Why not...
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465 karma · joined November 16, 2016
xrb_196w8xumh5ewazrpfrdzspjf74ba8ijsqgrf8eemau8t8tqiekn7ishzouc7
1) What's the probability of the cryptographic ciphers behind cryptocurrencies being broken?
2) If they were broken, is it possible to 'move' BTC or other coins to a new cipher?
I apologize if I'm using the 'cipher' terminology incorrectly.
But if it really is just online gambling, is that in and of itself enough to sustain over the long term? I don't mean the ~20k BTC price, but the ecosystem itself. After all, casinos/lottery/gambling have been around for a long, long time.
Avoiding the debate of whether there are any widespread real-world usage for cryptocurrencies, is it possible that cryptocurrency is valuable (and has long-term staying power) simply because the ecosystem surrounding it is so much fun for a non-negligible swath of people?
I know this sounds ridiculous. But hear me out. The entire process of setting up exchange accounts, reading about alternative coins, jumping into the drama, worrying about massive gains or losses, buying hardware wallets, chatting with others doing the same thing, thinking about complex systems and new coins, etc. is a lot of fun.
Could it be that cryptocurrencies are somewhat of a 'simulation currency trading market' that just so happens to allow you to cash in and cash out with fiat currency?
I have little doubt that the current value of BTC and other coins will blow up in the future, and if you've got anything more than disposable income invested in it, you'll probably get burnt really bad. I think it's almost a given. But I suspect that the idea of cryptocurrency is here to stay, at least for my lifetime. Solely because it's fun. But of course like anything, I could be wrong.
Use historical pricing data, NLP and comment volume on cryptocurrency subreddits and threads, frequency of search terms from Google analytics, major exchange status (Coinbase, Gemini, Kraken, etc. - whether they are up or down as dummy variables), frequency of crypto-related news, etc. all plugged in as features into models in Python and linked with Gemini trading API.
I started working on it and then got lazy (and bad RSI from being at my computer way too much) and it never materialized. Plus, I guarantee somebody else is doing it, or models like that probably aren't too successful anyway.
Could be fun though.
I joined a non-tech company as their first data scientist, and am now tasked with building up the data science efforts of the company. Your comment is really ringing true, though I've found the company here to be generally great to work with in terms of their flexibility. Funny enough, it took me months of working with the IT department to get a VM with 8-cores and 64 GB of RAM, or the ability to spin up AWS instances (none of the analysts at the company had ever asked for that). I'm happy with it though, it's a fascinating challenge and I'll admit at times I feel a bit in over my head.
In my experience, many companies are wanting to hire a lot more data scientists (and paying them six-figure salaries to boot), when what they really are looking for are what has traditionally been called a "data analyst" position. I.E., the entire sum of the job is Excel reporting and basic statistics, but instead wrapped in some "Machine Learning" marketing language.
As opposed to more long-term focus on what business problems can be solved by carefully cleaning data, building models, testing predictive power, and then deploying these models into production. Which obviously takes a lot more time, a lot more effort focusing on a single problem, and doesn't have nearly as many immediate deliverables that higher-ups can open their Outlook inbox and see. Naturally they then wonder why they are paying such a premium for someone who doesn't bring as many deliverables to the table as their 60-70k analysts. And then the data scientist wonders why he's spending most of his time sending Excel attachments. Definitely more of a problem in non-tech companies.
In my opinion, I feel like we are at peak "!@#@ data science! @#$@", and there will naturally be some deflation and correction in expectations just like any other occupation that comes out of the woodwork and is the 'sexiest job of the _____'. It's just the way it is. The article author seems to be carving himself out a niche to capitalize on this (which is fine).
My opinion is that the proper way to deal with the privacy implications of this are to come up with better alternatives - open-source voice assistants and entire ecosystems that do everything locally and that are easy to setup by Joe Shmoe. More in-depth auditing by security experts to see exactly what data is being sent from these devices back to their servers. Privacy laws that will take a company down and send people to jail when violated. Encryption on everything that can feasibly be encrypted.
The truth is that I have succumbed to the utility of my Google Home (and the enormous amount of fun I get from having it interface with Home Assistant), but I'd like to be a part of whatever the front line is in making sure these are as privacy-conscious as possible.
The main reason I bought them was the ability to display multiple colors - I absolutely wear that out. I love having a red-colored room when I'm playing video games. Dimmed 'sunset' colors when watching a movie. Party colors when friends are over.
I think they're really fun, sometimes useful.
As long as the risks of x are acknowledged, people can make their own choice as to whether they confer enough benefit from x to accept the y level of risk.
It'd be dishonest to pretend that alcohol doesn't have any benefits (social or otherwise). After all, it's been used for millennia. We can argue about whether those benefits outweigh the risks, especially as we learn more about what the risks are (i.e. like we did with tobacco). But I don't think it's particularly surprising or amusing. I'm sure you could name an activity in your life that has some amount of risk that you still choose to partake in because you gain benefit from it (and perhaps be inclined to comment on a story about it too - but I could be wrong).
I wonder at what point the data cleaning, feature engineering, and model building will all be entirely automated. And what that means for the role of a data scientist within a non-tech organization.
If I recall correctly, all the major neuroimaging packages (AFNI, SPM, FSL) had options for cluster-size thresholding at the time. Along with tools like alpha sim to estimate cluster-level FDR (but I think that ultimately had issues with it's algorithm, discovered only a few years later...).
I just remember thinking that if the salmon paper had a reasonable cluster-threshold, none of the spurious voxels would have been considered in the final analysis.
Granted, several years later, a paper came out suggesting that method would inflate false positives (http://www.pnas.org/content/113/28/7900.full).
I imagine the neuroimaging field, particularly the stats part, has changed rapidly since I left.
The Pixel 2 XL checks all of my boxes, which really aren't much: runs Android, has a large screen, is fast, has a good camera, is fast, is supported with updates for a few years, and is fast. My S7 Edge has made me truly understand the life pain and suffering that even minor lag can cause.
I implicitly assumed that any phone which would fit those criteria and be in the $700-$800 price range would also have a flawless screen. I'm not even talking about a Note8-level "holy shit that's amazing" screen - more like an S7 level of "nice screen" screen.
A speedy Android UI is so important to me though that I'm at least going to give the phone a shot when I receive it. "Maybe it's just a bad batch," I'm telling myself. And promptly RMA it or return it if there's any issues.
Part of me feels bad that I'm still willing to part with my money to try this phone out. Does that make me a sucker?