This post introduces you to emerging AI technologies that will potentially lead growth of artificial intelligence applications for next two to five years.

1. Generative Adversarial Networks

Generative Adversarial Networks (GANs) use two models: a generator and a discriminator, both trained with same data. Generator generates new examples from given data and throws them to discriminator, along with some original or real examples. Discriminator classifies these samples as real or fake. Two models work as adversaries. With each round, discriminator gets better at telling real from fake as generator at creating better fakes. Read more about GANs here.

2. Capsule Networks

When our eyes see a 3D object, it can identify hierarchical relationships between object parts. In simple terms, humans can identify objects from different poses! But internal data representation of a convolutional neural network does not take into account important spatial hierarchies between simple and complex objects. This is where capsule networks can help.

Capsule theory has two important parts : collection of neurons called “capsules” and an algorithm for “dynamic routing between capsules”. The algorithm allows capsules to communicate to create what will be similar to scene graphs in computer graphics. This can drastically improve the efficiency of image classification or object identification tasks. Further reading on Capsule Networks.

3. Conscious Machines

Machine consciousness means that the machine is aware of situation or fact. In scary terms, it is like “Skynet of Terminator series becoming self-aware.” In demonstration of this, Columbia Engineering researchers have created a robot that learns what it is. Without any prior knowledge of its build, the robot can create a self-simulation. The robot can then use that self-simulator internally to contemplate and adapt to different situations, handling new tasks as well as detecting and repairing damage in its own body. Further reading on conscious machines : Forbes, Columbia University

4. Contextual AI

Contextual AI refers to applications that can understand user’s context. The system can see the human perspective with enough information about the environment, situation and context. Contextual AI makes applications more personalized. For example, a smart home assistant knows your preferences and learns your habits to provide a more personal experience. Read more about contextual AI at IBM

5. Custom AI Chips

There was a time when only gamers needed GPUs where as today GPUs are used in variety of ML and AI applications.Given the rise of GPU, chip manufacturers such as Intel are creating specialized chips with computing power up to 3 TOPS (Trillion operations per second). Few examples are : Intel Neural Compute Stick

6. Debating Systems

Think of a system that scans newspaper and magazine articles to present a dueling narrative for a topic. That’s what IBM’s Project Debater did! Such systems can help humans build persuasive arguments and make better informed decisions.

What other emerging AI technologies to include in the list? Put your suggestions in the comments below.

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