Artificial Intelligence Definitions
Chances are, you may be interested in artificial intelligence. But there’s just so much information out there. You probably won’t know where to start.
Want to learn about, say Machine Learning and Neural Networks? I’ve done some research and put together a glossary of artificial intelligence terms. Pre-formatted in simple plain language for the people like me.
Expand your mind.
What I’ve discovered is that regardless of how much I try to learn, there’s still more to uncover. I’ve managed to learn thus far that there are a lot of misconceptions going on in terms of artificial intelligence. AI has been a popular theme in pop-culture and science-fiction. Capturing our imaginations for ages. As a result, this is the version of AI many of us have set in our minds.
Don't worry. AI isn’t sentient, nor self-aware. It’s time to separate fact from science-fiction. Ask Google Assistant, Alexa or Siri for the meaning of life. You’re bound to get a myriad of entertaining answers. Not as a result of deep philosophical contemplation. An algorithm processes the input, then defines an appropriate response based on a set of predefined ‘trained’ rules.
It’s not here to take over the world. It’s here to make our world, primarily the digital version of it, easier to deal with. It’s a tool that is becoming increasingly relevant in our day and age. The volume of data the average professional has to deal with has increased exponentially. Not a very productive use of the human mind, repetitively organizing endless streams of data.
AI may help us solve this, for example. For many industries it isn’t a question of if artificial intelligence should be implemented, but how soon?
Competition in the information age is fast and fierce. If you’re going to effectively harness the potential of AI, you’ll need to understand the opportunity it represents. Does this mean benefits for your business or end up being another cool gadget gathering dust?
At Talkwalker our AI-powered insights enable our customers to spend less time managing data and more time putting the data to good use. Focusing their energies on creating insights and strategies for impact!
It’s a long list, but here are a few outtakes. You can read the full list on my blog post at Talkwalker here: Artificial Intelligence Definitions, Upgrade your AI IQ. For starters:
The analysis of a statement or situation by AI and finding the simplest and most likely explanation for it.
Example: You notice that the roads are wet. The most likely hypothesis is… rain. It’s the obvious explanation.
This is a process typically used to train deep neural networks and refine the results of artificial neural networks. Information is processed by the system and results are sent back up the pipeline in reverse order for verification.
A field that deals with how computers find, process, and analyzes digital images. Computer vision powers emerging technologies, including facial recognition, augmented reality, and image recognition. Can be seen at work in Talkwalker’s proprietary image recognition technology.
The furthest evolution of AI at present. It learns by example and uses multiple layers of nonlinear processing units to achieve phenomenal results.
It requires a lot of computer processing power, and large amounts of labeled data, to comprehend the task at hand. But, it can achieve the highest levels of data accuracy.
Reinforcement learning is a type of machine learning with less specific goals. Instead, the goals are more abstract, such as “maximize brand mentions.”
During training, the AI learns by acting towards the goal and evaluating its contribution after each effort.
Combination of natural language processing (NLP), computational linguistics, and text analytics. Applied together to identify and extract subjective information from content. Its goal is to understand the attitude of a person.
Supervised and unsupervised learning are two different AI training methods. Supervised training, includes labeled data sets. This allows the artificial intelligence to learn from the expected labels, and extrapolate that into wider data sets.
Unsupervised learning requires no labeling, and it is up to the AI to self-categorize its output. While unsupervised learning can perform more complex learning functions, it can also create unnecessary or complicated categories of data. Giving more clutter rather than less.
Developed by Alan Turing in the 1950s. The Turing test was devised to see if people could distinguish between the machine or human interactions. The standard interpretation is to have an interviewer blind-question both a human and computer subject. Then see if the interviewer can accurately predict which subject is the AI, based on their results.
What’s next for artificial intelligence?
The field itself is constantly expanding and evolving at the moment. The potential applications and ramifications are up for debate. Best bet is to get educated, at least start with the basics. Stay informed as it develops further.
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