Kate Crawford is a Fake. Our metrics, ourselves: A hundred years of self-tracking from the weight scale to the wrist wearable device - Kate Crawford, Jessa Lingel, Tero Karppi, 2015 by [deleted] in MachineLearning

[–][deleted] 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

There is a constant barrage of complaints that women are not being recognized for their supposed achievements as much as men are. However, almost inevitably when I see women, especially American women, engage with STEM, it ends up being from some touchy-feely perspective. As soon as you have some blabbering about the social implications of this or that technology, you will have people like Kate Crawford. The only thing I was surprised about was that she did not have her pronouns in her Twitter bio. But, inevitably, their timeline will be full with some virtue signaling for the latest social causes, like BLM, something about how women are oppressed, and just about the softest version of the scientific field they are in one can imagine.

Despite their whaling about the oppression of women and support for girl power, industry seems to hunger for women and minorities it can decorate their leadership positions with. When people like Elizabeth Holmes enter the stage, we often ask in hindsight who could have known, why those people go on undetected for so long, while we virtually beg for scammers like these to prop up their ranks among diversity hires and minority quotas. It's the same, tired, regurgitated take on current politics and social issues. You can practically read the script without having done an ounce of research, which is why these positions are perfect for people like that. Women oppressed, white men bad, society biased. Apply this to any field you like and you have it made. Be it gaming, sports, STEM, math, machine learning - you name it. The formula fits everywhere.

Teach Yourself Programming in Ten Years - Classic Peter Norvig Article by [deleted] in MachineLearning

[–][deleted] 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

Walk into any bookstore, and you'll see how to Teach Yourself Java in 24 Hours alongside endless variations offering to teach C, SQL, Ruby, Algorithms, and so on in a few days or hours. The Amazon advanced search for [title: teach, yourself, hours, since: 2000 and found 512 such books. Of the top ten, nine are programming books (the other is about bookkeeping). Similar results come from replacing "teach yourself" with "learn" or "hours" with "days."

The conclusion is that either people are in a big rush to learn about programming, or that programming is somehow fabulously easier to learn than anything else. Felleisen et al. give a nod to this trend in their book How to Design Programs, when they say "Bad programming is easy. Idiots can learn it in 21 days, even if they are dummies." The Abtruse Goose comic also had their take.

[...]

Microsoft to Offer AMD Based GPU Accelerated Machine Learning Under Windows -- Too Little, Too Late? by [deleted] in MachineLearning

[–][deleted] 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

Because of this, there is an increasing demand to learn the fundamentals of machine learning by both existing software engineers and students. However, one of the challenges many users face is the accessibility of machine learning workflows and tools – they will commonly need to use Linux software solutions, often on separate hardware from what they use for their day-to-day computing tasks.

But you folks have known that for years. Not only did you initially mock Linux from high above, with a smug sense of superiority, now you also have to make concessions for its popularity on your cloud platform, Windows Azure, where you are offering Linux images for VM role deployment. Now, you are several years late to the machine learning game. Arguably, you were behind the cloud computing trend in many ways, too, speaking of the cloud. And yet again, it was a competitor, with Amazon, and maybe a second with Google, that were investing in Linux. If one of the largest software companies in the world cannot keep up with the latest industry developments in a timely fashion, is there something wrong with its corporate culture?

Maybe one could excuse this oversight, given that, by any measure, the embrace of machine learning by the industry was sudden and rapid. Despite machine learning having been a developing field in academia for decades, there was the AI winter of the 70s and many developing technologies initially take a long time to find their way into corporate environments that can afford the investment in often complex deployment and setup processes, let alone end-users. Machine learning, however, had announced itself in such a broad variety of fashions, time and time again, that it seems nothing short of absolute corporate blindness to have yet again failed to acknowledge the enduring significance of Linux and the increasing insularity of the Windows ecosystem.

It might sound far-fetched to bring this up in the same context, but with another major product release, Microsoft Edge, now apparently having abandoned their previous core, in favor of yet more open source, Linux community derived developments, those being Chromium, V8, and Blink, after having ditched EdgeHTML.

One has to therefor ask, is Microsoft too incompetent to keep up with some of the most significant industry developments? Is this company that is employing tens of thousands of people world-wide no longer capable of developing a usable web browser on their own? Is there an equivalent of senility that infects corporations that grow too large?

Speaking of Windows, why do people still use it at this point? It is more of a pain in the rear nowadays and even the crummy setup experience you used to have with Linux is now a smooth ride. You are often browsing the Internet while your Linux distribution is still installing the system, using pre-compiled networking drivers and bootstrapping systems that are loaded into RAM disk. I think people are primarily using Windows because of the driver support and the software available. They install Windows despite Windows. It is a means to an end. The operating system more likely gets in the way and people seem relieved when they have to mess with Windows as little as possible, whereas people under Linux often love investing weeks, months or years into customizing every part of their base system. In short, I believe Microsoft is in many ways riding out the momentum they had built in decades past with the ecosystem they have built. It is not Windows that people want, it is access to the software Microsoft has brought to an increasingly inferior platform through its business connections. People install Windows for its ecosystem, not for Windows, whereas people who use Linux often times like its design philosophy and for its own sake. Long term, this cannot bode well for Windows or Microsoft and their arthritic lethargy when it comes to adjusting to new trends: they were spectacularly wrong on the Internet, they lost the browser war long ago, they were late to the cloud, and now they dropped the ball on machine learning. The only way they seem to have made it this long is by having decisively crippled the competition in the OS market, an area that is conspicuously monopolistic, especially by the standards of the fast-paced software industry.

The Sleeping Beauty Problem: A Data Scientist’s Perspective by Stankmango in MachineLearning

[–]JasonCarswell 1 insightful - 2 fun1 insightful - 1 fun2 insightful - 2 fun -  (0 children)

That was painfully tedious and I gave up after half way when I finally figured out what it was aiming at.

Also, that's Snow White, not Sleeping Beauty.

Mona Lisa Frown: Machine Learning Brings Old Paintings and Photos to Life by Stankmango in MachineLearning

[–]happysmash27 2 insightful - 1 fun2 insightful - 0 fun3 insightful - 1 fun -  (0 children)

Machine learning tends to do that.

Mona Lisa Frown: Machine Learning Brings Old Paintings and Photos to Life by Stankmango in MachineLearning

[–]Snow 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

They use photos of stars and politicians to make that long time ago.

Mona Lisa Frown: Machine Learning Brings Old Paintings and Photos to Life by Stankmango in MachineLearning

[–]magnora7 2 insightful - 1 fun2 insightful - 0 fun3 insightful - 1 fun -  (0 children)

Wow that is crazy. Looks so real

Mona Lisa Frown: Machine Learning Brings Old Paintings and Photos to Life by Stankmango in MachineLearning

[–]wizzwizz4 1 insightful - 1 fun1 insightful - 0 fun2 insightful - 1 fun -  (0 children)

Well, Samsung's technology only works for the upper torso at the moment, so it's not that much of a problem. Deepfakes are concerning, but it's honestly nothing a skilled human artist can't do.

Ooh! Could this technology be used to automate The Snowman III: Electric Boogaloo?

Mona Lisa Frown: Machine Learning Brings Old Paintings and Photos to Life by Stankmango in MachineLearning

[–]Stankmango[S] 3 insightful - 4 fun3 insightful - 3 fun4 insightful - 4 fun -  (0 children)

Some sites are running the news from another angle.

Mona Lisa Frown: Machine Learning Brings Old Paintings and Photos to Life by Stankmango in MachineLearning

[–]wizzwizz4 3 insightful - 2 fun3 insightful - 1 fun4 insightful - 2 fun -  (0 children)

Brilliant and scary and brilliant.