One of the more annoying things about the Pixel 1, aside from the Google Assistant that I had to disable on privacy grounds, is that it’s a sealed case — so no way to replace the battery. This started to become an issue a few weeks ago. It wasn’t just that battery life had gotten noticeably worse, you expect that after a couple of years, it was that the battery would go from c.20% to dead without any warning.
It turns out, however, that there’s an entire chain of Google-certified phone repair joints with the silly name of ubreakifix that will replace the battery in a Pixel in a couple of hours for about $80. That’s a lot cheaper than buying a new phone. I was afraid the thing would have horrible scars from being pried open, but no. “We have tools” the tech told me smugly, and it indeed there’s no sign the case has been opened, but battery life is 50% greater than it was last week.
So now I’m likely good until Google orpahans the phone, which could come as soon as in October, at which point supposedly they’ll stop doing patches for it. The idea of course is to make me buy a new phone. Sadly, it will probably work. I hope the Pixel 4 is better than the Pixel 3 or I may to switch to Samsung.
VotingWorks aims to shake up the voting equipment market by creating a new non-profit voting systems manufacturer with the mission of being the public works for voting systems. VotingWorks will do this by developing voting equipment that 1) embody the state-of-the-art in usability, security, design, and development; 2) are affordable to maximize any benefit to all sizes of election jurisdictions; 3) allow speedy, efficient voting processes; and, 4) that is extensible to the needs of all types of localities. And all of this will be developed in the open for the public good.
The need here is very real. Election officials often find themselves stuck between a rock and a hard place when choosing a new voting system; there are often few expensive choices that come with serious limitations in how these systems can be used, modified, improved, and studied. CDT has advised localities in procurement decisions in the past and contributed to efforts where jurisdictions are designing their own voting systems – such as the Los Angeles County VSAP project – and the common factor in all these cases is the wide variety of needs and requirements that elections present, and how few systems can meet them all.
CDT will serve as a home for VotingWorks until it becomes its own non-profit entity. This partnership means VotingWorks is working closely with the CDT’s experienced team to rapidly ramp up operations and begin in earnest the development of affordable, secure, open-source voting machines for use in US public elections.
Someday, perhaps soon, diagnostics generated by machine learning (ML) will have demonstrably better success rates than those generated by human doctors. What will the dominance of ML diagnostics mean for medical malpractice law, for the future of medical service provision, for the demand for certain kinds of doctors, and—in the longer run—for the quality of medical diagnostics itself?
This article argues that once ML diagnosticians, such as those based on neural networks, are shown to be superior, existing medical malpractice law will require superior ML-generated medical diagnostics as the standard of care in clinical settings. Further, unless implemented carefully, a physician’s duty to use ML systems in medical diagnostics could, paradoxically, undermine the very safety standard that malpractice law set out to achieve. In time, effective machine learning could create overwhelming legal and ethical pressure to delegate the diagnostic process to the machine. Ultimately, a similar dynamic might extend to treatment also. If we reach the point where the bulk of clinical outcomes collected in databases are ML-generated diagnoses, this may result in future decision scenarios that are not easily audited or understood by human doctors. Given the well-documented fact that treatment strategies are often not as effective when deployed in real clinical practice compared to preliminary evaluation, the lack of transparency introduced by the ML algorithms could lead to a decrease in quality of care. The article describes salient technical aspects of this scenario particularly as it relates to diagnosis and canvasses various possible technical and legal solutions that would allow us to avoid these unintended consequences of medical malpractice law. Ultimately, we suggest there is a strong case for altering existing medical liability rules in order to avoid a machine-only diagnostic regime. We argue that the appropriate revision to the standard of care requires the maintenance of meaningful participation by physicians in the loop.
I hope that it will be of interest to to lawyers, doctors, computer scientists, and a range of medical service providers and policy-makers. Comments welcome!
AN AMAZON ECHO SPEAKER has been blamed for starting a “rave” in a sixth floor flat in Hamburg.
The owner was out at a real nightclub when the speaker decided to start blasting out bangin’ tunes at top volume at 0150 CET. Neighbours called the police who broke down the door to find no one in, just the Alexa speaker havin’ it large all on its own.
Speaking to German paper Die Welt, the flat (and speaker) owner, Oliver Haberstroh explained that he’d not had any problems with the digital task monkey up until this point.
Neighbours raised the alarm after shouting and banging on the door didn’t work.