Even though cats and dogs are different animals, what is the point of distinction between them? You might term the ability to distinguish between a cat and a dog to be common sense, but it is described as deep learning. People are not programmed to recognize different attributes in an object by inputting external information. These capabilities are inherent and cannot be induced through external stimuli, and thus are unnoticeable for us humans.
Computers, on the other hand, need gradual feeding – in the form of deterministic algorithms- to detect even the most simple judgments. Despite the surge in machine learning and connectivity, a computer cannot do what a toddler does unintentionally. The following are the developments in deep learning –
- Over the past six years, deep learning, which is a branch of artificial intelligence, has made tremendous progress, taking inspiration from the neural networks of the human brain. Facebook’s AI lab has built a system that can answer basic questions, to which it has never been exposed. Amazon’s smart speaker, Echo uses deep learning as well. And three years back, Microsoft’s chief research officer took everyone by surprise at a lecture in China where the machine used deep learning to translate his English into Mandarin with the output in his voice and an error rate of only 7%.
- Powerful tech companies have inculcated deep learning in all their software, but apparently, Google has invested more than others. They have been including deep learning wherever possible and have increased accuracy by 25% after deploying the same into their voice search. They were able to beat one of the players at Go- the most complex board game in the world.
- For decades computers have been able to accomplish complex tasks but cannot do so without including detailed deterministic algorithms at every step. But the machines failed at tasks for which no explicit direction can be given: like facial recognition or answering novel questions. These tasks could have been achieved by hand-coding, scores of attributes of the required answers, but the process was too labor-intensive. In loose comparisons with the brain, these machines learn just like us, from their experiences and are now becoming as complex and meticulous as humans at speech and recognition.
- There have been many scientists who have contributed to deep learning procedures but the biggest breakthrough arrived from a team at Stanford when they discovered that GPUs or graphics processing unit chips, that were created for video games could be repurposed into deep learning chips.
The recent discoveries might seem impressive, but this is just the tip of the iceberg. If compared to the present personal computer scenario, deep learning is still at the black and green DOS screen. The biggest barrier to the progress of deep learning is primarily, data. We need large-scale data for adopting a machine that can rationalize problems on its own. There are several possibilities for the aid of deep learning and there are so many more lurking in the corner. The possibilities are endless because humans have sought out to build a machine that can learn, analyze, and pass judgments on their own.
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