Machine Learning - Unlocking the Future
Traditional software comprises instructions that electronic systems follow to perform specified actions. Machine learning inverts that by employing algorithms that instead enable systems to learn as they go along.
AI/ML analyzes incoming data by looking for patterns. It makes predictions or categorizations based on the patterns it has detected, and then it tests its predictions. The predictive model that the AI/ML creates is continuously revised and optimized.
AI/ML is well-suited for applications with large data sets or steady streams of data. It is especially advantageous when this kind of data must be processed quickly if not in real time. Typically data is structured, deliberately formatted before submitted to computation. AI/ML can certainly use structured data, but because it is capable of learning it is particularly adept at processing unstructured data.
Some of the applications AI/ML works well for include:
- Analytics and data
- Autonomous systems
- Environmental monitoring
Key Machine Learning Applications
Analytics & Data
AI/ML excels at rapidly detecting patterns in vast amounts of data, which can be impractical (and sometimes impossible) to do with traditional computational systems. Furthermore, because AI/ML learns, it is also adaptable, meaning that with more data it can improve its models or even revise them if something fundamental in the nature of the incoming data changes. Almost any application can benefit from AI/ML, but it is of particular value in applications that rely on sensor input, notably in autonomous and semi-autonomous systems.
Autonomous and semi-autonomous systems
Autonomous and semi-autonomous systems are entirely dependent on steady flows of sensor data. While autonomous systems can be trained in advance, it is not possible for a programmer to anticipate every object or circumstance any given autonomous system might confront. The capability to learn is critical. It enables the autonomous system to adapt to unexpected stimuli – a new object, or unfamiliar behavior from recognized objects. It would not be practical to create products such as self-driving cars without AI/ML.
Environmental Monitoring
AI/ML is applicable to environmental monitoring for many of the same reasons that it is applicable to autonomous systems. Environmental monitoring is similarly a sensor-based application that produces a steady stream of data that needs to be analyzed in real time. The environments to be monitored can be anything from a clean room, where even tiny changes can be highly significant, to the outdoors, where conditions can be highly changeable. Either way, environmental monitoring benefits from AI/ML’s strengths.
Sound Analysis, Speech & Audio Processing
Analysis of sound is useful to identify the contexts related to audio signals, and to have an understanding of the environmental sounds. Speech & audio processing helps to analyze large amounts of natural language data to support the interactions between computers and human "natural" languages. This includes speech recognition, natural language understanding, and natural language generation.
- Natural language processing (NLP) to generate human-computer interactions. NLP includes speech recognition, natural language understanding, and natural language generation.
- Acoustic scene classification (ASC) to categorize audio signals into predetermined classes. ASC is similar to speech recognition, except that the target classes are different and more heterogeneous. ASC can identify the contexts (classes) related to the audio signals and can be used in different applications including security and surveillance for gunshot detection, or just to identify the environmental situation you are in (restaurant, metro station, city street, etc...).
- Keyword Spotting (KWS) to detect predefined keywords in an audio stream. KWS is a technique to provide a hands-free interface for on voice-activated devices with limited resources and is used similar to AI voice assistants to detect wake words and keywords to trigger an action.
Machines that Learn: A Deep Dive into AI/ML Models and Algorithms
Artificial intelligence (AI) and machine learning (ML) can be used to pull insights out of huge volumes of information quickly and efficiently. AI/ML can also give machines the ability to process information similar to the way humans do.
AI/ML can perform recognition and classification, predictive analytics, natural language understanding, and other tasks that are difficult or impossible to accomplish with traditional computing.
These capabilities lead to an impressive variety of use cases: voice recognition, autonomous driving, epidemiology, pharmaceutical design, software coding, financial trading, and more.
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