Gemini fast: „Walking: It will take you about 45 seconds. You will arrive refreshed and full of steps, but you will be standing next to a high-pressure hose with no car to spray.“
Foundational models can work where so far „needs human intuition“ was the state of things. I can picture a time series model with large enough Training corpus being able to deal quite well with typical quirks of seasonalities, shocks, outliers, etc.
I fully agree regarding how things have been so far, but I’m excited to see practitioners try out models such as the one presented here — it might just work.
- energy management (shifting loads to times when energy is cheap) for consumer/commercial/industrial use cases
- energy markets, especially power trading: often highly algorithmic, and driven by models that turn fundamentals data (weather, calendar, …) into supply/demand predictions, and from there into price predictions
The same applies within large companies. If you’re within a business team, and you’re requesting work from a design team/engineering team/data science team, you’ll face the same issues with scope creep + churn + competing priorities etc.
I wouldn’t blame agencies for being bad, this is people being people plus a bit of other things. Anticipating and steering around/against these dynamics has been one of my biggest career learnings over the last years. The author has some good suggestions for how to do it — if you work in a large company, take another look and ask yourself if they don’t also apply to your work!
At least in e-commerce it’s very much possible. The company that I work for does such experiments regularly (there’s a dedicated Data Science team for measurement) and I’ve personally been involved in lift studies for Google Ads. They work, you just have to be careful with the ‘how much’ combined with ‘for what’.
Happy to chat with anyone who is interested in the topic (pfalke at pfalke dot com).
Haven’t had a chance to listen to the podcast, apologies if that made me miss the point of the parent post!
Slightly OT: Can someone elaborate on "Do not share Wifi"? What can be problematic when using a shared but encrypted (say, WPA2 with pre-shared key) Wifi?
This is a common setup in public places like cafes. I've always wondered in what ways this can cause problems.
Did the same thing to get a wedding registration appointment with the Berlin city administration, but used Ghostery instead of AWS+Twilio. The responsible administration department currently doesn’t give out appointments and advises not to plan weddings because they are so understaffed, but I didn’t feel like waiting.
Got a notification email from Gostery in the middle of the night, registered the only available slot et voilà!
For online services (e.g., HackerNews), what's the scenario where an attacker cracks an 8 character password in 8 hours? I assume the attacker would need to download a copy of the service's password store and in that case the service has been hacked to a degree that the attacker won't need to crack passwords anymore.
As a non-infosec guy, could someone shed more light on the implications for end users?
I get that the combination of password reuse, short passwords and the fact that some services store passwords in plain text or as MD5 hashes makes it easy to break into accounts once a single service is compromised.
So my takeaway is not to use longer passwords, but to use a password manager and have unique passwords for every service. My current setup is 8 character passwords for online services (easier to occasionally type in manually).
Am I running a risk by not using 12 character passwords?
Could someone explain why password length is so important when logging into web services? I get that it's important for encryption, but when you implement a web service that has rate limiting, a basic password length of say six characters should be sufficient, no?
Could a similar approach work to find the impact of foods on other health factors (sleep quality, digestion, ...) in a personal experiment?
I picture logging my coffee consumption (or even all foods + sports etc) and sleep over a few weeks, then using machine learning to find a possible relationship.
I'd appreciate any links to OSS projects/commercial apps that work on this.