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The ArIN project is co-funded by the European Union. The opinions and views expressed in this document are those of the author(s) and do not necessarily reflect those of the European Union or the Spanish Service for the Internationalization of Education (SEPIE). Neither the European Union nor the SEPIE National Agency can be held responsible for them.

Key concepts (process)

Predictive analytics, Face recognition, Natural language services, Data modeling APIs

Data search services, Image processing services, Corporation support services

Engineering services, Financial services, Data storage services

MLaaS refers to different ML services that are offered as a component of cloud computing services. What can be such services?

Key concepts (process)

Right answer!

Together, these services represent a range of AI-powered capabilities that can be harnessed in various industries and applications. They demonstrate the potential of AI to provide advanced analytics, recognize and process human features, understand and generate natural language, and model data in intelligent and efficient ways.

Key concepts (process)

Data Pre-processing and Feature Extraction, Training a Machine Learning model, Deploying the model, Invoking the model

Installing ML model to Cloud computing service, Proper formulation of a request, Receiving answers

Data acquisition, Flushing a ML model with data, Receiving answers to questions

Of what stages a typical machine learning pipeline would consist?

Key concepts (process)

Right answer!

These stages collectively form a typical machine learning pipeline, starting from data pre-processing and feature extraction, followed by model training, deployment, and finally, invoking the model to make predictions. Each stage is crucial in the overall process of building and utilizing machine learning models effectively.

Key concepts (process)

Identify objects and scenes in images that are specific to your business needs

Process Big Data and acquire information valuable for your business growth

Utilize large data storages for your business needs

What can you do in Amazon Rekognition Custom Labels platform?

Key concepts (process)

Right answer!

Amazon Rekognition Custom Labels empowers businesses to create and deploy custom machine learning models for identifying specific objects and scenes in images. This functionality provides businesses with a powerful tool to automate image analysis and leverage AI-driven object recognition tailored to their unique requirements.

AI - HUMAN INTERACTION

Cloud computing & AI

Edge computing & Cloud computing

Edge computing & AI

Edge AI is the combination of:

AI - HUMAN INTERACTION

Right answer!

By combining AI with edge computing, AI algorithms can be deployed directly on devices such as smartphones, IoT devices, or edge servers, enabling real-time and efficient processing of data at the network edge.

AI - HUMAN INTERACTION

Self-driving cars

Google

Chatbot

Which of the following is a practical example of Edge AI?

AI - HUMAN INTERACTION

Right answer!

These vehicles rely on AI algorithms and edge computing capabilities to process sensor data in real time, make driving decisions, and respond to the environment without relying solely on cloud computing.

AI - HUMAN INTERACTION

Lower latency

Higher computing capacity

Increased robustness

Which of these is NOT one of the advantages of Edge AI?

AI - HUMAN INTERACTION

Right answer!

Higher computing capacity is not one of the advantages of Edge AI. Edge AI focuses on bringing AI capabilities to edge devices with limited computing resources, such as smartphones, IoT devices, or edge servers.:

Advantages and AI applications

Code generation

Optimization

Limited domain expertise

Which of these is NOT an advantage of AI in programming?

Advantages and AI applications

Right answer!

AI systems are typically specialized and designed for specific domains or tasks. They may not possess broad knowledge or expertise outside their designated area, limiting their applicability in diverse problem domains. However, this is a fast evolving field, and soon the capabilities of IA can be expanded, allowing it to perform more complex tasks.

Advantages and AI applications

Faster and more accurate diagnoses

Increased efficiency

Enhanced patient communication

How can AI benefit the field of medicine and healthcare?

question 3/6 - HISTORY

Right answer!

AI algorithms can analyze medical images and patient data, assisting doctors in making diagnoses with greater speed and accuracy. This can lead to earlier detection of diseases and improved treatment outcomes for patients.

Advantages and AI applications

Increased customer satisfaction and market responsiveness

Improved reliability and reduced maintenance costs

Enhanced safety and accuracy in quality control

How can AI benefit industries when applied to various processes?

question 3/6 - HISTORY

Right answer!

When AI is applied to various processes in industries, it can benefit them by improving reliability and reducing maintenance costs. AI-powered predictive maintenance can help identify potential equipment failures in advance, allowing for timely maintenance to prevent breakdowns.

Risks & Threats

Only to avoid bias and discrimination in AI.

To implement appropriate security measures, protecting the users’ data.

to control the use of AI in the everyday life.

What was the objective of the development of the European Union's General Data Protection Regulation (GDPR)?

Risks & Threats

Right answer!

The objective of the European Union's General Data Protection Regulation (GDPR) was to establish a comprehensive framework for data protection and privacy for individuals within the EU.

Risks & Threats

Mainly the bias already present in the data used, which leads AI systems to reinforce those same stereotypes.

Only the human input, because data cannot be intrinsically biased.

Bias in data, but also the human input during data treatment, as well as the system's limited perspective on human life.

What can cause AI's lack of neutrality?

Risks & Threats

Right answer!

AI's lack of neutrality can also be influenced by human input during data treatment and analysis, as well as the limitations and perspectives inherent in the system itself. Understanding and addressing bias in AI is crucial for promoting fairness and ensuring AI systems are neutral and unbiased in their decision-making.

Risks & Threats

It’s impossible to reduce the bias - it will be always present in some way.

Selecting more diverse data, auditing the system, providing training to those involved in the process, and establishing more guidelines.

The most efficient measure is just to try to correct human input by training and capacitating workers.

What measures can be taken to minimise bias in AI?

Risks & Threats

Right answer!

Minimizing bias in AI requires a multifaceted approach. It involves being mindful of the data used, ensuring it represents diverse perspectives and avoiding biased datasets. Regular audits of the AI system can help identify and address biases.

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