AI Email Response Generator Free

AI Email Response Generator Free — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Verbot

  The Verbot (short for Verbal-Robot) was a chatbot program and artificial intelligence software development kit (SDK) designed for Windows and web platforms. == Early beginning == The origin of verbot traces back to Michael Mauldin's research during his time as a graduate student and post-doctoral fellow at Carnegie Mellon University. The creative foundation also stems from Peter Plantec's work in personality psychology and art direction. === Historic outline === In 1994, Michael Loren Mauldin, founder of Lycos, Inc., developed a prototype chatbot, Julia, which competed in the internationally known Turing test, for the coveted Loebner Prize. The Turing test matches computer scientist judges against machines to see if they can distinguish a computer from a real human. Julia was refined and developed, and in 1997, Dr. Mauldin and Peter Plantec, a clinical psychologist and animator, formed Virtual Personalities, Inc. (now Conversive, Inc.) in order to create a virtual human interface that would incorporate real-time animation as well as speech and natural language processing. The initial release, a stand-alone virtual person called Sylvie, was beta-tested to the public. This release was well received, and finally, after several versions, the production release (deemed version 3) of the Verbally Enhanced Software Robot, or Verbot, was deployed in fall 2000. The grandfather of all Verbots is Rog-O-Matic, which, although it could not talk, could and did explore a virtual world. Julia has been active on the internet in one form or another since 1989. A close cousin of Julia is Lycos, a robot that explores the World Wide Web and answers questions about it. Sylvie was the first Verbot with a face and a voice. Sylvie was the first Virtual Human with advanced, flexible interfacing capability. === Beginnings === The Virtual Personalities story goes back to 1978, where Mauldin was attending Rice University. Fascinated by the idea of ELIZA, he proceeded to write a program called "PET" for his 8 kilobyte Commodore PET Computer. PET included simple induction as a way to post new information, for example: Subject: I like my friend (later) Subject: I like food. PET: I have heard that food is your friend. Meanwhile, Plantec was separately designing a personality for "Entity", a theoretical virtual human that would interact comfortably with humans without pretending to be one. At that time the technology was not advanced enough to realize Entity. Mauldin got so involved with this that he majored in Computer Science and minored in Linguistics. === Rogue === In the late seventies and early eighties, a popular computer game at universities was Rogue, an implementation of Dungeons and Dragons where the player would descend 26 levels in a randomly created dungeon, fighting monsters, gathering treasure, and searching for the elusive "Amulet of Yendor". Mauldin was one of four grad students who devoted a large amount of time to building a program called "Rog-O-Matic" capable of retrieving the amulet and emerging victorious from the dungeon. === TinyMUD === In 1989, when James Aspnes at Carnegie Mellon created the first TinyMUD (a descendant of MUD and AberMUD), Mauldin was one of the first to create a computer player that would explore the text-based world of TinyMUD. But his first robot, Gloria, gradually accreted more and more linguistic ability, to the point that it could pass the "unsuspecting" Turing test. In this version of the test, the human has no reason to suspect that one of the other occupants of the room is controlled by a computer, and so is more polite and asks fewer probing questions. The second generation of Mauldin's TinyMUD robots was Julia, created on Jan. 8, 1990. Julia slowly developed into a more and more capable conversational agent, and assumed useful duties in the TinyMUD world, including tour guide, information assistant, note-taker, and message-relayer. She could even play the card game hearts along with the other human players. In 1991, Julia attended the first Loebner Prize contest in Boston, Massachusetts. Although she only finished third, she was ranked by one judge as more human than one of the human confederates, winning a coveted certificate of humanness in the world's first restricted Turing test. Julia continued to log in to various TinyMUD's and TinyMucks for the next seven years, and chatted with hundreds of people a month over the internet. === Lycos === Julia's job was to explore a virtual world consisting of pages of textual descriptions, with links between them, and to construct an internal map of that world and answer questions about it (including path information such as the shortest route from one room to another, and matching information, such as which rooms contained a certain kind of object or textual description). It was therefore only a very short cognitive leap from Julia to Lycos, another robotic agent that explores a virtual world made of hyperlinked pages of text, and which answers questions about those pages. Sylvie was born and her abilities were expanded greatly to include interfacing with computers and control systems via her serial ports. === Sylvie === Sylvie was the first intelligent animated virtual human. She was designed both as a conversation agent and as a virtual human interface that would form a bridge between the two. She became more popular as a conversation agent, but her designers believe she serves as a prototype for future virtual human interface design that will help us all cope with the increasing complexity of technology. As an aside, Plantec noticed that a large number of Sylvies have been sold in Southeast Asia. Upon investigation, he found out that students had discovered a "test" mode that would allow them to type in English sentences that Sylvie would pronounce in her somewhat stylized English. == Ownership == In 1997, Dr. Mauldin and Peter Plantec formed Virtual Personalities, Inc. to create Natural Language Processing solutions for companies. In 2001 Virtual Personalities, Inc. became Conversive, Inc. to reflect the focus on providing Customer Service and Marketing to the Enterprise Market. In late 2012 Avaya, Inc. acquired Conversive's assets including Verbots. == Verbot versions == The Verbot 4 version was created and released in 2004. In 2005 Version 4.1 of the Verbot Software was released with many feature enhancements and bug fixes, including built-in support for embedding C# code in outputs and conditionals. In early 2006 Conversive launched Verbots Online allowing Verbot 4 users to upload their knowledge and show off their bots to the world. In 2009 Version 5 was released, completely free and fully featured. In early 2012 the last version of Verbot, 5.0.1.2, was released to the general public with support for Windows 7. Later in 2012 Verbots Online completely shut down. == Verbots today == Verbots.com, its community of users, and its forums no longer exist, but the software and users can still be found. There has been no active development since the early 2012 release of Verbot 5.0.1.2.

  Read more →
• 

  Emma Brunskill

  Emma Patricia Brunskill is an American computer scientist. Her research combines machine learning with human–computer interaction by studying the effects of AI systems in human-centered applications including educational software and healthcare, and the theory of reinforcement learning in situations where mistakes impose high risks or costs. She is an associate professor of computer science at Stanford University, where she also holds a courtesy appointment in the Stanford Graduate School of Education and is an affiliate of the King Center on Global Development. == Education and career == Brunskill grew up in Seattle and Edmonds, Washington, and entered the University of Washington at age 15. She graduated magna cum laude in 2000, with a bachelor's degree in computer engineering and physics. A Rhodes Scholarship took her to Magdalen College, Oxford in England, where she received a master's degree in neuroscience in 2002. After a summer working in Rwanda, she became a graduate student of computer science at the Massachusetts Institute of Technology, where she completed her Ph.D. in 2009. Her doctoral dissertation, Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains, was supervised by Nicholas Roy. After working as an NSF Postdoctoral Research Fellow at the University of California, Berkeley, she joined Carnegie Mellon University (CMU) in 2011 as an assistant professor of computer science. She moved from CMU to Stanford University in 2017. == Recognition == Brunskill was a 2014 recipient of the National Science Foundation CAREER Award and a 2015 recipient of the Office of Naval Research Young Investigator Award. She was one of two alumni of the University of Washington's Paul G. Allen School of Computer Science and Engineering to be honored in 2020 by the school's Alumni Impact Awards. She was elected as a Fellow of the Association for the Advancement of Artificial Intelligence in 2025, "for significant contributions to the field of reinforcement learning, and applications for societal benefit, in particular AI for education".

  Read more →
• 

  Madhan Karky

  Madhan Karky Vairamuthu is an Indian lyricist, screenwriter, research associate, software engineer, and entrepreneur. A holder of a doctorate in computer science from the University of Queensland, Karky began his professional career as an assistant professor at the College of Engineering, Guindy, and soon after ventured into the Tamil cinema, working as a lyricist and dialogue writer. He resigned from his teaching profession in early 2013 and began working full-time in the film industry, while also launching the Karky Research Foundation (KaReFo), an educational research organization which primarily focuses on language computing and language literacy. He also founded the Mellinam Education, which develops educational games and story books designed to propagate learning among children, and DooPaaDoo, an online music platform which promotes independent music and serves a distributor for film soundtracks. == Early life == Karky is the eldest son of seven-time National Award winning lyricist Vairamuthu and Ponmani, a Tamil scholar and veteran professor at the Meenakshi College for Women. He has a younger brother, Kabilan, who is a novelist and also works as a lyricist and dialogue writer for Tamil films. === Education === He grew up in Chennai and was educated at the Loyala Matriculation School in Kodambakkam. By his own admission, he was not a good student, excelling primarily only in Tamil and English. During his time in high school, he gained an interest in computer science He got admission in College of Engineering, Guindy which is affiliated with the Anna University. He began his undergraduate education in the field of Computer engineering in the year 1997. While in CEG, as part of his final year project, Karky developed a program called the Tamil Voice Engine, under the supervision of Professor T.V. Geetha. The goal of the project was construction of a text to speech engine for the Tamil language. The research paper on the project was officially selected at the Tamil Internet Conference in Kuala Lumpur, Malaysia. Other projects during his tenure include the Name Generator, which was part of his course on Creativity, Innovation and New Product Development (the objective being to generate random names that are pronounceable with respect to Indian phonetics) and Compiler Design, for which a high level programming language was conceived, with the goal of proper specification and interpretation of lexical rules and grammar rules. For Chennai Kavigal, he created a Spell Checker for a Tamil Word Processor. The project involved a lot of Natural Language Processing elements, based on a root dictionary built as a part of the morphological analyzer for the Tamil Language. The endgame being determining the correctness of words. Following the completion of his bachelor's degree in 2001, Karky began his master's degree at the University of Queensland in the year 2003. In that particular stint, he developed a project based on the theory of computation and strong mathematics (under the supervision of Dr. George Havas). It aimed at analyzing an existing algorithm of reducing any kind of matrix format to a standard format called 'Hermite Normal form', which is a unit upper triangular matrix. Some of his other projects during this course include the Disciplined Software Process Project (whose objective was to introduce and practice the software development process for individuals called Personal Software Process), the On-Line Art Store Website (which involved the creation of a website that trades paintings through the Internet) and the Text Based Voice Chat (for which a Proxy Voice Chat system was designed and developed in Visual Basic that incorporated the predominant computing aspects). In addition to his academics, Karky also served as Academic tutor at the university. He conducted class room tutorials and laboratory sessions on subjects such as Relational Database Systems and Programming Languages. As part of his PhD program on information technology, he developed a Java-based simulation platform called SENSE (Simulated Environment of Networked Sensor Experiments), to test different heuristics. This project was done under the guidance of Dr. Maria Orlowska and Dr. Shazia Sadiq. His thesis is titled "Design considerations for query dissemination in wireless sensor networks". === Teaching career === Upon his return to India following the completion of his post-graduation, Karky returned to CEG Anna University in December 2007. He was a Senior Research Fellow for the next six months, managing research projects as well as multiple student projects at an undergrad and postgrad levels. In addition to those, he handled courses and labs for students who pursued their master's degrees. He also served as a Project Scientist between July 2008 and July 2009, managing projects of research groups as well as ME & MBA students. Starting from August 2009, he began his role as an assistant professor. He lectured Computer Science students who were pursuing their Bachelors and master's degrees as well as coordinated the Tamil Computing Lab at the university. He also served as counsellor for NRI and foreign national students, as well as the Staff treasurer of Computer Science Engineering Association. Some of the subjects he taught include Advanced Databases, Ethics for Engineers, Principles of Programming Languages, Environmental Science and Tamil Computing (for PhD students). === Family and personal life === Karky's been married to Nandini Eswaramoorthy, a fellow alum at Anna University, since June 22, 2008. Nandini Karky now works in the Tamil film industry as a subtitler for feature films and documentaries. They have a son named Haiku Karky, who was born in 2009. == Film career == === Debut === During his teaching stint at Anna University, Karky also began his career in the Tamil film industry with the science-fiction film Enthiran (2010), the magnum opus of director Shankar. Karky had approached the director in 2008 with some of the songs he had written, and was brought him on board to help with the dialogues of the film, especially assisting with technical terminology. He stated that there were three sets of dialogues written for almost every scene of the film; one by Shankar, one by Karky, and the other by the late Sujatha, a frequent collaborator with the director who had died during the early stages of the film's pre-production. Shankar would go through all the three drafts and implement those that fit best. The climax was the only portion that didn't have multiple hands, as it was written solely by Karky. In addition to the dialogue, Karky wrote 2 songs for the film, as well: "Irumbile oru Irudhaiyam" (the first song of his career, which was partially crooned by A.R. Rahman) and "Boom Boom Robo Da". However, Kanden Kadhalai (2009), in which he had written the song "Ododi Poren" (composed by Vidyasagar), became his first release. For his work on Enthiran, Karky was named Best Find of the Year at the 2011 Vijay Awards. === Lyric writer === Following his work on Enthiran, Karky became one of the most sought after lyricists in the Tamil film industry, having multiple collaborations with A.R. Rahman, Harris Jayaraj, G. V. Prakash Kumar, D. Imman, M.M. Keeravani, Yuvan Shankar Raja, S. Thaman, Sanjay Leela Bhansali, Anirudh Ravichander and Sam CS. In addition to his native Tamil, he is known for penning songs in multiple languages; some of which include "Asku Laska" from Nanban (which features 16 different languages), "The Rise of Damo" from 7 Aum Arivu (written in Mandarin) and "Continua" from Nootrenbadhu (in Portuguese). His work is also characterized by infusing uncommon Tamil words that aren't normally used in everyday lexicon, as part of lyrics (like "Kuviyamillaa Kaatchi Paezhai" from Ko and "Panikoozh" from I). He also wrote the first palindrome song in Tamil cinema for the film Vinodhan. As of the end of 2025, he has over one thousand songs to his credit. Some of Karky's most popular songs include "Irumbile oru Irudhaiyam" (Enthiran), "Enamo Edho" (Ko), "Nee Koorinal" (Nootrenbadhu), "Asku Laska" (Nanban), "Google Google" (Thuppakki), "Elay Keechaan" (Kadal), "Osakka" (Vanakkam Chennai), "Selfie Pulla" (Kaththi), "Pookkalae Sattru Oyivedungal" (I), "Mei Nigara" (24), "Azhagiye" (Kaatru Veliyidai), "Endhira Logathu Sundariye" (2.0) and "Kurumba" (Tik Tik Tik). === Dialogue writer === On the heels of the success with Enthiran, Karky once again collaborated as a dialogue writer with director Shankar for Nanban. An adaptation of the Hindi blockbuster 3 Idiots, he infused a twang to the dialogue that aimed to showcase college life in a different manner. He also collaborated as a technical advisor with Shankar with 2.0 (the sequel to Enthiran). Karky's also known for his successful collaboration with Telugu director S.S. Rajamouli, on his two-part magnum opus Baahubali; the second part being the most profitable South Indian film of all time, and RRR. His o

  Read more →
• 

  Best AI Writing Assistants in 2026

  In search of the best AI writing assistant? An AI writing assistant is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI writing assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

  Read more →
• 

  Convolutional neural network

  A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. CNNs are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently been replaced—in some cases—by newer architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization that comes from using shared weights over fewer connections. For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution (or cross-correlation) kernels, only 25 weights for each convolutional layer are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features. Some applications of CNNs include: image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are also known as shift invariant or space invariant artificial neural networks, based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. Feedforward neural networks are usually fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks makes them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) Robust datasets also increase the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set. Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field. CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This simplifies and automates the process, enhancing efficiency and scalability overcoming human-intervention bottlenecks. == Architecture == A convolutional neural network consists of an input layer, hidden layers and an output layer. In a convolutional neural network, the hidden layers include one or more layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel with the layer's input matrix. This product is usually the Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers. Here it should be noted how close a convolutional neural network is to a matched filter. === Convolutional layers === In a CNN, the input is a tensor with shape: (number of inputs) × (input height) × (input width) × (input channels) After passing through a convolutional layer, the image becomes abstracted to a feature map, also called an activation map, with shape: (number of inputs) × (feature map height) × (feature map width) × (feature map channels). Convolutional layers convolve the input and pass its result to the next layer. This is similar to the response of a neuron in the visual cortex to a specific stimulus. Each convolutional neuron processes data only for its receptive field. Although fully connected feedforward neural networks can be used to learn features and classify data, this architecture is generally impractical for larger inputs (e.g., high-resolution images), which would require massive numbers of neurons because each pixel is a relevant input feature. A fully connected layer for an image of size 100 × 100 has 10,000 weights for each neuron in the second layer. Convolution reduces the number of free parameters, allowing the network to be deeper. For example, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 neurons. Using shared weights means there are many fewer parameters, which helps avoid the vanishing gradients and exploding gradients problems seen during backpropagation in earlier neural networks. To speed processing, standard convolutional layers can be replaced by depthwise separable convolutional layers, which are based on a depthwise convolution followed by a pointwise convolution. The depthwise convolution is a spatial convolution applied independently over each channel of the input tensor, while the pointwise convolution is a standard convolution restricted to the use of 1 × 1 {\displaystyle 1\times 1} kernels. === Pooling layers === Convolutional networks may include local and/or global pooling layers along with traditional convolutional layers. Pooling layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. Local pooling combines small clusters, tiling sizes such as 2 × 2 are commonly used. Global pooling acts on all the neurons of the feature map. There are two common types of pooling in popular use: max and average. Max pooling uses the maximum value of each local cluster of neurons in the feature map, while average pooling takes the average value. === Fully connected layers === Fully connected layers connect every neuron in one layer to every neuron in another layer. It is the same as a traditional multilayer perceptron neural network (MLP). Each neuron in the fully connected layer receives input from all the neurons in the previous layer. These inputs are weighted and summed with the corresponding biases, and then passed through an activation function to perform a nonlinear transformation, generating the output. The flattened matrix goes through a fully connected layer to classify the images. === Receptive field === In neural networks, each neuron receives input from some number of locations in the previous layer. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. Typically the area is a square (e.g. 5 by 5 neurons). Whereas, in a fully connected layer, the receptive field is the entire previous layer. Thus, in each convolutional layer, each neuron takes input from a larger area in the input than previous layers. This is due to applying the convolution over and over, which takes the value of a pixel into account, as well as its surrounding pixels. When using dilated layers, the number of pixels in the receptive field remains constant, but the field is more sparsely populated as its dimensions grow when combining the effect of several layers. To manipulate the receptive field size as desired, there are some alternatives to the standard convolutional layer. For example, atrous or dilated convolution expands the receptive field size without increasing the number of parameters by interleaving visible and blind regions. Moreover, a single dilated convolutional layer can comprise filters with multiple dilation ratios, thus having a variable receptive field size. === Weights === Each neuron in a neural network computes an output value by applying a specific function to the input values received from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning consists of iteratively adjusting these biases and weights. The vectors of weights and biases are called filters and represent particular features of the input (e.g., a particular shape). A distinguishing feature of CNNs is that many neurons can share the same filter. This reduces the memory footprint because a single bias and a single vector of weights are used across all receptive fields that share that filter, as opposed to each receptive field having its own bias and vector

  Read more →
• 

  Top 10 AI Presentation Makers Compared (2026)

  Trying to pick the best AI presentation maker? An AI presentation maker 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 presentation maker slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

  Read more →
• 

  Timo Honkela

  Timo Untamo Honkela (August 4, 1962 – May 9, 2020) was a computer scientist at the University of Helsinki, Aalto University School of Science and Aalto University School of Art, Design and Architecture. He holds a PhD from Helsinki University of Technology. From 2014 until 2018 he held a fixed-term professorship at the University of Helsinki. Before joining the University of Helsinki he worked as a non-tenured professor in two Schools of the Aalto University, The School of Art, Design and Architecture and the School of Science. He has presented his thoughts on his studies and work in the joint blog 375 Humanists. Timo Honkela conducted research on several areas related to knowledge engineering, cognitive modeling and natural language processing. Honkela was born in Kalajoki. From 1998 to 2000 he worked as a professor in the Aalto Media Lab. To the media Lab Honkela brought his expertise in Kohonen self-organising map (SOM) and worked closely with artist and designers around the topic. In 2001 Honkela collaborated with George Legrady to produce an interactive museum installation, Pockets Full of Memories to the Centre Georges Pompidou, National Museum of Modern Art in Paris. The concept, created by Legrady, provided for visitors a possibility to scan their own objects to a database and then organise them by Kohonen Self-Organizing Map algorithm. In 2017 Honkela published a book in Finnish. The book Rauhankone (English: Peace Machine) presents his idea of designing artificial intelligence and machine learning to serve humanity, in practice to help people to live in peace with each other. He died in Helsinki. == Publications == Timo Honkela, Wlodzislaw Duch, Mark Girolami and Samuel Kaski (editors): Artificial Neural Networks and Machine Learning, Springer, 2011. Jorma Laaksonen and Timo Honkela (editors): Advances in Self-Organizing Maps, Springer, 2011. Timo Honkela: Rauhankone. Gaudeamus, 2017.

  Read more →
• 

  Lior Ron (business executive)

  Lior Ron (born March 16, 1977) is an Israeli businessman. He is the founder, chairman and former CEO of logistics technology company Uber Freight, co-founder of self-driving truck company Otto, and COO of self-driving technology company Waabi. == Early life and education == Ron grew up in Israel near Haifa. He attended the Technion – Israel Institute of Technology in Haifa, where he earned a bachelor's degree in computer science in 1997. He then joined Israeli Army Intelligence, where he served until 2004. After the Army, he earned a master's degree in computer science at Technion, incorporating artificial intelligence as he developed a biomedical device to assist patients suffering with Parkinson's disease. He then moved to California and earned an MBA from The Stanford Graduate School of Business. His undergraduate work and master's thesis were centered around AI when it was still in its early stages. == Career == === Google === In 2007, Ron joined Google as the Product Lead for Google Maps. He then worked at Motorola Mobility after it was acquired by Google, and in Google's robotics research effort. === Otto === In 2016, Ron left Google to found Otto, a company that makes self-driving kits to retrofit big rig trucks. Quoted in Wired, Ron said he left Google because he “felt an obligation to bring this technology to society sooner rather than later.” Otto launched in May 2016, and was acquired by Uber in late July of the same year. The Uber partnership allowed Ron and Otto the opportunity to develop a freight marketplace for truck drivers. === Uber Freight === On May 18, 2017, Ron and Uber launched Uber Freight, a unit of Uber initially designed as an app connecting long-haul truck drivers with companies in need of cargo shipping, with Ron as CEO. In August 2018, Uber Freight launched a new digital platform focused on shippers, to help them find the right driver for their needs. In 2021, Uber Freight acquired Transplace for $2.25 billion, expanding its services to include managed transportation, logistics software, and consulting. With Ron as CEO, Uber Freight has evolved into a full-scale logistics technology company for shippers and drivers, as Ron introduced more advanced generative AI capabilities to Uber Freight's software and Insights AI logistics platform. In September 2024, the company announced it manages nearly $20 billion in freight, and serves one in three Fortune 500 companies. In May 2025, the company launched the transportation industry's first large-scale AI-powered logistics network, with its large language model embedded directly into its transportation management system. === Waabi === On August 12, 2025, it was reported that Ron had been named chief operating officer of Waabi, a company developing autonomous driving technology using artificial intelligence. He remains as chairman of Uber Freight, with Rebecca Tinucci taking over as CEO. == Controversy == Ron co-founded Otto with Anthony Levandowski, who faces a lawsuit brought in 2017 from Google's parent company Alphabet that alleges Levandowski stole trade secrets while working for Alphabet's self-driving car division before he and Ron co-founded Otto.

  Read more →
• 

  LCD crosstalk

  LCD crosstalk is a visual defect in an LCD screen which occurs because of interference between adjacent pixels. Owing to the way rows and columns in the display are addressed, and charge is pushed around, the data on one part of the display has the potential to influence what is displayed elsewhere. This is generally known as crosstalk, and in matrix displays typically occurs in the horizontal and vertical directions. Crosstalk used to be a serious problem in the old passive-matrix (STN) displays, but is rarely discernable in modern active-matrix (TFT) displays. A fortunate side effect of inversion (see above) is that, for most display material, what little crosstalk there is largely cancelled out. For most practical purposes, the level of crosstalk in modern LCDs is negligible. Certain patterns, particularly those involving fine dots, can interact with the inversion and reveal visible crosstalk. If you try moving a small Window in front of the inversion pattern (above) which makes your screen flicker the most, you may well see crosstalk in the surrounding pattern. Different patterns are required to reveal crosstalk on different displays (depending on their inversion scheme).

  Read more →
• 

  AI Customer-support Bots Reviews: What Actually Works in 2026

  Looking for the best AI customer-support bot? An AI customer-support bot 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 customer-support bot slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

  Read more →
• 

  Collostructional analysis

  Collostructional analysis is a family of methods developed by (in alphabetical order) Stefan Th. Gries (University of California, Santa Barbara) and Anatol Stefanowitsch (Free University of Berlin). Collostructional analysis aims at measuring the degree of attraction or repulsion that words exhibit to constructions, where the notion of construction has so far been that of Goldberg's construction grammar. == Collostructional methods == Collostructional analysis so far comprises three different methods: collexeme analysis, to measure the degree of attraction/repulsion of a lemma to a slot in one particular construction; distinctive collexeme analysis, to measure the preference of a lemma to one particular construction over another, functionally similar construction; multiple distinctive collexeme analysis extends this approach to more than two alternative constructions; covarying collexeme analysis, to measure the degree of attraction of lemmas in one slot of a construction to lemmas in another slot of the same construction. == Input frequencies == Collostructional analysis requires frequencies of words and constructions and is similar to a wide variety of collocation statistics. It differs from raw frequency counts by providing not only observed co-occurrence frequencies of words and constructions, but also (i) a comparison of the observed frequency to the one expected by chance; thus, collostructional analysis can distinguish attraction and repulsion of words and constructions; (ii) a measure of the strength of the attraction or repulsion; this is usually the log-transformed p-value of a Fisher-Yates exact test. == Versus other collocation statistics == Collostructional analysis differs from most collocation statistics such that (i) it measures not the association of words to words, but of words to syntactic patterns or constructions; thus, it takes syntactic structure more seriously than most collocation-based analyses; (ii) it has so far only used the most precise statistics, namely the Fisher-Yates exact test based on the hypergeometric distribution; thus, unlike t-scores, z-scores, chi-square tests etc., the analysis is not based on, and does not violate, any distributional assumptions.

  Read more →
• 

  Top 10 AI Analytics Tools Compared (2026)

  Shopping for the best AI analytics tool? An AI analytics tool 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 analytics tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

  Read more →
• 

  Lost Art-Database

  The Lost Art-Datenbank is an online database published by the German Lost Art Foundation (Deutsches Zentrum Kulturgutverluste. It contains information on cultural objects looted from Jewish collectors or transferred due to Nazi persecution during the Nazi era. Until 2015, it was managed by the Koordinierungsstelle für Kulturgutverluste (Magdeburg Coordination Office). == Creation == Following the Washington Conference of 1998, and the commitments to provide more transparency regarding looted art, Germany launched the Lost Art Database in 2000 order to help Holocaust victims and their families track down artworks that had been looted from them or lost due to Nazi persecution. == Functionality == The Lost Art Database lists art and books and other cultural objects that were lost, seized, stolen or forceably sold during the Nazi era. The database is divided into search requests from victims' families, heirs or institutions and "found" reports from cultural institutions on items with unresolved provenance gaps from the Nazi periods. The section on reports of finds lists objects that are known to have been unlawfully seized or relocated as a result of the war. In addition, reports are published here on cultural objects for which an uncertain or incomplete provenance may indicate a possible unlawful seizure or war-related relocation. The publication of reports in the Lost Art Internet Database is carried out on behalf of and with the consent of the reporting persons and institutions. The responsibility for the content of the reports lies with these legal or natural persons. There have been controversies over which items should be included in the database. Lost Art is based on the Washington Principles adopted in 1998, which Germany has committed itself to implementing (Joint Declaration, 1999). The Lost Art Database is considered a key resource in the search for looted art and the victims of persecution. Every item in the Lost Art Database has an identifier, known as a Lost Art ID. Proveana is the linked research database. == Other lost art databases == Other countries have launched databases to help identify Nazi looted art. Each database has its own area of focus. The German Lost Art Database allows families or heirs to submit information. Other countries have databases that focus on looted artworks that have not been found or artworks that were repatriated to the national authorities after the defeat of the Nazis but were never returned to their original owners. Other databases have been created for stolen antiquities, looted art from colonial era, art stolen from Syria, Iraq, Ukraine, or from museums or collectors.

  Read more →
• 

  AI Copywriting Tools Reviews: What Actually Works in 2026

  Shopping for the best AI copywriting tool? An AI copywriting tool 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 copywriting tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

  Read more →
• 

  The Best Free AI Subtitle Generator for Beginners

  In search of the best AI subtitle generator? An AI subtitle generator is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI subtitle generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

  Read more →