What is Interactive Machine Learning

Introducing Interactive Machine Learning

Machine Learning is a subset of Artificial Intelligence, which has become immensely popular in the last decade. Machine Learning algorithms are used to make predictions and automate processes based on data. These algorithms rely on historical data to generate models that can be used to make predictions will also define policies, and control decision-making processes. Machine Learning can drive large-scale automation in industries like finance, manufacturing, e-commerce, etc. With machine learning, data can be transformed into a competitive advantage. But, what if the process of Machine Learning could be made interactive, and humans could play an active role in engaging with the machine becomes more efficient? That is where Interactive Machine Learning comes in.

Interactive Machine Learning Defined

Interactive Machine Learning (IML) is a concept that combines machine learning and human interaction. In this system, humans actively participate in the learning phase of the algorithm. Humans aid a particular machine learning algorithm to learn from their input, and in the end, the algorithm improves its accuracy over time. IML thrives on human input since it can solve the issues that traditional machine learning struggles with like incorrect data processing or labeling.

What are the Benefits of Interactive Machine Learning?

Interactive machine learning has many benefits. Some of the advantages of IML include:

  • Efficiency: Interactive Machine Learning can enable rapid learning about complex systems, resulting in faster decision-making.
  • Improved accuracy: With IML, humans can provide additional context and feedback that may help increase the algorithm's overall performance, resulting in more accurate results.
  • Increased human control: Interactive Machine Learning allows humans to have greater control of the learning process. Humans can decide what information is relevant to the algorithm and can change the algorithm's priorities depending on their specific needs.
  • Faster insight: IML offers quick insight, which can help to discover abnormality or identify trends. The interaction process does not need supervision or involvement from a data scientist
  • More comprehensive analytics: Interactive Machine learning can easily analyze faint signals in data, which traditional Machine Learning algorithms would have missed.
  • Improved scalability: With IML, human interaction can be scaled up for large datasets, which alleviates the shortage of labeled or preprocessed data. This leads to limiting "black box" tendencies of the algorithm in traditional machine learning.
Types of Interactive Machine Learning

IML defines two distinct types of interactions human machine interaction and human feedback interaction.

  • Human-Machine Interaction: In this paradigm, the machine and human collaborate to generate the desired outputs. In this model, human interaction can be used in a variety of ways, including selecting relevant features for analysis, influencing the learning schedule, and identifying the significance of individual data points.
  • Human Feedback Interaction: In this model, the machine generates outputs based on the provided data, and humans provide feedback to the machine that helps augment the model for the next iteration. The algorithm can refine itself by engaging in multiple human observations and correcting its accuracy until it becomes more reliable.
Interactive Machine Learning Applications

Interactive Machine Learning applications span several domains. Below are examples of how IML is applied in various areas of life:

  • Healthcare: IML is applied in precision medicine as a new means of treatment recommendations that take into account the patient's history of allergies or medication resistance. By continual learning from rapidly updated patient medical data, IML can increasingly offer personalized and precise medical services to patients.
  • Sales and Marketing: IML can be used in lead generation and sales prediction for businesses. The algorithm will prioritize significant leads based on customer behavior within the CRM system, customer feedback, prior orders, etc.
  • Education: IML can apply in personalized educational models in online learning platforms. In this domain, the algorithm observes interactions between students and content and uses its observations to create a personalized learning path.
  • Autonomous vehicles: Cars that operate autonomously require a constant stream of sensor data, which may become overwhelming. IML algorithms allow the vehicle to learn a user's driving style and preferences and ultimately anticipate their inputs.
  • E-commerce: Personalized recommendations can improve customer experience and ultimately encourage customer retention. By analyzing user purchases and browsing behavior, IML algorithms can offer next-best-offer recommendations in real-time.
Interactive Machine Learning Trends

Interactive Machine Learning has gone through significant development since its inception. The following are latest trends of IML:

  • Explainable AI: One of the aspects of machine learning is a lack of interpretability, inscrutable models that are challenging to understand. As IML applications continue to grow, this has become a focus of the AI community, who believe that AI with more transparency is more effective with broader applications.
  • Rapid development frameworks: Frameworks for rapid prototyping of IML applications, such as DeepForge, have been developed. It allows researchers, developers, domain experts to rapidly explore complex domains and machine learning models. It helps to cut down the time required to design and deploying models in production and improve the workflow of the Developers.
  • Semi-supervised learning: Semi-Supervised learning deploys a mix of supervised and unsupervised learning for more efficient and scalable learning. The proposed model is first exposed to labeled datasets that can aid it in the initial phase of learning. Then additional data is fed into the model through unsupervised learning to correct the inaccurate and imbalanced dataset.
  • Online or Active Learning: In active learning, the machine is designed to pose questions to humans based on its inferences, allowing for information that may not have been available before. In online learning, new examples are fed into the model automatically to improve annotation, incorporation, and decision-making at scale.
The Future of Interactive Machine Learning

As the market matures, and applications become more widespread, the future of interactive Machine Learning is looking very promising. IML can generate better AI models with increased transparency, speed, and efficiency, especially in domains with limited data. IML has the potential to expand applications at large scale, making predictions that will revolutionize ways of doing things. The opportunity for collaboration between machines and humans enabled by IML in the future is enormous.