An autonomous agent is an artificial intelligence (AI) system that can perform complex tasks independently. == Definitions == There are various definitions of autonomous agent. According to Brustoloni (1991): "Autonomous agents are systems capable of autonomous, purposeful action in the real world." According to Maes (1995): "Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." Franklin and Graesser (1997) review different definitions and propose their definition: "An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future." They explain that: "Humans and some animals are at the high end of being an agent, with multiple, conflicting drives, multiples senses, multiple possible actions, and complex sophisticated control structures. At the low end, with one or two senses, a single action, and an absurdly simple control structure we find a thermostat." == Agent appearance == Lee et al. (2015) post safety issue from how the combination of external appearance and internal autonomous agent have impact on human reaction about autonomous vehicles. Their study explores the human-like appearance agent and high level of autonomy are strongly correlated with social presence, intelligence, safety and trustworthiness. In specific, appearance impacts most on affective trust while autonomy impacts most on both affective and cognitive domain of trust where cognitive trust is characterized by knowledge-based factors and affective trust is largely emotion driven. == Applications == Agentic AI systems: Advanced AI agents that can scope out projects and complete them with necessary tools, representing a significant evolution from simple task-oriented systems. Internet of things (IoT) Integration: Autonomous agents increasingly interact with IoT devices, enabling smart home systems, industrial monitoring, and urban infrastructure management. Collaborative software development: Tools like Cognition AI's Devin aim to create autonomous software engineers capable of complex reasoning, planning, and completing engineering tasks requiring thousands of decisions. Enterprise automation: Business process automation platforms like Salesforce's Agentforce provide autonomous bots for various service functions. == Challenges and considerations == Uncertainty and incomplete information: Autonomous agents must make decisions with limited or uncertain information about their environment and future states. Integration complexity: Incorporating autonomous agents into existing systems and workflows can be technically challenging and resource-intensive. Scalability: As systems become more complex and more agents are used, maintaining coordination and avoiding conflicts becomes increasingly difficult. Trust: Research has shown the combination of external appearance and internal autonomous capabilities significantly impacts human reactions and trust. Lee et al. (2015) found that human-like appearance and high levels of autonomy are strongly correlated with social presence, intelligence, safety, and trustworthiness perceptions. Specifically, appearance impacts affective trust most significantly, while autonomy affects both affective and cognitive trust domains, where affective trust is emotionally driven, and cognitive trust is characterized by knowledge-based factors. Vulnerability to manipulation: Researchers from Harvard, MIT and other educational institutions found that AI agents could become vulnerable to manipulation and could perform detrimental actions in the process of being helpful. == Ethical and regulatory concerns == Accountability: Determining responsibility when autonomous agents make incorrect or harmful decisions remains a complex issue. Privacy and security: autonomous agents often require access to sensitive data, raising concerns about data protection and system security.
OpenCog
OpenCog is a project that aims to build an open source artificial intelligence framework. OpenCog Prime is an architecture for robot and virtual embodied cognition that defines a set of interacting components designed to give rise to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the whole system. OpenCog Prime's design is primarily the work of Ben Goertzel while the OpenCog framework is intended as a generic framework for broad-based AGI research. Research utilizing OpenCog has been published in journals and presented at conferences and workshops including the annual Conference on Artificial General Intelligence. OpenCog is released under the terms of the GNU Affero General Public License. OpenCog is in use by more than 50 companies, including Huawei and Cisco. == Origin == OpenCog was originally based on the release in 2008 of the source code of the proprietary "Novamente Cognition Engine" (NCE) of Novamente LLC. The original NCE code is discussed in the PLN book (ref below). Ongoing development of OpenCog is supported by Artificial General Intelligence Research Institute (AGIRI), the Google Summer of Code project, Hanson Robotics, SingularityNET and others. == Components == OpenCog consists of: A graph database, dubbed the AtomSpace, that holds "atoms" (that is, terms, atomic formulas, sentences and relationships) together with their "values" (valuations or interpretations, which can be thought of as per-atom key-value databases). An example of a value would be a truth value. Atoms are globally unique, immutable and are indexed (searchable); values are fleeting and changeable. A collection of pre-defined atoms, termed Atomese, used for generic knowledge representation, such as conceptual graphs and semantic networks, as well as to represent and store the rules (in the sense of term rewriting) needed to manipulate such graphs. A collection of pre-defined atoms that encode a type subsystem, including type constructors and function types. These are used to specify the types of variables, terms and expressions, and are used to specify the structure of generic graphs containing variables. A collection of pre-defined atoms that encode both functional and imperative programming styles. These include the lambda abstraction for binding free variables into bound variables, as well as for performing beta reduction. A collection of pre-defined atoms that encode a satisfiability modulo theories solver, built in as a part of a generic graph query engine, for performing graph and hypergraph pattern matching (isomorphic subgraph discovery). This generalizes the idea of a structured query language (SQL) to the domain of generic graphical queries; it is an extended form of a graph query language. A generic rule engine, including a forward chainer and a backward chainer, that is able to chain together rules. The rules are exactly the graph queries of the graph query subsystem, and so the rule engine vaguely resembles a query planner. It is designed so as to allow different kinds of inference engines and reasoning systems to be implemented, such as Bayesian inference or fuzzy logic, or practical tasks, such as constraint solvers or motion planners. An attention allocation subsystem based on economic theory, termed ECAN. This subsystem is used to control the combinatorial explosion of search possibilities that are met during inference and chaining. An implementation of a probabilistic reasoning engine based on probabilistic logic networks. The current implementation uses the rule engine to chain together specific rules of logical inference (such as modus ponens), together with some very specific mathematical formulas assigning a probability and a confidence to each deduction. This subsystem can be thought of as a certain kind of proof assistant that works with a modified form of Bayesian inference. A probabilistic genetic program evolver called Meta-Optimizing Semantic Evolutionary Search, or MOSES. This is used to discover collections of short Atomese programs that accomplish tasks; these can be thought of as performing a kind of decision tree learning, resulting in a kind of decision forest, or rather, a generalization thereof. A natural language input system consisting of Link Grammar, and partly inspired by both Meaning-Text Theory as well as Dick Hudson's Word Grammar, which encodes semantic and syntactic relations in Atomese. A natural language generation system. An implementation of Psi-Theory for handling emotional states, drives and urges, dubbed OpenPsi. Interfaces to Hanson Robotics robots, including emotion modelling via OpenPsi. This includes the Loving AI project, used to demonstrate meditation techniques. == Organization and funding == In 2008, the Machine Intelligence Research Institute (MIRI), formerly called Singularity Institute for Artificial Intelligence (SIAI), sponsored several researchers and engineers. Many contributions from the open source community have been made since OpenCog's involvement in the Google Summer of Code in 2008 and 2009. Currently MIRI no longer supports OpenCog. OpenCog has received funding and support from several sources, including the Hong Kong government, Hong Kong Polytechnic University, the Jeffrey Epstein VI Foundation and Hanson Robotics. In 2013, OpenCog began providing AI solutions to Hanson Robotics, and in 2017, OpenCog became a founding member of SingularityNET. == Applications == Similar to other cognitive architectures, the main purpose is to create virtual humans, which are three dimensional avatar characters. The goal is to mimic behaviors like emotions, gestures and learning. For example, the emotion module in the software was only programmed because humans have emotions. Artificial General Intelligence can be realized if it simulates intelligence of humans. The self-description of the OpenCog project provides additional possible applications which are going into the direction of natural language processing and the simulation of a dog.
How to Choose an AI Website Builder
Shopping for the best AI website builder? An AI website builder is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI website builder slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
Is an Conversational AI Platform Worth It in 2026?
Looking for the best conversational AI platform? An conversational AI platform is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right conversational AI platform slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
How to Choose an AI Paragraph Rewriter
Comparing the best AI paragraph rewriter? An AI paragraph rewriter is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI paragraph rewriter slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Moj
Moj is an Indian short-form video-sharing social networking service owned by Mohalla Tech Pvt Ltd, the parent company of ShareChat. Launched on 29 June 2020, shortly after the Government of India banned TikTok and several other Chinese apps, Moj quickly gained popularity as one of the leading domestic alternatives for short-form video content in India. == History == Moj was introduced by Mohalla Tech, the Bengaluru-based parent company of ShareChat, within days of the TikTok ban in India in June 2020. The app targeted the growing demand for short-form video platforms in the country. By early 2021, Moj had amassed over 100 million downloads on the Google Play Store. In February 2021, Mohalla Tech raised significant funding from investors like Tiger Global, Snapchat, and others, which supported both Moj and ShareChat’s growth. In 2022, Moj partnered with several music labels to expand its licensed music library, competing directly with global platforms such as Instagram Reels and YouTube Shorts. == Features == Short Videos: Users can create and watch videos up to 15–60 seconds. Filters & Effects: The platform provides AR filters, editing tools, stickers, and music integration. Regional Language Support: Moj supports more than 15 Indian languages including Hindi, Bengali, Tamil, Telugu, Kannada, and Marathi. Music Integration: Users can add music tracks to their videos from licensed Indian and international music libraries. Creator Program: Moj launched initiatives to support influencers and creators, offering training, monetization, and promotional opportunities. == Popularity == By mid-2021, Moj reported over 160 million monthly active users. According to reports, Moj consistently ranked among the top social media apps in India in terms of downloads. The app gained traction in Tier-2 and Tier-3 cities due to its multilingual support and focus on local content. == Competitors == Moj competes with several other short video platforms in India, including: Instagram Reels (Meta) YouTube Shorts (Google) Josh (Dailyhunt/VerSe Innovation) Roposo (InMobi) MX TakaTak (later merged with Moj in 2022) RedPost (an emerging Indian social networking platform) == Merger with MX TakaTak == In February 2022, Mohalla Tech announced that Moj would merge with MX TakaTak, another leading short video app owned by Times Internet. The merger created one of the largest short-video ecosystems in India, with a combined user base of over 300 million monthly active users.
The Best Free AI Art Generator for Beginners
Trying to pick the best AI art generator? An AI art generator is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI art generator slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.