If you’re interested in AI, you’ve seen your share of news and content surrounding how it changes everything from the workplace to daily life.
On the workplace side, there are as many questions as there are opportunities to learn how to incorporate AI into a job.
However, if you have interests in skills like software engineering and machine learning, you might also be wondering:
Could I create an AI platform or program?
Well…yes. Someone had to develop the ones we know today, right?
So, the bigger question is:
How do you create your own AI program? Let’s see what the experts say.
AI Software Development as a Job
Andril Bas, the Co-Founder of uptech, says there is tremendous potential for companies that invest in AI software development and shared his experience in building AI software.
Drawing from the experiences gleaned during the development of Dyvo.ai, Andril shared three invaluable tips for AI-based product creation:
Employing machine learning is critical – It enables systems to learn and evolve, ultimately refining accuracy and performance.
Agility is critical – As AI constantly evolves, an agile team of developers who can swiftly adapt to changes is indispensable.
Collaborating with seasoned AI developers - Given the complexities, partnering with experts possessing an in-depth understanding of the technology is invaluable.
Even though there is a strong need to be agile and learn a lot, Adnril says that building AI software is similar to conventional software development but with key differences.
These differences require understanding the AI-focused tasks of problem-solving, data collection, testing, training and deployment.
Meanwhile, PlatAI points out that AI falls under the branch of computer science and provides examples of AI applications, such as intelligent assistants (Siri, Alexa), self-driving cars, conversational bots, and email spam filters.
They say the core of building an AI lies in collecting relevant data to train AI and that more sophisticated AI technology can analyze vast amounts of data to learn how to perform specific tasks.
Their roadmap for developing your own AI looks like this:
1. Understand the Purpose: Clearly define the problem you want the AI to solve and the value it will provide to users. This forms the foundation of your project.
2. Prioritize Quality Data: Focus on acquiring high-quality data relevant to your AI's intended task. Data is crucial for training the AI model effectively.
3. Data Cleaning is Vital: Ensure the data you collect is clean and error-free. Data cleaning improves the quality of the training process.
4. Algorithm Selection and Development: Choose or create suitable algorithms that instruct the computer on effectively performing the desired tasks.
5. Prioritize Training and Accuracy: Devote significant attention to AI algorithm training. Optimize it to achieve high accuracy, as this is a critical factor in the success of your AI model.
6. Platform Choice Matters: Decide whether to use in-house or cloud-based frameworks for your AI project. Consider factors like scalability, resource availability, and deployment speed.
7. Select the Right Programming Language: Choose a programming language based on your project's goals and your familiarity with the language. Python, C++, Java, and R are popular choices with distinct strengths.
8. Deployment and Monitoring are Ongoing Processes: After deploying your AI, monitor its performance to ensure it continues to operate effectively. Be prepared to make adjustments as needed.
So, yes, it is possible to create your own AI to serve various purposes. There is no shortage of demand for professionals who found ways to learn software engineering or how to apply AI so that you can take your first AI creation steps today.