Every day, Artificial Intelligence (A.I.) is becoming more advanced, more applications are being found for it, and more businesses are using it to their advantage. But what drives the fledgling A.I. we use today? What will feed A.I. in the future?
The answer is data. Lots of data.
What is A.I.?
Artificial Intelligence is one of three new-age computing terms that you may have heard over the years in Sci-fi films, news stories, research projects and now technology products; deep learning, machine learning and artificial intelligence.
Artificial Intelligence is the broad term that describes any attempt to integrate human-like intelligence into digital services.
Machine Learning is part of the Artificial Intelligence landscape. It refers to a program’s ability to take in data and modify its response or output over time. This is achieved using algorithms that discover patterns and generate insights from data.
Deep learning is the most advanced part of A.I., and brings us closer to enabling machines to learn and think as much like humans as possible. It involves analysing huge sets of data in order to teach A.I. about how the data is represented, and is not focused on developing task specific algorithms.
Why does A.I. need data?
Each year, the amount of data we produce doubles and it is predicted that within the next decade there will be 150 billion networked sensors (see our article on the ‘intelligent edge’). This data is instrumental in helping A.I. devices learn how humans think and feel, and accelerates their learning curve. The more information a system has to process, the more it learns and ultimately the more accurate it becomes. Artificial Intelligence is now capable of learning without human interaction. A good example of this is Google’s DeepMind A.I. which recently taught itself how to win at 49 Atari games!
In the past, A.I.’s growth was restrained due to limited data sets and representative samples of data rather than real-time, real-life data and the inability to analyse massive amounts of data in seconds. Todays rapid access to big data storage has propelled A.I. and machine learning forward, and allowed the transition to a data-first approach. Our technology has now evolved enough to access these giant datasets which rapidly evolve A.I. and machine-learning applications.
You and Your Data
To feed A.I.’s insatiable hunger for data, a lot of it is needed. You might not realise, but you and your online presence are providing that data right now.
If you use Siri, Google Now, Cortana or Alexa, you are providing data for A.I. to analyse. They work by recording your voice, uploading the recording to the cloud, then processing the words and sending back the answer. After you’ve got your answer, you forget about asking the initial question. Despite this, your recorded voice, the text extracted from it, and the entire context of the back-and-forth conversations is still being used to build the A.I. that drives these virtual assistants. Everything you say to your virtual assistant is fed through machine learning software to help the A.I. improve and learn over time.
It’s not just personal assistants that gather your data for use by Artificial Intelligence. Social media platforms, search engines and email services all gather and distribute data about you, your language and how you behave as a human. The artificial intelligence revolution we are just at the start of is as much about the availability of massive data sets as it is about intelligent software. The bigger the data sets, the smarter the A.I.
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