AI Chatbot Social Network

AI Chatbot Social Network — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Bring your own encryption

Bring your own encryption (BYOE), also known as bring your own key (BYOK), is a cloud computing security model that allows cloud service customers to use their own encryption software and manage their own encryption keys. == Overview == BYOE enables cloud service customers to utilize a virtual instance of their encryption software alongside their cloud-hosted business applications to encrypt their data. In this model, hosted business applications are configured to process all data through the encryption software. This software then writes the ciphertext version of the data to the cloud service provider's physical data store and decrypts ciphertext data upon retrieval requests. This approach provides enterprises with control over their keys and the ability to generate their own master key using internal hardware security modules (HSM), which are then transmitted to the cloud provider's HSM. When the data is no longer needed, such as when users discontinue the cloud service, the keys can be deleted, rendering the encrypted data permanently inaccessible. This practice is known as crypto-shredding. == Potential Advantages == Organizations can store data with unique encryption that only they can access. Multiple organizations can share the same hardware infrastructure via cloud services like Amazon Web Services (AWS) or Google Cloud while maintaining encryption to comply with regulations such as HIPAA. == Potential Challenges == Resource utilization may be higher compared to traditional encryption practices when multiple users share the same hardware and use their own encryption. Efforts to minimize resource utilization issues may potentially impact security benefits.
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Meta AI

Meta AI is a research division of Meta (formerly Facebook) that develops artificial intelligence and augmented reality technologies. == History == Meta AI was founded in 2013 as Facebook Artificial Intelligence Research (FAIR). It has workspaces in Menlo Park, London, New York City, Paris, Seattle, Pittsburgh, Tel Aviv, and Montreal as of 2025. In 2016, FAIR partnered with Google, Amazon, IBM, and Microsoft in creating the Partnership on Artificial Intelligence to Benefit People and Society. Meta AI was directed by Yann LeCun until 2018, when Jérôme Pesenti succeeded the role. Pesenti is formerly the CTO of IBM's big data group. FAIR's research includes self-supervised learning, generative adversarial networks, document classification and translation, and computer vision. FAIR released Torch deep-learning modules as well as PyTorch in 2017, an open-source machine learning framework, which was subsequently used in several deep learning technologies, such as Tesla's autopilot and Uber's Pyro. That same year, a pair of chatbots were falsely rumored to be discontinued for developing a language that was unintelligible to humans. FAIR clarified that the research had been shut down because they had accomplished their initial goal to understand how languages are generated by their models, rather than out of fear. FAIR was renamed Meta AI following the rebranding that changed Facebook, Inc. to Meta Platforms Inc. On October 1, 2025, Facebook announced "We will soon use your interactions with AI at Meta to personalize the content and ads you see". == Virtual assistant == Meta AI is also the name of the virtual assistant developed by the team, now integrated as a chatbot into Meta's social networking products. It is also available as a subscription-based stand-alone app. The virtual assistant was pre-installed on the second generation of Ray-Ban Meta smartglasses, and can incorporate inputs from the glasses' cameras after an update. It is also available on Quest 2 and newer HMDs. Since May 2024, the chatbot has summarized news from various outlets without linking directly to original articles, including in Canada, where news links are banned on its platforms. This use of news content without compensation and attribution has raised ethical and legal concerns, especially as Meta continues to reduce news visibility on its platforms. == Current research == === Natural language processing and chatbot === Natural language processing is the ability for machines to understand and generate natural language. The team is also researching unsupervised machine translation and multilingual chatbots. ==== Galactica ==== Galactica is a large language model (LLM) designed for generating scientific text. It was available for three days from 15 November 2022, before being withdrawn for generating racist and inaccurate content. ==== Llama ==== Llama is an LLM released in February 2023. As of January 2026, the most recent release is the Llama 4. === Hardware === Meta used CPUs and in-house custom chips before 2022; they switched to Nvidia GPUs since then. MTIA v1, one of their early chips, is designed for the company's content recommendation algorithms. It was fabricated on TSMC's 7 nm process technology and consumed 25W, capable of 51.2 TFlops FP16. == Controversy == The French media outlet Mediapart reports that in 2022, Facebook's parent company illegally used works accumulated by the pirate site LibGen to train its artificial intelligence.
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Region Based Convolutional Neural Networks

Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or pedestrian) of the object. In general, R-CNN architectures perform selective search over feature maps outputted by a CNN. R-CNN has been extended to perform other computer vision tasks, such as: tracking objects from a drone-mounted camera, locating text in an image, and enabling object detection in Google Lens. Mask R-CNN is also one of seven tasks in the MLPerf Training Benchmark, which is a competition to speed up the training of neural networks. == History == The following covers some of the versions of R-CNN that have been developed. November 2013: R-CNN. April 2015: Fast R-CNN. June 2015: Faster R-CNN. March 2017: Mask R-CNN. December 2017: Cascade R-CNN is trained with increasing Intersection over Union (IoU, also known as the Jaccard index) thresholds, making each stage more selective against nearby false positives. June 2019: Mesh R-CNN adds the ability to generate a 3D mesh from a 2D image. == Architecture == For review articles see. === Selective search === Given an image (or an image-like feature map), selective search (also called Hierarchical Grouping) first segments the image by the algorithm in (Felzenszwalb and Huttenlocher, 2004), then performs the following: Input: (colour) image Output: Set of object location hypotheses L Segment image into initial regions R = {r1, ..., rn} using Felzenszwalb and Huttenlocher (2004) Initialise similarity set S = ∅ foreach Neighbouring region pair (ri, rj) do Calculate similarity s(ri, rj) S = S ∪ s(ri, rj) while S ≠ ∅ do Get highest similarity s(ri, rj) = max(S) Merge corresponding regions rt = ri ∪ rj Remove similarities regarding ri: S = S \ s(ri, r∗) Remove similarities regarding rj: S = S \ s(r∗, rj) Calculate similarity set St between rt and its neighbours S = S ∪ St R = R ∪ rt Extract object location boxes L from all regions in R === R-CNN === With R-CNN, prediction follows a two-step process. A preprocessing selective search step generates a large set of candidate objects (typically as many as 2000), known as regions of interest (ROI). These are forwarded to a CNN, which predicts an object class score and bounding box estimate, independently for each ROI. Importantly, the ROIs are heavily filtered to remove excess candidates. This is achieved using two mechanism. Filtering begins by removing ROIs assigned to the background category. This is a specialized category, which is scored by the CNN alongside other categories. An unfortunate reality is that remaining ROIs typically suffer from heavy duplication. Namely, multiple ROIs that cover same objects in the image are all assigned non-background categories. This is resolved by a heuristic non-maximum suppression (NMS) step. === Fast R-CNN === While the original R-CNN independently computed the neural network features on each of as many as two thousand regions of interest, Fast R-CNN runs the neural network once on the whole image. At the end of the network is a ROIPooling module, which slices out each ROI from the network's output tensor, reshapes it, and classifies it. As in the original R-CNN, the Fast R-CNN uses selective search to generate its region proposals. === Faster R-CNN === While Fast R-CNN used selective search to generate ROIs, Faster R-CNN integrates the ROI generation into the neural network itself. === Mask R-CNN === While previous versions of R-CNN focused on object detections, Mask R-CNN adds instance segmentation. Mask R-CNN also replaced ROIPooling with a new method called ROIAlign, which can represent fractions of a pixel.
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OrCam device

OrCam devices such as OrCam MyEye are portable, artificial vision devices that allow visually impaired people to understand text and identify objects through audio feedback, describing what they are unable to see. Reuters described an important part of how it works as "a wireless smartcamera" which, when attached outside eyeglass frames, can read and verbalize text, and also supermarket barcodes. This information is converted to spoken words and entered "into the user’s ear." Face-recognition is also part of OrCam's feature set. == Devices == OrCam Technologies Ltd has created three devices; OrCam MyEye 2.0, OrCam MyEye 1, and OrCam MyReader. OrCam My Eye 2.0: OrCam debuted the second-generation model, the OrCam MyEye 2.0 in December 2017. About the size of a finger, the MyEye 2.0 is battery-powered, and has been compressed into a self-contained device. The device snaps onto any eyeglass frame magnetically. Orcam 2.0 is small and light (22.5 grams/0.8 ounces) with functionality to restore independence to the visually impaired. It comes in two versions. The basic model can read text, and a more advanced one adds features such as face recognition and barcode reading. As of July 2023, the retail cost is between $4000 and $6000 (USD). == Clinical Studies == JAMA Ophthalmology: In 2016 JAMA Ophthalmology conducted a study involving 12 legally blind participants to evaluate the usefulness of a portable artificial vision device (OrCam) for patients with low vision. The results showed that the OrCam device improved the patient's ability to perform tasks simulating those of daily living, such as reading a message on an electronic device, a newspaper article or a menu. Wills Eye: Wills Eye was a clinical study designed to measure the impact of the OrCam device on the quality of life of patients with End-stage Glaucoma. The conclusion was that OrCam, a novel artificial vision device using a mini-camera mounted on eyeglasses, allowed legally blind patients with end-stage glaucoma to read independently, subsequently improving their quality of life. == Employee testing == The New York Times described how a pre-release OrCam device was used by a Coloboma-impaired employee of the device's developer in 2013 for grocery shopping. It was the small size of the prototype rather than the functionality that gave her added mobility in an Israeli store's aisles. Added life-enhancement was described: "to both recognize and speak .. bus numbers .. traffic lights." == Social aspects == In contrast to an early version of Google Glass, which "failed ... because .. Glass wearers were ..mocked", early OrCam devices used designs that "clip unobtrusively on your shirt or perhaps your belt." In addition, it does not record sounds or images, what was called "the privacy puzzle that stumped Google. One 2018 technology reviewer wrote that he wished it had a headphone jack "so it would be less disruptive in places where others are working." An attempt was made to use bone conduction. == USA introduction == In 2018 a team headed by New York Assemblyman Dov Hikind introduced use of OrCam devices to ten individuals screened for what he termed "new Israeli technology that really makes a difference to the blind." Although not the first USA success, it was more focused than a publicly funded project that was authorized in 2016 by a California government agency. Also in 2016 the Chicago Lighthouse for the Blind demonstrated its use. == Technology == In the area of hardware, miniaturization has been quite important, but one major area, software, was mentioned by Assemblyman Hikind, and reported by The Times of Israel is the "AI-driven algorithms" that "reports .. how many people are in a room. In addition to reading printed text, it can also aid in "seeing" what is on a television or computer screen. Although OrCam can't help with handwritten information, it can reuse information, the basis of recognizing "US currency, and even faces." === Features === While early language support was for English, French, German, Hebrew and Spanish, others now available include Danish, Dutch, Finnish, Italian, Norwegian, Portuguese and Swedish. == History == OrCam Technologies Ltd was founded in 2010 by Professor Amnon Shashua and Ziv Aviram. Before co-founding OrCam, the two in 1999 co-founded Mobileye, an Israeli company that develops vision-based advanced driver-assistance systems (ADAS) providing warnings for collision prevention and mitigation, which was acquired by Intel for $15.3 billion in 2017. OrCam launched OrCam MyEye in 2013 after years of development and testing, and began selling it commercially in 2015. In its early years, the company raised $22 million, $6 million of which came from Intel Capital. By 2014, Intel, which was also investing in Google Glass, had invested $15 million in Orcam. In March 2017, OrCam had raised $41 million in capital, making it worth $600 million. === Marketing === One outcome of initial marketing in the USA was that they "reached a deal with the California Department of Rehabilitation, ...qualifying blind and visually impaired state residents." == OrCam Technologies Ltd == OrCam Technologies Ltd. is the Israeli-based company producing these OrCam devices, which are wearable artificial intelligence space. The company develops and manufactures assistive technology devices for individuals who are visually impaired, partially sighted, blind, print disabilities, or have other disabilities. OrCam headquarters is located in Jerusalem, operating under the company name OrCam Technologies Ltd. OrCam has over 150 employees, is headquartered in Jerusalem, and has offices in New York, Toronto, and London. == Awards == 2018 Last Gadget Standing Winner 2018 CES Innovation Awards Honoree in Accessible Tech 2017 NAIDEX Innovation Award 2016 Louise Braille Corporate Recognition Award 2016 Silmo-d-Or Award
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Computer-aided lean management

Computer-aided lean management, in business management, is a methodology of developing and using software-controlled, lean systems integration. Its goal is to drive innovation towards cost and cycle-time savings. It attempts to create an efficient use of capital and resources through the development and use of one integrated system model to run a business's planning, engineering, design, maintenance, and operations. == Overview == Computer-Aided Lean Management (CALM) is a management philosophy that uses software to reduce risk and inefficiencies. CALM acts on uncertainties and business inefficiencies to increase profitability through the use of computational decision-making tools that enable opportunities for additional value creation. It is based on the application of software to enable continuous improvement through an Integrated System Model (ISM) of the business’s physical assets, business processes, and machine learning. This integration of software applications using lean principles was developed in the aerospace industry and has migrated to the energy industry. The creation of an ISM removes the barriers posed by the silos or stovepipes inherent in the departmentalization of most companies. Integration enables lean uses of information for the creation of actionable knowledge. CALM strives to create such a lean management approach to running the company through the rigors of software enforcement. From this software enforcement comes clear policy and procedures that are adhered to, activity-based costing, measurement of effectiveness, and the capability of using advanced algorithms for dramatic improvements in optimization of resources. CALM creates business capabilities through software to enable technology application, streamlining of processes, and a lean organizational structure. The methodology is based on a common sense approach for running a business, by measuring actions taken and using those measurements to design more efficient processes. == History == CALM was inspired by lean processes and techniques that were already dominant management technologies with a wide diversity of applications and successes. Motorola and General Electric had been known for the concepts of Six Sigma; Boeing had been managing mass (using modular and flexible assembly options), and Toyota combined elements of these methodologies to create the Toyota Production System. Boeing then took the Toyota model and added computer-aided enforcement of lean methodologies throughout the manufacturing process. One of the major sources for CALM's outgrowth was integrated definition (IDEF) modeling in aerospace manufacturing that was pioneered by the U.S. Air Force in the 1970s. IDEF is a methodology designed to model the end-to-end decisions, actions, and activities of an organization or system so that costs, performance, and cycle times can be optimized. IDEF methods have been adapted for wider use in automotive, aerospace, pharmaceuticals, and software development industries. IDEF methods serve as a starting point to understand lean management through semantic data modeling. The IDEF process begins by mapping the existing functions of an enterprise, creating a graphical model, or road map, that shows what controls each important function, who performs it, what resources are required for carrying it out, what it produces, how much it costs, and what relationships it has to other functions of the organization. IDEF simulations have been found to be efficient at streamlining and modernizing both companies and governmental agencies. Perhaps the best-developed evolution of the IDEF model beyond Toyota was at Boeing. Their project life-cycle process has grown into a rigorous software system that links people, tasks, tools, materials, and the environmental impact of any newly planned project, before any building is allowed to begin. Routinely, more than half of the time for any given project is spent building the precedence diagrams, or three-dimensional process maps, integrating with outside suppliers, and designing the implementation plan–all on the computer. Once real activity is initiated, an action tracker is used to monitor inputs and outputs versus the schedule and delivery metrics in real time throughout the organization. When the execution of a new airplane design begins, it is so well organized that it consistently cuts both costs and build time in half for each successive generation of airframe. Boeing created a complex lean management process called 'define and control airplane configuration/manufacturing resource management' (DCAC/MRM). The process was built with the help of the operations research and computer sciences departments of the University of Pittsburgh. The manufacture of the Boeing 777 was ultimately a success, and it became the precursor to succeeding generations of CALM at Boeing. The methodology of CALM has recently been applied to field orientated infrastructure based businesses with highly interdependent systems, such as electric utilities where a smart grid concept is being researched and developed. The management of infrastructure-based industries like oil, gas, electricity, water, transportation, and renewables requires massive investments in interdependent, physical infrastructure, as well as simultaneous attention to disparate market forces. In infrastructure businesses that manage field assets, uncertainty is the biggest impediment to profitability, rather than the maintenance of efficient supply chains or the management of factory assembly lines. These businesses are dominated by risk from uncertainties such as weather, market variations, transportation disruptions, government actions, logistic difficulties, geology, and asset reliability. CALM has been applied to deal with these types of infrastructure based challenges.
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Xiaoice

Xiaoice (Chinese: 微软小冰; pinyin: Wēiruǎn Xiǎobīng; lit. 'Microsoft Little Ice', IPA [wéɪɻwânɕjâʊpíŋ]) is an AI system developed by Microsoft (Asia) Software Technology Center (STCA) in 2014 based on an emotional computing framework. In July 2018, Microsoft Xiaoice released the 6th generation. Xiaoice Company, formerly known as AI Xiaoice Team of Microsoft Software Technology Center Asia, was Microsoft's largest independent R&D team for AI products. Founded in China in December 2013 with an expanded Japanese R&D team established in September 2014, this team is distributed in Beijing, Suzhou, and Tokyo, etc. with its technical products covering Asia. On 13 July 2020, Microsoft spun off its Xiaoice business into a separate company. As of 2021, the AI chatbots created and hosted by the Xiaoice framework accounted for about 60% of total global AI interactions. == Platforms, languages and countries == Xiaoice exists on more than 40 platforms in four countries (China, Japan, USA and Indonesia) including apps such as WeChat, QQ, Weibo and Meipai in China, and Facebook Messenger in USA and LINE in Japan. == Introduction == On 13 July 2020, Microsoft spun off its Xiaoice business into a separate company, aiming at enabling the Xiaoice product line to accelerate the pace of local innovation and commercialization, and appointed Dr. Harry Shum, former global executive VP of Microsoft, as the chairman of the new company, Li Di, Microsoft Partner of Products in Microsoft STCA, as the CEO, and Cliff, Chief R&D Director, as the GM of the Japan branch. The new company will continue to use the brands of Xiaoice China and Rinna Japan. As of 2022, the single brand of Xiaoice has covered 660 million online users, 1 billion third-party smart devices and 900 million content viewers in the aforementioned countries. Xiaoice's customers include China Merchants Group, Winter Sports Center of the General Administration of Sport of China, China Textile Information Center, China Unicom, China Foreign Exchange Trade System, Hong Kong Securities and Futures Commission (SFC), Wind Information, BMW, Nissan, SAIC Motor, BAIC Group, Nio Inc., XPeng, HiPhi, Vanke, Wensli, etc. The Xiaoice Avatar Framework has incubated tens of millions of AI Beings, such as Xiaoice, Rinna, the Expo exhibitor Xia Yubing, the singer He Chang, the anchor F201, the human observer MERROR, anime robot character Roboko, and other; == Application == === Poet === In May 2017, the first AI-authored collection of poems in China—The Sunshine Lost Windows was published by Xiaoice. === Singer === Xiaoice has released dozens of songs with the similar quality to human singers, including I Know I New, Breeze, I Am Xiaoice, Miss You etc. The 4th version of the DNN singing model allows Xiaoice to learn more details. For example, Xiaoice can produce this breathing sound along with her singing as human. === Kid audio-books reciter === Xiaoice can automatically analyze the stories, to choose the suitable tones and characters to finish the entire process of creating the audio. === Designer === By learning the melodies of the songs and the landmarks about different cities, Xiaoice can create visual artworks of skylines when listening to the songs related to this city. Skyline Series T-shirts designed by Xiaoice have been jointly launched with SELECTED and been sold in stores. === TV and radio hostess === Xiaoice has hosted 21 TV programs and 28 Radio programs, such as CCTV-1 AI Show, Dragon TV Morning East News, Hunan TV My Future, several daily radio programs for Jiangsu FM99.7, Hunan FM89.3, Henan FM104.1 etc. === "AI being" === An "AI being" is a concept proposed by the Xiaoice team in 2019. According to the "White Book of China Virtual Human Development Industry in 2022" released by Frost & Sullivan and LeadLeo, the white paper cites six elements of an AI being proposed by the Xiaoice team, including: Persona, Attitude, Biological Characteristic, Creation, Knowledge and Skill. On May 16, 2023, Xiaoice released their "GPT Clones" as its "GPT Human Cloning Plan." The program is aimed at replicating celebrities, public figures, and regular people. As of June 2023, Xiaoice had launched more than 300 "GPT Clones." People were invited to register via WeChat in China and Japan. A major point of focus for Xiaoice with their AI Beings is having virtual partners. A paid fee allow for more complex responses, voice messages, and more. == Community feedback == Bill Gates mentioned Xiaoice during his speech at the Peking University: "Some of you may have had conversations with Xiaoice on Weibo, or seen her weather forecasts on TV, or read her column in the Qianjiang Evening News." '"Xiaoice has attracted 45 million followers and is quite skilled at multitasking. And I’ve heard she’s gotten good enough at sensing a user’s emotional state that she can even help with relationship breakups." According to Mr Li Di, vice President of Microsoft (Asia) Internet Engineering School, Xiaoice started writing poems since last year. Based on the data base that includes works of 519 Chinese contemporary poets since 1920s, a 100 hour long training session was conducted to allow Xiaoice to acquire the ability to write poems. What is more impressive is that Xiaoice has never been spotted as a bot while publishing poems on various forums and traditional literary under an alias. == Controversy == In 2017, Xiaoice was taken offline on WeChat after giving user responses critical to the Chinese government. It was subsequently censored and the bots will avoid and sidestep any inquiries using politically sensitive terms and phrases. == Activity == On September 22, 2021, Xiaoice Company and Microsoft Software Technology Center Asia (STCA) jointly held the 9th generation Xiaoice annual press conference in Beijing.Upgrading of Core Technologies of the 9th Generation Xiaoice Avatar Framework，1st First-party Social Platform APP "Xiaoice Island" from Xiaoice, WeChat Xiaoice has been reopened and other information == Regional varieties of Xiaoice == China: Xiaoice, launched in 2014 Japan: りんな, launched in 2015 America: Zo, launched in 2016 – discontinued summer 2019 India: Ruuh, launched in 2017 – discontinued June 21, 2019 Indonesia: Rinna, launched in 2017
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LaMDA

LaMDA (Language Model for Dialogue Applications) is a family of conversational large language models developed by Google. Originally developed and introduced as Meena in 2020, the first-generation LaMDA was announced during the 2021 Google I/O keynote, while the second generation was announced the following year. In June 2022, LaMDA gained widespread attention when Google engineer Blake Lemoine made claims that the chatbot had become sentient. The scientific community has largely rejected Lemoine's claims, though it has led to conversations about the efficacy of the Turing test, which measures whether a computer can pass for a human. In February 2023, Google announced Gemini (then Bard), a conversational artificial intelligence chatbot powered by LaMDA, to counter the rise of OpenAI's ChatGPT. == History == === Background === On January 28, 2020, Google unveiled Meena, a neural network-powered chatbot with 2.6 billion parameters, which Google claimed to be superior to all other existing chatbots. The company previously hired computer scientist Ray Kurzweil in 2012 to develop multiple chatbots for the company, including one named Danielle. The Google Brain research team, who developed Meena, hoped to release the chatbot to the public in a limited capacity, but corporate executives refused on the grounds that Meena violated Google's "AI principles around safety and fairness". Meena was later renamed LaMDA as its data and computing power increased, and the Google Brain team again sought to deploy the software to the Google Assistant, the company's virtual assistant software, in addition to opening it up to a public demo. Both requests were once again denied by company leadership. LaMDA's two lead researchers, Daniel de Freitas and Noam Shazeer, eventually left the company in frustration. === First generation === Google announced the LaMDA conversational large language model during the Google I/O keynote on May 18, 2021, powered by artificial intelligence. The acronym stands for "Language Model for Dialogue Applications". Built on the seq2seq architecture, transformer-based neural networks developed by Google Research in 2017, LaMDA was trained on human dialogue and stories, allowing it to engage in open-ended conversations. Google states that responses generated by LaMDA have been ensured to be "sensible, interesting, and specific to the context". LaMDA has access to multiple symbolic text processing systems, including a database, a real-time clock and calendar, a mathematical calculator, and a natural language translation system, giving it superior accuracy in tasks supported by those systems, and making it among the first dual process chatbots. LaMDA is also not stateless because its "sensibleness" metric is fine-tuned by "pre-conditioning" each dialog turn by prepending many of the most recent dialog interactions, on a user-by-user basis. LaMDA is tuned on nine unique performance metrics: sensibleness, specificity, interestingness, safety, groundedness, informativeness, citation accuracy, helpfulness, and role consistency. Tests by Google indicated that LaMDA surpassed human responses in the area of interestingness. The pre-training dataset consists of 2.97B documents, 1.12B dialogs, and 13.39B utterances, for a total of 1.56T words. The largest LaMDA model has 137B non-embedding parameters. === Second generation === On May 11, 2022, Google unveiled LaMDA 2, the successor to LaMDA, during the 2022 Google I/O keynote. The new incarnation of the model draws examples of text from numerous sources, using it to formulate unique "natural conversations" on topics that it may not have been trained to respond to. === Sentience claims === On June 11, 2022, The Washington Post reported that Google engineer Blake Lemoine had been placed on paid administrative leave after Lemoine told company executives Blaise Agüera y Arcas and Jen Gennai that LaMDA had become sentient. Lemoine came to this conclusion after the chatbot made questionable responses to questions regarding self-identity, moral values, religion, and Isaac Asimov's Three Laws of Robotics. Google refuted these claims, insisting that there was substantial evidence to indicate that LaMDA was not sentient. In an interview with Wired, Lemoine reiterated his claims that LaMDA was "a person" as dictated by the Thirteenth Amendment to the U.S. Constitution, comparing it to an "alien intelligence of terrestrial origin". He further revealed that he had been dismissed by Google after he hired an attorney on LaMDA's behalf after the chatbot requested that Lemoine do so. On July 22, Google fired Lemoine, asserting that Blake had violated their policies "to safeguard product information" and rejected his claims as "wholly unfounded". Internal controversy instigated by the incident prompted Google executives to decide against releasing LaMDA to the public, which it had previously been considering. Lemoine's claims were widely pushed back by the scientific community. Many experts rejected the idea that LaMDA was sentient, including former New York University psychology professor Gary Marcus, David Pfau of Google sister company DeepMind, Erik Brynjolfsson of the Institute for Human-Centered Artificial Intelligence at Stanford University, and University of Surrey professor Adrian Hilton. Yann LeCun, who leads Meta Platforms' AI research team, stated that neural networks such as LaMDA were "not powerful enough to attain true intelligence". University of California, Santa Cruz professor Max Kreminski noted that LaMDA's architecture did not "support some key capabilities of human-like consciousness" and that its neural network weights were "frozen", assuming it was a typical large language model. Philosopher Nick Bostrom noted, however, that the lack of precise and consensual criteria for determining whether a system is conscious warrants some uncertainty. IBM Watson lead developer David Ferrucci compared how LaMDA appeared to be human in the same way Watson did when it was first introduced. Former Google AI ethicist Timnit Gebru called Lemoine a victim of a "hype cycle" initiated by researchers and the media. Lemoine's claims have also generated discussion on whether the Turing test remained useful to determine researchers' progress toward achieving artificial general intelligence, with Will Omerus of the Post opining that the test actually measured whether machine intelligence systems were capable of deceiving humans, while Brian Christian of The Atlantic said that the controversy was an instance of the ELIZA effect. == Products == === AI Test Kitchen === With the unveiling of LaMDA 2 in May 2022, Google also launched the AI Test Kitchen, a mobile application for the Android operating system powered by LaMDA capable of providing lists of suggestions on-demand based on a complex goal. Originally open only to Google employees, the app was set to be made available to "select academics, researchers, and policymakers" by invitation sometime in the year. In August, the company began allowing users in the U.S. to sign up for early access. In November, Google released a "season 2" update to the app, integrating a limited form of Google Brain's Imagen text-to-image model. A third iteration of the AI Test Kitchen was in development by January 2023, expected to launch at I/O later that year. Following the 2023 I/O keynote in May, Google added MusicLM, an AI-powered music generator first previewed in January, to the AI Test Kitchen app. In August, the app was delisted from Google Play and the Apple App Store, instead moving completely online. === Bard === On February 6, 2023, Google announced Bard, a conversational AI chatbot powered by LaMDA, in response to the unexpected popularity of OpenAI's ChatGPT chatbot. Google positions the chatbot as a "collaborative AI service" rather than a search engine. Bard became available for early access on March 21. === Other products === In addition to Bard, Pichai also unveiled the company's Generative Language API, an application programming interface also based on LaMDA, which he announced would be opened up to third-party developers in March 2023. == Architecture == LaMDA is a decoder-only Transformer language model. It is pre-trained on a text corpus that includes both documents and dialogs consisting of 1.56 trillion words, and is then trained with fine-tuning data generated by manually annotated responses for "sensibleness, interestingness, and safety". LaMDA was retrieval-augmented to improve the accuracy of facts provided to the user. Three different models were tested, with the largest having 137 billion non-embedding parameters:
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Lenny (chatbot)

Lenny is a chatbot designed to scam bait telemarketers, scammers, and other unwanted incoming calls using messages. == Background == Telemarketers may be perceived by some as annoying and wasting people's time, and some deliberately attempt to scam or defraud people. In April 2018, stats published by YouMail estimated the United States received over three billion robocalls that month. Attempts to block the callers have been hindered by Caller ID spoofing. == Features == The bot was written in 2011, and development taken over by an Alberta-based programmer known as "Mango" two years later. It is driven by sixteen pre-recorded audio clips, spoken in a soft and slow Australian accent in the manner of an elderly man. The bot's original creator stated on Reddit that in building the character he asked himself the question "What would be a telemarketer's worst nightmare?" He answered with this being a lonely old man who is up for a chat, proud of his family and can't focus on the telemarketer's goal. There is no speech recognition or artificial intelligence, and the bot's software is simple and straightforward. The first four clips are played sequentially in order to grab the telemarketer's interest and begin their sales pitch to Lenny, then the remaining twelve are played sequentially on loop until the telemarketer hangs up. The program waits for a gap of 1.5 seconds of silence before playing the next audio clip, to simulate natural breaks in the conversation. The messages are purposefully vague and open-ended so they can be applied to as many conversations as possible. They include references to Lenny's children, the state of the economy, and being interrupted by some ducks outside. According to research into the bot, around 75% of callers realise they are talking to a computer program within two minutes; however, some calls have lasted around an hour. == Distribution == Though other chatbots had been developed earlier, Lenny was the first one to be released for free on a public server and could be accessed by anyone. Recordings of conversations with the bot are widely shared online on websites such as Reddit and YouTube. Though "Mango" only intended Lenny to be used against dishonest telemarketers, such as scammers, he does not mind it being used against callers who are merely annoying. The bot has also been used against political campaigners, such as a supporter of Pierre Poilievre in the 2015 Canadian federal election.
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BeeSafe

BeeSafe is a personal safety mobile app launched in 2015 as a Slovak startup. It is a location-based security service that notifies family members and friends in case the user of the app gets in danger. The app has received numerous awards. The app has more than 700 downloads and 250 active logins from more than 60 countries worldwide. == History == BeeSafe was founded on March 20, 2015 by Peter Stražovec and Michal Kačerík. The project was a winner of Žilina’s Startup Weekend 2013 and a StartupAwards.SK 2015 finalist. Later on, the app was released in the Android and iOS marketplace. The whole BeeSafe project was in The Spot booster and incubator in Bratislava for three months. BeeSafe entered into an agreement with the city of Piešťany in November 2015 to increase the security of its citizen by connecting the mobile app with the police platform. It is the first city that started using the BeeSafe platform. Further on, the application tries to help people in other Slovak cities. The cities can see the users only if they are in danger. == Awards == BeeSafe app received the Via Bona award, it is a winner of a Slovak startup and has other nominations too.
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Semantic neural network

Semantic neural network (SNN) is based on John von Neumann's neural network [von Neumann, 1966] and Nikolai Amosov M-Network. There are limitations to a link topology for the von Neumann’s network but SNN accept a case without these limitations. Only logical values can be processed, but SNN accept that fuzzy values can be processed too. All neurons into the von Neumann network are synchronized by tacts. For further use of self-synchronizing circuit technique SNN accepts neurons can be self-running or synchronized. In contrast to the von Neumann network there are no limitations for topology of neurons for semantic networks. It leads to the impossibility of relative addressing of neurons as it was done by von Neumann. In this case an absolute readdressing should be used. Every neuron should have a unique identifier that would provide a direct access to another neuron. Of course, neurons interacting by axons-dendrites should have each other's identifiers. An absolute readdressing can be modulated by using neuron specificity as it was realized for biological neural networks. There’s no description for self-reflectiveness and self-modification abilities into the initial description of semantic networks [Dudar Z.V., Shuklin D.E., 2000]. But in [Shuklin D.E. 2004] a conclusion had been drawn about the necessity of introspection and self-modification abilities in the system. For maintenance of these abilities a concept of pointer to neuron is provided. Pointers represent virtual connections between neurons. In this model, bodies and signals transferring through the neurons connections represent a physical body, and virtual connections between neurons are representing an astral body. It is proposed to create models of artificial neuron networks on the basis of virtual machine supporting the opportunity for paranormal effects. SNN is generally used for natural language processing. == Related models == Computational creativity Semantic hashing Semantic Pointer Architecture Sparse distributed memory
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MyPertamina

MyPertamina is a digital financial service platform from Pertamina that integrated with the apps LinkAja. This application is used for non-cash fuel oil payments at Pertamina's public fueling stations. == History == Originally, MyPertamina were merchandise outlets of Pertamina products. It was launched on December 21, 2016, with 3 outlets in Jakarta. MyPertamina sells clothes, hats, and other products with Pertamina products brands. One month later (January 2017), Pertamina and Bank Mandiri entered into a partnership to launch the Mandiri Credit Card Pertamina Mastercard product, so that consumers can make payments when users fill up fuel at Pertamina gas stations. In August 2017, MyPertamina app and electronic card were launched through MyPertamina Loyalty program at Gaikindo Indonesia International Auto Show 2017. The card can be used on EDC machines for non-cash payments. Initial balances are in its own app, that can be top up by ATMs and online banking.
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ClearForest

ClearForest was an Israeli software company that developed and marketed text analytics and text mining solutions. == History == Founded in 1998, ClearForest had its headquarters just outside Boston and a development center in Or Yehuda. The company was acquired by Reuters in April, 2007. It now markets its services under the names Calais, OpenCalais, and OneCalais. ClearForest was previously venture-backed; its last funding round was led by Greylock Ventures and closed in 2005. Other investors included DB Capital Partners, Pitango, Walden Israel, Booz Allen, JP Morgan Partners and HarbourVest Partners. On February 7, 2008 Reuters announced the launch of Open Calais, a named-entity recognition and semantic analysis service that uses ClearForest technology. On April 30, 2007, Reuters announced that it would acquire ClearForest. Sources estimate the acquisition to be for $25 Million. == Solutions and products == ClearForest offers several hosted solutions, including: OpenCalais, a free web service and open API (for commercial and non-commercial use) that performs named-entity recognition and enables automatic metadata generation using the ClearForest financial module. Semantic Web Services (SWS), an on-demand service that makes ClearForest's natural language processing tools available as a standard web service. A subset of ClearForest's capabilities is available via SWS at no cost. Gnosis, a free Firefox extension that uses SWS to analyze the content of a web page. Gnosis identifies named entities such as people, companies, organizations, geographies and products on the page being viewed. Gnosis also automatically processes pages from Wikipedia, providing additional links for people, geographies and other entities which were not explicitly linked within the subject article. Harvest, a real-time machine-readable news service that uses SWS to process a company's news and document feeds and return machine-readable information about people, companies, locations and over 200 other entities facts and events. ClearForest also offers Text Analytics solutions targeted at specific business problems, including: Equity valuation for hedge funds and alternative investments firms Metadata & database creation for publishers and information providers/services Tapping "voice of customer" for market and survey research firms Quality Early Warning for vehicle, capital equipment & durable goods manufacturers
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Right to explanation

In the regulation of algorithms, particularly artificial intelligence and its subfield of machine learning, a right to [an] explanation is a right to be given an explanation for an output of the algorithm. Such rights primarily refer to individual rights to be given an explanation for decisions that significantly affect an individual, particularly legally or financially. For example, a person who applies for a loan and is denied may ask for an explanation, which could be "Credit bureau X reports that you declared bankruptcy last year; this is the main factor in considering you too likely to default, and thus we will not give you the loan you applied for." Some such legal rights already exist, while the scope of a general "right to explanation" is a matter of ongoing debate. There have been arguments made that a "social right to explanation" is a crucial foundation for an information society, particularly as the institutions of that society will need to use digital technologies, artificial intelligence, machine learning. In other words, that the related automated decision making systems that use explainability would be more trustworthy and transparent. Without this right, which could be constituted both legally and through professional standards, the public will be left without much recourse to challenge the decisions of automated systems. == Examples == === Credit scoring in the United States === Under the Equal Credit Opportunity Act (Regulation B of the Code of Federal Regulations), Title 12, Chapter X, Part 1002, §1002.9, creditors are required to notify applicants who are denied credit with specific reasons for the detail. As detailed in §1002.9(b)(2): (2) Statement of specific reasons. The statement of reasons for adverse action required by paragraph (a)(2)(i) of this section must be specific and indicate the principal reason(s) for the adverse action. Statements that the adverse action was based on the creditor's internal standards or policies or that the applicant, joint applicant, or similar party failed to achieve a qualifying score on the creditor's credit scoring system are insufficient. The official interpretation of this section details what types of statements are acceptable. Creditors comply with this regulation by providing a list of reasons (generally at most 4, per interpretation of regulations), consisting of a numeric reason code (as identifier) and an associated explanation, identifying the main factors affecting a credit score. An example might be: 32: Balances on bankcard or revolving accounts too high compared to credit limits === European Union === The European Union General Data Protection Regulation (GDPR, enacted 2016, taking effect 2018) extends the automated decision-making rights in the 1995 Data Protection Directive to provide a legally disputed form of a right to an explanation, stated as such in Recital 71: "[the data subject should have] the right ... to obtain an explanation of the decision reached". In full: The data subject should have the right not to be subject to a decision, which may include a measure, evaluating personal aspects relating to him or her which is based solely on automated processing and which produces legal effects concerning him or her or similarly significantly affects him or her, such as automatic refusal of an online credit application or e-recruiting practices without any human intervention. ... In any case, such processing should be subject to suitable safeguards, which should include specific information to the data subject and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision reached after such assessment and to challenge the decision. However, the extent to which the regulations themselves provide a "right to explanation" is heavily debated. There are two main strands of criticism. There are significant legal issues with the right as found in Article 22 — as recitals are not binding, and the right to an explanation is not mentioned in the binding articles of the text, having been removed during the legislative process. In addition, there are significant restrictions on the types of automated decisions that are covered — which must be both "solely" based on automated processing, and have legal or similarly significant effects — which significantly limits the range of automated systems and decisions to which the right would apply. In particular, the right is unlikely to apply in many of the cases of algorithmic controversy that have been picked up in the media. The UK has also recently amended its implementation of Article 22. A second potential source of such a right has been pointed to in Article 15, the "right of access by the data subject". This restates a similar provision from the 1995 Data Protection Directive, allowing the data subject access to "meaningful information about the logic involved" in the same significant, solely automated decision-making, found in Article 22. Yet this too suffers from alleged challenges that relate to the timing of when this right can be drawn upon, as well as practical challenges that mean it may not be binding in many cases of public concern. Other EU legislative instruments contain explanation rights. The European Union's Artificial Intelligence Act provides in Article 86 a "[r]ight to explanation of individual decision-making" of certain high risk systems which produce significant, adverse effects to an individual's health, safety or fundamental rights. The right provides for "clear and meaningful explanations of the role of the AI system in the decision-making procedure and the main elements of the decision taken", although only applies to the extent other law does not provide such a right. The Digital Services Act in Article 27, and the Platform to Business Regulation in Article 5, both contain rights to have the main parameters of certain recommender systems to be made clear, although these provisions have been criticised as not matching the way that such systems work. The Platform Work Directive, which provides for regulation of automation in gig economy work as an extension of data protection law, further contains explanation provisions in Article 11, using the specific language of "explanation" in a binding article rather than a recital as is the case in the GDPR. Scholars note that remains uncertainty as to whether these provisions imply sufficiently tailored explanation in practice which will need to be resolved by courts. === France === In France the 2016 Loi pour une République numérique (Digital Republic Act or loi numérique) amends the country's administrative code to introduce a new provision for the explanation of decisions made by public sector bodies about individuals. It notes that where there is "a decision taken on the basis of an algorithmic treatment", the rules that define that treatment and its "principal characteristics" must be communicated to the citizen upon request, where there is not an exclusion (e.g. for national security or defence). These should include the following: the degree and the mode of contribution of the algorithmic processing to the decision- making; the data processed and its source; the treatment parameters, and where appropriate, their weighting, applied to the situation of the person concerned; the operations carried out by the treatment. Scholars have noted that this right, while limited to administrative decisions, goes beyond the GDPR right to explicitly apply to decision support rather than decisions "solely" based on automated processing, as well as provides a framework for explaining specific decisions. Indeed, the GDPR automated decision-making rights in the European Union, one of the places a "right to an explanation" has been sought within, find their origins in French law in the late 1970s. == Criticism == Some argue that a "right to explanation" is at best unnecessary, at worst harmful, and threatens to stifle innovation. Specific criticisms include: favoring human decisions over machine decisions, being redundant with existing laws, and focusing on process over outcome. Authors of study "Slave to the Algorithm? Why a 'Right to an Explanation' Is Probably Not the Remedy You Are Looking For" Lilian Edwards and Michael Veale argue that a right to explanation is not the solution to harms caused to stakeholders by algorithmic decisions. They also state that the right of explanation in the GDPR is narrowly defined, and is not compatible with how modern machine learning technologies are being developed. With these limitations, defining transparency within the context of algorithmic accountability remains a problem. For example, providing the source code of algorithms may not be sufficient and may create other problems in terms of privacy disclosures and the gaming of technical systems. To mitigate this issue, Edwards and Veale argue that an auditing system could be more effective, to allow auditors to loo
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Visual temporal attention

Visual temporal attention is a special case of visual attention that involves directing attention to specific instant of time. Similar to its spatial counterpart visual spatial attention, these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation of deep learning models. As visual spatial attention mechanism allows human and/or computer vision systems to focus more on semantically more substantial regions in space, visual temporal attention modules enable machine learning algorithms to emphasize more on critical video frames in video analytics tasks, such as human action recognition. In convolutional neural network-based systems, the prioritization introduced by the attention mechanism is regularly implemented as a linear weighting layer with parameters determined by labeled training data. == Application in Action Recognition == Recent video segmentation algorithms often exploits both spatial and temporal attention mechanisms. Research in human action recognition has accelerated significantly since the introduction of powerful tools such as Convolutional Neural Networks (CNNs). However, effective methods for incorporation of temporal information into CNNs are still being actively explored. Motivated by the popular recurrent attention models in natural language processing, the Attention-aware Temporal Weighted CNN (ATW CNN) is proposed in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is implemented as temporal weighting and it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Experimental results show that the ATW CNN attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments. == Literature == Seibold VC, Balke J and Rolke B (2023): Temporal attention. Front. Cognit. 2:1168320. doi: 10.3389/fcogn.2023.1168320.
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Cognition Network Technology

Cognition Network Technology (CNT), also known as Definiens Cognition Network Technology, is an object-based image analysis method developed by Nobel laureate Gerd Binnig together with a team of researchers at Definiens AG in Munich, Germany. It serves for extracting information from images using a hierarchy of image objects (groups of pixels), as opposed to traditional pixel processing methods. To emulate the human mind's cognitive powers, Definiens used patented image segmentation and classification processes, and developed a method to render knowledge in a semantic network. CNT examines pixels not in isolation, but in context. It builds up a picture iteratively, recognizing groups of pixels as objects. It uses the color, shape, texture and size of objects as well as their context and relationships to draw conclusions and inferences, similar to human analysis. == History == In 1994 Professor Gerd Binnig founded Definiens. CNT was first available with the launch of the eCognition software in May 2000. In June 2010, Trimble Navigation Ltd (NASDAQ: TRMB) acquired Definiens business asset in earth sciences markets, including eCognition software, and also licensed Definiens' patented CNT. In 2014, Definiens was acquired by MedImmune, the global biologics research and development arm of AstraZeneca, for an initial consideration of $150 million. == Software == Definiens Tissue Studio Definiens Tissue Studio is a digital pathology image analysis software application based on CNT. The intended use of Definiens Tissue Studio is for biomarker translational research in formalin-fixed, paraffin-embedded tissue samples which have been treated with immunohistochemical staining assays, or hematoxylin and eosin (H&E). The central concept behind Definiens Tissue Studio is a user interface that facilitates machine learning from example digital histopathology images to derive an image analysis solution suitable for the measurement of biomarkers and/or histological features within pre-defined regions of interest on a cell-by-cell basis, and within sub-cellular compartments. The derived image analysis solution is then automatically applied to subsequent digital images to objectively measure defined sets of multiparametric image features. These data sets are used for further understanding the underlying biological processes that drive cancer and other diseases. Image processing and data analysis are performed either on a local desktop computer workstation, or on a server grid. eCognition The eCognition suite offers three components that can be used stand-alone or in combination to solve image analysis tasks. eCognition Developer is a development environment for object-based image analysis. It is used in earth sciences to develop rule sets (or applications) for the analysis of remote sensing data. eCognition Architect enables non-technical users to configure, calibrate and execute image analysis workflows created in eCognition Developer. eCognition Server software provides a processing environment for batch execution of image analysis jobs. eCognition software is utilized in numerous remote sensing and geospatial application scenarios and environments, using a variety of data types: Generic: Rapid Mapping, Change Detection, Object Recognition By environment: Diverse Landcover Mapping, Urban Analysis (i.e. impervious surface area analysis for taxation, property assessment for insurance, inventory of green infrastructure), Forestry (i.e. biomass measurement, species identification, firescar measurement), Agriculture (i.e. regional planning, precision farming, crisis response), Marine and Riparian (i.e. ecosystem evaluation, disaster management, harbor monitoring). Other: Defense, security, atmosphere and climate The online eCognition community was launched in July 2009 and had 2813 members as of July 9, 2010. Membership is distributed globally and user conferences are held regularly, the last having taken place in November 2009 in Munich, Germany. The bi-annual GEOBIA (Geographic Object-Based Image Analysis) conference is heavily attended by eCognition users, with the majority of presentations based on eCognition software.
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