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This blog is the first part of a 3-blog series on developing intelligent solutions using artificial intelligence.

As we immerse ourselves in the ever-evolving world of technology, one area that continues to captivate our curiosity is Artificial Intelligence (AI). The practical applications and possibilities offered by AI have transformed industries and enriched our lives in ways we could have only imagined. From the moment we wake up and interact with our smartphones to the time we stream our favorite shows on platforms like Netflix, AI is at work behind the scenes, enhancing our experiences.

  • What exactly makes a solution an AI solution?
  • What behaviors and characteristics set it apart from conventional approaches?

These are the questions we aim to answer as we explore the fascinating world of AI.

AI solutions demonstrate an extraordinary range of abilities. They possess the power to adapt and learn from their experiences, communicate with us naturally, make informed decisions, and solve complex problems. These behaviors lie at the heart of what makes an AI solution truly intelligent. The table below captures these behaviors along with examples of real world AI systems exhibiting such behaviors.

Behavior Description Real-world Examples
Adaptability AI systems can adapt and refine their performance over time by learning from past experiences and feedback. AlphaGo, an AI program developed by DeepMind, improved its gameplay by playing millions of matches against itself, analyzing strategies, and refining its gameplay based on feedback from human Go experts.
Communication AI systems can process and comprehend human language, including speech and text, and respond accordingly. Virtual assistants like Amazon's Alexa or Apple's Siri can understand voice commands, answer questions, and perform tasks based on natural language instructions. They can also detect emotions through voice analysis
Credibility  AI systems should provide an indication of the confidence level in their responses, reflecting the reliability of their conclusions or recommendations.  When a voice assistant provides an answer to a question, it can also provide a confidence score or express uncertainty if the answer is based on limited or ambiguous information.
Decision-making AI systems should be capable of analyzing multiple options, evaluate their potential outcomes, and make informed decisions. Google's search algorithm ranks web pages based on relevance, popularity, and user feedback, providing the most relevant search results based on user queries.
Interpretation AI systems can comprehend and interpret various forms of human expression, such as spoken or written language and visual data. Google Translate can translate text, speech, and images between different languages, allowing users to communicate and understand content across language barriers.
Learnability AI systems can be initially trained by human experts and continuously learn from new data and experiences Self-driving cars are trained by human experts to recognize objects, traffic signs, and driving rules. However, they also learn from their own experiences on the road, adapting their driving behavior based on real-world scenarios.
Memorization AI systems can capture and retain learned information, allowing them to recall and utilize acquired knowledge for future tasks IBM's Watson, for example, can store vast amounts of medical literature, research papers, and patient records, enabling it to retrieve relevant information for medical diagnoses and treatment recommendations.
AI systems can analyze large datasets and identify meaningful patterns or correlations that may not be apparent to humans. Netflix uses AI algorithms to analyze user behavior, viewing history, and preferences to identify patterns and recommend new movies and TV shows tailored to each user's interests.
Problem solving

AI systems can devise strategies, evaluate different options, and execute a sequence of actions to achieve specific objectives.

DeepMind's AlphaFold employs AI to predict the three-dimensional structure of proteins, aiding in drug discovery and understanding the molecular basis of diseases.
Rationality AI systems can apply logical and statistical reasoning to process information, draw inferences, and derive meaningful insights. AI-based fraud detection systems analyze patterns in financial transactions, identify anomalies, and generate conclusions about potentially fraudulent activities to help prevent financial fraud.
Scalability AI systems can handle and process massive amounts of data from various sources, accommodating different formats and structures. Social media platforms like Facebook analyze enormous volumes of user-generated content, including text, images, and videos, to personalize user experiences, deliver targeted ads, and detect harmful content.
Sensing AI systems can process sensory inputs, such as images or sensor data, to understand and interpret the environment Self-driving cars use various sensors, including cameras, LiDAR, and radar, to perceive and interpret their surroundings, detect obstacles, and navigate safely.

It is generally fair to say that for a solution to be called an AI solution, it should exhibit some or most of the behaviors mentioned above. These behaviors encompass key characteristics associated with artificial intelligence systems and are often considered fundamental to AI capabilities. However, the extent to which an AI solution exhibits these behaviors can vary. It’s important to note that AI is a broad field, and there are different types and levels of AI systems. Some AI solutions may excel in specific behaviors, while others may focus on a subset of characteristics depending on their intended purpose and domain. For example, a speech recognition system may prioritize natural language understanding and communication abilities, while a self-driving car system may emphasize perception, decision-making, and problem-solving capabilities.

Therefore, while the behaviors listed here provide a useful framework for understanding AI systems, it’s not necessarily too restrictive to expect an AI solution to exhibit these behaviors to some degree. However, the specific requirements for labeling a solution as an AI solution may vary depending on context, application, and the level of sophistication needed.

In the next part, we will see how these traits can be observed in an AI solution.

About the author

Gopalakrishna Kuppuswamy is dedicated to driving innovative solutions around AI and Decision Intelligence at

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