Cultural algorithms (CA) are a branch of evolutionary computation where there is a knowledge component that is called the belief space in addition to the population component. In this sense, cultural algorithms can be seen as an extension to a conventional genetic algorithm. Cultural algorithms were introduced by Reynolds (see references). == Belief space == The belief space of a cultural algorithm is divided into distinct categories. These categories represent different domains of knowledge that the population has of the search space. The belief space is updated after each iteration by the best individuals of the population. The best individuals can be selected using a fitness function that assesses the performance of each individual in population much like in genetic algorithms. === List of belief space categories === Normative knowledge A collection of desirable value ranges for the individuals in the population component e.g. acceptable behavior for the agents in population. Domain specific knowledge Information about the domain of the cultural algorithm problem is applied to. Situational knowledge Specific examples of important events - e.g. successful/unsuccessful solutions Temporal knowledge History of the search space - e.g. the temporal patterns of the search process Spatial knowledge Information about the topography of the search space == Population == The population component of the cultural algorithm is approximately the same as that of the genetic algorithm. == Communication protocol == Cultural algorithms require an interface between the population and belief space. The best individuals of the population can update the belief space via the update function. Also, the knowledge categories of the belief space can affect the population component via the influence function. The influence function can affect population by altering the genome or the actions of the individuals. == Pseudocode for cultural algorithms == Initialize population space (choose initial population) Initialize belief space (e.g. set domain specific knowledge and normative value-ranges) Repeat until termination condition is met Perform actions of the individuals in population space Evaluate each individual by using the fitness function Select the parents to reproduce a new generation of offspring Let the belief space alter the genome of the offspring by using the influence function Update the belief space by using the accept function (this is done by letting the best individuals to affect the belief space) == Applications == Various optimization problems Social simulation Real-parameter optimization
Indic computing
Indic Computing means "computing in Indic", i.e., Indian Scripts and Languages. It involves developing software in Indic Scripts/languages, Input methods, Localization of computer applications, web development, Database Management, Spell checkers, Speech to Text and Text to Speech applications and OCR in Indian languages. Unicode standard version 15.0 specifies codes for 9 Indic scripts in Chapter 12 titled "South and Central Asia-I, Official Scripts of India". The 9 scripts are Bengali, Devanagari, Gujarati, Gurmukhi, Kannada, Malayalam, Oriya, Tamil and Telugu. A lot of Indic Computing projects are going on. They involve some government sector companies, some volunteer groups and individual people. == Government sector == Indian Union Government made it mandatory for Mobile phone companies whose handsets manufactured, stored, sold and distributed in India to have support for displaying and typing text using fonts for all 22 languages. This move has seen rise in use of Indian languages by millions of users. === TDIL === The Department of Electronics and Information Technology, India initiated the TDIL (Technology Development for Indian Languages) with the objective of developing Information Processing Tools and Techniques to facilitate human-machine interaction without a language barrier; creating and accessing multilingual knowledge resources; and integrating them to develop innovative user products and services. In 2005, it started distributing language software tools developed by Government/Academic/Private companies in the form of CD for non commercial use. Some of the outcomes of TDIL program have been deployed on Indian Language Technology Proliferation & Deployment Centre. This Centre disseminates all the linguistic resources, tools & applications which have been developed under TDIL funding. This programme took to exponential expansion under the leadership of Dr. Swaran Lata who also created international foot-print of the programme. She has now retired. === C-DAC === C-DAC is an India based government software company which is involved in developing language related software. It is best known for developing InScript Keyboard, the standard keyboard for Indian languages. It has also developed lot of Indic language solutions including Word Processors, typing tools, text to speech software, OCR in Indian languages etc. ==== BharateeyaOO.org ==== The work developed out of CDAC, Bangalore (earlier known as NCST, Bangalore) became BharateeyaOO. OpenOffice 2.1 had support for over 10 Indian languages. ==== BOSS ==== BOSS linux was developed by the Centre for Development of Advanced Computing (CDAC) to promote use of open-source software in India. == NGO and Volunteer groups == === Indlinux === Indlinux organisation helped organise the individual volunteers working on different indic language versions of Linux and its applications. === Sarovar === Sarovar.org is India's first portal to host projects under Free/Open source licenses. It is located in Trivandrum, India and hosted at Asianet data center. Sarovar.org is customised, installed and maintained by Linuxense as part of their community services and sponsored by River Valley Technologies. Sarovar.org is built on Debian Etch and GForge and runs off METTLE. === Pinaak === Pinaak is a non-government charitable society devoted to Indic language computing. It works for software localization, developing language software, localizing open source software, enriching online encyclopedias etc. In addition to this Pinaak works for educating people about computing, ethical use of Internet and use of Indian languages on Internet. === Ankur Group === Ankur Group is working toward supporting Bengali language (Bengali) on Linux operating system including localized Bengali GUI, Live CD, English-to-Bengali translator, Bengali OCR and Bengali Dictionary etc. === BhashaIndia === === SMC === SMC is a free software group, working to bridge the language divide in Kerala in the technology front and is today the biggest language computing community in India. == Input methods == === Full size keyboards === With the advent of Unicode inputting Indic text on computer has become very easy. A number of methods exist for this purpose, but the main ones are:- ==== InScript ==== Inscript is the standard keyboard for Indian languages. Developed by C-DAC and standardized by Government of India. Nowadays it comes inbuilt in all major operating systems including Microsoft Windows (2000, XP, Vista, 7), Linux and Macintosh. ==== Phonetic transliteration ==== This is a typing method in which, for instance, the user types text in an Indian language using Roman characters and it is phonetically converted to equivalent text in Indian script in real time. This type of conversion is done by phonetic text editors, word processors and software plugins. Building up on the idea, one can use phonetic IME tools that allow Indic text to be input in any application. Some examples of phonetic transliterators are Xlit, Google Indic Transliteration, BarahaIME, Indic IME, Rupantar, SMC's Indic Keyboard and Microsoft Indic Language Input Tool. SMC's Indic Keyboard has support for as many as 23 languages whereas Google Indic Keyboard only supports 11 Indian languages. They can be broadly classified as: Fixed transliteration scheme based tools – They work using a fixed transliteration scheme to convert text. Some examples are Indic IME, Rupantar and BarahaIME. Intelligent/Learning based transliteration tools – They compare the word with a dictionary and then convert it to the equivalent words in the target language. Some of the popular ones are Google Indic Transliteration, Xlit, Microsoft Indic Language Input Tool and QuillPad. ==== Remington (typewriter) ==== This layout was developed when computers had not been invented or deployed with Indic languages, and typewriters were the only means to type text in Indic scripts. Since typewriters were mechanical and could not include a script processor engine, each character had to be placed on the keyboard separately, which resulted in a very complex and difficult to learn keyboard layout. With the advent of Unicode, the Remington layout was added to various typing tools for sake of backward compatibility, so that old typists did not have to learn a new keyboard layout. Nowadays this layout is only used by old typists who are used to this layout due to several years of usage. One tool to include Remington layout is Indic IME. A font that is based on the Remington keyboard layout is Kruti Dev. Another online tool that very closely supports the old Remington keyboard layout using Kruti Dev is the Remington Typing tool. === Braille === IBus Sharada Braille, which supports seven Indian languages was developed by SMC. === Mobile phones with Numeric keyboards === Mobile/Hand/cell phone basic models have 12 keys like the plain old telephone keypad. Each key is mapped to 3 or 4 English letters to facilitate data entry in English. For inputting Indian languages with this kind of keypad, there are two ways to do so. First is the Multi-tap Method and second uses visual help from the screen like Panini Keypad. The primary usage is SMS. 140 characters size used for English/Roman languages can be used to accommodate only about 70 language characters when Unicode Proprietary compression is used some times to increase the size of single message for Complex script languages like Hindi. A research study of the available methods and recommendations of proposed standard was released by Broadband Wireless Consortium of India (BWCI). ==== Transliteration/Phonetic methods ==== English is used to type in Indian languages. QuillPad IndiSMS ==== Native methods ==== In native methods, the letters of the language are displayed on the screen corresponding to the numeral keys based on the probabilities of those letters for that language. Additional letters can be accessed by using a special key. When a word is partially typed, options are presented from which the user can make a selection. === Smart phones with Qwerty keyboards === Most smart phones have about 35 keys catering primarily to the English language. Numerals and some symbols are accessed with a special key called Alt. Indic input methods are yet to evolve for these types of phones, as support of Unicode for rendering is not widely available. === For Smart Phones with Soft/Virtual keyboards === Inscript is being adopted for smart phone usage. For Android phones which can render Indic languages, Swalekh Multilingual Keypad Multiling Keyboard app are available. Gboard offers support for several Indian languages. == Localization == Localization means translating software, operating systems, websites etc. various applications in Indian language. Various volunteers groups are working in this direction. === Mandrake Tamil Version === A notable example is the Tamil version of Mandrake linux(defunct since 2011). Tamil speakers in Toronto (Canada) released Mandrake,
Attribute–value system
An attribute–value system is a basic knowledge representation framework comprising a table with columns designating "attributes" (also known as "properties", "predicates", "features", "dimensions", "characteristics", "fields", "headers" or "independent variables" depending on the context) and "rows" designating "objects" (also known as "entities", "instances", "exemplars", "elements", "records" or "dependent variables"). Each table cell therefore designates the value (also known as "state") of a particular attribute of a particular object. == Example of attribute–value system == Below is a sample attribute–value system. It represents 10 objects (rows) and five features (columns). In this example, the table contains only integer values. In general, an attribute–value system may contain any kind of data, numeric or otherwise. An attribute–value system is distinguished from a simple "feature list" representation in that each feature in an attribute–value system may possess a range of values (e.g., feature P1 below, which has domain of {0,1,2}), rather than simply being present or absent (Barsalou & Hale 1993). == Other terms used for "attribute–value system" == Attribute–value systems are pervasive throughout many different literatures, and have been discussed under many different names: Flat data Spreadsheet Attribute–value system (Ziarko & Shan 1996) Information system (Pawlak 1981) Classification system (Ziarko 1998) Knowledge representation system (Wong & Ziarko 1986) Information table (Yao & Yao 2002)
Tim Houlne
Tim Houlne is an American business executive, entrepreneur, and author known for his work in outsourcing and homeshoring, remote working, and artificial intelligence (AI) in customer service. He is the founder and CEO of Humach, a company that uses human agents and AI in customer experience solutions. Previously, he was co-founder and CEO of Working Solutions, a virtual contact center company in the United States. == Early life and education == Houlne graduated from Missouri Western State University (MWSU) in 1986 with a bachelor's degree in business administration and from the University of Texas in Dallas with an MBA. In 2024, MWSU and North Central Missouri College renamed the Convergent Technology Alliance Center to the Houlne Center for Convergent Technology. The 20,000 square-foot learning laboratory provides training and applied education experiences in industries such as AI, cybersecurity, manufacturing and construction, and service technologies. == Career == In 1998, Houlne co-founded Working Solutions, a Plano, Texas-based U.S. outsourcing company that provides customer service using remote, home-based agents. As CEO, he oversaw the development of a virtual workforce model that routes service calls to either domestic or offshore agents, according to client needs and service requirements. In 2015, Houlne founded Humach, a customer experience outsourcing provider that uses human service agents with AI-based digital agents. The company derives its name from the combination of services provided by humans and machines. Its clients include Amazon, Carfax and McDonald's. The company acquired InfiniteAI in 2020, and Markets EQ in 2025. In 2013, Houlne was named a finalist for the Ernst & Young Entrepreneur of the Year Award (Southwest Region).He is the co-author of several books focused on the evolution of work, the gig economy, and the influence of AI in customer-facing roles. == Works == The New World of Work: From the Cube to the Cloud (2013) ISBN 0982562276 OCLC 813933360 The New World of Work, Second Edition: The Cube, the Cloud and What's Next (2023) ISBN 9781642258318 OCLC 1389815847 The Intelligent Workforce: How Humans & Machines Will Co-Create a Better Future (2024) ISBN 9798887501604 OCLC 1439598569
GENESIS (software)
GENESIS (The General Neural Simulation System) is a simulation environment for constructing realistic models of neurobiological systems at many levels of scale including: sub-cellular processes, individual neurons, networks of neurons, and neuronal systems. These simulations are “computer-based implementations of models whose primary objective is to capture what is known of the anatomical structure and physiological characteristics of the neural system of interest”. GENESIS is intended to quantify the physical framework of the nervous system in a way that allows for easy understanding of the physical structure of the nerves in question. “At present only GENESIS allows parallelized modeling of single neurons and networks on multiple-instruction-multiple-data parallel computers.” Development of GENESIS software spread from its home at Caltech to labs at the University of Texas at San Antonio, the University of Antwerp, the National Centre for Biological Sciences in Bangalore, the University of Colorado, the Pittsburgh Supercomputing Center, the San Diego Supercomputer Center, and Emory University. == Neurons and Neural Systems == GENESIS works by creating simulation environments for constructing models of neurons or neural systems. "Nerve cells are capable of communicating with each other in such a highly structured manner as to form neuronal networks. To understand neural networks, it is necessary to understand the ways in which one neuron communicates with another through synaptic connections and the process called synaptic transmission". Neurons have a specialized structure for their function, they "are different from most other cells in the body in that they are polarized and have distinct morphological regions, each with specific functions". The two important regions of a neuron are the dendrite and the axon. "Dendrites are the region where one neuron receives connections from other neurons. The cell body or soma contains the nucleus and the other organelles necessary for cellular function. The axon is a key component of nerve cells over which information is transmitted from one part of the neuron (e.g., the cell body) to the terminal regions of the neuron". The third important piece of a neuron is the synapse. "The synapse is the terminal region of the axon this is where one neuron forms a connection with another and conveys information through the process of synaptic transmission". Neural networks like the ones simulated with GENESIS software can quickly become highly complex and difficult to understand. "Just a few interconnected neurons (a microcircuit) can perform sophisticated tasks such as mediate reflexes, process sensory information, generate locomotion and mediate learning and memory. Even more complex networks, macrocircuits, consist of multiple embedded microcircuits. Macrocircuits mediate higher brain functions such as object recognition and cognition". GENESIS endeavors to simulate neural systems as they are found in nature. Often, "a neuron can receive contacts from up to 10,000 presynaptic neurons, and, in turn, any one neuron can contact up to 10,000 postsynaptic neurons. The combinatorial possibility could give rise to enormously complex neuronal circuits or network topologies, which might be very difficult to understand". == History == GENESIS was developed by Dr. James M. Bower, in the Caltech laboratory, and first released to the public in 1988 in association with the first Methods in Computational Neuroscience Course at the Marine Biological Laboratory in Woods Hole, MA. Full source code for the software was released in the same year under an open software model for development. It's now supported by the Computational Biology Initiative at the University of Texas at San Antonio and is available free along with tutorial guides on its use. P-GENESIS, a parallel version of GENESIS, was first run in 1990 on the Intel Delta, which was the prototype for the Intel Paragon family of massively parallel supercomputers. == How GENESIS Works == GENESIS is useful in creating a simulation environment for constructing models of neurobiological systems, such as: sub-cellular processes individual neurons networks of neurons neuronal systems The GENESIS system is complicated, but relatively easy to use. An individual can input commands through one of three ways: script files, graphical user interface, or the GENESIS command shell. These commands are then processed by the script language interpreter. "The Script Language Interpreter processes commands entered through the keyboard, script files, or the graphical user interface, and passes them to the GENESIS simulation engine. The simulation engine also loads compiled object libraries, reads and writes data files, and interacts with the graphical user interface". Below is a graphical representation of the user input process and a sample GENESIS output. == Applications == Most current applications for GENESIS involve realistic simulations of biological systems. It is usually used to simulate the behavior of larger brain structures, for example the cerebral cortex. These studies most often occur in lab courses in neural simulation at Caltech and the Marine Biological Laboratory at Woods Hole, Massachusetts. GENESIS can be used in combination with Yale University’s software called NEURON as a means for scientists to collaborate to construct a physical description of the nervous system. The GENESIS software can also be used with Kinetikit in the modeling of signal transduction pathways. GENESIS has been used in many studies. Some of these studies involve research that focuses on the development of software that would be useful across many disciplines. Others are studies of neurons, such as Purkinje cells. These studies used GENESIS to simulate Purkinje cells and could be useful for the planning and development of later experiments using the GENESIS software. There may also be biomedical applications of the software. For example, St. Jude Medical in Europe has developed an implanted GENESIS device.
Normal distributions transform
The normal distributions transform (NDT) is a point cloud registration algorithm introduced by Peter Biber and Wolfgang Straßer in 2003, while working at University of Tübingen. The algorithm registers two point clouds by first associating a piecewise normal distribution to the first point cloud, that gives the probability of sampling a point belonging to the cloud at a given spatial coordinate, and then finding a transform that maps the second point cloud to the first by maximising the likelihood of the second point cloud on such distribution as a function of the transform parameters. Originally introduced for 2D point cloud map matching in simultaneous localization and mapping (SLAM) and relative position tracking, the algorithm was extended to 3D point clouds and has wide applications in computer vision and robotics. NDT is very fast and accurate, making it suitable for application to large scale data, but it is also sensitive to initialisation, requiring a sufficiently accurate initial guess, and for this reason it is typically used in a coarse-to-fine alignment strategy. == Formulation == The NDT function associated to a point cloud is constructed by partitioning the space in regular cells. For each cell, it is possible to define the mean q = 1 n ∑ i x i {\displaystyle \textstyle \mathbf {q} ={\frac {1}{n}}\sum _{i}\mathbf {x_{i}} } and covariance S = 1 n ∑ i ( x i − q ) ( x i − q ) ⊤ {\displaystyle \textstyle \mathbf {S} ={\frac {1}{n}}\sum _{i}\left(\mathbf {x} _{i}-\mathbf {q} \right)\left(\mathbf {x} _{i}-\mathbf {q} \right)^{\top }} of the n {\displaystyle n} points of the cloud x 1 , … , x n {\displaystyle \mathbf {x} _{1},\dots ,\mathbf {x} _{n}} that fall within the cell. The probability density of sampling a point at a given spatial location x {\displaystyle \mathbf {x} } within the cell is then given by the normal distribution e − 1 2 ( x − q ) ⊤ S − 1 ( x − q ) {\displaystyle e^{-{\frac {1}{2}}\left(\mathbf {x} -\mathbf {q} \right)^{\top }\mathbf {S} ^{-1}\left(\mathbf {x} -\mathbf {q} \right)}} . Two point clouds can be mapped by a Euclidean transformation f {\displaystyle f} with rotation matrix R {\displaystyle \mathbf {R} } and translation vector t {\displaystyle \mathbf {t} } f R , t ( x ) = R x + t {\displaystyle f_{\mathbf {R} ,\mathbf {t} }(\mathbf {x} )=\mathbf {R} \mathbf {x} +\mathbf {t} } that maps from the second cloud to the first, parametrised by the rotation angles and translation components. The algorithm registers the two point clouds by optimising the parameters of the transformation that maps the second cloud to the first, with respect to a loss function based on the NDT of the first point cloud, solving the following problem arg min R , t { − ∑ i NDT ( f R , t ( x i ) ) } {\displaystyle \arg \min _{\mathbf {R} ,\mathbf {t} }\left\{-\sum _{i}\operatorname {NDT} \left(f_{\mathbf {R} ,\mathbf {t} }\left(\mathbf {x_{i}} \right)\right)\right\}} where the loss function represents the negated likelihood, obtained by applying the transformation to all points in the second cloud and summing the value of the NDT at each transformed point f R , t ( x ) {\displaystyle f_{\mathbf {R} ,\mathbf {t} }(\mathbf {x} )} . The loss is piecewise continuous and differentiable, and can be optimised with gradient-based methods (in the original formulation, the authors use Newton's method). In order to reduce the effect of cell discretisation, a technique consists of partitioning the space into multiple overlapping grids, shifted by half cell size along the spatial directions, and computing the likelihood at a given location as the sum of the NDTs induced by each grid.
AutoGPT
AutoGPT is an open-source autonomous software agent that uses OpenAI's large language models, such as GPT-4, to attempt to achieve a goal specified by a user in natural language. Unlike chatbots that require continuous user commands, AutoGPT works autonomously by breaking the main goal into smaller sub-tasks and using tools like web browsing and file management to complete them. Released in March 2023, the project quickly gained popularity on GitHub and social media, with users creating agents for tasks like software development, market research, and content creation. One notable experiment, ChaosGPT, was tasked with destroying humanity, which brought mainstream attention to the technology's potential. However, AutoGPT is known for significant limitations, including a tendency to get stuck in loops, hallucinate information, and incur high operational costs due to its reliance on paid APIs. == Background == AutoGPT was released on March 30, 2023, by Toran Bruce Richards, the founder of video game company Significant Gravitas Ltd. It was one of the first widely accessible applications to showcase the autonomous capabilities of GPT-4, which had been released weeks earlier. Richards's goal was to create a model that could respond to real-time feedback and pursue objectives with a long-term outlook without needing constant human intervention. The application operates by prompting a user to define an agent's name, role, and main objective, including up to five sub-goals to achieve it. AutoGPT then works independently to reach its objective. The project is publicly available on GitHub but requires users to install it in a development environment like Docker and have a paid OpenAI account to obtain the necessary API key. In October 2023, the project's parent company, Significant Gravitas Ltd., raised $12 million in venture funding to support further development. == Capabilities == The overarching capability of AutoGPT is the breaking down of a large task into various sub-tasks without the need for user input. These sub-tasks are then chained together and performed sequentially to yield a larger result as originally laid out by the user input. One of the distinguishing features of AutoGPT is its ability to connect to the internet. This allows for up-to-date information retrieval to help complete tasks. In addition, AutoGPT maintains short-term memory for the current task, which allows it to provide context to subsequent sub-tasks needed to achieve the larger goal. Another feature is its ability to store and organize files so users can better structure their data for future analysis and extension. AutoGPT is also multimodal, which means that it can take in both text and images as input. With these features, AutoGPT is claimed to be capable of automating workflows, analyzing data, and coming up with new suggestions. == Applications == === Software === AutoGPT can be used to develop software applications from scratch. AutoGPT can also debug code and generate test cases. Observers suggest that AutoGPT's ability to write, debug, test, and edit code may extend to AutoGPT's own source code, enabling self-improvement. === Business === AutoGPT can be used to do market research, analyze investments, research products and write product reviews, create a business plan or improve operations, and create content such as a blog or podcast. One user has used AutoGPT to conduct product research and write a summary on the best headphones. Another user has used AutoGPT to summarize recent news events and prepare an outline for a podcast. === Other === AutoGPT was used to create ChefGPT, an AI agent able to independently explore the internet to generate and save unique recipes. AutoGPT was also used to create ChaosGPT, an AI agent tasked to “destroy humanity, establish global dominance, cause chaos and destruction, control humanity through manipulation, and attain immortality”. ChaosGPT reportedly researched nuclear weapons and tweeted disparagingly about humankind. == Limitations == AutoGPT is susceptible to frequent mistakes, primarily because it relies on its own feedback, which can compound errors. In contrast, non-autonomous models can be corrected by users overseeing their outputs. Furthermore, AutoGPT has a tendency to hallucinate or to present false or misleading information as fact when responding. AutoGPT can be constrained by the cost associated with running it as its recursive nature requires it to continually call the OpenAI API on which it is built. Every step required in one of AutoGPT's tasks requires a corresponding call to GPT-4 at a cost of at least about $0.03 for every 1000 tokens used for inputs and $0.06 for every 1000 tokens for output when choosing the cheapest option. For reference, 1000 tokens roughly result in 750 words. Another limitation is AutoGPT's tendency to get stuck in infinite loops. Developers believe that this is a result of AutoGPT's inability to remember, as it is unaware of what it has already done and repeatedly attempts the same subtask without end. Andrej Karpathy, co-founder of OpenAI which creates GPT-4, further explains that it is AutoGPT's “finite context window” that can limit its performance and cause it to “go off the rails”. Like other autonomous agents, AutoGPT is prone to distraction and unable to focus on its objective due to its lack of long-term memory, leading to unpredictable and unintended behavior. == Reception == AutoGPT became the top trending repository on GitHub after its release and has since repeatedly trended on Twitter. In April 2023, Avram Piltch wrote for Tom's Hardware that AutoGPT 'might be too autonomous to be useful,' as it did not ask questions to clarify requirements or allow corrective interventions by users. Piltch nonetheless noted that such tools have "a ton of potential" and should improve with better language models and further development. Malcolm McMillan from Tom's Guide mentioned that AutoGPT may not be better than ChatGPT for tasks involving conversation, as ChatGPT is well-suited for situations in which advice, rather than task completion, is sought. Will Knight from Wired wrote that AutoGPT is not a foolproof task-completion tool. When given a test task of finding a public figure's email address, he noted that it was not able to accurately find the email address. Clara Shih, Salesforce Service Cloud CEO commented that "AutoGPT illustrates the power and unknown risks of generative AI," and that due to usage risks, enterprises should include a human in the loop when using such technologies. Performance is reportedly enhanced when using AutoGPT with GPT-4 compared to GPT-3.5. For example, one reviewer who tested it on a task of finding the best laptops on the market with pros and cons found that AutoGPT with GPT-4 created a more comprehensive report than one by GPT 3.5.