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ujal | 7 years ago

I don't get the sentiment of the article either. I can't speak for researchers but software engineers are living through very exciting times.

  State of the art in numbers:
  Image Classification - ~$55, 9hrs (ImageNet)
  Object Detection - ~$40, 6hrs (COCO)
  Machine Translation - ~$40, 6hrs (WMT '14 EN-DE)
  Question Answering - ~$5, 0.8hrs (SQuAD)
  Speech recognition - ~$90, 13hrs (LibriSpeech)
  Language Modeling - ~$490, 74hrs (LM1B)
"If you think Deep (Reinforcement) Learning is going to solve AGI, you are out of luck" --

I don't know. Duplex equipped with a way to minimize his own uncertainties sounds quite scary.

discuss

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varelse|7 years ago

Duplex was impressive but cheap street magic: https://medium.com/@Michael_Spencer/google-duplex-demo-witch...

Microsoft OTOH quietly shipped the equivalent in China last month: https://www.theverge.com/2018/5/22/17379508/microsoft-xiaoic...

Google has lost a lot of steam lately IMO. Facebook is releasing better tools and Microsoft, the company they nearly vanquished a decade ago, is releasing better products. Google does remain the master of its own hype though.

sah2ed|7 years ago

> Microsoft, the company they nearly vanquished a decade ago, is releasing better products.

Google nearly vanquished Microsoft a decade ago? Where can I read more about this bit of history :) ?

IMO, Axios [0] seem to do a better job of criticizing Google's Duplex AI claims, as they repeatedly reached out to their contacts at Google for answers.

0: https://www.axios.com/google-ai-demo-questions-9a57afad-9854...

placebo|7 years ago

My thoughts on AGI (at least in the sense of being indistinguishable from interaction with a human) are the same as my thoughts on extraterrestrial life: I'll believe it only when I see it (or at least when provided with proof that the mechanism is understood). This extrapolation on a sample size of one is something I don't understand. How is the fact that machine learning can do specific stuff better than humans different in principle than the fact that a hand calculator can do some specific stuff better than humans? On what evidence can we extrapolate from this to AGI?

We haven't found life outside this planet, and we haven't created life in a lab, therefore n=1 for assessing probability of life outside earth (which means we can't calculate a probability for this yet). Likewise, we haven't created anything remotely like animal intelligence (let alone human) and we have no good theory regarding how it works, so n=1 for existing forms of general intelligence.

Note that I'm not saying there can be no extraterrestrial life or that we will never develop AGI, just that I haven't seen any evidence at this point in time that any opinions for or against their possibility are anything more than baseless speculation.

ujal|7 years ago

This is what we know from Google about Duplex:

"To train the system in a new domain, we use real-time supervised training. This is comparable to the training practices of many disciplines, where an instructor supervises a student as they are doing their job, providing guidance as needed, and making sure that the task is performed at the instructor’s level of quality. In the Duplex system, experienced operators act as the instructors. By monitoring the system as it makes phone calls in a new domain, they can affect the behavior of the system in real time as needed. This continues until the system performs at the desired quality level, at which point the supervision stops and the system can make calls autonomously." --

sqrt17|7 years ago

If the dollar amounts refer to the training cost for the cheapest DL model, do you have references for them? A group of people at fast.ai trained an ImageNet model for 26$, presumably after spending a couple hundered on getting everything just right: http://www.fast.ai/2018/04/30/dawnbench-fastai/