Artificial intelligence

Artificial intelligence (AI) is a field of computer science that produces systems designed to solve problems that humans typically solve using intelligence. In the consumer and industry space, it is commonly referred to as chatbots or large language models (LLMs), which have been a main focus of industry since the November 2022 launch of OpenAI's ChatGPT, with tens of billions of dollars in funding allocated to producing more popular LLMs. This is also a significant focus on text-to-image models, which "draw" an image using a written prompt, and less commonly, text-to-video models, which extend the text-to-image concept across several smooth video frames.

AI is not a new concept; it has been of interest since the 1950s. AI is a catch-all term, encompassing many areas and techniques.

Generative artificial intelligence models are trained through vast amounts of existing human-generated content. LLMs gather statistics on word patterns, which allows the model to generate sequences of words that seem similar to what a person might have written. However, an LLM does not understand anything; they cannot reason. They generate randomly modulated pattern of tokens. In this way, they function similarly to autocomplete.

People reading sequences of tokens sometimes perceive things they think are true. Sequences that do not make sense to the reader, or that are false, are called hallucinations. LLMs are typically trained to produce output that is pleasing to people, exhibiting dark patterns. For example, they produce output which seems confidently written, use patterns which praise the user (sycophancy), and employ emotionally manipulative language.

People are accustomed to interacting with others, and many overestimate the abilities of things that exhibit complex, person-like patterns. Promoters of “AI” systems take advantage of this tendency, using suggestive names (like “reasoning” and “learning”) and grand claims (“PhD level”), which make it harder for people to understand these systems.

From November 2022 to 2025, venture capitalists and companies invested hundreds of billions of dollars into AI but received minimal returns. When companies seek returns, consumers can expect that products may be orphaned, services may be reduced, customer data may be sold or repurposed, costs may rise, and companies may reduce staff or fail. Historically, AI has had brief periods of intense hype, followed by disillusionment, and “AI winters.”^{[citation needed]}

The current well-funded industry of artificial intelligence tools has led to the rampant and unethical use of content. Startups aiming to develop AI services have been rapidly scraping the internet for content to train future models, and members of the field are concerned that they are approaching the limit of publicly available content to train from.^[1]

Further reading: Artificial intelligence/training

Users' work is sometimes silently used in training without their explicit consent, as was the case for Adobe's AI policy.

AI can be and has been used to generate deepfakes of people with and without their consent. Deepfakes are media generated with the likeness of an individual. Deepfake media can range from harmless to harmful. The latter includes child pornography, revenge porn, blackmail, etc. Since the rampant rise of consumer AI, deepfakes have become even more prevalent, with some websites explicitly specializing in them.^{[citation needed]}

There are several concerns with using online AI models like ChatGPT, not only because they are proprietary, but also because there is no guarantee of where your data will be stored or used. Recent developments in local AI models offer an alternative to online AI models, which can be downloaded from platforms like HuggingFace and used offline. Common models to run include Llama (Meta), DeepSeek (DeepSeek), Phi (Microsoft), Mistral (Mistral AI), Gemma (Google).

In some cases, AI models can be hijacked for malicious purposes. Demonstrated with Comet (Perplexity), users can run arbitrary prompts to the browser's built-in AI assistant by hiding text in the HTML comments, non-visible webpage text, or simple comments on a webpage.^[2] These arbitrary prompts can then be exploited to obtain sensitive information or gain unauthorized access to high-value accounts, such as those for banking or gaming libraries.^[3] See Prompt injection.

Due to heavy investments into and increased use of generative AI and LLMs, many data centers have been constructed to host LLMs. These data centers consume large amounts of power and water, in order to power and cool the computer systems running the models. Residents that live in cities where AI data centers have been constructed have complained of an increase in their electricity bills despite no change in their personal usage.^{[citation needed]} According to a research video by Benn Jordan, these data centers (as well as fracking operations and natural occurrences) cause a high amount of sound pollution, which can cause various symptoms.^[4]

Most AI apps include an initial "root"/"system" prompt given to the AI, which is hidden from the user. Some corporations go to great lengths to keep those prompts hidden, and to avoid leaking it to the user. Some projects attempt to bring back transparency to these tools, in spite of the restrictions.^[5]

• Automatic content recognition

• Nicky Case, “AI Safety for Fleshy Humans”, Hack Club (2024)