Most people do not know how Google uses Machine Learning in the Search algorithms. This talk covers everything from Rank Brain to the Helpful Content Update to Neural Matching, how they work and what you need to do to take advantage of how they work.
The more you understand how Google works, the better you can increase your site's visibility in Search.
Google is using Large Language Models and Machine Learning in the algorithms that rank your sites and show them to users.
This talk will help you better understand from BERT to Rank Brain to Neural Matching and SGE, how they work, and what you should do about it.
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
This session will look at how ChatGPT and other AI are changing the affiliate eco system. What benefits does this technology bring with it, and what are its limitations? The session will focus on how to use A.I tools and when you shouldn't. We will separate the hype vs the reality.
Basic SEO by Andrea H. Berberich @webpresenceoptiAndrea Berberich
SEO stands for Search Engine Optimization and everyone who uses the Internet will eventually use a search engine. SEO is a huge field and everyone who works in the digital sphere is impacted by the guidelines, findings and rules of SEO.
Bearish SEO: Defining the User Experience for Google’s Panda Search LandscapeMarianne Sweeny
The search sun shifted in March 2011 when Google started rolling out the beginning of the Panda update. Instead of using the famous PageRank, a link-based relevance calculation, Panda rests on a machine interpretation of user experience to decide which sites are most relevant to a searchers quest for knowledge. This means that IA and UX practitioners need to start thinking about the machine implications of the way they structure information on the web, and think ahead about the human implications for how search engines present their sites in response to searcher queries. Bearish SEO will present real, actionable methods for content providers, information architects and user experience designers to directly influence search engine discoverability. Need is an experience. It is a state of being. The goal for this presentation is to ensure that user experience professionals become an integral part of designing search experience.
Why is SEO still important to content marketing and content creation professionals? Because search engines are getting better at optimizing for humans!
An Overview of the area and the current potential for the open technologies to be used, and some suggestions as to why they are not as heavily used as they should be.
Google is using Large Language Models and Machine Learning in the algorithms that rank your sites and show them to users.
This talk will help you better understand from BERT to Rank Brain to Neural Matching and SGE, how they work, and what you should do about it.
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
This session will look at how ChatGPT and other AI are changing the affiliate eco system. What benefits does this technology bring with it, and what are its limitations? The session will focus on how to use A.I tools and when you shouldn't. We will separate the hype vs the reality.
Basic SEO by Andrea H. Berberich @webpresenceoptiAndrea Berberich
SEO stands for Search Engine Optimization and everyone who uses the Internet will eventually use a search engine. SEO is a huge field and everyone who works in the digital sphere is impacted by the guidelines, findings and rules of SEO.
Bearish SEO: Defining the User Experience for Google’s Panda Search LandscapeMarianne Sweeny
The search sun shifted in March 2011 when Google started rolling out the beginning of the Panda update. Instead of using the famous PageRank, a link-based relevance calculation, Panda rests on a machine interpretation of user experience to decide which sites are most relevant to a searchers quest for knowledge. This means that IA and UX practitioners need to start thinking about the machine implications of the way they structure information on the web, and think ahead about the human implications for how search engines present their sites in response to searcher queries. Bearish SEO will present real, actionable methods for content providers, information architects and user experience designers to directly influence search engine discoverability. Need is an experience. It is a state of being. The goal for this presentation is to ensure that user experience professionals become an integral part of designing search experience.
Why is SEO still important to content marketing and content creation professionals? Because search engines are getting better at optimizing for humans!
An Overview of the area and the current potential for the open technologies to be used, and some suggestions as to why they are not as heavily used as they should be.
Search engines have changed a lot over the last 15 years and optimizing Websites for them must keep up. This presentation looks at the search landscape and present strategies and tactics for optimizing for today's search.
Talent Sourcing and Matching - Artificial Intelligence and Black Box Semantic...Glen Cathey
A deep dive into resume and LinkedIn sourcing and matching solutions claiming to use artificial intelligence, semantic search, and NLP, including how they work, their pros, cons, and limitations, and examples of what sourcers and recruiters can do that even the most advanced automated search and match algorithms can't do. Topics covered include human capital data information retrieval and analysis (HCDIR & A), Boolean and extended Boolean, semantic search, dynamic inference, dark matter resumes and social network profiles, and what I believe to be the ideal resume search and matching solution.
The Actionable Guide to Doing Better Semantic Keyword Research #BrightonSEO (...Paul Shapiro
For a detailed recap: http://pshapi.ro/SemanticKWR
My BrightonSEO presentation...
1st Half: What is semantic search and why does it matter to SEOs.
2nd Half: Using KNIME to do semantic keyword research using SERP and Twitter data.
Machine Learning for Marketers - CTAConf 2019Britney Muller
Explore CTAConf's Marketing IQ theme with a layer of AI.
You don't have to be a data scientist to think of the next genius ML application!!! ANYONE CAN!
Machine Learning is power at your fingertips! Learn more about how you can apply Machine Learning to your day to day life here.
Croud Presents: How to Build a Data-driven SEO Strategy Using NLPDaniel Liddle
Exploring how you can harness the huge amounts of data available to build an effective, empirically-led SEO strategy using machine learning resource such as natural language processing (NLP). Including useful and practical tips on areas such as topic modelling, categorisation and clustering, so you can get started on using NLP in your own SEO strategy right away.
Understanding Semantic Search and AI Content to Drive Growth in 2023 March 2023TysonStockton1
Exploring modern search engines, semantic search, and AI technology to better understand how we can integrate into SEO strategy and content initiatives.
With the rise of ChatGPT there has been a lot of discussion around if SEO content is good or bad. To best determine how to leverage this technology in SEO workflows we must revisit how a modern search engine works and where we are at with AI technology.
SXSWedu 2018: Making Critical Thinking Real with Digital ContentJulie Evans
Everyone from employers to educators are talking about the need for today’s students to develop effective critical thinking and problem solving skills-but few people know what that really looks like in a classroom or how to measure student competency in a meaningful way. This workshop is designed to take the conceptual understanding of critical thinking to a more practical reality. Grounded in research about employers’ expectations and educators’ challenges in this area, the workshop will use innovative digital content and games to demonstrate how students can effectively develop problem solving muscles, and how teachers can measure student competencies. Features Arts, Science and Civics.
From Dr. Julie Evans (Project Tomorrow) and Dr. Kari Stubbs (BrainPOP)
SEO in the Age of Entities: Using Schema.org for FindabilityJonathon Colman
How is SEO changing to support microdata like Schema.org? And why is this metadata good for information retrieval and organic search engine optimization?
In this introductory guest lecture for the University of Washington, I present some of the problems in information retrieval for unstructured content ("blobs") and how to solve for these challenges using Schema.org microdata to define "entities".
There's a simple Schema.org markup exercise to expose students to the basics as well as jokes about horror movies, The Simpsons, Keanu Reeves, and even Joss Whedon just to keep things light-hearted and fun.
You can learn more about Jonathon Colman at http://www.jonathoncolman.org/
Brian Spiering, a faculty member at the University of San Francisco's MS in Data Science, provides practical advice on how best to navigate the seemingly unlimited choices. He covers how to learn programming skills you'll need, how much Machine Learning is enough, and how to develop the necessary communication skills.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
The growth of Artificial Intelligence (AI) is leading to transformation in our workplaces. Professor Klaus Schwab of the World Economic Forum is calling this change The Fourth Industrial Revolution. There are mixed predictions about AI and automation: some people see a reduction in the need for some jobs while predicting an increase in others. There is no doubt AI is changing the nature of work and learning.
In this presentation you’ll get an insider’s view into--
- Some of the jargon behind the technologies e.g. what data scientists mean when they talk about ‘training a model’
- How AI is being used in L&D today to gain insights and automate learning
- Why you should be looking to use chatbots in your learning programs
- How to get started with recommendation engines
- What the issues and challenges are using AI in learning.
- Even if you aren’t a technical person you will get an understanding of leading edge technologies by attending this webinar.
Afraid the next Google update will kill your site's traffic? Already been hammered by one and trying to recover? Google unleashed a lot of updates this fall, and a lot of sites were negatively affected, especially those in the e-commerce and affiliate space. This talk will help you understand better how Google's machine-learning algorithms work. When Google rewards sites and when they "punish" sites by taking away their traffic. We will also look at how AI content might affect you going forward.
Understanding Google's machine learning algorithms will help you protect your site from the wrath of a Google update going forward as well as help you learn how to better grow your existing site traffic and revenue.
#ASW24
Discussion using real world examples of how those often disregarded anomalies in your crawl and site data can be the breadcrumbs that lead you to discover serious site issues that go so often overlooked.
Attendees will learn how to not only find these hidden anomalies but how to turn those findings into actionable site fixes that improve your site presence and visibility in organic search.
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Search engines have changed a lot over the last 15 years and optimizing Websites for them must keep up. This presentation looks at the search landscape and present strategies and tactics for optimizing for today's search.
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A deep dive into resume and LinkedIn sourcing and matching solutions claiming to use artificial intelligence, semantic search, and NLP, including how they work, their pros, cons, and limitations, and examples of what sourcers and recruiters can do that even the most advanced automated search and match algorithms can't do. Topics covered include human capital data information retrieval and analysis (HCDIR & A), Boolean and extended Boolean, semantic search, dynamic inference, dark matter resumes and social network profiles, and what I believe to be the ideal resume search and matching solution.
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1st Half: What is semantic search and why does it matter to SEOs.
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Machine Learning for Marketers - CTAConf 2019Britney Muller
Explore CTAConf's Marketing IQ theme with a layer of AI.
You don't have to be a data scientist to think of the next genius ML application!!! ANYONE CAN!
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Croud Presents: How to Build a Data-driven SEO Strategy Using NLPDaniel Liddle
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Understanding Semantic Search and AI Content to Drive Growth in 2023 March 2023TysonStockton1
Exploring modern search engines, semantic search, and AI technology to better understand how we can integrate into SEO strategy and content initiatives.
With the rise of ChatGPT there has been a lot of discussion around if SEO content is good or bad. To best determine how to leverage this technology in SEO workflows we must revisit how a modern search engine works and where we are at with AI technology.
SXSWedu 2018: Making Critical Thinking Real with Digital ContentJulie Evans
Everyone from employers to educators are talking about the need for today’s students to develop effective critical thinking and problem solving skills-but few people know what that really looks like in a classroom or how to measure student competency in a meaningful way. This workshop is designed to take the conceptual understanding of critical thinking to a more practical reality. Grounded in research about employers’ expectations and educators’ challenges in this area, the workshop will use innovative digital content and games to demonstrate how students can effectively develop problem solving muscles, and how teachers can measure student competencies. Features Arts, Science and Civics.
From Dr. Julie Evans (Project Tomorrow) and Dr. Kari Stubbs (BrainPOP)
SEO in the Age of Entities: Using Schema.org for FindabilityJonathon Colman
How is SEO changing to support microdata like Schema.org? And why is this metadata good for information retrieval and organic search engine optimization?
In this introductory guest lecture for the University of Washington, I present some of the problems in information retrieval for unstructured content ("blobs") and how to solve for these challenges using Schema.org microdata to define "entities".
There's a simple Schema.org markup exercise to expose students to the basics as well as jokes about horror movies, The Simpsons, Keanu Reeves, and even Joss Whedon just to keep things light-hearted and fun.
You can learn more about Jonathon Colman at http://www.jonathoncolman.org/
Brian Spiering, a faculty member at the University of San Francisco's MS in Data Science, provides practical advice on how best to navigate the seemingly unlimited choices. He covers how to learn programming skills you'll need, how much Machine Learning is enough, and how to develop the necessary communication skills.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
The growth of Artificial Intelligence (AI) is leading to transformation in our workplaces. Professor Klaus Schwab of the World Economic Forum is calling this change The Fourth Industrial Revolution. There are mixed predictions about AI and automation: some people see a reduction in the need for some jobs while predicting an increase in others. There is no doubt AI is changing the nature of work and learning.
In this presentation you’ll get an insider’s view into--
- Some of the jargon behind the technologies e.g. what data scientists mean when they talk about ‘training a model’
- How AI is being used in L&D today to gain insights and automate learning
- Why you should be looking to use chatbots in your learning programs
- How to get started with recommendation engines
- What the issues and challenges are using AI in learning.
- Even if you aren’t a technical person you will get an understanding of leading edge technologies by attending this webinar.
Similar to Google Machine Learning Algorithms and SEO (20)
Afraid the next Google update will kill your site's traffic? Already been hammered by one and trying to recover? Google unleashed a lot of updates this fall, and a lot of sites were negatively affected, especially those in the e-commerce and affiliate space. This talk will help you understand better how Google's machine-learning algorithms work. When Google rewards sites and when they "punish" sites by taking away their traffic. We will also look at how AI content might affect you going forward.
Understanding Google's machine learning algorithms will help you protect your site from the wrath of a Google update going forward as well as help you learn how to better grow your existing site traffic and revenue.
#ASW24
Discussion using real world examples of how those often disregarded anomalies in your crawl and site data can be the breadcrumbs that lead you to discover serious site issues that go so often overlooked.
Attendees will learn how to not only find these hidden anomalies but how to turn those findings into actionable site fixes that improve your site presence and visibility in organic search.
This presentation was from 2021 and explains to viewers what Core Updates are, but also importantly, what they are not. Also how to fix your site an regain your traffic.
This was the training session follow up to the general talk on ChatGPT. This talk has a bit more detail on prompt writing along with the power and limitations of ChatGPT for Marketing.
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NOTE: SOME SLIDES CENSORED for Public View.
Ungagged conference sessions can only be shared by the permission of the speaker. Most of this slide deck can be viewed publicly, but a few slides are not for public viewing and items, in part or in whole, have been redacted.
____________________
This slide deck was presented at the Ungagged LA Conference Nov 2019.
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Google Algorithms, Your Site, and Moving towards Mobile First indexing in a Post Update World.
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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And...
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- Visualization tools to display your network;
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Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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Learn about:
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• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
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See how to accelerate model training and optimize model performance with active learning
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
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What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Leading Change strategies and insights for effective change management pdf 1.pdf
Google Machine Learning Algorithms and SEO
1. @schachin
Kristine Schachinger
Kristine Schachinger
• Started at a front-end dev & designer
Claim to Fame – Designed Reba McEntire’s site
• Started in SEO 2005
• Consultant 2009 – Present
• Some sites I have worked with:
GoodRx, Vice Media, Zappos, Instacart, Healthline, Jack in the Box, Discover,
USA.gov (*GSA), Salon.com, Paychex,com, AndroidHeadlines.com, Patch Media etc
• Judge: US Search Awards, UK Search Awards, EU Search Awards
and since I said yes to all the Search Awards during the pandemic there might be more.
• Specialties: Site Auditing, Site Recoveries, Technical SEO, and all the rest.
• Articles in: WIX SEO, Search Engine Journal, Marketing Land, Search Engine Land,
and Search Engine Watch -- among others.
• Speaker: BarbadosSEO, UngaggedUK/US, State of Search. Leeds, Pubcon, SMX,
RIMC, SXSWi -- and others.
10. @schachin
Kristine Schachinger
In ONE SECOND today, there were
http://www.internetlivestats.com/google-search-statistics/
http://www.internetlivestats.com/google-search-statistics/
15. @schachin
Kristine Schachinger
Google Myth: AI, machine learning, & deep learning are all the same thing
While artificial intelligence (AI) is a convenient and commonplace term, it has no
widely agreed-upon technical definition. One helpful way to think about AI is as the
science of making things smart. Much of the recent progress we’ve seen in AI is based
on machine learning (ML), a subfield of AI where computers learn and recognize
patterns from examples, rather than being programmed with specific rules. There are
many different ML techniques, but deep learning is a particularly popular one right
now. Deep learning is based on neural network technology, an algorithm whose
architecture is inspired by the human brain and can learn to recognize pretty complex
patterns, such as what “hugs” are or what a “party” looks like.
https://ai.google/static/documents/exploring-6-myths.pdf
16. @schachin
Kristine Schachinger
Google
Myth: AI is approaching
human intelligence
“While AI systems are
nearing or outperforming
human beings at
increasingly complex tasks
like generating musical
melodies or playing the
game of Go, they remain
narrow and brittle, and lack
true agency or creativity.”
https://ai.google/static/documents/exploring-6-myths.pdf
DALL-E image for “Robot Learning English”
17. @schachin
Kristine Schachinger
Google
THERE ARE THREE PLACES GOOGLE APPLIES MACHINE LEARNING
IN THE ORGANIC SEARCH ENGINE.
+ PRE-SCORING
LANGUAGE MODELS
+ AD HOC POST-SCORING
RANK BRAIN
NEURAL MATCHING
+ LIVE RANKING FACTORS
HELPFUL CONTENT UPDATE
THE BIG DADDY! MUM IN A CLASS BY ITSELF.
23. @schachin
Kristine Schachinger
In the beginning there was…
Word2Vec the Embedded Word Model
Semantic Search.
https://www.tensorflow.org/tutorials/representation/word2vec
24. @schachin
Kristine Schachinger
Word Embedding
Vector space models (VSMs) represent
(embed) words in a continuous vector space
where semantically similar words are
mapped to nearby points
('are embedded nearby each other').
Word2Vec
https://www.tensorflow.org/tutorials/representation/word2vec
27. @schachin
Kristine Schachinger
• Words go in.
• Words get assigned a mathematical address in a vector.
• Similar and related words sit close to each other in the vector space.
• Words are retrieved based on your query and the words it locates in the “best fit” vector.
• These word “interpretations” are used to return results.
Begging of Semantic Search.
31. @schachin
Kristine Schachinger
Sesame Street and Search
What is BERT?
Natural Language Processing pre-training called Bidirectional
Encoder Representations from Transformers, or BERT.
Moving from NLU into early NLP
32. @schachin
Kristine Schachinger
Google
https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746
BERT. ”BERT, Bidirectional Encoder Representations from Transformers, came in 2019, it is a neural
network-based technique for natural language processing pre-training. looking at the sequence of words
on a page, so even seemingly unimportant words in your queries are counted for in the result.”
• Year Launched: 2019
• Used For Ranking: No
• Looks at the query and content language
• All languages
• Language Training Model: Prescoring
• Very commonly used for many queries
• Can you optimize for it? No
34. @schachin
Kristine Schachinger
https://bensen.ai/elmo-meet-bert-recent-advances-in-natural-language-embeddings/
BERT, or Bidirectional Encoder Representations from Transformers, improves upon
standard Transformers by removing the unidirectionality constraint by using a masked language
model (MLM) pre-training objective. The masked language model randomly masks some of the tokens
from the input, and the objective is to predict the original vocabulary id of the masked word based only
on its context. Unlike left-to-right language model pre-training, the MLM objective enables the
representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional
Transformer. In addition to the masked language model, BERT uses a next sentence prediction task
that jointly pre-trains text-pair representations.
There are two steps in BERT: pre-training and fine-tuning. During pre-training, the model is trained on
unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with
the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the
downstream tasks. Each downstream task has separate fine-tuned models, even though they are
initialized with the same pre-trained parameters.
Sesame Street and Search: BERT Definition
36. @schachin
Kristine Schachinger
Sesame Street and Search: Why is BERT Special?
BERT can disambiguate words from the sentence and apply meaning forward and backward to those
words in order to predict a masked word using those applied contexts. This is SUPER EFFICIENT!
37. @schachin
Kristine Schachinger
Because BERT can go forward and backwards
to predict an unknown (masked) term and/or sentence.
Also uses root words, so play for player/playing/played are the same
Sesame Street and Search: Why is BERT Special?
https://blog.google/products/search/search-language-understanding-bert/
39. @schachin
Kristine Schachinger
Simply put BERT or language modeling is
“Language modeling – although it sounds formidable –
is essentially just predicting words in a blank.”
40. @schachin
Kristine Schachinger
Why does it matter to us as SEOs?
It mostly doesn’t.
It was a breakthrough in Language Model
Processing, because it is …
+ VERY Fast
+ Uses fewer resources
+ Provides better understanding of content
41. @schachin
Kristine Schachinger
• Collection of NLP Pre-Requisites
https://towardsdatascience.com/a-collection-of-must-known-pre-requisite-resources-
for-every-natural-language-processing-nlp-a18df7e2e027
• NLU vs NLP: What’s the Difference?
https://www.bmc.com/blogs/nlu-vs-nlp-natural-language-understanding-processing/
• BERT State of Art Pre Learning AI
https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html
• GITHUB or BERT
https://github.com/google-research/bert
• GOOGEL PAPER: BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding
https://arxiv.org/abs/1810.04805
• Google Brings in BERT to Improve its Search Results
https://techcrunch.com/2019/10/25/google-brings-in-bert-to-improve-its-search-
results/
• Google Blog on BERT
https://www.blog.google/products/search/search-language-understanding-bert/
• Future of AI in 2021
https://www.bmc.com/blogs/state-of-ai/
BERT the Deep Dive.
44. @schachin
Kristine Schachinger
Rank Brain.
Rank Brain & Neural Matching & the
Document Relevancy Model (DRAM)
“Document relevance ranking, also known as adhoc retrieval
is the task of ranking documents from a large collection using
the query and the text of each document only.”
Rank Brain.
45. @schachin
Kristine Schachinger
Rank Brain vs Neural Matching.
Both are used to re-ordered the results post retrieval
according to “ad hoc retrieval” methods and ”dynamic relevancy”
Ranking with ONLY the document text
• https://www.searchenginejournal.com/google-neural-matching/271125/
• http://www2.aueb.gr/users/ion/docs/emnlp2018.pdf
58. @schachin
Kristine Schachinger
• When do you see it?
• Relationships are weak or unknown
• -- enter Rank Brain.
• Behind the scenes, data is continually fed into the machine
learning process, to make results more relevant the next time.
• Can be combined with other algorithms such as neural matching
• No way to optimize for it
• BUT you can help prevent your page from getting one of these
results check your results for your queries.
Make sure Google is NOT CONFUSED.
Rank Brain.
61. @schachin
Kristine Schachinger
Google
https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746
Neural matching. Neural matching was released in 2018 - expanded to the local search results in 2019.
Neural matching does specifically help Google rank search results and is part of the POST ad-hoc
ranking algorithms.
Links CANNOT affect this ranking sort.
• Year Launched: 2018
• Used For Ranking: Yes (but post scoring)
• Looks at the query and content language
• Works for all languages
• Very commonly used for many queries
• Applied post scoring ad hoc
• Can you optimize for it? Yes and No
66. @schachin
Kristine Schachinger
Rank Brain vs Neural Matching.
RankBrain helps Google better relate pages to concepts.
Neural Matching helps Google better relate words to searches.
• Rank Brain = page concepts
• Neural Matching = linking words to the page concepts
“…neural matching, – AI method to better connect words to concepts.” - Google
https://www.seroundtable.com/google-explains-neural-matching-vs-rankbrain-27300.html
68. @schachin
Kristine Schachinger
Google Helpful Content Update
“Our classifier for this update runs continuously, allowing it to monitor newly-launched sites and
existing ones. As it determines that the unhelpful content has not returned in the long-term, the
classification will no longer apply.
This classifier process is entirely automated, using a machine-learning model.”
https://developers.google.com/search/blog/2022/08/helpful-content-update
69. @schachin
Kristine Schachinger
Google Helpful Content Update
From CMSWire
https://www.cmswire.com/digital-marketing/google-helpful-content-update-improves-customer-experience-and-seo-strategy/
https://developers.google.com/search/blog/2022/08/helpful-content-update
71. @schachin
Kristine Schachinger
Google Helpful Content Update
Main Points
• Ranking signal NOT an update
• First known ranking signal that has machine learning
• Continually rolling but with delays, so can take 2-3
months to catch-up with your site
• Sitewide but severity based on the number of issued
pages
• Other factors can lessen the devaluation (like
content quality on other pages)
• Seems to target what Panda and Penguin did with an
additional focus on the quality of “usefulness” or
“helpfulness”
• Is your content differentiating itself?
DALL-E image for “Angry SEO”
72. @schachin
Kristine Schachinger
Google Helpful Content Update
Read these documents for all the details.
• Google Search's helpful content update and
your website
https://developers.google.com/search/updates/helpful-content-update
• What creators should know about Google's
August 2022 helpful content update.
https://developers.google.com/search/docs/essentials
• Googlee Essentials
https://developers.google.com/search/blog/2022/08/helpful-content-
update
73. @schachin
Kristine Schachinger
Google
Myth: can’t detect AI content.
AI systems can predict that content is
likely created by AI.
How?
AI cannot create anything. It is only
able to use what is knows to detect
patterns and then in the case of
content, use those patterns to “write
content”
So, AI can recognize patterns of how
AI would “write” and determine a
likelihood that this item is written by
AI.
It is not 100%, but it can be done.
https://ai.google/static/documents/exploring-6-myths.pdf
74. @schachin
Kristine Schachinger
Maybe the Helpful Content
should be the
“Bo Hodas” Update?
#BoHodasUpdate #StateofSearch2022
DALL-E image for “Helpful Content Robot”
76. @schachin
Kristine Schachinger
GoogleMUM (Multitask Unified Model)
“…has the potential to transform how Google helps you with complex tasks. MUM
uses the T5 text-to-text framework and is 1,000 times more powerful than BERT.
MUM not only understands language, but also generates it.”
Built on top of BERT.
____________
Possible related patent
https://www.searchenginejournal.com/what-is-google-mum/407844/
https://blog.google/products/search/introducing-mum/
https://www.fastcompany.com/90681337/google-mum-search
77. @schachin
Kristine Schachinger
“A brief explanation about the significance of the multimodal model: Multimodal is a composite
machine learning technique which compares and combines information from multiple sources
to form a single response.
The "modal" in multimodal refers to the aggregation of data within media, such as visual data
from images and video, language data from text documents, and audio data from music and
sound recordings.
Modalities are incorporated into the training dataset for machine learning models. Multimodal
sentiment analysis, for example, can inspect various combinations of text, audio and visual
data to assess the sentiment towards an event or occurrence.
With MUM, Google is treating media as modalities to improve the user experience with its
search.”
https://www.cmswire.com/digital-marketing/what-marketers-can-expect-from-google-mum/
GoogleMUM (Multitask Unified Model)
78. @schachin
Kristine Schachinger
“The choice of multimodal models fits Google because of the increased number of non-text
based sources, such as video in the form of livestreams or similar, and audio files, as in the
case of podcasts. To develop MUM, Google trained the algorithm "across 75 different
languages and many different tasks at once" to refine its comprehension of information and
digital details.
MUM also considers knowledge across languages, comparing a query to sources that aren’t
written in the user's native language to bring better information accuracy.
As a result Google claims MUM is 1,000 times more powerful than
BERT.”
https://www.cmswire.com/digital-marketing/what-marketers-can-expect-from-google-mum/
GoogleMUM (Multitask Unified Model)
79. @schachin
Kristine Schachinger
Reid acknowledges that MUM carries its own risks. “Any time you’re training a model based on
humans, if you’re not thoughtful, you’ll get the best and worst parts,” she says. She emphasizes
that Google users human raters to analyze the data used to train the algorithm and then assess
the results, based on extensive published guidelines.
“Our raters help us understand what is high quality content, and that’s
what we use as the basis,” she says. “But even after we’ve built the
model, we do extensive testing, not just on the model overall, but trying
to look at slices so that we can ensure that there is no bias in the
system.”
The importance of this step is one reason why Google isn’t
deploying all its MUM-infused features today.”
https://www.cmswire.com/digital-marketing/what-marketers-can-expect-from-google-mum/
GoogleMUM (Multitask Unified Model)
82. @schachin
Kristine Schachinger
AI is ever-changing and unfixed.
Don’t waste the time and resources on gaming it.
But you can make it easier for the machine
learning to get it right.
Do you optimize for Machine Learning?
88. @schachin
Kristine Schachinger
Simple answer to a very complex issue?
Do your normal query research, check the SERPs for Rank
Brain issues and then just write in
natural and conversational language.
Using specificity (topical hubs) PLUS
depth & breadth to create holistic content.
89. @schachin
Kristine Schachinger
Write holistic content? Does your content have depth, breadth, & semantic relationships?
Use terms that are semantically related. Image search is great for showing related terms.
92. @schachin
Kristine Schachinger
What is Structured Data?
Structured data for SEO purposes is on-page markup that
enables search engines to better understand the information
currently on your site’s web pages, and then use this information
to improve search results listing by better matching user intent.
98. @schachin
Kristine Schachinger
We can help give Google a clearer understanding.
That helps us help Google better answer
the questions users ask
and to better surface our content for those users
We give our data meaning
Google Understands
101. @schachin
Kristine Schachinger
Well Formed Text & Parsey McParseFace.
http://www.kurzweilai.net/google-open-sources-natural-language-understanding-tools
Ray Kurzweil on Google NLU
102. @schachin
Kristine Schachinger
Questions = Well Formed Text
https://ai.google/research/pubs/pub47323
“Understanding natural language queries is fundamental to many practical NLP
systems. Often, such systems comprise of a brittle processing pipeline, that is not
robust to "word salad" text ubiquitously issued by users. However, if a query
resembles a grammatical and well-formed question, such a pipeline is able to
perform more accurate interpretation, thus reducing downstream compounding
errors.”
106. @schachin
Kristine Schachinger
Takeaways.
• Think Search Queries NOT Simple Keywords
• Write in natural, conversational language
• Write using holistic content
• Focus on depth and breadth with related terms
• Add Structured Data
• Use well formed text (ie questions) when you can.
Takeaways.