Image and video retrieval by their semantic content has been an important and challenging task for years, because it ultimately requires bridging the symbolic/subsymbolic gap. Recent successes in deep learning enabled detection of objects belonging to many classes greatly outperforming traditional computer vision techniques. However, deep learning solutions capable of executing retrieval queries are still not available. We propose a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution. Specifically, we use YOLOv2 and OpenCog. Queries allowing the retrieval of video frames containing objects of specified classes and specified spatial arrangement are implemented.

Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.

Random weights in convolutional neural networks have shown promising results in previous studies yet remain below par compared to trained networks on image benchmarks. We explore depthwise convolutional neural networks with thousands of random filters in each layer, the sign activation function in between layers, and training performed only at the last layer with a linear support vector machine. We show that our network attains higher accuracies than previous random networks and is comparable to trained large networks on large images from the STL10 and ImageNet benchmarks. Since our network lacks a gradient due to the sign activation it is not possible to produce gradient-based adversarial examples targeting it. We show that our network is also less affected by gradient based adversarial examples produced from state of the art networks that considerably hamper their performance. As a possible explanation for our network's accuracy with random weights we show that the the margin of the linear support vector machine is larger on our final representation compared to the original dataset and increases with the number of random filters. Our network is simple and fast to train and predict, attains high classification accuracy particularly on large images, is hard to attack with adversarial examples, and is less affected by gradient based adversarial examples compared to state of the art networks.

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people's everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and offline.

DBSCAN may not be sufficient when the input data type is heterogeneous in terms of textual description. When we aim to discover clusters of geo-tagged records relevant to a particular point-of-interest (POI) on social media, examining only one type of input data (e.g., the tweets relevant to a POI) may draw an incomplete picture of clusters due to noisy regions. To overcome this problem, we introduce DBSTexC, a newly defined density-based clustering algorithm using spatio--textual information. We first characterize POI-relevant and POI-irrelevant tweets as the texts that include and do not include a POI name or its semantically coherent variations, respectively. By leveraging the proportion of POI-relevant and POI-irrelevant tweets, the proposed algorithm demonstrates much higher clustering performance than the DBSCAN case in terms of $\mathcal{F}_1$ score and its variants. While DBSTexC performs exactly as DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with the textually heterogeneous inputs. Furthermore, to further improve the clustering quality by fully capturing the geographic distribution of tweets, we present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates the notion of fuzzy clustering into the DBSTexC. We then demonstrate the robustness of F-DBSTexC via intensive experiments. The computational complexity of our algorithms is also analytically and numerically shown.

Boon of technology has changed the human civilization for good, as constant evolution in technology being intensively tracked and leveraged by new-age entrepreneurs, it only becomes imminent that aspects such as the Internet-of-Things (IoT), artificial Intelligence (AI), Machine Learning (ML), Deep Learning and more; are utilised by entrepreneurs to develop technology-driven models to actually solve societal problems. In this regard, with futuristic technology penetration being the focal point; Entrepreneur India lists the relevance of a domain called Machine Learning for entrepreneurs in 2018 to harness and develop solutions such that societal problems are mitigated whilst driving recognition for developing unconventional solutions. "Machine Learning has the potential to help create a utopian world without any disease, crime, and poverty," states Yogesh Bhatt who is Vice President at Bengaluru-based Manipal Prolearn; a unit of Manipal Global Education Services. This is substantiated when we consider the fact that data scientists today have been working at the heart of societal issues in domains healthcare and medicine; which are undoubtedly in a definite need for disruption. Specifically, Machine Learning is now driving doctors through predictive analytics; towards aspects such as enhancing the success rates when it comes to diagnosing and treating life threatening ailments such as clinical depression and cancer. These aspects potentially provide exciting prospects for entrepreneurs, to come up with models driven by ML, as there practically (not theoretically) exists opportunity for impact to introduce positive changes in the lives of people.

We're in a golden age for hollow corporate statements sold as high-minded ethical treatises. Last year, in the thick of its fake news scandal, Facebook released a 5,000-word document outlining, well, I'm still not sure exactly. The letter attempted to pull the company out of its public opinion black hole by posing probing questions, including the head-scratcher: "How do we help people build supportive communities that strengthen traditional institutions in a world where membership in these institutions is declining?" The answers were generally of the "build more Facebook" variety. It was a masterstroke in corporate pablum, though not so masterful that it saved the company from the onslaught of bad press.

Elizabeth Holm, a professor of materials science and engineering at the College of Engineering at Carnegie Mellon University and a computational materials scientist at Sandia National Laboratories says we're in the midst of an artificial intelligence (AI) culture shift. She also says that machines won't replace human experts. "Machines are great at handling things, like large amounts of data, but machines still need an expert, a human, to analyze the data, set parameters and guide decisions," said Holm. "Engineering and science decisions are based on understanding how things work. How does a bridge support its load? How does an engine convert fuel into motion? In contrast, AI transforms data into decisions without understanding any underlying principles," said Holm. "Applying AI to engineering and science will require a culture shift: either we will learn to trust decisions that we do not understand, or AIs will evolve to base their decisions on principals that humans can interpret and control."

Statisticians at German technical university Technische Universitat Dortmund built a model that used machine learning to predict Spain will win the 2018 World Cup. The prediction is based on 100,000 simulations of the tournament. Spain was followed by Germany, Brazil, France and Belgium in terms of their chances of winning. And it should be a good tournament because Spain, with a 17.8 percent chance of winning, is only slightly ahead of Germany at 17.1 percent. Brazil follows with 12.3 percent, and then it's France (11.2 percent) and Belgium (10.4 percent).

As the number of people who work on a volunteer basis or try to contribute for good causes increase in Turkey, we look to the future with confidence. Furthermore, if these volunteers comprise of scientists, academicians and youths, supporting such formations mean paving the way of social developments. The Deep Learning Turkey community, which teaches and guides high school students and undergraduates, turning artificial intelligence into a social responsibility project, has reached out to thousands of youths although it was established only in August of last year. The community helps youths who are interested in artificial intelligence and want to have a career in this field as well as providing information sharing on an open platform for scientists. Deep Learning Turkey is the biggest and the most effective artificial intelligence community in Turkey.