- Think of reading as an active pursuit rather than being passive.
- Don’t get bogged down in details, try to sprint through them and look for the gist of the author.
- Read with the intention of finding out the gist and pace yourself accordingly.
- Learn to adjust your speed based on the complexity of the content.
- Skim through the content if you know what he is going to say.
- Stop vocalization during reading.
- Don’t be a word by word reader, be a phrase reader.
- Try to speed up yourself in your reading, that will help you more concentrated on the content.
- Develop your vocabulary.
- Like any other skill, more you practice, better you become. Make sure you practice with the above principles.
- Finally, Don’t consider it as an ephemeral course but as a lifelong journey of betterment.
Let’s say we have a machine that understands all the high-level abstractions and patterns in data. Let’s say it has built on its own models to predict what’s coming next. Data science ends here presenting models and predictions to Entrepreneurs or executives to make a decision. But Is it intelligent at this stage? No, it has to be able to make decisions on its own to be truly intelligent.
So, what guides decision making process?
A company makes its decision to maximize its profit. Profit is their value and that guides their decision-making process.
A man’s value of his survival deeply coded in his DNA motivates him to make proper decisions that could help him get proper food, shelter, clothes, sex etc.
Evolution has managed to deduce very rich and distinct emotions that could help us identify reinforcements in the real world. This makes it an imperative for any machine to be hard coded with these rules of value system to make use of reinforcement learning or other latest tech meme of that kind.
Whether it is chess or Go, if there is something it should definitely know before starting the game, it has to be the winning state and possibly evaluation functions for all different states possible in the Game. But that’s a small world to code valuations/reinforcements. How would you go about writing such codes for a machine to evaluate the humongous set of states, instances, objects in the real world? This makes me believe that modeling the value system or giving to a machine ability to build its own value system is at the core of AI. Too early to ignore Minsky’s rule-based systems.
Understanding customer personality from user data.
Objective: Learn the habits of the user from his digital activities. Reflect it on him for retro. predict his behavior next day.
Digital data: what apps and sites he checks with what frequency?
Data about the lifetime of a product would be very valuable to recommend another product of that category at the right time.
Suppose you have a function add with arguments a and b. We usually have certain assumptions or requirements about what format that input has to be in. In this case, it might be some numbers. what if they are strings? it will through an error. but of we give same input to a human, he would probably concatenate two strings or map each char to an integer and add the result or something of that sort.
So, input modifier is an imaginative agent which would manipulate input we have/given to fit into the function argument requirements. This would allow for more collaboration among different functionalities at our disposal and may open the door for creativity in computation. However, there would be a trade-off of little uncertainty in it.
When you give circle and square arguments to put together function, input modifier can think of it as one inside the other, one after the other and so many other possibilities. this uncertainty is inevitable even in human cognition.
Universal evaluation of each person’s impact on the world and fairly distributing wealth based on that.
Principle component analysis for text summarization.
App for updating customers with latest IPOs and their prospectus.
App for local retail – buying things based on their closeness for faster delivery.
Train a classifier to identify a student’s stage in the Flow state graph in Udacity.
Model a situation as a recurrent neural network fed with a sequence where each sequence image is learned by a convolutional neural network.
Text summarization mandatory for NLP? when you read a book, you constantly summarize and build a model in your brain and continue reading the book with that summary in context. So, summarizing text and feeding it back in an LSTM network can NLU more efficient.
Buy a CNC machine and customers.
Induction -> models -> prediction -> decision -> action
For example, finding a number in the telephone directory.
Inducts that numbers are sorted by alphabetical order of names from the data.
Models or imagines a straight line domain for all data.
Predicts at what part of the book the number can be found.
Takes decision and action to open that part.
Again infers from data and updates its predictions, decisions, and acts accordingly with the new information.
MARCUS HUTTER approach
It is not necessary for a theory to be computable in general, at least in most of the sciences. But as the Artificial intelligence sits as a branch of computer science, most of AI researchers looks for the theory of AI from the perspective of its computability. MARCUS HUTTER suggests that at least a small chunk of researchers should look for theory of intelligence without considering computational resource constraints and then we can approximate that theory to computability once we find it.
Solomonoff’s theory of induction – Algorithmic probability and Kolmogorov complexity.
In most of the AI algorithms, it is often the case that you enumerate in some sort and search through all the possible states and check whether we are at the goal state or particular constraints are being satisfied for the different combinations of states. This pretty much looks like a brute force and doesn’t seem to be intelligent though it could solve a pretty huge number of problems because of computational power we are endowed with. There would be a hard upper bound to the kind of problems we can tackle because of the computational limitation.
To make the search more efficient and more directional, we try to use human thought up heuristics into the search algorithms to guide the search. This is the crucial part of Human-machine interaction, leveraging the benefits of computational power and accuracy to the uniquely human-like heuristic intuition.
But the main question is, could we ever be able to make a computer think up its own heuristics appropriate to problem domain to guide its search?
Another question, How do we come up with heuristics in the first place? Do we develop it by copying the practices of our peers and mentors through our experience in the domain? or did we develop an innate sense of what to do when in some regular day to day chore settings because of millions of years of evolution? Is the ability to think up right heuristics is what true intelligence is?
Life is filled with lots of domains to figure out and pursue all of them. Think of life as a tree whose branches go down to different domains and then subdomains etc. If you are a computer science student, you must be knowing the breadth first and depth first search algorithms to explore through the tree. It is this choice of your exploration that determines the course of your life.
- People who opt to explore in breadth-first strategy, look out for all the options, judge them by the standards of their interests, aptitude and most importantly benefits it can provide to us. They are pretty much confused at this stage itself because it is not always binary that you like it or not, rather it is a gray scale somewhere in between. They will be jumping from one thing to another quite often, takes a lot of time go deep and settle on the next level of any domain. Life is too short for this kind of people to succeed. However, they tend to be very knowledgeable and opinionated on very diverse courses of life.
- If you look at depth first kind, who are more focused, they go deeper and deeper through mere perseverance for success or unmatchable motivation or whatever it is that drives them. This is the success group or most famously called outliers. They will be very highly skilled in a very narrow domain which will thrill the whole humanity and attracts everyone to notice them and is very marketable. But this is restricted to his narrow domain and remains pretty much dumb in many other aspects or domains of life. This wouldn’t be a problem because of the financial success or celebrity status they enjoy, they can hire or find other people to manage those things and they love to remain in the aura of the tall kingdom they have built for themselves. This narrow domain is mostly decided by the culture of his environment or the aptitude and practices of his family etc.
Irrespective of love, people always try to marry or date the most worthy person available in their vicinity. In this view, marriage can be considered as a self-imposed disciplinary action against falling prey for instant gratification.
What you don’t use you lose. If you stop practising something, you will eventually lose it. Skill is like a bicycle, Easy to balance at the fast pace. However, slow, patient and persistent riders are essential to unravel deeply hidden secrets of science.
Confidence, happiness and worth attract women to a man. During my first love, she approached me looking at above qualities of mine. As relationship went for few months, I became dependent on her love. Then, she found a guy who is relatively more confident and happy than me, she moved on when I am mentally weak, vulnerable and craving for a sense of belongingness. As someone said, women look out for the mental state of a person through different omens. The practice of killing sadness at yourself and spreading happy moments around will bring you more friends and happiness into your life.
Which tool to use when is one of the most important skills you should learn. In academy and other institutions, often you will be taught about how to use a tool or technique to counter a problem. The questions of which tool or technique we need to pick at what problem setting is something you have to learn yourself through experience and practice most of the times.
Ability to come up with a right measure for quantifying things is a very valuable skill in science and technology.
We are biologically evolved to feel and culturally evolved to think.
Computer science is great because it has developed analytic methods(complexity analysis) to evaluate any algorithmic ideas in their domain. Most other domains like Entrepreneurship, Moral philosophy etc. don’t have these kinds of rigorous analytic methods for their ideas. This makes most of their ideas debatable for longer periods of time if not forever.
Passivity leads to boredom.
Focused education is the best kind of investment in the early stage of your life.
If you are worried about something, just step back and ask yourself whether that something is really necessary for the long term. This might resolve lots of your worries.
Spend for comfort not luxury.
In my early days, with lots of youthful arrogance, when there are two problems to tackle, I used to always choose the hard one. Naturally, that decision of mine made me fail many times and killed my confidence to darkest depths. When I started to tackle smaller and easier problems in my vicinity, they is some progress I could measure from time to time which gave me confidence and also tackling these small problems looked like stepping stones to harder ones. So, start small to make it big rather to start big and fail forever.