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.
Uncertain data
In computer science, uncertain data is data that contains noise that makes it deviate from the correct, intended or original values. In the age of big data, uncertainty or data veracity is one of the defining characteristics of data. Data is constantly growing in volume, variety, velocity and uncertainty (1/veracity). Uncertain data is found in abundance today on the web, in sensor networks, within enterprises both in their structured and unstructured sources. For example, there may be uncertainty regarding the address of a customer in an enterprise dataset, or the temperature readings captured by a sensor due to aging of the sensor. In 2012 IBM called out managing uncertain data at scale in its global technology outlook report that presents a comprehensive analysis looking three to ten years into the future seeking to identify significant, disruptive technologies that will change the world. In order to make confident business decisions based on real-world data, analyses must necessarily account for many different kinds of uncertainty present in very large amounts of data. Analyses based on uncertain data will have an effect on the quality of subsequent decisions, so the degree and types of inaccuracies in this uncertain data cannot be ignored. Uncertain data is found in the area of sensor networks; text where noisy text is found in abundance on social media, web and within enterprises where the structured and unstructured data may be old, outdated, or plain incorrect; in modeling where the mathematical model may only be an approximation of the actual process. When representing such data in a database, an appropriate uncertain database model needs to be selected. == Example data model for uncertain data == One way to represent uncertain data is through probability distributions. Let us take the example of a relational database. There are three main ways to do represent uncertainty as probability distributions in such a database model. In attribute uncertainty, each uncertain attribute in a tuple is subject to its own independent probability distribution. For example, if readings are taken of temperature and wind speed, each would be described by its own probability distribution, as knowing the reading for one measurement would not provide any information about the other. In correlated uncertainty, multiple attributes may be described by a joint probability distribution. For example, if readings are taken of the position of an object, and the x- and y-coordinates stored, the probability of different values may depend on the distance from the recorded coordinates. As distance depends on both coordinates, it may be appropriate to use a joint distribution for these coordinates, as they are not independent. In tuple uncertainty, all the attributes of a tuple are subject to a joint probability distribution. This covers the case of correlated uncertainty, but also includes the case where there is a probability of a tuple not belonging in the relevant relation, which is indicated by all the probabilities not summing to one. For example, assume we have the following tuple from a probabilistic database: Then, the tuple has 10% chance of not existing in the database.
AI Virtual Assistants: Free vs Paid (2026)
Trying to pick the best AI virtual assistant? An AI virtual assistant 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 virtual assistant slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
Topic model
In natural language processing, a topic model is a type of probabilistic, neural, or algebraic model for discovering the abstract topics that occur in a collection of documents. Topic modeling is a frequently used text mining tool for discovering hidden semantic features and structures in a text. The topics produced by topic models are generated through a variety of mathematical frameworks, including probabilistic generative models, matrix factorization methods based on word co-occurrence, and clustering algorithms applied to semantic embeddings. Topic models are commonly used to organize and discover latent features in large collections of unstructured text and other forms of big data. Beyond text mining, topic models have also been used to uncover latent structures in fields such as genetic information, bioinformatics, computer vision, and social networks. == History == An early topic model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998. Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999. Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization of PLSA. Developed by David Blei, Andrew Ng, and Michael I. Jordan in 2002, LDA introduces sparse Dirichlet prior distributions over document-topic and topic-word distributions, encoding the intuition that documents cover a small number of topics and that topics often use a small number of words. Other topic models are generally extensions on LDA, such as Pachinko allocation, which improves on LDA by modeling correlations between topics in addition to the word correlations which constitute topics. Hierarchical latent tree analysis (HLTA) is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics. == Topic models for context information == Approaches for temporal information include Block and Newman's determination of the temporal dynamics of topics in the Pennsylvania Gazette during 1728–1800. Griffiths & Steyvers used topic modeling on abstracts from the journal PNAS to identify topics that rose or fell in popularity from 1991 to 2001 whereas Lamba & Madhusushan used topic modeling on full-text research articles retrieved from DJLIT journal from 1981 to 2018. In the field of library and information science, Lamba & Madhusudhan applied topic modeling on different Indian resources like journal articles and electronic theses and resources (ETDs). Nelson has been analyzing change in topics over time in the Richmond Times-Dispatch to understand social and political changes and continuities in Richmond during the American Civil War. Yang, Torget and Mihalcea applied topic modeling methods to newspapers from 1829 to 2008. Mimno used topic modelling with 24 journals on classical philology and archaeology spanning 150 years to look at how topics in the journals change over time and how the journals become more different or similar over time. Yin et al. introduced a topic model for geographically distributed documents, where document positions are explained by latent regions which are detected during inference. Chang and Blei included network information between linked documents in the relational topic model, to model the links between websites. The author-topic model by Rosen-Zvi et al. models the topics associated with authors of documents to improve the topic detection for documents with authorship information. HLTA was applied to a collection of recent research papers published at major AI and Machine Learning venues. The resulting model is called The AI Tree. The resulting topics are used to index the papers at aipano.cse.ust.hk to help researchers track research trends and identify papers to read, and help conference organizers and journal editors identify reviewers for submissions. To improve the qualitative aspects and coherency of generated topics, some researchers have explored the efficacy of "coherence scores", or otherwise how computer-extracted clusters (i.e. topics) align with a human benchmark. Coherence scores are metrics for optimising the number of topics to extract from a document corpus. == Algorithms == In practice, researchers attempt to fit appropriate model parameters to the data corpus using one of several heuristics for maximum likelihood fit. A survey by D. Blei describes this suite of algorithms. Several groups of researchers starting with Papadimitriou et al. have attempted to design algorithms with provable guarantees. Assuming that the data were actually generated by the model in question, they try to design algorithms that probably find the model that was used to create the data. Techniques used here include singular value decomposition (SVD) and the method of moments. In 2012 an algorithm based upon non-negative matrix factorization (NMF) was introduced that also generalizes to topic models with correlations among topics. Since 2017, neural networks has been leveraged in topic modeling in order to improve the speed of inference, and leading to further advancements like vONTSS, which allows humans to incorporate domain knowledge via weakly supervised learning. In 2018, a new approach to topic models was proposed based on the stochastic block model. Topic modeling has leveraged LLMs through contextual embedding and fine tuning. == Applications of topic models == === To quantitative biomedicine === Topic models are being used also in other contexts. For examples uses of topic models in biology and bioinformatics research emerged. Recently topic models has been used to extract information from dataset of cancers' genomic samples. In this case topics are biological latent variables to be inferred. === To analysis of music and creativity === Topic models can be used for analysis of continuous signals like music. For instance, they were used to quantify how musical styles change in time, and identify the influence of specific artists on later music creation.
Simon Godsill
Simon John Godsill (born 2 December 1965) is professor of statistical signal processing at the University of Cambridge, and a professorial fellow at Corpus Christi College. He is also a member of the Centre for Science and Policy. His main area of research is Bayesian statistics and stochastic sampling methodologies, particularly particle filtering. == Education == Godsill obtained both undergraduate and Ph.D. degrees from the Department of Engineering at Cambridge University, whilst a member of Selwyn College. He obtained a first class degree in the Electrical and Information Sciences Tripos. The title of his 1993 Ph.D. thesis was "The Restoration of Degraded Audio Signals" and his Ph.D. supervisor was Peter Rayner, whom he shared with Michael Richard Lynch. == Career == Godsill has published over 250 articles in peer reviewed journals, along with the books Digital audio restoration: a statistical model based approach and Compressed sensing & sparse filtering. == Business interests == Godsill is currently a director of CEDAR Audio Ltd, a Cambridge-based company that applies Bayesian mathematics for purposes of noise reduction in audio data. In February 2005, the company received a Sci-Tech Academy Award (a 'Technical Oscar') for its services to the movie industry, and a stream of innovations appeared over the following years with corresponding recognition including induction into the Audio Technology Hall of Fame (2008), a Cinema Audio Society Award (2009). Godsill is also a director at Input Dynamics Ltd, a Cambridge-based company that applies Bayesian techniques to touch screen technology. Godsill is involved with the research effort at BMLL Technologies, a Cambridge spin-off working in the field of machine learning application in the financial sector.
Corona-Warn-App
Corona-Warn-App was the official and open-source COVID-19 contact tracing app used for digital contact tracing in Germany made by SAP and Deutsche Telekom subsidiary T-Systems. It had been downloaded 22.8 million times as of 19 November 2020 and 26.2 million times as of 18 March 2021. The app has been promoted by billboard and broadcast advertisements, e.g. in cooperation with the German Football Association (DFB) and other prominent companies. The German government has announced that the app would no longer exchange tracing information as of April 30, 2023 & would enter hibernation as of June 1, 2023. == Effectiveness == Experts believe that time saved by using the app can be critical for improving the effectiveness contact tracing efforts. Some virologists say when at least 60% of people in Germany use it, it would be very effective. == Functioning == The app works with the Exposure Notification Framework (what is implemented in Google Play Services for Android and in iOS) by using Bluetooth to exchange codes with app users that are within 1.5 meters of each other for a period of at least 10 minutes. Anyone who tests positive for COVID-19 can share this information voluntarily with the app. Other app users are then notified about when, how long and at what distance they had contact with the infected person within a 14-day period. Testing is available for persons on a voluntary basis. === Server architecture === Based on the Client–server model five servers are operated within the app backend: the Corona-Warn-App server. It stores the authorized keys of infected users, referred to as diagnosis keys, from the past 14 days in its database. Stored diagnosis keys are grouped into regularly updated blocks which are transmitted to the Content Delivery Network. This interface supplies the keys for the app clients to download and locally compute a potential exposure risk. the Verification server. It is responsible for documenting the approval of the user to share their positive test result with the app and also to verify the test result. the Portal Server. It generates a so-called teleTAN token if the user did not give their consent to share their test result with the app at first but then changed their mind or if the local public health authority or test laboratory is not connected to the app system yet. the Test Result Server. It saves the test results provided by the local public health authorities or test laboratories for further use within the backend. the Federation Gateway Server. It connects to the national Corona-Warn-App servers of participating EU countries to enable transnational key exchange. By the distribution of the data on different servers the decoupling of the data becomes possible and results in an obstructed tracing of the app users. ==== Report of a positive COVID-19 test ==== The app provides a function to warn other app users by uploading their positive test result on a voluntarily and anonymous basis to the Corona-Warn-App server. In case the local public health authority or test laboratory is already connected to the app system, the user receives a QR-Code when the swab specimen is taken that can be scanned in the app. After scanning the QR-Code und the user getting authorized by the Verification server, the app receives an individual Registration token which gets stored locally and with which the status and the result of the test can be checked manually as well as automatically. If the local public health authority or test laboratory is not connected to the app system yet and the user wants to share their positive test result with other app users, it is required to request a teleTAN token by calling the verification hotline of the app. In both cases, the user can upload their diagnosis keys of the last 14 days to the Corona-Warn-App server in case their consent to share the information is given. The Corona-Warn-App server then verifies the uploaded keys by asking the Verification server if the keys are valid and if they are, the Corona-Warn-App server stores them in its database. == Privacy == The use of the app is voluntary. The app implements decentralized data storage to ensure data privacy. Employers can require that Corona-Warn be installed on company phones, but can not compel its use on private phones. == Funding == The open source app, which costs €20 million to develop is intended to supplement human contact tracing efforts, which Germany put in place during the early stages of the COVID-19 pandemic in Germany. In August 2022, a spokesperson for the German ministry of health announced that the total costs including all additional developments are now estimated to be closer to €150m. == Interoperability == At its start the app only worked in Germany, and Jens Spahn, than Federal Minister of Health (CDU), has said the development of a Europe-wide system is a future goal. With the update published on 19 October 2020 the app supports key-exchanges with the EU Interoperability Gateway and is therefore able to communicate with contact tracing apps from Ireland and Italy. Austria, Belgium, Czech Republic, Croatia, Cyprus, Denmark, Finland, Ireland, Italy, Latvia, Malta, Netherlands, Norway, Poland, Slovenia, Spain and Switzerland had joined the gateway as well and are also able to exchange keys with Corona-Warn-App. The app can be downloaded in many App stores outside of Germany. However, as of August 2021, the app is still unavailable for those of notable national German minorities like Turks, Russians or Ukrainians, who use App stores of their home countries. == Software variants == An unofficial Corona-Warn-App has been released on F-Droid, making the app available without proprietary components on Android phones. == Literature == Thomas Köllmann: Die Corona-Warn-App – Schnittstelle zwischen Datenschutz- und Arbeitsrecht. In: Neue Zeitschrift für Arbeitsrecht. Nr. 13, 10. Juli 2020, S. 831–836.
The Best Free AI Voice Assistant for Beginners
Looking for the best AI voice assistant? An AI voice assistant 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 AI voice assistant 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.