- Companies realize that their customers want to have more personalized products and services, and in order to satisfy their customers’ needs, they need to collect as much data as possible to understand the profiles of their customers.
- Once the data is collected, Artificial Intelligence (AI) can be used to understand and construct customer profiles that reveal the needs of each individual customer.
- In a similar manner, the data of our “digital selves” and our interactions and activities using our digital devices reveal interesting properties of our profiles.
- Recommender systems, or expert systems, are actually examples of how data is used to build profiles and act based on it.
- Based on this insight of different profiles and segmentations, a recommender system can build a model to be able to predict the behavior of users and customers.
One of the most sought commodities today is data. The digitalization has given rise to a technical revolution that could be of the same magnitude as that…
Continue reading “The Value Of Data In A Digital World”
- It’s tempting to think of machine learning as a magic black box.
- If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
- The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
- There’s also a pipeline for data as it flows through machine learning solutions.
- Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.
Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
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Best quote from NAS DS Round Table: “I mean, do we need deep learning to analyze 30 subjects?” – B Caffo @simplystats #datascienceinreallife
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- Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle.
- was eventually turned into a tool that transformed drawings into clip art based on the best results it got, helping people add a visual icon to their work without requiring any particular artistic talent.
- I wasn’t very confident in my frog (croak) so I felt that adding “Ribbit” to the drawing might provide context, even if the AI might not be able to read.
- Face is a neat one too — I’d guess that depending on the artist, most of these drawings were interpreted as a self portrait.
- While you can argue that most people see frog in mostly the same way, dragon produced a variety of results — from fire-breathing to horned.
Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle. Quick, Draw! was eventually turned…
Continue reading “Google made a site that shows how millions of people draw the same object”
- If you are a digital marketer and you don’t understand what machine learning is, it’s high time to learn about this amazing digital technology.
- Just as artificial intelligence, social automation and DIY tools, programmatic buying, and other latest technologies have revolutionized different aspects of marketing world, machine learning is changing the whole process of marketing – from how marketers handle simple tasks to how they create marketing campaigns and create brand stories.
- Machine learning is an advanced tool that could improve things because of its efficiency and ability to handle complex tasks.
- Data is the most critical aspect of any digital marketing strategy, and machine learning can effectively dovetail complex data.
- Machine learning is not a new tool; it’s something that has advanced over time and recently gained significant new strengths and potential.
If you are a digital marketer and you don’t understand what machine learning is, it’s high time to learn about this amazing digital technology.
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- Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
- Let’s say you had to determine whether a home is in San Francisco or in New York.
- In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
- Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
- The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.
This article was written by Stephanie and Tony on R2D3.
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
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- Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.
- “Over the last five years it’s been pretty incredible, the progress of deep [neural] nets,” says Grant Van Horn, lead competition organizer and graduate student at California Institute of Technology.
- Van Horn says this latest Google competition differs from ImageNet, which forces algorithms to identify a wide variety of objects like cars and houses and boats, because iNat requires AI to examine the “nitty-gritty details” that separate one species from another.
- On a scale from general image recognition (ImageNet) to specific (facial recognition,where most faces generally look the same and only slight variations matter), iNat lies somewhere in the middle, Van Horn says.
- Van Horn, who has specialized in building AI that distinguishes differences between birds, said that the iNat competition illustrates how AI is beginning to help people learn about the world around them, rather than just help them organize their photos, for instance.
In 2012, Google made a breakthrough: It trained its AI to recognize cats in YouTube videos. Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy. It was a 70% improvement over any other machine learning at the time. Five years later,…
Continue reading “Five years ago, artificial intelligence was struggling to identify cats. Now it’s trying to tackle 5000 species — Quartz”