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Over the last year it’s been difficult to escape Deep Learning chatter — and in 2018, we’re already seeing more of the same. The good news? In recent months, the progress on delivering key tasks has been anchored in increasingly quantifiable results. And this, likely, will be a big leap forward in grounding some of the “big picture” thinking in reality.
Understanding Deep Learning
Artificial intelligence, machine Learning and neural networks define the notion of deep learning. Machine learning’s one of the division called deep learning, which connected with algorithms inspired by the architecture of the brain known as artificial neural networks. Through deep learning, a computer model learns directly from text, images, or sound and performs certain tasks, often with human-level capabilities — or better. As rapid research and advancements continue to push envelopes and improve these algorithms, deep learning will be increasingly applied to real life scenarios starting with tasks like:
- Pattern recognition analysis
- Self-driving vehicles and advanced games
- Integrated applications for art and healthcare
A good example? Using audio to create high-quality video. Researchers at University of Washington built a system which creates video from audio. Using audio of President Barack Obama, the team synthesized his words to lip motions from existing video footage. This exciting technology instantly opened door to modeling anyone, by analyzing everyday applications such as Whatsapp, Skype and Google Duo footage, for example.
The synthesis of a video could allow lip-reading from over-the-phone audio, especially for those with hearing impairments. Beyond that, though, this technology is a huge leap forward for things like film special effects and games. Now, it’s easier to identify video footage and, from a person’s lip motion, integrate custom audio. This is also a huge win for the advancement of robots — leveraging this, we can predict more accurate and intelligent human robots in the future, enabling these machines to not only answer questions in a smart way, but also to observe the nature of the communicator, surrounding situations and the overall atmosphere, so it can answer accordingly.
From Photo Restoration to Counter Terrorism
And that’s just the beginning. Leveraging deep learning technology, for example, old photos can be restored — deep learning networks can easily predict what colors should be in the image. Already, Google has created high-resolution images from low-quality pictures using Pixel Recursive Super Resolution technique. It’s the next evolution of image processing, data recovery and security system. But this one goes farther than just simple photo restoration — this approach can help automation, robotics and game technology going forward by enabling the machine to recognize humans with greater accuracy. This same approach can help recognize faces and motions which relate to things like theft, terrorist patterns, spy field or computer security-breaking algorithms — the implications, then, are endless. By observing the behaviour of any person or community we can assume the future situations and circumstances.
And then, of course, there are the seemingly cinematic applications — applications that seem so high-tech and futuristic that it’s hard to believe it’s here and now. Self-driving cars exist and, more recently, self-driven flying taxis have come into play in Dubai. Passengers can operate the autonomous Ehang 184, they simply required to enter their destination and let the machine handle the rest. Researchers are also working on self-driven drone and robots which boost the way of transportation of human, products or medical resources. Deep learning networks make this true by self-learning techniques.
What Comes NEXT — and NOW?
While this may sound far-off and, again, incredibly futuristic, the reality is that future is here and now. A new non-profit called OpenAI recently rolled out in a big way, with the aim of democratizing artificial intelligence and deep learning technology. Their open source platform allows users to test deep learning on thousands of games and sites.
Beyond that, though, Artificial Intelligence is a reality for a majority of enterprise businesses, enabling them to build and integrate smart technology. While energy can be saved in a smart way using deep learning method. Check out how WorkFusion’s Automation Solutions enable to monitor business activities in a smart way — their webinar digs into AI technology and what enterprise automation problems will be solved. For many companies getting started with AI and deep learning, this is an optimal jumping-off point.
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