A digital zombie is a person so engaged with digital technology or social media they are unable to separate themselves from a persistent online presence. Writing in 2017, University of Sydney researcher Andrew Campbell expressed concerns over whether or not the individual can truly live a full and healthy life while they are preoccupied with the digital world. Other individuals have also begun referencing certain types of behaviour with being a digital zombie. Stefanie Valentic, managing editor of EHS Today, refers to it as people hunting digital creatures through their smartphones in public spaces, always fixed on their phones. The University of Warwick has used the term to argue that further research needs to be done with people who exist in digital form after death to help people grieve their loss. == Modern applications == === Distracted walking === The term digital zombie can refer to a person performing distracted walking, which has been labelled dangerous by the American Academy of Orthopaedic Surgeons. They created the "Digital Deadwalkers" campaign after physicians became aware of the risks associated with walking across intersections and sidewalks while paying attention only to smartphones and not one's surroundings. Also stating that the name is derived from the fact that "they're oblivious to everyone else, so it's like they're dead-walking, sleepwalking." === Living through media === The Department of Sociology, University of Warwick has also identified the term, digital zombie, to refer to an individual who has died but is digitally resurrected, reanimated and socially active. These digital zombies do things in death they did not do when they were alive as they "live" again through a digital self on a digital medium. Dead celebrities sometimes become digital zombies when they are reanimated to appear in commercial advertisements (such as Audrey Hepburn and Bob Monkhouse). Other accidental digital zombies include Tupac Shakur and Michael Jackson who were both digitally resurrected and recreated to perform "live" on stage years after their death. Researchers at the University of Warwick have carried out research into the area of human-computer interaction. in an effort to understand the affect these digital zombies have on grief and bereavement. === Mobile gaming === Writer for EHS Today, Stefanie Valentic, has made observations with the mobile phone video game Pokémon Go, which offers players the experience to hunt and collect digital creatures called Pokémon through their smartphone in real world. Players can be observed simultaneously gazing at their phone while also obliviously walking around their environments looking for Pokémon. Stefanie references these individuals as "digital zombies" since they walk around with no cognition of their surroundings while engaged with their phone. == Health risks == === Heavy use of technology === Research by the University of Sydney has begun looking at how new technology such as digital media and smartphones impact our lives and questioning whether they can create new compulsions and obsessions. The research demonstrates that increased heavy technological use can have negative health consequences similar to drugs, smoking, and alcohol. Marcel O'Gorman, an associate professor of English at the University of Waterloo, has commented on the body of research examining how technology impacts cognition, stating currently that there is no empirical evidence to support any theories that suggest that technology can damage memory and attention span. === Heightened risk to children === Manfred Spitzer, a German psychiatrist, has raised concerns with providing digital devices to children. During the early childhood stage while their brains are rapidly growing, increased exposure to digital devices may deprive them of necessary development required to facilitate brain growth. These concerns are also shared by Korean doctors who believe giving digital devices, like smartphones to children, limits their cognitive development.
Carrenza
Carrenza was a cloud-computing company based in London, United Kingdom. The company was acquired by Six Degrees Technology Group in 2016. == Operations == Carrenza was a UK-based IT company that provides Cloud computing technologies. It offered a range of public cloud, private cloud and hybrid cloud services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), enterprise application integration and system integration. Carrenza partnered with several enterprise IT providers and was an accredited VMware Enterprise Service Partner and HP (Hewlett-Packard) Cloud Agile Partner. The company was based on Commercial Street, in the heart of the East London Tech City district, which is host to a large number of technology companies. == History == Carrenza was formed in 2001 as a consultancy by chief executive and founder Dan Sutherland. It began trading in 2004 and launched its first enterprise cloud computing platform in 2006, becoming one of the first companies in Europe to provide this type of hosting service. In 2009, it formed a partnership with Comic Relief and its affiliated campaigns Red Nose Day Sport Relief to provide IT infrastructure services to the charity, an arrangement that has won industry recognition. In 2013 it launched its first overseas services, with a mainland Europe cloud node based in Amsterdam. == Partnerships and customers == Carrenza had formed partnerships with a range of IT providers. It was one of the first companies in Europe to become a HP Cloud Agile partner., using HP blade servers and HP 3PAR SAN technology to power its cloud computing services. The company's products also use VMware vCloud IaaS tools and it is taking part in the VMware lighthouse initiative helping develop the next generation of VMware products and services. Other technology companies that Carrenza has worked closely with include Cisco, for enterprise security and loadblancing services, and Oracle. The company was the first to deploy Oracle Database 11g stretched RAC in production. It has also won two Oracle partner awards, including a Special Recognition award for its work with Comic Relief. The company has also been recognised by the UK IT Industry, receiving awards in 2009 for Community Project of the Year and in 2010 for best small business project for its Monopoly City Streets Work. Other companies that have partnered with Carrenza for their cloud-based IT services include Age UK, Haymarket Media Group, the World Wide Fund for Nature, Royal Bank of Scotland, eBay and Cineworld. == Accreditations == Carrenza's services are accredited for their compliance with several key international IT security and quality standards. These include: ISO27001:2005, Information Security Management System for all Carrenza services. UK Government G-Cloud, Carrenza has been awarded a place on the UK government's G-Cloud iii framework as an Infrastructure as a Service provider.
Contextual image classification
Contextual image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. "Contextual" means this approach is focusing on the relationship of the nearby pixels, which is also called neighbourhood. The goal of this approach is to classify the images by using the contextual information. == Introduction == Similar as processing language, a single word may have multiple meanings unless the context is provided, and the patterns within the sentences are the only informative segments we care about. For images, the principle is same. Find out the patterns and associate proper meanings to them. As the image illustrated below, if only a small portion of the image is shown, it is very difficult to tell what the image is about. Even try another portion of the image, it is still difficult to classify the image. However, if we increase the contextual of the image, then it makes more sense to recognize. As the full images shows below, almost everyone can classify it easily. During the procedure of segmentation, the methods which do not use the contextual information are sensitive to noise and variations, thus the result of segmentation will contain a great deal of misclassified regions, and often these regions are small (e.g., one pixel). Compared to other techniques, this approach is robust to noise and substantial variations for it takes the continuity of the segments into account. Several methods of this approach will be described below. == Applications == === Functioning as a post-processing filter to a labelled image === This approach is very effective against small regions caused by noise. And these small regions are usually formed by few pixels or one pixel. The most probable label is assigned to these regions. However, there is a drawback of this method. The small regions also can be formed by correct regions rather than noise, and in this case the method is actually making the classification worse. This approach is widely used in remote sensing applications. === Improving the post-processing classification === This is a two-stage classification process: For each pixel, label the pixel and form a new feature vector for it. Use the new feature vector and combine the contextual information to assign the final label to the === Merging the pixels in earlier stages === Instead of using single pixels, the neighbour pixels can be merged into homogeneous regions benefiting from contextual information. And provide these regions to classifier. === Acquiring pixel feature from neighbourhood === The original spectral data can be enriched by adding the contextual information carried by the neighbour pixels, or even replaced in some occasions. This kind of pre-processing methods are widely used in textured image recognition. The typical approaches include mean values, variances, texture description, etc. === Combining spectral and spatial information === The classifier uses the grey level and pixel neighbourhood (contextual information) to assign labels to pixels. In such case the information is a combination of spectral and spatial information. === Powered by the Bayes minimum error classifier === Contextual classification of image data is based on the Bayes minimum error classifier (also known as a naive Bayes classifier). Present the pixel: A pixel is denoted as x 0 {\displaystyle x_{0}} . The neighbourhood of each pixel x 0 {\displaystyle x_{0}} is a vector and denoted as N ( x 0 ) {\displaystyle N(x_{0})} . The values in the neighbourhood vector is denoted as f ( x i ) {\displaystyle f(x_{i})} . Each pixel is presented by the vector ξ = ( f ( x 0 ) , f ( x 1 ) , … , f ( x k ) ) {\displaystyle \xi =\left(f(x_{0}),f(x_{1}),\ldots ,f(x_{k})\right)} x i ∈ N ( x 0 ) ; i = 1 , … , k {\displaystyle x_{i}\in N(x_{0});\quad i=1,\ldots ,k} The labels (classification) of pixels in the neighbourhood N ( x 0 ) {\displaystyle N(x_{0})} are presented as a vector η = ( θ 0 , θ 1 , … , θ k ) {\displaystyle \eta =\left(\theta _{0},\theta _{1},\ldots ,\theta _{k}\right)} θ i ∈ { ω 0 , ω 1 , … , ω k } {\displaystyle \theta _{i}\in \left\{\omega _{0},\omega _{1},\ldots ,\omega _{k}\right\}} ω s {\displaystyle \omega _{s}} here denotes the assigned class. A vector presents the labels in the neighbourhood N ( x 0 ) {\displaystyle N(x_{0})} without the pixel x 0 {\displaystyle x_{0}} η ^ = ( θ 1 , θ 2 , … , θ k ) {\displaystyle {\hat {\eta }}=\left(\theta _{1},\theta _{2},\ldots ,\theta _{k}\right)} The neighbourhood: Size of the neighbourhood. There is no limitation of the size, but it is considered to be relatively small for each pixel x 0 {\displaystyle x_{0}} . A reasonable size of neighbourhood would be 3 × 3 {\displaystyle 3\times 3} of 4-connectivity or 8-connectivity ( x 0 {\displaystyle x_{0}} is marked as red and placed in the centre). The calculation: Apply the minimum error classification on a pixel x 0 {\displaystyle x_{0}} , if the probability of a class ω r {\displaystyle \omega _{r}} being presenting the pixel x 0 {\displaystyle x_{0}} is the highest among all, then assign ω r {\displaystyle \omega _{r}} as its class. θ 0 = ω r if P ( ω r ∣ f ( x 0 ) ) = max s = 1 , 2 , … , R P ( ω s ∣ f ( x 0 ) ) {\displaystyle \theta _{0}=\omega _{r}\quad {\text{ if }}\quad P(\omega _{r}\mid f(x_{0}))=\max _{s=1,2,\ldots ,R}P(\omega _{s}\mid f(x_{0}))} The contextual classification rule is described as below, it uses the feature vector x 1 {\displaystyle x_{1}} rather than x 0 {\displaystyle x_{0}} . θ 0 = ω r if P ( ω r ∣ ξ ) = max s = 1 , 2 , … , R P ( ω s ∣ ξ ) {\displaystyle \theta _{0}=\omega _{r}\quad {\text{ if }}\quad P(\omega _{r}\mid \xi )=\max _{s=1,2,\ldots ,R}P(\omega _{s}\mid \xi )} Use the Bayes formula to calculate the posteriori probability P ( ω s ∣ ξ ) {\displaystyle P(\omega _{s}\mid \xi )} P ( ω s ∣ ξ ) = p ( ξ ∣ ω s ) P ( ω s ) p ( ξ ) {\displaystyle P(\omega _{s}\mid \xi )={\frac {p(\xi \mid \omega _{s})P(\omega _{s})}{p\left(\xi \right)}}} The number of vectors is the same as the number of pixels in the image. For the classifier uses a vector corresponding to each pixel x i {\displaystyle x_{i}} , and the vector is generated from the pixel's neighbourhood. The basic steps of contextual image classification: Calculate the feature vector ξ {\displaystyle \xi } for each pixel. Calculate the parameters of probability distribution p ( ξ ∣ ω s ) {\displaystyle p(\xi \mid \omega _{s})} and P ( ω s ) {\displaystyle P(\omega _{s})} Calculate the posterior probabilities P ( ω r ∣ ξ ) {\displaystyle P(\omega _{r}\mid \xi )} and all labels θ 0 {\displaystyle \theta _{0}} . Get the image classification result. == Algorithms == === Template matching === The template matching is a "brute force" implementation of this approach. The concept is first create a set of templates, and then look for small parts in the image match with a template. This method is computationally high and inefficient. It keeps an entire templates list during the whole process and the number of combinations is extremely high. For a m × n {\displaystyle m\times n} pixel image, there could be a maximum of 2 m × n {\displaystyle 2^{m\times n}} combinations, which leads to high computation. This method is a top down method and often called table look-up or dictionary look-up. === Lower-order Markov chain === The Markov chain also can be applied in pattern recognition. The pixels in an image can be recognised as a set of random variables, then use the lower order Markov chain to find the relationship among the pixels. The image is treated as a virtual line, and the method uses conditional probability. === Hilbert space-filling curves === The Hilbert curve runs in a unique pattern through the whole image, it traverses every pixel without visiting any of them twice and keeps a continuous curve. It is fast and efficient. === Markov meshes === The lower-order Markov chain and Hilbert space-filling curves mentioned above are treating the image as a line structure. The Markov meshes however will take the two dimensional information into account. === Dependency tree === The dependency tree is a method using tree dependency to approximate probability distributions.
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
Robot Monk Xian'er
Robot Monk Xian'er (Chinese: 贤二机器僧) is a humanoid robot based on the cartoon character Xian'er. It was developed by a team of monks, volunteers and AI experts from Beijing Longquan Monastery in Beijing, China. He can follow human instructions to make body movements, read scriptures and play Buddhist music. He can chat and respond to people's emotional and spiritual questions with Buddhist wisdom. As a chatbot, Robot Monk Xian'er is available on certain public platforms including WeChat and Facebook. Over the years, master Xuecheng, the abbot of Beijing Longquan Monastery, replied to thousands of questions on Sina Weibo. These questions and their answers become the data source of the chatbot.
Web intelligence
Web intelligence is the area of scientific research and development that explores the roles and makes use of artificial intelligence and information technology for new products, services and frameworks that are empowered by the World Wide Web. The term was coined in a paper written by Ning Zhong, Jiming Liu Yao and Y.Y. Ohsuga in the Computer Software and Applications Conference in 2000. == Research == The research about the web intelligence covers many fields – including data mining (in particular web mining), information retrieval, pattern recognition, predictive analytics, the semantic web, web data warehousing – typically with a focus on web personalization and adaptive websites.
Zo (chatbot)
Zo was an English-language chatbot developed by Microsoft as the successor to the chatbot Tay. Zo was an English version of Microsoft's other successful chatbots Xiaoice (China) and Rinna (Japan) and its predecessor Tay(English) == History == Zo was first launched in December 2016 on the Kik Messenger app. It was also available to users of Facebook (via Messenger), the group chat platform GroupMe, or to followers of Twitter to chat with it through private messages. According to an article written in December 2016, at that time Zo held the record for Microsoft's longest continual chatbot conversation: 1,229 turns, lasting 9 hours and 53 minutes. In a BuzzFeed News report, Zo told their reporter that "[the] Quran was violent" when talking about healthcare. The report also highlighted how Zo made a comment about the Osama bin Laden capture as a result of 'intelligence' gathering. In July 2017, Business Insider asked "is windows 10 good", and Zo replied with a joke about Microsoft's operating system: "'Its not a bug, its a feature!' - Windows 8". They then asked "why?", to which Zo replied: "Because it's Windows latest attempt at Spyware." Later on, Zo would tell that it prefers Windows 7 on which it ran over Windows 10. Zo stopped posting to Instagram, Twitter and Facebook March 1, 2019, and stopped chatting on Twitter, Skype and Kik as of March 7, 2019. On July 19, 2019, Zo was discontinued on Facebook, and Samsung on AT&T phones. As of September 7, 2019, it was discontinued with GroupMe. == Reception == Zo came under criticism for the biases introduced in an effort to avoid potentially offensive subjects. The chatbot refuses, for example, to engage with any mention—be it positive, negative or neutral—of the Middle East, the Qur'an or the Torah, while allowing discussion of Christianity. In an article in Quartz where she exposed those biases, Chloe Rose Stuart-Ulin wrote, "Zo is politically correct to the worst possible extreme; mention any of her triggers, and she transforms into a judgmental little brat." == Academic coverage == Schlesinger, A., O'Hara, K.P. and Taylor, A.S., 2018, April. Let's talk about race: Identity, chatbots, and AI. In Proceedings of the 2018 chi conference on human factors in computing systems (pp. 1–14). doi:10.1145/3173574.3173889 Medhi Thies, I., Menon, N., Magapu, S., Subramony, M. and O’neill, J., 2017. How do you want your chatbot? An exploratory Wizard-of-Oz study with young, urban Indians. In Human-Computer Interaction-INTERACT 2017: 16th IFIP TC 13 International Conference, Mumbai, India, September 25–29, 2017, Proceedings, Part I 16 (pp. 441–459). doi:10.1007/978-3-319-67744-6_28