What is artificial intelligence?
At its simplest form, artificial intelligence is a field which combines computer science and robust data sets to enable problem solving. It also encompasses subfields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence.
These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data. Over the years, artificial intelligence has gone through many cycles of hype, but even to skeptics, the release of OpenAI’s chat GPT seems to mark a turning point. The last time Generative AI loomed this large, the breakthroughs were in computer vision, but now the leap forward is in natural language processing. And it’s not just language. Generative models can also learn the grammar of software code, molecules, natural images, and a variety of other data types.
The applications for this technology are growing every day, and we’re just starting to explore the possibilities. But as the hype around the use of AI in business takes off, conversations around ethics become critically important. Types of Artificial Intelligence, Weak AI versus Strong AI. Weak AI, also called Narrow AI or Artificial Narrow Intelligence, or ANI, is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow might be a more accurate descriptor for this type of AI as it is anything but weak.
It enables some very robust applications, such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and Autonomous Vehicles. Strong AI is made up of Artificial General Intelligence, or AGI, and Artificial Superintelligence, or ASI. Artificial General Intelligence, AGI, or General AI, is a theoretical form of AI where a machine would have an intelligence equal to humans. It would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future.
Artificial Superintelligence, ASI, also known as Superintelligence, would surpass the intelligence and ability of the human brain. While Strong AI is still entirely theoretical with no practical examples in use today, that doesn’t mean AI researchers aren’t also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as Hal 9000, the superhuman rogue computer assistant in 2001, a space odyssey. Deep Learning versus Machine Learning. Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Both deep learning and machine learning are subfields of Artificial Intelligence, and deep learning is actually a subfield of machine learning.
Deep learning is actually comprised of neural networks. Deep in deep learning refers to a neural network comprised of more than three layers, which would be inclusive of the inputs and the output. A neural network with more than three layers can be considered a deep learning algorithm. The way in which deep learning and machine learning differ is in how each algorithm learns.
Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as, quote, scalable machine learning. Classical or non-deep machine learning is more dependent on human intervention to learn.
Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn. Deep machine learning can leverage labeled data sets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled data set. It can ingest unstructured data in its raw form, for example text or images, and it can automatically determine the hierarchy of features which distinguish different categories of data from one another.
Unlike machine learning, it doesn’t require human intervention to process data, allowing us to scale machine learning in more interesting ways. The rise of generative models. Generative AI refers to deep learning models that can take raw data, say all of Wikipedia or the collected works of Rembrandt, and learn to generate statistically probable outputs when prompted. At a high level, generative models encode a simplified representation of their training data and draw from it to create new work that’s similar but not identical to the original data. Generative models have been used for years in statistics to analyze numerical data. The rise of deep learning, however, made it possible to extend them to images, speech, and other complex data types.
Among the first class of models to achieve this crossover feat were variational auto encoders, or VAEs, introduced in 2013. VAEs were the first deep learning models to be widely used for generating realistic images and speech. Artificial intelligence applications. There are numerous real-world applications of AI systems today. Speech recognition. It’s also known as automatic speech recognition, or ASR, computer speech recognition, or speech to text, and it’s a capability which uses natural language processing, or NLP, to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search, for example, Siri, or provide more accessibility around texting. Customer service. Online virtual agents are replacing human agents along the customer journey.
They answer frequently asked questions or FAQs around topics, like shipping, or provide personalized advice, cross-selling products, or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bonds on e-commerce sites with virtual agents and messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice search. Computer vision. This AI technology enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and based on those inputs, it can take action.
The ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging and social media, radiology imaging and healthcare, and self-driving cars within the automotive industry. Recommendation Engines. Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers. Automated Stock Trading. Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
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