Enlarge / Take a peek inside the Ars vault with us! (credit: Aurich Lawson | Getty Images)

A bit over three years ago, just before COVID hit, we ran a long piece on the tools and tricks that make Ars function without a physical office . Ars has spent decades perfecting how to get things done as a distributed remote workforce, and as it turns out, we were even more fortunate than we realized because that distributed nature made working through the pandemic more or less a non-event for us. While other companies were scrambling to get work-from-home arranged for their employees, we kept on trucking without needing to do anything different.

However, there was a significant change that Ars went through right around the time that article was published. January 2020 marked our transition away from physical infrastructure and into a wholly cloud-based hosting environment. After years of great service from the folks at Server Central (now Deft ), the time had come for a leap into the clouds—and leap we did.

There were a few big reasons to make the change, but the ones that mattered most were feature- and cost-related. Ars fiercely believes in running its own tech stack, mainly because we can iterate new features faster that way, and our community platform is unique among other Condé Nast brands. So when the rest of the company was either moving to or already on Amazon Web Services (AWS), we could hop on the bandwagon and take advantage of Condé’s enterprise pricing. That—combined with no longer having to maintain physical reserve infrastructure to absorb big traffic spikes and being able to rely on scaling—fundamentally changed the equation for us.

Enlarge (credit: Bruno Vincent / Getty Images )

The UK’s communications watchdog has called for a probe into Microsoft and Amazon’s dominance of the country’s cloud computing market in the latest challenge to the tech giants from global regulators.

Ofcom said on Wednesday it was “particularly concerned” by the practices of Amazon Web Services and Microsoft, which together control between 60 and 70 per cent of the UK cloud market. It has proposed referring the sector to the Competition and Markets Authority for further investigation.

Cloud computing is dominated by Amazon and Microsoft, and has become a crucial driver of revenue at the tech giants.

Enlarge / Anker's cameras store their footage on a local base. Thumbnail images of faces, however, were uploaded to cloud servers. (credit: Eufy)

Eufy, a smart home brand of tech accessory firm Anker, had become popular among some privacy-minded security camera buyers. Its doorbell camera and other devices proudly proclaimed having " No Clouds or Costs ," and that "no one has access to your data but you."

That's why security consultant and researcher Paul Moore's string of tweets and videos, demonstrating that Eufy cameras were uploading name-tagged thumbnail images to cloud servers to alert owners' phones, likely unencrypted, stung smart home and security enthusiasts so hard this week.

Moore , based in the UK, started asking Eufy rhetorical questions about its practices on Twitter starting November 21. "Why is my 'local storage" #doorbellDual storing every face, without encryption, to your servers? Why can I stream my camera without #authentication?!" Moore also posted lines from " source code & API responses " that suggested a very weak AES key was being used to encrypt video footage.

Enlarge / Microsoft says its latest cloud licensing terms are meant to give customers more flexibility and cost control—just not on Amazon, Google, or Alibaba servers. (credit: Getty Images)

Facing European antitrust scrutiny, Microsoft has made it easier to virtualize its software on non-Microsoft cloud infrastructure—just so long as that infrastructure isn't owned by notable competitors Amazon, Google, or Alibaba.

The conflict, months in the making, is striking for a company that has largely avoided the antitrust scrutiny of its rivals, and eagerly sought to distance itself from the anti-competitive complaints and government actions that beset Microsoft in the late 1990s.

Microsoft outlined the changes that would take effect on October 1 in a blog post . Nicole Dezen, chief partner officer, wrote that Microsoft "believes in the value of the partner ecosystem" and changed outsourcing and hosting terms that "will benefit partners and customers globally."

Enlarge (credit: Aurich Lawson | Getty Images)

I am not a data scientist. And while I know my way around a Jupyter notebook and have written a good amount of Python code, I do not profess to be anything close to a machine learning expert. So when I performed the first part of our no-code/low-code machine learning experiment and got better than a 90 percent accuracy rate on a model, I suspected I had done something wrong.

If you haven't been following along thus far, here's a quick review before I direct you back to the first two articles in this series. To see how much machine learning tools for the rest of us had advanced—and to redeem myself for the unwinnable task I had been assigned with machine learning last year—I took a well-worn heart attack data set from an archive at the University of California-Irvine and tried to outperform data science students' results using the "easy button" of Amazon Web Services' low-code and no-code tools.

The whole point of this experiment was to see:

Enlarge / Ahhh, the easy button! (credit: Aurich Lawson | Getty Images)

This is the second episode in our exploration of "no-code" machine learning. In our first article , we laid out our problem set and discussed the data we would use to test whether a highly automated ML tool designed for business analysts could return cost-effective results near the quality of more code-intensive methods involving a bit more human-driven data science.

If you haven't read that article, you should go back and at least skim it . If you're all set, let's review what we'd do with our heart attack data under "normal" (that is, more code-intensive) machine learning conditions and then throw that all away and hit the "easy" button.

As we discussed previously, we're working with a set of cardiac health data derived from a study at the Cleveland Clinic Institute and the Hungarian Institute of Cardiology in Budapest (as well as other places whose data we've discarded for quality reasons). All that data is available in a repository we've created on GitHub, but its original form is part of a repository of data maintained for machine learning projects by the University of California-Irvine. We're using two versions of the data set: a smaller, more complete one consisting of 303 patient records from the Cleveland Clinic and a larger (597 patient) database that incorporates the Hungarian Institute data but is missing two of the types of data from the smaller set.

Enlarge / Is our machine learning yet?

Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience . (Spoiler alert: It has not .)

And as Ars' Matt Ford recently pointed out here , artificial intelligence may be artificial, but it's not "intelligence"—and it certainly isn't magic. What we call "AI" is dependent upon the construction of models from data using statistical approaches developed by flesh-and-blood humans, and it can fail just as spectacularly as it succeeds. Build a model from bad data and you get bad predictions and bad output—just ask the developers of Microsoft's Tay Twitterbot about that.

For a much less spectacular failure, just look to our back pages. Readers who have been with us for a while, or at least since the summer of 2021, will remember that time we tried to use machine learning to do some analysis—and didn't exactly succeed. ("It turns out 'data-driven' is not just a joke or a buzzword," said Amazon Web Services Senior Product Manager Danny Smith when we checked in with him for some advice. "'Data-driven' is a reality for machine learning or data science projects!") But we learned a lot, and the biggest lesson was that machine learning succeeds only when you ask the right questions of the right data with the right tool.

We are very pleased to announce that Lenovo , Infomaniak , Roche , Amazon Web Services (AWS) and Google , have committed to supporting DebConf21 as Platinum sponsors .

As a global technology leader manufacturing a wide portfolio of connected products, including smartphones, tablets, PCs and workstations as well as AR/VR devices, smart home/office and data center solutions, Lenovo understands how critical open systems and platforms are to a connected world.

Infomaniak is Switzerland's largest web-hosting company, also offering backup and storage services, solutions for event organizers, live-streaming and video on demand services. It wholly owns its datacenters and all elements critical to the functioning of the services and products provided by the company (both software and hardware).

Roche Roche is a major international pharmaceutical provider and research company dedicated to personalized healthcare. More than 100.000 employees worldwide work towards solving some of the greatest challenges for humanity using science and technology. Roche is strongly involved in publicly funded collaborative research projects with other industrial and academic partners and have supported DebConf since 2017.

Amazon Web Services (AWS) is one of the world's most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally (in 77 Availability Zones within 24 geographic regions). AWS customers include the fastest-growing startups, largest enterprises and leading government agencies.

Google is one of the largest technology companies in the world, providing a wide range of Internet-related services and products such as online advertising technologies, search, cloud computing, software, and hardware.

Google has been supporting Debian by sponsoring DebConf for more than ten years, and is also a Debian partner sponsoring parts of Salsa 's continuous integration infrastructure within Google Cloud Platform.

With these commitments as Platinum Sponsors, Lenovo, Infomaniak, Roche, Amazon Web Services and Google are contributing to make possible our annual conference, and directly supporting the progress of Debian and Free Software, helping to strengthen the community that continues to collaborate on Debian projects throughout the rest of the year.

Thank you very much for your support of DebConf21!

Participating in DebConf21 online

The 22nd Debian Conference is being held Online, due to COVID-19, from August 22nd to 28th, 2021. There are 8 days of activities, running from 10:00 to 01:00 UTC. Visit the DebConf21 website at https://debconf21.debconf.org to learn about the complete schedule, watch the live streaming and join the different communication channels for participating in the conference.

Značky: #debconf21, #lenovo, #google, #debconf, #sponsors, #roche, #aws, #Debian, #infomaniak