I think it depends on the amount of business that you want. I do freelance as a side job and only have a few clients a year that give me an extra ~10-20k of income. I mostly do web scraping, data science, data management, and teaching (workshops, taught a couple university classes, adult education)
Networking events and word of mouth from other clients have been about half of my work, and the rest just sort of email me out of the blue based on either my LinkedIn profile or a book I wrote.
I don't blog, I don't have much of a professional website. The book I wrote is fairly popular, but I find that the people who email generally haven't read the book (although they might have bought it... to see that it's real, I guess) but they find me through that.
Will agree that cold outreach doesn't work at all unless you're doing it on a massive scale (and then you're just a professional salesperson...). I've done it, but it's literally never worked for me.
In a computer security class I had at Harvard, the professor made the comment: "Your mother's maiden name is considered a 'secret.' Which is funny, given the building we're in."
We were in the Maxwell Dworkin building, named after the surnames of Bill Gates' and Paul Allens' mothers. Their mothers' maiden names were literally carved in a huge stone sign outside the building.
Individuals who have androgen insensitivity syndrome (XY chromosomes, but that don't respond to testosterone and are, for most intents and purposes, sterile females) will have large hips/pelvic bones.
So, yes, hip size takes a few modified base pairs (although probably all within one gene, or pretty close together on the same chromosome), but evolution only needs to "decide" (heavy emphasis on the quotes there) that this physiological change is hormonally triggered. Many skeletal changes are hormonally triggered, so this may have even been the easiest/most likely path.
I made a post about this, above, but I would argue that urine isn't always possible to completely clean in some situations. We lived in a place with a carpet and a thick pad under the carpet. Combined with a cat that spontaneously decided to habitually urinate in that one spot (no history of it). We tried EVERYTHING -- it was a massive project for over a year to get her to stop and spent hundreds of dollars on cleaning. Every enzyme product and shampoo, hours of elbow grease, soaking, drying, re-soaking, re-drying. We caught it as soon as it happened, gave LOTS of care and attention, but no dice.
Even the most responsible owners with the best pets can have problems that can't be fixed without just replacing the entire carpet. It happens.
My husband and I replaced a carpet in our last apartment ourselves before move out (hired a professional, got it matched exactly with the rest of the carpets, replaced carpet pad as well, cleaned concrete subfloor) because our cat decided that one corner of the living room was an appropriate place to urinate.
She's usually a well-behaved cat, never did anything like this before, and we tried everything we could think of to get her to stop. We spent over a year and hundreds of dollars trying to repeatedly clean the spot, shampooing, steaming, enzymes, but nothing worked.
Our deposit was only $500, but we spent about $1600 replacing the carpet (it was the right thing to do and, like you said, they can sue us). There was NOTHING you could do to get that out besides just replacing it. And that was just one room. Multiply by 5 or 6 for replacing carpets all over the house, and that's more than a landlord can even reasonably hold as a security deposit. And what if we had had a wood subfloor instead of concrete? That would quickly get into the 5 figures.
It seemed like an ideal pet situation -- good cat, no history of urinating outside the litter box, responsible pet owners, regular vet checkups, doing everything we could to try and clean and get her to stop, cared very much about the cleanliness of our living space, but we STILL had to replace the carpet.
Honestly, especially after that experience, I don't blame landlords at all. I own a house now and don't think I'd ever want to, say, rent a room to a stranger with a cat. And I have a cat!
I've been at my current company (satellite of a larger company) for a year and a half, we have 8 people in the office, max, every day. There are a couple people who usually work from home, some days it's only one or two people in the office. Our office is 4,200 sq ft and it's an "open office." We're not growing, we didn't just shrink from a massive size -- the company just picked an office and that was that.
I get paid well above market rate, I own a home just outside Boston, my macbook pro had an issue a few months ago and the company just issued me a new one without blinking. The company just doesn't give a crap. Open offices are cool and just what programmers do these days, right?
My last company was very similar. 5 people, > 2,000 sq ft, great salaries, open office. The CEO talked about how awesome it was all the time, and actually said he wanted MORE talking in the office. Annoying as hell.
I don't think it's a money issue. I really think it's a culture issue. The only company I ever worked at that had cubicles was my first job out of college and they were actually relatively strapped for cash. The difference is the founder was in his 60's and they were in business consulting, not software specifically.
There are really a few problems here that I think the article is getting a little mixed together, and I wanted to lay them out.
First, that the image corpuses used for machine learning have a strong gender bias, perhaps more than exists in the "real world." More images of men than women, more images of men working on computers, more images of women in kitchens, etc.
These images are sourced from http://imsitu.org/ which is sourced from http://image-net.org/, which (after some digging) looks like it gets most of its images from Flickr, stock photography sites, and random corporate sites. Are these representations of the "real world"? I would argue not. Professional photography, stock photography, and photos taken for the purpose of being used in an unknown future context, and/or to appeal to the most people, tends to err on the side of being "universally applicable" and emphasizing the "common idea of a thing" rather than how the thing actually is, with all its variations. An image of a man in a kitchen be perceived as more controversial and may be less universally usable than an image of a woman in a kitchen. So if you want to take a photo with as many possible uses as possible, you'd tend to fall back on established social norms MORE often than they might actually occur.
Second, machine learning tends to emphasize small differences when it has nothing else to go on, or is improperly trained. If you have a dataset featuring people in kitchens where 75% of the time the person in a photo is a woman, you could get an algorithm that is 75% accurate simply by saying "the person in the photo is a woman" every single time. While the dataset reflects 75% women, the algorithm reflects 100% women. It emphasizes small differences in order to gain accuracy.
This isn't just hypothetical. Many times, I've worked on a categorization/labeling dataset that turns out to have no actual underlying pattern, but I wind up, after many hours, getting a best fit algorithm that, say, predicts the dataset correctly 85.166667% of the time... only to realize that my dataset is spectacularly unbalanced and exactly (EXACTLY) 85.166667% of the dataset is in a single category. It's amazing how it just sort of snaps to that when you start layering the machine learning algorithms and you realize that the real problem is that there's no real pattern in the data (something data scientists don't often like to admit).
Third, sometimes the algorithms just get it wrong in ways that seem minor and rare from a data science perspective, but have large social consequences. Like improperly labeling a black couple as gorillas. It might actually be the case that the algorithm was improperly trained because it lacked photos of black people and photos of gorillas and didn't have much to go on (an example of the first issue) but I don't know enough about the situation to say for sure.
And fourth, of course, is that these patterns DO exist to some degree in the "real world," and this is a point that's been hammered on over and over again on Hacker News. The problem is that machine learning is a sort of big leveler that finds these patterns wherever it can and applies them universally (and often while emphasizing the differences for the reasons stated above). I mean, that's the point of it, after all! But knowing this fact, I think it makes sense to be careful where and how it's used.
Completely agree, and I was about to comment on the same thing that the author was drawing some dangerous conclusions from the anecdote about her kids, the same sort of conclusions I see a lot of other parents with young children making as well.
I'm female. When I was 7/8 years old I literally cried about having to do multiplication and addition flash cards. I was failing math. My dad said "Listen, you're not going to be a mathematician, but you have to learn multiplication!" Hated hated hated it.
Things started to turn around in middle school. My teachers kept pushing me, I kept taking math, I started to enjoy it. By 16 I was cross-registering at a local community college to take calculus and was in differential equations my senior year. I have a bachelor's in engineering and a master's in software engineering.
The preferences of seven year olds are almost meaningless, and their career aspirations are definitely meaningless. When I was seven I wanted to be either a baker or a manicurist or president of the United States. I haven't been interested in the culinary arts, cosmetology, or politics since. My sister was an avid artist, now she's an actuary who doesn't even do art as a hobby.
Almost every parent (especially parents of mixed-gendered children) I have this discussion with ends up saying something along the lines of "Just wait until you have kids -- boys and girls are super different and my son likes trucks and machines and my daughter likes dolls!" Yeah, but what does that have to do with which classes they enjoy in middle school or high school? I don't know. Maybe I will change my mind, but this extrapolation of early childhood preferences into adulthood career paths bugs the heck out of me.
I've had great experiences with Amazon the few times something went wrong.
- Had a fire extinguisher shipped to me but the pin apparently got knocked out in the box and it discharged during shipping. They sent out a new fire extinguisher immediately, I got to keep the old one and got it recharged for $10.
- A water filter was held by USPS and I wasn't able to pick it up in time. They sent out a new water filter (different shipping so it got to my door) even before the first one was sent back for non-pickup.
- Ordered through Amazon Fresh and some bread got crushed by a 2L soda. Sent a one-line message to them "Bread was crushed by 2L soda" Bam -- refund for bread.
- Called, questioning a $10 charge I had on my credit card, even though I hadn't ordered anything. They tracked it down to a Prime Video subscription I had made that had ended its "free trial period." I just said "Oh! I was going to cancel that!" and they removed the charge and cancelled it for me right then and there without me even asking. I've never heard of any company doing something like that that easily. In my experience "free trial periods" are just traps trying to get people to forget and let the subscription run, so I didn't even bother asking, but they went ahead and removed it.
In addition, the package (listed on the USPS documentation) didn't have the correct weight, which should have been further proof that something was wrong. The package was 8oz, the lens itself, even without packaging, weighs 3.2 pounds.
Yup that seems like it, everything needed to prove that the transaction was fraudulent on the seller's part is included here by third party sources. Amazon completely blew it.
So it seems that as a buyer, if you receive the wrong item, or the item is delivered to the wrong address, Amazon will do nothing. I'm not sure why the seller went to the trouble of sending to a different address, but didn't bother to bulk up the mass.
Technically, this also comes under Mail Fraud which is a federal crime[1]. This suggests that our author should contact both the FBI and the postmaster of their town because someone is using the Mail Service to commit their fraud. And as the latest rounds of advertisments from the Post Office state, "When you use the mail to do your business, it becomes our business."
Networking events and word of mouth from other clients have been about half of my work, and the rest just sort of email me out of the blue based on either my LinkedIn profile or a book I wrote.
I don't blog, I don't have much of a professional website. The book I wrote is fairly popular, but I find that the people who email generally haven't read the book (although they might have bought it... to see that it's real, I guess) but they find me through that.
Will agree that cold outreach doesn't work at all unless you're doing it on a massive scale (and then you're just a professional salesperson...). I've done it, but it's literally never worked for me.