Artificial Intelligence; possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. We are really close to living in some sort of sci-fi, so it’s a good idea to have a look at the most possible and promising machine learning and AI trends for the upcoming 2018 and ask ourselves if we are ready for them. What all we read in science fiction novels or saw in movies like ‘The Matrix’ could someday materialize into reality.
In science fiction, artificial intelligence (AI) systems are often bent on overthrowing human civilization, or they are benevolent caretakers of our species. In reality, machine-learning is already with us, evolving out of search engines like Google and seeping into our everyday lives without much fanfare.
Artificial intelligence is front and center, with business and government leaders pondering the right moves. But what’s happening in the lab, where discoveries by academic and corporate researchers will set AI’s course for the coming year and beyond? Team of researchers from Artificial Intelligence Lab homed in on the leading developments both technologists and business leaders should watch closely.
Bill Gates, the founder of Microsoft, once said that ‘AI can be our friend’ and is good for the society.
Let’s have a look at the trends In AI in 2018 that will have a huge impact in years to come.
Deep learning theory: The information bottleneck principle explains how a deep neural network learns.
What it is: Deep neural networks, which mimic the human brain, have demonstrated their ability to “learn” from image, audio, and text data. Yet even after being in use for more than a decade, there’s still a lot we don’t yet know about deep learning, including how neural networks learn or why they perform so well. That may be changing, thanks to a new theory that applies the principle of an information bottleneck to deep learning. In essence, it suggests that after an initial fitting phase, a deep neural network will “forget” and compress noisy data — that is, data sets containing a lot of additional meaningless information — while still preserving information about what the data represents.
Why it matters: Understanding precisely how deep learning works enables its greater development and use. For example, it can yield insights into optimal network design and architecture choices, while providing increased transparency for safety-critical or regulatory applications. Expect to see more results from the exploration of this theory applied to other types of deep neural networks and deep neural network design.