![]() ![]() Google spinoff Dandelion unveiled on Wednesday a smart heating and air conditioning system that uses energy from the ground to regulate your home’s temperature. ![]() ![]() Google spinoff Dandelion uses ground energy to heat, cool homes As for components, almost everything is still being sourced from China.Įurope and the US have had their rapid growth now it’s going to be the slow slide to saturation. So far most have been able to get around the recently introduced India import tariffs by doing final device assembly at local India manufacturing plants. Chinese OEMs will continue their strategy of selling large volumes of low-end devices by shifting their focus from China to India. The biggest upside in Asia/Pacific continues to be India with volumes expected to grow 14% and 16% in 20. Tough times are expected to continue in 2018 as IDC forecasts consumption in China to decline another 7.1% before flattening out in 2019. Why does aquamacs use so much cpu driver#The biggest driver of the 2017 downturn was China, which saw its smartphone market decline 4.9% year over year. Looking further out, IDC expects the market is to grow roughly 3% annually from 2019 onwards with worldwide shipment volume reaching 1.654bn in 2022 and a five year compound annual growth rate (CAGR) of 2.5%. According to the International Data Corporation (IDC) Worldwide Quarterly Mobile Phone Tracker, smartphone shipments are forecast to drop 0.2% in 2018 to 1.462bn units, which is down from 1.465bn in 2017 and 1.469bn in 2016. Worldwide smartphone volumes will remain down in 2018 before returning to growth in 2019 Īfter declining 0.3% in 2017, the worldwide smartphone market is expected to contract again in 2018 before returning to growth in 2019 and beyond. Just because you can’t see the submarine doesn’t mean it isn’t making progress – perhaps a lot. The point is, Google/DeepMind tends to go a long time in submarine mode, then pop up with something big. I’m not sure I agree with him on all of this, but refuting it isn’t trivial. ![]() OK, so we can now train AlexNet in minutes rather than days, but can we train a 1000x bigger AlexNet in days and get qualitatively better results? Apparently not… Particularly AlphaGo Zero and slightly more general AlphaZero take ridiculous amount of compute, but are not applicable in the real world applications because much of that compute is needed to simulate and generate the data these data hungry models need. The latest three points on that graph, interestingly show reinforcement learning related projects, applied to games by Deepmind and OpenAI. Neural machine translation is a big effort by all the big web search players and no wonder it takes all the compute it can take (and yet google translate still sucks, though has gotten arguably better). So at 100 times more compute than AlexNet we pretty much saturated architectures in terms of vision, or image classification to be precise. Xception is a variation of google inception architecture and actually only slightly outperforms inception on ImageNet, arguably actually slightly outperforms everyone else, because essentially AlexNet solved ImageNet. So in terms of applications for vision we see that VGG and Resnets saturated somewhat around one order of magnitude of compute resources applied (in terms of number of parameters it is actually less). We had the AlexNet in 2012 which had ~60M parameters, we probably now have models with at least 1000x that number right? Well probably we do, the question however is – are these things 1000x as capable? Or even 100x as capable? A study by openAI comes in handy: One of the key slogans repeated about deep learning is that it scales almost effortlessly. AI winter is well on its way įilip Piekniewski is sceptical on the AI/ML front: You’ll need to click a confirmation link, so no spam.Ī selection of 10 links for you. You can sign up to receive each day’s Start Up post by email. Don’t just sit there, look at the geotargeted ads on your phone. ![]()
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