A sub-field of artificial intelligence that enables systems to learn from data.
Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed.
B.
The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
C.
A field of artificial intelligence that enables computers to gain high-level understanding from digital images or videos. If AI is the brain, then this is the eye that enables the computer to observe and understand. It works the same as the human eye.
D.
An area of machine learning concerned with artificial neural networks.
These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimics biological neural networks.
Deep learning is a subset of machine learning that uses multiple layers of artificial neural networks to learn from data and perform complex tasks. The term “deep” refers to the number of layers in the network, which can range from a few to hundreds or even thousands. Each layer consists of a set of nodes that perform mathematical operations on the input data and pass the output to the next layer. The network learns by adjusting the weights of the connections between the nodes based on the feedback from the desired output. Deep learning can handle various types of data, such as images, text, speech, or video, and can automatically extract features and patterns from them without human intervention. Deep learning is behind many applications of artificial intelligence, such as computer vision, natural language processing, speech recognition, and generative models123.
References: 1: What is Deep Learning? | IBM 2: What Is Deep Learning? Definition, Examples, and Careers | Coursera 3: Deep learning - Wikipedia
Question 2
What information should be filled in when adding an entity label for the OOB (Out Of the Box) labeling template?
Options:
A.
Name. Data Type. Attribute name, and Color.
B.
Name, Data Type. Attribute name. Shortcut, and Color.
C.
Name, Shortcut, and Color.
D.
Name. Input to be labeled. Attribute name. Shortcut, and Color.
Answer:
D
Explanation:
The OOB labeling template is a predefined template that you can use to label your text data for entity recognition models. The template comes with some preset labels and text components, but you can also add your own labels using the General UI or the Advanced Editor. When you add an entity label, you need to fill in the following information:
Name: the name of the new label. This is how the label will appear in the labeling tool and in the exported data.
Input to be labeled: the text component that you want to label. You can choose from the existing text components in the template, such as Date, From, To, CC, and Text, or you can add your own text components using the Advanced Editor. The text component determines the scope of the text that can be labeled with the entity label.
Attribute name: the name of the attribute that you want to extract from the text. You can use this to create attributes such as customer name, city name, telephone number, and so on. You can add more than one attribute for the same label by clicking on + Add new.
Shortcut: the hotkey that you want to assign to the label. You can use this to label the text faster by using the keyboard. Only single letters or digits are supported.
Color: the color that you want to assign to the label. You can use this to distinguish the label from the others visually.
References: AI Center - Managing Data Labels, Data Labeling for Text - Public Preview
Question 3
Which technology enables UiPath Communications Mining to analyze and enable action on messages?
Options:
A.
Natural Language Processing (NLP)
B.
Virtual Reality.
C.
Cloud Computing.
D.
Robotic Process Automation
Answer:
A
Explanation:
UiPath Communications Mining is a new capability to understand and automate business communications. It uses state-of-the-art AI models to turn business messages—from emails to tickets—into actionable data. It does this in real time and on all major business communications channels1. Natural Language Processing (NLP) is the branch of AI that deals with analyzing, understanding, and generating natural language. NLP enables UiPath Communications Mining to extract the most important data from any message, such as reasons for contact, data fields, and sentiment2. NLP also allows UiPath Communications Mining to deploy custom AI models in hours, not weeks, by using automatic labeling and annotation2.