Here's A Cheat Sheet On AI Buzzwords

It has taken decades for artificial intelligence to reach the point of being able to offer sophisticated - but sometimes erroneous - answers to a vast array of questions, such as the ones asked by the ChatGPT chatbot in late 2022. This was the first major milestone in artificial intelligence of its kind for decades. Scientists were experimenting with the concept of "computer vision" as far back as the 1960s, giving machines the capability of reading and understanding text as well. During the era of the Beatles, chatbots were still a relatively new invention. In today's world, it is becoming increasingly likely that a computer will be able to perform the majority of human tasks better than a human would. Whether you are worried about being replaced by a machine, or just curious about the possibilities that AI could bring, here is a list of some popular AI buzzwords and what they actually mean, so that you can understand what they mean. 

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Artificial Intelligence

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The term 'machine learning' refers to the use of technology in order to model the capabilities of humans. With artificial intelligence, you will be able to deliver personalized products and news feeds, and services at a lower cost, faster, and free of human error. Factory managers or transportation network operators may be able to use this information to make better use of their engineers' time and to spot component failures before they happen. The term "Big Data" was coined in the 1950s by computer scientist John McCarthy, but it didn't really take off until the 2000s when technology giants such as Google, Meta Platforms Inc., and Microsoft Corp. got together to combine vast computing power with vast pools of user information. There is no question that artificial intelligence can display human-like abilities in the way it processes data or converses, but machines are still unable to know what they are doing or saying. At the moment, they are still relying primarily on algorithms in order to perform their tasks. 

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Algorithm

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Algorithms are step-by-step processes used to solve problems that require a wide range of steps to be taken. An input is taken, some logic is applied, and an output is produced. It has been centuries since humans have used algorithms to solve problems in order to solve them. There are some financial analysts who spend most of their careers constructing algorithms that can predict future events and help them to make money in the process. There has been a shift towards using “machine learning” in recent years, which builds on the idea that our world works on these “traditional” algorithms.

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Machine Learning 

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The process of feeding data into algorithms is what allows them to become more sophisticated and refined over time based on the data they receive. By using this method, a computer is able to "learn" without having to be trained on the specifics of the task at hand in order to perform the task. Consider the iPhone photo app as an example. Initially, it doesn't know what you look like because it doesn't know how you look. When you start tagging your face in photos taken across many years and in a variety of situations, the machine "learns" to recognize you as the person in the photo, once you start tagging your face in pictures taken across many years. The more data that is fed into it, the more effective it becomes as a result. In order to achieve that, there will need to be an impressive model underneath that can distinguish between a human face and, for example, two fried eggs, a mushroom, and a sausage placed on a plate. 

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Natural Language Processing

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Natural Language Processing (NLP) is a branch of artificial intelligence that helps computer systems to understand, process, and generate speech and text in a manner similar to what a human would do. NLP uses machine-learning algorithms in order to extract information from written texts, translate languages, identify handwritten words, and understand meaning and context in order to extract data from written texts. Virtual assistants like Siri and Alexa are powered by AI, which is the underlying technology that allows them to understand a variety of requests and respond in a natural language based on the requests they are given. In addition, NLP is also capable of identifying the emotion in text, which is the reason why Siri will suggest that you call a friend or loved one when you tell it that you're sad. As part of everyday applications, spam filtering can be applied to emails, web searches, spellchecking, and text prediction, among other things. 

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Chatbots

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Chatterbots, as they were known in the 1990s, are products such as ChatGPT that have the capability to hold advanced, human-like conversations with people about anything from historical trivia to lists of creative recipes using a watermelon. One of the first examples of this is the tools provided by companies on their "Contact Us" pages as a first line of defense when a customer needs help, as a first line of defense. In some ways, these systems are similar to voice-activated virtual assistants in that they are relatively simplistic and limited in terms of their conversational capabilities. Recent advances in artificial intelligence are expected to lead to a rapid improvement in chatbots in the near future. 

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Computer Vision 

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An application of artificial intelligence enables computers to analyze visual information such as images and videos as well as identify and classify objects and people from that information. Depending on the situation, the systems may take or recommend a particular action based on what they see. Technology such as this is being used in the tracking of wildlife for the purpose of conservation and in the guidance of autonomous vehicles. Despite the fact that such a system has been used in military operations and policing for many years, there have been concerns about its usage because it has been shown to be racially biased and to lack the precision for reliably identifying a particular individual. 

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Deep Learning

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AI is one of the most common forms of artificial intelligence in which software is taught to classify something, such as a video or a loan application, from a large set of labeled data, using a set of rules. The age-old computing adage "garbage in, garbage out" is still applicable in this case. If the training data isn't good - it doesn't reflect the real world or incorporates human biases into the data - then the artificial intelligence (AI) won't work as intended or it will include those biases in the AI. The software that was used to assist in making decisions about bail, sentencing, and parole indicated that black defendants are more likely to commit future crimes than their white counterparts. The use of deep learning software has also led to businesses denying services to people from ethnic minorities in areas with a higher concentration of residents from ethnic minorities in some instances. 

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Generative AI

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Creative works can be produced from simple prompts, such as pictures, music, text, poetry, or sea shanties. It includes technologies like Stability AI's Stable Diffusion, or OpenAI's DALL-E, which can create elaborate and detailed imagery in seconds. Additionally, Google has developed (but hasn't released) a system for making music from keywords. AI systems like these aren't to be confused with those used by Adobe, for instance, in its Photoshop product, which corrects and improves existing photographs. Instead, generative AI creates entirely new work after being trained on vast quantities of preexisting material, and this is causing artists and agencies to sue. 

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Neural Networks

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It is a type of artificial intelligence (AI) in which a computer is programmed to learn in a manner similar to how a human brain learns, which is through trial and error, as a kind of learning mechanism. As a result of each success or failure, future attempts and adaptations are reinforced. As a child's brain maps neural pathways based on what it is taught, the virtual neurons learn to generate responses based on the information they have been fed and what is said to be correct, just as the human brain maps neural pathways based on what it has been taught. However, to become proficient, it can take thousands or even millions of attempts. By layering these neurons together, you end up with a network of neurons that is multileveled and very complex. Thus, deep learning is also known as machine learning, as it is a method of learning from large amounts of data using neural networks. 

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Large Language Models

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There are very large neural networks that are being trained using massive amounts of text from all over the internet in a particular language, including e-books, news articles, and Wikipedia pages in that particular language. The LLM process is the backbone of natural language processing, as it is able to recognize, summarize, translate, predict, and generate text based on billions of parameters that are available for it to learn from. One of the most well-known LLMs is GPT-3. GPT-3 is the engine that drives ChatGPT and is probably the best-known one. The Meta company has developed LLaMA, and the Google company has developed LaMDA. 

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GPT 

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In the field of machine learning, this is a type of LLM whose initials stand for Generative Pre-trained Transformer. In terms of complexity, GPT's "transformer" component is the most complex of the three components and does not appear to be specific to its creator, OpenAI, who created ChatGPT. An example would be a computer system that could take strings of inputs and process them all at once rather than individually so that the context and word order of the input can be captured as well. Having a clear understanding of order, syntax, and meaning for a translation is crucial, for instance: "Her dog, Poppy, ate in the kitchen" could be translated into the French equivalent of "Poppy ate her dog in the kitchen" without paying sufficient attention to order, syntax, and meaning. 

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OpenAI

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As a nonprofit organization, the San Francisco-based laboratory behind ChatGPT was co-founded by Sam Altman, a programmer, and entrepreneur, with the mission of developing AI technology that would benefit humanity as a whole. The foundation of LinkedIn co-founder Reid Hoffman, as well as Elon Musk, who ended his involvement in 2018, were early investors in the company. In 2019, when Microsoft invested $1 billion in OpenAI, the company shifted to create a for-profit entity. A new version of Microsoft's Bing search engine and Edge browser, which use the tech that Microsoft has invested in, was launched in January after Microsoft pumped in another $10 billion. Hugging Face is one of the rival AI labs, which is working with Amazon.com Inc., and Baidu Inc., which announced in October that it will roll out Wenxin Yiyan, or Ernie Bot in English, shortly and will directly compete with OpenAI. Additionally, Anthropic, a startup closely partnered with Google, is also testing a ChatGPT competitor called Claude. 

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