Google’s work in artificial intelligence is impressive. It includes networks of hardware and software that are very similar to the system of neurons in the human brain. By analyzing huge amounts of data, the neural nets can learn all sorts of tasks, and, in some cases like with AlphaGo, they can learn a task so well that they beat humans. They can also do it better and in a bigger scale.
AI seems to be the future of Google Search and of the technology world in general. This specific method, called deep learning, is reinventing many of the Internet’s most popular and interesting services.
Google, during its conception and growth, has relied predominantly on algorithms that followed exact rules set by programmers (think ‘if this then that’ rules). Even with that apparent reassurance of human control, there’s still some concerns about the world of machine learning because even the experts don’t fully understand how neural networks work. However, in recent years great strides have been made in understanding the human brain and thus how neural networks could be wired. If you feed enough photos of a dog into a neural net, it is able to learn to identify a dog. In some cases, a neural net can handle queries better than algorithms hand-coded by humans. Artificial intelligence is the future of Google Search and that means it’s probably a big influencer of everything else.
Based on Google Search advances and AI like AlphaGo, experts expect to see:
- More radical deep learning architectures
- Better integration of symbolic and subsymbolic systems
- Expert dialogue systems
- Deep learning for more intricate robotic planning and motor control
- High-quality video summarization
- More creative and higher-resolution dreaming
Experts consider these methods can accelerate scientific research per se. The idea of having scientists working alongside artificially intelligent systems that can hone in on areas of research is not a farfetched idea anymore. It might happen soon and 2016 looks like a good year for it.
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Source: 3blades – What Will Be The Key Deep Learning Breakthrough in 2016?