"These end-to-end voice translation system uses Automatic Speech Recognition (ASR), Machine Translation and Speech-to-Text to convert Mandarin to English and is designed to help armed forces, intelligence agencies and local law enforcement authorities in improving communication systems and giving substantial leeway to the Indian defense forces," the company in its statement said. The solution has a wide range of applications that includes cross border intelligence, voice surveillance, monitoring telephone/internet conversations, intercepting Radio/Satellite communication, and to bridge interactions during border meetings & joint exercises. Its unique features include noise reduction, dialect/accent detection, and support for all audio file formats. Speaking on the launch, Ananth Nagaraj, Co-founder & CTO, Gnani.ai said, "AI-based Speech Recognition technology is a necessity and is quickly making its way in becoming part of modern warfare. We believe AI has the potential to transform and improve the communication systems and will help strengthen Indian Armed forces." "Understanding linguistic nuances such as phoneme and dialects is a challenge especially when it comes to Mandarin.

Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches rely on similarities in geometric structure of word embedding spaces across languages, to learn structure-preserving linear transformations using adversarial networks and refinement strategies. However, such techniques, in practice, tend to suffer from instability and convergence issues, requiring tedious fine-tuning for precise parameter setting. This paper proposes BioSpere, a novel framework for unsupervised mapping of bi-lingual word embeddings onto a shared vector space, by combining adversarial initialization and refinement procedure with point set registration algorithm used in image processing. We show that our framework alleviates the shortcomings of existing methodologies, and is relatively invariant to variable adversarial learning performance, depicting robustness in terms of parameter choices and training losses. Experimental evaluation on parallel dictionary induction task demonstrates state-of-the-art results for our framework on diverse language pairs.

Speaking to UKTN, Marko Hozjan, co-founder and CEO of TAIA, explains, "TAIA helps businesses translate their content more efficiently by providing professional translators with AI assistance. Files are automatically analysed and a price quote with delivery times is available in under a minute. Users can select between a range of services and delivery times to order a translation service that best fits their needs and budget. Once the project is ordered, it's automatically translated using Neural Machine Translation and prefilled with existing translations from customers' unique Translation memory. This way your projects get translated faster and more consistently with every order. Users can monitor the progress of their project in the convenient web application and easily manage all their translation needs in one place, keeping their data secure and their costs optimised."

Multiple-choice questions (MCQs) offer the most promising avenue for skill evaluation in the era of virtual education and job recruiting, where traditional performance-based alternatives such as projects and essays have become less viable, and grading resources are constrained. The automated generation of MCQs would allow assessment creation at scale. Recent advances in natural language processing have given rise to many complex question generation methods. However, the few methods that produce deployable results in specific domains require a large amount of domain-specific training data that can be very costly to acquire. Our work provides an initial foray into MCQ generation under high data-acquisition cost scenarios by strategically emphasizing paraphrasing the question context (compared to the task). In addition to maintaining semantic similarity between the question-answer pairs, our pipeline, which we call AGenT Zero, consists of only pre-trained models and requires no fine-tuning, minimizing data acquisition costs for question generation. AGenT Zero successfully outperforms other pre-trained methods in fluency and semantic similarity. Additionally, with some small changes, our assessment pipeline can be generalized to a broader question and answer space, including short answer or fill in the blank questions.

To obtain fast and accurate inference on edge devices, a model has to be optimized for real-time inference. Fine-tuned state-of-the-art models like VGG16/19, ResNet50 have 138 million and 23 million parameters respectively and inference is often expensive on resource-constrained devices. Previously I've talked about one model compression technique called "Knowledge Distillation" using a smaller student network to mimic the performance of a larger teacher network (Both student and teacher network has different network architecture). Today, the focus will be on "Pruning" one model compression technique that allows us to compress the model to a smaller size with zero or marginal loss of accuracy. In short, pruning eliminates the weights with low magnitude (That does not contribute much to the final model performance).

With the advancement of machine translation, there is a recent movement towards large-scale empirical techniques that have prompted exceptionally massive enhancements in translation quality. Machine Translation is the technique of consequently changing over one characteristic language into another, saving the importance of the info text. The ongoing research on Image description presents a considerable challenge in the field of natural language processing and computer vision. To overcome this issue, multimodal machine translation presents data from other methods, for the most part, static pictures, to improve the interpretation quality. Here, we will cover the absolute most well-known datasets that are utilized in machine translation.

Artificial intelligence (AI) has become a digital frontier that will have a profound impact on the world. It will have immense technological, economic, and social consequences and will transform the way humans work, live, and produce and distribute goods and services. Although it is too early to say, it is clear that AI will affect traditional intellectual property (IP) concepts. Commercial AI-generated music and AI-created inventions are not so far, and it is expected that it will define the concepts of the'composer', 'author', and'inventor'. But how that will happen is not clear yet.

Lilt, the AI-powered enterprise translation software and services company, announced that it has been awarded an AFWERX SBIR Phase II contract

Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning. Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions. We refer to these relations as relative roles and leverage them to make each token role-aware using attention. This results in a more structured and interpretable architecture that incorporates modality-specific inductive biases for the captioning task. Intuitively, the model is able to learn spatial, temporal, and cross-modal relations in a given pair of video and text. The disentanglement achieved by our proposal gives the model more capacity to capture multi-modal structures which result in captions with higher quality for videos. Our experiments on two established video captioning datasets verifies the effectiveness of the proposed approach based on automatic metrics. We further conduct a human evaluation to measure the grounding and relevance of the generated captions and observe consistent improvement for the proposed model. The codes and trained models can be found at https://github.com/hassanhub/R3Transformer

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves, i know that sounds a little bit confuse but will be clear at the end. At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the banana and the apple.