I didn't say that it does mean that. I said it has come to be used that way. I support some prescriptiveness in language, but I am a realist about technical terms getting broad to the point of meaninglessness when they enter the mainstream. Fighting this is a frustrating and likely pointless endeavour (see: hacker).

Your theory seems to be that writers at TechCrunch think when they say a startup is using "AI" to improve recruiting or the sales process or even to process large volumes of textual data that they really think they mean artificial general intelligence. I believe they are using a vague catchphrase or abbreviation that gets clicks, and that they know that's not "real AI" but don't care.

Maybe the journalists at the New York Times or Washington Post are a more credulous lot and they really do think that is what it means, but I doubt it. They are all just capitalizing on confusion between Hollywood AI and machine learning for clickbait. That is what gets them paid.

At the same time, AI has come to be used in the tech press and mainstream press to mean "machine learning and related statistical techniques". Obviously this hurts the brains of many of us who still understand AI to mean "strong AI" or the newer moniker, "AGI" (artificial general intelligence), but we sort of have to roll with the language on this one.

Wikipedia insists that machine learning is a subset of AI. OK, sure, I guess that's fine, in that it is one of a series of techniques that can provide reasonable performance in solving certain human-level intelligence tasks.

When I'm speaking with a technical audience I go with more precise terminology - deep learning, reinforcement learning, unsupervised learning and try to avoid the headache, since we all know we are talking about statistical techniques in machine learning and none of it yet comes close to strong AI. Getting the mainstream press to correct their use of the abbreviation AI is about as likely as getting them to correct their use of the word "hacking".

In fact... this kind of service has always been held up as a shining example of how businesses are *supposed* to make money off of GPL.

Why? For large memory-requiring jobs and places where the latency of occasional cold start cases isn't acceptable, Lambda isn't the right tool. For periodic batch jobs, components in workflows, or response handling logic where some latency variability is acceptable, it's a great tool and saves a lot of costs over having dedicated but grossly underutilized EC2s.

"democratic forum"... "trash heap full of retards and emotionally-stunted dipshits" - I fail to see how these are materially different?

Just look at what we do with our democratic elections in the United States right now.

Plenty of groundbreaking things have happened, just not so much in physics. Which is why despite having a degree in it, I work in an area where far more exciting things are going on these days (machine learning).

Demographic changes, abortion, technology (video games, cashless society, security systems), and economic changes. And maybe also some lead in the air.

ROFD beats Blockchain every time. Regular Old Fucking Database.

I heard that the alzheimers brains were preserved in a solution containing aluminum. The healthy brains were not. But this is a bit different. They claim that their model has been used to reverse some of the symptoms of Alzheimers. That's a stronger claim. It's still a small study. There will be at least a few cases that don't fit the model. But it seems well designed and worth taking note of.

AI == machine learning. All of these are machine learned systems. AI doesn't mean thinking magic. And yes, we do actually have DNN-based models that do simple reasoning now. Look up the Facebook bAbI challenge, for example, or question answering systems like FlowQA.

"AI" generally just means machine learning these days. And all of those are machine learned, statistical model-driven systems, not algorithmic systems. While things like SVM or Naive Bayesian classifiers have been around for years, DNN-based systems that perform well on much harder tasks that require large amounts of data to learn have only existed since 2011 or so.

Most of those billion Google Assistant devices were software updates pushed by Google to existing Android devices. This can be better thought of as the count of Android devices, and a small percentage of Google Home devices. Most of the 100 million Alexa devices, on the other hand, are actual devices sold with Alexa enabled, many of which were purchased specifically for use with Alexa.

It's all about the GDP/IQ ratio.

Apparently they missed "startups 101" - the goal is to sell your company to a bagholder before the exponential growth dies off. Failure to find a bagholder is also known as "failure" in Silicon Valley.

This has nothing to do with AWS auto-scaling. The system that had issues doesn't run in public AWS. I can't say more than that unfortunately, but some random professor speculating based on leaked posts without any knowledge of the actual systems involved is a terrible source of information.

Source: I work at Amazon.