Forget the jargon. Forget the hype. This isn't your average "How to Build AI" guide.
Building AI software is about designing human-centered solutions. This article guides you through a thoughtful and real approach that breaks the mold of boilerplate, formulaic methods.
Having been navigating the wild world of IT since 2007, we've seen the good, the bad, and the downright misleading when it comes to building AI. This guide aims to cut through the noise and dive into the practical trenches of crafting real-world AI solutions that solve problems, not chase trends.
How to Create an AI Software: Major Steps
Planning Your AI Software (Defining Business Goals)
All in all, building AI is about mimicking human intelligence, i.e., learning, logical reasoning, making decisions, and, ultimately, solving problems. Machine Learning is usually a starting point for many businesses in the AI journey because they want to learn from vast amounts of data and build optimal solutions that go beyond the limitations of human error and cognitive capacity. Then, if ML models begin to learn and self-optimize effectively without human facilitation, there is a possibility of them becoming true AI.
This perspective sounds a little intimidating, but it is considered a North Star for the majority of businesses today, regardless of whether IT is a part of their value proposition or not. McKinsey reports that over 40% of respondents will increase AI investments. Peter H. Diamandis, a future-focused serial entrepreneur, puts it very clear: “In 2023, more than 1 in 4 dollars invested by VCs in US startups went to an AI-related company.”
Let’s say you decided it’s time to roll up your sleeves and start the AI project. As we mentioned, you can’t go wrong with problem-solving. Identify areas where other AIs currently struggle and work on engineering solutions. You might also consider making custom AI on demand (for example, in healthcare or construction). That would be smart because of the specificity of datasets and relationships between hyperparameters. However, this approach is based on scarcity (create something exclusive that no one else will have), – which is extremely hard to scale. On the other hand, if you aim at creating some core algorithm that gracefully resolves complexities that everyone else is having, you might achieve the true North Star of your business sooner than you know.
Alternatively, consider the reverse psychology principle if you don’t like to go with the flow. For example, if everyone is working on AI, what impact does it have on our use of data and electrical signals? Maybe we can make data storage or transmission more efficient. You need an idea that will truly stand out to get your foot in the door of venture investments. Sometimes, instead of pursuing a vague mission of making the world a better place, choosing a niche and sticking with value creation for that particular AI niche is more beneficial.
To put it simply, imagine AI companies like different floors in a giant building, each adding value in a unique way.
- The common area (Applications) is filled with companies creating finished products like apps. It can be tough for them to stand out from the crowd, just like competitors selling similar products in a store.
- The middle floor (Infrastructure) holds the tools that help AI engineers build these applications, kind of like the workshop where tools are made for different projects. Can you create a library or a framework? Go for it!
- The boiler room (Models) is where you boldly go, while no man has ever been there before. These are the special ingredients (parameters) and secret recipes (formulas) that make AI tools work their wonders. The primary focus is on advanced mathematical operations and complex logic between them.
- Foundation, pillars, ceiling (Hardware) This level includes the powerful machinery that runs everything, similar to the power plant that keeps the whole building functioning. While important, working on this floor can be challenging, just like building and maintaining a power plant requires a lot of resources.
Once you decide on the right way to go, it’s time to move on to the next step.
Data Collection and Preparation
A lot of novice AI makers are tempted by vast amounts of public data that they can get absolutely free. However, it might be useful to look beyond those common sources. Instead, what if you collect niche industry data? This strategy might result in more accurate predictions because your model will not be distracted by noise, and you won’t have to spend so much resources on cleaning your data. Try to diversify your data sources: smart devices, citizen science initiatives, and likewise pools of information with a wealth of real-world scenarios might be more valuable than just downloading public data that everyone else is likely to use for building their models. If you have no idea where to take this data, aim at partnering with subject matter experts, researchers, and organizations that are collecting this data and are interested in enhancing their insights.
Implement a pipeline that preserves its initial state and tracks modification to ensure data integrity. This might be based on the blockchain of version control systems. This will help you eliminate bias and errors down the line.
Finally, you need to make sure that the data you collect is interpretable. Use techniques like feature importance analysis and counterfactual explanations to understand how data points to influence your model's decisions. This can help identify potential biases and ensure responsible AI development.
AI Model Selection and Development
The selection of an AI model depends on the business value that you decide to pursue. After crafting a business case and project charter, your software engineering stakeholders will proceed with outlining a network architecture, overarching logic of the solution, technical roadmap, and tech stack.
Finding experienced software folks who code in specialized AI-oriented libraries might be challenging. If you are a novice startup, you might not possess that experience yourself. If you need to move fast, the viable solution here is to gather brilliant software developers and embark on a learning journey.
To get started with coding, you need to decide which of the available AI architectures you are going to use to achieve your business objectives.
If you have never programmed neural networks before, keep calm! Just think of them as colleagues in your company:
Convolutional Neural Networks (CNNs)
- Imagine these as image processing experts. They're like programs that break down images into smaller pieces using multiple "filters." By analyzing these pieces, they can identify objects, classify images, and even segment different parts within an image.
Recurrent Neural Networks (RNNs)
- Think of these as business intelligence specialists. They excel at handling data that comes in order, like text or time series. They have a "memory" that allows them to remember past information and use it to understand the current data point better. This makes them perfect for tasks like analyzing text sentiment, predicting future patterns, or understanding trends over time.
Generative Adversarial Networks (GANs)
- Imagine these as a developer and product owner. They consist of two parts: a "generator" and a "discriminator." The generator tries to create new data based on existing knowledge, while the discriminator tries to distinguish real data from the generated one and give feedback on what is acceptable and what is not. This review process pushes the generator to become better at creating data.
Autoencoders
- Think of these as storage optimization experts. They're like programs that learn to represent complex data in a simpler way. They achieve this by compressing the data into a smaller form (a "latent representation") while still capturing the important features. This compressed data can then be used for other tasks like extracting informative features for further analysis or detecting anomalies in datasets.
Transformers
- These are like customer support experts in the world of AI, particularly for tasks involving text. They use a special mechanism called "attention" that allows them to focus on specific parts of a text sequence, just like you might focus on certain keywords in a sentence. This helps them understand complex relationships between words, making them powerful for tasks like machine translation, analyzing emotions in text, and summarizing large amounts of text data.
By understanding particular AI roles in simple terms, you can manipulate their functionality more easily without getting lost in their mathematical complexity.
Training and Evaluation
AI training is not a one-off activity. In fact, it requires multiple iterations of fine-tuning the algorithm. This doesn’t mean that the algorithm you built was wrong or buggy. On the contrary, fine-tuning is quite beneficial, provided that it gives valuable improvements with every iteration.
To boot, some interesting findings are only discoverable in the process of experimentation. You wouldn’t have known them otherwise. If additional robustness is needed, use augmentation to increase dataset diversity. Examples of fine-tuning-worthy parameters include: Regularization Strength, batch size, learning rate/schedule, decay rate/schedule, number of hidden layers, and dropout rate.
Testing the Model
While algorithms consistently outperform humans in tasks like predicting employee success and optimizing supply chains, people struggle to trust them due to a phenomenon called "algorithm aversion." This lack of trust stems from our inability to understand the inner workings of algorithms and learn from their mistakes, unlike human advisors.
Studies show that simply demonstrating an algorithm's ability to learn through its past performance significantly increases user trust and preference for the algorithm compared to humans, even when both have the same success rate. Additionally, even implying an algorithm's potential for future learning with terms like "machine learning" can boost user acceptance.
On a technical level, by employing a comprehensive testing strategy and embracing XAI techniques, QA specialists can ensure the development of reliable, trustworthy, and user-friendly AI software.
A testing routine could look something like this:
- Test preparation:
- Identify the desired user experience.
- Establish quantifiable metrics.
- Data preparation:
- Evaluate data quality and diversity.
- Test for data poisoning and manipulation.
- Implement data augmentation techniques.
- Running functionality testing:
- Test core functionalities.
- Run scenario testing.
- Stress test the system.
- Ensuring explainability:
- Utilize explainable AI (XAI) techniques.
- Test for fairness and bias.
- Testing integration and security:
- Test integration with other systems.
- Perform security testing.
- User testing:
- Involve real users to gather feedback.
- Conduct A/B testing against humans and/or non-AI software.
Integration and Deployment
Relying on cloud infrastructure might be efficient, but to go even further, consider deploying lightweight AI models on edge devices closer to data sources. As a result, you can communicate a lot of interesting value props, like reducing latency, improving data privacy by keeping sensitive data localized, and increasing system resilience in case of network disruptions.
Before fully deploying the AI, you can also try to experiment by running it in a "shadow mode" alongside existing systems. This allows you to observe its performance in real-world scenarios, compare its outputs to human decisions, and refine the model or decision-making logic based on insights gleaned from this shadow phase.
Last but not least, ensure ethical AI deployment. Go beyond the technical level by creating policies that address possible bias and unethical usage. Propose detection and mitigation strategies for unacceptable content. For example, implement human-in-the-loop to monitor the output. You might also include environmental considerations like designing efficient architectures that minimize computational requirements and memory footprint.
Best Practices to Develop AI Software from Scratch
If you’ve gone this far in this blog post, you already know that ensuring efficient and reliable AI software development requires a multifaceted approach. To ensure you can keep track of multiple initiatives when you build AI software, adopting version control systems is a good idea. This allows for tracking different model architectures, hyperparameter configurations, and training runs, simultaneously ensuring that ethical concerns are met.
Secondly, containerization with tools like Docker is a valuable strategy. It packages your code and dependencies into a self-contained unit, streamlining deployment across various environments and guaranteeing consistent execution. It is also easier to revert to more successful images or to go back and see why less successful experiments didn’t work.
Finally, proactive problem identification is key. A robust logging system captures information about model training, inference, and system errors. This data proves invaluable for debugging, performance analysis, and pinpointing potential issues before they escalate in production.
Common Challenges in Developing AI Software
Scarce or Biased Data
This consideration is like a double-edged sword. If you feed your model with too much diverse data, it might be difficult for it to learn, which will lead to errors. On the contrary, if you have too little data or biased data, the model will make errors, too. Unfortunately, there is no uniform solution when you decide how to build AI software. You need to experiment and fine-tune your algorithms for your particular use cases.
Lack of Model Explainability
To effectively debug the model, you need to be able to trace the reason for problems that arise. However, many AI models come to their conclusions in mysterious ways. That is why developers strive to create Explainable AI (XAI), where it will be possible to understand how the model came to a particular outcome.
High Costs of Training and Deployment
Training an AI model might require you to go wild with those CPU cores. As a result, infrastructure and computational costs skyrocket, and it becomes difficult to achieve a substantial ROI. When designing a technological roadmap, consider the future need for optimizing resource allocation.
After-Release Fears
Integrating AI models into production environments holds a certain degree of risk when building AI software. The erroneous output might be offensive to certain groups of people or even plainly wrong. That is why AI developers can’t afford just to let AI go wherever it wants to go. Instead, they need to set up continuous monitoring, logging, and incident response routines.
AI Software Solutions: Success Stories
While AI models are still regarded as a technology of the distant future, some businesses have already embraced their power and are now enjoying benefits.
JP Morgan Chase
One of the world’s oldest chain of banks, recently started using an anti-fraud AI model. OmniAI works as a helper of human data scientists, helping them extract valuable insights from vast varieties of unstructured information. The result is quite promising: the company claims to have reduced fraudulent transaction attempts by 80%.
Duolingo
Duolingo Max uses ChatGPT 4 to personalize the learning experience. They also host English language proficiency exams that automatically adapt to the knowledge level of the student while taking the exam. While using the app, learners can ask Duo to explain why the previous answer was wrong in the chat window. Additionally, they can practice simulated real-world conversations by chatting with AI.
Future Trends in AI Software Development
While generative AI is on top right now, emerging trends bring those models closer to edge devices. As intelligence gets closer to the data source, it becomes easier to create AI software for correct decision-making. Such models reduce latency and dependence on third-party cloud processing, thereby enhancing user experience. Some examples include developing AI for autonomous vehicles or industrial process control.
Development tools like low-code and no-code platforms are emerging as well, empowering software developers with a broad range of skill sets to contribute to creating and training AI models. This trend makes AI more accessible and democratizes the market.
Wrapping Up
Building AI software is currently a top trend in the IT industry. Companies that offer proprietary insights receive generous funding and explore ways of improving AI output. However, there are still some substantial risks to be addressed when it comes to collecting relevant data, maintaining efficient learning, mitigating ethical concerns, and optimizing infrastructure costs.
Intellectsoft offers 15+ years of experience in custom software development, system architecture, and team augmentation. Talk to our experts today to discover how you can improve your AI development pipelines, conduct efficient project management, and meet your business goals.
