QUESTION: 25

What are the enablers that contribute towards the growth of artificial intelligence and its related technologies?

The introduction of 5G networks and the expansion of internet service provider coverage
The development of blockchain technology and quantum computing
The abundance of data, lower cost high-performance compute, and improved algorithms
The creation of the Internet and the widespread use of cloud computing

Answer(s): C

Explanation:

Several key enablers have contributed to the rapid growth of artificial intelligence (AI) and its related technologies. Here's a comprehensive breakdown:
Abundance of Data: The exponential increase in data from various sources (social media, IoT devices, etc.) provides the raw material needed for training complex AI models. High-Performance Compute: Advances in hardware, such as GPUs and TPUs, have significantly lowered the cost and increased the availability of high-performance computing power required to train large AI models.
Improved Algorithms: Continuous innovations in algorithms and techniques (e.g., deep learning, reinforcement learning) have enhanced the capabilities and efficiency of AI systems.

Reference:

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. Dean, J. (2020). AI and Compute. Google Research Blog.

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QUESTION: 26

What is feature-based transfer learning?

Transferring the learning process to a new model
Training a model on entirely new features
Enhancing the model's features with real-time data
Selecting specific features of a model to keep while removing others

Answer(s): D

Explanation:

Feature-based transfer learning involves leveraging certain features learned by a pre-trained model and adapting them to a new task. Here's a detailed explanation:
Feature Selection: This process involves identifying and selecting specific features or layers from a pre-trained model that are relevant to the new task while discarding others that are not. Adaptation: The selected features are then fine-tuned or re-trained on the new dataset, allowing the model to adapt to the new task with improved performance. Efficiency: This approach is computationally efficient because it reuses existing features, reducing the amount of data and time needed for training compared to starting from scratch.

Reference:

Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How Transferable Are Features in Deep Neural Networks? In Advances in Neural Information Processing Systems.

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QUESTION: 27

What strategy can an Al-based company use to develop a continuous improvement culture?

Limit the involvement of humans in decision-making processes.
Focus on the improvement of human-driven processes.
Discourage the use of Al in education systems.
Build a small Al community with people of similar backgrounds.

Answer(s): B

Explanation:

Developing a continuous improvement culture in an AI-based company involves focusing on the enhancement of human-driven processes. Here's a detailed explanation:
Human-Driven Processes: Continuous improvement requires evaluating and enhancing processes that involve human decision-making, collaboration, and innovation. AI Integration: AI can be used to augment human capabilities, providing tools and insights that help improve efficiency and effectiveness in various tasks. Feedback Loops: Establishing robust feedback loops where employees can provide input on AI tools and processes helps in refining and enhancing the AI systems continually. Training and Development: Investing in training employees to work effectively with AI tools ensures that they can leverage these technologies to drive continuous improvement.

Reference:

Deming, W. E. (1986). Out of the Crisis. MIT Press.

Senge, P. M. (2006). The Fifth Discipline: The Art & Practice of The Learning Organization. Crown Business.

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QUESTION: 28

What are common misconceptions people have about Al? (Select two)

Al can think like humans.
Al can produce biased results.
Al can learn from mistakes.
Al is not prone to generate errors.

Answer(s): A

Explanation:

There are several common misconceptions about AI. Here are two of the most prevalent:
Misconception: AI can think like humans.
Many people believe that AI systems possess human-like thinking and understanding. However, AI, including advanced systems like neural networks, does not "think" in the human sense. AI operates based on complex algorithms and large datasets, processing information and making predictions or decisions based on patterns within the data. Reality: AI lacks consciousness, emotions, and subjective experiences. It processes information syntactically rather than semantically, meaning it does not understand content in the way humans do.

Reference:

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson. Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.
Misconception: AI is not prone to generate errors.

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QUESTION: 25

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QUESTION: 28

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