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The robot-depicted world of our not-so-distant future relies heavily on our ability to deploy artificial intelligence (AI) successfully. However, transforming machines into thinking devices is not as easy as it may seem. Strong AI can only be achieved with machine learning (ML) to help machines understand as humans do. Machine learning can be confusing, so it is important that we begin by clearly defining the term:
How Does Machine Learning Work?Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin. The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly. Why Is Machine Learning Important?Machine learning as a concept has been around for quite some time. The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming. Samuel designed a computer program for playing checkers. The more the program played, the more it learned from experience, using algorithms to make predictions. As a discipline, machine learning explores the analysis and construction of algorithms that can learn from and make predictions on data. ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes.
Machine Learning Is Widely AdoptedMachine learning is not science fiction. It is already widely used by businesses across all sectors to advance innovation and increase process efficiency. In 2021, 41% of companies accelerated their rollout of AI as a result of the pandemic. These newcomers are joining the 31% of companies that already have AI in production or are actively piloting AI technologies.
Training Methods for Machine Learning DifferMachine learning offers clear benefits for AI technologies. But which machine learning approach is right for your organization? There are many to ML training methods to choose from including:
Let’s see what each has to offer. Supervised Learning: More Control, Less BiasSupervised machine learning algorithms apply what has been learned in the past to new data using labeled examples to predict future events. By analyzing a known training dataset, the learning algorithm produces an inferred function to predict output values. The system can provide targets for any new input after sufficient training. It can also compare its output with the correct, intended output to find errors and modify the model accordingly. Unsupervised Learning: Speed and ScaleUnsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. At no point does the system know the correct output with certainty. Instead, it draws inferences from datasets as to what the output should be. Reinforcement Learning: Rewards OutcomesReinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovering errors or rewards. The most relevant characteristics of reinforcement learning are trial and error search and delayed reward. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback — known as the reinforcement signal — is required for the agent to learn which action is best. Machine Learning Is Not PerfectIt is important to understand what machine learning can and cannot do. As useful as it is in automating the transfer of human intelligence to machines, it is far from a perfect solution to your data-related issues. Consider the following shortcomings before you dive too deep into the ML pool:
The Future of Machine Learning: Hybrid AIFor all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI, or a rule-based methodology toward processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships. Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why, this powerful approach is transforming how data is used across the enterprise. Discover what differentiates hybrid AI from traditional machine learning and how it will transform the way we use our data. Learn More |