Through this Relearning-basedPostgraduate diploma, you will master the most innovative Artificial Intelligence techniques to optimize machine translation in multilingual environments”

The advancement of Artificial Intelligence techniques is offering unprecedented opportunities for experts managing multilingual environments. For example, Deep Neural Networks are enabling automatic interpretation in many languages and optimizing complex tasks (including real-time translation and content personalization). However, to enjoy its benefits, translators need to acquire advanced skills to handle digital tools such as TensorFlor, PyTorch or Google Dialogflow with precision. Only then will professionals be able to develop interfaces such as chatbots to improve the quality of multilingual communications in real time.

To facilitate this task, TECH presents a pioneering program in Integration of Artificial Intelligence Techniques for Multilanguage Support. The academic itinerary will delve into issues ranging from the training of Machine Learning models to the use of specific applications for automatic interpretation with speech recognition. In this way, graduates will develop advanced skills to skillfully use translation tools such as Speechmatics, Kaldi or OTTER.ai. Also, the syllabus will delve into the creation of digital interfaces such as virtual assistants through Deep Learning systems, which will allow graduates to adapt to the linguistic preferences of users and perform more rigorous interpretations according to the tone of the conversion.

It should be noted that the methodology of this university program reinforces its innovative nature. TECH offers a 100% online academic environment, adapted to the needs of busy translators looking to experience a leap in quality in their careers. It also uses its revolutionary Relearning methodology, based on the repetition of key concepts to fix knowledge and facilitate learning. On the other hand, the only thing students will need is an electronic device with Internet access (such as a cell phone, computer or tablet) to access the Virtual Campus and enjoy the most dynamic academic materials on the educational market.

You will increase your knowledge from real cases and the resolution of complex situations in simulated learning environments”

This Postgraduate diploma in Integration of Artificial Intelligence Techniques for Multilanguage Support contains the most complete and up-to-date program on the market. The most important features include: 

• The development of case studies presented by experts in Artificial Intelligence applied to Translation and Interpreting
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning.
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Do you want to apply the most effective Machine Learning techniques to ensure consistency in the terminology of translated content? Get it through this university program”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts in the field of educational coaching with extensive experience.   

You will delve into the latest trends to improve automatic interpretation with speech recognition and ensure data privacy"

You will study from the comfort of your home and update your knowledge online with TECH, the world's largest online university"

Conceived by renowned specialists in Artificial Intelligence applied to Translation and Interpreting, the course will delve into the implementation of modern Machine Learning algorithms. In this way, students will develop advanced skills to train and customize Machine Learning and Deep Neural Network models to optimize the quality of automatic interpretations in different languages and linguistic contexts. In addition, the teaching materials will analyze the keys to design interfaces such as chatbots through specialized tools such as TensorFlow, OpeanAI and PyTorch. Thanks to this, professionals will create various multilingual virtual assistants to improve translation efficiency.

You will master the most sophisticated Natural Language Processing strategies to improve the fluency of machine translations in various languages” 

Module 1. Artificial Intelligence and Real-Time Translation 

1.1. Introduction to Real-Time Translation with Artificial Intelligence

1.1.1. Definition and Basic Concepts
1.1.2. Importance and Applications in Different Contexts
1.1.3. Challenges and Opportunities
1.1.4. Tools such as Fluently or Voice Tra

1.2. Artificial Intelligence Fundamentals in Translation

1.2.1. Brief Introduction to Artificial Intelligence
1.2.2. Specific Applications in Translation
1.2.3  Relevant Models and Algorithms

1.3. AI-Based Real-Time Translation Tools

1.3.1. Description of the Main Tools Available
1.3.2. Comparison of Functionalities and Features
1.3.3. Use Cases and Practical Examples

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

1.4.1. Principles and Operation of NMT Models
1.4.2. Advantages over Traditional Approaches
1.4.3. Development and Evolution of NMT Models

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

1.5.1. Basic NLP Concepts Relevant to Translation
1.5.2. Preprocessing and Post-Processing Techniques
1.5.3. Improving the Coherence and Cohesion of the Translated Text

1.6. Multilingual and Multimodal Translation Models

1.6. 1 Translation Models that Support Multiple Languages
1.6.2. Integration of Modalities such as Text, Speech and Images
1.6.3. Challenges and Considerations in Multilingual and Multimodal Translation

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

1.7.1. Translation Quality Assessment Metrics
1.7.2. Automatic and Human Evaluation Methods. iTranslate Voice
1.7.3. Strategies to Improve Translation Quality

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

1.8.1. Use of Translation Tools in Daily Work
1.8.2. Integration with Content Management and Localization Systems
1.8.3. Adaptation of Tools to Specific User Needs

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

1.9.1. Biases and Discrimination in Machine Translation
1.9.2. Privacy and Security of User Data
1.9.3. Impact on Linguistic and Cultural Diversity

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

1.10.1. Emerging Trends and Technological Advances
1.10.2. Future Prospects and Potential Innovative Applications
1.10.3. Implications for Global Communication and Language Accessibility

Module 2. Integration of Speech Recognition Technologies in Machine Interpreting

2.1. Introduction to the Integration of Speech Recognition Technologies in Machine Interpreting

2.1.1. Definition and Basic Concepts
2.1.2. Brief History and Evolution. Kaldi
2.1.3. Importance and Benefits in the Field of Interpretation

2.2. Principles of Speech Recognition for Machine Interpreting

2.2.1. How Speech Recognition Works
2.2.2. Technologies and Algorithms Used
2.2.3. Types of Speech Recognition Systems

2.3. Development and Improvements in Speech Recognition Technologies

2.3.1. Recent Technological Advances. Speech Recognition
2.3.2. Improvements in Accuracy and Speed
2.3.3. Adaptation to Different Accents and Dialects

2.4. Speech Recognition Platforms and Tools for Machine Interpreting

2.4.1. Description of the Main Platforms and Tools Available
2.4.2. Comparison of Functionalities and Features
2.4.3. Use Cases and Practical Examples. Speechmatics

2.5. Integrating Speech Recognition Technologies into Machine Interpreting Systems

2.5.1. Design and Implementation of Machine Interpreting Systems with Speech Recognition
2.5.2. Adaptation to Different Interpreting Environments and Situations
2.5.3. Technical and Infrastructure Considerations

2.6. Optimization of the User Experience in Machine Interpreting with Speech Recognition

2.6.1. Design of Intuitive and Easy to Use User Interfaces
2.6.2. Customization and Configuration of Preferences. OTTER.ai
2.6.3. Accessibility and Multilingual Support in Machine Interpreting Systems

2.7. Assessment of the Quality in Machine Interpreting with Speech Recognition

2.7.1. Interpretation Quality Assessment Metrics
2.7.2. Machine vs. Human Evaluation
2.7.3. Strategies to Improve the Quality in Machine Interpreting with Speech Recognition

2.8. Ethical and Social Challenges in the Use of Speech Recognition Technologies in Machine Interpreting

2.8.1. Privacy and Security of User Data
2.8.2. Biases and Discrimination in Speech Recognition
2.8.3. Impact on the Interpreting Profession and on Linguistic and Cultural Diversity

2.9. Specific Applications of Machine Interpreting with Speech Recognition

2.9.1. Real-Time Interpreting in Business and Commercial Environments
2.9.2. Remote and Telephonic Interpreting with Speech Recognition
2.9.3. Interpreting at International Events and Conferences

2.10. Future of the Integration of Speech Recognition Technologies in Machine Interpreting  

2.10.1. Emerging Trends and Technological Developments. CMU Sphinx
2.10.2. Future Prospects and Potential Innovative Applications
2.10.3. Implications for Global Communication and Elimination of Language Barriers

Module 3. Design of Multilanguage Interfaces and Chatbots Using Artificial Intelligence Tools 

3.1. Fundamentals of Multilanguage Interfaces

3.1.1. Design Principles for Multilingualism: Usability and Accessibility with Artificial Intelligence
3.1.2. Key Technologies: Using TensorFlow and PyTorch for Interface Development
3.1.3. Case Studies: Analysis of Successful Interfaces Using Artificial Intelligence

3.2. Introduction to Chatbots with Artificial Intelligence

3.2.1. Evolution of Chatbots: from Simple to Artificial Intelligence-Driven
3.2.2. Comparison of Chatbots: Rules vs. Artificial Intelligence-Based Models
3.2.3. Components of AI-Driven Chatbots: Use of Natural Language Understanding (NLU)

3.3. Multilanguage Chatbot Architectures with Artificial Intelligence

3.3.1. Designing Scalable Architectures with IBM Watson
3.3.2. Integrating Chatbots into Platforms with Microsoft Bot Framework
3.3.3. Updating and Maintenance with Artificial Intelligence Tools

3.4. Natural Language Processing (NLP) for Chatbots

3.4.1. Syntactic and Semantic Parsing with Google BERT
3.4.2. Language Model Training with OpenAI GPT
3.4.3. Application of PLN Tools such as spaCy in Chatbots

3.5. Development of Chatbots with Artificial Intelligence Frameworks

3.5.1. Implementation with Google Dialogflow
3.5.2. Creating and Training Dialog Flows with IBM Watson
3.5.3. Advanced Customization Using AI APIs such as Microsoft LUIS

3.6. Conversation and Context Management in Chatbots

3.6.1. State Models with Rasa for Chatbots
3.6.2. Conversational Management Strategies with Deep Learning
3.6.3. Real-Time Ambiguity Resolution and Corrections Using Artificial Intelligence

3.7. UX/UI Design for Multilanguage Chatbots with Artificial Intelligence

3.7.1. User-Centered Design Using Artificial Intelligence Analytics
3.7.2. Cultural Adaptation with Automatic Localization Tools
3.7.3. Usability Testing with Artificial Intelligence-Based Simulations

3.8. Integration of Multi-Channel Chatbots with Artificial Intelligence

3.8.1. Omni-Channel Development with TensorFlow
3.8.2. Secure and Private Integration Strategies with Artificial Intelligence Technologies
3.8.3. Security Considerations with Artificial Intelligence Cryptography Algorithms

3.9. Data Analysis and Chatbot Optimization

3.9.1. Use of Analytics Platforms such as Google Analytics for Chatbots
3.9.2. Performance Optimization with Machine Learning Algorithms
3.9.3. Machine Learning for Continuous Chatbot Refinement

3.10. Multilanguage Chatbot Architectures with Artificial Intelligence

3.10.1. Project Definition with Artificial Intelligence Management Tools
3.10.2. Technical Implementation Using TensorFlow or PyTorch
3.10.3. Evaluation and Tuning Based on Machine Learning and User Feedback

A highly flexible curriculum based on free access to content, which you can access from your electronic device of choice. Even from your smartphone!”