Genius Machine Learning Advice for 11 Minutes Straight

Summary

The 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

00:00

🔍 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.

05:00

💡 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.

10:02

🚀 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.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. In the video, the speaker emphasizes the importance of understanding machine learning algorithms and the need for debugging skills to improve their performance. The script mentions that experts in debugging machine learning algorithms can be significantly more efficient in making systems work, highlighting the practical application of machine learning in solving problems.

💡Data Collection

Data collection refers to the process of gathering and measuring information from various sources to gain insights. The video script points out a common pitfall where engineers might spend excessive time collecting more data under the assumption that 'more data is valuable,' without first testing whether additional data will actually improve the model. This illustrates the importance of strategic data collection in the context of machine learning.

💡Debugging

Debugging is the process of identifying and removing bugs or errors from a system. The speaker in the video underscores the value of being adept at debugging in the field of machine learning, suggesting that those who are skilled at it can be 10x to 100x more efficient. Debugging is crucial for refining machine learning algorithms and ensuring they perform as expected.

💡Architecture

In the context of machine learning, architecture refers to the structure or design of a model, including the arrangement of layers and connections in a neural network. The script suggests modifying the architecture as a potential solution when a machine learning model is not performing well, indicating that changing the underlying structure can sometimes be more effective than simply collecting more data.

💡Optimization Algorithms

Optimization algorithms are used in machine learning to improve the performance of models by adjusting parameters. The video mentions trying different optimization algorithms as a strategy for troubleshooting and enhancing machine learning models, emphasizing the role of these algorithms in the iterative process of model development.

💡Regularization

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The script briefly mentions regularization as one of the potential changes to consider when a machine learning model is not working as expected, highlighting its role in balancing model complexity and performance.

💡Programming

Programming is the process of writing sequences of instructions to direct a computer to perform specific tasks. The video transcript discusses the importance of learning programming as a foundational skill, with the speaker advocating for hands-on practice and problem-solving as the primary means of learning, rather than passively watching videos or tutorials.

💡10,000 Hours Rule

The 10,000 Hours Rule, popularized by Malcolm Gladwell, suggests that it takes approximately 10,000 hours of practice to achieve mastery in a field. The speaker in the video subscribes to this concept, arguing that deliberate and sustained effort over time is key to becoming an expert in any area, including machine learning and programming.

💡Cost Functions

Cost functions, also known as loss functions or objective functions, are used in machine learning to measure the performance of a model. The video script describes cost functions as a way to quantify how well a model is performing, with the speaker advocating for their use in guiding the development and refinement of machine learning models.

💡Deep Learning

Deep learning is a subset of machine learning that focuses on artificial neural networks with many layers, or 'deep' architectures. The transcript discusses deep learning's initial underestimation and the critical role of large amounts of data and computational power in its success. The speaker reflects on the importance of conviction in the potential of deep learning to achieve results.

💡Reinforcement Learning (RL)

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The video mentions reimplementing RL from scratch as a method for deeply understanding the mechanics and principles behind it, indicating the value of hands-on learning in mastering complex concepts.

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

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