Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine. However, the lack of consistent terminology and architectural descriptions makes it difficult to compare different existing solutions. We address these challenges by proposing a new taxonomy for describing such systems based on trends found in recent publications on multimodal classification. Many of the most difficult aspects of unimodal classification have not yet been fully addressed for multimodal datasets including big data, class imbalance, and instance level difficulty. We also provide a discussion of these challenges and future directions.

The Tsetlin Machine (TM) is a novel machine-learning algorithm based on propositional logic, which has obtained state-of-the-art performance on several pattern recognition problems. In previous studies, the convergence properties of TM for 1-bit operation and XOR operation have been analyzed. To make the analyses for the basic digital operations complete, in this article, we analyze the convergence when input training samples follow AND and OR operators respectively. Our analyses reveal that the TM can converge almost surely to reproduce AND and OR operators, which are learnt from training data over an infinite time horizon. The analyses on AND and OR operators, together with the previously analysed 1-bit and XOR operations, complete the convergence analyses on basic operators in Boolean algebra.

The automatic identification of complex features in images has already become a reality thanks to artificial neural networks. Some examples of software exploiting this technique are Facebook's automatic tagging system, Google's image search engine and the animal and plant recognition system used by iNaturalist. We know that these networks are inspired by the human brain, but their working mechanism is still mysterious. New research, conducted by SISSA in association with the Technical University of Munich and published for the 33rd Annual NeurIPS Conference, proposes a new approach for studying deep neural networks and sheds new light on the image elaboration processes that these networks are able to carry out. Similar to what happens in the visual system, neural networks used for automatic image recognition analyse the content progressively, through a chain of processing stages.

Research project'Technology Foresight on Biometrics' from EU border security agency Frontex, which studies the impact of emerging biometric technologies on the facilitation of border crossing at the EU external borders, has completed two new steps. Frontex announced the tender for the project in September 2021 with a contract value of EUR 500,000 (US$590,000). The project, led by Steinbeis 2i together with three subcontracted partners (4CF, ERREQUADRO and WAT) has been examining how to maximize the future benefits of biometrics in border management while minimizing its risks and ensuring full compliance with the existing legal, ethical and technological constraints. The research team created a taxonomy of biometric technologies and carried out a Delphi survey to gather information on key technologies. Looking at the taxonomy of technologies early on provides foresight into areas which need to be addressed, according to the report, and establishes a common and systematic understanding of the technological field.

Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero- and few-shot settings. For zero-shot performance, it makes sense to design patterns that closely resemble the text seen during self-supervised pretraining because the model has never seen anything else. Supervised training allows for more flexibility. If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies. Contrasting patterns where a "token" can be any continuous vector vs. those where a discrete choice between vocabulary elements has to be made, we call our method CONtinuous pAtterNs (CONAN). We evaluate CONAN on two established benchmarks for lexical inference in context (LIiC) a.k.a. predicate entailment, a challenging natural language understanding task with relatively small training sets. In a direct comparison with discrete patterns, CONAN consistently leads to improved performance, setting a new state of the art. Our experiments give valuable insights into the kind of pattern that enhances a PLM's performance on LIiC and raise important questions regarding our understanding of PLMs using text patterns.

Artificial intelligence is one of the fastest growing fields of computer science today and the demand for excellent AI Engineers is increasing day in and day out. This course will help you stay competitive in the AI job market by teaching you how to create a Deep Learning End-to-End product on your own. Most courses focus on the basics of Deep Learning and teach you about the very basics of different models. In this course, however, you will learn how to write a whole End-to-End pipeline, from data preprocessing across choosing the right hyper-parameters, to showing your users results in a browser. The case that we will tackle in this course is an engine for Image to Image Search.

"VICReg could be used to model the dependencies between a video clip and the frame that comes after, therefore learning to predict the future in a video." Humans have an innate capability to identify objects in the wild, even from a blurred glimpse of the thing. We do this efficiently by remembering only high-level features that get the job done (identification) and ignoring the details unless required. In the context of deep learning algorithms that do object detection, contrastive learning explored the premise of representation learning to obtain a large picture instead of doing the heavy lifting by devouring pixel-level details. But, contrastive learning has its own limitations.

The artificial intelligence systems used by image recognition tools, such as those that certain connected cars use to identify street signs, can be tricked to make an incorrect identification by a low-cost but effective attack using a camera, a projector and a PC, according to Purdue University researchers. A research paper describes an Optical Adversarial Attack, or OPAD, which uses a projector to project calculated patterns that alter the appearance of the 3D objects to AI-based image recognition systems. The paper will be presented in October at an ICCV 2021 Workshop. In an experiment, a pattern was projected onto a stop sign, causing the image recognition to read the sign as a speed limit sign instead. The researchers say this attack method could also work with image recognition tools in applications ranging from military drones to facial recognition systems, potentially undermining their reliability.

Description This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus.

Like snowflakes and people, no two digital enterprises are exactly alike. Digital transformation, as with countless technology initiatives over the decades, will look different from company to company. However, there are basic patterns that characterize successful digital initiatives. A survey out of BCG shows that 70% of digital transformations fall short of their objectives, often with profound consequences. With so much at stake to build digital capabilities, why do so many companies -- no matter how technically savvy, fail?