Deep learning will be next big thing in artificial intelligence

Deep learning will be next big thing in artificial intelligence

Deep learning relies on simulating large, multilayered webs of virtual neurons, which enable a computer to learn to recognize abstract patterns.

There is a huge investment opportunity with deep-learning, not only because of the technology itself but also because of how it is leveraging other technologies to become more powerful.

The volumes of data available due to the proliferation of online services, improvements in storage, advancements in GPU and computing power, the abundance of cloud computing, development of cheap sensors, and the rise of new data generated by the Internet of Things.

As a result, deep learning has opportunities to solve challenges across all types of industries.

All the big software companies are investing heavily in building deep-learning capabilities and incorporating it into many of their products.

These companies are not only pushing it for internal use; they are advancing the entire industry by releasing their software frameworks and libraries.

Google recently announced that it is open-sourcing its latest Machine Learning system, TensorFlow; Facebook is releasing, for free, the designs of a powerful new server intended to run AI software; IBM open-sourced its machine learning code, SystemML; and Elon Musk and others founded OpenAI, a nonprofit AI research group; among many other examples.

In some ways, deep learning is following a course similar to that of big data in its early days.

In the long run, it turned out that almost every company is a big data company, as they all need to store and analyze huge amounts of data.

We see many new startups that claim their advantage is in deep learning.

In the long run, what interests us most as investors are companies that can leverage deep learning to build a data network effect.

These insights should translate to more customers willing to share their data, which will improve the quality and size of the data set, resulting in a virtuous cycle.

New competitors will confront the classic chicken and egg problem: Without customer data, it will be impossible to match the deep-leaning algorithm, but without a better algorithm, they will not be able to get customer data.

 

Source – VB