Un programa exhaustivo y 100% online, exclusivo de TECH y con una perspectiva internacional respaldada por nuestra afiliación con European Society for Translation Studies” 

Según un reciente informe realizado por la Organización de las Naciones Unidas, la implementación de herramientas emergentes de la Inteligencia Artificial ha permitido optimizar un 50% la accesibilidad a contenido multilingüe en los proyectos de desarrollo global. Por eso, es fundamental que los especialistas se mantengan al corriente de las técnicas más sofisticadas de Deep Learning y entrenamiento de algoritmos para mejorar la Traducción en sectores críticos como la salud, la educación o los Derechos Humanos.

Con el objetivo de facilitar esta puesta al día, TECH ha creado un pionero Postgraduate diploma en Application of Artificial Intelligence Techniques for Machine Translation. Diseñado por referencias en este ámbito, el itinerario académico ahondará en cuestiones que abarcan desde los diferentes modelos probabilísticos de lingüística o sistemas de detección de emociones hasta la generación de texto autorregresivos. De esta manera, los egresados obtendrán competencias avanzadas tanto para diseñar como entrenar y optimizar algoritmos como Redes Neuronales. Asimismo, los materiales didácticos profundizarán en el manejo de software de última generación con el objetivo de que los alumnos realicen interpretaciones automáticas de voz en situaciones especiales que requieren una comunicación inmediata y directa.

En lo que respecta a la metodología de la titulación universitaria, se imparte de forma 100% online para que los profesionales de la Traducción puedan planificar individualmente sus horarios y ritmo de estudio. Además, TECH emplea su disruptivo método del Relearning, consistente en la reiteración natural y progresiva de los conceptos esenciales del temario para garantizar su óptima comprensión.

Como miembro de la European Society for Translation Studies (EST), TECH ofrece a sus alumnos acceso a recursos exclusivos, becas para escuelas de verano, premios para jóvenes investigadores, descuentos en libros especializados y la posibilidad de participar en congresos internacionales. Esta afiliación potencia su capacitación académica, fomenta la colaboración con expertos globales y amplía sus oportunidades profesionales en el ámbito de los estudios de traducción.

Extraerás valiosas lecciones a través de casos prácticos reales en entornos simulados de aprendizaje”

Esta Postgraduate diploma enApplication of Artificial Intelligence Techniques for Machine Translation contiene el programa universitario más completo y actualizado del mercado. Sus características más destacadas son:

• El desarrollo de casos prácticos presentados por expertos en Inteligencia Artificial enfocada a la Traducción e Interpretación
• Los contenidos gráficos, esquemáticos y eminentemente prácticos con los que está concebido recogen una información práctica sobre aquellas disciplinas indispensables para el ejercicio profesional
• Los ejercicios prácticos donde realizar el proceso de autoevaluación para mejorar el aprendizaje
• Su especial hincapié en metodologías innovadoras
• Las lecciones teóricas, preguntas al experto, foros de discusión de temas controvertidos y trabajos de reflexión individual
• La disponibilidad de acceso a los contenidos desde cualquier dispositivo fijo o portátil con conexión a internet

¿Buscas implementar en tu praxis diaria las técnicas más modernas de la Inteligencia Artificial para traducir automáticamente lenguajes complejos como jergas o tecnicismos? Lógralo con esta titulación”

El programa incluye en su cuadro docente a profesionales del sector que vierten en esta capacitación la experiencia de su trabajo, además de reconocidos especialistas de sociedades de referencia y universidades de prestigio.

Su contenido multimedia, elaborado con la última tecnología educativa, permitirá al profesional un aprendizaje situado y contextualizado, es decir, un entorno simulado que proporcionará una capacitación inmersiva programada para entrenarse ante situaciones reales.

El diseño de este programa se centra en el Aprendizaje Basado en Problemas, mediante el cual el profesional deberá tratar de resolver las distintas situaciones de práctica profesional que se le planteen a lo largo del curso académico. Para ello, contará con la ayuda de un novedoso sistema de vídeo interactivo realizado por reconocidos expertos.

Profundizarás en el empleo de plataformas de Traducción Asistida avanzadas como Wordbee, lo que te permitirá realizar controles de calidad a fin de detectar inconsistencias terminológicas habituales como faltas de ortografía"

Con la disruptiva metodología Relearning aplicada por TECH, afianzarás los conceptos más complejos del temario de forma natural y progresiva” 

This program has been designed by real experts in Artificial Intelligence applied to Machine Translation. The curriculum will delve into aspects ranging from the implementation of Linguistic Learning models or sentiment analysis systems to different speech recognition methods. In this way, students will develop advanced skills to train and adjust Deep Learning techniques according to different languages and contexts. In addition, the syllabus will analyze the most cutting-edge strategies of Natural Language Processing, which will allow graduates to perform translations of complex grammatical structures in real time and generate fluent texts.

You will handle the most sophisticated algorithms to optimize diverse Machine Translation systems based on Artificial Intelligence, which will allow you to adapt your interpretations to different linguistic contexts”

Module 1. Linguistic Models and Artificial Intelligence Application 

1.1. Classical Models of Linguistics and their Relevance to Artificial Intelligence

1.1.1. Generative and Transformational Grammar
1.1.2. Structural Linguistic Theory
1.1.3. Formal Grammar Theory
1.1.4. Applications of Classical Models in Artificial Intelligence

1.2. Probabilistic Models in Linguistics and Their Application in Artificial Intelligence

1.2.1. Hidden Markov Models (HMM)
1.2.2. Statistical Language Models
1.2.3. Supervised and Unsupervised Learning Algorithms
1.2.4. Applications in Speech Recognition and Text Processing

1.3. Rule-Based Models and Their Implementation in AI. GPT

1.3.1. Formal Grammars and Rule Systems
1.3.2. Knowledge Representation and Computational Logic
1.3.3. Expert Systems and Inference Engines
1.3.4. Applications in Dialog Systems and Virtual Assistants

1.4. Deep Learning Models in Linguistics and Their Use in Artificial Intelligence

1.4.1. Convolutional Neural Networks for Text Processing
1.4.2. Recurrent Neural Networks and LSTM for Sequence Modeling
1.4.3. Attention Models and Transformers. APERTIUM
1.4.4. Applications in Machine Translation, Text Generation and Sentiment Analysis

1.5. Distributed Language Representations and Their Impact on Artificial Intelligence

1.5.1. Word Embeddings and Vector Space Models
1.5.2. Distributed Representations of Sentences and Documents
1.5.3. Bag-of-Words Models and Continuous Language Models
1.5.4. Applications in Information Retrieval, Document Clustering and Content Recommendation

1.6. Machine Translation Models and Their Evolution in AI. Lilt

1.6.1. Statistical and Rule-Based Translation Models
1.6.2. Advances in Neural Machine Translation
1.6.3. Hybrid Approaches and Multilingual Models
1.6.4. Applications in Online Translation and Content Localization Services

1.7. Sentiment Analysis Models and Their Usefulness in Artificial Intelligence

1.7.1. Sentiment Classification Methods
1.7.2. Detection of Emotions in Text
1.7.3. Analysis of User Opinions and Comments
1.7.4. Applications in Social Networks, Analysis of Product Opinions and Customer Service

1.8. Language Generation Models and Their Application in AI. TransPerfect Globallink

1.8.1. Autoregressive Text Generation Models
1.8.2. Conditioned and Controlled Text Generation
1.8.3. GPT-Based Natural Language Generation Models
1.8.4. Applications in Automatic Typing, Text Summarization, and Intelligent Conversation

1.9. Speech Recognition Models and Their Integration in Artificial Intelligence

1.9.1. Audio Feature Extraction Methods
1.9.2. Speech Recognition Models Based on Neural Networks
1.9.3. Improvements in Speech Recognition Accuracy and Robustness
1.9.4. Applications in Virtual Assistants, Transcription Systems and Speech-based Device Control

1.10. Challenges and Future of Linguistic Models in Artificial Intelligence

1.10.1. Challenges in Natural Language Understanding
1.10.2. Limitations and Biases in Current Linguistic Models
1.10.3. Research and Future Trends in Artificial Intelligence Linguistic Modeling
1.10.4. Impact on Future Applications such as General Artificial Intelligence (AGI) and Human Language Understanding. SmartCAt

Module 2. Artificial Intelligence and Real-Time Translation

2.1. Introduction to Real-Time Translation with Artificial Intelligence

2.1.1. Definition and Basic Concepts
2.1.2. Importance and Applications in Different Contexts
2.1.3. Challenges and Opportunities
2.1.4. Tools such as Fluently or Voice Tra

2.2. Artificial Intelligence Fundamentals in Translation

2.2.1. Brief Introduction to Artificial Intelligence
2.2.2. Specific Applications in Translation
2.2.3. Relevant Models and Algorithms

2.3. Artificial Intelligence-Based Real-Time Translation Tools

2.3.1. Description of the Main Tools Available
2.3.2. Comparison of Functionalities and Features
2.3.3. Use Cases and Practical Examples

2.4. Neural Machine Translation (NMT) Models. SDL Language Cloud

2.4.1. Principles and Operation of NMT Models
2.4.2. Advantages over Traditional Approaches
2.4.3. Development and Evolution of NMT Models

2.5. Natural Language Processing (NLP) in Real-Time Translation. SayHi TRanslate

2.5.1. Basic NLP Concepts Relevant to Translation
2.5.2. Preprocessing and Post-Processing Techniques
2.5.3. Improving the Coherence and Cohesion of the Translated Text

2.6. Multilingual and Multimodal Translation Models

2.6.1. Translation Models that Support Multiple Languages
2.6.2. Integration of Modalities such as Text, Speech and Images
2.6.3. Challenges and Considerations in Multilingual and Multimodal Translation

2.7. Quality Assessment in Real-Time Translation with Artificial Intelligence

2.7.1. Translation Quality Assessment Metrics
2.7.2. Automatic and Human Evaluation Methods. iTranslate Voice
2.7.3. Strategies to Improve Translation Quality

2.8. Integration of Real-Time Translation Tools in Professional Environments

2.8.1. Use of Translation Tools in Daily Work
2.8.2. Integration with Content Management and Localization Systems
2.8.3. Adaptation of Tools to Specific User Needs

2.9. Ethical and Social Challenges in Real-Time Translation with Artificial Intelligence

2.9.1. Biases and Discrimination in Machine Translation
2.9.2. Privacy and Security of User Data
2.9.3. Impact on Linguistic and Cultural Diversity

2.10. Future of AI-Based Real-Time Translation. Applingua

2.10.1. Emerging Trends and Technological Advances
2.10.2. Future Prospects and Potential Innovative Applications
2.10.3. Implications for Global Communication and Language Accessibility

Module 3. Artificial Intelligence-Assisted Translation Tools and Platforms

3.1. Introduction to Artificial Intelligence-Assisted Translation Tools and Platforms

3.1.1. Definition and Basic Concepts
3.1.2. Brief History and Evolution
3.1.3. Importance and Benefits in Professional Translation

3.2. Main Artificial Intelligence-Assisted Translation Tools

3.2.1. Description and Functionalities of the Leading Tools on the Market
3.2.2. Comparison of Features and Prices
3.2.3. Use Cases and Practical Examples

3.3. Professional AI-Assisted Translation Platforms. Wordfast

3.3.1. Description of Popular Artificial Intelligence-Assisted Translation Platforms
3.3.2. Specific Functionalities for Translation Teams and Agencies
3.3.3. Integration with Other Project Management Systems and Tools

3.4. Machine Translation Models Implemented in AI-Assisted Translation Tools

3.4.1. Statistical Translation Models
3.4.2. Neural Translation Models
3.4.3. Advances in Neural Machine Translation (NMT) and Its Impact on AI-Assisted Translation Tools

3.5. Integration of Linguistic Resources and Databases in AI-Assisted Translation Tools

3.5.1. Using Corpus and Linguistic Databases to Improve Translation Accuracy
3.5.2. Integrating Specialized Dictionaries and Glossaries
3.5.3. Importance of Context and Specific Terminology in Artificial Intelligence-Assisted Translation

3.6. User Interface and User Experience in AI-Assisted Translation Tools

3.6.1. User Interface Design and Usability
3.6.2. Customization and Preference Settings
3.6.3. Accessibility and Multilingual Support on AI-Assisted Translation Platforms

3.7. Quality Assessment in Artificial Intelligence-Assisted Translation

3.7.1. Translation Quality Assessment Metrics
3.7.2. Machine vs. Human Evaluation
3.7.3. Strategies to Improve the Quality of Artificial Intelligence-Assisted Translation

3.8. Integration of AI-Assisted Translation Tools into the Translator's Workflow

3.8.1. Incorporation of AI-Assisted Translation Tools into the Translation Process
3.8.2. Optimizing Workflow and Increasing Productivity
3.8.3. Collaboration and Teamwork in Artificial Intelligence-Assisted Translation Environments

3.9. Ethical and Social Challenges in the Use of AI-Assisted Translation Tools

3.9.1. Biases and Discrimination in Machine Translation
3.9.2. Privacy and Security of User Data
3.9.3. Impact on the Translation Profession and on Linguistic and Cultural Diversity

3.10. Future of AI-Assisted Translation Tools and IA. Wordbee

3.10.1. Emerging Trends and Technological Developments
3.10.2. Future Prospects and Potential Innovative Applications
3.10.3. Implications for Training and Professional Development in the Field of Translation

A unique training experience, key and decisive to boost your professional development"