{"id":4702,"date":"2023-09-27T06:06:42","date_gmt":"2023-09-27T12:06:42","guid":{"rendered":"https:\/\/scrumdigital.com\/blog\/?p=4702"},"modified":"2024-04-23T06:16:49","modified_gmt":"2024-04-23T12:16:49","slug":"what-are-the-greatest-challenges-to-developing-quality-ai-apps","status":"publish","type":"post","link":"https:\/\/scrumdigital.com\/blog\/what-are-the-greatest-challenges-to-developing-quality-ai-apps\/","title":{"rendered":"What Are The Greatest Challenges to Developing Quality AI Apps?"},"content":{"rendered":"

McKinsey<\/span><\/a> Global Survey report indicates that more than 40% of organizations are ready to increase their investment in AI. This growing interest in AI underscores its transformative potential in multiple industries, from healthcare and finance to entertainment and logistics.\u00a0<\/span><\/p>\n

However, as this technology progresses, considerable hurdles are closed. Despite the elevated interest and investment, developing quality AI applications is complex. The juggling between data, algorithms, and real-world applications brings a suite of challenges for developers.\u00a0<\/span><\/p>\n

In this article, we’ll look at the challenges developers can face when making a quality AI app.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n
Challenge<\/b><\/td>\nSolution<\/b><\/td>\n<\/tr>\n
1. Insufficient Data<\/span><\/td>\n.<\/strong> Augment data<\/span><\/p>\n

.<\/strong> Use transfer learning<\/span><\/p>\n

.<\/strong> Utilize synthetic data<\/span><\/td>\n<\/tr>\n

2. Overfitting<\/span><\/td>\n.<\/strong> Regularization<\/span><\/p>\n

.<\/strong> Use dropout<\/span><\/p>\n

.<\/strong> Increase training data<\/span><\/td>\n<\/tr>\n

3. Model Interpretability<\/span><\/td>\n.<\/strong> Use explainable AI tools\u00a0<\/span><\/p>\n

.<\/strong> Adopt simpler models if possible<\/span><\/td>\n<\/tr>\n

4. Handling Bias in Data<\/span><\/td>\n.<\/strong> Audit datasets<\/span><\/p>\n

.<\/strong> Implement fairness<\/span><\/p>\n

.<\/strong> Enhancing interventions<\/span><\/td>\n<\/tr>\n

5. Scalability Issues<\/span><\/td>\n.<\/strong> Optimize algorithms<\/span><\/p>\n

.<\/strong> Use distributed training<\/span><\/td>\n<\/tr>\n

6. Model Deployment<\/span><\/td>\n .<\/strong> Containerization (e.g., Docker)<\/span><\/p>\n

.<\/strong> Use deployment platforms like AWS SageMaker<\/span><\/td>\n<\/tr>\n

7. Real-time Processing<\/span><\/td>\n.<\/strong> Use edge computing<\/span><\/p>\n

.<\/strong> Optimize model architecture<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Technical Challenges to Developing Quality AI Apps<\/h2>\n

1. Data Complexity<\/h3>\n

\"Data<\/p>\n

Data is the cornerstone of any business. However, only <\/span>3% <\/span>of companies produce data that meets their basic quality standards. Most companies’ poor-quality data costs them an average of<\/span> $15 million<\/span> annually. <\/span><\/p>\n

The importance of data is true for AI, too. It helps AI systems learn and decide things. But getting good and different data is hard. It’s not just about collecting data; it has to be right and clean. Data can have mistakes, be incomplete, or be biased. Real data from the world can be messy and need cleaning.<\/span><\/p>\n

Consider Tay, <\/span>Microsoft’s 2016 chatbot for Twitter<\/span>. It was designed to learn and improve through user interactions. But shortly after its launch, Tay started posting unsuitable tweets and was quickly taken offline.<\/span><\/p>\n

If data is not balanced, AI can give wrong results. For instance, If an AI learns mostly from one group’s data, it might not work well for a different group. So, what can be done about it?\u00a0<\/span><\/p>\n

Fortunately, there’s no shortage of tools to help manage data. Some notable names are \u2013 Apache Hadoop, MongoDB, and Databricks Lakehouse. <\/span><\/p>\n

But how can you ensure effective data collection and management? Begin by diversifying your data sources; depending solely on one channel can be limiting. Incorporating data from various sources provides a broader, unbiased perspective.\u00a0\u00a0<\/span><\/p>\n

It’s equally vital to invest in data-cleaning utilities, with platforms like <\/span>OpenRefine <\/span>and <\/span>Talend<\/span> being instrumental in refining and preparing data for AI integration.<\/span><\/p>\n

Ensure that your data adheres to prevailing regulations, such as the GDPR in Europe or CCPA in California. Staying updated with legal changes and routinely revisiting your data collection practices is crucial. <\/span><\/p>\n

With meticulous planning and management, your data can become an invaluable resource, simplifying the integration of AI into your software solutions.<\/span>
\n<\/b><\/p>\n

3. The AI Model Limitations<\/h3>\n

\"The<\/p>\n

Every AI system uses an algorithm, a set of instructions for making choices. But these algorithms aren’t perfect. They are extremely complex. This complexity is the biggest challenge in developing a near-perfect AI app.\u00a0<\/span><\/p>\n

Consider image recognition as a case in point. Deep convolutional neural networks are typically the first pick for such tasks, and they perform excellently. <\/span><\/p>\n

Yet, for simpler image datasets, straightforward supervised learning methods like logistic regression or support vector machines (SVMs) might do the trick. They’re also more straightforward to set up.<\/span><\/p>\n

Take sentiment analysis as another instance. Simple supervised techniques like Naive Bayes can match the performance of intricate deep-learning solutions. Selecting between advanced and basic AI models hinges on your particular requirements.<\/span><\/p>\n

For vast datasets, you’d naturally lean towards the sophisticated models. However, with smaller datasets, opting for a more straightforward model can be both efficient and cost-saving. A notable drawback of intricate models is their opacity. <\/span><\/p>\n

They can seem like “mysteries” – you get results without clear insight into the processes inside. This lack of transparency makes it challenging to grasp their decision-making rationale.<\/span><\/p>\n

Would you embed something you can’t decipher into your product? Likely not. So, when bringing AI into your product, where should you begin? Starting with a basic model is a wise strategy. Gauge its effectiveness and then ponder the need for complexity.<\/span><\/p>\n

Tools such as AutoML or H2O Driverless AI can assist in automating the AI model development, guiding you in algorithm selection and training. Techniques like LIME and SHAP come in handy to address the opacity issue. <\/span><\/p>\n

They aim to demystify the decisions of sophisticated AI models. For optimal AI deployment, striking a balance between model intricacy and real-world applicability is key. Always pick the most fitting solution for your specific task.<\/span>
\n<\/b><\/p>\n

Read More: 10 Best Programming Languages for AI Development<\/strong><\/a><\/p>\n

3. Computational Costs<\/h3>\n

\"Computational<\/p>\n

Developing AI applications goes beyond the development of merely models and algorithms; it requires significant financial and infrastructural investments. Global expenditures on AI-driven systems are projected to surge to <\/span>$154 billion in 2023<\/span>, marking a notable 26.9% hike from the previous year. And by 2026, this figure is anticipated to double, crossing a staggering $300 billion.\u00a0<\/span><\/p>\n

An illustrative case is DeepMind’s <\/span>AlphaGo,<\/span> an AI construct engineered to outclass world champions in the game of Go. It’s estimated that the training expenses for this singular model amounted to an eye-watering <\/span>$35 million <\/span>for DeepMind. This was solely due to the high computational costs.\u00a0<\/span><\/p>\n

Like many tech problems, new solutions are coming up. To deal with the high computational costs, companies can think about making some changes.<\/span><\/p>\n