Computing: It Really Is Part Of Everything People Do

You might not be aware of this, but computing, and computer tech in general, affect our life in so many ways. For example, computer tech can be found in most modern cars and can definitely be found in just about all of the movies out there. Businesses and governments around the world rely on both computer technology and computing to deal with people in a number of ways.

Those are only a handful of the ways that computing and computer technology is part of everyday life. The chances are in the future, people will be relying even more on computing and computer technology.

Why CS Grads Should Get Into Retail

The labor market for Computer Science graduates is ever growing. Interestingly, the supply is yet to meet the demand. Silicon Valley makes an obvious choice for the majority considering the ample opportunities and high wages. But, according to new research, the retail sector may just be catching up. Retail companies are looking for computer science graduates or persons with related backgrounds. In addition, they are willing to pay comparable compensation. Tech–talent needs in the retail sector are a result of a bold initiative by key industry players to adopt a bold technology. More research shows that out of the 118,000 computer science jobs posted in 2017, 5,000 comprised those from the retail sector alone.

This means that 15% of all college graduates have a chance of landing into relevant jobs within the industry. And, the numbers only keep rising. In some cases, compensation in the retail sector outmatches that of the Silicon Valley. One of the biggest advantages is that of geographical diversity. College graduates do not have to necessarily live or work in areas of high cost of living such as the Silicon Valley. With the economic and practical parts aside, graduates also get to be choosy. They get a front seat to observe and experience advancements that can transform people’s shopping experiences. Lastly, they get an opportunity to go beyond the traditional tech employers.

Research source link:

https://nrf.com/blog/4-reasons-computer-science-grads-should-be-looking-jobs-retail

Computer Science Ethics

According to Brent Hecht, researchers should disclose any potential negative consequences that may arise in society as a result of artificial intelligence. This is mainly on matters privacy and data use and management. Hecht is a computer scientist and chair of the FCA (Future of Computing Academy). He was part of the team of young leaders that made the policy proposal in March. He believes that without such standards, computer scientists might continue producing software and machines without taking into consideration what effects they have on the society. Hecht’s peer-review proposal says that when a paper is handed to a peer reviewer, in addition to evaluating the intellectual rigor within, the author’s claims of impact should be put into consideration.

Basically, the process should also include trying to identify unintended uses and expected side effects. However, Hecht and his team do not propose social impact to be the foundation on which publications get rejected. Their recommendation is that authors should disclose all the potential negative impacts that arise from the use of the new technology. He thinks that for successful prediction, social scientists should be involved. They should be included in the execution process. Google has already responded to the proposal. Publicly disclosing the negative impacts of new technology will create new pathways of solving emerging problems that would have otherwise gone unsolved.

Research source link:

https://www.nature.com/articles/d41586-018-05791-w

Doctors’ Gut Feelings A Major Determinant of Number of Tests for Patients

In a recent study carried out by MIT, computer scientists discovered that a doctor’s gut feelings play a significant role in a patient’s treatment process. Scientists arrived at this conclusion after analyzing notes written by doctors looking after intensive-care patients. According to research affiliate Mohammad Ghassemi from the Institute for Medical Engineering and Science (IMES) at MIT, based on a doctor’s training, practice and years of experience, he/she is able to look into a patient’s recovery process and see if they are doing well or not. Many tech companies are currently working on AI systems that can assist in the diagnosis and treatment of patients based on their medical data.

The latest study shows that technology may not be able to perform as well as doctors. This is because human doctors possess something that technology lacks. A doctor’s intuition plays an even bigger role on the first days of a patient’s visit to the hospital. This is because the doctor doesn’t have a lot of data to go by yet. Among the factors that doctors consider when deciding what kind of tests to give to patients include symptoms, family history, lifestyle habits and the severity of illness. Gut feelings come in as a result of experience. Computer scientists were able to find out about this based on sentiment analyses carried out on written notes.

Research source link:

https://techxplore.com/news/2018-07-scientists-physicians-gut-patients.html

Accurate Extrapolation by First Machine Learning Technique

Until now, machine learning methods have only been able to predict situations based on other known situations. Projecting occurrences outside this scope – a method known as extrapolation – was impossible. Scientists from the Max Planck Institute for Intelligent Systems (MPI for Intelligent Systems) and the Institute of Science and Technology Austria (IST Austria) have developed a new machine learning method that provides accurate predictions for all feasible conditions controlled by similar physical dynamics. The objective is to help scientists develop robots that can guarantee safe operation under different circumstances. But how does one tell robotic limits without damaging the robot?

The new technique provides interpretable and simple descriptions to the underlying physics. Interpolation requires sufficient data and, acquiring such takes a lot of time and resources. Scientists need to collect data from dangerous and extreme situations. The machine learning method solves such problems and, is the first to accurately predict unseen situations. It takes in data and works to showcase the true dynamics of the occurrence. It provides equations that describe these situations. Once scientists get a hold of these equations, they can predict all occurrences based on specific conditions without necessarily having seen them. The model also provides insights into the link between results and conditions. Equations provided are similar to those found in textbooks.

Research source link:

https://www.eurekalert.org/pub_releases/2018-07/iosa-fml071218.php

Machine Learning As An Independent Study

Machine learning expertise is in high demand. The online payment mogul PayPal plans to hire 600 experts from the field of Machine learning and artificial intelligence. American and Canadian universities are leading the pack when it comes to meeting this demand. When comparing machine learning to computer science, one wonders if it’s necessary to separate the two fields and have them studied independently. Machine learning has a wider scope of applications and also commands better compensation. Companies that are continuously in need of machine learning expertise include Amazon, Flipkart, Accenture, and Samsung. Breaking into this highly competitive industry will require a good programming background. If you already have a degree in CS and are wondering what to do for your Master’s degree, take ML.

Companies are increasingly creating space for machine-learning centered jobs. Data science interviews are focused on code and algorithm architecture. In an ML-oriented company, a solid CS foundation can help you secure a job. Computer science helps students to acquire skills in areas such as software development, theory algorithms, information systems and system architecture. Most programs also feature fields such as machine learning and data mining. If you prefer pursuing research opportunities, an MS in machine learning would go a long way. Also, ML fits well with students that have a strong statistics background.

Research source link:

https://analyticsindiamag.com/should-machine-learning-be-a-separate-major-like-computer-science/

A Problem Exclusively for Quantum Computers

Scientists have been looking for a problem that only quantum computers can solve since 1993. They knew that such a problem would work well to showcase the potential that lies in quantum computers when it comes to tackling hard calculations. Finally, 25 years later, scientists have found the problem. In a recent paper written by computer scientists Avishay Tal and Ran Raz, there’s strong evidence to show that the computing capacity embedded in quantum computing surpasses classical computers by far. They go ahead to prove that a quantum computer can efficiently solve this problem while a conventional computer would take years to do the job. Such problems are categorized in a class known as “BQP”.

Engineers are still trying to build a useable quantum computer. The report does not in any way undermine classical computers in an effort to elevate quantum machines. What the paper does is to show how both are worlds apart. Scientists developed various complexity classes long time ago to categorize computational problems. For example, building an algorithm that can find the prime factors to large numbers belongs to a complexity class different from an already-built algorithm that does that for smaller numbers. The two most common classes are P and NP. BQP was developed in 93’ by computer scientists Umesh Vazirani and Ethan Bernstein. It was, and still is, meant to contain all problems whose answer is a yes or no, and can be solved efficiently only using a quantum computer.

Research source link:

https://www.quantamagazine.org/finally-a-problem-that-only-quantum-computers-will-ever-be-able-to-solve-20180621/

Summit, The New Worlds Fastest Computer

Summit is said to be 8 times faster than its predecessor: Titan. It was unveiled by the US Department of Energy as the world’s fastest computer. The machine occupies an area equivalent to two tennis courts. It’s powered by Nvidia’s Tensor Core GPUs and IBM’s Power 9 processors. It can produce about 13MW of power when running on all cylinders. This beast was designed and built by Nvidia and IBM and incorporates computing architecture based on AI capabilities. This means that it can be used in developing scientific models and simulations. Thanks to conventional high-performance computing capabilities, it can also process machine-learning assignments.

According to ORNL’s outreach project manager Katie Beathea, contractual costs for Summit could hit up to 280 million USD. This includes maintenance and any other potential activities to be undertaken. Summit occupies about 9,000 square feet, space that was renovated to specifically accommodate the new system. Evaporative cooling towers were built on site while 15MW of utilities and power were transported to the location. The cooling system churns over 4,000 gallons of water to dissipate heat produced by the computer. China’s Sunway TaihuLight was the fastest supercomputer before Summit. In addition to being the fastest, Summit is also the smartest. It performs tasks at speeds of about 300 gigabytes per second. Posts from the Nvidia blog indicate that the machine has been booked for various cancer, fusion energy and disease and addiction projects.

Research source link:

http://www.datacenterknowledge.com/supercomputers/ibm-nvidia-build-world-s-fastest-supercomputer-us-government

New System Allows Personal Computer to Analyze Huge Graphs

In data science, graphs are made using connecting lines and node structures. These are used to build and study multiplex data relationships. Such developments are used to rank webpages, plot neuron structures and analyze relationship networks that influence political insights. Sometimes, huge graphs made of billions of lines and nodes can reach more than a terabyte in size. This creates need for servers that are heavily dependent on a lot of power and the use of costly dynamic random access memory (DRAM). A group of researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT have developed a new device that uses an inexpensive flash storage, similar to the one used in smartphones, which can process huge graphs using a personal computer.

Flash storages are usually slower. But for the researchers, they equipped the device with a computation accelerator and a flash chip array which gives the flash storage DRAM-like capabilities. The device is run by a new algorithm which makes it easy for the flash storage to access data while reducing computing resources such as time, bandwidth and memory. Results from the research showed that it was possible to analyze large amounts of data while utilizing less power and temperature. The team was able to accomplish the same amount of work done by traditional systems using a personal computer.

Research source link:

http://news.mit.edu/2018/device-allows-personal-computer-process-huge-graphs-0531

JOB OPPORTUNITIES FOR COMPUTER SCIENCE MAJORS

This field requires analytical thinking. Analyze a problem and know how to handle it. Be able to explain a technical problem in a language that a non-technical person would understand. You are required to have a passion for creativity. The technology is rapidly changing now and then. Computer science majors should be able to understand this and be updated on the changes. There are several job opportunities in the computer science field. The jobs depend on the interest, skills, and specializations of an individual.

One of the examples of job opportunities is computer hardware engineers. This requires testing computer components like memory devices and routers. Designing and developing computers is also one of the duties performed. Skills in technical and creativity are used widely. The engineers must have the patient to perform one test, again and again, to ensure it is working correctly. It is a well-paying job.

Computer systems analyst is another job opportunity. Here, one is required to analyze the organization’s computer systems. For efficiency of the company’s operations, the analyst is expected to recommend necessary changes on both hardware and software of the computers. Interpersonal skills are required to convince the managers and the employees on the required changes.

Computer network architect implements, designs, and maintain data communications and networking. They assist in data sharing and communication in an organization. The three job opportunities in this article are well-paying jobs.

Reference: https://www.thebalancecareers.com/top-jobs-for-computer-science-majors-2059634