Do a Google search on how to get your best body and you’ll be inundated with pages of training tips. For those who want to take that same, proactive approach to creating your best relationship, I have your “exercise regimen” below. 1. Do the things you did the first year you were dating. As the months and years roll on, we tend to slink into our proverbial sweatpants and get lazy in our relationship. We lose our patience, gentleness, thoughtfulness, understanding and the general effort we once made toward our mate. Think back to the first year of your relationship and write down all the things you used to do for your partner. Now start doing them again. 2. Ask for what you want. Over time, we assume that our partner knows us so well that we don’t need to ask for what we want. What happens when we make this assumption? Expectations are set and just as quickly, they get deflated. Those unmet expectations can leave us questioning the viability of our partnership...
A team of researchers from MIT introduced a new computer chip optimized for deep-learning, an approach to artificial intelligence that is gaining popularity. The chip, dubbed “Eyeriss” which was introduced two days ago could allow mobile devices to perform tasks like natural language processing and facial recognition without being connected to the internet. It’s the latest attempt to make the complex operations of machine learning more portable. That means that our smartphones, wearables, robots, self-driving cars, and other IoT devices could begin performing complex deep learning processes locally — something that until now has been very difficult to do.
Deep learning has traditionally demanded large amounts of computer processing. GPUs, computer chips designed to render the graphics we see on our computer screens, are a good enough workhorse to handle the task. But GPUs come with a drawback: they suck up a ton a power. This makes them impractical to use for deep learning on mobile devices. The workaround to this has been to take raw data collected by devices, upload it over the internet, perform deep learning on powerful GPU servers, then shoot the results back over the internet to the device.
Eyeriss is the latest in a number of chips being announced that are taking deep learning out of remote servers. Qualcomm just revealed its Snapdragon 820A and 820Am processors at CES 2016, which allow cars to detect multiple lane markings and understand traffic signals using deep learning. Nvidia also flaunted its machine learning wares at CES, demonstrating how its Tegra processor could use deep learning for be used for autonomous driving. And Google recently announced a partnership with chip maker Movidius aimed at improving facial recognition on phones.
The MIT research team, headed by Vivienne Sze of MIT’s department of electrical engineering and computer science, introduced the chip at the International Solid State Circuits Conference in San Francisco, where they used it to perform an image recognition task. Plans for when the Eyeriss will reach devices have not been announced.
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