Harold Borko (1922-2012) was an American psychologist and researcher working primarily in the field of information science. == Biography == Borko was born in 1922 in New York City, New York. After serving in the US Army from 1942 to 1946 he obtained a BA in Psychology from the University of California, Los Angeles in 1948 and both his MA and PhD from the University of Southern California in Psychology in 1952. He returned to the army as a psychologist until 1956 after which he began a career working in and teaching information science. He died in California in 2012. == Information Science Career == After leaving the military Borko began working at the RAND Corporation as a Systems Training Specialist in 1956 and moved to the Systems Development Corporation a year later working in the Language Processing and Retrieval department. Alongside this work he taught Psychology at USC from 1957-65 and then moved into teaching Library Science at UCLA from 1965. In 1967 Borko left his role at the Systems Development Corporation and continued as a full-time professor at UCLA until his retirement in 1993.. From 1961 to 1995 Borko authored and co-authored over 100 articles on new developments in the field as well as the historiography of information science. He served as an editor of the Journal of Educational Data Processing from 1963-1975 and as President of the American Society for Information Science in 1966 == Partial list of works == Borko, H. (1962, May). The construction of an empirically based mathematically derived classification system. In Proceedings of the May 1-3, 1962, spring joint computer conference (pp. 279-289). Borko, H., & Bernick, M. (1963). Automatic document classification. Journal of the ACM (JACM), 10(2), 151-162. Borko, H. (1964). The Storage and Retrieval of Educational Information. Journal of Teacher Education, 15(4), 449-452. Borko, H. (1964). Measuring the reliability of subject classification by men and machines. American Documentation, 15(4), 268-273. Borko, H. (1965). The conceptual foundations of information systems. Borko, H. (1968), Information science: What is it?†. Amer. Doc., 19: 3-5. https://doi.org/10.1002/asi.5090190103 Borko, H. (1970). Experiments in book indexing by computer. Information storage and retrieval, 6(1), 5-16. Borko, H. (1985). An introduction to computer-based library systems (Lucy A. Tedd). Education for Information, 3(1), 61.
Caffe (software)
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface. == History == Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in Spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on GitHub. == Features == Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. == Applications == Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework. == Caffe2 == In April 2017, Facebook announced Caffe2, which included new features such as recurrent neural network (RNN). At the end of March 2018, Caffe2 was merged into PyTorch.
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Ayanna Howard
Ayanna MacCalla Howard (born January 24, 1972) is an American roboticist, entrepreneur, and educator currently serving as the dean of the College of Engineering at Ohio State University. Assuming this role in March 2021, Howard became the first woman to lead the Ohio State College of Engineering. Howard previously served as the chair of the School of Interactive Computing in the Georgia Tech College of Computing, the Linda J. and Mark C. Smith Endowed Chair in Bioengineering in the School of Electrical and Computer Engineering, and the director of the Human-Automation Systems (Humans) Lab. == Early life and education == As a little girl, Howard was interested in aliens and robots. Her favorite TV show was The Bionic Woman. Howard received her B.S. in engineering from Brown University in 1993 and her M.S. and Ph.D. in electrical engineering from the University of Southern California in 1994 and 1999, respectively. Her thesis, Recursive Learning for Deformable Object Manipulation, was advised by George A. Bekey. In addition, Howard's Doctoral thesis was triggered by the AIDS epidemic with focus on sorting hospital waste by using robots. Howard has also received an MBA from Claremont Graduate University. == Career == Howard's early interest in artificial intelligence led her to pursue a senior position at Seattle-based Axcelis Inc, where she helped develop Evolver, the first commercial genetic algorithm, and Brainsheet, a neural network developed in partnership with Microsoft. From 1993 to 2005, she worked at the NASA Jet Propulsion Laboratory, holding multiple roles such as senior robotics researcher and deputy manager in the Office of the Chief Scientist. In 2005, she joined Georgia Tech as an associate professor and founder of the Human-Automation Systems (Humans) lab. She has also served as the associate director of research for Georgia Tech's Institute for Robotics and Intelligent Machines and as chair of the multidisciplinary robotics Ph.D. program at Georgia Tech. In 2017, she became the chair of the School of Interactive Computing at Georgia Tech. In 2008, Howard received worldwide attention for her SnoMote robots, designed to study the impact of global warming on the Antarctic ice shelves. In 2013, she founded Zyrobotics, which has released their first suite of therapy and educational products for children with special needs. Howard has authored 250 publications in reputable journals and conferences, including serving as co-editor/co-author of more than a dozen books and book chapters. She has also received four patents and given over 140 invited talks and keynotes. She is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the Institute of Electrical and Electronics Engineers (IEEE). Among her many honors, Howard received the Computer Research Association's A. Nico Habermann Award and the Richard A. Tapia Achievement Award. In a 2020 interview on Marketplace, Howard outlined how companion robots could alleviate the effects of social distancing caused by the COVID-19 pandemic in the United States. On November 30, 2020, the Columbus Dispatch reported that Howard would become the next dean of the College of Engineering at Ohio State University on March 1, pending approval by the board of trustees. On March 1, 2021, she assumed this role, becoming the first woman to hold the position. In 2021, Howard received the Athena Lecturer Award from Association for Computing Machinery (ACM) for her Contributions to Robotics, AI and Broadening Participation in Computing. In June 2022, Howard was elected a trustee of Brown University. == Research == Howard's research interests include human-robot interaction, assistive/rehabilitation robotics, science-driven/field robotics, and perception, learning, and reasoning. Howard's research and published works span across various topics in robotics and AI, including intelligent learning, virtual reality for rehabilitation and robotics in the role of pediatric therapy. Her research is highlighted by her focus on technology development for intelligent agents that must interact with and in a human-centered world. Her work, which addresses issues of human-robot interaction, learning, and autonomous control, has resulted in more than 200 peer-reviewed publications. == Honors and awards == Howard's numerous accomplishments have been documented in more than a dozen featured articles. In 2003, she was named to the MIT Technology Review TR100 as one of the top 100 innovators in the world under the age of 35. She was featured in Time magazine's "Rise of the Machines" article in 2004. She was also featured in a USA Today Science & Space article. Some of Howard's notable awards include: Lew Allen Award for Excellence (formerly the Director's Research Achievement Award of the Jet Propulsion Laboratory) for significant technical contributions, 2001 MIT Technology Review Top 100 Young Innovators of the Year, 2003 NAE Gilbreth Lectureship, 2010 A. Richard Newton Educator ABIE Award, Anita Borg Institute, 2014 Computer Research Association's A. Nico Habermann Award, 2016 Brown Engineering Alumni Medal (BEAM), 2016 AAAS-Lemelson Invention Ambassador, 2016-2017 Atlanta magazine's Women Making a Mark, 2017 Walker's Legacy #WLPower25 Atlanta Award, 2017 Forbes America's Top 50 Women In Tech, 2018 ACM Athena Lecturer Award, 2021 2021 class of Fellows of the American Association for the Advancement of Science. IEEE Fellow, 2021, "for contributions to human-robot interaction systems" 2023 AAAI/EAAI Patrick Henry Winston Outstanding Educator Award
Neural style transfer
Neural style transfer (NST) software algorithms are able to manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. Common uses for NST are the creation of artificial artwork from photographs, for example by transferring the appearance of famous paintings to user-supplied photographs. Several notable mobile apps use NST techniques for this purpose, including DeepArt and Prisma. This method has been used by artists and designers around the globe to develop new artwork based on existent style(s). == History == NST is an example of image stylization, a problem studied for over two decades within the field of non-photorealistic rendering. The first two example-based style transfer algorithms were image analogies and image quilting. Both of these methods were based on patch-based texture synthesis algorithms. Given a training pair of images–a photo and an artwork depicting that photo–a transformation could be learned and then applied to create new artwork from a new photo, by analogy. If no training photo was available, it would need to be produced by processing the input artwork; image quilting did not require this processing step, though it was demonstrated on only one style. NST was first published in the paper "A Neural Algorithm of Artistic Style" by Leon Gatys et al., originally released to ArXiv 2015, and subsequently accepted by the peer-reviewed CVPR conference in 2016. The original paper used a VGG-19 architecture that has been pre-trained to perform object recognition using the ImageNet dataset. In 2017, Google AI introduced a method that allows a single deep convolutional style transfer network to learn multiple styles at the same time. This algorithm permits style interpolation in real-time, even when done on video media. == Mathematics == This section closely follows the original paper. === Overview === The idea of Neural Style Transfer (NST) is to take two images—a content image p → {\displaystyle {\vec {p}}} and a style image a → {\displaystyle {\vec {a}}} —and generate a third image x → {\displaystyle {\vec {x}}} that minimizes a weighted combination of two loss functions: a content loss L content ( p → , x → ) {\displaystyle {\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}})} and a style loss L style ( a → , x → ) {\displaystyle {\mathcal {L}}_{\text{style }}({\vec {a}},{\vec {x}})} . The total loss is a linear sum of the two: L NST ( p → , a → , x → ) = α L content ( p → , x → ) + β L style ( a → , x → ) {\displaystyle {\mathcal {L}}_{\text{NST}}({\vec {p}},{\vec {a}},{\vec {x}})=\alpha {\mathcal {L}}_{\text{content}}({\vec {p}},{\vec {x}})+\beta {\mathcal {L}}_{\text{style}}({\vec {a}},{\vec {x}})} By jointly minimizing the content and style losses, NST generates an image that blends the content of the content image with the style of the style image. Both the content loss and the style loss measures the similarity of two images. The content similarity is the weighted sum of squared-differences between the neural activations of a single convolutional neural network (CNN) on two images. The style similarity is the weighted sum of Gram matrices within each layer (see below for details). The original paper used a VGG-19 CNN, but the method works for any CNN. === Symbols === Let x → {\textstyle {\vec {x}}} be an image input to a CNN. Let F l ∈ R N l × M l {\textstyle F^{l}\in \mathbb {R} ^{N_{l}\times M_{l}}} be the matrix of filter responses in layer l {\textstyle l} to the image x → {\textstyle {\vec {x}}} , where: N l {\textstyle N_{l}} is the number of filters in layer l {\textstyle l} ; M l {\textstyle M_{l}} is the height times the width (i.e. number of pixels) of each filter in layer l {\textstyle l} ; F i j l ( x → ) {\textstyle F_{ij}^{l}({\vec {x}})} is the activation of the i th {\textstyle i^{\text{th}}} filter at position j {\textstyle j} in layer l {\textstyle l} . A given input image x → {\textstyle {\vec {x}}} is encoded in each layer of the CNN by the filter responses to that image, with higher layers encoding more global features, but losing details on local features. === Content loss === Let p → {\textstyle {\vec {p}}} be an original image. Let x → {\textstyle {\vec {x}}} be an image that is generated to match the content of p → {\textstyle {\vec {p}}} . Let P l {\textstyle P^{l}} be the matrix of filter responses in layer l {\textstyle l} to the image p → {\textstyle {\vec {p}}} . The content loss is defined as the squared-error loss between the feature representations of the generated image and the content image at a chosen layer l {\displaystyle l} of a CNN: L content ( p → , x → , l ) = 1 2 ∑ i , j ( A i j l ( x → ) − A i j l ( p → ) ) 2 {\displaystyle {\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}},l)={\frac {1}{2}}\sum _{i,j}\left(A_{ij}^{l}({\vec {x}})-A_{ij}^{l}({\vec {p}})\right)^{2}} where A i j l ( x → ) {\displaystyle A_{ij}^{l}({\vec {x}})} and A i j l ( p → ) {\displaystyle A_{ij}^{l}({\vec {p}})} are the activations of the i th {\displaystyle i^{\text{th}}} filter at position j {\displaystyle j} in layer l {\displaystyle l} for the generated and content images, respectively. Minimizing this loss encourages the generated image to have similar content to the content image, as captured by the feature activations in the chosen layer. The total content loss is a linear sum of the content losses of each layer: L content ( p → , x → ) = ∑ l v l L content ( p → , x → , l ) {\displaystyle {\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}})=\sum _{l}v_{l}{\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}},l)} , where the v l {\displaystyle v_{l}} are positive real numbers chosen as hyperparameters. === Style loss === The style loss is based on the Gram matrices of the generated and style images, which capture the correlations between different filter responses at different layers of the CNN: L style ( a → , x → ) = ∑ l = 0 L w l E l , {\displaystyle {\mathcal {L}}_{\text{style }}({\vec {a}},{\vec {x}})=\sum _{l=0}^{L}w_{l}E_{l},} where E l = 1 4 N l 2 M l 2 ∑ i , j ( G i j l ( x → ) − G i j l ( a → ) ) 2 . {\displaystyle E_{l}={\frac {1}{4N_{l}^{2}M_{l}^{2}}}\sum _{i,j}\left(G_{ij}^{l}({\vec {x}})-G_{ij}^{l}({\vec {a}})\right)^{2}.} Here, G i j l ( x → ) {\displaystyle G_{ij}^{l}({\vec {x}})} and G i j l ( a → ) {\displaystyle G_{ij}^{l}({\vec {a}})} are the entries of the Gram matrices for the generated and style images at layer l {\displaystyle l} . Explicitly, G i j l ( x → ) = ∑ k F i k l ( x → ) F j k l ( x → ) {\displaystyle G_{ij}^{l}({\vec {x}})=\sum _{k}F_{ik}^{l}({\vec {x}})F_{jk}^{l}({\vec {x}})} Minimizing this loss encourages the generated image to have similar style characteristics to the style image, as captured by the correlations between feature responses in each layer. The idea is that activation pattern correlations between filters in a single layer captures the "style" on the order of the receptive fields at that layer. Similarly to the previous case, the w l {\displaystyle w_{l}} are positive real numbers chosen as hyperparameters. === Hyperparameters === In the original paper, they used a particular choice of hyperparameters. The style loss is computed by w l = 0.2 {\displaystyle w_{l}=0.2} for the outputs of layers conv1_1, conv2_1, conv3_1, conv4_1, conv5_1 in the VGG-19 network, and zero otherwise. The content loss is computed by w l = 1 {\displaystyle w_{l}=1} for conv4_2, and zero otherwise. The ratio α / β ∈ [ 5 , 50 ] × 10 − 4 {\displaystyle \alpha /\beta \in [5,50]\times 10^{-4}} . === Training === Image x → {\displaystyle {\vec {x}}} is initially approximated by adding a small amount of white noise to input image p → {\displaystyle {\vec {p}}} and feeding it through the CNN. Then we successively backpropagate this loss through the network with the CNN weights fixed in order to update the pixels of x → {\displaystyle {\vec {x}}} . After several thousand epochs of training, an x → {\displaystyle {\vec {x}}} (hopefully) emerges that matches the style of a → {\displaystyle {\vec {a}}} and the content of p → {\displaystyle {\vec {p}}} . As of 2017, when implemented on a GPU, it takes a few minutes to converge. == Extensions == In some practical implementations, it is noted that the resulting image has too much high-frequency artifact, which can be suppressed by adding the total variation to the total loss. Compared to VGGNet, AlexNet does not work well for neural style transfer. NST has also been extended to videos. Subsequent work improved the speed of NST for images by using special-purpose normalizations. In a paper by Fei-Fei Li et al. adopted a different regularized loss metric and accelerated method for training to produce results in real-time (three orders of magnitude faster than Gatys). Their idea was to use not the pixel-based loss defined above but rather a 'perceptual loss' measuring t
Angelo Dalli
Angelo Dalli (born 14 April 1978) is a computer scientist specialising in artificial intelligence, a serial entrepreneur, and business angel investor. == Early life and education == Dalli was born in Malta and grew up in the town of Birżebbuġa. Dalli was educated at the Archbishop's Seminary, Malta and represented Malta in the Young European Environmental Research contest held in Cologne in 1994. Dalli represented Malta in the International Olympiad in Informatics held in Eindhoven in 1995, where he won a bronze medal. Dalli started selling computer software as a teenager, and worked for the International Data Group as a freelance contributor for PC World. == Academic work == After graduating from the University of Malta, Dalli spent time lecturing on artificial intelligence and natural language processing before reading for his PhD at the University of Sheffield under the supervision of Yorick Wilks. Dalli has published over 23 peer reviewed papers in the artificial intelligence and natural language processing fields, including one of the earliest methods on timestamp extraction from documents that is now commonly used in most email applications. Angelo has also contributed to the encoding of European languages in Unicode, in particular for the Common Locale Data Repository. In the field of Bioinformatics Dalli has found a particularly useful integer sequence (sequence A062208 in the OEIS) which efficiently computes all alignments of strings of length 3 together with other generalisations (sequence A062204 in the OEIS), (sequence A062205 in the OEIS) for applications in natural language and sequence alignment. Dalli has an Erdős number of 3. Dalli has led the Maltese national informatics team in the International Olympiad in Informatics at IOI 2002 in Seoul, South Korea and IOI 2004 in Athens, Greece. == Artificial intelligence == === Trustworthy AI and Hybrid Intelligence === Angelo has been a vocal proponent of trustworthy AI that impacts society positively and believes that AI should be properly regulated. Angelo has co-founded UMNAI in 2019, with the aim of creating a new form of trustworthy AI that can explain the decisions and steps that the AI has taken to output an answer, based on a neurosymbolic AI architecture that combines neural and symbolic AI in an auditable and certain manner. === AI and society === Angelo led the Government of Malta taskforce that produced Malta's new AI regulation and national AI strategy, and is an active member of the IEEE, AAAI, ACM and the ACL. === AI in transport === Angelo had led the introduction of different machine learning techniques in intelligent transport systems (ITS), including parking, controlled vehicle access zones and dynamic traffic interchange control. His intelligent transport company, Traffiko, operated in Europe, Australia and the Middle East, and was eventually sold to Q-Free in Norway in 2015. === AI in gaming === Angelo is a well known speaker in the online gambling industry. Angelo setup one of the first companies that applied artificial intelligence in the online gambling industry, called Bit8 (now part of Intralot), with the most notable work being on algorithms that estimate and maximise player lifetime value and personalised bonusing systems. These techniques have since been widely adopted by the online gambling industry Intralot subsequently bought Bit8 in 2017. === AI and creativity === Angelo has been collaborating various artists and creatives to teach AI about creativity. The results of this collaboration is the UMA AI entity, short for Universal Machine Artist. Angelo has also co-founded the Creative Science and Arts Institute to act as a foundation for future research into AI, science, technology and creativity. UMA is creating original artwork using a modified Generative adversarial network has a third component, the human artist, to produce different learning results than standard generative AI models. The underlying discriminator in UMA started from an anti-fraud detection system and has now gradually evolved to add stable diffusion and procedural generation methods. The first two artworks generated by UMA were auctioned in October and November 2018 respectively, with all proceeds donated to charity and good causes. Ongoing work in improving UMA and furthering collaboration with other artists is ongoing. Notable exhibitions include Tomorrow's Blossoms with Selina Scerri at Esplora Museum in 2024, which explored the theme of AI and emotions. == Angel investor == Angelo is an angel investor active in the high-tech startup scene, and is a member of EBAN, and World Business Angel Forum senator. Angelo has been encouraging Maltese startups via various public events including the Zest and Budding Rockstars conferences and co-founded BAM, the Malta Business Angel network, in 2019. == Awards and honours == === Entrepreneurial and scientific === Bronze Medal, International Olympiad in Informatics (1995) Malta Top Entrepreneur Award (2019) Malta Top Entrepreneur Award (2014) WIPO IP Enterprise Award for the UMNAI Neuro-symbolic AI architecture (2022) === Corporate awards === Intralot Bit8 EGR Rising Star Award (2014) Intralot Bit8 Malta Communication Authority eBusiness Award for the Best B2B application (2015) Intralot Bit8 Malta iGaming Award for Excellence (2017)