Post by Keithr0Post by ClockyPost by DarylPost by jonzThe Chinese made AI Chip
No, the chips are made by the US company Nvidia. probably in
Taiwan, so some might say that's China, but not the same China
as DeepSeek is from. DeepSeek write software that runs on them.
Seems the Chinese are probably lying
Since it's open-source, people will find that out when they try to
build their own chatbots based on it. In that regard they're
easier to trust than the US companies with closed-source software.
¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬¬
I downloaded it a couple of days ago, and I`m impressed!.
Chucked a couple of queries at it, (vehicle related) and got back
everything I could possibly need.....Concise!!!. 😉🙂
My only question is why would you want/need to use it?
My Google phone has "Gemini" AI installed with the last update and
I'm trying to work out how to get rid of the cunt of a thing.
Ordinary Google is good enough, no need for AI shit.
Google = AI shit, dopey.
Google search is not.
It would seem you are wrong
https://searchengineland.com/how-google-uses-artificial-intelligence-in-google-search-379746
How Google uses artificial intelligence In Google Search
From RankBrain, Neural Matching, BERT and MUM - here is how Google uses
AI for understanding language for query, content and ranking purposes.
Barry Schwartz on February 3, 2022 at 12:00 pm | Reading time: 8 minutes
Chat with SearchBot
As Google continues to leverage more artificial intelligence and machine
learning in Google Search, one may wonder in what ways does AI and
machine learning help Google Search perform its daily tasks. Since 2015,
when Google introduced its first AI into search named RankBrain, Google
has continued to deploy AI systems to better understand language and
thus improve the search results Google presents to its searches.
Several months ago we sent Google a number of questions around how
Google uses its AI in search, including RankBrain, neural matching, BERT
and Google’s latest AI breakthrough – MUM. We’ve come up with more of an
understanding of when Google uses AI, which AI does what in Google
Search, how these various AI algorithms may work together, how they have
changed over the years and what, if anything, search marketers need to
know when it comes to how Google uses AI in search.
We spoke with Danny Sullivan, the Public Liaison for Google Search, to
help with the answers to many of these questions. In short, RankBrain,
neural matching and BERT are used in Google’s ranking system across
many, if not most, queries and look at understanding the language of
both the query and content it is ranking. However, MUM is not currently
used for ranking purposes, it is currently only used for COVID vaccine
naming and powers the related topics in videos results.
It starts by writing content for humans
You hear it all the time from Google representatives and from many SEOs:
write content for humans. In the older days of SEO, when the algorithms
were maybe simpler, you would have many SEOs who would craft content for
each and every search engine (back then there were dozens of different
search engines). Now, there is primarily Google, with a little bit of
Bing and some ruffling from DuckDuckGo – but the algorithms are much
more complex and with machine learning and AI, the algorithms understand
language more like a human would understand language.
So the advice Google has given is write for humans, and that you can’t
optimize your site for BERT or any AI. If you write content that humans
understand, then the algorithms and AI search engines use will also
understand it. In short, this article is not aimed at trying to give you
SEO tips on how to optimize your sites for any specific AI, but rather
to communicate how Google uses AI in Google Search.
Overview of AI used in Google Search
RankBrain. It starts with RankBrain, Google’s first attempt at using AI
in search dates back to 2015. Google told us RankBrain helps Google
understand how words are related to concepts and can take a broad query
and better define how that query relates to real-world concepts. While
it launched in 2015 and was used in 15% of queries, Google said it is
now, in 2022, widely used in many queries and in all languages and
regions. RankBrain does specifically help Google rank search results and
is part of the ranking algorithm.
Year Launched: 2015
Used For Ranking: Yes
Looks at the query and content language
Works for all languages
Very commonly used for many queries
Here is an example provided by Google of how RankBrain is used, if you
search for “what’s the title of the consumer at the highest level of a
food chain,” Google’s systems learn from seeing those words on various
pages that the concept of a food chain may have to do with animals, and
not human consumers. By understanding and matching these words to their
related concepts, RankBrain helps Google understand that you’re looking
for what’s commonly referred to as an “apex predator.”
Neural matching. Neural matching was the next AI Google released for
search, it was released in 2018 and then expanded to the local search
results in 2019. In fact, we have an article explaining the differences
between RankBrain and neural matching over here. Google told us neural
matching helps Google understand how queries relate to pages by looking
at the entire query or content on the page and understanding it within
the context of that page or query. Today, neural matching is used in
many, if not most, queries, for all languages, in all regions, across
most verticals of search. Neural matching does specifically help Google
rank search results and is part of the ranking algorithm.
Year Launched: 2018
Used For Ranking: Yes
Looks at the query and content language
Works for all languages
Very commonly used for many queries
Here is an example provided by Google of how neural matching is used, if
you search for “insights how to manage a green,” for example. Google
said “if a friend asked you this, you’d probably be stumped.” “But with
neural matching, we’re able to make sense of this quizzical search. By
looking at the broader representations of concepts in the query —
management, leadership, personality and more — neural matching can
decipher that this searcher is looking for management tips based on a
popular, color-based personality guide,” Google told us.
BERT. BERT, Bidirectional Encoder Representations from Transformers,
came in 2019, it is a neural network-based technique for natural
language processing pre-training. Google told us BERT helps Google
understand how combinations of words express different meanings and
intents, including looking at the sequence of words on a page, so even
seemingly unimportant words in your queries are counted for. When BERT
launched, it was used in 10% of all English queries but expanded to more
languages and used in almost all English queries early on. Today it is
used in most queries and is supported in all languages. BERT does
specifically help Google rank search results and is part of the ranking
algorithm.
Year Launched: 2019
Used For Ranking: Yes
Looks at the query and content language
Works for all languages but Google said BERT “plays critical role in
almost every English query”
Very commonly used for many queries
Here is an example provided by Google of how BERT is used, if you search
for “if you search for “can you get medicine for someone pharmacy,” BERT
helps us understand that you’re trying to figure out if you can pick up
medicine for someone else. Before BERT, we took that short preposition
for granted, mostly surfacing results about how to fill a prescription,”
Google told us.
MUM. MUM, Multitask Unified Model, is Google’s most recent AI in search.
MUM was introduced in 2021 and then expanded again at the end of 2021
for more applications, with a lot of promising uses for it in the
future. Google told us that MUM helps Google not just with understanding
languages but also generating languages, so it can be used to understand
variations in new terms and languages. MUM is not used for any ranking
purposes right now in Google Search but does support all languages and
regions.
Year Launched: 2021
Used For Ranking: No
Not query or languages specific
Works for all languages but Google not used for ranking purposes today
Used for a limited number of purposes
Currently, MUM is used to improve searches for COVID-19 vaccine
information, and Google said it is “looking forward to offering more
intuitive ways to search using a combination of both text and images in
Google Lens in the coming months.”
AI used together in search but may be specialized for search verticals
Danny Sullivan from Google also explained that while these are
individual AI-based algorithms, they often work together to help with
ranking and understanding the same query.
Google told us that all of these AI systems “are used to understand
language including the query and potentially relevant results,” adding
that “they are not designed to act in isolation to analyze just a query
or a page.” Previously, it may have been assumed and understood that one
AI system may have looked more at understanding the query and not the
content on the page, but that is not the case, at least not in 2022.
Google also confirmed that in 2022 RankBrain, neural matching, and BERT
are used globally, in all languages that Google Search operates in.
And when it comes to web search versus local search versus images,
shopping and other verticals, Google explained that RankBrain, neural
matching, and BERT are used for web search. Other modes or verticals of
Google Search such as images or shopping mode use separate, specialized
AI systems, according to Google.
What about core updates and AI
As explained above, Google uses RankBrain, neural matching, and BERT in
most queries you enter into Google Search, but Google also has core
updates. The Google broad core updates that Google rolls out a few times
per year is often noticed by site owners, publishers, and SEOs more than
when Google releases these larger AI-based systems.
But Google said these all can work together, with core updates. Google
said these three, RankBrain, neural matching, and BERT are the larger AI
systems they have. But they have many AI systems within search and some
within the core updates that Google rolls out.
Google told us they do have other machine learning systems in Google
Search. “RankBrain, neural matching, and BERT are just some of our more
powerful and prominent systems,” Google said. Google added, “there are
other AI elements that can impact core updates that don’t pertain to
those specific three AI systems.”
And then there is this;
https://searchengineland.com/google-search-testing-ai-mode-451672
Google Search testing ‘AI Mode’
Google is reportedly internally testing a new feature called 'AI Mode',
which is a new way to ask open-ended and exploratory questions.
Barry Schwartz on February 6, 2025 at 3:21 pm | Reading time: 2 minutes
Chat with SearchBot
Google is reportedly testing a new search feature named “AI Mode.” AI
Mode in Google seems like a new way to search by asking more open-ended
and exploratory questions, with the option to follow up.
What is AI Mode. AI Mode is described as “Search intelligently
research[ing] for you – organizing information into easy-to-digest
breakdowns with links to explore content across the web,” according to a
leaked email obtained by 9to5Google.
AI Mode is more for open-ended and exploratory questions that may not be
served well by what we see today from the Google Search results. This
includes queries that ask Google Search for advice and comparisons, as
well as exchanges that allow for follow-up questions.
It uses Gemini 2.0, according to the details.
What it looks like. Here is a screenshot obtained by 9to5Google:
Google Search AI Mode Dogfood Cover
Types of queries. Here is a list of the types of queries AI Mode is good
at answering:
“How many boxes of spaghetti should I buy to feed 6 adults and 10
children, and have enough for seconds?”
“Compare wool, down, and synthetic jackets in terms of insulation, water
resistance, and durability”
“What do I need to get started with aquascaping?”
Follow-up: “What are some nearby stores to buy supplies?”
More. We first learned about hints of AI Mode back in early December
with through an Android APK finding, in the code of the app. Android
Authority than shared some screenshots of AI Mode buttons.
I do wonder if the AI Overview comparison view is part of this or not.
Why we care. AI Mode might be coming to the Google Search apps and maybe
somehow directly to Google Search. This may impact how people consume
search results and ultimately click to your site.
These AI Overviews are already having a very negative impact on
click-through rates from Google to your site. Will AI Mode make it even
worse?
--
Xeno
Nothing astonishes Noddy so much as common sense and plain dealing.
(with apologies to Ralph Waldo Emerson)