We have been discussing with professionals and innovators in the search industry ongoing challenge, Trending Opportunitiesand the techniques people and companies use to stay relevant in competitive search results.
One of the trends driving great progress in search technology is the shift from keywords to data that better represents the meaning of the query, and what is known about it.
Keyword search has been driving content discovery since 1230 AD. It was then that Cardinal Hugh de St Cher, a French cardinal and Bible commentator, completed the first known index in history.
Vector search marks a major shift from this traditional method of information retrieval to a future where all the complex data that makes up modern content assets can be put to use.
So what do you need to know now?
we got in touch or freeformer head of Amazon’s AI Labs and current CEO of Pinecone, for a primer on vector search and why you might want to put the technology on your radar.
We ask freedom:
- How will vector search redefine traditional keyword search?
- How would you explain vector search to a 5-year-old?
- What challenges have you encountered using ML algorithms for Amazon Web Services (AWS) customers, and how have you overcome them?
- What is a pine cone and what does it do?
- What advice or advice do you have for SEO beginners just entering the world of ML and AI?
Let’s start with this – why is natural language processing (NLP) so important to the future of SEO and how can marketers prepare for what’s next?
We Burned the Ship of Keyword Searches
Edo Freedom: “Like SEO Master the PageRank Algorithmthey now need to understand NLP to succeed and beat the competition.
However, unlike PageRank, NLP is growing rapidly And there are thousands of contributors.
It will take more effort than following Matt Cutts (from Google) on Twitter and tracking SERP changes.
Thankfully, while NLP is a more complex topic, it’s not as shrouded in mystery as PageRank.
Much of NLP’s work is done in the open, with free and plentiful research papers, open source software, and for free NLP Online Course.
One thing is clear about NLP: it’s here to stay.
It’s far from perfect, but it’s improving fast, and the big tech companies have burned the keyword search boats, and there’s no turning back. “
Vector search allows us to search the way we speak
How vector search will redefine tradition keyword search?
Edo Freedom: “vector search Does not redefine keyword search; it replaces the whole cloth.
Instead of using keywords and their synonyms and misspellings, vector searches use vector embedding.
This is a piece of data that represents the meaning of the search phrase and other known information about the query or user.
(To humans, vector embeddings are unrecognizable and look like a long string of numbers.)
The search phrase and this representation of the user are then used to rank a large number of embeddings representing other content and user preferences to find the most relevant results.
From a user’s perspective, this means they can search for the way they speak.
They no longer have to learn the quirks and syntax of search engines.
From an SEO standpoint, this means they can really focus on the subject and topic and not have to worry about precise keywords. “
How would you explain vector search to a 5-year-old?
Edo Freedom: “Our article explains Vector Search Basics close.
ELI5 version, as I practice in my own family: if I say “Italian food”, you probably think of pizza or pasta.
You already know these things are related because you remember eating pizza at an Italian restaurant or learning that pasta is popular in Italy.
But computers never learn this. So the term “Italian food” means just that, and doesn’t contain information saying it’s related to pasta or pizza.
So when I ask my computer to search for “Italian restaurants,” it might ignore pizza places.
Machine learning is a method of helping computers understand the meaning of what we say or input.
Vector search is a way for these computers to search everything they know based on meaning rather than exact words.
So now, if I ask a computer to recommend an Italian place, it might suggest your favorite pizza place just like you.
Organizations can finally focus on creating and organizing human content.
Thousands of scientists and engineers work tirelessly to make ML and NLP resemble the human mind.
Do you really want to object? The winning strategy for SEO is to optimize for the human mind. “
Overcoming challenges in machine learning
What challenges have you encountered using ML algorithms for Amazon Web Services (AWS) customers, and how have you overcome them?
Edo Freedom: “I can’t talk about specific projects or challenges at AWS. I can say more broadly, in my experience, I see that ML algorithms are no longer the bottleneck.
To be sure, they’re far from perfect, and there’s still a lot of work to do, but it’s happening at a breakneck pace.
The next challenge is to run these algorithms at the scale needed to support consumer products and enterprise applications.
Those representations I mentioned earlier, vector embeddings, are computationally expensive to search.
Indexing of only 1 million items (vector embeddings) already requires specialized software and careful tuning; indexing 100 million items requires specialized software and infrastructure; indexing 1B or more items requires you to be Google or Amazon.
(By the way, that’s why I started Pinecone: to make it easy for engineering teams to add vector search to their applications.)”
What are pine cones?
What is a pine cone and what does it do?
Edo Freedom: Today, Pinecone makes it easy for engineers to build fast, fresh and filtered vector searches into their applications.
It provides engineering teams with the search infrastructure needed to run vector searches at scale, all packaged in a managed service via a simple API.
(We removed the version number because releases are fast, and because as a hosted service, users always get the latest version and don’t need to worry about updates.)
Working with algorithms is fun and definitely worth the challenge.
With vector search, we are at the intersection of cutting-edge algorithms, database architectures, and serverless applications.
And, we’re seeing our customers apply this technology to products that are revolutionizing consumer and enterprise applications, such as semantic search, recommender systems, IT security, wearables, computer vision, and more.
Introduction to Machine Learning and Artificial Intelligence
What advice or advice do you have for SEO beginners just entering the world of ML and AI?
Edo Freedom: “Don’t be afraid. Even the brightest researchers in the field are ‘figuring it out’.
Learning Artificial Intelligence/Machine Learning Articles that go beyond the surface will make you a better SEO professional, and there are plenty of free resource can help you do this.
For those interested in careers in this field, we are currently Recruit across all teams: Engineering, Research, Customer Success, Sales, Marketing and Operations.
More resources:
Featured Image: Courtesy of Pinecone
