Artificial Intelligence and Machine Learning

While AI is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You’ll see how these two technologies work, with examples and a few funny asides. Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points and draw conclusions.

Artificial intelligence contains many subfields:

  • Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude.
  • A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
  • Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
  • Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
  • Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

The difference between Machine Learning and Artificial Intelligence

Artificial Intelligence refers to a very large field of research that encompasses a number of techniques aimed at developing computers that can learn and solve problems:

  • Computer Vision
  • Supervised and Unsupervised Learning
  • Reinforcement Learning and Genetic Algorithms
  • Natural Language Processing
  • Robotics (Motion)

Machine Learning is the field of Artificial Intelligence concerned with learning from data on its own.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Some Machine Learning Methods

Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  • In contrast, unsupervised 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. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
  • Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
  • Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Understanding AI technologies and how they lead to smart applications

The mutually beneficial relationship between the internet of things (IoT) and Artificial Intelligence (AI) is enabling disruptive innovations in wearables and implantable biomedical devices for healthcare monitoring; smart surveillance and monitoring applications such as the use of an autonomous drone for disaster management and rescue operations. The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous. This convergence of AI and IoT is evolving the nature of emerging applications from being assisted to augmented and ultimately to autonomous intelligence. This continuum will impact all industries ranging from manufacturing, retail, healthcare, telecommunication, and transportation, etc. IoT sensors will allow the collection of a vast amount of data, whereas AI can help derive intelligence for devising smarter applications for a smarter world. Moreover, the emerging 5G landscape provides a foundation for realizing the full potential of AI-powered IoT. The massive connectivity offered by 5G along with ultra-low latency capability will open up avenues for exciting applications across all verticals.

This emerging era of AI and IoT applications has three main components (i) smart devices (ii) intelligent systems of systems and (iii) end-to-end analytics. Numerous challenges exist in implementing such systems that include algorithmic and design innovations to meet Quality of Service requirements (latency, bandwidth, delay, etc); mechanisms to preserve IoT data privacy and provide secure services for interconnected users; achieving high performance systems that can process both high volume and fast speed IoT data leveraging Edge AI. Moreover, from an application front, there is still a need to design scalable and intelligent IoT data solutions that make better use of federated learning and collaborative sensing concepts for collective intelligence.

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