Home TechnologyArtificial Intelligence 5 AI Trends to Watch in 2020

5 AI Trends to Watch in 2020

by CIO AXIS

There are too many artificial intelligence trends to keep up with. What AI trends should you keep an eye on? Here are 5 AI trends that Udemy, a global training platform for learning and teaching online, asks businesses to follow in 2020 and beyond.

1. Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a simple AI technology, but also one of the most disruptive. It is used to perform a high-volume, repetitive task on the computer.

Key RPA applications: invoicing, billing, payroll processing, data extraction and aggregation, shipment scheduling and tracking.

2. Natural language processing (NLP)
Natural language processing applies machine learning models to teach computers how to understand what is said in written and spoken language. Because of its rich and growing applications, natural language processing is arguably one of the top branches of AI in overall economic value. It’s becoming especially popular as consumers adopt voice interface technology like Google Home or Amazon Alexa. Instead of writing or interacting with graphics on a screen, we talk to devices that can understand our casual language.

NLP can be divided into two sub-applications:

  • Natural language understanding, consists of a machine reviewing a text and accurately interpreting its meaning.
  • Natural language generation, where a system generates a logical response to a text or input.

Key natural language processing applications: Sentiment analysis, chatbots, machine translation, automatic summarization, auto video captioning.

3. Reinforcement learning
Reinforcement learning is an input- and output-based system that trains itself over trial and error to reach a certain goal, while using a reward system to reinforce its decisions. So, an AI takes as input some data and returns as output an action. When it does this correctly, it receives an award. The better it performs its task, the more rewards the system is given and vice versa.

Key reinforcement learning applications: Personalized recommendations, advertising budget optimization, and advertising content optimization.

4. Edge computing
With smartphones, smartwatches, and Internet of Things-enabled devices in our homes and cars, there is a lot of data flying around. Processing all this data is a complex exercise requiring information sent to cloud computing machines based on servers hundreds or even thousands of miles away.

Enter edge computing, which takes the servers and data storage required for devices to access their smarts, and puts it directly on the device. This is real-time data processing that results in much faster computing responses and avoids network latency. If cloud computing is big data, edge computing is instant data.

Another type of edge computing is performed on nodes. An edge computing node is a mini-server close to a local telecommunications provider. Using a node creates a bridge between cloud and local computing options. This technique results in lower costs and less time spent on data computation, making for a faster experience for the consumer.

Key edge computing applications: the interconnection of more devices, growth of Internet of Things technology.

5. Open-source AI frameworks
The programming world is built on libraries and frameworks that take redundancies out of everyday coding work. Likewise, open-source AI programming frameworks have allowed the development of AI technology to expand quickly.

Key open-source AI framework applications: Prototype and train complex AI algorithms; build pipelines to define, optimize, and assess an AI model; automate the training of a reinforcement learning module; build neural networks with just a few lines of code.

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