Limitations of Perceptron
The slide explains a limitation which applies to any linear model. It would equally apply to linear regression for example. 


  What does he mean by hand generated features? 


This means any features generated by analysis of the problem. For instance if you wanted to categorise a building you might have its height and width. A hand generated feature could be deciding to multiply height by width to get floor area, because it looked like a good match to the problem.


  I don't get the binary input example and why it is a table look-up type problem and why it won't generalize?


A table look-up solution is just the logical extreme of this approach. If you have a really complex classification, and your raw features don't relate directly (as a linear multiple of the target), you can craft very specific manipulations of them that give just the right answer for each input example. Essentially this is the same as marking each example in your training data with the correct answer, which has the same structure, conceptually, as a table of input: desired output with one entry per example.

In fact this might generalize, but only exactly as well as the crafted features do. In practice, when you have a complex problem and sample data that only partially explains your target variable (i.e. in most data science scenarios), then generating derived features until you find some that explain the data is strongly related to overfitting.

From your comment:


  In his video lecture, he says "Suppose for example we have binary input vectors. And we create a separate feature unit that gets activated by exactly one of those binary input vectors. We'll need exponentially many feature units. But now we can make any possible discrimination on binary input vectors. So for binary input vectors, there's no limitation if you're willing to make enough feature units." 1.What feature? 2.Why are we creating this feature? And why adding exponential such features we can discriminate these vectors?


Here is an example of the scheme that Geoffrey Hinton describes. Say you have 4 binary features, associated with one target value and see the following data:

data 0 1 1 0 -&gt; class 1
data 1 1 1 0 -&gt; class 2 
data 0 1 0 1 -&gt; class 1
data 1 1 1 0 -&gt; class 2
data 0 1 1 1 -&gt; class 2
data 0 1 0 0 -&gt; class 1


It is possible to get a perceptron to predict the correct output values by crafting features as follows:

data 0 1 1 0 -&gt; features 1 0 0 0 0 -&gt; class 1
data 1 1 1 0 -&gt; features 0 1 0 0 0 -&gt; class 2
data 0 1 0 1 -&gt; features 0 0 1 0 0 -&gt; class 1
data 1 1 1 0 -&gt; features 0 1 0 0 0 -&gt; class 2
data 0 1 1 1 -&gt; features 0 0 0 1 0 -&gt; class 2
data 0 1 0 0 -&gt; features 0 0 0 0 1 -&gt; class 1


Each unique set of original data gets a new one-hot-encoded category assigned. It is clear that ultimately if you had $n$ original features, you would need $2^n$ such derived categories - which is an exponential relationship to $n$. 

Working like this, there is no generalisation possible, because any pattern you had not turned into a derived feature and learned the correct value for would not have any effect on the perceptron, it would just be encoded as all zeroes. However, it would learn to fit the training data very well, it could just associate each unique vector with a weight equal to the training output - this is effectively a table lookup.

The whole point of this description is to show that hand-crafted features to "fix" perceptrons are not a good strategy. Even though they can be made to work for training data, ultimately you would be fooling yourself.