From Software Engineering To Artificial Intelligence — Making The Career Move
Technology has been an unstoppable force over the years, with particularly astounding growth and breadth of its presence across all business domains. Automation and robotics, powered by artificial intelligence (AI), are changing how work is done in organizations, and AI-driven algorithms are already powering driverless cars on the roads.
What, really, is artificial intelligence?
Tracing its roots back to computer science, AI essentially makes machines or software programs smart, quite like how the human brain works. Machines can now work and think like humans, identifying faces and recognizing speech, driving cars, and performing robotics surgeries. Doing this is no mean feat, as making what is in essence a conglomeration of nuts and bolts learn and take informed decisions while improving basis mistakes is huge. From online shopping recommendations to automatic tagging of faces on social networking sites, AI is already in many parts of our lives.
Could AI take away human jobs?
The fear is not completely unfounded. AI has taken away and will take away more jobs, but those will largely be the repetitive, low-value-adding kind. AI advancements will bring in new technologies and tools to tackle highly complex jobs, and will improve our day-to-day lives.
Software engineers and technology workers are, naturally, concerned. Letting machines do what humans did all this while is no easy task, and AI is in fact expected to take over nearly 1.8 million jobs by the end of 2020. By the end of 2037, from cashiers to sports referees and from telemarketers to journalists, automation could be replacing many jobs.
Is this a good time to switch to a career in AI?
AI is here to stay, and it is expected to create many more jobs than it takes away. Its spread into different fields has brought in several options for a creative and lucrative career. Technology professionals with AI skills will outshine their peers, consigning routine skills from most industries into obsolescence. There will be a number of job opportunities in programming on the technical engineering front of data science and, by 2022, the World Economic Forum (WEF) estimates 130 million new jobs in AI.
Are software engineers well placed for AI careers?
Most certainly. A C++ developer, for instance, may have an interest in image processing, an aspect where machine learning (ML) has made inroads. What is important to understand is that AI and robots are not gunning for software engineering jobs, and professionals in the latter already need to stay up to pace with the most current frameworks, technologies, and tools. Their interest in and familiarity with picking up new skills positions them well for moving into AI careers. What they must do is understand technology trends in detail, along with getting a strong grasp of theoretical concepts. They also have to be able to present problems in a manner that is non-deterministic.
What knowledge do they need?
Effective ML engineers do more than train out-of-the-box models on curated data sets, aiming at high accuracy. They must understand:
- Data type, its statistical distribution, and inbuilt biases
- Statistical models applicable to the relevant dataset and their potential to be successful
- Metrics to consider to optimize model output
Along with engineering basics, they must know the fundamentals of linear algebra, optimization, and statistics. This is essential for model integration, deployment, and debugging. Issues to consider include data acquisition, labeling, and preprocessing to build, update, and serve an inference model.
Which skills help in making the transition?
The following skills will help software engineers to make the move into AI careers:
- Software engineering: The job already requires considering user needs and designing and developing applications accordingly. These skills help to create code that accelerates experiments, test data pipeline functions and model interference timelines, include model checkpointing, and set up distributed infrastructure.
- Machine learning: Already present in several business domains, ML uses data to train neural networks. Important things to know are the working of loss of function, building functional models and effectively communicating results, and backpropagation benefits.
- Data munging: Another name for data wrangling, this is essential to correlate a model to the data quantity and quality. It involves pre- and post-processing of data as well as looking for reliable and accurate data sources.
- Statistics: This is a fundamental skill for the transition, and it is important to grasp over- and under-fitting, provide the right attribution to model results, and know how to determine and measure model success.
- Debugging and tuning: ML models are brought down most commonly by poor output predictions. What is required is finding the right architecture and parameters to test different configurations.
- Programming: Build a base in the popular languages, such as Java, Julia, Lisp, MySQL, and Python.
What else can help to ease the move to an AI career?
Find an unmet need or something that could simplify everyday lives through a data-driven approach. Look for relevant labeled datasets, or consider how to find similar data. See if the available data amount and quality will allow accomplishment of the task at hand. Iterate on deployment and modeling, and maximize learnings from these experiences. Follow good newsletters and other sources such as Insight Blog, Hacker News, and Import AI.
Are AI certifications useful for aspiring AI professionals?
For a software engineer, an AI certification is a good way to move into an AI career. An artificial intelligence certification proves the candidate has the most current skills in the field, and is capable and desirous of moving into higher roles and responsibilities.
The last word…
The trajectory taken by AI and ML indicates the high prospects in this line, and software engineers, by virtue of their existing skills and nature of work, are particularly well placed to capitalize on the opportunities!
Originally published at https://www.linkedin.com.
