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Once rooted in the realm of science fiction, artificial intelligence (AI) is now a huge part of our everyday lives. We just don’t recognize it.
Have you ever:
Yes? Surprise! These are all examples of AI in action.
Okay, but what does that really mean?
Put simply, AI is where machines observe, make sense of, learn from, and interface with the external world – without humans explicitly programming it.
In three main steps, AI:
Here’s how it looks in the real world:
Is machine learning the same thing as AI?
AI is an umbrella term – under it is machine learning (ML) and deep learning (DL). ML is the set of techniques and tools that allow computers to “think” by creating mathematical algorithms based on accumulated data. DL uses neural network models to do things like image recognition and language processing.
It’s all about data
The more data you give to a machine to learn, the more accurate the machine gets at predicting things. As the complexity of the learning goes up, so do the data requirements to make sense of it. The higher performance the compute power, the faster computers can learn.
The average person generates 600 to 700 megabytes of data per day just doing normal things like posting to Snapchat, sending emails, and playing games.
It’s estimated that number will jump to 1.5 GB per day by 2020. And that’s only accounting for people. The average autonomous vehicle will generate 4,000 GB per day and a smart factory will produce 1 million GB of data daily!
As the use of AI and ML technologies expands, the data requirements will be astounding – as will the need for more sophisticated and powerful computing.
But what about killer robots?
There’s a healthy level of skepticism about AI – and a vague fear that machines might take over the world. But a computer’s ability to learn helps humanity in many ways. AI augments what humans do, helping us research faster and make better decisions more efficiently.
For example, ever tried navigating a new city with a paper map? Data-rich, dynamic map apps that recognize real-time road hazards and recalculate the quickest route make life much easier – with nary a robot overlord in sight.
Beyond everyday conveniences, both AI and ML technologies have been used for decades in fields such as education, finance, and medicine.
These technologies continue to evolve and help society, from reducing online harassment to helping tackle issues such as human trafficking, global hunger, and beyond – AI's potential for good is unlimited.
AI = possibility
From science fiction to everyday life, AI has come a long way. Imagine what it’ll help make possible next.
Interested in pushing possible forward with us? Learn about AI careers at Intel.
This blog is authored by Deb Landua.
Have you ever:
- Received a bank alert for suspected fraudulent charges?
- Been reminded to exercise … by your smartphone?
- Had Alexa recognize your voice and play that song you couldn’t get out of your head?
Yes? Surprise! These are all examples of AI in action.
Okay, but what does that really mean?
Put simply, AI is where machines observe, make sense of, learn from, and interface with the external world – without humans explicitly programming it.
In three main steps, AI:
- Perceives the world and uses data to detect patterns.
- Recognizes those patterns.
- Takes an action based on that recognition.
Here’s how it looks in the real world:
- You post pictures to Facebook of you hiking with a friend.
- The algorithms notice, recognizing the person you’re with and that you like hiking.
- In turn, it suggests other people or area hikes you might like.
Is machine learning the same thing as AI?
AI is an umbrella term – under it is machine learning (ML) and deep learning (DL). ML is the set of techniques and tools that allow computers to “think” by creating mathematical algorithms based on accumulated data. DL uses neural network models to do things like image recognition and language processing.
It’s all about data
The more data you give to a machine to learn, the more accurate the machine gets at predicting things. As the complexity of the learning goes up, so do the data requirements to make sense of it. The higher performance the compute power, the faster computers can learn.
The average person generates 600 to 700 megabytes of data per day just doing normal things like posting to Snapchat, sending emails, and playing games.
It’s estimated that number will jump to 1.5 GB per day by 2020. And that’s only accounting for people. The average autonomous vehicle will generate 4,000 GB per day and a smart factory will produce 1 million GB of data daily!
As the use of AI and ML technologies expands, the data requirements will be astounding – as will the need for more sophisticated and powerful computing.
But what about killer robots?
There’s a healthy level of skepticism about AI – and a vague fear that machines might take over the world. But a computer’s ability to learn helps humanity in many ways. AI augments what humans do, helping us research faster and make better decisions more efficiently.
For example, ever tried navigating a new city with a paper map? Data-rich, dynamic map apps that recognize real-time road hazards and recalculate the quickest route make life much easier – with nary a robot overlord in sight.
Beyond everyday conveniences, both AI and ML technologies have been used for decades in fields such as education, finance, and medicine.
These technologies continue to evolve and help society, from reducing online harassment to helping tackle issues such as human trafficking, global hunger, and beyond – AI's potential for good is unlimited.
AI = possibility
From science fiction to everyday life, AI has come a long way. Imagine what it’ll help make possible next.
Interested in pushing possible forward with us? Learn about AI careers at Intel.
This blog is authored by Deb Landua.
About the Author
We make the impossible possible and empower millions around the world through great technology, good corporate citizenship, and inclusive culture. We are Intel, and these are our stories.
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