Tag Archive | "Learning"

Now Live for Your SEO Learning Pleasure: The NEW Beginner’s Guide to SEO!

Posted by FeliciaCrawford

It feels like it’s been a king’s age since we first began our long journey to rewrite and revamp the Beginner’s Guide to SEO. For all the long months of writing and rewriting, of agonizing over details and deleting/replacing sections every so often as Google threw us for a loop, it’s hard to believe it’s finally ready to share:

The new Beginner’s Guide to SEO is here!

What makes this new version so darn special and sparkly, anyway?

I’m glad you asked! Our design team would breathe a sigh of relief and tell you it’s because this baby is on-brand and ready to rock your eyeballs to next Tuesday with its use of fancy, scalable SVGs and images complete with alt text descriptions. Our team of SEO experts would blot the sweat from their collective brow and tell you it’s because we’ve retooled and completely updated all our recommendations to ensure we’re giving fledgling learners the most accurate push out of the digital marketing nest that we can. Our developers would tell you it’s because it lives on a brand-spankin’-new CMS and they no longer have to glare silently at my thirteenth Slack message of the day asking them to fix the misplaced period on the fourth paragraph from the top in Chapter 7.

All joking aside, every bit of the above is true, and each perspective pulls together a holistic answer: this version of the Beginner’s Guide represents a new era for the number-one resource for learning SEO, one where we can update it at the drop of a Google algorithm-shaped hat, where it’s easier than ever to access and learn for a greater variety of people, where you can rely on the fact that the information is solid, up-to-date, and molded to best fit the learning journey unique to SEO.

I notice the structure is a little different, what gives?

We can’t escape your eagle eyes! We structured the new guide quite differently from the original. Everything is explained in our introduction, but here’s the gist: taking inspiration from Maslow’s hierarchy of needs, we built each chapter based on the core foundation of how one ought to go about doing SEO, covering the most integral needs first before leveling up to the next.

A pyramid of SEO needs mimicking Maslow's Hierarchy of Needs theory of psychology.

We affectionately call this “Mozlow’s Hierarchy of Needs.” Please forgive us.

A small but mighty team

While it may have taken us a full year and a half to get to this point, there was but a small team behind the effort. We owe a huge amount of gratitude to the following folks for balancing their other priorities with the needs of the new Beginner’s Guide and putting their all into making this thing shine:

Britney Muller, our brilliant SEO scientist and the brains behind all the new content. Words cannot do justice to the hours she spent alone and after hours before a whiteboard, Post-Its and dry-erase notes making up the bones and muscles and soul of what would someday become this fully-fleshed-out guide. For all the many, many blog comments answered and incorporated, for all the emails and Twitter messages fielded, for all the love and hard work and extra time she spent pouring into the new content, we have to give a heartfelt and extremely loud and boisterous THANK YOU. This guide wouldn’t exist without her expertise, attention to detail, and commitment to excellence.

Kameron Jenkins, our SEO wordsmith and all-around content superheroine. Her exquisite grasp of the written word and extensive experience as an agency SEO were paramount in pulling together disparate feedback, finessing complicated concepts into simple and understandable terms, and organizing the information in ways most conducive to aiding new learners. Again, this guide wouldn’t be here without her positive attitude and incredible, expert help.

Trevor Klein, editor extraordinaire. His original vision of organizing it according to the SEO hierarchy of needs provided the insight and architecture necessary to structuring the guide in a completely new and utterly helpful way. Many of the words, voice, and tone therein belong to him, and we deeply appreciate the extra polish and shine he lent to this monumental effort.

Skye Stewart, talented designer and UX aficionado. All the delightful images you’ll find within the chapters are compliments of her careful handiwork, from the robo-librarian of Chapter 2 to the meat-grinder-turned-code-renderer of Chapter 5. The new Beginner’s Guide would be an infinitely less whimsical experience without her creativity and vision.

Casey Coates, software engineer and mystical CMS-wizard-come-miracle-maker. I can safely say that there is no way you would be exploring the brand-new Beginner’s Guide in any coherent manner without his help. For all the last-minute additions to CMS deploys, for calmly fielding all the extra questions and asks, for being infinitely responsive and helpful (case in point: adding alt text to the image block less than two minutes after I asked for it) and for much, much more, we are grateful.

There are a great many other folks who helped get this effort underway: Shelly Matsudaira, Aaron Kitney, Jeff Crump, and Cyrus Shepard for their integral assistance moving this thing past the finish line; Rand Fishkin, of course, for creating the original and longest-enduring version of this guide; and to all of you, our dear community, for all the hours you spent reading our first drafts and sharing your honest thoughts, extremely constructive criticisms, and ever-humbling praise. This couldn’t exist without you!

Y’all ready for this?

With tender pride and only a hint of the sort of naturally occurring anxiety that accompanies any big content debut, we’re delighted and excited for you to dive into the brand-new Beginner’s Guide to SEO. The original has been read over ten million times, a mind-boggling and truly humbling number. We can only hope that our newest incarnation is met by a similar number of bright minds eager to dive into the exhilarating, challenging, complex, and lucrative world of SEO.

Whether you’re just starting out, want to jog your memory on the fundamentals, need to clue in colleagues to the complexity of your work, or are just plain curious about what’s changed, we hope from the bottom of our hearts that you get what you need from the new Beginner’s Guide.

Dive in and let us know what you think!

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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.

The post AWS CEO Announces Textract to Extract Data Without Machine Learning Skills appeared first on WebProNews.


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SearchCap: Eric Schmidt changes role, enterprise SEO & machine learning for PPC

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

The post SearchCap: Eric Schmidt changes role, enterprise SEO & machine learning for PPC appeared first on Search Engine Land.



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Machine Learning and It’s Impact on Search

The terms machine learning (ML) and artificial intelligence (AI) have been cropping up more often when it comes to organic and paid search. Now a recent report by Acquisio has confirmed just how effective machine learning for search results.

According to Acquisio, paid search accounts that have been optimised for machine learning have 71% higher conversion rates and have lower cost-per-click (CPC). But these were not the only benefits that accounts using machine learning enjoyed. The web marketing company also revealed these accounts were also able to reach their target spending levels and had lower churn rates.

The data implies that small marketing teams and CMOs now stand on an even playing field with more established companies now that ML is more affordable, effective and accessible to everyone.

This doesn’t mean that marketers should ignore organic search and original, value-laden content. Paid search might be the easiest way to rank high in search engines, particularly since AI will be doing the bulk of the work, developing campaigns that have greater odds of being seen by the right searchers at the proper time. However, organic search is more authentic and will last longer than paid searches.

The goal now is to understand how ML impacts the search system and how to take advantage of the technology’s evolution that made paid and organic searches more effective.

Paid vs Organic Search: Which Wins in the End?

There’s been an ongoing debate as to which is better – paid or organic searches. Interestingly, both have come out on top, but at different times and conditions. The results have depended on the type of research done and other outside factors. For instance, a study conducted in 2011 showed that organic search was more effective. However, paid search has outpaced its counterpart from 2013 onwards. But this appears to be due to the changes Google has made to its algorithm.

So which is better? Andy Taylor, the Associate Director of Research at Merkle, believes that flexibility is the best option. Instead of just sticking to one approach, companies should determine what search strategy is ideal for their business at the moment and the technology that’s currently available. After all, the ideal marketing strategy for your company now will probably change in a few months as customers change their expectations and technologies expand.

Machine Learning is Changing More Than Search

The rise of machine learning has also resulted in a shift to data-driven models instead of the conventional attribution models. This multi-touch attribution model (MTA) relies on an analytics scale that’s more descriptive and takes into account various touchpoint outputs, like ad interactions, ad creative, or exposure order. It also allows marketers to have a better understanding of how factors, like a distinct set of keywords and ad words, can affect a conversion.

But it’s not just search capacities that machine learning has an impact on. The technology is also being used to refine and make algorithm changes. It has been theorized that Google’s RankBrain utilizes machine learning to assess if the company has to revise its own rankings based on what the consumer searches for and whether the user was satisfied with the result.

Machine Learning Will Push for More Sophisticated Content

Because machine learning technology is developing more advanced SEM capacities and sophisticated algorithms, search engines are pushing marketers and content producers to deliver more refined content. This would eventually lead to search engines becoming more discerning to the quality of online content a company is putting out. This means producing high-quality content that particularly targets what the consumer is looking for becomes more vital than ever before.

Machine learning and AI are impacting every aspect of marketing. Companies should start understanding them and how to utilize ML-optimized tools effectively in their marketing campaigns.  

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Learning to Re-Share: 4 Strategies to Renew, Refresh, and Recycle Content for Bigger Reach

Posted by jcar7

In the nearly three years the MeetEdgar blog went live, we’ve published more than 250 posts, written over 300,000 words, searched for hundreds of .gifs, and used our own tool to share our content 2,600 times to over 70,000 fans on social media.

After all that work, it seems silly to share a post just once. Nobody crumples up an oil painting and chucks it in the trash after it’s been seen one time — and the same goes for your content.

You’ve already created an “art gallery” for your posts. Resharing your content just lets the masses know what you’ve got on display. Even if hundreds or thousands of people have seen it all before, there’s always someone new to your content.

In a social media landscape that’s constantly changing, building a solid foundation of evergreen content that can be shared and shared again should be a key part of your social media strategy.

Otherwise, your art gallery is just another building in the city.

But wait… aren’t we supposed to be writing fresh content?

Yes! One of the biggest misconceptions about resharing is that it’s a spammy tactic. This is just not true — provided that you’re resharing responsibly. We’ll explain how to do that in just a moment.

Resharing actually does double-duty for your brand. It not only gets the content that you spent your valuable time creating in front of more eyeballs (and at optimal times, if you want to get fancy), it also frees you up to have more authentic, real-time social interactions that drive people to your site from social media — since you’ve got content going out no matter what.

Did we mention that resharing is good for SEO? Moz Blog readers know that the more people engage with a post, the better your blog or site looks to search engines. And that’s only one facet of the overall SEO boost (and traffic boost!) resharers can see.

How resharing impacts SEO

Big brands are probably the most prolific content resharers. Heck, they don’t even think twice about it:

BuzzFeed is a perfect example of the value of repeating social updates, because they don’t necessarily NEED to.

So why do they do it anyway? Because it gets results.

Social sharing alone has an impact on SEO, but social engagement is really where it’s at. Quality content is totally worth the up-front time and cost, but only if it gets engagement! You up your chances of engagement with your content if you simply up your content’s exposure. That’s what resharing does awesomely.

With literally zero tweaks to the content itself, BuzzFeed made each of those social posts above double in value. Chances are, the people who saw these posts the first time they were shared are not the same people who saw them when they were reshared.

But simply resharing social posts isn’t the only way to get more engagement with your content. This post covers how companies large and small do resharing right, and highlights some of the best time-saving content strategies you can implement for your brand right now.

1 – Start at the source: Give old posts a new look

Lots has changed in five years — the world got three new Fast & Furious movies and LKR Social Media transformed from a consulting service into social media automation software.

We’ve done the math: three months is one Internet year and five years is basically another Internet epoch. (This may be a slight exaggeration.) So when we transferred some of our founder’s older evergreen blog posts to the new MeetEdgar blog, we took stock of which of those posts had picked up the most organic traffic.

One thing that hadn’t changed in five years? A blog post about how Vin Diesel was winning the social media game was still insanely popular with our readers:

Screen Shot 2017-07-24 at 11.53.06 AM.pngScreen Shot 2017-07-24 at 11.54.34 AM.png

Writing blog posts with an eye toward making them as evergreen as possible is one of the smartest, most time-saving-est content marketing strategies out there.

There weren’t a ton of tweaks to make, but we gave this popular post some love since so many people were finding it. We pepped up the headline, did a grammar and content rundown, refreshed links and images, updated social share buttons, and added more timely content. The whole process took less time than writing a brand new post, and we got to share it with tens of thousands of followers who hadn’t seen it when it was originally published.

So… check your metrics! Which evergreen posts have performed the best over time? Which have lots of awesome organic traffic? Make a list, do a content audit, and start updating!

2 – Find your social sharing “sweet spot” by repackaging your content

When you read studies that say many social media users reshare social posts without ever clicking through to the content itself… it can be a little disheartening.

Okay, a LOT disheartening.

You’ve probably spent tons of time creating your content, and the thought that it’s not getting read NEARLY as often as it could be is a recipe for content marketing burnout. (We’ve all been there.)

But it’s not all for naught — you might just need to experiment until you find the “sweet spot” that gets people to read and share. One way to do that is to simply repackage content you’ve already written.

The tried-and-true “best of” post offers a reprieve from the content-creation grind while still delivering tons of value to your fans and readers.

Repackaging is best when it reframes your content with a new focus — like rounding up similar posts based on a theme. (You can do this in reverse, too, and turn one great post into a bunch of fresh content to then share and reshare!)

If you can get people to your site, a “best of” post encourages readers to stay longer as they click links for the different articles you’ve gathered up, and engage with content they may never have thought to look up separately.

Most fun of all, you can repackage your content to target new or different subsets of your audience on social media. (More on that in the next section.)

3 – Social shake-up: Reaching and testing with different audiences

“What if the same person recognizes something that I’ve already posted in the past?” you might be asking right about now. “I don’t want to annoy my followers! I don’t want to be spammy!”

Forget about people resharing social posts without reading the content behind the links — most people don’t see your social posts at all in the first place.

This is just one of those uncomfortable facts about the Internet, like how comment sections are always a minefield of awful, and how everyone loves a good startled cat .gif.

That doesn’t mean you should repeat yourself, word-for-word, all the time. Chances are, you have more than one type of reader or customer, so it’s important not just to vary your content, but also to vary how you share it on social media.

Savvy marketers are all over this tactic, marketing two sides (or more) of the same coin. Here are a couple of examples of social sharing images from a Mixpanel blog post:

Option A

Option B

Both Option A and Option B go to the same content, but one highlights a particularly juicy stat (problem statement: “97% of users churn”) and the other hits the viewer with an intriguing subheader (solution statement: “behavior-based messaging”). In this way, Mixpanel can find out what pulls in the most readers and tweak and promote that message as needed.

Pull a cool anecdote from your post or highlight a different stat that gets people excited. It can be as easy as changing up the descriptions of your posts or just using different images. There’s so much to test and try out — all using the same post.

4 – Automate, automate, automate

Remember, your best posts are only as good as the engagement they get. That fact, however, doesn’t mean you have to keep manually resharing them on social media day in and day out.

Unless, of course, you’re into that boring busywork thing.

Automating the whole process of resharing evergreen content saves tons of time while keeping your brand personality intact. It also frees you up to have real-time interactions with your fans on social media, brainstorm new post ideas, or just go for a walk, and it solves the time crunch and the hassle of manually re-scheduling posts, while actually showcasing more of your posts across the massive social media landscape. Just by spacing out your updates, you’ll be able to hit a wider range of your followers.

(This is probably a good time to check whether your social media scheduling tool offers automatic resharing of your content.)

Now, social media automation isn’t a substitute for consistently creating great new content, of course, but it does give your existing evergreen content an even better opportunity to shine.

Win with quality, get things DONE with resharing

It’s noisy out there. The law of diminishing returns — as well as declining social reach — means that a lot of what you do on social media can feel like shouting into the void.

And there’s not a huge ROI for shouting into voids these days.

Responsible resharing is an important part of your overall content marketing strategy. As long as you keep your content fresh, create new quality content regularly, and talk to your fans where and when they’re most active, chances are people won’t see the same thing twice. The data shows you’ll get more clicks, more traffic, and better SEO results — not a bad bonus to that whole “saving lots of time” thing.

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Apple Shares Source Code For Machine Learning Framework at WWDC 2017

Apple’s recent WWDC (Worldwide Developers Conference) saw the unheralded release of Core ML, which will reportedly make it easier for developers to come up with machine learning tools across the Apple ecosystem.

The way this works is that developers need to convert their creations into an API that is compatible with the Core ML. They then have to load their programs into the Apple Xcode development before it can be installed on the iOS.

Developers can use any of the following frameworks: Keras, XGBoost, LibSVM, Caffe, and scikit-learn. To make it even easier for them to load their models, Apple is allowing them to come up with their own converter.

According to Apple, the Core ML is “a new foundational machine learning framework used across Apple products, including Siri, Camera, and QuickType.”

The company explained that this new machine learning tool would be “the foundation for domain-specific frameworks and functionality.”

One of the primary advantages of the Core ML is that it speeds up the artificial intelligence on the Apple Watch, iPhone, iPad, and perhaps the soon-to-be-released Siri speaker. If it works the way that is billed, any AI task on the iPhone, for instance, would be six times quicker compared to the Android.

The machine learning tools supported by Apple Core ML include linear models, neural networks, and tree ensembles. The company also promised that private data by users won’t be compromised by this new endeavor. This means that developers can’t just tinker with any phone to steal private information.

Core ML also supports:

  • Foundation for Natural Language Processing
  • Vision for Image Analysis
  • Gameplay Kit

“Core ML itself builds on top of low-level primitives like Accelerate and BNNS, as well as Metal Performance Shaders,” the company added.

But Apple is reportedly not content with just releasing the Core ML. According to rumors, the company is looking to fulfill its promise of helping to build a very fast mobile platform. In fact, if the rumors are true, the company is also building a much better chip that can handle AI tasks without compromising performance.

Though Core ML seems promising, Apple is certainly not blazing the trail when it comes to machine learning. In fact, Facebook and Google have already unveiled their own machine learning frameworks to optimize the mobile user’s experience.

The new machine learning framework is still part of Apple’s Core Brand, which already includes Core Audio, Core Location, and Core Image as announced earlier.

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SearchCap: AMP images, Google Maps promoted places & machine learning

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

The post SearchCap: AMP images, Google Maps promoted places & machine learning appeared first on Search Engine Land.



Please visit Search Engine Land for the full article.


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