While Si-Fi Movies portray that machine learning could lead to a real-life Terminator series, a Skynet attack is not happening soon. Artificial Intelligence is still in the very early phase.
Machine learning is the ability of a computer (or a machine) to learn and achieve things without explicitly programming them to do so. Instead, the computer learns by leveraging algorithms from past data to predict accurate results. For example, ML uses “pattern recognition” to produce reliable and informed results.
We can divide machine learning into 4 types:
Data scientists provide the machines with algorithms labeled with training data and define the variables they want the algorithm to assess for correlations. In this type of machine learning, both the input and output of the algorithm are specified. The advantages are that it is accurate and the most simple to build.
For example, suppose you build a tech support chatbot using supervised learning. In that case, the developer/user provides training statements and their pre-labeled intents. To overcome the limitations of supervised learning, the industry began moving towards advanced unsupervised learning.
In this type, developers do not provide the machines with labeled data. Instead, such algorithms train on unlabeled data. The algorithm then skims and scans the data for connections. The data that algorithms train on and the predictions or recommendations they give as output are predetermined.
For example, if you built the same chatbot using unsupervised learning, it could answer queries without depending on scripts. Instead, the chatbot learns from the customer’s questions. When unsupervised AI delivers self-service to users, the conversational interaction can leverage conversational AI to become richer and more natural. The downside is the chatbot can have difficulty maintaining context in lengthy conversations.
This is a hybrid of supervised and unsupervised learning. How so? In this kind of machine learning, data scientists provide the algorithm with labeled and classified data. However, the machine’s algorithm has the freedom to explore and look for connections or patterns in the data.
How does this type apply to chatbots? Semi-supervised learning combines human and computer initiatives to develop a useable chatbot. It relies on interactions between computer-guided segmentation of data in intents and response-driven human annotations imposing constraints on clusters to improve relevance.
Reinforcement learning is a goal-oriented algorithm that learns over many steps how to achieve a complex target (goal) or maximize along a specific dimension. In this type, a data scientist designs an algorithm to perform a task and provides it with positive or negative feedback. Then, the algorithm selects what actions to take on it’s own.
Self-improving chatbots are challenging to build because of choosing and prioritizing chatbot performance evaluation metrics. One option is to add user satisfaction questions at the end of each interaction.
In a nutshell, Machine Learning, a part of AI, is the art and ability of computers to learn how to make better predictions and find accurate results. According to Nasdaq, the machine learning market is ready for lift-off, and it’s expected to grow to $20 billion by 2025. Want to learn more about machine learning? Click here to do a deep dive with Sara Brown of MIT.
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