Boffins from UC Berkeley, MIT, and the Institute for Advanced Study in the United States have devised techniques to implant undetectable backdoors in machine learning (ML) models.

Their work suggests ML models developed by third parties fundamentally cannot be trusted.

In a paper that's currently being reviewed – "Planting Undetectable Backdoors in Machine Learning Models" – Shafi Goldwasser, Michael Kim, Vinod Vaikuntanathan, and Or Zamir explain how a malicious individual creating a machine learning classifier – an algorithm that classifies data into categories (eg "spam" or "not spam") – can subvert the classifier in a way that's not evident.

After exiting BOINC and removing the mlds tasks from Task Manager, can I safely delete the slots folders that contain the mlds.exe for any slot that is currently not in use? Are there any other BOINC files that need to be updated?

<core_client_version>7.16.11</core_client_version>
<![CDATA[
<message>
(unknown error) - exit code 195 (0xc3)</message>
<stderr_txt>
14:00:43 (4452): wrapper (7.7.26016): starting
14:00:44 (4452): wrapper: running mlds.exe (--nthreads 1 -c --maxepoch 128)
16:18:42 (4452): mlds.exe exited; CPU time 8004.645311
16:18:42 (4452): app exit status: 0x3
16:18:42 (4452): called boinc_finish(195)

</stderr_txt>
]]>

This is so frustrating. The error is intermittent even on the same host, and appears to be happening in a bowels of the pytorch library, so it's not (necessarily) anything we've done in the client. And you're sure this system runs reliably with other intense projects? I don't doubt it, but I can't rule out a cache or memory issue endemic to your system either.

Oddly the linux client seems to be a bit more unstable too.. maybe this is a pytorch v1.9 issue?

... They were surprisingly accurate. The worst performing was able to predict the right answer 80 per cent of the time, and the best was able to do this 99 per cent, according to the paper.

To put it simply, by using the quantum kernel approach, a quantum computer can distinguish between more features and, therefore, see patterns even in a huge database, where a classical computer would only see random noise.

The goal is to simplify the integration and scaling of big data and AI workflows onto the hybrid cloud, the company said.

IBM Wednesday announced CodeFlare, an open-source, serverless framework designed to simplify the integration and efficient scaling of big data and AI workflows onto the hybrid cloud. CodeFlare is built on top of an emerging open-source distributed computing framework for machine learning applications known as Ray.

The play R.U.R.: Rossum’s Universal Robots, by Czech playwright Karl Čapek. And it is now 100 years old, having first been staged in Prague on January 25, 1921.

R.U.R. is important for a lot of reasons. It is universally celebrated as the work of art that gave the world the very word “robot”. What is less often remarked that R.U.R. also gave us the basic plot of so very many of our stories about robots and AI that have been made in the last hundred years.

... But now horse-trading artificial intelligence that can act on its own has begun appearing in business.

If you sell to Walmart, you might have already met one. An AI system developed by Pactum was tested by the giant US retailer.

A chatbot contacts suppliers, and invites them to renegotiate together contracts over things like price and payment terms.

GPT-3 has been created by OpenAI, a research business co-founded by Elon Musk and has been described as the most important and useful advance in AI for years.

But there’s some confusion over exactly what it does (and indeed doesn’t do), so here I will try and break it down into simple terms for any non-techy readers interested in understanding the fundamental principles behind it. I’ll also cover some of the problems it raises, as well as why some people think its significance has been overinflated somewhat by hype.

Nowadays, computers appear to do a lot more than unravel mathematical equations. In the last half- century, programming has become the ultimate tool to automate tasks and save time, but how much can we automate, and how do we go about doing so? Can a computer observe a photograph and say, “Aha, I see a lovely couple walking over a bridge under an umbrella in the rain”? Can software make medical decisions as accurately as trained professionals can?