Tag Archive | "Machine"

Internet Wayback Machine Adds Historical TextDiff

The Wayback Machine has a cool new feature for looking at the historical changes of a web page.

The color scale shows how much a page has changed since it was last cached & you can select between any two documents to see how a page has changed over time.

You can then select between any two documents to see a side-by-side comparison of the documents.

That quickly gives you an at-a-glance view of how they’ve changed their:

  • web design
  • on-page SEO strategy
  • marketing copy & sales strategy

For sites that conduct seasonal sales & rely heavily on holiday themed ads you can also look up the new & historical ad copy used by large advertisers using tools like Moat, WhatRunsWhere & Adbeat.

Categories: 

SEO Book

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Machine Learning 101 – Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn’t have to be — and you don’t have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week’s episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I’m talking about all things machine learning, something, as many of you know, I’m super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven’t really reached artificial intelligence. But it’s just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it’s a bit flipped, and it works extremely well. There are two primary types of machine learning:

  1. You have supervised, which is where you’re basically feeding a model labeled training data, 
  2. And then unsupervised, which is where you’re feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You’ll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there’s all of this data that you’re putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It’s a really important part to understand, because unless you have the right types of data to feed a model, you’re not going to get the desired outcome that you would like. 

A machine learning model example

So let’s look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it’s going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don’t want it to do is to fit every single data point and have a line that looks like that — that’s also known as overfitting — because it doesn’t play nice for new data points. You don’t want a model to get so calculated to your dataset that it doesn’t predict accurately in the future.

A way to look at that is by the loss function. That’s maybe getting a bit deeper in this, but that’s how you would measure how the line is being fit. Let’s see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don’t suggest you do this for primary pages. This isn’t going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It’s really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what’s in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It’s pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I’ve got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you’ll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You’ll see things like TSA has put up over $ 1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It’s also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it’s something to definitely think about. 

TensorFlow is a great resource. It’s what Google put out, and it’s what a lot of their machine learning models is built off of. They’ve got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don’t care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that’s pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can’t express that enough. So a lot of machine learning and data scientists, it’s all data cleaning and parsing, and that’s the bulk of the work in this field.

It’s important to be aware of that. So that’s it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com


If you enjoyed this episode of Whiteboard Friday, you’ll be delighted by all the cutting-edge SEO knowledge you’ll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney’s talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn’t love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!


Moz Blog

Posted in IM NewsComments Off

Machine Learning 101 – Whiteboard Friday

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn’t have to be — and you don’t have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week’s episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I’m talking about all things machine learning, something, as many of you know, I’m super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven’t really reached artificial intelligence. But it’s just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it’s a bit flipped, and it works extremely well. There are two primary types of machine learning:

  1. You have supervised, which is where you’re basically feeding a model labeled training data, 
  2. And then unsupervised, which is where you’re feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You’ll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there’s all of this data that you’re putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It’s a really important part to understand, because unless you have the right types of data to feed a model, you’re not going to get the desired outcome that you would like. 

A machine learning model example

So let’s look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it’s going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don’t want it to do is to fit every single data point and have a line that looks like that — that’s also known as overfitting — because it doesn’t play nice for new data points. You don’t want a model to get so calculated to your dataset that it doesn’t predict accurately in the future.

A way to look at that is by the loss function. That’s maybe getting a bit deeper in this, but that’s how you would measure how the line is being fit. Let’s see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don’t suggest you do this for primary pages. This isn’t going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It’s really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what’s in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It’s pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I’ve got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you’ll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You’ll see things like TSA has put up over $ 1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It’s also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it’s something to definitely think about. 

TensorFlow is a great resource. It’s what Google put out, and it’s what a lot of their machine learning models is built off of. They’ve got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don’t care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that’s pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can’t express that enough. So a lot of machine learning and data scientists, it’s all data cleaning and parsing, and that’s the bulk of the work in this field.

It’s important to be aware of that. So that’s it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com


If you enjoyed this episode of Whiteboard Friday, you’ll be delighted by all the cutting-edge SEO knowledge you’ll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney’s talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn’t love movie day in school? ;-)

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!


Moz Blog

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New Opportunities for Image SEO: How to Leverage Machine Vision for Strategic Wins

Posted by KristinTynski

Image search results used to give you the option to “view image” without having to navigate to the site the image was hosted on.

When it started in 2013, sites saw a 63% decline in organic traffic from image results.

Why?

Because there was no need to click through when the image could be viewed in full from within the search results.

And then everything changed

In February 2018, Google decided to remove the “view image” button. Now searchers must visit the site hosting that image directly, restoring image results to their former organic search driving power.

According to some recent studies, this change has increased organic image traffic a massive 37%.

Given image results’ return to value, marketers are asking themselves how they can make the most out of this search mechanism.

So what are some new ways we can leverage tools to better understand how to optimize images for ranking?

To explore this, I decided to see if Google’s Vision AI could assist in unearthing hidden information about what matters to image ranking. Specifically, I wondered what Google’s image topic modeling would reveal about the images that rank for individual keyword searches, as well as groups of thematically related keywords aggregated around a specific topic or niche.

Here’s what I did — and what I found.

A deep dive on “hunting gear”

I began by pulling out 10 to 15 top keywords in our niche. For this article, we chose “hunting gear” as a category and pulled high-intent, high-value, high-volume keywords. The keywords we selected were:

  • Bow hunting gear
  • Cheap hunting gear
  • Coyote hunting gear
  • Dans hunting gear
  • Deer hunting gear
  • Discount hunting gear
  • Duck hunting gear
  • Hunting gear
  • Hunting rain gear
  • Sitka hunting gear
  • Turkey hunting gear
  • Upland hunting gear
  • Womens hunting gear

I then pulled the image results for the Top 50 ranking images for each of these keywords, yielding roughly ~650 images to give to Google’s image analysis API. I made sure to make note of the ranking position of each image in our data (this is important for later).

Learning from labels

The first, and perhaps most actionable, analysis the API can be used for is in labeling images. It utilizes state-of-the-art image recognition models to parse each image and return labels for everything within that image it can identify. Most images had between 4 and 10 identifiable objects contained within them. For the “hunting gear” related keywords listed above, this was the distribution of labels:

[full interactive]

At a high level, this gives us plenty of information about Google’s understanding of what images that rank for these terms should depict. A few takeaways:

  • The top ranking images across all 13 of these top keywords have a pretty even distribution across labels.
  • Clothing, and specifically camouflage, are highly represented, with nearly 5% of all images containing camo-style clothing. Now, perhaps this seems obvious, but it’s instructive. Including images in your blog posts related to these hunting keywords with images containing camo gear likely gives you improved likelihood of having one of your images included in top ranking image results.
  • Outdoor labels are also overrepresented: wildlife, trees, plants, animals, etc. Images of hunters in camo, out in the wild, and with animals near them are disproportionately represented.

Looking closer at the distribution labels by keyword category can give use a deeper understanding of how the ranking images differ between similar keywords.

[full interactive]

Here we see:

  • For “turkey hunting gear” and “duck hunting gear,” having birds in your images seems very important, with the other keywords rarely including images with birds.
  • Easy comparisons are possible with the interactive Tableau dashboards, giving you an “at a glance” understanding of what image distributions look like for an individual keyword vs. any other or all others. Below I highlighted just “duck hunting gear,” and you can see similar distribution of the most prevalent labels as the other keywords at the top. However, hugely overrepresented are “water bird,” “duck,” “bird,” “waders,” “hunting dog,” “hunting decoy,” etc., providing ample ideas for great images to include in the body of your content.

[full interactive]

Ranking comparisons

Getting an intuition for the differences in top ranking (images ranking in the first 10 images for a keyword search) vs. bottom ranking (images ranking in the 41st to 50th positions) is also possible.

[full interactive]

Here we can see that some labels seem preferred for top rankings. For instance:

  • Clothing-related labels are much more common amongst the best ranking images.
  • Animal-related labels are less common amongst the best ranking images but more common amongst the lower ranking images.
  • Guns seem significantly more likely to appear in top ranking images.

By investigating trends in labels across your keywords, you can gain many interesting insights into the images most likely to rank for your particular niche. These insights will be different for any set of keywords, but a close examination of the results will yield more than a few actionable insights.

Not surprisingly, there are ways to go even deeper in your analysis with other artificial intelligence APIs. Let’s take a look at how we can further supplement our efforts.

An even deeper analysis for understanding

Deepai.org has an amazing suite of APIs that can be easily accessed to provide additional image labeling capabilities. One such API is “Image Captioning,” which is similar to Google’s image labeling, but instead of providing single labels, it provides descriptive labels, like “the man is holding a gun.”

We ran all of the same images as the Google label detection through this API and got some great additional detail for each image.

Just as with the label analysis, I broke up the caption distributions and analyzed their distributions by keyword and by overall frequency for all of the selected keywords. Then I compared top and bottom ranking images.

A final interesting finding

Google sometimes ranks YouTube video thumbnails in image search results. Below is an example I found in the hunting gear image searches.

It seems likely that at least some of Google’s understanding of why this thumbnail should rank for hunting gear comes from its image label detection. Though other factors, like having “hunting gear” in the title and coming from the NRA (high topical authority) certainly help, the fact that this thumbnail depicts many of the same labels as other top-ranking images must also play a role.

The lesson here is that the right video thumbnail choice can help that thumbnail to rank for competitive terms, so apply your learnings from doing image search result label and caption analysis to your video SEO strategy!

In the case of either video thumbnails or standard images, don’t overlook the ranking potential of the elements featured — it could make a difference in your SERP positions.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!


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Our Machine Learning Platform Helps Brands Retain Their Customers, Says Medallia CEO

“We’re a platform that helps some of the biggest brands in the world really understand their customers in live time and communicate with them while they’re in an experience,” says Medallia CEO Leslie Stretch. “Instead of a survey after they’ve left a hotel, they communicate them while they’re there, check in on the experience and improve it. This helps them retain their customer and perhaps sell them another experience. It’s this machine learning platform that does that.”

Leslie Stretch, President and CEO of Medallia, discusses the company’s IPO and how the company uses machine learning to react to customer signals in real-time rather than after they leave an experience in an interview on CNBC:

Our Machine Learning Platform Helps Brands Retain Their Customers

We’re a Silicon Valley tech company. We’re a platform that helps some of the biggest brands in the world really understand their customers in live time and communicate with them while they’re in an experience. So instead of a survey after they’ve left a hotel, they communicate them while they’re there, check in on the experience and improve it. This helps them retain their customer and perhaps sell them another experience. It’s this machine learning platform that does that.

Anything is a signal to us, a survey, an IOT signal, a transaction, somebody buys something, they have a bad experience at the pool, or they’re on an airline and they don’t quite like the service that they’re getting, they can feed that back immediately instead of waiting until the experience is finished. We’re all about platform and signal. We’re very different from the survey companies, the feedback companies, which are the old experience economy companies. It’s the application of deep Silicon Valley technology to the problem.

The Customer Is At the Center of Every Digital Transformation

Customer experience has become really a major theme for every big brand in the world today. I also think that our technology is innovative and very different. The application of machine learning and the platform and just the operationalization of a private Silicon Valley company are really what I’ve done in the past. Just bringing basic blocking and tackling to go to market and marketing and building up the salesforce. So very simple and taking the story out to a bigger market.

We actually just signed a revenue share partnership with Salesforce. We have a partnership for Marketing Cloud with Adobe. They’re great alliances for us. We can present our machine learning, our unstructured data, into their Marketing Cloud, Sales Cloud, and Service Cloud. That’s brand new for us this year. It’s great to go to market with leaders like that. Both Adobe and Salesforce completely understand the customer is at the center of every digital transformation and we are at the center of that.

It’s Not For the Faint-Hearted, But We Invested a Ton In It

We spent more than a half a billion dollars building this plot platform. That sets us apart from the traditional simple survey vendor. We’ve spent a ton of money on the privacy layer and on the security layer. We’ve worked already for a decade with some of the biggest brands in the world whose customer information is precious. We’re HIPAA certified for healthcare as well. So we take that very seriously. It’s not for the faint-hearted, but we invested a ton in it and it’s worth it.

Our Machine Learning Platform Helps Brands Retain Their Customers, Says Medallia CEO Leslie Stretch

The post Our Machine Learning Platform Helps Brands Retain Their Customers, Says Medallia CEO appeared first on WebProNews.


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How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

“Our acquisition of Dynamic Yield has brought us a lot of excitement,” says McDonald’s CEO Steve Easterbrook. “Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website will automatically suggest two or three things to go along with it. We’re the first business that we’re aware of that can bring that into the physical world. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.”

Steve Easterbrook, CEO of McDonald’s, discusses how the company is using technology to elevate the customer experience and accelerate growth in an interview on CNBC:

Continue To See How We Can Elevate the Customer Experience

As we’ve executed the growth plan we’ve spent the first two years, three or four years ago, turning the business around. Now we’ve had a couple of years of growth. We’re confident now that we’re beginning to identify further opportunities to further accelerate growth. That takes a little bit of research and development cost. It means you’ve got to bring some expertise into the business to help us do that. We’re still managing to effectively run the business. G&A is staying the same and we’re putting a little bit more into innovation.

We continue to see how can we help continue to elevate the experience for customers. With this pace of change in the world and with different technology and different innovations, whether it’s around food, technology, or design, we’re seeing opportunities that we think can either make the experience more fun and enjoyable or smoother for customers. If we can find that we’re going to go hard at it.

We need to continue growing. If where we are investing that money is helping drive growth across 38,000 restaurants then I think the shareholders and investors would be satisfied. We want to bring our owner-operators along with us as well. They’re investing their hard-earned dollars so that always means we got a business case. The owner-operators will want to see a return on their investment just the same as a shareholder would. We’ve got a wonderful check and balance in the system to help us make sure we spend that innovative money in the right way.

Using Data, Machine Learning, and AI to Accelerate Growth

Our acquisition of Dynamic Yield has brought us a lot of excitement. It was our first acquisition for 20 years. It was an acquisition in a way that was different from the past. It wasn’t looking at different restaurant businesses to try and expand our footprint. It’s bringing a capability, an IP and some talent, into our business that can help us accelerate the growth model. We completed the deal mid-April and within two weeks we had that technical capability in 800 drive-throughs here in the U.S. It’s a very rapid execution and implementation.

Very simply put, in the online world when we’re shopping and we pick an item and put it into our shopping basket, any website we’re on these days will automatically suggest two or three things to go along with it. People who buy that tend to like these things as well. We’re the first business that we’re aware of that can bring that into the physical world. As customers are at the menu board, maybe they’re ordering a coffee and we can suggest a dessert or they’re ordering a quarter pounder with cheese and we can suggest making that into a meal. It’s really just taking data and machine learning and AI, all these sorts of technical capabilities.

Mining All of the Data Will Improve the Business

The best benefit for customers is we’re more likely to suggest things they do want and less likely to suggest things they don’t. It’ll just be a nicer experience for the customer. But yes, for the restaurant itself, because we can put our drive-thru service lines in there, for example, the technical capability by mining all of the data will be to suggest items are easier to make at our busier times. That’ll help smooth the operation as well. The immediate result will be some ticket (increases). But frankly, if the overall experience is better customers come back more often. That’s ultimately where the success will be, driving repeat visits and getting people back more often.

Across the entire sector, traffic is tight right now and people are eating out less. They have been progressively eating out less for a number of years. Whether it’s the advent of home delivery, for example, which is something we participate in, but at the moment it’s just a little bit tight out there. It’s a fight for market share. Anyone who is getting growth, typically it’s because they’re adding new units. People are finding it hard to (increase) guest count growth. It’s something that we have stated as an ambition of ours. We think that’s a measure of the true health of the business. Last quarter, we did grow traffic and we’ve grown traffic for the last couple of years, but only modestly. We want to be stronger than that.

How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth

The post How McDonald’s Is Using Data, Machine Learning, and AI to Accelerate Growth appeared first on WebProNews.


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How Palo Alto Networks Blocks 30,000 New Pieces of Malware Daily Via AI, Machine Learning, and Big Data

“The platform we have uses big data analytics and machine learning in the cloud to process and find all of the unknown malware, make it known and be able to block it,” says Scott Stevens, SVP, Global  Systems Engineering at Palo Alto Networks. “We find 20-30 thousand brand new pieces of malware every day. We’re analyzing millions and millions of files every day to figure out which ones are malicious. Once we know, within five minutes we’re updating the security posture for all of our connected security devices globally.”

Scott Stevens, SVP, Global  Systems Engineering at Palo Alto Networks, discusses how the company uses AI, machine learning, and big data to find and block malware for its customers in an interview with Jeff Frick of theCUBE which is covering RSA Conference 2019 in San Francisco:

We Find 20-30 Thousand New Pieces of Malware Every Day

There are two ways to think about artificial intelligence, machine learning, and big data analytics. The first is if we’re looking at how are we dealing with malware and finding unknown malware and blocking it, we’ve been doing that for years. The platform we have uses big data analytics and machine learning in the cloud to process and find all of the unknown malware, make it known and be able to block it.

We find 20-30 thousand brand new pieces of malware every day. We’re analyzing millions and millions of files every day to figure out which ones are malicious. Once we know, within five minutes we’re updating the security posture for all of our connected security devices globally.

Whether it’s endpoint software or it’s our inline next gen firewalls we’re updating all of our signatures so that the unknown is now known and the known can be blocked. That’s whether we’re watching to block the malware coming in or the command-and-control that’s using via DNS and URL to communicate and start whatever it’s going to do. You mentioned crypto lockers and there are all kinds of things that can happen. That’s one vector of using AI NML to prevent the ability for these attacks to succeed.

Machine Learning Uses Data Lake to Discover Malware

The other side of it is how do we then take some of the knowledge and the lessons we’ve learned for what we’ve been doing now for many years in discovering malware and apply that same AI NML locally to that customer so that they can detect very creative attacks very and evasive attacks or that insider threat that employee who’s behaving inappropriately but quietly.

We’ve announced over the last week what we call the cortex XDR set of offerings. That involves allowing the customer to build an aggregated data lake which uses the Zero Trust framework which tells us how to segment and also puts sensors in all the places of the network. This includes both network sensors an endpoint as we look at security the endpoint as well as the network links. Using those together we’re able to stitch those logs together in a data lake that machine learning can now be applied to on a customer by customer basis.

Maybe somebody was able to evade because they’re very creative or that insider threat again who isn’t breaking security rules but they’re being evasive. We can now find them through machine learning. The cool thing about Zero Trust is the prevention architecture that we needed for Zero Trust becomes the sensor architecture for this machine learning engine. You get dual purpose use out of the architecture of Zero Trust to solve both the in-line prevention and the response architecture that you need.

How Palo Alto Networks Blocks 30,000 New Pieces of Malware Daily

>> Read a companion piece to this article here:

Zero Trust Focuses On the Data That’s Key to Your Business

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How LinkedIn is Using Machine Learning to Determine Skills

One of the more interesting reveals that Dan Francis, Senior Product Manager for LinkedIn Talent Insights, provided in a recent talk about the Talent Insights tool is how LinkedIn is using machine learning to determine skills of people. He says that there are now over 575 million members in the LinkedIn database and there are 35,000 standardized skills in LinkedIn’s skills taxonomy. The way LinkedIn is figuring out what skills a member has is via machine learning technology.

Dan Francis, Senior Product Manager, LinkedIn Talent Insights, discussed Talent Insights in a recent LinkedIn video embedded below:

LinkedIn Using Machine Learning to Determine Skills

The skills data in Talent Insights comes from a variety of sources, mainly from a member’s profile. There are over 35,000 standardized skills that we have in LinkedIn’s skills taxonomy, and the way we’re figuring out what skills a member has is using machine learning. We can identify skills that a member has that’s based on things that they explicitly added to their profile.

The other thing that we’ll do is look at the text of the profile. There’s a field of machine learning called natural language processing and we’re basically using that. It’s scanning through all the words that are on a member’s profile, and when we can determine that it’s pertaining to the member, as oppose the company or another subject, we’ll say okay, we think that this member has this skill. We also look at other attributes, like their title or the company, to make sure they actually are very likely to have that skill.

The last thing that we’ll do is look at the skills a member has and figure out what are skill relationships. So as an example, let’s say that a member has Ember, which is a type of JavaScript framework, since we know that they know Ember, they also know JavaScript. So if somebody’s running a search like that, we’ll surface them in the results. I think that the most important reason why this is helpful and the real benefit to users of the platform is when you’re searching, you want to get as accurate a view of the population as possible. What we’re trying to do is look at all the different signals that we possibly have to represent that view.  

575 Million People on LinkedIn Globally and Adding 2 Per Second

Today, LinkedIn has over 575 million members that are on the platform globally. This is actually growing at a pretty rapid clip, so we’re adding about two members per second. One of the great things about LinkedIn is that we’re actually very well represented in terms of the professional workforce globally. If you look at the top 30 economies around the world, we actually have the majority of professionals in all of those economies.

LinkedIn is the World’s Largest Aggregator of Jobs

I think there’s often a perception that most of the data’s directly from LinkedIn, stuff that’s posted on LinkedIn and job status is one notable exception to that. Plenty of companies and people will post jobs on LinkedIn, and that’s information that does get surfaced. However, we’re also the world’s largest aggregator of jobs. At this point there are over 20 million jobs that are on LinkedIn.

The way that we’re getting that information is we’re working with over 40,000 partners. These are job boards, ATS’s, and direct customer relationships. We’re collecting all of those jobs, standardizing them, and showing them on our platform. The benefit is not just for displaying the data in Talent Insights, the benefit is also when members are searching on LinkedIn.com, we’re giving them as representative a view of the job market as possible.

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Search Buzz Video Recap: Google Algorithm Chatter, Bad Links, News Bugs, Bing Machine Learning & More

This week I covered a possible small Google algorithm update that touched down sometime over the week. Google said you really shouldn’t have to worry about bad links but if you do…


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AWS CEO Announces Textract to Extract Data Without Machine Learning Skills

AWS CEO Andy Jassy announced Amazon Textract at the AWS re:Invent 2018 conference. Textract allows AWS customers to automatically extract formatted data from documents without losing the structure of the data. Best of all, there are no machine learning skills required to use Textract. It’s something that many data-intensive enterprises have been requesting for many years.

Amazon Launches Textract to Easily Extract Usable Data

Our customers are frustrated that they can’t get more of all those text and data that are in documents into the cloud, so they can actually do machine learning on top of it. So we worked with our customers, we thought about what might solve these problems and I’m excited to announce the launch of Amazon Textract. This is an OCR plus plus service to easily extract text and data from virtually any document and there is no machine learning experience required.

This is important, you don’t need to have any machine learning experience to be able to use Textract. Here’s how it generally works. Below is a pretty typical document, it’s got a couple of columns and it’s got a table in the middle of the left column.

When you use OCR it just basically captures all that information in a row and so what you end up with is the gobbledygook you see in the box below which is completely useless. That’s typically what happens.

Let’s go through what Textract does. Textract is intelligent. Textract is able to tell that there are two columns here so actually when you get the data and the language it reads like it’s supposed to be read. Textract is able to identify that there’s a table there and is able to lay out for you what that table should look like so you can actually read and use that data in whatever you’re trying to do on the analytics and machine learning side. That’s a very different equation.

Textract Works Great with Forms

What happens with most of these forms is that the OCR can’t really read the forms or actually make them coherent at all. Sometimes these templates will kind of effectively memorize in this box is this piece of data. Textract is going to work across legal forms and financial forms and tax forms and healthcare forms, and we will keep adding more and more of these.

But also these forms will change every few years and when they do something that you thought was a Social Security number in this box turns out now not to be a date of birth. What we have built Textract to do is to recognize what certain data items or objects are so it’s able to tell this set of characters is a Social Security number, this set of characters is a date of birth, this set of characters is an address.

Not only can we apply it to many more forms but also if those forms change Textract doesn’t miss a beat. That is a pretty significant change in your capability in being able to extract and digitally use data that are in documents.

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