The document addresses popular myths surrounding machine learning, emphasizing that it is distinct from AI and not all data is useful for training models. It highlights misconceptions about data requirements, the skills needed to develop machine learning systems, and the potential biases that can be amplified through data. Additionally, it discusses the dual-use nature of machine learning technologies and their impact on jobs, suggesting they will create new opportunities while also changing existing roles.

1. rockInterview.in
Popular Machine Learning Myths

2. rockInterview.in
It is tempting to assume that
machine learning will
solve every problem
Percentage of executives who expect AI will be
behind all their new innovations by 2021
42%
But you’ll get better results if you look
beyond the hype and avoid these common
myths by understanding what machine
learning can and can’t deliver.

3. rockInterview.in
Myth 1: Machine Learning is AI
Machine learning and artificial intelligence are frequently
used as synonyms, but they are not the same.
Beware the buzzwords and be precise. Machine learning is
about learning patterns and predicting outcomes from large
data sets; the results might look “intelligent” but at heart it’s
about applying statistics at unprecedented speed and
scale.
What is Machine
Learning and AI
Machine learning is the technique that’s most
successfully made its way out of research labs
into the real world.
AI is a broad field covering areas such as
computer vision, robotics and natural language
processing, as well as approaches such as
constraint satisfaction that don’t involve machine
learning.

4. rockInterview.in
Myth 2: All data is useful
You need data for Machine learning, but not all data is
useful for machine learning.
All the data you use for training needs to be well labelled,
and labelled with the features that match the questions
you’re going to ask the machine learning system, which
takes a lot of work.
Don’t assume the data you already have is clean, clear,
representative or easy to label.
What kind of data you
need to train your system?
Representative data that covers the patterns
and outcomes your machine learning system
will need to handle.
Data that doesn’t have irrelevant patterns
included (such as photos that show all the men
standing up and all the women sitting down, or
all the cars being in a garage and all the bikes
being in a muddy field) because the machine
learning model you create will reflect those
overly specific patterns and look for them in the
data you use it with.

5. rockInterview.in
Myth 3: You always need a lot of data
The major advances made recently in image recognition,
machine reading comprehension, language translation and
other areas have happened because of better tools,
computing hardware such as GPUs that can process large
amounts of data in parallel, and large labelled data sets,
including ImageNet and the Stanford Question Answering
Dataset.
But thanks to a trick called transfer learning, you can
customize a pre-trained system to your own problem with a
relatively small amount of data.
Transfer Learning
With transfer learning, you don’t always need a
large data set to get good results in a specific
area; instead you can teach a machine learning
system how to learn using one large data set
and then have it transfer that ability to learn to
your own, much smaller training data set.

6. rockInterview.in
Myth 4: Anyone can build
a machine learning system
There are plenty of open source tools and frameworks for
machine learning and countless courses showing you how
to use them.
Getting machine learning right takes experience; if you’re
just getting started, look to APIs to pre-trained models you
can call from inside your code while you acquire or hire
data science and machine learning expertise to build
custom systems.
Things you need
to know for building
a machine learning system
How to prepare data and partition it for training
and testing
How to choose the best algorithm and what
heuristics to use with it
How to turn that into a reliable system in
production
You also need to monitor the system to make
sure the results stay relevant over time; you
need to keep checking that the model still fits
your problem.

7. rockInterview.in
Myth 5: All patterns in
the data are useful
“Black box” models are efficient but don’t make it clear
what pattern they have learned.
More transparent, intelligible algorithms like Generalized
Additive Models make it clearer what the model has
learned so you can decide if it’s useful to deploy.
Some patterns are not useful
Often times, there are several unhelpful
anti-patterns in your data set which can skew
your predictions, unless you already know about
them.
In other cases, a system can learn a valid
pattern (like a controversial facial recognition
system that accurately predicted sexual
orientation from selfies) that isn’t useful because
it doesn’t have a clear and obvious explanation.

8. rockInterview.in
Myth 6: Reinforcement
learning is ready to use
Curating and creating clearly labelled data sets takes time
and effort. So there’s a lot of interest in unsupervised forms
of learning, especially reinforcement learning (RL) — where
an agent learns by trial and error, by interacting with its
environment and receiving rewards for correct behaviour.
RL is less common outside of research though. The problem
is that few real-world environments have easily discoverable
rewards and immediate feedback, and it’s particularly tricking
allocating rewards when the agent takes multiple actions
before anything happens.
RL applications in and
outside research
DeepMind’s AlphaGo system used RL
alongside supervised learning to beat
high-ranking Go players
Google uses DeepMind to save power in its
data centers by learning to cool them more
efficiently
Microsoft uses a specific, limited version of RL
called contextual bandits to personalize news
headlines for visitors to MSN.com.

9. rockInterview.in
Myth 7: Machine Learning
is unbiased
Because machine learning learns from data, it’s going to
replicate any biases in the data set. But it turns out that
machine learning also amplifies bias.
Getting similar recommendations on a shopping site is
useful, but it’s problematic when it comes to sensitive areas
and can produce a feedback loop.
It’s important to be aware of the issues of bias in machine
learning. If you can’t remove bias in your training data set,
use techniques to reduce bias.
Biases that can be created
while learning from data
Training image recognition systems on the
COCO data set can create associations such as
men with computer hardware and women with
kitchen equipment.
Gender stereotypes can be learned such as “he
is a doctor” and “she is a nurse”.
If you join a Facebook group opposed to
vaccination, Facebook’s recommendation engine
will suggest other groups focused on conspiracy
theories or the belief that the Earth is flat.

10. rockInterview.in
Myth 8: Machine Learning is only
used for good
Machine learning powers anti-virus tools, looking at the behaviour of brand-new attacks to
find them as soon as they’re launched.
But equally, hackers are using machine learning to probe the defenses of anti-virus tools, as
well as to craft targeted phishing attacks at scale by analyzing large amounts of public data or
analyzing how successful previous phishing attempts were.

11. rockInterview.in
Myth 9: Machine Learning will
replace people
It’s common to fret that AI will take away jobs and it will
certainly change what jobs we do and how we do them;
machine learning systems improve efficiency and
compliance and reduce costs.
What machine learning has already started doing is creating
new business opportunities, such as improving customer
experience with predictive maintenance, and offering
suggestions and support to business decision makers.
Machine Learning will
create new jobs
In the long run machine learning will create new
roles in the business as well as making some
current positions obsolete.
But many of the tasks machine learning
automates simply weren’t possible before, either
because of complexity or scale; you couldn’t hire
enough people to look at every photograph
posted to social media to see whether it features
your brand, for example.