People notice genuine learners. They’ll want to help you. Don’t tell them, but they just became your mentors. This is very important: Pick up what they put down. Think of them as offering up quests for you to complete. When they say “Anyone willing to help with __ __?” you’re that kid in the first row with your hand already raised. These are senior engineers, some of the most in-demand people in tech. They’ll spend time with you, 1 on 1, if you help them out (p.s. and there’s always something they want help on). You can’t pay for this stuff. They’ll teach you for free. Most people don’t see what’s right in front of them. But not you.
The problem is, who do you speak to? Clearly the phone operators are just doing there job, while the various trades are doing theirs. (Note: we have had two different trades turn up randomly on the wrong day) Do we speak with the project manager in charge? Probably, if you can get a hold of them and they are able to actually bring your job up on the computer?
Although the ‘rise of the robots’ may resolve some of this, I think it comes back to care amd customer service. It really has me considering what cover we get and how much we pay. Maybe sometimes you pay for what you get?
I think this experience is no different to other novels set in the future as well as the fast:
The books challenge us how we live without telling us how to live.
I love listening to music with my daughters. One minute it might be a Disney classic, the next some pop song off the radio. What interests me is when I introduce something new to see the response. Each decision influences the next choice. This rather than sandwiches captures the challenges and complexities associated with ‘algorithms’ and ‘machine learning’.
Somewhere between 2000 and 2019 the IT industry seamlessly transitioned from
“we provide precision engineering tools, your data is yours, you should not need to trust us or anyone, mathematics is your guarantee, crypto 4 ever. ”
“give us your data. all of it. give. no secrets. hold nothing back. in return we will… train AIs on it.. and provide unspecified ‘services’… for someone, who may be you… that can change at any time… and we are funded by, uh. Look, a unicorn!”
“Mariners Apartment Complex,” “Venice Bitch,” and “hope is a dangerous thing for a woman like me to have – but i have it” continue Lana’s lyrical hot streak. … She continues to tease the tropes that have so often been used to pigeonhole her, including femme-fatale melodrama, sadness as a form of rebellion, kitschy sexuality, and her beloved Americana imagery, all prim debutantes in pastels.
The list below is far from exhaustive and exclusive. That is the beauty at the same time. Nothing has to be done, everything is possible. Take advantage of this list and of course fill in the comments.
Accidents will happen. And occasionally, maybe they should. Accidents are not welcome in most schools. Children are usually told to be more careful and ‘not to do it again’ when mishaps occur. Yet accidents can often be just as important in our education as learning knowledge and skills. What’s more, they probably prepare students for a world of work where mistakes may not necessarily be a bad thing.
Opinion: The 2009 vs. 2019 profile picture trend may or may not have been a data collection ruse to train its facial recognition algorithm. But we can’t afford to blithely play along.
Humans are the richest data sources for most of the technology emerging in the world.
Ian O’Byrne thinks there are two possibilities on this debate:
One, Facebook (and other companies) is getting us to share/label photos for learning possibilities. I think this could definitely be used to help the machines learn.
Second, Facebook (and other companies) are getting us to play “remember when” as user attitudes and trust are making us question the business/organization. They’re hoping to have users emote and connect to strengthen bonds as they’re fraying.
I threw the hairbands away.
This had me wondering if a way of thinking about ‘lookup tables’ is the collection of ideas and values that we reference each and every day? As with different applications, maybe there are those whose foundations are more visible and obvious that others? As Ben Werdmuller suggests,
We’ve all got red lines. They’re ours alone to draw.
I have a reputation, which is the trace of past events and current relationships in a social system. But that reputation isn’t really separate from the techniques others use to decode and utilize my reputation for decision-making.
This relationship is synergistic.
I have this strange feeling that it is not Parse This that is the problem, but it is where I am noticing the issue.
Morio & Buchholz (2009) separated this into three levels (visual anonymity, disassociation with real and online identities, and lack of identifiability.
- Visual anonymity – When individuals communicate without seeing each other. A good example of that is using text-based chatting programs over the Internet. People’s physical appearances are obscured in that scenario.
- Dissociation of real and online identities – A single individual can create more than one online identity using more than one screen name & avatars. Individuals then have the ability to become more than one person with dissimilar personalities. They also have the ability to adopt new genders & races.
- Lack of identifiability – This is the level closest to true anonymity online. When individuals cannot be identified, their behaviors are not distinguishable from others. An example would be an online forum in which people can post anonymous comments without attaching usernames to that post.
Place names can be damning evidence of colonial history. On a map of Australia, you’ll see Murderers Flat, Massacre Inlet, Haunted Creek, and Slaughterhouse Gully.
And Uber and Lyft’s apps are encrypted on your phone, so to reverse-engineer them, you’d have to decrypt them (probably by capturing an image of their decrypted code while it was running in a virtual phone simulated on a desktop computer). Decrypting an app without permission is “bypassing an effective means of access control” for a copyrighted work (the app is made up of copyrighted code).
Uber and Lyft can use DMCA 1201 to stop you from figuring out how to use them to locate co-op drivers, and they can use the CFAA to stop you from flipping your booking from Uber to Meta-Uber.