Weibo

Weibo (Chinese: 微博; pinyin: Wēibó), or Sina Weibo (Chinese: 新浪微博; pinyin: Xīnlàng Wēibó), is a Chinese microblogging (weibo) website. Launched by Sina Corporation on 14 August 2009, it is one of the biggest social media platforms in China, with over 582 million monthly active users (252 million daily active users) as of Q1 2022. The platform has been highly successful but has faced criticism for heavy censorship. Sina had gone public on the Nasdaq in 2000. In March 2014, Sina announced a spinoff of Weibo and filed an IPO under the symbol WB. Sina carved out 11% of Weibo in the IPO, with Alibaba owning 32% post-IPO. The company began trading publicly on 17 April 2014. In March 2017, Sina launched Sina Weibo International Version. In November 2018, Sina Weibo suspended its registration function for minors under the age of 14. In July 2019, Sina Weibo announced that it would launch a two-month campaign to clean up pornographic and vulgar information, named "Project Deep Blue" (蔚蓝计划). On 29 September 2020, the company announced it would go private again due to rising tensions between the US and China. == Name == "Weibo" (微博) is the Chinese word for "microblog". Sina Weibo launched its new domain name weibo.com on 7 April 2011, deactivating and redirecting from the old domain, t.sina.com.cn, to the new one. Due to its popularity, the media sometimes refers to the platform simply as "Weibo", despite the numerous other Chinese microblogging services including Tencent Weibo, Sohu Weibo, and NetEase Weibo. However, the latter three have stopped providing services. == Background == Sina Weibo is a platform based on fostering user relationships to share, disseminate, and receive information. Through the website or the mobile app, users can upload pictures and videos publicly for instant sharing, with other users being able to comment with text, pictures and videos, or use a multimedia instant messaging service. The company initially invited a large number of celebrities to join the platform at the beginning and has since invited many media personalities, government departments, businesses and non-governmental organizations to open accounts for the purpose of publishing and communicating information. To avoid the impersonation of celebrities, Sina Weibo uses verification symbols; celebrity accounts have an orange letter "V" and organizations' accounts have a blue letter "V". Sina Weibo has more than 500 million registered users; out of these, 313 million are monthly active users, 85% use the Weibo mobile app, 70% are college-aged, 50.10% are male and 49.90% are female. There are over 100 million messages posted by users each day. With more than 100 million followers, actress Xie Na holds the record for the most followers on the platform. Despite fierce competition among Chinese social media platforms, Sina Weibo remains the most popular. == History == After the July 2009 Ürümqi riots, China shut down most domestic microblogging services, including Fanfou, the very first weibo service. Many popular non-China-based microblogging services like Twitter, Facebook, and Plurk have since been blocked. Sina Corporation CEO Charles Chao considered this to be an opportunity, and on 14 August 2009, Sina launched the tested version of Sina Weibo. Basic functions including message, private message, comment and reposting were made available that September. A Sina Weibo–compatible API platform for developing third-party applications was launched on 28 July 2010. On 1 December 2010, the website experienced an outage, which administrators later said was due to the ever-increasing numbers of users and posts. Registered users surpassed 100 million in February 2011. Since 23 March 2011, t.cn has been used as Sina Weibo's official shortened URL in lieu of sinaurl.cn. On 7 April 2011, weibo.com replaced t.sina.com.cn as the new main domain name used by the website. The official logo was also updated. In June 2011, Sina announced an English-language version of Sina Weibo would be developed and launched, though content would still be governed by Chinese law. On 11 January 2013, Sina Weibo and Alibaba China (a subsidiary of Alibaba Group) signed a strategic cooperation agreement. With more and more foreign celebrities using Sina Weibo, language translation has become an urgent need for Chinese users who wish to communicate with their idols online, especially Korean. In January 2013, Sina Weibo and NetEase.com announced that they had reached a strategic cooperation agreement. When users browse foreign language content, they can now directly obtain translation results through the YouDao Dictionary. The Sina Weibo financial report in February 2013 showed that its total revenue was approximately US$66 million and that the number of registered users had exceeded the 500 million mark. In April 2013, Sina officially announced that Sina Weibo had signed a strategic cooperation agreement with Alibaba. The two sides conducted in-depth cooperation in areas such as user account interoperability, data exchange, online payment, and internet marketing. At the same time, Sina announced that Alibaba, through its wholly owned subsidiary, had purchased the preferred shares and common shares issued by Sina Weibo Company for US$586 million, which accounted for approximately 18% of Weibo's fully diluted and diluted total shares. === Ownership === On 9 April 2013, Alibaba Group announced that it would acquire 18% of Sina Weibo for US$586 million, with the option to buy up to 30% in the future. Alibaba exercised this option when Weibo was listed on the NASDAQ in April 2014. == Users == According to iResearch's report on 30 March 2011, Sina Weibo had 56.5% of China's microblogging market based on active users and 86.6% based on browsing time over competitors such as Tencent Weibo and Baidu. According to research by Sina Corporation, the number of active users reached over 400 million by Q1 2018, making Sina Weibo the 7th platform with at least 400 million active users, and daily usage increased by 21%. As of 2017, approximately 80% of its users were in their 20s and 30s. The top 100 users had over 485 million followers combined. More than 5,000 companies and 2,700 media organizations in China use Sina Weibo. The site is maintained by a growing microblogging department of 200 employees responsible for technology, design, operations, and marketing. Sina executives invited and persuaded many Chinese celebrities to join the platform. Users now include Asian celebrities, movie stars, singers, famous business and media figures, athletes, scholars, artists, organizations, religious figures, government departments, and officials from Hong Kong, Mainland China, Malaysia, Singapore, Taiwan, and Macau, as well as some famous foreign individuals and organizations, including Kevin Rudd, Boris Johnson, David Cameron, Narendra Modi, Toshiba, and the Germany national football team. Sina Weibo has a verification program for known people and organizations. Once an account is verified, a verification badge is added beside the account name. == Features == Many of Sina Weibo's features resemble those of Twitter. A user may post with a 140-character limit (increased to 2,000 as of January 2016 with the exception of reposts and comments). An analysis of 29 million Weibo posts found the median length was 14 characters. Users may mention or talk to other people using "@UserName" formatting, add hashtags, follow other users to make their posts appear in one's own timeline, re-post with "//@UserName" similar to Twitter's retweet function "RT @UserName", select posts for one's favorites list, and verify the account if the user is a celebrity, brand, business or otherwise of public interest. URLs are automatically shortened using the domain name t.cn, akin to Twitter's t.co. Official and third-party applications can access Sina Weibo from other websites or platforms. Users may: Submit up to 18 images/video files in every post Send personal messages to followers Follow others and be followed Post "stories" like on Instagram React to posts using different emojis Receive monetary rewards that can be used in a digital store linked to Weibo View posts identified as "hot" or popular Display the location they post from Hashtags differ slightly between Sina Weibo and Twitter, using the double-hashtag "#HashName#" format (the lack of spacing between Chinese characters necessitates a closing tag). Users can own a hashtag by requesting hashtag monitoring; the company reviews these requests and responds within one to three days. Once a user owns a hashtag, they have access to a wide variety of functions available only to them on the condition that they remain active (less than 1 post per calendar week revokes these privileges). Additionally, comments appear as a list below each post. A commenter can also choose to re-post the comment, quoting the whole original post, to their own page. Unregistered users can only browse a few post

Document mosaicing

Document mosaicing is a process that stitches multiple, overlapping snapshot images of a document together to produce one large, high resolution composite. The document is slid under a stationary, over-the-desk camera by hand until all parts of the document are snapshotted by the camera's field of view. As the document slid under the camera, all motion of the document is coarsely tracked by the vision system. The document is periodically snapshotted such that the successive snapshots are overlap by about 50%. The system then finds the overlapped pairs and stitches them together repeatedly until all pairs are stitched together as one piece of document. The document mosaicing can be divided into four main processes. Tracking Feature detecting Correspondences establishing Images mosaicing. == Tracking (simple correlation process) == In this process, the motion of the document slid under the camera is coarsely tracked by the system. Tracking is performed by a process called simple correlation process. In the first frame of snapshots, a small patch is extracted from the center of the image as a correlation template. The correlation process is performed in the four times size of the patch area of the next frame. The motion of the paper is indicated by the peak in the correlation function. The peak in the correlation function indicates the motion of the paper. The template is resampled from this frame and the tracking continues until the template reaches the edge of the document. After the template reaches the edge of the document, another snapshot is taken and the tracking process performs repeatedly until the whole document is imaged. The snapshots are stored in an ordered list to facilitate pairing the overlapped images in later processes. == Feature detecting for efficient matching == Feature detection is the process of finding the transformation that aligns one image with another. There are two main approaches for feature detection. Feature-based approach : Motion parameters are estimated from point correspondences. This approach is suitable for the case that there is plenty supply of stable and detectable features. Featureless approach : When the motion between the two images is small, the motion parameters are estimated using optical flow. On the other hand, when the motion between the two images is large, the motion parameters are estimated using generalised cross-correlation. However, this approach requires a computationally expensive resources. Each image is segmented into a hierarchy of columns, lines, and words to match the organised sets of features across images. Skew angle estimation and columns, lines and words finding are the examples of feature detection operations. === Skew angle estimation === Firstly, the angle that the rows of text make with the image raster lines (skew angle) is estimated. It is assumed to lie in the range of ±20°. A small patch of text in the image is selected randomly and then rotated in the range of ±20° until the variance of the pixel intensities of the patch summed along the raster lines is maximised. To ensure that the found skew angle is accurate, the document mosaic system performs calculation at many image patches and derive the final estimation by finding the average of the individual angles weighted by the variance of the pixel intensities of each patch. === Columns, lines and words finding === In this operation, the de-skewed document is intuitively segmented into a hierarchy of columns, lines and words. The sensitivity to illumination and page coloration of the de-skewed document can be removed by applying a Sobel operator to the de-skewed image and thresholding the output to obtain the binary gradient, de-skewed image. The operation can be roughly separated into 3 steps: column segmentation, line segmentation and word segmentation. Columns are easily segmented from the binary gradient, de-skewed images by summing pixels vertically. Baselines of each row are segmented in the same way as the column segmentation process but horizontally. Finally, individual words are segmented by applying the vertical process at each segmented row. These segmentations are important because the document mosaic is created by matching the lower right corners of words in overlapping images pair. Moreover, the segmentation operation can organize the list of images in the context of a hierarchy of rows and column reliably. The segmentation operation involves a considerable amount of summing in the binary gradient, de-skewed images, which done by construct a matrix of partial sums whose elements are given by p i y = ∑ u = 1 i ∑ v = 1 j b u v {\displaystyle p_{iy}=\sum _{u=1}^{i}\sum _{v=1}^{j}b_{uv}} The matrix of partial sums is calculated in one pass through the binary gradient, de-skewed image. ∑ u = u 1 u 2 ∑ v = v 1 v 2 b u v = p u 2 v 2 + p u 1 v 1 − p u 1 v 2 − p u 2 v 1 {\displaystyle \sum _{u=u_{1}}^{u_{2}}\sum _{v=v_{1}}^{v_{2}}b_{uv}=p_{u_{2}v_{2}}+p_{u_{1}v_{1}}-p_{u_{1}v_{2}}-p_{u_{2}v_{1}}} == Correspondences establishing == The two images are now organized in hierarchy of linked lists in following structure : image=list of columns row=list of words column=list of row word=length (in pixels) At the bottom of the structure, the length of each word is recorded for establishing correspondence between two images to reduce to search only the corresponding structures for the groups of words with the matching lengths. === Seed match finding === A seed match finding is done by comparing each row in image1 with each row in image2. The two rows are then compared to each other by every word. If the length (in pixel) of the two words (one from image1 and one from image2) and their immediate neighbours agree with each other within a predefined tolerance threshold (5 pixels, for example), then they are assumed to match. The row of each image is assumed a match if there are three or more word matches between the two rows. The seed match finding operation is terminated when two pairs of consecutive row match are found. === Match list building === After finishing a seed match finding operation, the next process is to build the match list to generate the correspondences points of the two images. The process is done by searching the matching pairs of rows away from the seed row. == Images mosaicing == Given the list of corresponding points of the two images, finding the transformation of the overlapping portion of the images is the next process. Assuming a pinhole camera model, the transformation between pixels (u,v) of image 1 and pixels (u0, v0) of image 2 is demonstrated by a plane-to-plane projectivity. [ s u ′ s v ′ s ] = [ p 11 p 12 p 13 p 21 p 22 p 23 p 31 p 32 1 ] [ u v 1 ] E q .1 {\displaystyle \left[{\begin{array}{c}su'\\sv'\\s\end{array}}\right]=\left[{\begin{array}{ccc}p_{11}&p_{12}&p_{13}\\p_{21}&p_{22}&p_{23}\\p_{31}&p_{32}&1\end{array}}\right]\left[{\begin{array}{c}u\\v\\1\end{array}}\right]\qquad Eq.1} The parameters of the projectivity is found from four pairs of matching points. RANSAC regression technique is used to reject outlying matches and estimate the projectivity from the remaining good matches. The projectivity is fine-tuned using correlation at the corners of the overlapping portion to obtain four correspondences to sub-pixel accuracy. Therefore, image1 is then transformed into image2's coordinate system using Eq.1. The typical result of the process is shown in Figure 5. === Many images coping === Finally, the whole page composition is built up by mapping all the images into the coordinate system of an "anchor" image, which is normally the one nearest the page center. The transformations to the anchor frame are calculated by concatenating the pair-wise transformations found earlier. The raw document mosaic is shown in Figure 6. However, there might be a problem of non-consecutive images that are overlap. This problem can be solved by performing Hierarchical sub-mosaics. As shown in Figure 7, image1 and image2 are registered, as are image3 and image4, creating two sub-mosaics. These two sub-mosaics are later stitched together in another mosaicing process. == Applied areas == There are various areas that the technique of document mosaicing can be applied to such as : Text segmentation of images of documents Document Recognition Interaction with paper on the digital desk Video mosaics for virtual environments Image registration techniques == Relevant research papers == Huang, T.S.; Netravali, A.N. (1994). "Motion and structure from feature correspondences: A review". Proceedings of the IEEE. 82 (2): 252–268. doi:10.1109/5.265351. D.G. Lowe. [1] Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, Boston, 1985. Irani, M.; Peleg, S. (1991). "Improving resolution by image registration". CVGIP: Graphical Models and Image Processing. 53 (3): 231–239. doi:10.1016/1049-9652(91)90045-L. S2CID 4834546. Shivakumara, P.; Kumar, G. Hemantha; Guru, D. S.; Nagabhushan, P. (2006). "

Nondeterministic finite automaton

In automata theory, a finite-state machine is called a deterministic finite automaton (DFA), if each of its transitions is uniquely determined by its source state and input symbol, and reading an input symbol is required for each state transition. A nondeterministic finite automaton (NFA), or nondeterministic finite-state machine, does not need to obey these restrictions. In particular, every DFA is also an NFA. Sometimes the term NFA is used in a narrower sense, referring to an NFA that is not a DFA, but not in this article. Using the subset construction algorithm, each NFA can be translated to an equivalent DFA; i.e., a DFA recognizing the same formal language. Like DFAs, NFAs only recognize regular languages. NFAs were introduced in 1959 by Michael O. Rabin and Dana Scott, who also showed their equivalence to DFAs. NFAs are used in the implementation of regular expressions: Thompson's construction is an algorithm for compiling a regular expression to an NFA that can efficiently perform pattern matching on strings. Conversely, Kleene's algorithm can be used to convert an NFA into a regular expression (whose size is generally exponential in the input automaton). NFAs have been generalized in multiple ways, e.g., nondeterministic finite automata with ε-moves, finite-state transducers, pushdown automata, alternating automata, ω-automata, and probabilistic automata. Besides the DFAs, other known special cases of NFAs are unambiguous finite automata (UFA) and self-verifying finite automata (SVFA). == Informal introduction == There are at least two equivalent ways to describe the behavior of an NFA. The first way makes use of the nondeterminism in the name of an NFA. For each input symbol, the NFA transitions to a new state until all input symbols have been consumed. In each step, the automaton nondeterministically "chooses" one of the applicable transitions. If there exists at least one "lucky run", i.e. some sequence of choices leading to an accepting state after completely consuming the input, it is accepted. Otherwise, i.e. if no choice sequence at all can consume all the input and lead to an accepting state, the input is rejected. In the second way, the NFA consumes a string of input symbols, one by one. In each step, whenever two or more transitions are applicable, it "clones" itself into appropriately many copies, each one following a different transition. If no transition is applicable, the current copy is in a dead end, and it "dies". If, after consuming the complete input, any of the copies is in an accept state, the input is accepted, else, it is rejected. == Formal definition == For a more elementary introduction of the formal definition, see automata theory. === Automaton === An NFA is represented formally by a 5-tuple, ( Q , Σ , δ , q 0 , F ) {\displaystyle (Q,\Sigma ,\delta ,q_{0},F)} , consisting of a finite set of states Q {\displaystyle Q} , a finite set of input symbols called the alphabet Σ {\displaystyle \Sigma } , a transition function δ {\displaystyle \delta } : Q × Σ → P ( Q ) {\displaystyle Q\times \Sigma \rightarrow {\mathcal {P}}(Q)} , an initial (or start) state q 0 ∈ Q {\displaystyle q_{0}\in Q} , and a set of accepting (or final) states F ⊆ Q {\displaystyle F\subseteq Q} . Here, P ( Q ) {\displaystyle {\mathcal {P}}(Q)} denotes the power set of Q {\displaystyle Q} . === Recognized language === Given an NFA M = ( Q , Σ , δ , q 0 , F ) {\displaystyle M=(Q,\Sigma ,\delta ,q_{0},F)} , its recognized language is denoted by L ( M ) {\displaystyle L(M)} , and is defined as the set of all strings over the alphabet Σ {\displaystyle \Sigma } that are accepted by M {\displaystyle M} . Loosely corresponding to the above informal explanations, there are several equivalent formal definitions of a string w = a 1 a 2 . . . a n {\displaystyle w=a_{1}a_{2}...a_{n}} being accepted by M {\displaystyle M} : w {\displaystyle w} is accepted if a sequence of states, r 0 , r 1 , . . . , r n {\displaystyle r_{0},r_{1},...,r_{n}} , exists in Q {\displaystyle Q} such that: r 0 = q 0 {\displaystyle r_{0}=q_{0}} r i + 1 ∈ δ ( r i , a i + 1 ) {\displaystyle r_{i+1}\in \delta (r_{i},a_{i+1})} , for i = 0 , … , n − 1 {\displaystyle i=0,\ldots ,n-1} r n ∈ F {\displaystyle r_{n}\in F} . In words, the first condition says that the machine starts in the start state q 0 {\displaystyle q_{0}} . The second condition says that given each character of string w {\displaystyle w} , the machine will transition from state to state according to the transition function δ {\displaystyle \delta } . The last condition says that the machine accepts w {\displaystyle w} if the last input of w {\displaystyle w} causes the machine to halt in one of the accepting states. In order for w {\displaystyle w} to be accepted by M {\displaystyle M} , it is not required that every state sequence ends in an accepting state, it is sufficient if one does. Otherwise, i.e. if it is impossible at all to get from q 0 {\displaystyle q_{0}} to a state from F {\displaystyle F} by following w {\displaystyle w} , it is said that the automaton rejects the string. The set of strings M {\displaystyle M} accepts is the language recognized by M {\displaystyle M} and this language is denoted by L ( M ) {\displaystyle L(M)} . Alternatively, w {\displaystyle w} is accepted if δ ∗ ( q 0 , w ) ∩ F ≠ ∅ {\displaystyle \delta ^{}(q_{0},w)\cap F\not =\emptyset } , where δ ∗ : Q × Σ ∗ → P ( Q ) {\displaystyle \delta ^{}:Q\times \Sigma ^{}\rightarrow {\mathcal {P}}(Q)} is defined recursively by: δ ∗ ( r , ε ) = { r } {\displaystyle \delta ^{}(r,\varepsilon )=\{r\}} where ε {\displaystyle \varepsilon } is the empty string, and δ ∗ ( r , x a ) = ⋃ r ′ ∈ δ ∗ ( r , x ) δ ( r ′ , a ) {\displaystyle \delta ^{}(r,xa)=\bigcup _{r'\in \delta ^{}(r,x)}\delta (r',a)} for all x ∈ Σ ∗ , a ∈ Σ {\displaystyle x\in \Sigma ^{},a\in \Sigma } . In words, δ ∗ ( r , x ) {\displaystyle \delta ^{}(r,x)} is the set of all states reachable from state r {\displaystyle r} by consuming the string x {\displaystyle x} . The string w {\displaystyle w} is accepted if some accepting state in F {\displaystyle F} can be reached from the start state q 0 {\displaystyle q_{0}} by consuming w {\displaystyle w} . === Initial state === The above automaton definition uses a single initial state, which is not necessary. Sometimes, NFAs are defined with a set of initial states. There is an easy construction that translates an NFA with multiple initial states to an NFA with a single initial state, which provides a convenient notation. == Example == The following automaton M, with a binary alphabet, determines if the input ends with a 1. Let M = ( { p , q } , { 0 , 1 } , δ , p , { q } ) {\displaystyle M=(\{p,q\},\{0,1\},\delta ,p,\{q\})} where the transition function δ {\displaystyle \delta } can be defined by this state transition table (cf. upper left picture): State Input 0 1 p { p } { p , q } q ∅ ∅ {\displaystyle {\begin{array}{|c|cc|}{\bcancel {{}_{\text{State}}\quad {}^{\text{Input}}}}&0&1\\\hline p&\{p\}&\{p,q\}\\q&\emptyset &\emptyset \end{array}}} Since the set δ ( p , 1 ) {\displaystyle \delta (p,1)} contains more than one state, M is nondeterministic. The language of M can be described by the regular language given by the regular expression (0|1)1. All possible state sequences for the input string "1011" are shown in the lower picture. The string is accepted by M since one state sequence satisfies the above definition; it does not matter that other sequences fail to do so. The picture can be interpreted in a couple of ways: In terms of the above "lucky-run" explanation, each path in the picture denotes a sequence of choices of M. In terms of the "cloning" explanation, each vertical column shows all clones of M at a given point in time, multiple arrows emanating from a node indicate cloning, a node without emanating arrows indicating the "death" of a clone. The feasibility to read the same picture in two ways also indicates the equivalence of both above explanations. Considering the first of the above formal definitions, "1011" is accepted since when reading it M may traverse the state sequence ⟨ r 0 , r 1 , r 2 , r 3 , r 4 ⟩ = ⟨ p , p , p , p , q ⟩ {\displaystyle \langle r_{0},r_{1},r_{2},r_{3},r_{4}\rangle =\langle p,p,p,p,q\rangle } , which satisfies conditions 1 to 3. Concerning the second formal definition, bottom-up computation shows that δ ∗ ( p , ε ) = { p } {\displaystyle \delta ^{}(p,\varepsilon )=\{p\}} , hence δ ∗ ( p , 1 ) = δ ( p , 1 ) = { p , q } {\displaystyle \delta ^{}(p,1)=\delta (p,1)=\{p,q\}} , hence δ ∗ ( p , 10 ) = δ ( p , 0 ) ∪ δ ( q , 0 ) = { p } ∪ { } {\displaystyle \delta ^{}(p,10)=\delta (p,0)\cup \delta (q,0)=\{p\}\cup \{\}} , hence δ ∗ ( p , 101 ) = δ ( p , 1 ) = { p , q } {\displaystyle \delta ^{}(p,101)=\delta (p,1)=\{p,q\}} , and hence δ ∗ ( p , 1011 ) = δ ( p , 1 ) ∪ δ ( q , 1 ) = { p , q } ∪ { } {\displaystyle \delta ^{}(p,1011)=\delta (p,1)\cup \delta (q,1)=\{p,q\}\cup \{\}} ; since that set is

Yejin Choi

Yejin Choi (Korean: 최예진; born 1977) is the Dieter Schwarz Foundation Professor and Senior Fellow at the Department of Computer Science at Stanford University and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) respectively. Her research considers natural language processing and computer vision. == Early life and education == Choi is from South Korea. She attended Seoul National University. After earning a bachelor's degree in Computer Science, Choi moved to the United States, where she joined Cornell University as a graduate student. There she worked with Claire Cardie on natural language processing. After earning her doctorate, Choi joined Stony Brook University as an Assistant Professor of Computer Science. At Stony Brook University Choi developed a statistical technique to identify fake hotel reviews. == Research and career == In 2018 Choi joined the Allen Institute for AI. Her research looks to endow computers with a statistical understanding of written language. She became interested in neural networks and their application in artificial intelligence. She started to assemble a knowledge base that became known as the atlas of machine commonsense (ATOMIC). By the time she had finished the creation of ATOMIC, the language model generative Pre-trained Transformer 2 (GPT-2) had been released. ATOMIC does not make use of linguistic rules, but combines the representations of different languages within a neural network. In 2020, Choi was endowed with the Brett Helsel Professorship, which she held until she became Chair of Computer Science in 2023. She has since made use of Commonsense Transformers (COMET) with Good old fashioned artificial intelligence (GOFAI). The approach combines symbolic reasoning and neural networks. She has developed computational models that can detect biases in language that work against people from underrepresented groups. For example, one study demonstrated that female film characters are portrayed as less powerful than their male counterparts. In 2023, Choi became The Wissner-Slivka Chair of Computer Science. Choi is also a scientific advisor to French research group Kyutai which is being funded by Xavier Niel, Rodolphe Saadé, Eric Schmidt, and others. In 2025, Stanford HAI announced the appointment of Choi as senior fellow and the Dieter Schwarz Foundation HAI Professor and Professor of Computer Science at Stanford University. == Awards and honours == 2013 International Conference on Computer Vision Marr Prize 2016 Institute of Electrical and Electronics Engineers AI One to Watch 2017 Facebook ParlAI Research Award 2018 Anita Borg Early Career Award 2020 Association for the Advancement of Artificial Intelligence Outstanding Paper Award 2021 Conference on Neural Information Processing Systems Outstanding Paper Award 2021 Association for Computational Linguistics Test-of-time Paper Award 2021 Conference on Computer Vision and Pattern Recognition Longuet-Higgins Prize 2022 North American Chapter of the Association for Computational Linguistics Best Paper Award 2022 International Conference on Machine Learning Outstanding Paper Award 2022 MacArthur Fellowship 2023 Association for Computational Linguistics Best Paper Award 2023 TIME100 Archived 2024-12-27 at the Wayback Machine AI 2023 2023 Empirical Methods in Natural Language Processing Outstanding Paper Award 2025 Association for Computational Linguistics Outstanding Paper Award 2025 Association for Computational Linguistics Best Demo Paper Award 2025 TIME100 AI 2025 == Select publications == Ott, Myle; Choi, Yejin; Cardie, Claire; Hancock, Jeffrey T. (2011). "Finding Deceptive Opinion Spam by Any Stretch of the Imagination". Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, USA: Association for Computational Linguistics: 309–319. arXiv:1107.4557. Bibcode:2011arXiv1107.4557O. ISBN 9781932432879. S2CID 2510724. Kulkarni, Girish; Premraj, Visruth; Ordonez, Vicente; Dhar, Sagnik; Li, Siming; Choi, Yejin; Berg, Alexander C.; Berg, Tamara L. (2013). "BabyTalk: Understanding and Generating Simple Image Descriptions". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (12): 2891–2903. Bibcode:2013ITPAM..35.2891K. CiteSeerX 10.1.1.225.5228. doi:10.1109/TPAMI.2012.162. ISSN 1939-3539. PMID 22848128. Choi, Yejin; Cardie, Claire; Riloff, Ellen; Patwardhan, Siddharth (2005). "Identifying sources of opinions with conditional random fields and extraction patterns". Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. Morristown, NJ, USA: Association for Computational Linguistics. pp. 355–362. doi:10.3115/1220575.1220620.

The Best Free AI Code Generator for Beginners

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Vero (app)

Vero (stylized as VERO) is a social media platform and mobile app company. Vero markets itself as a social network free from advertisements, data mining and algorithms. == History == The app was founded by French-Lebanese billionaire Ayman Hariri who is the son of former Lebanese prime minister Rafic Hariri. The name is taken from the Italian word for true. The app launched officially in 2015 as an alternative to Facebook and their popular photo-blogging app Instagram. Within weeks of its release the app surged in popularity although users expressed mixed reports with some feeling confused about how the app worked. Cosplayers were early to adopt the app as their photo-sharing platform of choice, favouring the app's pinch and zoom magnification feature over Instagram's zoom feature. Other creative communities soon followed, and the app became popular with niche groups of makeup artists, tattoo artists, and skateboarders. In March 2018, Vero's popularity surged, partly helped by an exodus from Facebook and Instagram following the Cambridge Analytica data scandal. In the wake of the scandal, Vero devised an advertising campaign aimed at defected Facebook and Instagram users, hoping the app's policies and privacy settings would assuage concerns over sharing personal information on the internet. Within the space of one week, the app went from being a small service, akin to Ello or Peach, to being the most downloaded app in eighteen countries. In December 2020, Vero released its most significant update to date, Vero 2.0 which introduced new features including voice and video calls, game and app posts and bookmarks, and refinements to the UI. In October 2021, Vero introduced their Desktop app (beta) with multiple post options and a re-sizable multi-column feed. == Concept and funding == Vero's content feed resembles Instagram's although users can share a wider variety of content and the app has a chronological content feed whereas Facebook and Instagram's feeds are algorithm based. Vero's business plan is also distinct from similar social media apps. Whereas its competitors such as Facebook or Instagram make money from in-app advertising revenue and the sale of user data, Vero's business plan was to invite the first one million users to use the app for free then charge any subsequent users a subscription fee. The app was entirely funded by its founder and generated additional revenues by charging affiliate fees when someone buys a product they find on Vero. == Awards == Vero was recognized at the 2021 Webbys, being named as an Honoree in the Best Visual Design - Aesthetic Category. == Controversies == === Privacy === Vero has faced some criticism over the wording of their manifesto, in particular, the statement "Vero only collects the data we believe is necessary to provide users with a great experience and to ensure the security of their accounts." Because this policy does not explicitly state that the app will not sell data on to third parties some users fear that the need to monetise the app through data might prove too tempting. Users have also complained about not being able to delete their accounts. While this was never the case, the option was hidden deep in the app's settings. === Russian involvement === Although Vero remains transparent about the app's Russian development team, they have been caught up in concerns about Russian interference on social media platforms. The app's founder Ayman Hariri was quick to dismiss the remarks as xenophobic and defend the nationality of his employees, stating in an interview with Time Magazine; "At the end of the day, where people are from is really not how anybody should judge anyone". === Criticism of the app's founder === Until 2013, Vero's founder Ayman Harari was deputy CEO and chairman of Saudi Oger, the Saudi Arabian construction company which collapsed in 2017, mired by controversies over the welfare and treatment of their employees. However, Hariri is quick to point out that he divested from the firm in 2014 and the worker's rights violations occurred after he had left the company.

Lenhart Schubert

Lenhart Karl Otto Schubert is a professor of Computer Science at the University of Rochester, as well as a member of the Center for Language Sciences and the Center for Computation and the Brain. Schubert is a prominent researcher in the field of common sense reasoning. == Biography == Schubert received his Ph.D. from the University of Toronto in 1970. He was on the faculty of the University of Alberta between 1973 and 1988 and joined the faculty at the University of Rochester in 1988. He was elected fellow of Association for Advancement of Artificial Intelligence in 1993 for "fundamental contributions in NLP, esp. in the formalization, representation, and practical implementation of non-first order concepts".