How to Build AI Products
- product management
- 5 min read
By Dhruv Rastogi – VP & Head of Data Science at IKS Health
Imagine a world where computers not only follow strict instructions but also learn and adapt on their own. That’s the power of artificial intelligence or AI. Unlike traditional algorithms that simply follow predefined rules, AI has the remarkable ability to learn from data and make decisions independently. But what exactly is AI, and how is it different from the traditional algorithms we’ve been using for years? Understanding the nuances of AI, its relationship with traditional algorithms, and its role in product development can be complex. In this guide, we’ll break down the differences between traditional algorithms and AI, explain how AI works, and show how it’s being used to create new products and solve problems.
Key Takeaways:
- Traditional algorithms rely on predefined rules, while AI aims to teach computers how to learn autonomously.
- AI includes ML, which involves statistical methods, and DL, which utilizes multi-layer neural networks for predictions.
- Supervised learning uses labeled data, unsupervised learning uncovers hidden patterns, and reinforcement learning enables machines to learn through interaction.
- AI enhances insight generation, operational efficiencies, innovation in product development, and the creation of new products.
- AI can serve as standalone solutions, core components, or feature enhancements in various products, enhancing user experiences and driving growth.
- Humans play a crucial role in handling edge cases, building training data sets, generating heuristics, and quantifying sample accuracy to make AI usable and reliable.
- The AI flywheel effect represents the cycle of growth and improvement fueled by user-generated data, driving innovation and enhancing product performance.
- Organizations can leverage open data sources, crowdsourcing, user-generated data, company data assets, and data vendors to gather valuable insights and drive innovation.
Artificial Intelligence vs Traditional Algorithms
The difference between traditional algorithms and artificial intelligence (AI) is their problem-solving and learning approach. Traditional algorithms are sets of predefined rules and procedures that instruct machines on what actions to take. For instance, a calculator operates based on a predetermined algorithm that dictates its functions.
On the other hand, artificial intelligence goes beyond instruction and aims to teach computers how to learn. AI enables computers to acquire knowledge and skills autonomously. This process mirrors human learning, where individuals absorb information and develop understanding through exposure and experience.
For instance, when teaching a baby about animals, we don’t provide a strict set of rules detailing every feature of each animal. Instead, we simply show the baby a cat and label it as such. The baby then learns to recognize cats based on its observations and experiences.
Similarly, AI systems possess cognitive capabilities such as reading, understanding, listening, and comprehension. They can perform tasks similar to those carried out by humans, demonstrating a more flexible and adaptive approach compared to traditional algorithms.
Artificial Intelligence vs Machine Learning and Deep Learning
It is very easy to get confused between AI, ML, and deep learning. It’s crucial to grasp the differences between AI, machine learning (ML), and deep learning (DL) as a product manager.
Machine learning, a subset of AI, relies on statistical methods and experimentation to enable machines to improve through experience. It includes various techniques involved in enhancing machine performance over time.
Deep learning, on the other hand, falls under the umbrella of machine learning techniques. It utilizes multi-layer neural networks to make predictions and discern patterns. These neural networks contribute to its predictive capabilities.
Determining when to use ML versus DL depends on several factors. ML systems offer higher interpretability and require less time and cost to build compared to DL systems. However, DL systems often boast superior accuracy, making them advantageous in certain scenarios. DL systems can function without extensive domain expertise, operating as black box systems.
Also, the efficacy of DL systems significantly improves with a critical mass of data. While ML and DL systems may perform similarly with limited data, as data volume increases, DL systems tend to outperform ML systems at a faster rate.
Types of Machine Learning Programs
Understanding the different types of machine learning algorithms is crucial for data scientists, developers, and AI enthusiasts. Let’s dive into the three primary categories: supervised, unsupervised, and reinforcement learning.
1. Supervised Learning
Supervised learning involves training models with labeled data, where each input example is paired with a corresponding output label. This type of ML technique works when there’s a clear relationship between input features and output labels. For instance, in email classification, the model discerns between spam and legitimate emails based on past data.
2. Unsupervised Learning
Unsupervised learning operates without explicit output labels. It focuses on uncovering hidden structures or patterns within the data. Common applications include clustering similar data points or performing anomaly detection, crucial in fraud detection scenarios.
3. Reinforcement Learning
Reinforcement learning takes a different approach by enabling machines to learn through interaction with an environment. Here, the algorithm doesn’t rely on labeled datasets but instead learns by trial and error. Through a system of rewards and penalties, the model refines its actions over time to achieve optimal performance. This approach finds widespread use in dynamic environments like robotics, gaming (e.g., chess), and autonomous vehicles.
AI Value Addition Methods
Let’s explore how AI adds value to businesses and drives tangible outcomes.
1. Insight Generation
AI empowers businesses to extract actionable insights from vast datasets, enabling informed decision-making. By leveraging advanced analytics techniques, organizations can uncover hidden patterns, trends, and correlations within their data.
2. Operational Efficiencies
AI revolutionizes operations by optimizing processes and enhancing efficiency across various business functions. From improving productivity and quality to streamlining supply chain management and optimizing HR processes, AI-driven solutions drive operational excellence.
3. Innovation in Product Development
AI fuels innovation by infusing existing products with new features and capabilities. From incorporating conversational interfaces into dashboards to leveraging advanced technologies like speech recognition and natural language processing, AI enables businesses to reimagine their products and services.
4. Building New Products
AI empowers businesses to create entirely new products and services. Leveraging cutting-edge technologies such as speech-to-text, language processing, and pattern mining, entrepreneurs can come up with groundbreaking solutions across diverse industries. By pushing the boundaries of innovation, AI-driven startups and enterprises can disrupt markets and drive transformative change.
Role of AI/ML in the Product
Let’s explore how AI and ML are shaping products across various dimensions and industries.
1. Feature to Core Integration
In some products, AI and ML serve as standalone black box solutions, providing essential functionalities independently. Examples include speech-to-text converters, image recognition tools, and language enhancement software. These products rely solely on AI and ML algorithms to deliver their core functionalities. For instance, a speech-to-text product operates as a standalone solution, extracting text from audio inputs without external dependencies.
On the other hand, certain products, like self-driving cars or surveillance drones, rely heavily on AI and ML as the core components. Without these technologies, the product loses its essential functionality. For instance, a self-driving car’s ability to detect and respond to objects on the road hinges entirely on AI-powered algorithms. In such cases, AI and ML are features integral to the product’s existence and functionality.
2. Enhancing User Experience
AI and ML also act as feature enhancements in many products. Consider Over-the-Top (OTT) streaming platforms like Amazon Prime, Netflix, or Hotstar. While the core functionality revolves around content delivery, recommendation systems powered by AI enrich the user experience. These recommendation features analyze user preferences and behaviors to suggest personalized content, ultimately increasing engagement and retention.
Role of Humans in the Loop
AI systems rely heavily on human input and oversight to function effectively. From initial development to ongoing refinement, humans are indispensable in ensuring the functionality and usability of AI technologies.
Let’s understand into the crucial role humans play in making AI usable and reliable.
1. Handling Edge Cases
One of the primary contributions of humans in the AI loop is handling edge cases. These are scenarios where AI algorithms struggle to make accurate predictions or decisions due to insufficient training data. Humans differentiate between correct and incorrect outcomes and impart contextual understanding that machines may lack.
2. Training Data Sets
AI systems rely on meticulously curated training data sets. Humans, including programmers, database experts, and domain specialists, play a pivotal role in generating these data sets. By labeling data, identifying patterns, and establishing ground truth, humans empower AI algorithms to make informed decisions and predictions.
3. Generating Heuristics
Heuristics are invaluable in guiding AI systems’ decision-making processes. Humans contribute by devising heuristics based on domain knowledge and practical experience. These heuristics serve as guiding principles, enabling AI algorithms to make informed choices in real-world applications.
4. Quantifying Sample Accuracy
Even as AI models undergo training and evaluation, humans remain essential in quantifying sample accuracy. Human evaluators assess the performance of AI systems, gauging their accuracy levels across different data samples. This ongoing evaluation ensures that AI systems meet predefined performance benchmarks and deliver reliable results.
AI Flywheel Effect
The AI flywheel effect represents the dynamic cycle of growth and improvement inherent in AI products. Unlike traditional tech products, where growth may stagnate over time, AI products thrive on data accumulation and algorithm refinement. As more users engage with the platform, they generate valuable data, which in turn enhances the algorithms’ intelligence. This, in a the form of a cycle, leads to improved product performance, attracting even more users and perpetuating the cycle of growth.
Leading tech companies have harnessed the AI flywheel effect to drive growth and innovation. Platforms like social media networks and networking platforms thrive on user-generated data, fueling AI algorithms to deliver tailored recommendations, optimize content delivery, and mitigate user attrition. Through strategic utilization of data, these companies have transformed their products.
Data Collection Methods
Let’s explore various data collection methods that organizations can leverage to gather valuable insights and drive innovation.
1. Leveraging Open Data Sources
One of the primary methods for data collection is leveraging open data sources. These repositories contain a lot of publicly available data across diverse domains, ranging from government databases to academic research datasets. Organizations can access a vast array of information to support their AI projects and initiatives.
2. Crowdsourcing Labeled Data
Crowdsourcing offers another avenue for data collection, particularly for tasks requiring labeled data for machine learning models. Through crowdsourcing platforms, organizations can enlist the help of individuals to annotate and label datasets, thereby enriching the data and enhancing its utility for AI applications.
3. User-Generated Data
User-generated data plays a pivotal role in fueling AI-powered products and services. By encouraging user interactions and engagement, organizations can collect valuable data directly from their user base. This data provides invaluable insights into user behavior, preferences, and interactions with the product, driving personalized experiences and informed decision-making.
4. Company Data Assets
Many organizations possess rich repositories of internal data, including customer data, vendor information, sales data, and product lifecycle data. By leveraging these company-owned data assets, organizations can gain deeper insights into their operations, customer behavior, and market trends.
5. Purchasing Data from Vendors
Data vendors offer access to curated datasets, often anonymized to protect privacy, which organizations can use to augment their existing data assets. With the proliferation of data marketplaces, organizations can explore a wide range of datasets tailored to their specific needs and objectives.
Artificial intelligence is no longer just a concept of the future—it’s a powerful force driving innovation and transformation across industries. By understanding the distinctions between traditional algorithms and AI, grasping the complexities of machine learning and deep learning, and recognizing the diverse applications of AI in product development, businesses can unlock new opportunities for growth and success.
Moreover, by leveraging various methods of data collection, organizations can utilize the full potential of AI, empowering themselves to make smarter decisions and stay ahead of the competition.
About the Author:
Dhruv Rastogi – VP & Head of Data Science at IKS Health
Frequently Asked Questions
AI in product management utilizes artificial intelligence, deep learning, or machine learning to solve product problems.
It is highly unlikely that AI will fully replace product management. AI is used to assist product managers and enhance aspects of product management, like data analytics and customer support. But it cannot completely replace a product manager.
Challenges associated with AI product management include working with more stakeholders, the ambiguity of outcomes, difficulty in explaining the rationale behind the outcomes, addressing fairness and bias concerns, adapting to new infrastructure and tools, and selecting the right problems to solve with AI.
In traditional product management, product behavior is usually binary and predetermined. AI product management deals with probabilistic outcomes.
Common data collection methods are leveraging open data sources, crowdsourcing labeled data, user-generated data, company data assets, and purchasing data from vendors.
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