This document discusses a new undergraduate course that blends computing, artificial intelligence, and robotics. It provides an overview of the curriculum and presents results from a student survey, which found high satisfaction with both the course and use of robots. The course has no prerequisites and serves both general education and computer science major requirements. It aims to teach basic computing and AI concepts through hands-on experience with robotics.

1. 1 Computer Science and Robotics
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Department of Computing and Technology, Iqra University, Islamabad, Pakistan, ,
3 Abstract: This paper describes a new undergraduate course that serves two purposes. First, it satisfies a general
4 education requirement in mathematical sciences, and second, it serves as one of four possible first courses for computer
5 science majors. The course has no prerequisites: the student population is drawn primarily from first-year college
6 students. This paper focuses on the curriculum, which blends basic computing, artificial intelligence, and robotics.
7 Results of a class survey are presented and discussed. Overall, satisfaction with both the course and the use of robots
8 was high.
10 1. Introduction
11 Robotics utilizes a broad range of disciplines within computer science and beyond, from mathematics to
12 mechanics to biology. Especially important tools from computer science include artificial intelligence and
13 sophisticated sensorprocessing. Roboticsistheintersectionofscience, engineeringand technologythatproduces
14 machines, called robots, that substitute for (or replicate) human actions. Pop culture has always been fascinated
15 with robots. R2-D2. Optimus Prime. WALL-E. These exaggerated, humanoid concepts of robots usually seem
16 like a caricature of the real thing or are they more forward-thinking than we realize? Robots are gaining
17 intellectual and mechanical capabilities that do not put the possibility of an R2-D2-like machine out of reach in
18 the future. The concept of artificial humans predates recorded history (see automaton), but the modernterm
19 robot derives from the Czech word robota (”forced labour” or ”serf”), used in Karel Čapek’s play R.U.R. (1920).
20 The play’s robots were manufactured humans, heartlessly exploited by factory owners until they revolted and
21 ultimately destroyed humanity. Whether biological, like the monster in Mary Shelley’s Frankenstein (1818),
22 or mechanical was not specified, the mechanical alternative inspired generations of inventors to build electrical
23 humanoids. ”Thefieldofmachinelearningisconcerned withthequestionofhowtoconstructcomputerprograms
24 that automatically improve with experience.” In simpler words, machine learning is the field ofcomputer science
25 that makes the machine capable of learning independently without being explicitly programmed. The point to
26 be noted here is that ML algorithms can learn on their own from past experiences, just like humansdo. When
27 exposed to new data, these algorithms learn, change and grow by themselves without you needing to change the
28 code every single time. Machine Learning algorithms utilize a variety of techniques to handle large amountsof
29 complex data to make decisions. These algorithms complete the task of learning from data with specific inputs
30 given to the machine. It is important to understand how these algorithms and a machine learning system work
31 so that we can get to know how these can be used in the future. Machine Learning is broadly divided into three
32 main areas, supervised learning, unsupervised and reinforcement learning.
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1 2. Methodology
2 nstead of advocating less regulation of labor, some have advocated more regulation of robots and computers.
3 Elon Musk famously predicted that “robots will be able to do everything better than us” said and warned that
4 government needs to regulate AI “before it’s too late” (as quoted in Breland, 2017). And yet when he triedto
5 extend the reach of robots to the final assembly of his Tesla Model 3 cars, he was only able to build far less than
6 half of the cars per week that he had promised. Later, in an April 2018 tweet, Musk admitted “yes, excessive
7 automation at Tesla was a mistake Humans are underrated” .When a robot killed an assembly line worker in
8 Germany in June 2015, it was not from Terminator-like intent, but from an inability to distinguish between
9 inert metal and human flesh (Max Tegmark as quoted by Hardy, 2015, p. B6). The greater danger usuallyis
10 not AI, but artificial stupidity
11 3. Virtualization Change Entertainment
12 Virtualizationisthetechnologythatallowsyoutocreatemultiplesimulatedenvironments ordedicatedresources
13 fromasinglephysicalhardwaresystem. Ahypervisor softwareconnectsdirectlyto thathardware andallows you
14 to split one system into separate, distinct, and secure environments known as virtual machines (V.M.s). These
15 V.M.s relyon thehypervisor’s abilityto separate the machine’s resources fromthe hardware and distribute them
16 appropriately. Virtualization helps you get the most value from previous investments. The physical hardware,
17 equipped with a hypervisor, is called the host, while the many VMs that use its resources are guests. Operators
18 can control virtual instances of CPU, memory, storage, and other resources, so guests receive the resources
19 they need when they need them. When the virtual environment is running, and a user or program issues an
20 instruction that requires additional resources from the physical environment, the hypervisor relays therequest
21 to the physical system and caches the changes—which all happens at close to native speed (particularly if the
22 request is sent through an open-source hypervisor based on KVM, the Kernel-based Virtual Machine).
23 3.1. Virtual Reality Change Education
24 Education is driving the future of V.R. more than any other industry outside of gaming. For many teachers, the
25 idea of implementing virtual reality (V.R.) into the curriculum seems like a far-fetched notion. The technology
26 seems too expensive, too primitive, or too impractical to fit into a typical class period. Virtualization is
27 technology that allows you to create multiple simulated environments or dedicated resources from a single,
28 physical hardware system. Software called a hypervisor connects directly to that hardware and allows you to
29 split 1 system into separate, distinct, and secure environments known as virtual machines (VMs). TheseVMs
30 rely on the hypervisor’s ability to separate the machine’s resources from the hardware and distribute them
31 appropriately. Virtualization helps you get the most value from previous investments. The physical hardware,
32 equipped with a hypervisor, is called the host, while the many VMs that use its resources are guests. Operators
33 can control virtual instances of CPU, memory, storage, and other resources, so guests receive the resources
34 they need when they need them. When the virtual environment is running and a user or program issues an
35 instruction that requires additional resources from the physical environment, the hypervisor relays therequest
36 to the physical system and caches the changes—which all happens at close to native speed (particularly if the

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37 request is sent through an open source hypervisor based on KVM, the Kernel-based Virtual Machine).

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1 3.2. Cloud Computing
2 Not that long ago, computers were large, expensive, largely unknown devices that punched out the financials
3 for large companies, payrolls, analysis and other information. No one, except a few computer people, had any
4 idea how this worked. It just worked. Now, smaller companies wanted these capabilities, but the costs were
5 unreasonable, so they would ”lease” capacity from large tech companies, like I.B.M. This approach allowed
6 them just the capacity needed, without the cost of the equipment. Also, it sounds a lot like today’s ”cloud”.
7 As one might expect, the smaller players were eaten up by larger players. Then larger players ate them up until
8 a select few companies owned vast amounts of customer data. Moving from one to the next was increasingly
9 difficult. Choices were reduced, the cost migration increased, and the entrenched companies squeezed for every
10 dime. Soon after came the P.C. revolution, driven by the idea of getting data out of these big back-office
11 systems and onto personal computers where it could be worked and analyzed. Then the Internet gave us the
12 ability to share data broadly and cheaply. So, now we have the ”cloud”. Essentially, large back-office systems
13 and platforms that consumers can ”lease” to run their businesses and analyze their data. Prettier, yes.Faster,
14 maybe. Betteremrains to be seen.
15 I’d say a big consequence is that there is a single point of failure. There have been major outages ofS3
16 (a storage system by Amazon Web Services), which affected many renowned services, such as Airbnb,Spotify,
17 and Slack. If a cloud provider suffers from a problem, services relying on that service suffer as well.
18 3.3. Stifled Growth
19 Owning and maintaining on-site I.T. resources generally costs more than having them in the cloud. Keep in
20 mind that traditional I.T. spending can siphon awayfunds that you could otherwise use to expand your business,
21 open a new office, hire a new employee, or launch a new marketing campaign.
22 3.4. Risk of Business Disruption
23 If your I.T. resources are on-site, what happens to your business in the event of a fire, hurricane or other
24 calamities? Your risk of business disruption is much higher if your applications and data are in the path of

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25 disaster instead of in the cloud.
26 3.5. Inability to Work Remotely
27 On-site I.T. keeps people tethered to the office, interfering with their productivity and making it harder to
28 achieve work/life balance. This negatively affects current employees it also makes it harder to attract top job
29 candidates in a tough hiring environment.
30 3.6. Loss of Competitive Advantage
31 If you don’t take advantage of the flexibility and agility that the cloud affords, you can be sure your competition
32 will. And if it allows them to respond more quickly and effectively to opportunity, it’s less likely to turn out
33 well for you. Not all of these consequences are measurable in hard dollar costs; some represent indirect costs.
34 But when you consider the relatively inexpensive monthly cost of a complete cloud services solution (about
35 150−175 per user for most of our clients – including the cost of managed services), the cost of not movingcan
36 be pretty steep.

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1 4. Robots and Humans
2 Reinforcement learning can be used together with robotics to achieve whatever goals you can represent as
3 numerical penalties or rewards. If this is something you’d classify as ”more intelligent”, then the answer to
4 your first question is ”yes”. They were making robots behave more as experts fall in the domain of imitation
5 learning. One approach to imitation learning is ”inverse reinforcement learning”: you observe (human) experts
6 and try to reconstruct the reward function they might be using, which will allow you to use that samereward
7 function in reinforcement learning in robots. Now, the key things that make humans intelligent are versatility
8 and communication. We are social animals, and communication allows us to share knowledge and distribute
9 tasks. We are also versatile and can adapt to many different environments. The fields of A.I. you need forthis
10 are: transfer learning: levering knowledge learned from1 domain to a different (but related) domain multi-agent
11 systems: haveagents that can collaborate, oreven learn what other agents can be trusted or not. All of the fields
12 above (imitation learning, transfer learning, and multi-agent systems) are pretty large research fields. Thereis
13 still a lot of work to be done. Until we have a lot more progress in all those fields, no artificial intelligencewill
14 be anywhere near the types of intelligence humans are good at. Of course, computers are already muchbetter
15 than humans at other kinds of intelligence (accuracy, calculating speed, memory.
16 5. Artificial Intelligence(AI) and Humans
17 If you’ve ever taught a dog to sit or shake, you’re familiar with the concept of reinforcement learning. Positive
18 reinforcement is when an animal—or a child if you’re lucky—learns a desired behavior based on the rewards it
19 receives for the steps it takes to reach the desired outcome. For example, you give your dog a treat for sitting
20 at the door when he needs to go out for a bathroom break, or you give your child a high five—or in my house,
21 when they do well on their spelling test. The subject—in this case, the dog or child—learns which behavior
22 is good or bad based on the response it receives along the way. The same concept can be applied to artificial
23 intelligence (A.I.).
24 Historically, one of the flaws of A.I. is traditionally, machines and computer programs can’t learn from
25 their mistakes. Instead, they rely on a complex set of data that helps them recognize words, things, and
26 missions. Rather than learning by trial and error, like humans do, they refer to their internal set of hard-coded
27 ”instructions” to determine right and wrong. And while deep learning allows them to be reprogrammed with
28 mass amounts of new data to achieve better outcomes, they can’t improve those outcomes independently. This
29 process, also called ”supervised learning” requires extensive involvement on the part of the programmer.
30 That’s where reinforcement learning comes in. Recently, tech giants like Alphabet and Google have been
31 working to teach artificial intelligence programs to think for themselves through reinforcement learning. In
32 other words, they’re helping them solve perceived problems, ”rather than being taught what solutions look
33 like.” Many would agree the technology is still in its infancy—or as one writer put it, it’s green-and-black-DOS-
34 screen stage. Although it’s been tremendously successful in gaming—including Google DeepMind/AlphaGo’s
35 much-hyped victory in the game Go—few have been able to find solid commercial uses quite yet, outside of
36 content personalization and ad placement or other somewhat insignificant victories such as saving power or
37 sorting trash, etc. The following are a few ways programmers will be working to develop the technology in
38 coming years to make it more useful in the commercial world, especially in marketing and our personal lives.

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1 5.1. AI and Machine Learning
2 A.I. currently uses reinforcement learning to move through scenarios that have a clear set of perceived rewards.
3 That’s one reason gaming has been such an easy place to start. In the future, however, programmers will
4 focus more on ”reward shaping”—teaching A.I. to work in situations where rewards are more nuanced, with
5 more action steps involved. This will allow robots to move from simple acts like moving through a maze, to
6 determining that a maze needs to be moved through to achieve another perceived purpose. These abilities
7 could manifest in A.I., providing better, more relevant recommendations after a customer makes a purchase.
8 A.I. already assists with content personalization for ad delivery, but imagine Netflix recommendations that are
9 exactly what you feel like viewing at that moment—every time—or Alexa taking a recent request andturning
10 it into a relevant Amazon order for an item you didn’t even realize you needed.
11 5.2. Complex Problem
12 Currently, reinforcement learning has been most successful in very specific, controlled situations. To create
13 machines and programs that are more effectual in our work or personal lives, they will need to move ”beyond
14 a single, narrow domain” to develop common sense and handle more complex, less structured challenges. In
15 other words, they’ll need to be able to infer when there is a real problem or mission in a living, changing
16 environment. Marketing professionals could apply these A.I. capabilities in order to be more responsive in
17 social media reputation management situations. A.I. algorithms could be trained to detect unhappycustomers
18 and go one step beyond today’s programs that only analyze sentiment to reply with a suggestion to solve a
19 problem.
20 5.3. Greater Curiosity
21 At the moment, machines and programs have no purpose to assess or improve situations on their own. In
22 the future, programmers will be working to build them with greater curiosity to improve the world around
23 them. A.I. that can explore the world around it and make suggestions for positive change could also, in theory,
24 create compelling thought leadership content, or at least, more relevant content marketing articles that are
25 indistinguishable from pieces written by human beings. Since content is such a huge piece of the marketing
26 puzzle today, taking this job off the plates of human experts can free them to work in areas like content strategy,
27 which still require a human touch.
28 6. Working in Less Controlled Environments
29 Humans aren’t always logical. In the past, self-driving cars have found it difficult to drive withhuman-driven
30 vehicles because their actions don’t always make sense—in essence, they can’t be anticipated. A.I. agentswill
31 need to learn to adjust their actions in human-centered environments, where actions often change based on
32 mood, rather than clear rules or logic. In the future, it’s clear deep reinforcement learning could be a game
33 changer in almost every industry. Not only does it free up programmers from creating cumbersome data sets,
34 it also creates limitless growth potential for A.I. This will be useful in the areas of self-driving vehicles (not
35 just cars, but planes and trains, alike), social media marketing, and customer service, as machines learn to
36 adjust to customer complaints and service issues. Indeed, with reinforcement learning, robots will be able to
37 take on even more ”human” qualities of discernment and complex decision-making. Soon, the question of when
38 personal robot assistants become a reality will be answered—and the only question we’ll be asking is what to do

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39 with all our free time. Since open-source is becoming more of a trend in computer science, how cancomputer

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1 programmers protect a device? Three main ways: Keeping open source components up to date Slecting the
2 open source components that do not extend the attack surface unknowingly. Protecting the system so that it
3 does not publish details about its components and versions outside. Old versions of open source components
4 have typically a set of known vulnerabilities. Badly configured system manifests its vulnerable versions and
5 helps the attackers. Sometimes you can easily extend any system with open source plugins but you might not
6 know that they open up new ways to access the resources over the web [2], e.g. creating files in the file system.
7 But because the components are open source, it is usually very easy to find material to properly use themand
8 you can fix the issues if wanted. On average, I think open source code is more is secure than closed sourcebut
9 you must keep up with the software updates and understand the inner functionaliry up to some degree. Ifyou
10 are thinking about DRM (e.g. protected movie files), this is quite difficult in Open Source as the encryption
11 algorithm would be completely readable including the key management. I believe this is done by havingsome
12 components that are not open source in the otherwise open source software (e.g. in Firefox there is a DRM
13 component by Google that you can switch on)
14 7. Big data and Bioinformatics
15 I would argue that data and bioinformatics are already changing biology. Biology is being taught as a data
16 science (computational biology). And while data is not the only aspect of biology, any biologist must deal with
17 data. Data-driven research and huge datasets are also placing bioinformatics at the center of many research
18 projects. Itay Yanai, the director of Institute for Computational Medicine at N.Y.U. recently expressed this in
19 the following words: ”. . . those who are able to make sense of the richness of data in the modern life sciences
20 have now been put in the driver’s seat.” Robotics can provide immense satisfaction to the Robotics[1] Engineer
21 while pursuing it as a future career if the person is passionate about it. If you want to learn robotics, the best
22 way to do so is developing computer science, coding, physics, and linear algebra. One of the key art of being a
23 Roboticist is applying one’s knowledge and common sense in the right way and at the right time.
24 8. Programming Language
25 The most usable language use in making the Robotic in top level, C/ C++ takes the top slot in Robotics
26 programming platform as most programmers/ aspiring ”Robotics Engineer” use C/C++ to ensure the peak
27 performance from the Robot. C/ C++ is a must-learn programming language if you are serious about building
28 a career in the Robotics industry because these two are considered the most mature programming languages in
29 Robotics because they allow easy interaction with low-level hardware. When the Robot is severely limited in
30 memory, standard ’C’ is preferred to save every byte possible; otherwise, ’C++’ is easy to work with. The C++
31 language can call the OS API directly and doesn’t need any wrapper which means that one can use platform-
32 specific libraries that are extremely quick to use. Python is easy to use and requires less time. Compared to
33 other object-oriented programming languages such as Java or C/C++, less coding work is required in Python
34 saving a lot of time. But it complicated for massive projects because of its inability to spot errors as it is
35 an interpreted language. Python is considered a high-level programming language, quite extensively used in
36 designing embedded systems in Robotics. Because of all such useful features, it has become a key player in
37 R.O.S. (Robots Operating System). C/ C++ is one of the few languages that excels at all of these andyields
38 good quality performance quickly. Python is recommended if you’re a novice making your way into Robotics
39 programming. MATLAB is best for data analysis.

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40 Programming Language is vocabulary and a collection of rules that command a computer, devices, an

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1 applications to work according to the written codes. The programing language enables us to write efficient
2 programs and develop online solutions such as- mobile applications, web applications, games, etc. Toadvance
3 your ability to develop real algorithms, most of the languages come with many features for the Programmers.
4 They can be used in a proper way to get the best results. To Improve Customization of Your CurrentCoding-
5 Using basic features of the existing programming language you can simplify things to program a better option
6 to write resourceful codes. There is no compulsion of writing code in a specific way. The thing which matters is
7 the usage of features used and clarity of the concept. To Increase Your Vocabulary Of beneficialProgramming
8 Constructs-Programmers use high-level languages to express thoughts. And, byusing the best features they can
9 easilyexplain the workingofaspecific application, device, etc. Robotsin medicinehelp relieve medical personnel
10 from routine tasks, take their time away from more pressing responsibilities, and make medical procedures safer
11 and less costly for patients. Robotic medical assistants monitor patient vital statistics and alert the nurses when
12 there is a need for a human presence in the room, allowing nurses to monitor several patients at once. These
13 robotic assistants also automatically enter information into the patient electronic health record. Robotic carts
14 may be seen moving through hospital corridors carrying supplies.
15 9. Robots as Personal assistant
16 Robotic personal assistants can be built to look friendly and the Japanese have taken the lead on this front.
17 One of their machines, called Paro, responds to human speech and looks like a decidedly non-threatening baby
18 seal. Robots are also being used for medical transportation to deliver medicines, meals to patients and staff, in
19 addition to optimizing communication Many healthcare facilities have started using robots to clean and disinfect
20 surfaces, especially with the rise in antibiotic-resistant bacteria and outbreaks of deadly infections like Ebola
21 Rehabilitation robots play a significant role in the recovery of people with disabilities by helping them improve
22 mobility, strength, coordination etc. Speed: Robots don’t get distracted or need to take breaks. They don’t
23 request vacation time or ask to leave an hour early. A robot will never feel stressed out and start running slower.
24 They also don’t need to be invited to employee meetings or training session Consistency: Robots never need
25 to divide their attention between a multitude of things. Their work is never contingent on the work of other
26 people. Perfection: Robots will always deliver quality. Since they’re programmed for precise, repetitive motion,
27 they’re less likely to make mistakes. In some ways, robots are simultaneously an employee and a quality control
28 system.
29 10. Conclusions
30 Drilling deep into the capabilities and motives of humans and robots, reassures us that humans are not in danger
31 of being substantially replaced by robots. At first glance, this conclusion seems at odds with recent unpublished
32 stylized econometric analysis that suggests that automation reduces the share of output attributable to labor
33 . Future research can explore whether the reason for the decline in labor’s share, is that growing government
34 regulations have reduced the productivity of humans more than they have reduced the productivity of robots
35 and computers. Although awaiting that future research, it is at least reassuring that the Autor and Salomons’
36 working paper also found that an increase in automation does not increase unemployment instead of advocating
37 less regulation of labor, some have advocated more regulation of robots and computers. Elon Musk famously
38 predicted that “robots will be able to do everything better than us” (as quoted in Clifford, 2017) and warned
39 that government needs to regulate AI “before it’s too late” (as quoted in Breland, 2017). And yet when he
40 tried to extend the reach of robots to the final assembly of his Tesla Model 3 cars, he was only able to build

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41 far less than half of the cars per week that he had promised. Later, in an April 2018 tweet, Musk admitted

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1 “yes, excessive automation at Tesla was a mistake . . . Humans are underrated” (as quoted in Wilkes,2018,
2 p. A8).When a robot killed an assembly line worker in Germany in June 2015, it was not from Terminator-like
3 intent, but from an inability to distinguish between inert metal and human flesh The greater danger usually is
4 not AI, but artificial stupidity