Genius Machine Learning Advice for 11 Minutes Straight
Summary
TLDRThe speaker emphasizes the importance of not blindly collecting more data in machine learning, suggesting that testing and modifying algorithms can be more effective. They highlight the need for systematic debugging and the value of hands-on learning in programming. The speaker advocates for sustained effort over time, the significance of understanding one's learning style, and the belief in the '10,000-hour rule' for expertise. They also discuss the maturation of AI, the importance of creating over consuming, and the power of deep learning despite initial skepticism. The talk concludes with advice for young people to find their passions, understand themselves, and the potential of cost functions in machine learning.
Takeaways
- 🔍 **Efficient Debugging**: The speaker emphasizes the importance of quickly identifying when collecting more data isn't the solution to a problem, suggesting that modifying the architecture or trying different approaches can be more effective.
- 🚀 **Expertise in Machine Learning**: Being adept at debugging machine learning algorithms can significantly speed up the process of getting a model to work, with experts being 10x to 100x faster than others.
- 🤔 **Systematic Problem Solving**: When faced with issues in machine learning, it's crucial to ask the right questions and systematically try different solutions like changing the architecture, adding more data, or adjusting the optimization algorithm.
- 💡 **Learning by Doing**: The speaker advocates for learning programming through hands-on experience, suggesting that one should not be afraid to get hands-on and dirty when trying to solve problems.
- 📚 **Deep Understanding Through Struggle**: Encourages spending time struggling with problems to learn more, rather than immediately seeking answers through quick searches, which can hinder the learning process.
- 📈 **Consistent Effort Over Time**: Success in fields like programming and machine learning comes from consistent effort over a long period, not from sporadic all-nighters or bursts of work.
- 📝 **Handwritten Notes for Retention**: The act of taking handwritten notes is recommended for better knowledge retention and understanding, as it requires recoding information in one's own words.
- 🤖 **Practical Application of AI**: When considering the application of AI, start with a specific problem rather than a general desire to use the technology, ensuring that the solution is targeted and effective.
- 🌟 **The Value of Creation**: The speaker promotes the idea of creation over consumption or redistribution, arguing that creating something new is more satisfying and fulfilling.
- 🧠 **Long-term Learning and Passion**: Encourages focusing on long-term learning and finding one's true passions, which can lead to a deeper understanding and more significant contributions in a field.
Q & A
Why do some engineers spend six months on a direction that might not be fruitful?
-Some engineers spend six months on a direction like collecting more data because they believe more data is valuable. However, they might not realize that the problem at hand might not benefit from more data, and a different approach, such as modifying the architecture, could be more effective.
How can one become proficient at debugging machine learning algorithms?
-Becoming proficient at debugging machine learning algorithms involves systematic questioning, such as why a model isn't working and what changes could be made, like altering the architecture, adding more data, adjusting regularization, or changing the optimization algorithm.
What is the best way to learn programming according to the transcript?
-The best way to learn programming is by doing it, not just watching videos. One should start a project, face challenges, and try to solve them without immediately seeking answers online, which allows for deeper learning and understanding.
What does 'getting your hands dirty' mean in the context of learning programming?
-'Getting your hands dirty' means spending time deeply engaged in trying to solve problems on your own, such as debugging a network that isn't converging, without immediately turning to Google for answers.
Why is sustained effort over time more valuable than sporadic all-nighters?
-Sustained effort over time is more valuable because it allows for consistent progress and learning, whereas all-nighters are not sustainable and can lead to burnout. Regular engagement with the material leads to better retention and understanding.
How does the concept of the '10,000 hours' theory apply to becoming an expert in a field?
-The '10,000 hours' theory suggests that spending a significant amount of time (10,000 hours) deliberately practicing and working on something can lead to expertise. It emphasizes the importance of consistent effort and time investment over natural talent.
What is the benefit of taking handwritten notes while studying?
-Taking handwritten notes increases retention by forcing you to recode the knowledge in your own words, which promotes long-term retention and understanding.
Why should one consider creating over consuming or redistributing?
-Creating is more satisfying and fulfilling than consuming or redistributing. It allows for the development of new ideas and innovations, which can have a more significant and lasting impact.
What advice is given for those interested in a career in AI?
-For those interested in a career in AI, the advice is to start with a clear goal of what you want to achieve with AI, such as creating a machine that can perform a task not currently possible, and then work backward to identify the steps and research needed to achieve it.
How has the perception of large neural networks changed over time?
-Initially, large neural networks were underestimated and not believed to be trainable. However, with the availability of large amounts of supervised data and computational power, along with the conviction that they could work, they have become successful and are now a cornerstone of deep learning.
Why are cost functions important in machine learning?
-Cost functions are important in machine learning because they provide a measurable way to assess the performance of a system. They allow for the optimization of models by minimizing or maximizing a specific measure, which is crucial for training and improving machine learning algorithms.
Outlines
🔍 The Importance of Debugging in Machine Learning
The speaker emphasizes the importance of debugging in machine learning, suggesting that engineers often waste time collecting more data without testing whether it will be beneficial. They argue for a more iterative approach, adjusting the architecture or trying different strategies rather than blindly collecting data. The speaker also highlights the value of those who are skilled at debugging machine learning algorithms, as they can be significantly faster at getting systems to work. They stress the importance of asking the right questions to avoid going down unproductive paths and suggest that learning programming is an active process that involves hands-on experience and persistence, rather than passive observation.
💡 Pursuing Passion and Understanding Oneself for Success
The speaker advises young individuals to discover their true passions by exploring various fields and to understand their own optimal working methods and personal strengths. They advocate for a deep understanding of foundational subjects like quantum mechanics and statistical physics, which have broad applications. The speaker also discusses the underestimated potential of deep learning before its success, highlighting the need for data, computational power, and conviction in the approach. They suggest reimplementing concepts at different levels of abstraction to deepen understanding and encourage setting ambitious goals in AI research, focusing on real-world applications rather than just improving on existing metrics.
🚀 Embracing Creation and Learning from Experience
The speaker shares personal advice on success, including the importance of self-belief, independent thinking, and risk-taking. They stress the value of hard work, boldness, and building a network. The speaker also reflects on their own approach to success, which involved ignoring advice and learning from their own experiences. They caution against blindly following advice from others, suggesting that individual paths to success can vary greatly.
Mindmap
Keywords
💡Machine Learning
💡Data Collection
💡Debugging
💡Architecture
💡Optimization Algorithms
💡Regularization
💡Programming
💡10,000 Hours Rule
💡Cost Functions
💡Deep Learning
💡Reinforcement Learning (RL)
Highlights
Engineers sometimes waste time pursuing unproductive directions like collecting more data, which could be better spent on modifying the architecture or trying new approaches.
Debugging machine learning algorithms effectively can lead to significant speed improvements, often by an order of magnitude.
The importance of asking the right questions when debugging, such as whether a model will eventually work and what changes could be tried.
Learning programming is best achieved through hands-on experience rather than just watching videos or tutorials.
The value of getting hands-dirty in programming, which means deeply engaging with problems and finding solutions independently.
The importance of sustained effort over time in learning and mastering a skill, such as reading research papers consistently.
The concept of the 10,000-hour rule in becoming an expert in a field through deliberate practice and effort.
The benefits of taking handwritten notes for better knowledge retention and understanding.
The humorous suggestion that machine learning should not be applied to macaroni and cheese production without a clear problem statement.
The advice to always choose creation over consumption or redistribution when given a choice, as it is more satisfying and impactful.
The maturity of the AI field and the growing responsibility to consider the impact of AI systems before releasing them.
The importance of learning foundational concepts with a long shelf life, such as quantum mechanics, over more transient skills.
Encouragement for the next generation to find their true passions and understand their optimal working methods.
The underestimated potential of deep learning before its success, highlighting the importance of conviction in the approach.
The suggestion to reimplement concepts at different levels of abstraction to deepen understanding.
The counterintuitive success of training large neural networks with relatively small amounts of data, defying traditional machine learning wisdom.
The role of cost functions in machine learning and their importance in measuring system performance.
Advice on how to be successful, including having self-belief, learning to think independently, and building a network.
Transcripts
I find that um even today unfortunately
there are Engineers that will spend 6
months trying to pursue a particular
direction uh such as collect more data
because we heard more data is valuable
but sometimes you could run some tests
could have figured out 6 months earlier
that for this particular problem
collecting more data isn't going to cut
it so just don't spend 6 months
collecting more data spend your time
modifying the architecture or trying
something else so is an evolving
discipline but I find that the people
that are really good at debugging
machine learning algorithms are easily
10x
maybe 100x faster at getting something
to work the often question is um why
doesn't it work yet or can I expect it
to eventually work uh and what are the
things I could try change the
architecture more data more
regularization different optimization
algorithm different types of data so to
answer those questions systematically so
you don't spend 6 months heading down
the blind alley before someone comes and
says why you spend six months doing this
we're never going to learn programming
by watching a video the only way to
learn programming I think and the only
one is the only way everyone I've ever
met who can program well learned it all
in the same way they had something they
wanted to do and then they tried to do
it and then they were like oh well okay
this is kind of you know it would be
nice if the computer could kind of do
this thing and then you know that's how
you learn you just keep pushing on a
project um so the only advice I have for
learning programming is go program don't
be afraid to get your hands dirty I
think that's the main thing so if
something doesn't work like really drill
into why things are not working can you
elaborate what your hands dirty means so
for example like if an algorithm if you
try to train a network and it's not
converging whatever rather than trying
to like Google the answer or trying to
do something like really spend those
like 5 8 10 15 20 whatever number of
hours really trying to figure it out
yourself cuz in that process you'll
actually learn a lot more Googling is of
course like a good way to solve it when
you need a quick answer but I think
initially especially like when you're
starting out it's much nicer to like
figure things out by yourself and I just
say that from experience because like
when I started out there were not a lot
of resources so we would like in the lab
a lot of us like we would look up senior
students and then the senior students
were of course busy and they would be
like hey why don't you go figure it out
because I just don't have the time I'm
working on my dissertation or whatever a
final PhD students and so then we would
sit down and like just try to figure it
out and that I think really helped me
it's often not about the birth of
sustain efforts and the all nighters
because you could only do that a limited
number of times it's the sustain effort
over a long time I think you know
reading two research papers is a nice
thing to do but the power is not reading
two research papers it's reading two
research papers a week week for a year
then you read a 100 papers and and you
actually learn a lot when you read the
100 papers beginners are often focused
on what to do and I think the focus
should be more like how much you do so I
I am kind of like believer on a high
level in this 10,000 hours kind of
concept where you just kind of have to
just pick the things where you can spend
time and you you care about and you're
interested in you literally have to put
in 10,000 hours of work it doesn't even
like matter as much like where you put
it and you're you'll iterate and you'll
improve and you'll waste some time but I
think it's actually really nice cuz I
feel like there's some sense of
determinism about being being an expert
at a thing if you spend 10,000 hours you
can literally pick an arbitrary thing
and I think if you spend 10,000 hours of
deliberate effort and work you actually
will become an expert at it one thing I
still do when I'm trying to study
something really deeply is uh take
handwritten notes we know that that act
of taking notes preferably handwritten
notes increases retention taking
handwritten notes it causes you to
recode the knowledge in your own words
more and that process of recoding
promotes long-term retention I heard
machine learning is important could you
help integrate machine learning with
macaroni and cheese production you just
I don't even you can't help these people
like who lets you run anything who lets
that kind of person run anything my
problem is not that they don't know
about machine learning my problem is
that they think that machine learning
has something to say about macaroni and
cheese production like I heard about
this new technology how can I use it for
why at least start with tell me about a
problem like if you have a problem
you're like you know some of my boxes
aren't getting enough macaroni in them
um can we use machine learning to this
problem that's much much better than how
do I apply machine learning to macaroni
and cheese you tweeted when you have the
choice between being a Creator consumer
or redistributor always go for creation
when you have the choice to create
something always go for creation it's so
much more satisfying and it also this is
what life is about I think the field of
AI has been in a state of childhood and
now it's exiting that state and it's
entering a state of maturity what that
means is that AI is very successful and
also very impactful and its impact is
not only large but it's also growing and
so for that reason it seems wise to
start thinking about the impact of our
systems before releasing them maybe a
little bit too soon rather than a little
bit too late try to get interested by
big questions things like what is
intelligence what is the universe made
of what's life all about things like
that like even like crazy big questions
like what's time like nobody knows what
time is and then learn basic things like
basic methods either from math from
physics or from engineering things I
have a long shelf life like if you have
a choice between learning uh you know
mobile programming on iPhone or quantum
mechanics take quantum mechanics because
you're going to learn things that you
have no idea exist you may never be a
Quantum physicist but you learn about
path integrals and path integrals are
used everywhere learn statistical
physics because all the math that comes
out for machine learning basically comes
out by statistical physicists in the you
know late 19 early 20th century right I
love giving talks to the Next Generation
what I say to them is actually two
things I think the most important things
to learn about uh and to find out about
when you're when you're young is what
are your true passions is first of all
there two things one is find your true
passions and I think the way to do that
is to explore as many things as possible
when you're young and you can take those
risks um I would also encourage people
to look at the finding the connections
between things uh in a unique way I
think that's a really great way to find
a passion second thing I would say
advise is know yourself so spend a lot
of time understanding how you work best
like what are the optimal times to work
what are the optimal ways that you study
what are your how do you deal with
pressure um sort of test yourself in
various scenarios and um try and improve
your weaknesses but also find out what
your unique skills and strengths are and
then hone those so then that's what you
will be your Super Value in the world
later on the key fact about deep
learning before deep learning started to
be successful is that it was
underestimated people didn't believe
that large neural networks could be
trained the ideas were all there the
thing that was missing was a lot of
supervised data and a lot of compute
once you have a lot of supervised data
and a lot of compute then there is a
third thing which is needed as well and
that is conviction conviction that if
you take the right stuff which already
exists and apply and mix with a lot of
data and a lot of compute that it will
in fact work and so that was the missing
piece it was you had the you needed the
data you needed the compute which showed
up in terms of gpus and you needed the
conviction to realize that you need to
mix them together I would say
reimplement everything on different
levels of abstraction in some sense but
I would say RL and something from
scratch rain PL and something from a
paper rain PL and something you know
from podcast that you have heard about
i' would say that's a powerful way to
understand things so it's often the case
that you read the description and you
think you understand but you truly
understand once you build it then you
actually know what really me that in the
description if someone who's a student
considering a career in AI like takes a
little while sits down and thinks like
what do I really want to see what I want
to see a machine do what I want to see a
natural language system and then
actually sit down and think about the
steps that are necessary to get there
and hopefully that thing is not a better
number on imet classification it's like
it's probably like an actual thing that
we can't do today that would be really
awesome and I think that thinking about
that and then backtracking from there
and Imagining the steps needed to get
there will actually lead to much better
research it'll lead to working on the
bottlenecks that other other people
aren't working on deep planning has been
kind of looked at with suspicion by a
lot of computer scientists because the
math is very different the math that you
use for deep planning you know it kind
of has more to do with you know
cybernetics uh the kind of math you do
in electrical engineering than the kind
of math you do in computer science and
nothing in in machine learning is exact
right computer science is all about
obiously compulsive attention to details
of like you know every index has to be
right and you can prove that an
algorithm is correct right uh machine
learning is the science of sloppiness
and so the big idea is the cost function
the cost function is a way of measuring
the performance of the system according
to some measure I'm a big fan of cost
functions I think cost functions are
great and they serve us really well and
I think that whenever we can do things
we with cost functions we should and you
know maybe there is a chance that we
will come up with some yet another
profound way of looking at things that
will involve cost functions in a less
Central way but I don't know I think
cost functions are I
mean I would not bet against cost
functions the fact that you can build
gigantic neural Nets train train them on
you know relatively small amounts of
data relatively with stochastic gr and
descent and that it actually works uh
breaks everything you read in every
textbook right every pre deep learning
textbook that told you you need to have
fewer parameters and you have data
samples all those things that you read
in textbook and they tell you stay away
from this and they all wrong it was kind
of obvious to me before I knew anything
that this was a good idea and then it
became surprising that it worked because
I started reading those textbooks okay
so okay can you talk through the
intuition of why it was obvious to you
if you remember well okay so the
intuition was it's it's sort of like you
know those people in the late 19th
century who proved that heavier than a
flight was impossible and of course you
have Birds right they do fly and so we
have the same kind of thing that we know
that the brain works we don't know how
but we know it works and we know it's a
large network of neurons in interaction
and that learning takes place by
changing the connection so kind of
getting this level of inspiration
without covering the details but sort of
trying to derive basic principles is
also the idea somehow that i' I've been
convinced of since I was on undergrad
that that intelligence is inseparable
from learning the idea somehow that you
can create an intelligent Machine by
basically programming for me it was a
nonstarter you know from the start every
intelligent entity that we know about
arrives at this intelligence F rning you
wrote a blog post a few years ago titled
how to be successful it's so succinct
and so brilliant compound yourself have
almost too much self-belief learn to
think independently get good at sales
and quotes make it easy to take risks
Focus work hard be bold be willful be
hard to compete with build a network you
get rich by owning things be internally
driven what stands out to you you from
that or Beyond as advice you can give
yeah no I think it is like good advice
in some sense but I also think it's way
too tempting to take advice from other
people and the stuff that worked for me
which I tried to write down there
probably may not work as well for other
people and I think I mostly got what I
wanted by ignoring advice and I tell
people not to listen to too much advice
listening to advice from other people
should be approached with great caution
[Music]
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