Forget the jargon. Forget the hype. This isn’t your average “How to Build AI” guide.<\/b><\/p>\n
Building AI software<\/span><\/a> is about designing <\/span>human-centered solutions<\/b>. This article guides you through a thoughtful and real approach that breaks the mold of boilerplate, formulaic methods.<\/span><\/p>\n 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<\/a> that solve problems, not chase trends.<\/span><\/p>\n All in all, building AI is about <\/span>mimicking human intelligence<\/b>, 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.<\/span><\/p>\n 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. <\/span>McKinsey<\/span><\/a> reports that over 40% of respondents will increase AI investments. <\/span>Peter H. Diamandis<\/span><\/a>, a future-focused serial entrepreneur, puts it very clear:<\/span> \u201cIn 2023, more than 1 in 4 dollars invested by VCs in US startups went to an AI-related company.\u201d<\/b><\/p>\n Let\u2019s say you decided it\u2019s time to roll up your sleeves and start the AI project. As we mentioned, you can\u2019t go wrong with <\/span>problem-solving<\/b>. Identify areas where other AIs currently struggle and work on engineering solutions. You might also consider making<\/span> custom AI on demand<\/b> (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), \u2013 which is extremely <\/span>hard to scale<\/b>. On the other hand, if you aim at creating some <\/span>core algorithm <\/b>that gracefully resolves complexities that everyone else is having, you might achieve the true North Star of your business sooner than you know.\u00a0<\/span><\/p>\n Alternatively, consider the reverse psychology principle if you don\u2019t 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.<\/span><\/p>\n To put it simply, i<\/span>magine AI companies like different floors in a giant building, each adding value in a unique way.<\/span><\/p>\n Once you decide on the right way to go, it\u2019s time to move on to the next step.<\/span><\/p>\n 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\u2019t 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.<\/span><\/p>\n 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.<\/span><\/p>\n 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.<\/span><\/p>\n The selection of an AI model depends on the business value that you decide to pursue. After crafting a business case and <\/span>project charter<\/span><\/a>, your software engineering stakeholders will proceed with outlining a network architecture, overarching logic of the solution, technical roadmap, and tech stack.\u00a0<\/span><\/p>\n 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.\u00a0<\/span><\/p>\n 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.\u00a0<\/span><\/p>\n If you have never programmed neural networks before, keep calm! Just think of them as colleagues in your company:<\/span><\/p>\n By understanding particular AI roles in simple terms, you can manipulate their functionality more easily without getting lost in their mathematical complexity.<\/span><\/p>\n AI training is not a one-off activity. In fact, it requires multiple iterations of <\/span>fine-tuning<\/b> the algorithm. This doesn\u2019t mean that the algorithm you built was wrong or buggy. On the contrary, fine-tuning is quite <\/span>beneficial,<\/b> provided that it gives valuable improvements with every iteration.\u00a0<\/span><\/p>\n To boot, some interesting findings are only discoverable in the process of experimentation. You wouldn\u2019t have known them otherwise. If additional robustness is needed, use augmentation to increase dataset diversity. <\/span>Examples of fine-tuning-worthy parameters include<\/b>: <\/span>Regularization Strength, batch size, learning rate\/schedule, decay rate\/schedule, number of hidden layers, and dropout rate.\u00a0<\/span><\/p>\n 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 <\/span>“algorithm aversion.”<\/b> This lack of trust stems from our inability to understand the inner workings of algorithms and learn from their mistakes, unlike human advisors.\u00a0<\/span><\/p>\n Studies show<\/span> 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.<\/span><\/p>\n 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.<\/span><\/p>\n A testing routine could look something like this:<\/span><\/p>\n Relying on cloud infrastructure might be efficient, but to go even further, consider <\/span>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.<\/span><\/p>\n 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.<\/span><\/p>\n 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.<\/span><\/p>\n If you\u2019ve 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 <\/span>build AI software<\/span>, adopting <\/span>version control systems<\/b> is a good idea. This allows for tracking different model architectures, hyperparameter configurations, and training runs, simultaneously ensuring that ethical concerns are met.\u00a0<\/span><\/p>\n Secondly, <\/span>containerization<\/b> 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\u2019t work.<\/span><\/p>\n Finally, proactive problem identification is key. A robust <\/span>logging system<\/b> 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.\u00a0<\/span><\/p>\n 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 <\/span>how to build AI software<\/span>. You need to experiment and fine-tune your algorithms for your particular use cases.<\/span><\/p>\n 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.<\/span><\/p>\n 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.<\/span><\/p>\n Integrating AI models into production environments holds a certain degree of risk when <\/span>building AI software<\/span>. The erroneous output might be offensive to certain groups of people or even plainly wrong. That is why AI developers can\u2019t afford just to let AI go wherever it wants to go. Instead, they need to set up continuous monitoring, logging, and incident response routines.\u00a0<\/span><\/p>\n 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.<\/span><\/p>\n One of the world\u2019s oldest chain of banks, recently started using an <\/span>anti-fraud AI model<\/span><\/a>. 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%.<\/span><\/p>\n Duolingo Max<\/span><\/a> 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.<\/span><\/p>\n 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 <\/span>create AI software<\/span> 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.<\/span><\/p>\n 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.<\/span><\/p>\n Building AI software<\/span> 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.\u00a0<\/span><\/p>\n Intellectsoft<\/a><\/strong><\/span> 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<\/a><\/strong><\/span> pipelines, conduct efficient project management, and meet your business goals.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" 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…<\/p>\n","protected":false},"author":85,"featured_media":27576,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[798,6],"tags":[],"class_list":["post-27575","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-software-development"],"acf":[],"yoast_head":"\nHow to Create an AI Software: Major Steps<\/h2>\n
Planning Your AI Software (Defining Business Goals)<\/h3>\n
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Data Collection and Preparation<\/h3>\n
AI Model Selection and Development<\/h3>\n
Convolutional Neural Networks (CNNs)<\/strong><\/h4>\n
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Recurrent Neural Networks (RNNs)<\/strong><\/h4>\n
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Generative Adversarial Networks (GANs)<\/strong><\/h4>\n
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Autoencoders<\/strong><\/h4>\n
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Transformers<\/strong><\/h4>\n
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Training and Evaluation<\/h3>\n
Testing the Model<\/h3>\n
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Integration and Deployment<\/h3>\n
Best Practices to Develop AI Software from Scratch<\/h2>\n
Common Challenges in Developing AI Software<\/h2>\n
Scarce or Biased Data<\/h3>\n
Lack of Model Explainability<\/h3>\n
High Costs of Training and Deployment<\/h3>\n
After-Release Fears<\/h3>\n
AI Software Solutions: Success Stories<\/h2>\n
JP Morgan Chase<\/h3>\n
Duolingo<\/h3>\n
Future Trends in AI Software Development<\/h2>\n
Wrapping Up<\/h2>\n