models are prone to generating falsehoods called "hallucinations", although this can be reduced with
RLHF and quality data. They are used in chatbots, which allow people to ask a question or request a task
in simple text.[122][123]
Current models and services include Gemini (formerly Bard), ChatGPT, Grok, Claude, Copilot, and
LLaMA.[124] Multimodal GPT models can process different types of data (modalities) such as images,
videos, sound, and text.[125]
In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-specific
enhancements and used with specialized TensorFlow software had replaced previously used central
processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine
learning models' training.[126] Specialized programming languages such as Prolog were used in early AI
research,[127] but general-purpose programming languages like Python have become predominant.[128]
The transistor density in integrated circuits has been observed to roughly double every 18 months—a
trend known as Moore's law, named after the Intel co-founder Gordon Moore, who first identified it.
Improvements in GPUs have been even faster.[129]
AI and machine learning technology is used in most of the essential applications of the 2020s, including:
search engines (such as Google Search), targeting online advertisements, recommendation systems
(offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense,
Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and
self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial
recognition (Apple's Face ID or Microsoft's DeepFace and Google's FaceNet) and image labeling (used
by Facebook, Apple's iPhoto and TikTok). The deployment of AI may be overseen by a Chief automation
officer (CAO).
The application of AI in medicine and medical research has the potential to increase patient care and
quality of life.[130] Through the lens of the Hippocratic Oath, medical professionals are ethically
compelled to use AI, if applications can more accurately diagnose and treat patients.[131][132]
For medical research, AI is an important tool for processing and integrating big data. This is particularly
important for organoid and tissue engineering development which use microscopy imaging as a key
technique in fabrication.[133] It has been suggested that AI can overcome discrepancies in funding
allocated to different fields of research.[133] New AI tools can deepen the understanding of biomedically
relevant pathways. For example, AlphaFold 2 (2021) demonstrated the ability to approximate, in hours
rather than months, the 3D structure of a protein.[134] In 2023, it was reported that AI-guided drug
discovery helped find a class of antibiotics capable of killing two different types of drug-resistant
bacteria.[135] In 2024, researchers used machine learning to accelerate the search for Parkinson's disease
Hardware and software
Applications
Health and medicine