Alexei "Alyosha" A. Efros (born 9 April 1975) is a Russian-American computer scientist and professor at University of California, Berkeley. He has contributed to the field of computer vision, and his work has been referenced in Wired, BBC News, The New York Times, and The New Yorker. == Early life and education == Efros was born in St. Petersburg in the Soviet Union. His father is Alexei L. Efros, then a physics professor at the Ioffe Physico-Technical Institute. His family emigrated to the United States when he was 14 to accommodate his father's career and the family settled in Salt Lake City in 1991. He graduated from the University of Utah in 1997, and attended University of California, Berkeley for his PhD, where he was advised by Jitendra Malik and graduated in 2003. He then spent a year as a research fellow at the University of Oxford, where he worked with Andrew Zisserman. == Career == Efros joined the faculty at Carnegie Mellon University in Pittsburgh, where he remained until 2013 when he joined the faculty of the University of California, Berkeley. He received a Guggenheim Fellowship in 2008. He received the 2016 ACM Prize in Computing.
Enterprise cognitive system
Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguous, multi-faceted, and fast-evolving, and what to do in such a situation is usually assessed today by the business user. An ECS is designed to synthesize a business context and link it to the desired outcome. It recommends evidence-based actions to help the end-user achieve the desired outcome. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome. While general-purpose cognitive systems can be used for different outputs, prescriptive, suggestive, instructive, or simply entertaining, an enterprise cognitive system is focused on action, not insight, to help in assessing what to do in a complex situation. == Key characteristics == ECS have to be: Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time. In the Enterprise, near-real time learning from data requires an agile information federation approach to ingest incremental data updates as they occur, and an unsupervised learning approach to ensure that new best practice is leveraged across the organization in a timely manner. Interactive: They must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and Cloud services, as well as with people. In the Enterprise, interactions are controlled via existing workflows and UIs. Therefore, embedding best practices directly into these existing interfaces, in the context of a specific step, is critical to ensure maximum end-user adoption. Iterative and stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. In the Enterprise, business context is often structured by a business process, and therefore sufficiently data-rich to make relevant recommendations without significant iterations from the end-user. A stateful memory of overall interactions across communication channels is critical for understanding of context, as a static profile will not capture intent and outcome potential the way behavior does. Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user's profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). In the Enterprise, Context is fragmented and must be aggregated across data types, sources, and locations. In most business environments, such data is captured in existing enterprise information systems, and the effort is linked to quickly source and unify such information. It is rare to have to directly process sensor, audio or visual data in real-time as direct input into the enterprise cognitive system. Instead, these data types are captured by Enterprise Applications and pre-processed into a binary or text format prior to consumption by the System. == Business applications powered by an ECS == Bottlenose – trends and brands monitoring Cybereason – security threat monitoring Dataminr – social media monitoring
European Society for Fuzzy Logic and Technology
The European Society for Fuzzy Logic and Technology (EUSFLAT) is a scientific association with the aims to disseminate and promote fuzzy logic and related subjects (sometimes comprised under the collective terms soft computing or computational intelligence) and to provide a platform for exchange between scientists and engineers working in these fields. The society is both open for academic and industrial members. == History == EUSFLAT was founded in 1998 in Spain as the successor of the National Spanish Fuzzy Logic Society, ESTYLF, with the aim to open the society for members from other European countries. Since then, the society managed to attract a large share of members from outside Spain, and even beyond Europe, with the Spanish members still being the largest group inside EUSFLAT. For these historical reasons, the society is officially registered in Spain. == Conferences == Starting with 1999, EUSFLAT has been organizing its biannual conferences in odd years. Previous meetings: Palma de Mallorca, Balearic Islands, Spain, September 22–25, 1999 (jointly with National Spanish conference, ESTYLF) Leicester, United Kingdom, September 5–7, 2001 Zittau, Germany, September 10–12, 2003 Barcelona, Catalonia, Spain, September 7–9, 2005 (jointly with 11th Rencontres Francophones sur la Logique Floue et ses Applications) Ostrava, Czech Republic, September 11–14, 2007 Lisbon, Portugal, July 20–24, 2009 (jointly with 13th World Congress of the International Fuzzy Systems Association) Aix-les-Bains, France, July 18–22, 2011 (jointly with Les Rencontres Francophones sur la Logique Floue et ses Applications) Milan, Italy, September 11–13, 2013 Gijón, Spain, June, 30–3 July 2015 == Publications == EUSFLAT publishes the proceedings of its conferences in an open access manner. Until 2010, Mathware & Soft Computing was the official journal of EUSFLAT. On July 1, 2010, the International Journal of Computational Intelligence Systems (Atlantis Press, ISSN 1875-6891 (print) / ISSN 1875-6883 (on-line)) became the official journal of EUSFLAT. EUSFLAT publishes an electronic newsletter with three issues a year. == Presidents == EUSFLAT is led by the President, who is elected for a two-year period, and cannot serve for more than two consecutive periods. Francesc Esteva (1998–2011) Luis Magdalena (2001–2005) Ulrich Bodenhofer (2005–2009) Javier Montero (2009–2013) Gabriella Pasi (2013–present)
Squirrel AI
Squirrel Ai Learning is an international educational technology company that specializes in intelligent adaptive learning and was one of the first companies in the world to offer large scale AI-powered adaptive education solutions. == Methodology == Squirrel Ai Learning uses artificial intelligence to tailor lesson plans to each individual student. The company's AI researchers have access to the world's largest student databases, which are used to train the AI algorithms. Squirrel Ai Learning works with teachers to identify the most fine-grained possible concepts ("knowledge points") for a course in order to precisely target learning gaps. For example, middle school mathematics is broken into over 10,000 points such as rational numbers, the properties of a triangle, and the Pythagorean theorem. Each point is linked to related items, forming a "knowledge graph". Each knowledge point is addressed by videos, examples and practice problems. A textbook might address 3,000 points; ALEKS, another adaptive learning platform, uses 1,000. Each student begins with a diagnostic test to identify where to begin their learning. The system continues to refine its graph as more students proceed. Learning is not student-directed. The system decides the order of topics. == History and milestones == Squirrel Ai Learning was founded by Derek Haoyang Li in 2014. In March, 2017, The Squirrel Ai Intelligent Adaptive Learning System (IALS) was launched. IALS utilizes artificial intelligence to customize lessons, practice and evaluations for each individual student. In 2018, Squirrel Ai Learning established a joint research lab of AI adaptive learning with the institute of Automation of the Chinese Academy of Sciences. By 2019, Squirrel Ai Learning had opened 2,000 learning centers in 200 cities and registered over a million students in Asia. In 2019, Squirrel Ai Learning opened a research lab in partnership with Carnegie Mellon University. As of 2019, Squirrel Ai Learning had raised over $180 million in funding and in 2018 it surpassed $1 billion in valuation. In 2020, Squirrel Ai Learning launched the $1 million AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity in partnership with AAAI. The inaugural award was given to Regina Barzilay for her work developing machine learning models to address drug synthesis and early-stage breast cancer diagnosis. In 2020, Squirrel Ai Learning established strategic partnership with DingTalk, Alibaba Group. As of 2021, Squirrel Ai Learning had served over 60,000 public schools, in over 1200 cities in Asia. Squirrel Ai plans to start offering its services in the United States in 2026. The American arm is separate from the Chinese company to avoid regulatory hurdles. As of January 2026, it had set up an "independent technology platform" in the US. == Recognition == Squirrel Ai Learning has gained recognition both in Asia and internationally including: Squirrel Ai Learning was named one of the World's Top 30 AI application case in the 2018 Synced Machine Intelligence Awards. In June 2019, Squirrel Ai Learning was named as one of the 50 smartest companies in China by MIT technology review. Squirrel Ai Learning won the GITEX 2019 Best Education Technology Award. In 2020, Squirrel Ai Learning won the UNESCO AI Innovation Award. Squirrel Ai Learning was listed in the 2020 CB Insight's AI 100, CB Insights' annual ranking of the 100 most promising AI startups in the world. Squirrel Ai Learning won Edtech Review's Best AI in Education Company of the Year award 2020.
Residuated Boolean algebra
In mathematics, a residuated Boolean algebra is a residuated lattice whose lattice structure is that of a Boolean algebra. Examples include Boolean algebras with the monoid taken to be conjunction, the set of all formal languages over a given alphabet Σ {\displaystyle \Sigma } under concatenation, the set of all binary relations on a given set X {\displaystyle X} under relational composition, and more generally the power set of any equivalence relation, again under relational composition. The original application was to relation algebras as a finitely axiomatized generalization of the binary relation example, but there exist interesting examples of residuated Boolean algebras that are not relation algebras, such as the language example. == Definition == A residuated Boolean algebra is an algebraic structure ( L , ∧ , ∨ , ¬ , 0 , 1 , ∙ , I , / , ∖ ) {\displaystyle (L,\wedge ,\vee ,\neg ,0,1,\bullet ,\mathbf {I} ,/,\backslash )} such that An equivalent signature better suited to the relation algebra application is ( L , ∧ , ∨ , ¬ , 0 , 1 , ∙ , I , ▹ , ◃ ) {\displaystyle (L,\wedge ,\vee ,\neg ,0,1,\bullet ,\mathbf {I} ,\triangleright ,\triangleleft )} where the unary operations x ∖ {\displaystyle x\backslash } and x ▹ {\displaystyle x\triangleright } are intertranslatable in the manner of De Morgan's laws via x ∖ y = ¬ ( x ▹ ¬ y ) {\displaystyle x\backslash y=\neg (x\triangleright \neg y)} , x ▹ y = ¬ ( x ∖ ¬ y ) {\displaystyle x\triangleright y=\neg (x\backslash \neg y)} , and dually / y {\displaystyle /y} and ◃ y {\displaystyle \triangleleft y} as x / y = ¬ ( ¬ x ◃ y ) {\displaystyle x/y=\neg (\neg x\triangleleft y)} , x ◃ y = ¬ ( ¬ x / y ) {\displaystyle x\triangleleft y=\neg (\neg x/y)} , with the residuation axioms in the residuated lattice article reorganized accordingly (replacing z {\displaystyle z} by ¬ z {\displaystyle \neg z} ) to read ( x ▹ z ) ∧ y = 0 ⇔ ( x ∙ y ) ∧ z = 0 ⇔ ( z ◃ y ) ∧ x = 0 {\displaystyle (x\triangleright z)\wedge y=0\ \Leftrightarrow \ (x\bullet y)\wedge z=0\ \Leftrightarrow \ (z\triangleleft y)\wedge x=0} This De Morgan dual reformulation is motivated and discussed in more detail in the section below on conjugacy. Since residuated lattices and Boolean algebras are each definable with finitely many equations, so are residuated Boolean algebras, whence they form a finitely axiomatizable variety. == Examples == Any Boolean algebra, with the monoid multiplication ∙ {\displaystyle \bullet } taken to be conjunction and both residuals taken to be material implication x → y {\displaystyle x\to y} . Of the remaining 15 binary Boolean operations that might be considered in place of conjunction for the monoid multiplication, only five meet the monotonicity requirement, namely 0 , 1 , x , y {\displaystyle 0,1,x,y} and x ∨ y {\displaystyle x\vee y} . Setting y = z = 0 {\displaystyle y=z=0} in the residuation axiom y ≤ x ∖ z ⇔ x ∙ y ≤ z {\displaystyle y\leq x\backslash z\ \Leftrightarrow \ x\bullet y\leq z} , we have 0 ≤ x ∖ 0 ⇔ x ∙ 0 ≤ 0 {\displaystyle 0\leq x\backslash 0\ \Leftrightarrow \ x\bullet 0\leq 0} , which is falsified by taking x = 1 {\displaystyle x=1} when x ∙ y = 1 {\displaystyle x\bullet y=1} , x {\displaystyle x} , or x ∨ y {\displaystyle x\vee y} . The dual argument for z / y {\displaystyle z/y} rules out x ∙ y = y {\displaystyle x\bullet y=y} . This just leaves x ∙ y = 0 {\displaystyle x\bullet y=0} (a constant binary operation independent of x {\displaystyle x} and y {\displaystyle y} ), which satisfies almost all the axioms when the residuals are both taken to be the constant operation x / y = x ∖ y = 1 {\displaystyle x/y=x\backslash y=1} . The axiom it fails is x ∙ I = x = I ∙ x {\displaystyle x\bullet \mathbf {I} =x=\mathbf {I} \bullet x} , for want of a suitable value for I {\displaystyle \mathbf {I} } . Hence conjunction is the only binary Boolean operation making the monoid multiplication that of a residuated Boolean algebra. The power set 2 X 2 {\displaystyle 2^{X^{2}}} made a Boolean algebra as usual with ∩ {\displaystyle \cap } , ∪ {\displaystyle \cup } and complement relative to X 2 {\displaystyle X^{2}} , and made a monoid with relational composition. The monoid unit I {\displaystyle \mathbf {I} } is the identity relation { ( x , x ) | x ∈ X } {\displaystyle \{(x,x)|x\in X\}} . The right residual R ∖ S {\displaystyle R\backslash S} is defined by x ( R ∖ S ) y ⇔ ∀ z ∈ X , z R x ⇒ z S y {\displaystyle x(R\backslash S)y\ \Leftrightarrow \ \forall z\in X,zRx\Rightarrow zSy} . Dually the left residual S / R {\displaystyle S/R} is defined by y ( S / R ) x ⇔ ∀ z ∈ X , x R z ⇒ y S z {\displaystyle y(S/R)x\ \Leftrightarrow \ \forall z\in X,xRz\Rightarrow ySz} . The power set 2 Σ ∗ {\displaystyle 2^{\Sigma ^{}}} made a Boolean algebra as for Example 2, but with language concatenation for the monoid. Here the set Σ {\displaystyle \Sigma } is used as an alphabet while Σ ∗ {\displaystyle \Sigma ^{}} denotes the set of all finite (including empty) words over that alphabet. The concatenation L M {\displaystyle LM} of languages L {\displaystyle L} and M {\displaystyle M} consists of all words u v {\displaystyle uv} such that u ∈ L {\displaystyle u\in L} and v ∈ M {\displaystyle v\in M} . The monoid unit is the language { ε } {\displaystyle \{\varepsilon \}} consisting of just the empty word ε {\displaystyle \varepsilon } . The right residual M ∖ L {\displaystyle M\backslash L} consists of all words w {\displaystyle w} over Σ {\displaystyle \Sigma } such that M w ⊆ L {\displaystyle Mw\subseteq L} . The left residual L / M {\displaystyle L/M} is the same with w M {\displaystyle wM} in place of M w {\displaystyle Mw} . == Conjugacy == The De Morgan duals ▹ {\displaystyle \triangleright } and ◃ {\displaystyle \triangleleft } of residuation arise as follows. Among residuated lattices, Boolean algebras are special by virtue of having a complementation operation ¬ {\displaystyle \neg } . This permits an alternative expression of the three inequalities y ≤ x ∖ z ⇔ x ∙ y ≤ z ⇔ x ≤ z / y {\displaystyle y\leq x\backslash z\ \Leftrightarrow \ x\bullet y\leq z\ \Leftrightarrow \ x\leq z/y} in the axiomatization of the two residuals in terms of disjointness, via the equivalence x ≤ y ⇔ x ∧ ¬ y = 0 {\displaystyle x\leq y\ \Leftrightarrow \ x\wedge \neg y=0} . Abbreviating x ∧ y = 0 {\displaystyle x\wedge y=0} to x # y {\displaystyle x\#y} as the expression of their disjointness, and substituting ¬ z {\displaystyle \neg z} for z {\displaystyle z} in the axioms, they become with a little Boolean manipulation ¬ ( x ∖ ¬ z ) # y ⇔ x ∙ y # z ⇔ ¬ ( ¬ z / y ) # x {\displaystyle \neg (x\backslash \neg z)\#y\ \Leftrightarrow \ x\bullet y\#z\ \Leftrightarrow \ \neg (\neg z/y)\#x} Now ¬ ( x ∖ ¬ z ) {\displaystyle \neg (x\backslash \neg z)} is reminiscent of De Morgan duality, suggesting that x ∖ {\displaystyle x\backslash } be thought of as a unary operation f {\displaystyle f} , defined by f ( y ) = x ∖ y {\displaystyle f(y)=x\backslash y} , that has a De Morgan dual ¬ f ( ¬ y ) {\displaystyle \neg f(\neg y)} , analogous to ∀ x ϕ ( x ) = ¬ ∃ x ¬ ϕ ( x ) {\displaystyle \forall x\phi (x)=\neg \exists x\neg \phi (x)} . Denoting this dual operation as x ▹ {\displaystyle x\triangleright } , we define x ▹ z {\displaystyle x\triangleright z} as ¬ x ∖ ¬ z {\displaystyle \neg x\backslash \neg z} . Similarly we define another operation z ◃ y {\displaystyle z\triangleleft y} as ¬ ( ¬ z / y ) {\displaystyle \neg (\neg z/y)} . By analogy with x ∖ {\displaystyle x\backslash } as the residual operation associated with the operation x ∙ {\displaystyle x\bullet } , we refer to x ▹ {\displaystyle x\triangleright } as the conjugate operation, or simply conjugate, of x ∙ {\displaystyle x\bullet } . Likewise ◃ y {\displaystyle \triangleleft y} is the conjugate of ∙ y {\displaystyle \bullet y} . Unlike residuals, conjugacy is an equivalence relation between operations: if f {\displaystyle f} is the conjugate of g {\displaystyle g} then g {\displaystyle g} is also the conjugate of f {\displaystyle f} , i.e. the conjugate of the conjugate of f {\displaystyle f} is f {\displaystyle f} . Another advantage of conjugacy is that it becomes unnecessary to speak of right and left conjugates, that distinction now being inherited from the difference between x ∙ {\displaystyle x\bullet } and ∙ x {\displaystyle \bullet x} , which have as their respective conjugates x ▹ {\displaystyle x\triangleright } and ◃ x {\displaystyle \triangleleft x} . (But this advantage accrues also to residuals when x ∖ {\displaystyle x\backslash } is taken to be the residual operation to x ∙ {\displaystyle x\bullet } .) All this yields (along with the Boolean algebra and monoid axioms) the following equivalent axiomatization of a residuated Boolean algebra. y # x ▹ z ⇔ x ∙ y # z ⇔ x # z ◃ y {\displaystyle y\#x\triangleright z\ \Leftrightarrow \ x\bullet y\#z\ \Leftrightarrow \ x\#z\triangleleft y} With this signature it remains the case that this axiomatization can be expressed as
ShowDocument
ShowDocument is an online web application that allows multiple users to conduct web meetings, upload, share and review documents from remote locations. The service was developed by the HBR Labs company, established in 2007. == Features == Users can collaborate on and review documents in real time, with annotations and text being visible to all users and accessible for co-editing. The idea of every user being able to annotate can cause conflicts within the sessions, and so main navigation options are under the "presenter"'s control - which can be given to a different user as well. An earlier version of the application, by contrast, had allowed all users to navigate and edit at once, causing the system to drop all incomplete edits. It is possible to draw and write on a virtual whiteboard, and to stream a YouTube video to a group in full synchronization. A feature also exists for co-browsing of Google Maps. Entering an open session in the application can be done with a given code number, or by receiving a link through an Email message. Different file formats can be uploaded and saved either online or offline, such as PDF. A PDF file's text cannot be edited - text is edited through the separate text editor. Although the platform contains a text chat, it is not intended to replace instant messaging software, as there are no extensive messaging features. The application has a paid and free version, with the free version having a few limitations: audio and video options are disabled, number of participants is limited and sessions are time-limited. == Development == ShowDocument was first developed in 2007. On September 8, 2009, HBR labs released a new update which included features such as secure online document storage and mobile device support.
The Machine That Won the War (short story)
"The Machine That Won the War" is a science fiction short story by American writer Isaac Asimov. The story first appeared in the October 1961 issue of The Magazine of Fantasy & Science Fiction, and was reprinted in the collections Nightfall and Other Stories (1969) and Robot Dreams (1986). It was also printed in a contemporary edition of Reader's Digest, illustrated. It is one of a loosely connected series of such stories concerning a fictional supercomputer called Multivac. == Plot summary == Three influential leaders of the human race meet in the aftermath of a successful war against the Denebians. Discussing how the vast and powerful Multivac computer was a decisive factor in the war, each of the men admits that in fact, he falsified his part of the decision process because he felt that the situation was too complex to follow normal procedures. John Henderson, Multivac's Chief Programmer, admits that he altered the data being fed to Multivac, since the populace could not be trusted to report accurate information in the current situation. Max Jablonski then admits that he altered the data that Multivac produced, since he knew that Multivac was not in good working order due to manpower and spare parts shortage. Finally, Lamar Swift, executive director of the Solar Federation, reveals that he had not trusted the reports produced by Multivac, and had made the final decisions purely on the toss of a coin.