Home » Blog » Artificial Intelligence (AI) » Will AI Render Software Developers Obsolete?

Artificial Intelligence (AI)

Will AI Render Software Developers Obsolete?

Every economic sector will be impacted by artificial intelligence technology, hopefully for the better. For those in software development, many are asking the question, will AI make programmers obsolete?

Published

3 years ago

June 18, 2020

TwinzTech

Will AI render Software Developers Obsolete

Artificial Intelligence (AI) and its closely related cousin Machine Learning have become hot topics of discussion around dinner tables and expert panel shows.

Unfortunately, whether expert or opinionated father a side is taken as to whether developments in the technology will bring about the end of humanity or usher in a new utopian age.

These are pretty broad strokes that AI cannot hope to make real, either good or bad, in the near term. However, dinner table assumptions aside, the technology will have a massive impact on how we work; in fact, it already is.

Every economic sector will be impacted by artificial intelligence (AI) technology, hopefully for the better. For those in software development, many are asking the question, will AI make programmers obsolete?

Artificial Intelligence (AI) Technology

1. Work Flow Improvements

A study conducted in 2013 predicted that 47% of jobs in the US might be automated by 2030. A more recent study predicted that between 40 million and 160 million women worldwide might need to transition to a different occupation by 2030, with clerical and bookkeeping roles being the most in danger of being automated.

Where does this leave the profession that may be responsible for creating the automation software?. This is a question of keen interest for us at DevsData, and we feel it should be looked at in two ways.

Firstly, AI can be of benefit to programmers, and this is being seen across the IT sector even with AI adoption being done at a rudimentary level.

Secondly, does this benefit mean that programmers can be done away with? The software development cycle has many elements that programmers may find tedious, which can be automated.

If we look at the period before any code is committed to memory, the first technology specifications and features need to be determined. Only then can code be written.

In-Demand Programming Languages (AI)

Once that is done, the process of testing, deployment, and maintenance begins, which is costly and time-consuming. AI has proven to be excellent at handling the tasks we find repetitive and frankly boring.

For programmers, AI can assist in code writing by preventing errors from cropping up based on a library of successful and unsuccessful code. Further, AI can assist in testing potentially been far faster and efficient as well as fixing bugs with the same efficiency.

2. So, are programmers at risk?

As to the second part, the short answer is not necessarily. While the technology sector will use AI to improve efficiency by allowing AI to take on the mundane but necessary tasks, this frees up programmers in surprising and creative ways.

Programmers will be able to focus on developing better code and better software. The reality is also that programmers are still needed to create the AI algorithms to put in place in the first place, analyzing and curating programmers will do the data for use in the algorithms for the immediate to near future.

This, of course, means that programmers will need to learn new skills and possibly new programming languages, but this has been the case for the profession even before the AI debate began.

Artificial Intelligence (AI)

How to Build a Rewarding Career in Data Science?

We’ll explore the interesting world of data science, investigate its numerous applications, and offer you valuable tips on how to establish and grow a successful career in this exciting new sector.

Published

2 months ago

October 15, 2023

TwinzTech

How to Build a Rewarding Career in Data Science

As the need for qualified data scientists grows, acquiring a Data Science Certification might lead to exciting new opportunities. Why Choose Data Science as a Career? This is a prevalent worry among students and recent grads interested in data science, predictive modelling, and AI/ML professions. If you are naturally strong with numbers, like solving riddles, and are naturally curious, consider a career in data science.

In this blog, we’ll explore the interesting world of data science, investigate its numerous applications, and offer you valuable tips on how to establish and grow a successful career in this exciting new sector.

Table of Contents

1. Understanding Data Science: What Is It All About?

Data science is the process of gleaning valuable information from enormous databases. Understanding data patterns and making informed business decisions are aided by a combination of principles from computer science, statistics, and domain expertise. Businesses in every industry are racing to harness the power of data. This is where the services of trained data scientists with the appropriate tools may be invaluable.

2. Why Choose Data Science as a Career?

Before we go into how to get started in the industry, let’s discuss the advantages of a career in data science. Technological innovation is at the forefront of data science. From medicine to finance to retail to the arts, data science is having a profound impact. The potential to have an influence, solve problems, and make a difference in the world is great for those who work in this sector.

3. The Path to Becoming a Data Scientist

You’re obviously interested enough in data science to want to know how to get started in the field, so let’s talk about that. The road ahead may seem challenging, but with focus and strategy, you may make good progress.

a. Educational Foundation

A solid educational foundation is crucial for a successful career in the data science sector. Many data scientists get their start in the field by earning a bachelor’s degree in a quantitative field like Computer Science, Mathematics, or English. If you want to stand out in this competitive field, consider pursuing a master’s degree in data science or a related field or being certified in data science.

b. Develop Technical Skills

Data science requires a diverse range of abilities because of its technological nature. You should study Python and R since they are both widely used programming languages with practical applications in data analysis and machine learning. Use tools for displaying data, such as Matplotlib, and libraries for altering data, such as Pandas.

Understanding Object Lock A Shield for Data Immunity

c. Dive into Statistics and Machine Learning

Statistics act as the backbone of the data science field. Study the ins and outs of creating AI algorithms and statistical analysis. To get the most out of these strategies, try applying them to actual problems.

d. Build a Portfolio

The best way to display your ability is by practical practice. Take part in hackathons and open-source projects. A portfolio is an excellent way to demonstrate your skills and passion.

e. Network and Collaborate

Building your network in the data science community might open doors to paid and unpaid internships. Learn from industry experts by participating in seminars and online discussion groups. The results of collaborative activities are often shared knowledge and shared responsibility for tasks.

f. Stay Updated

Data science is a dynamic field that’s constantly evolving. Be informed about the latest advances in the area. Continuous learning is essential for keeping one’s competitive edge in the workplace.

4. Job Opportunities in Data Science

If you have a varied set of talents and a data science certification, you’ll be eligible for a variety of opportunities in the data science ecosystem.

Data Analyst: Determine actionable insights from data via analysis.
Machine Learning Engineer: Machine learning engineers develop and implement intelligent software that can teach itself.
Data Engineer: Manage data pipelines and infrastructure.
AI Research Scientist: Work as a scientist who focuses on artificial intelligence and does research to better the field.
Data Scientist: Data Scientists use their knowledge and expertise in specialized fields to solve complex problems.

5. Final Thoughts

Data science may be a fulfilling and intellectually stimulating career. Start a productive career by investing in your education, technical skills, and experience. Data science certification might help you succeed professionally. Data science may be appropriate for you if you love data and want to change the world. If you study hard and work hard in this field, you can influence global enterprises.