Machine learning in the cloud
Summary
TLDRThe video script explores the challenges of teaching computers to understand the complex, messy world as humans do. It discusses the evolution of artificial intelligence, machine learning, and deep learning, emphasizing their applications in everyday life like Google Maps and search recommendations. The script highlights the importance of training data in machine learning, the adaptability of algorithms across different use cases, and the ease of entry into the field due to advancements in technology. It also introduces Google Cloud's machine learning options, including BigQuery ML, AutoML, custom training, and pre-built APIs.
Takeaways
- 🌐 The world is complex for computers, which lack the intuitive understanding humans have for everyday objects.
- 💡 Early computing relied on logic, but real-world challenges require systems that can learn from their environment.
- 🤖 Artificial Intelligence (AI) aims to create machines capable of learning from their surroundings and improving over time.
- 📈 There are various approaches to AI, including pattern recognition, neural networks, reinforcement learning, and statistical inference.
- 🔄 Progress in AI is tied to advancements in technology, such as increased computing power and larger data sets.
- 🌟 AI applications are ubiquitous, often rebranded once they become commonplace, like search recommendations and language translation.
- 📊 Machine Learning (ML) is a subset of AI that uses data examples to enable machines to learn and improve without explicit programming.
- 📚 Training an ML model requires a dataset with inputs and corresponding labels to teach the model to recognize patterns.
- 🔍 Deep Learning is a subset of ML that adds layers for more complex learning, particularly useful for unstructured data like images and speech.
- 🔧 Algorithms or ML models are standardized and can be applied to different use cases after being trained on specific data.
- 🌉 Google Cloud provides multiple options for building ML models, including BigQuery ML, AutoML, custom training, and pre-built APIs.
Q & A
What is the difference between machine learning and traditional computing logic?
-Traditional computing relies on hard logical rules, while machine learning allows computers to learn from data, examples, and mistakes, rather than being explicitly programmed for every possible scenario.
How has machine learning improved the things we use daily, like Maps or search engines?
-Machine learning enhances tools like Maps and search engines by allowing them to learn from vast amounts of data. This enables features such as real-time traffic updates, more accurate search results, and personalized recommendations.
What is the significance of data in machine learning models?
-Data is crucial in machine learning because it helps train the models. A machine learning model is only as good as the data it learns from, requiring large amounts of high-quality, labeled examples to make accurate predictions.
How does deep learning differ from other forms of machine learning?
-Deep learning is a subset of machine learning that uses neural networks with multiple layers, enabling it to work with unstructured data like images, speech, or natural language, while other machine learning methods often handle structured data.
What role does artificial intelligence (AI) play in modern technology?
-AI allows machines to mimic human intelligence and make decisions based on patterns in data. It powers a wide range of technologies, from anti-lock braking systems to email spam filters, and translation services.
How do neural networks help machines recognize images or objects?
-Neural networks learn to recognize images by processing input through multiple layers, starting with simple patterns like edges and colors, and gradually building towards identifying objects such as cats or dogs.
What are some common methods used in AI and machine learning today?
-Common methods include pattern recognition, artificial neural networks, reinforcement learning, statistical inference, supervised learning, and unsupervised learning. Each method serves different purposes and can be combined for better results.
Why has machine learning made significant progress in recent years?
-Machine learning has advanced due to improvements in technology, including faster computers, larger datasets, and better algorithms, allowing for more complex models and solutions at scale.
What are some tools Google Cloud provides for building machine learning models?
-Google Cloud offers tools like BigQuery ML for SQL-based models, AutoML for no-code solutions, custom training for coding flexibility, and pre-built APIs for using pre-trained machine learning models.
Why don’t machine learning models use logical rules like traditional programming?
-Machine learning models rely on patterns and data to learn and make decisions. They use functions, not logical rules, to differentiate between categories in data, such as images, making them adaptable to different problems.
Outlines
🌐 Understanding the Messy World Through Machine Learning
This paragraph introduces the challenges computers face in understanding the complex and nuanced world around us, which humans can easily interpret. It discusses how machine learning is being used to improve various technologies such as Google Maps, search recommendations, video suggestions, and translations. The video 'Making Sense of a Messy World' is referenced, where Google engineers and researchers explain the evolution of computing from logic-based to machine learning-based approaches. The narrative emphasizes the shift from hard logical rules to learning systems that can adapt and improve over time, touching on various subfields like pattern recognition, artificial neural networks, reinforcement learning, and probabilistic machine learning. The importance of computational power and large data sets for advancing AI is highlighted, along with the idea that intelligence in machines is not a singular achievement but a collection of capabilities working together.
🤖 The Evolution of Machine Learning and Deep Learning
This section delves into the definitions and differences between artificial intelligence (AI), machine learning, and deep learning. AI is presented as an overarching term for technologies that mimic human intelligence, while machine learning is described as a subset that uses data examples to enable computers to learn without explicit programming. Deep learning is introduced as a subset of machine learning that adds layers for more profound learning, especially useful for unstructured data like images and speech. The paragraph explains how machine learning models are trained using examples, with inputs and corresponding labels, and how the quality of the training data directly impacts the model's performance. It also touches on the standardization of machine learning algorithms, which can be applied to various use cases after being trained on specific data sets. The excitement around machine learning is attributed to the lowered barriers to entry due to increased data availability, algorithm sophistication, and computing power.
🎨 Interactive Learning with Neural Networks
The final paragraph focuses on practical applications of machine learning, specifically mentioning Google Cloud's machine learning options. It outlines four approaches: BigQuery ML for SQL-based machine learning, AutoML for no-code model creation, custom training for tailored machine learning environments, and pre-built APIs for utilizing existing models. The paragraph invites viewers to participate in a doodling game at quickdraw.withgoogle.com, which contributes to a doodling data set for machine learning research. It also introduces deep neural networks (DNNs) and how they mimic the human brain's perception process, learning from basic visual elements to complex decisions. The TensorFlow Neural Network Playground is mentioned as a resource for understanding how these models work, highlighting the progress in machine learning that allows for scalable model building, exemplified by Google Photos.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Machine Learning
💡Deep Learning
💡Pattern Recognition
💡Neural Networks
💡Data
💡Training
💡Algorithms
💡BigQuery ML
💡AutoML
💡Custom Training
💡Pre-built APIs
Highlights
The world is often messy and complicated for computers.
Machine learning is improving computers' ability to understand the world.
Machine learning enhances applications like Maps, search, video recommendations, and translations.
Early computing relied on logic, but real-world challenges require learning systems.
Artificial intelligence aims to build machines that learn from their environment.
There is no single 'right path' in artificial intelligence; various approaches are being explored.
The progress in AI depends on advancements in technology and data availability.
Artificial intelligence is now integrated into everyday applications, often without us realizing.
Intelligence in machines requires training with examples.
Machine learning models learn from a dataset of inputs and their corresponding labels.
The quality of machine learning models is directly tied to the quality of the training data.
Machine learning algorithms can be applied to various use cases once trained.
Deep learning is a subset of machine learning that works with unstructured data.
Image classification is a common application of deep learning.
Machines become intelligent through training and do not start out intelligent.
Google Cloud offers various options for building machine learning models.
BigQuery ML allows for creating and executing machine learning models using SQL queries.
AutoML provides a no-code solution for building machine learning models.
Custom training offers flexibility and control over the machine learning pipeline.
Pre-built APIs allow the use of machine learning models already trained by Google.
The barriers to entry for machine learning have fallen due to increased data, mature algorithms, and computing power.
Deep neural networks mimic the human brain's perception of stimuli.
The TensorFlow Neural Network Playground is an interactive tool for understanding machine learning models.
Transcripts
the world is filled with things that
most of us can understand and react to
without much thought for example a stop
sign partially covered by snow is still
a stop sign and a chair that's five
times bigger than usual is still a place
to sit
but for computers which don't have the
benefit of growing up and learning the
nuances of these objects the world is
often messy and complicated
in this first section you'll start by
watching a video called making sense of
a messy world where Google engineers and
researchers discuss how machine learning
is improving computers and many of the
things we use them for such as Maps
search recommending videos and
translations
let's watch this short video now
yeah thank you
[Music]
there have been a number of shifts in
the way we think about computing
over the past few decades the
terminology artificial intelligence has
come in and out of favor in the
scientific Community sometimes it's
called machine learning we tend to call
it machine intelligence these days I
just call it intelligence and sometimes
it's just the effort to build machines
that are better
so in the early days everything was
built on logic
doing mathematical integration problems
playing chess
but we realized that what the real
challenges were were the things that
people can do every day the real world
is actually very messy
hard logical rules are not the way to
solve really interesting real world
problems you have to have a system
that'll learn to get the knowledge in
you can't just program it all in
artificial intelligence is an effort to
build machines that can learn from their
environment from mistakes and from
people we're still at the stage where we
don't know what is the right path and
the right breakthrough so I mean there's
certainly a whole raft of different
approaches one of the subfields we call
pattern recognition artificial neural
network reinforcement learning for
example statistical inference and
probabilistic machine learning
supervised learning unsupervised
learning and we're not quite sure what
technique is going to lead to better
systems and in fact it's probably not
one technique for everything it's
probably a bunch of different techniques
and combinations of those techniques any
progress we make in building truly
intelligent systems is going to depend
on progress in technology generally and
until recently we didn't have computers
that were fast enough or data sets that
were big enough to to do that and so
being able to take a particular problem
and spread it out over lots and lots of
machines is a very important approach
because it makes our research faster
so there's applications of artificial
intelligence around us all the time when
it begins to work or it does work it's
all of a sudden given another name we're
all already using it and very
comfortable with it things that now we
regard as routine
um 30 years ago would have been regarded
as amazing examples of artificial
intelligence anti-lock braking autopilot
systems for planes search
recommendations Maps
to decide whether or not this particular
email is Spam or not spam
the ability to translate one language to
another with your phone
ten years ago if you tried to talk to
your computer or to your phone you know
that would just be hopeless we we're
seeing a steady torrent of these tricks
one after the other getting figured out
right now and I think a lot of people
that are close to the field have this do
have that kind of breathless sense that
things are moving quickly it's a
progressive thing it's about building
things that are slightly better slightly
better slightly better intelligence is
really not going to be something that we
ever succeed in defining in a succinct
and singular way it's really this whole
constellation of capabilities that you
know all kind of are beautifully
orchestrated and working together
predicting the long-term future is very
difficult
nobody can really do it
and the bad thing to do is take
whatever's working best now
and assume the future is going to be
like that forever
[Music]
in that video you heard a few different
definitions of artificial intelligence
and machine learning let's explore the
differences together
artificial intelligence or AI is an
umbrella term that includes anything
related to computers mimicking human
intelligence
for example in an online word processor
robots perform human actions for
spelling and grammar checks
machine learning is a tool set like
Newton's Laws of mechanics
just as you can use Newton's laws to
learn how long it will take a ball to
fall to the ground if you drop it off a
cliff you can use machine learning to
solve certain kinds of problems at scale
by using data examples but without the
need for custom code
you might have also heard the term deep
learning or deep neural networks
deep learning is a subset of machine
learning that adds layers in between
input data and output results to make a
machine learn at more depth it's a type
of machine learning that works even when
the data is unstructured like images
speech video natural language text and
so on
image classification is a type of deep
learning a machine can learn how to
classify images into categories when
it's shown lots of examples
the basic difference between machine
learning and other techniques in AI is
that in machine learning machines learn
they don't start out intelligent they
become intelligent
so how do machines become intelligent
intelligence requires training
to train a machine learning model
examples are required
for example to train a model to estimate
how much you'll owe in taxes you must
show the model many many examples of tax
returns
or if you want to train a model to
estimate trip time between one location
and another you'll need to show it many
examples of previous Journeys
the first stage of ml is to train an ml
model with examples
an example consists of an input and the
correct answer for that input
this is called the label
in the case of structured data that's
rows and columns an input can simply be
a single row of data
in unstructured data like images an
input could be a single image of a cloud
that you want to classify as a rain
cloud or not
imagine you work for a manufacturing
company and you want to train a machine
learning model to detect defects in the
parts before they are assembled into
products
you'd start by creating a data set of
images of parts
some of those images would be good and
some parts would be defective
for each image you'll assign a
corresponding label and use that set of
examples to train the model
an important detail to emphasize is that
a machine learning model is only as good
as the data used to train it
and a good model requires a lot of
training data of historical examples of
rejected parts and parts in good
condition
with these elements you can train a
model to categorize Parts as defective
or not
the basic reason why ml models need high
quality data is because they don't have
human general knowledge data is the only
thing they have access to
after the model has been trained it can
be used to make predictions on data it's
never seen before
in this example the input for the
trained model is an image of a part
because the model has been trained on
specific examples of good and effective
Parts it can correctly predict that this
part is in good condition
algorithms or ml models are standard
that means that they exist independently
of the use case
although detecting manufacturing defects
in images and detecting disease leaves
and images are two different use cases
the same algorithm which is an image
classification network works for both
similarly standard algorithms predict
the future value of a Time series or to
transcribe human speech to text
resnet for example is a standard
algorithm for image classification
it's not essential to understand how an
image classification algorithm Works
only that it's the algorithm we should
use if we want to classify images of
automotive parts
when we use the same algorithm on
different data sets different features
or inputs are relevant to the different
use cases and we can see them
represented visually here
you might be wondering isn't the logic
different
you can't possibly use the same rules to
identify defects in manufacturing that
you use to identify leaves
the logic is different but machine
learning doesn't use logical rules
the image classification network isn't a
set of basic if this then that rules but
rather a function that learns how to
differentiate between categories of
images
so although you start with the same
standard algorithm after training the
trained model that classifies leaves is
different from the train model that
classifies parts
and you can actually reuse the same code
for other use cases that are focused on
the same kind of task
in our example you identified
manufacturing defects but the higher
level task classified images
this means you can reuse the same code
for another image classification problem
like finding examples of products in
photos posted on social media
however you still have to train it
separately for each use case
much of the excitement around ml is
because the barriers to entry have
fallen you don't need to be an
astrophysicist to do machine learning
this is because of the convergence of
several factors
the increasing availability of data
the increasing maturity and
sophistication of ml algorithms
and the increasing power and
availability of computing hardware and
software
Google Cloud offers four options for
building machine learning models
the first option is bigquery ml you'll
remember from the previous module of
this course that bigquery ml is a tool
for using SQL queries to create and
execute machine learning models in
bigquery if you already have your data
in bigquery and your problems fit the
predefined ml models this could be your
best choice
the second option is automl which is a
no code solution so you can build your
own machine learning models on vertex AI
through a point-and-click interface
the third option is custom Training
through which you can code your own
machine learning environment the
training and the deployment which
provides you with flexibility and
control over the ml pipeline
and finally there are pre-built apis
which are application programming
interfaces
this option lets you use machine
learning models that have already been
built and trained by Google so you don't
have to build your own machine learning
models if you don't have enough training
data or sufficient machine learning
expertise in-house
let's play a quick game where we'll see
how a neural network can learn how to
recognize doodling
during the process we'll help to teach
it by adding our drawings to the world's
largest doodling data set and help with
machine learning research
pause the video now and in a different
browser tab or window head to
quickdraw.withgoogle.com when you're
done head back here to continue the
video
here's the model behind the game we just
played it's called a deep neural network
or DNN
the layers are meant to mimic our own
human brains in the way we perceive
stimuli
with each layer the model learns more
about the image of this dog that's
hiding in a laundry basket starting from
basic edges and colors to a final
decision of cat or dog you'll build your
own image recognition model later in the
course spoiler alert you don't need to
write any code but if you want
additional information about how these
model types work see the tensorflow
neural network playground
the tensorflow neural network playground
at
playground.tensflow.org is a great
Interactive Learning tool for
understanding how computers think and
shows how far ml has come that we can
build these models at scale like in
Google photos
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