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Devansh- Machine Learning Made Simple
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Apr 14

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THE TRUTH BEHIND GOOGLE’S MACHINE LEARNING RESEARCH


A LOT OF PEOPLE HAVE BEEN FOCUSING ON THE WRONG ASPECTS

The last 2–3 weeks have been insane for Deep Learning. GPT3 can now edit lines
of text and change given pieces of code/text to match styles (or even into other
programming languages). Google has released another massive model, PaLM, which
can explain jokes (and more). Meanwhile, DALL-E 2, another large language model
based on transformers, can now take text input and generate images based on that
input.


The number of different tasks that an AI would have to do to be successful in
this task. Google really pushed boundaries with this one.

With all the boundaries being pushed, it is easy to give in to the hype, and
fall for the narratives spread constantly (“AGI s here”, “Big Tech will replace
all our jobs with Robots” etc). In this article, I will analyze the Machine
Learning Research done by Google, and contextualize it with respect to how
Google will use this research to actually make money in the future.


SOME CONTEXT

If you just casually go over the content of Google’s AI research, it won’t
really make sense. The company seems to be involved in everything under the sun,
from Computer Vision, Math solver, Neural Architecture Search, Deep Learning
Theory and so much more (we’ve covered all of this on my YouTube and Medium, so
make sure you’re connected with me to not miss out).


I will cover this research in my upcoming YouTube video- “State of Machine
Learning, March 2022”.

And last month, they created an absolutely mammoth protein classification
project that could classify a protein correctly out of 18,000 labels (and many
other accomplishments).

Their projects have been exceptional, and no doubt have progressed the field of
Machine Learning research, but you might be wondering, “What was the point?”.
Google is a for-profit company, so they invest in all of this? What are they
hoping to gain from this? Is there a method to their madness, that we can
decipher?


WHY COMPANIES SPENT BILLIONS ON DEEP LEARNING

Let’s think about how Google makes money. It’s easy to say ads. And that is
true. They use all the user data collected through YouTube, Maps, Search,
Travel, App Store, and their other services to build up a user profile. Then let
businesses and users advertise get to advertise to very specific profiles. This
is a much higher ROI than the traditional ads like banners and commercials
because they reach people who would need something. Google knows I’m a digital
nomad (through my searches, purchases, and constant location change). So I get a
lot of ads for hotels, traveling services, online services, etc. As a YouTuber,
I also get tons of ads for digital assistants, cameras, courses, etc. All of
these are products that I am more likely to buy when compared to others.


As we see the rise in the Metaverse etc, we will see this amount rise.

To do so, we have to be able to get information from large amounts of data. This
is where Machine Learning shines. It takes giant amounts of data and provides us
with some interesting insights. Coming up with these insights is quite literally
my full-time job. The reason companies pay so much for this service is simple,
Machine Learning operates at scale. If you can make decisions 1% better, the
return becomes massive after 1000 decisions (1.01¹⁰⁰⁰=21,000). And Google’s AI
is taking trillions of decisions regularly.


They have been able to grow because of their constant ventures (android, GCP
etc.)

It would be easy for me to end things here and call it a day. And worse analysis
platforms would do that. But my regular readers have come to expect better. And
that is what I shall give you. So, what exactly is Google hoping to accomplish
with its projects?


High-quality analysis only. No propaganda or clickbait. Meta Article shared at
the end, so keep reading.


DECIPHERING A TREND

What do these projects have in common (aside from the fact that Google sponsored
them):

 1. AlphaGo: A reinforcement-learning-based AI agent that could play the
    notoriously hard Go. It beat the World’s best, shaking up the community.
 2. AlphaCode: An algorithm that could read question prompts given in
    competitive coding challenges and actually solve them. Slightly better than
    competitive coders can.
 3. A Recurrent Neural Network based AI agent that could create new activation
    functions for deep learning. Extremely well. Read more about it here.
 4. Multiple iterations of AI, all aimed at cracking the Neural Architecture
    Search problem (automating the creation of neural networks).

Take a second to think about it. Don’t scroll down to check immediately, that’s
no fun.


No cheating.

The answer is that they all solve search problems. Number 1 was able to go
through many possible moves to find the best one. AlphaCode generated and tested
code samples relevant to the problem. To do so it would have to effectively
search over 20,000 choices. Number 3 searched through solution spaces for good
activation functions, and 4 searched for good Neural Network configurations.


If you know the word for this large number, drop it in the comments below.

I’m not making this up. These were all framed as search problems in their
formulations (I looked into all the papers). If you are interested in learning
about the technical details, make sure you use the links at the end to connect
with me on different platforms (especially LinkedIn, YouTube, and Medium). I
won’t go beyond this here since this article will get too long.

So how do these various search problems help with Google’s Business Model?
Google has already dominated the search engine, smartphone, and many other
important data markets. So it’s not as though they have competition. Time for
the final section.


GOOGLE’S MASTERPLAN

Google has data. Lots of it. And they already have very refined AI systems that
ensure that advertisers and businesses keep bringing Google their dollars. So
what is Google trying to accomplish?

There are two ways that a company can boost profits. They can either boost their
revenue or reduce operational costs. At this point, adding to their ML will not
help them get more customers. All the big businesses are already advertising on
Google products either way. So the marginal utility in customer acquisition will
be minimal.


Most sales/marketing is based on templates. With Deep Learning, these templates
can get very specific.

However, that doesn’t tell the full story. As they continue to refine different
search algorithms they will be entering the next frontier of advertising:
hyper-specific advertising. This will be a level of personalization of ads never
possible without extreme investment and very refined AI. They will be able to
create user profiles at a level of detail impossible before. To do so, the
ability to search through large amounts of data and come up with combinations of
characteristics will be crucial. Doesn’t that sound like the search problems we
discussed?

Google would be able to cater to their customers at an unprecedented level of
efficiency, which ultimately would allow them to bring in more revenue.

Another move that this would facilitate would be an entry into the consulting
market. As a creator, and someone involved in a startup, I know how much work
things like Customer Discovery and Market Research are. With all the data and
insights Google generates, is not farfetched to see how they could use their
analysis to consult businesses. Such consultations will give them very
fine-grained data by working with different customers, allowing them to refine
their products. This would start a positive reinforcing loop between the core
business and the consulting verticle.


Google is already establishing itself in the Platform As a Service business.

The SAAS (Software as a Service) market would be another such avenue. Google
could start packaging and selling the insights as different scripts so that
businesses could leverage insights without paying for consultations. Google’s
investments into BigQuery and constant refinement of their analytics platform
hint that they are looking at this avenue.


These are all things I have done. Google’s resources will allow them to do it at
a very high level.

However, it doesn’t end here. Google can also use search to reduce operational
costs. This is actually something that I’ve worked on personally. We frame the
system (or aspects of a system) in various ways, and then use AI to highlight
possible areas of redundancy and find the least/most important aspects.
Companies can then figure out how to handle this waste. At Google’s scale, they
will be able to run these searches on complex multi-objective functions. This
would require the development of very advanced search algorithms that can
traverse various search spaces. And shockingly, Google is doing exactly this.


SO HOW CAN YOU USE THIS TO BENEFIT YOURSELF?

To maximize your chances of leveraging this information, you need to (very
ironically) get into the basics. Solidify your Math, theoretical knowledge,
ability to frame ambiguous situations, and coding skills. These core skills will
allow you to tackle problems at scale (which is what Google is doing). This
article will tell you how to get into the Deep Learning field, step-by-step. If
you liked this article, check out my article on Facebook’s AI research, where I
conduct a similar analysis.



For Machine Learning, a base in Software Engineering is crucial. It will help
you conceptualize, build, and optimize your ML. My daily newsletter, Coding
Interviews Made Simple covers topics in Algorithm Design, Math, Recent Events in
Tech, Software Engineering, and much more to make you a better developer. I am
currently running a 20% discount for a WHOLE YEAR, so make sure to check it out.



To help me write better articles and understand you fill out this survey
(anonymous). It will take 3 minutes at most and allow me to improve the quality
of my work.

Feel free to reach out if you have any interesting jobs/projects/ideas for me as
well. Always happy to hear you out. Reach out to me on LinkedIn, Instagram, or
Twitter.

For monetary support of my work following are my Venmo and Paypal. Any amount is
appreciated and helps a lot. Donations unlock exclusive content such as paper
analysis, special code, consultations, and specific coaching:

Venmo: https://account.venmo.com/u/FNU-Devansh

Paypal: paypal.me/ISeeThings



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