10 Everyday Machine Learning Use Cases
Key Points
- Machine learning (ML), a broader field than generative AI, is already integral to daily life and is projected to become a $200 billion industry by 2029.
- Natural language processing (NLP) powers chatbots for customer service, voice assistants like Siri and Alexa, and automatic transcription in platforms such as Slack and YouTube.
- Mobile applications leverage ML for personalized recommendations (e.g., Spotify, LinkedIn) and on‑device tasks like computational photography, facial unlock, and image classification to organize photo libraries.
- ML models run directly on smartphones, enabling real‑time, privacy‑preserving processing without needing cloud resources.
- Financial institutions use ML and deep‑learning classifiers to monitor the roughly 150 million daily credit‑card transactions in the U.S., flagging suspicious activity for fraud detection at a scale impossible for manual review.
Full Transcript
# 10 Everyday Machine Learning Use Cases **Source:** [https://www.youtube.com/watch?v=CiSaY2xl9V4](https://www.youtube.com/watch?v=CiSaY2xl9V4) **Duration:** 00:07:01 ## Summary - Machine learning (ML), a broader field than generative AI, is already integral to daily life and is projected to become a $200 billion industry by 2029. - Natural language processing (NLP) powers chatbots for customer service, voice assistants like Siri and Alexa, and automatic transcription in platforms such as Slack and YouTube. - Mobile applications leverage ML for personalized recommendations (e.g., Spotify, LinkedIn) and on‑device tasks like computational photography, facial unlock, and image classification to organize photo libraries. - ML models run directly on smartphones, enabling real‑time, privacy‑preserving processing without needing cloud resources. - Financial institutions use ML and deep‑learning classifiers to monitor the roughly 150 million daily credit‑card transactions in the U.S., flagging suspicious activity for fraud detection at a scale impossible for manual review. ## Sections - [00:00:00](https://www.youtube.com/watch?v=CiSaY2xl9V4&t=0s) **Everyday Machine Learning Use Cases** - The speaker outlines how machine learning, beyond generative AI, powers everyday applications such as chatbots, voice assistants, transcription services, and music recommendation engines, highlighting its growing economic impact. ## Full Transcript
everybody is talking about generative AI
but gen AI is a subset of the larger
field of machine learning and I'm going
to give you 10 use cases of how machine
learning or ml is used today in everyday
life and by Machine learning I'm talking
about these sub field of artificial
intelligence in which machines learn
from data sets and past experiences by
recognizing patterns and generating
predictions now now machine learning is
projected to become a $200 billion
industry by
2029 but it's already very much here
today so let's get into it now one
aspect of machine learning that's seen
huge utility is NLP or natural language
processing that's the ability for
machines to make sense of the
unstructured mess that we like to call
human language so use case number one
is customer service text based queries
can be handled by chatbots which act as
virtual agents that many businesses
provide on their e-commerce sites the
chatbots can resolve many queries
themselves and where they can't they can
routes customers to where they can find
the appropriate help from a human
customer service
representative ml also Powers voice
assistance things like Siri and Alexa
where first speech to text and then NLP
machine learning models help understand
a spoken command that same capability is
used by services like slack and YouTube
to power Auto transcription of spoken
words in video content now number three
is ML and mobile apps where would we be
without spotify's ml models for
generating song recommendations or
linkedin's use of ml to make employment
suggestions your phone is likely filled
with apps that call out to Services
running machine learning models and
actually ml in smartphones really
deserves its own category because with
the power of modern smartphones some of
that machine learning is performed
directly on the device such as
computational photography to generate
background blur and your selfie shots or
unlocking your phone with facial
recognition or onboard device image
classification models that help you to
search your photo library like that time
I was trying to find this picture of my
cat where he jumped into the dryer ml
helped me to find that without me
spending a ton of time scrolling through
my photos
app hey the dry wasn't actually on now
now that's an example of a needle in a
hyack problem thousands of images and
there's only one I'm looking for which
in a way is is similar to use case
number five that is financial
transactions now in the us alone there
are
150 million credit card transactions
every day and the vast majority of those
are legitimate how to detect the
fraudulent ones well ML and deep
learning are widely used in fraud
detection where financial institutions
train ml models and classification
algorithms to rec ize suspicious online
transactions and flag them for further
investigation 150 million credit card
transactions every day is
1,739 every second so this is a task
that would be near impossible to perform
manually well did you also know that
between 60 and 73% of all Stock Market
trading is conducted by ml algorithms
and that percentage is increasing every
year all right let's quickly knock out a
couple more so ml is used frequently
in cyber security reinforcement learning
uses ml to train models to identify and
respond to cyber attacks and detect
intrusions ml informs a lot of our
transportation these days for instance
Google Maps uses ml algorithms to check
current traffic conditions and determine
the fastest route and right sharing apps
like uber and lift use ml to match
Riders to the drivers and ml plays a
large role in filtering email messages
as well through classification of
incoming messages and autocomplete
responses now number nine that's Health
Care this is one example where machine
learning can help augment and speed up
human capabilities now it's estimated
that doctors evaluating mammograms Miss
between 30 to 40% of cancers and the
rate of false positives is even higher
ml is already helping here where pattern
recognition models are trained to
classify tumors that are hard to see
with a human eye this is increasing not
only the accuracy of interpreting
Radiology Imaging but it's also
increasing the reading time of
Radiologists allowing them to focus
their attention on the more suspicious
examinations flagged by the ml models
there are also ml successes in early
lung cancer screening and finding bone
fractures okay one last one and and a
question for you in general which
department in an organization uses Ai
and machine learning the most well
according to Forbes it is the marketing
and sales department marketers use ml
for lead generation data analytics and
search engine optimization and they
often build on top of existing ml models
so for example consider how
recommendation algorithms like those at
net nflix make TV and movie suggestions
as to what to watch next based on your
derived tastes and interests well the
marketing and sales department can use
those same ml models for targeted
personalized marketing campaigns
tailored to those very same tastes and
interests look we we hear so much these
days about the future of AI and in
particular a GI artificial general
intelligence that will one day match and
surpass the intelligence of humans but
but right now that level of AI doesn't
exist it's theoretical but machine
learning that's AI that is already here
and it really is very much part of our
everyday
lives if you have any questions please
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