{"id":38737,"date":"2019-04-29T09:48:47","date_gmt":"2019-04-29T09:48:47","guid":{"rendered":"http:\/\/icloud.pe\/blog\/?guid=c1cce06079a3bea0a0bf025dab568236"},"modified":"2019-04-29T09:48:47","modified_gmt":"2019-04-29T09:48:47","slug":"what-can-you-do-with-deep-learning","status":"publish","type":"post","link":"https:\/\/icloud.pe\/blog\/what-can-you-do-with-deep-learning\/","title":{"rendered":"What can you do with deep learning?"},"content":{"rendered":"


\n Cloud Pro<\/a><\/span>
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29 Apr, 2019<\/span><\/div>\n<\/p><\/div>\n<\/div>\n

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If there\u2019s one resource the world isn\u2019t going to run out of anytime soon it\u2019s data. International analyst firm IDC estimates the \u2018Global Datasphere\u2019 \u2013 or the total amount of data stored on computers across the world \u2013 will grow from 33 zettabytes in 2018 to 175 zettabytes in 2025. Or to put that in a more relatable form, 175 billion of those terabyte hard disks you might find inside one of today\u2019s PCs.<\/p>\n

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\nThat data pool is an enormous resource, but one that\u2019s far too big for humans to exploit. Instead, we\u2019re going to need to rely on deep learning to make sense of all that data and discover links we don\u2019t know even exist yet. The applications of deep learning are, according to Intel\u2019s AI Technical Solution Specialist, Walter Riviera, \u201climitless\u201d.<\/p>\n

\u201cThe coolest application for deep learning is yet to be invented,\u201d he says.<\/p>\n

So, what is deep learning and why is it so powerful?<\/p>\n

Teaching the brain<\/h4>\n

Deep learning is a subset of machine learning and artificial intelligence. It is specifically concerned with neural networks \u2013 computer systems that are designed to mimic the behaviour of the human brain.<\/p>\n

In the same way that our brains make decisions based on multiple sources of \u2018data\u2019 \u2013 i.e. sight, touch, memory \u2013 deep learning also relies on multiple layers of data. A neural network is comprised of layers of \u201cvirtual or digital neurons,\u201d says Riviera. \u201cThe more layers you have, the deeper you go, the cleverer the algorithm.\u201d<\/p>\n

There are two key steps in deep learning: training and inference. The first is teaching that virtual brain to do something, the second is deploying that brain to do what it\u2019s supposed to do. Riviera says the process is akin to playing a guitar. When you pick up a guitar, you normally have to tune the strings. So you play a chord and see if it matches the sound of the chord you know to be correct. \u201cUnconsciously, you match the emitted sound with the expected one,\u201d he says. \u201cSomehow you\u2019re measuring the error \u2013 the difference between the two.\u201d<\/p>\n

If the two chords don\u2019t match, you twiddle the tuning pegs and strum the chord again, repeating the process until the sound from the guitar matches the one in your head. \u201cIt\u2019s an iterative process and after a while you can basically drop the guitar, because that\u2019s ready to go,\u201d says Riviera. \u201cWhat song can you play? Whatever, because it\u2019s good to go.\u201d<\/p>\n

In other words, once you\u2019ve trained a neural network to work out what\u2019s right and wrong, it can be used to solve problems that it doesn\u2019t already know the answer to. \u201cIn the training phase of a neural network, we provide data with the right answer\u2026 because we know what is the expected sound. We allow the neural network to play with that data until we are happy with the expected answer,\u201d says Riviera.<\/p>\n

\u201cOnce we\u2019re ready to go, because we think the guitar is playing well, so the neural network is actually giving the expected answer or the error is very close to zero, it\u2019s time to take that brain and put it in a camera, or to take decisions in a bank system to tell us that it\u2019s a fraud behaviour.\u201d
\nDeep learning as a concept isn\u2019t new \u2013 indeed, the idea has been around for 40 years. What makes it so exciting now is that we finally have all the pieces in place to unlock its potential.<\/p>\n

\u201cWe had the theory and the research papers, we had all the concepts, but we were missing two important components, which were the data to learn from and the compute power,\u201d says Riviera. \u201cToday, we have all of these three components \u2013 theory, data and infrastructures \u2013 but what we\u2019re missing is the fourth pillar, which is creativity. We still don\u2019t know what we can and can\u2019t achieve with deep learning.\u201d<\/p>\n

Deeper learning<\/h4>\n

That\u2019s not to say that deep learning isn\u2019t already being put to amazingly good use.<\/p>\n

Any regular commuter will know the sheer fist-thumping frustration of delays and cancelled trains. However, Intel technology is being used to power Resonate\u2019s Luminate platform, which helps one British train company better manage more than 2,000 journeys per day.<\/p>\n

Small, Intel-powered gateways are placed on the trackside, monitoring the movements of trains across the network. That is married with other critical data, such as timetables, temporary speed restrictions and logs of any faults across the network. By combining all this data and learning from past behaviour, Luminate can forecast where problems might occur on the network and allow managers to simulate revised schedules without disrupting live rail passengers. The system can also make automatic adjustments to short-term schedules, moving trains to where they are most needed.<\/p>\n

The results have been startling. On-time arrivals have increased by 9% since the adoption of the system, with 92% of trains now running to schedule.<\/p>\n

Perhaps just as annoying as delayed trains is arriving at the supermarket to find the product you went there for is out of stock. Once again, Intel\u2019s deep learning technology is being used to avert this costly situation for supermarkets.<\/p>\n

The Intel-powered Vispera ShelfSight system has cameras mounted in stores, keeping an eye on the supermarket shelves. Deep-learning algorithms are used to train the system to identify individual products and to spot empty spaces on the shelves, or even products accidentally placed in the wrong areas by staff.<\/p>\n

Staff are alerted to shortages using mobile devices, so that shelves can be quickly restocked and lost sales are kept to a minimum. And because all that data is fed back to the cloud, sales models can be adjusted and the chances of future shortages of in-demand products are reduced.<\/p>\n

Only the start<\/h4>\n

Yet, as Riviera said earlier, these applications of deep learning are really only the start. He relays the story of the Italian start-up that is using deep learning to create a system where drones carry human organs from hospital to hospital, eliminating the huge disadvantages of helicopters (too costly) and ambulances (too slow) when it comes to life-critical transplants.<\/p>\n

It\u2019s not the only life-saving application he can see for the technology, either. \u201cI\u2019d like to see deep learning building an autonomous system \u2013 robots \u2013 that can go and collect plastic from the oceans,\u201d he says. \u201cWe do have that capability, it\u2019s just about enabling it and developing it.\u201d<\/p>\n

\u201cThe best [use for deep learning] is yet to be invented,\u201d he concludes.<\/p>\n

Discover more about data innovations at Intel.co.uk<\/strong><\/em><\/a> <\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

Cloud Pro<\/p>\n

29 Apr, 2019 <\/p>\n

If there\u2019s one resource the world isn\u2019t going to run out of anytime soon it\u2019s data. International analyst firm IDC estimates the \u2018Global Datasphere\u2019 \u2013 or the total amount of data stored on computer…<\/p>\n","protected":false},"author":404,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-38737","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/38737","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/users\/404"}],"replies":[{"embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/comments?post=38737"}],"version-history":[{"count":1,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/38737\/revisions"}],"predecessor-version":[{"id":38738,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/posts\/38737\/revisions\/38738"}],"wp:attachment":[{"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/media?parent=38737"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/categories?post=38737"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/icloud.pe\/blog\/wp-json\/wp\/v2\/tags?post=38737"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}