Reinforcement learning is a type of machine learning that is based on learning from outcomes to make decisions. Reinforcement learning algorithms learn from their own actions and experiences in an environment, rather than from labeled data or explicit feedback. The goal of reinforcement learning is to find an optimal policy that maximizes a cumulative reward over time. A policy is a rule that determines what action to take ineach state of the environment. A reward is a feedback signal that indicates how good or bad an action was for achieving a desired objective. Reinforcement learning involves a trial-and-error process of exploring different actions and observing their consequences, and then updating the policy accordingly. Some of the challenges and components of reinforcement learning are:
Exploration vs exploitation: Balancing between trying new actions that might lead to higher rewards in the future (exploration) and choosing known actions that yield immediate rewards (exploitation).
Markov decision process (MDP): A mathematical framework for modeling sequential decision making problems under uncertainty, where the outcomes depend only on the current state and action, not on the previous ones.
Value function: A function that estimates the expected long-term return of each state or state-action pair, based on the current policy.
Q-learning: A popular reinforcement learning algorithm that learns a value function called Q-function, which represents the quality of taking a certain action in a certain state.
Deep reinforcement learning: A branch of reinforcement learning that combines deep neural networks with reinforcement learning algorithms to handle complex and high-dimensional problems, such as playing video games or controlling robots. References: : Reinforcement learning - Wikipedia, What is Reinforcement Learning? – Overview of How it Works - Synopsys
Questions 5
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Options:
A.
They ensure that the model size, training time, and data size are balanced for optimal results.
B.
They disregard model size and prioritize high-quality data only.
C.
They focus on increasing the number of tokens while keeping the model size constant.
D.
They prioritize larger model sizes to achieve better performance.
Large language models are trained on massive amounts of data to capture the complexity and diversity of natural language. Larger model sizes mean more parameters, which enable the model to learn more patterns and nuances from the data. Larger models also tend to generalize better to new tasks and domains. However, larger models also require more computational resources, data quality, and data size to train and deploy. Therefore, large language models handle the trade-off by prioritizing larger model sizes to achieve better performance, while using various techniques to optimize the training and inference efficiency4. References: Artificial Intelligence (AI) | Oracle
Questions 6
What is the primary function of Oracle Cloud Infrastructure Speech service?
Oracle Cloud Infrastructure Speech is an AI service that applies automatic speech recognition (ASR) technology to transform audio-based content into text. Developers can easily make API calls to integrate Speech’s pretrained models into their applications. Speech can be used for accurate, text-normalized, time-stamped transcription via the console and REST APIs as well as command-line interfaces or SDKs. You can also use Speech in an OCI Data Science notebook session. With Speech, you can filter profanities, get confidence scores for both single words and complete transcriptions, and more1. References: Speech AI Service that Uses ASR | OCI Speech - Oracle
Questions 7
What is "in-context learning" in the realm of large Language Models (LLMs)?
Options:
A.
Teaching a mode! through zero-shot learning
B.
Training a model on a diverse range of tasks
C.
Modifying the behavior of a pretrained LLM permanently
D.
Providing a few examples of a target task via the input prompt
In-context learning is a technique that leverages the ability of large language models to learn from a few input-output examples provided in the input prompt. By conditioning on these examples, the model can infer the task and the format of the desired output, and generate a suitable response. In-context learning does not require any additional training or fine-tuning of the model, and can be used for various tasks such as text summarization, question answering, text generation, and more45. In-context learning is also known as few-shot learning or prompt-based learning. References: [2307.12375] In-Context Learning in Large Language Models Learns Label …](https://arxiv.org/abs/2307.12375), [2307.07164] Learning to Retrieve In-Context Examples for Large Language Models] (https://arxiv.org/abs/2307.07164)
