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AI is Transforming the Future of Technology?
AI (Artificial Intelligence):
There is no doubt that Big Data has been one of the most popular topics among marketers and tech enthusiasts for several years.
Within the big data domain one of the most promising fields is deep learning which has evolved into one of tech’s most exciting and promising disciplines in the field of AI (Artificial Intelligence).
The popularity of deep learning peaked in March 2016, when Google’s DeepMind AI program called AlphaGo bested Lee Sedol, the celebrated player of the board game “Go”, by winning four out of five games. After the match it was revealed that a relatively new AI technique called “deep learning” was responsible for the victory. According to scientists, deep learning technology has the potential to transform the entire AI area.
Deep learning can be defined as a new area of machine learning working to improve areas like voice search, image and language processing or solve unstructured data challenges. We might not be aware of deep learning, but it is everywhere around us. It’s giving us personalized recommendations on Amazon, it’s on our smartphones by the name of Siri (or another voice-activated assistant). Deep learning can enable us to constructing our digital marketing activities by transforming the contents of our websites and mobile applications into tailor made offers.
Deep Learning is transforming the future of technology in many ways. Here are some of the most crucial areas;
Voice Search & Voice-Activated Assistants
One of the most popular usage areas of deep learning is voice search & voice-activated intelligent assistants. With the big tech giants having already made significant investments in this area, voice-activated assistants can be found on nearly every smartphone. Apple’s Siri is in the market since October 2011. Google Now, the voice-activated assistant for Android, was launched less than a year after Siri. The newest of the voice-activated intelligent assistants is Microsoft Cortana.
Major companies such as Netflix, Amazon, Google, and Facebook have been using recommendation systems on their web and mobile applications for years. These tech giants have access to a vast wealth of user generated data and they strive to use these data in order to add value to both users and their business bottom line. For instance, 35% of Amazon’s sales are generated from recommendations, and two thirds of the movies users watch on Netflix are recommended by Netflix’s recommendation engine.
Another popular area regarding deep learning is image recognition. It aims to recognize and identify people and objects in images as well as to understand the content and context. Image recognition is already being used in several sectors like gaming, social media, retail, tourism, etc…
For instance the video below shows how image recognition and AI might potentially transform the music industry.
Advertising is another key area that has been transformed by deep learning. It has been used by both publishers and advertisers to increase relevancy of their ads and boost the return on investment of their advertising campaigns. For instance, deep learning makes it possible for ad networks and publishers to leverage their content in order to create data-driven predictive advertising, real-time bidding (RTB) for their ads, precisely targeted display advertising, and more.
Deep learning is becoming an important area for all types of economy sectors and the tech giants are heavily investing in deep learning in terms of R&D and M&A. For instance, Apple acquired an AI startup VocalIQ (a company which used deep learning to understand the context in which words are spoken) to make Siri smarter and more humane. Similarly, Facebook acquired Wit.ai (a startup whose deep learning technology enables developers to add natural language processing to apps) in order to develop smarter personal assistants.
To wrap up, deep learning can be evaluated as a disruptive technology that is being used by more and more companies to build innovative products and to even spawn new business models.