The key difference between Artificial intelligence and human intelligence is that the former is demonstrated by a machine. Artificial Intelligence is not a one size fits all concept but requires a nuanced understanding of what constitutes intelligence in a machine, system, or program.
Generally speaking, AI:
We can classify AI into three distinct categories.
Narrow or Weak AI is defined as a field of artificial intelligence that is used for a specific task or purpose. It allows powerful systems to match or exceed human intelligence that is required for functioning in or performing actions that fall within the limited focus of the AI system in question. Examples can include news feed curation features on apps, voice assistants, and AI-powered speech recognition tools.
This, at the moment, is a hypothesized state of AI and is not something the world has currently achieved. It is where artificial intelligence equals human intelligence as far as cognitive abilities are concerned. Strong AI is also referred to as artificial general intelligence (AGI). To reach the point of having Strong AI, a system, program, or machine must be endowed with general intelligence and the ability to mimic human intelligence.
This is the ultimate stage of artificial intelligence where AI meets and supersedes both general human intelligence and general human cognitive abilities.
What, then, is the difference between AI and Machine Learning?
Machine Learning is a subset of AI. It is a learning concept that has fixed outcome goals such as the improvement of a system based on experience. “Improvement” here can be classified in many ways, such as correct responses to a customer’s queries, correctly identifying different images, identifying the presence or absence of cancer cells in patient scans, and more. The idea is to have machines trained with exposure to relevant datasets for them to have an independent learning technique instead of needing to be programmed for every new use case or scenario.
For example, an AI program can be “taught” what cats and dogs look like to determine which of thousands of photos contain cats vs. dogs. However, instead of reprogramming the system from scratch to teach it to differentiate between cats and cars, the system should be able to “learn” that a scanned object is or is not a cat and the system should then be able to provide the correct output or response based on experience.
ML can be categorized into the following:
Supervised Machine Learning: This is when an ML algorithm uses predefined categories that are specified by a human operator and tries to group data under those provided categories.
Unsupervised Machine Learning: This is where there are no pre-decided parameters for the system. Instead, the system is designed for pattern discovery. That is what constitutes the “learning” of the algorithm.
Semi-Supervised Machine Learning: These models combine both supervised and unsupervised elements in the learning process of the ML system or application.
Reinforcement Machine Learning: This approach rewards the process of trial and error. It can be used when there is a lack of training data so that a system can “learn” each time a correct vs. an incorrect response is provided, rather than learning by processing vast amounts of training data.
Characteristics of Machine Learning (ML)
Some of the key characteristics of machine learning are as follows.
Effective processing of data and information is possible when ML delivers results by analyzing massive data sets. AI and cognitive technologies can then be applied to ML learning models to deliver an effective way to process data and information. From there, the applications of ML are only limited by the use case and the ingenuity of the user. Applications of ML technology include search algorithms and facial recognition systems, diagnostic testing (of machines and patients), flagging suspicious digital activity (used by financial systems to prevent fraud), learning user behavior patterns (used in ML-driven targeted ads and data curation), and much more.
The natural extension of Machine Learning is Deep Learning. Deep Learning is a facet of machine learning where the neural networks used to process data are larger and more powerful, allowing these systems to parse bigger data sets or handle more complex problems. Deep Learning utilizes the same neural networks and machine learning models that are used in general ML, although on a significantly larger scale.
The transformation of data into actionable insight must be maintained. If it is, we can then act on these data-driven insights with greater speed and efficiency and incorporate those insights into strategic plans, whether that is in the area of disease identification, marketing, financial services, or otherwise.
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