Map of Computer Science

Summary

This script explores the expansive field of computer science, from its theoretical foundations with Turing machines and computability theory to practical applications like artificial intelligence and machine learning. It delves into computer engineering, discussing CPU design, multiprocessing, and programming languages, before highlighting the impact of computer science on society and its potential future, including the challenges of miniaturization and the promise of new computing paradigms.

Takeaways

• 🧠 Computers were originally designed to solve arithmetic but have evolved to perform a multitude of tasks including running the internet, creating lifelike graphics, and simulating the universe.
• 📱 The computing power in modern devices, such as smartphones, surpasses that of the entire world in the mid-1960s, highlighting the rapid advancements in technology.
• 🔍 Computer science is a diverse field divided into three main areas: theoretical computer science, computer engineering, and applications.
• 🤖 Alan Turing is considered the father of theoretical computer science, with his concept of the Turing machine, which serves as a foundation for the general purpose computer.
• 🔄 A Turing machine's components, including an infinitely long tape, a head, a state register, and a list of instructions, are analogous to modern computer components like RAM, CPU, and memory.
• 🚫 Computability Theory explores the limits of what computers can and cannot compute, with the halting problem being a famous example of an undecidable problem.
• 🔍 Algorithm development is crucial in computer science as it provides the instructions to solve problems efficiently, independent of hardware or programming language.
• 🔐 Cryptography is essential for secure internet communication, relying on complex mathematical problems to encrypt and protect data.
• 🛠 Computer engineering involves designing computers capable of handling various tasks optimally, with the CPU at the core of processing.
• 💻 Software engineering focuses on writing instructions for computers, emphasizing efficiency, creativity, and error minimization.
• 🌐 The future of computer science includes developing AI systems capable of independent thought and learning from large datasets, expanding human cognitive abilities.

Q & A

What was the original purpose of building computers?

Originally, scientists built computers to solve arithmetic problems.

How has the computing power in devices evolved over time?

Computers have become significantly smaller and more powerful at an incredible rate, with more computing power in a modern cell phone than there was in the entire world in the mid-1960s.

What is the significance of a Turing machine in computer science?

A Turing machine is significant as it provides a simple description of a general-purpose computer and is considered the foundation of computer science. It formalizes the concept of computability.

What are the main components of a Turing machine?

A Turing machine consists of an infinitely long tape split into cells containing symbols, a head that can read and write symbols to the tape, a state register that stores the state of the head, and a list of possible instructions.

How does the concept of a Turing machine relate to modern computers?

In modern computers, the tape is analogous to the working memory or RAM, the head is similar to the central processing unit, and the list of instructions is held in the computer’s memory.

What is the halting problem in computability theory?

The halting problem is a famous example of a problem that is inherently unsolvable by a computer or a human, where one tries to predict whether a program will stop running or continue indefinitely.

What is an algorithm and why is it important in computer science?

An algorithm is a set of instructions, independent of hardware or programming language, designed to solve a particular problem. It is important because it serves as a recipe for building a program and a lot of work goes into developing efficient algorithms.

What is the role of Information Theory in computer science?

Information Theory studies the properties of information, including how it can be measured, stored, and communicated. It has applications such as data compression, making data take up less memory while preserving information.

What is Cryptography and why is it important for internet security?

Cryptography is the practice of secure communication, which involves encryption schemes to scramble data, usually relying on complex mathematical problems to keep the information secure. It is important for maintaining the privacy and integrity of information sent over the internet.

What is the relationship between computer architecture and CPU design?

Computer architecture refers to how a processor is designed to perform tasks. Different architectures are good at different things, such as CPUs being general-purpose, GPUs being optimized for graphics, and FPGAs being very fast at a narrow range of tasks.

How does software engineering contribute to building efficient and error-free computer programs?

Software engineering involves writing bundles of instructions for the computer, following best practices and design philosophies to ensure the programs are efficient, logically sound, and as free of errors as possible.

What are some real-world applications of optimization problems in computer science?

Optimization problems, such as finding the best path or the most efficient combination of parts, can save businesses millions of dollars. They appear everywhere, including in planning the best trip for the money when going on vacation.

What is the current forefront of computer science research?

The forefront of computer science research is developing computer systems that can think for themselves, known as Artificial Intelligence, which includes prominent fields like machine learning, computer vision, and natural language processing.

How does Big Data contribute to machine learning?

Big Data provides the large amounts of data necessary for machine learning algorithms to learn from and improve. It involves managing and analyzing large datasets to extract valuable insights.

What is the significance of the Internet of Things (IoT) in data collection?

The Internet of Things adds data collection and communication capabilities to everyday objects, contributing to an exponential growth in data, which in turn fuels advancements in fields like Big Data and Artificial Intelligence.

Outlines

00:00

🌟 Evolution and Fundamentals of Computer Science

This paragraph delves into the origins and evolution of computers, which were initially designed for arithmetic but expanded into a myriad of applications, including internet operations, graphics, artificial intelligence, and more. It emphasizes the exponential growth in computing power, with modern smartphones possessing more power than the entire world in the mid-1960s. The theoretical foundation of computer science is attributed to Alan Turing and his concept of the Turing machine, which serves as a model for general-purpose computers. The paragraph also covers Computability Theory, focusing on the limits of what can be computed, such as the halting problem, and Computational Complexity, which categorizes solvable problems based on their scalability. The importance of algorithms, Information Theory, Coding Theory, and Cryptography are also highlighted, showcasing their roles in efficient data compression, secure data transmission, and the development of encryption schemes.

05:03

🛠️ The Engineering and Application Aspects of Computer Science

The second paragraph focuses on the engineering challenges of computer design, which requires versatility to solve a wide range of problems. It discusses the central processing unit (CPU), its role in multitasking and the complexity of scheduling tasks efficiently. The paragraph introduces the concept of multiprocessing and the impact on scheduling, as well as the variety of computer architectures, each optimized for specific tasks. Moving beyond hardware, the paragraph explores the layers of software, programming languages, and the significance of compilers in translating code into CPU instructions. It also touches on the design of programming languages and compilers, the role of the operating system, and the art of software engineering. The paragraph concludes with a look at various applications of computer science, including optimization, Boolean Satisfiability, and the growing fields of Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, and Big Data.

10:07

🚀 The Future and Impact of Computer Science

The final paragraph discusses the future of computer science, highlighting the ongoing development of new types of computers to overcome hardware limitations. It touches on the significant impact of computers on society and speculates on the potential evolution of technology over the next century. The paragraph also promotes a platform called 'brilliant.org' for learning more about computer science through problem-solving courses in logic, algorithms, machine learning, and artificial intelligence. It encourages viewers to engage with these resources to deepen their understanding of the subject, providing a link for further exploration and learning.

Keywords

💡Computer Science

Computer Science is a broad field that encompasses the study of computers and computational systems. It is the main theme of the video, as it discusses the various aspects and subfields that make up this discipline. In the script, computer science is divided into three main parts: the fundamental theory, computer engineering, and applications, highlighting its diverse nature and its impact on various domains such as the internet, graphics, and artificial intelligence.

💡Turing Machine

A Turing machine is a theoretical device in computer science that provides a simple abstraction of the internal workings of a computer. It was introduced by Alan Turing and is foundational to the concept of an algorithm. In the script, the Turing machine is described as having an infinitely long tape, a head, a state register, and a list of instructions, which collectively form the basis for the functionality of modern computers.

💡Computability Theory

Computability Theory, also known as computability, is a subfield of computer science that deals with what can and cannot be computed by an algorithm. The script mentions the halting problem as a famous example of a problem that is undecidable by a computer, illustrating the limits of computation.

💡Algorithm

An algorithm is a set of instructions or rules to be followed in order to solve a problem. The script emphasizes the importance of algorithms in computer science, as they are the recipes for building programs and are crucial for getting the best performance out of computers. Different algorithms can achieve the same result, such as sorting numbers, but with varying efficiencies.

💡Information Theory

Information Theory is a field that studies the quantification, storage, and communication of information. The script discusses its application in data compression, which is a technique to reduce the amount of data needed to represent information while preserving its essential aspects.

💡Cryptography

Cryptography is the practice of secure communication techniques that involve the use of codes to protect information. In the script, it is mentioned as an important aspect of computer science that ensures the security of data transmitted over the internet, relying on complex mathematical problems to secure the information.

💡CPU (Central Processing Unit)

The CPU is the primary component of a computer that performs most of the processing. The script explains that every task run on a computer goes through the CPU, which is controlled by a scheduler to manage multitasking efficiently. The design of the CPU and its architecture is critical in computer engineering.

💡Programming Language

A programming language is a formal language designed to communicate instructions to a computer. The script discusses the variety of programming languages, ranging from low-level languages like assembly to high-level languages like Python and JavaScript, which are used for coding websites and apps.

💡Operating System

An operating system is the most crucial software on a computer, managing the execution of other applications and the hardware. The script mentions that engineering a good operating system is a significant challenge and is the interface through which users interact with the computer.

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the development of computer systems that can perform tasks that would require human intelligence if done by a person. The script highlights AI as a prominent area of computer science research, with machine learning being a key technique that enables computers to learn from data and make decisions or classifications.

💡Big Data

Big Data refers to the large volumes of data that are generated and collected from various sources. The script discusses big data in the context of managing and analyzing large datasets to extract valuable insights. It is closely related to machine learning algorithms, which have improved due to the increased availability of data.

💡Internet of Things (IoT)

The Internet of Things is a concept where everyday objects are connected to the internet, allowing for data collection and communication. The script briefly mentions IoT as a source of even more data for big data analysis, indicating the growing interconnectedness of devices and the potential for enhanced computational capabilities.

Highlights

Computers were originally built to solve arithmetic but have evolved to perform a wide range of functions.

The rapid development of computing power has made modern smartphones more powerful than all computers in the mid-60s.

The Apollo moon landing could have been executed on a few Nintendo consoles, showcasing the immense growth in computing power.

Computer science is divided into three main parts: theoretical computer science, computer engineering, and applications.

Alan Turing formalized the concept of a Turing machine, laying the foundation for computer science.

A Turing machine consists of an infinitely long tape, a head, a state register, and a list of instructions, similar to modern computer components.

Lambda calculus is a basis for research in programming languages and is equivalent to the computability of a Turing machine.

Computability Theory classifies what is and isn't computable, with some problems being inherently unsolvable like the halting problem.

Algorithms are independent sets of instructions designed to solve specific problems, with efficiency varying between them.

Information theory explores the properties of information, including data compression and communication.

Cryptography is crucial for securing information sent over the internet, relying on complex mathematical problems.

Computer engineering involves designing versatile computers capable of solving a wide array of problems.

CPUs, GPUs, and FPGAs represent different computer architectures optimized for various tasks.

Programming languages and compilers are essential tools for software engineers, needing to be both user-friendly and versatile.

Operating systems are vital as they manage how programs run on hardware and interact with users.

Software engineering involves translating creative ideas into logical instructions, following best practices to ensure efficiency and error minimization.

Artificial Intelligence is a prominent field in computer science, focusing on developing systems that can think and learn from data.

Machine learning is a subset of AI, involving algorithms that enable computers to learn and make decisions based on data.

Computer vision, natural language processing, and knowledge representation are fields that contribute to AI's ability to understand and interact with the world.

Big data and the Internet of Things are expanding the scope of data collection and analysis, influencing AI and machine learning advancements.

Hacking, while not an academic discipline, is an important aspect of computer science, focusing on finding and exploiting system weaknesses.

Computational Science applies computer power to scientific inquiries, often utilizing supercomputing for complex simulations.

Human-Computer Interaction aims to design intuitive and user-friendly computer systems, including virtual and augmented reality technologies.

Robotics is the field that combines computer science with physical mechanisms, creating intelligent machines from simple to complex.

Despite hardware limitations, computer science continues to evolve, with ongoing research into new types of computers.

The impact of computers on society has been immense, and the future of computer technology holds great potential for further transformation.

Transcripts

We built computers to expand our brains.

Originally scientists build computers to solve arithmetic, but they turned out to be incredibly

useful for many other things as well: running the entire internet, lifelike graphics, artificial

brains or simulating the Universe, but amazingly all of it boils down to just flipping zeros

and ones.

Computers have become smaller and more powerful at an incredible rate.

There is more computing power in your cell phone then there was in the entire world in

the mid 60s.

And the entire Apollo moon landing could have been run on a couple of Nintendos.

Computer science is the subject that studies what computers can do.

It is diverse and overlapping field but I’m going to split it into three parts.

The fundamental theory of computer science, computer engineering, and Applications.

We’ll start with the father of theoretical computer science: Alan Turing, who formalised

the concept of a Turing machine which is a simple description of a general purpose computer.

People came up with other designs for computing machines but they are all equivalent to a

Turing machine which makes it the foundation of computer science.

A Turing machine contains several parts: An infinitely long tape that is split into cells

containing symbols.

There is also a head that can read and write symbols to the tape, a state register that

stores the state of the head and a list of possible instructions.

In todays computers the tape is like the working memory or RAM, the head is the central processing

unit and the list of instructions is held in the computer’s memory.

Even though a Turing machine is a simple set of rules, it’s incredibly powerful, and

this is essentially what all computers do nowadays.

Although our computers obviously have a few more parts like permanent storage and all

the other components.

Every problem that is computable by a Turing machine is computable using Lambda calculus

which is the basis of research in programming languages.

Computability Theory attempts to classify what is and isn’t computable.

There are some problems that due to their very nature, can never be solved by a computer,

a famous example is the halting problem where you try to predict whether a program will

stop running or carry on forever.

There are programs where this is impossible to answer by a computer or a human.

Many problems are theoretically solvable but in practice take too much memory or more steps

than lifetime of the Universe to solve, and computational complexity attempts to categorise

these problems according to how they scale.

There are many different classes of complexity and many classes of problem that fall into

each type.

There are a lot of real world problems that fall into these impossible to solve categories,

but fortunately computer scientists have a bunch of sneaky tricks where you can fudge

things and get pretty good answers but you’ll never know if they are the best answer.

An algorithm is a set of instructions independent of the hardware or programming language designed

to solve a particular problem.

It is kind of like a recipe of how to build a program and a lot of work is put into developing

algorithms to get the best out of computers.

Different algorithms can get to the same final result, like sorting a random set of numbers

into order, but some algorithms are much more efficient than others, this is studied in

O(n) complexity.

Information theory studies the properties of information and how it can be measured,

stored and communicated.

One application of this is how well you can compress data, making it take up less memory

while preserving all or most of the information but there are lots of other applications.

Related to information theory is coding theory.

And Cryptography is obviously very important for keeping information sent over the internet

secret.

There are many different encryption schemes which scramble data and usually rely on some

very complex mathematical problem to keep the information locked up.

These are the main branches of theoretical computer science although there are many,

more I don’t have time to go into Logic, Graph Theory, Computational Geometry, Automata

Theory, Quantum Computation, Parallel Programming, Formal Methods and Datastructures, but lets

move on to computer engineering.

Designing computers is difficult because they have to do so many different things.

Designers need to try and make sure they are capable of solving many different kinds of

problem as optimally as possible.

Every single task that is run on the computer goes through the core of the computer: the

CPU.

When you are doing lots of different things at the same time, the CPU needs to switch

back and forth between these jobs to make sure everything gets done in a reasonable

time.

This is controlled by a scheduler, which chooses what to do when and tries to get through the

tasks in the most efficient way, which can be very difficult problem.

Multiprocessing helps speed things up because the CPU has several cores that can execute

multiple jobs in parallel.

But this makes the job of the scheduler even more complex.

Computer architecture is how a processor is designed to perform tasks and different architectures

are good at different things.

CPUs are general purpose, GPUs are optimised for graphics and FPGAs can be programmed to

be very fast at a very narrow range of task.

On top of the raw hardware there are many layers of software, written by programmers

using many different programming languages.

A programming language is how humans tell a computer what to do and they vary greatly

depending on the job at hand from low level languages like assembly through to high level

languages like python or javascript for coding websites and apps.

In general, the closer a language is to the hardware, the more difficult it is for humans

to use.

At all stages of this hierarchy the code that programmers write needs to be turned into

raw CPU instructions and this is done by one or several programs called compilers.

Designing programming languages and compilers is a big deal, because they are the tool that

software engineers use to make everything and so they need to be as easy to use as possible

but also be versatile enough to allow the programmers to build their crazy ideas.

The operating system is the most important piece of software on the computer as it is

what we interact with and it controls how all of the other programs are run on the hardware,

and engineering a good operating system is a huge challenge.

This brings us to software engineering: writing bundles of instructions telling the computer

what to do.

Building good software is an art form because you have to translate your creative ideas

into logical instructions in a specific language, make it as efficient as possible to run and

as free of errors as you can.

So there are many best practices and design philosophies that people follow.

Some other important areas are getting many computers to communicate and work together

together to solve problems.

Storing and retrieving large amounts of data.

Determining how well computer systems are performing at specific tasks, and creating

highly detailed and realistic graphics.

Now we get to a really cool part of computer science, getting computers to solve real world

problems.

These technologies underlie a lot of the programs, apps and websites we use.

When you are going on vacation and you want to get the best trip for the money you are

solving an optimisation problem.

Optimisation problems appear everywhere and finding the best path or most efficient combination

of parts can save businesses millions of dollars.

This is related to Boolean Satisfiability where you attempt to work out if a logic formula

can be satisfied or not.

This was the first problem proved to be NP-complete and and so widely considered to be impossible

to solve, but amazing development of new SAT solvers means that huge SAT problems are solved

routinely today especially in artificial intelligence.

Computers extend our brains multiply our cognitive abilities.

The forefront of computer science research is developing computer systems that can think

for themselves: Artificial Intelligence.

There are many avenues that AI research takes, the most prominent of which is machine learning

which aims to develop algorithms and techniques to enable computers to learn from large amounts

of data and then use what they’ve learned to do something useful like make decisions

or classify things.

And there are many different types of machine learning.

Closely related are fields like computer vision, trying to make computers able to see objects

in images like we do, which uses image processing techniques.

Natural language processing aims to get computers to understand and communicate using human

language, or to process large amounts of data in the form of words for analysis.

This commonly uses another field called knowledge representation where data is organised according

to their relationships, like words with similar meanings are clustered together.

Machine learning algorithms have improved because of the large amount of data we give

them.

Big data looks at how to manage and analyse large amounts of data to get value from it.

And will get even more data from the Internet of Things, adding data collection and communications

to everyday objects.

Hacking is not a traditional academic discipline but definitely worth mentioning.

Trying to find weaknesses in computer systems, and take advantage of them without being noticed.

Computational Science uses computers to help answer scientific questions from fundamental

physics to neuroscience, and often makes use of Super Computing which throws the weight

of the worlds most powerful computers at very large problems, often in the area of Simulation.

Then there is Human Computer Interaction which looks at how to design computer systems to

be easy and intuitive to use.

Virtual reality, Augmented Reality and Teleprescence enhancing or replacing our experience of reality.

And finally Robotics which gives computers a physical embodiment, from a roomba to trying

to make intelligent human like machines.

So that is my Map of Computer Science, a field that is still developing as fast as it ever

has despite that fact that the underlying hardware is hitting some hard limits as we

struggle to miniaturise transistors any more, so lots of people are working on other kinds

of computers to try and overcome this problem.

Computers have had an absolutely huge impact on human society and so it is going to be

interesting to see where this technology goes in the next one hundred years.

Who knows, perhaps one day, we'll all be computers.

As per usual if you want to get hold of this map as a poster I have made it available so

check in the description below for some links, and also if you want to find out more about

computer science I recommend you check out the sponsor for this video brilliant dot org.

People often ask me how to go about learning more about the kinds of subjects I cover in

these videos, and as well as watching videos, a really great way is to get down and solve

some real problems.

And brilliant does and excellent job at this.

It's a really cool website and also an app which helps you learn by getting you to solve

interesting problems in science, mathematics and computer science.

And each of the courses starts off kind of easy and fun and then gets more and more challenging

as you master the concepts.

If you want to learn specifically about computers science they have got whole courses built

around topics in this video like logic, algorithms, machine learning, artificial intelligence,

so if you want to check that out brilliant dot org slash dos, or even better click the

link in the description below because that lets them know that you have come from here.

So thanks again for watching, and I'll be back soon with a new video.