions of users and decent amount of content and a lot can be inferred, generally correctly. Throw in a recommendation engine (https://www2.seas.gwu.edu/~simhaweb/champalg/cf/papers/wroberts.pdf, this one isn't particularly impressive and requires a lot of eingineering but was novel), external data (rating), etc. and it becomes possible to at least recommend something reasonably close.

I'd second this. NYS (and many other states) are stepping up their examination practices (audit) to close Corona-related revenue shortfalls. Here is an example (https://esd.ny.gov/doing-business-ny/requests-proposals/2020-mwbe-personal-net-worth-study-and-workforce-diversity). Cross-border activities have always been a priority for states but only in recent years have the capabilities (tighter integration with IRS data, cross-state data sharing, technology, etc.) improved enough to really make a difference. You'll need to be really buttoned up should someone stumble on you (or this thread).

This is a very astute observation. In terms of data vs. algo, building algo from data is VERY hard. This is especially the case if TikTok hands over the raw data and doesn't disclose how the data was transformed, what metrics are being used to train the models, etc.. Think this, GPT-3 has something like 140 million features (that's columns if you think in terms of rows/column of excel). I also doubt that there is "one algorithm that rules them all" and there is probably 1000s micro-algos. Keep in mind, while ago is being built, data will probably evolve. Long way of saying, feasible but expensive and difficult.

sold some UNG 9/12 Jan 22 bull spreads.

I've been trying to sell some bull spreads all morning. So far got nothing.

This is true of wash sales (IRS clarified this in Revenue Ruling 2008-5). Selling in taxable for a loss and buying in IRA would trigger a wash sale. This is my take and if I get audited, I'd challenge the IRS to show me a ruling where this was clarified. For option spreads, the two legs are not considered the same security so buying one in IRA and selling one in taxable is not identical.