This is the official Chicago book launch event for Alex Castrounis' new O'Reilly book, AI for People and Business (book description below). Alex will present an overview of his book and the conceptual frameworks and models that the book covers, which can help people understand and leverage AI and machine learning successfully. The presentation will be followed by a networking session where 20 free copies of AI for People and Business will be given away and signed (see below for how to qualify for a book)! Alex will also sign copies that you bring, so feel free to grab a copy in advance from Amazon at https://amzn.to/31HnEpp Light refreshments (not a full dinner) will be provided.

Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for addressing the HDA problem. The proposed STN not only aligns the discriminative directions of domains but also matches both the marginal and conditional distributions across domains. To circumvent negative transfer, STN aligns the conditional distributions by using the soft-label strategy of unlabeled target data, which prevents the hard assignment of each unlabeled target data to only one category that may be incorrect. Further, STN introduces an adaptive coefficient to gradually increase the importance of the soft-labels since they will become more and more accurate as the number of iterations increases. We perform experiments on the transfer tasks of image-to-image, text-to-image, and text-to-text. Experimental results testify that the STN significantly outperforms several state-of-the-art approaches.

Existing computational methods for the analysis of corpora of text in natural language are still far from approaching a human level of understanding. We attempt to advance the state of the art by introducing a model and algorithmic framework to transform text into recursively structured data. We apply this to the analysis of news titles extracted from a social news aggregation website. We show that a recursive ordered hypergraph is a sufficiently generic structure to represent significant number of fundamental natural language constructs, with advantages over conventional approaches such as semantic graphs. We present a pipeline of transformations from the output of conventional NLP algorithms to such hypergraphs, which we denote as semantic hypergraphs. The features of these transformations include the creation of new concepts from existing ones, the organisation of statements into regular structures of predicates followed by an arbitrary number of entities and the ability to represent statements about other statements. We demonstrate knowledge inference from the hypergraph, identifying claims and expressions of conflicts, along with their participating actors and topics. We show how this enables the actor-centric summarization of conflicts, comparison of topics of claims between actors and networks of conflicts between actors in the context of a given topic. On the whole, we propose a hypergraphic knowledge representation model that can be used to provide effective overviews of a large corpus of text in natural language.

The Visual Question Answering (VQA) task combines challenges for processing data with both Visual and Linguistic processing, to answer basic `common sense' questions about given images. Given an image and a question in natural language, the VQA system tries to find the correct answer to it using visual elements of the image and inference gathered from textual questions. In this survey, we cover and discuss the recent datasets released in the VQA domain dealing with various types of question-formats and enabling robustness of the machine-learning models. Next, we discuss about new deep learning models that have shown promising results over the VQA datasets. At the end, we present and discuss some of the results computed by us over the vanilla VQA models, Stacked Attention Network and the VQA Challenge 2017 winner model. We also provide the detailed analysis along with the challenges and future research directions.

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi-Sugeno-Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems.

Emerging advanced technological solutions today have reached such a peak in growth that they are increasingly leaving deeper imprints on both the professional and personal lives of people around the world. According to CompTIA (via TechRepublic), among the emerging solutions offering the greatest business and financial opportunities in the digital age are artificial intelligence, the Internet of Things (IoT), and 5G networks. And while these technologies could transform the business landscape and how organizations operate on a daily basis, one of the most visible effects they have had so far is how they are (or could) transform the productivity and growth speed of corporations, small or large. One of the bigger players in the business arena today is my industry, AI. McKinsey noted that AI solutions can automate many tasks performed by humans.

Precise medical imaging and analysis could enable early detection of lung cancer, help determine its exact size and location, and significantly improve diagnosis and treatment. This is usually done in a process called segmentation, which uses computers to identify the boundaries of the lung from surrounding thoracic tissue on CT images. From this process, a detailed 3-D map of the airways may be generated that can help to plan and navigate a bronchoscopy procedure to obtain biopsy samples and to perform other clinical interventions. "Until now, this process was very difficult because you need the radiologist, or even the surgeon, to spend much time to understand how to get to the specific place [where the lesion is located]. And this is sometimes prone to error," said Ron Soferman, founder and CEO of RSIP Vision, in an interview with MD DI. "It's very critical [to know the precise location] because, if you miss the lesion, you will take a biopsy from some random part of the lung and it will give a negative result."

"I'm using artificial intelligence techniques to predict the size of hailstorms," explained Gagne. Working with computer-simulated storms, he created software that is trained to determine which storms produce hail and then to recognize patterns associated with the storms behind the largest hailstones. "The shape of storms is really important." His latest work is published in Monthly Weather Review. Gagne's novel approach started with his PhD dissertation between 2014 and 2015.

It is no secret that algorithms today can generate very realistic deepfakes – images or videos that are totally fake but very hard to distinguish from the real ones. You can make Mark Zuckerberg talking about "one man with total control of billions of people stolen data" and the suspicion will come only because Mark is not likely to say these exact words while the video itself looks very realistic. So, let's see what are some of the state-of-the-art approaches to video generation. If these summaries of scientific AI research papers are useful for you, you can subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries. If you'd like to skip around, here are the papers we featured: We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature.

Breast cancer is the most common cancer and is the leading cause of cancer death among women worldwide. Detection of breast cancer, while it is still small and confined to the breast, provides the best chance of effective treatment. Computer Aided Detection (CAD) systems that detect cancer from mammograms will help in reducing the human errors that lead to missing breast carcinoma. Literature is rich of scientific papers for methods of CAD design, yet with no complete system architecture to deploy those methods. On the other hand, commercial CADs are developed and deployed only to vendors' mammography machines with no availability to public access. This paper presents a complete CAD; it is complete since it combines, on a hand, the rigor of algorithm design and assessment (method), and, on the other hand, the implementation and deployment of a system architecture for public accessibility (system). (1) We develop a novel algorithm for image enhancement so that mammograms acquired from any digital mammography machine look qualitatively of the same clarity to radiologists' inspection; and is quantitatively standardized for the detection algorithms. (2) We develop novel algorithms for masses and microcalcifications detection with accuracy superior to both literature results and the majority of approved commercial systems. (3) We design, implement, and deploy a system architecture that is computationally effective to allow for deploying these algorithms to cloud for public access.