16. Emerging Technology Flashcards
(119 cards)
1
Q
MACHINE LEARNING
What is machine learning?
A
Machine learning is a branch of artificial intelligence (AI) that enables computers to learn and improve without being explicitly programmed.
2
Q
What is artificial intelligence
(Al)
A
a computer science/ IT discipline that aims to build computer systems that can replicate human
intelligence
3
Q
How does machine learning differ from expert systems?
A
Expert systems use pre-set rules to make decisions, while machine learning identifies patterns in data and learns from them without predefined rules.
4
Q
What is a limitation of expert systems compared to machine learning?
A
Expert systems operate in a narrow scope and struggle with data that doesn’t fit predetermined rules.
5
Q
What is the core process involved in machine learning?
A
Training the system with large quantities of data so it can identify patterns and make predictions.
6
Q
Why is machine learning useful for identifying patterns?
A
It can detect complex correlations and trends that might be difficult for humans to spot.
7
Q
What are the benefits of using machine learning in various industries?
A
It provides insights that may go unnoticed and can lead to performance improvements or a competitive edge.
8
Q
CONCEPT, FEATURES AND FUNCTIONS OF MACHINE LEARNING
What are the four main features of a machine learning system?
A
Algorithms, data, training, and predictions.
9
Q
Algorithms
What is the role of algorithms in machine learning?
A
Algorithms are sets of instructions that help the system learn from data and improve over time through mathematical and statistical processes.
10
Q
What are pre-programmed AI model packages used for?
A
They allow companies to identify problem types and choose suitable pre-written algorithms to solve them.
11
Q
the main types of problems that are solved by
machine learning are
A
Regression problems
Classification problems:
Clustering problems
Anomaly detection:
12
Q
What is a regression problem in machine learning?
A
A supervised learning method used to predict continuous outcomes by fitting a straight line to data, such as forecasting future sales.
13
Q
What is a classification problem in machine learning?
A
A supervised learning technique used to predict categorical class labels based on past data, like identifying spam emails.
14
Q
What is clustering in machine learning?
A
An unsupervised learning method that groups similar data points based on characteristics, e.g., grouping customers by purchase history.
15
Q
what is anomaly detection in machine learning?
A
A method used in both supervised and unsupervised models to identify data points that deviate from normal behavior, like detecting fraudulent transactions.
16
Q
Data
What has contributed to the recent growth of machine learning?
A
The increasing amount of data generated by computer systems and connected devices.
17
Q
What types of data can be used in machine learning?
A
Text, images, numbers, and sounds.
18
Q
What determines the machine learning model that will be used?
A
The type of data and the type of problem being solved.
19
Q
Training
What does the ‘training’ phase in machine learning involve?
A
Feeding data to the algorithm so that the system can learn from it by identifying patterns and adjusting its internal parameters.
20
Q
How is the data typically divided for training machine learning models?
A
training set and a test set.
21
Q
What is the purpose of the training set?
A
To teach the model by allowing it to learn from the patterns in the data and reduce its prediction error over time.
22
Q
How does a machine learning model adjust during training?
A
It compares its predictions with actual outcomes and adjusts its parameters to improve accuracy.
23
Q
What is the purpose of the test set?
A
To evaluate the model’s performance using unseen data, helping assess accuracy and generalization.
24
Q
Why might multiple iterations of training and testing be necessary?
A
To refine the model and ensure it performs as required, especially in complex problems.
25
Supervised and unsupervised learning
What are the two main approaches to training a machine learning model?
Supervised learning and unsupervised learning.
26
What is supervised learning?
the use of labelled data to train a machine learning model
27
What is the goal of supervised learning?
To determine a mapping function between the input data and the output labels so the model can make accurate predictions on new data.
28
What types of problems is supervised learning used for?
egression and classification problems.
29
What is unsupervised learning:
the use of unlabelled data to train a machine learning
model
30
How does unsupervised learning differ from supervised learning?
It does not use labelled data and instead looks for patterns or structures within the data without prior knowledge of what those patterns might be.
31
What is the main goal of unsupervised learning?
To identify hidden patterns or structures in data to draw inferences and insights.
32
Why is unsupervised learning useful for companies?
It helps uncover hidden patterns without being influenced by user bias or assumptions.
33
What types of problems is unsupervised learning used for?
Clustering and dimensionality reduction problems.
34
IMPACT AND POSSIBILITIES OF MACHINE LEARNING
machine learning have tools that can have a large impact on individuals and organisations.
What are they
Natural language processing
Speech recognition
Image recognition
Pattern recognition
35
Natural language processing
What is Natural Language Processing (NLP)?
NLP is a branch of machine learning that focuses on enabling computers to understand, interpret, and generate human language in a meaningful and useful way.
36
What advantage does NLP have over traditional computer language processing?
NLP considers context and regional language variations, improving accuracy and relevance of results.
37
NLP continues to have an impact on a number of areas.
which are
1. Improved search engine results:
By understanding user intent more accurately, leading to more relevant and useful search outcomes.
2. Virtual assistants:
It helps them understand accents, regional variations, and non-typical phrasing to better interpret spoken commands.
3. Automated language translation
It enables systems to produce translations closer to those of native speakers, rather than just literal word-for-word translations.
4. Chatbots for customer service
NLP enhances chatbot accuracy and efficiency, handling routine queries and freeing human agents for more complex issues.
38
Why is NLP significant for businesses and organizations?
It boosts communication capabilities, enhances customer service, and supports automation of language-based tasks.
39
Speech recognition
What has contributed to the improved accuracy and usability of modern speech recognition?
Increases in computing power,
access to large datasets
more complex algorithms.
39
How do modern tools like Siri and Alexa enhance speech recognition?
By using vast data stores and natural language processing to accurately understand spoken language.
40
Image recognition
What is the goal of image recognition in machine learning?
To improve a computer’s accuracy in processing and understanding visual content.
41
Why is image recognition challenging for computers?
Because of the significant variation in the content of images, which makes interpretation complex.
42
applications of image recognition
1. Self-driving cars:
To identify objects like pedestrians, vehicles, and traffic signs to support safe decision-making.
2. Medical imaging:
By detecting abnormalities in scans or X-rays that may be too small for doctors to see, enabling earlier intervention.
3. Security and surveillance:.
It helps identify suspicious activities or individuals in video footage.
4. Social media:
For automatically tagging people or objects in photos.
5. Product search:
By enabling faster and more accurate searches using images.
6. Assistive technology:
image recognition uses the camera of a smartphone to identify objects and provides audible descriptions of the objects for people with sight-loss.
43
Pattern recognition
What is pattern recognition in the context of machine learning?
The process of identifying trends, correlations, and similarities in data sets.
44
How is pattern recognition used in machine learning?
Machine learning algorithms use it to discover, learn, and adapt to patterns in data.
45
why can machine learning models often outperform humans in pattern recognition?
Because they can process and analyze data on a much larger scale and identify complex patterns that humans may miss.
46
Is pattern recognition limited to a specific area of machine learning?
No, it is a core concept used across all aspects of machine learning.
47
**VIRTUAL AND AUGMENTED REALITY**
VIRTUAL REALITY
What is virtual reality (VR)?
A technology that provides users with an immersive experience within a computer-generated environment.
48
What is the goal of VR technology?
To make users feel as if they are actually present in a virtual world.
49
VR systems have several key components:
which are
1. Headsets
VR headsets display the virtual world using stereoscopic screens and often include headphones and head-tracking to create a 3D immersive effect.
2. Software:
software generates the visuals, sounds, and haptic feedback that create the virtual experience in VR
3. Controllers
To interact with the virtual environment, simulating real-world actions like picking up objects or moving through landscapes.
50
: How is VR used in education?
It allows students to explore subject content in immersive ways, such as visiting historical sites or exploring underwater environments.
50
Uses of VR
Education
Training
Design and engineering:
50
What is an example of VR use in training?
Pilots can practice emergency scenarios and doctors can rehearse complex surgeries in a safe, controlled environment.
50
How does VR benefit design and engineering?
It provides 3D simulations of buildings, helping architects detect design flaws and gather feedback before construction begins.
50
uses of AR
Education:
Navigation
Entertainment
51
AUGMENTED REALITY
How does augmented reality (AR) differ from virtual reality (VR)?
AR blends virtual and real worlds by overlaying computer-generated content onto the real world, while VR fully immerses the user in a simulated environment.
52
What is an example of AR in retail?
Letting users preview how furniture like chairs or tables would look in their room before purchasing.
53
How is AR used in education?
By adding virtual tags or labels to physical items to help users learn about components, such as in maintenance training.
54
What is an example of AR in navigation?
Superimposing directions and information onto buildings and roads through augmented maps.
55
How is AR used in entertainment?
Through interactive games that combine digital elements with the real world to create immersive experiences.
56
**INTERNET OF THINGS**
What is the Internet of Things (IoT)?
A network of non-traditionally smart devices (like appliances) that can generate, send, and receive data.
57
What are examples of IoT devices in the home?
Thermostats, lights, and security cameras that can be monitored and controlled via smartphone apps.
58
three key concepts that drive the loT:
Connecting devices
Exchanging data:
Automating tasks
59
Give an example of an automated IoT task.
A system that waters plants based on environmental data or alerts users about high pollen levels.
60
IMPACT OF THE INTERNET OF THINGS
How has the Internet of Things (IoT) impacted daily life?
It enables convenience and automation, such as tracking orders and managing home devices, even for people without fully smart homes.
61
BENEFITS OF IoT
name them
Convenience
Efficiency
Safety
62
Convenience
Give an example of how IoT provides convenience.
A coffee machine communicating with an alarm clock to brew coffee when you wake up.
63
Efficiency
How does IoT contribute to efficiency?
By automating routine tasks, saving time, energy, and resources for individuals and organizations.
64
Safety
What role does IoT play in improving safety?
Connected devices can detect hazards and send alerts to users or personnel.
65
Give an example of IoT safety in industrial settings.
Remote 24/7 monitoring of hazardous systems with alerts sent to key personnel.
66
How does IoT enhance safety at home?
Using sensors to monitor carbon monoxide levels and alert users if they become unsafe.
67
How do ride-hailing services like Uber use IoT for safety?
By sharing route and live location data with both drivers and passengers.
68
CHALLENGES OF IoT
What are some key challenges associated with the Internet of Things (IoT)?
Security
privacy
standardisation
environmental concerns.
69
Security
Why is security a major concern for IoT devices?
Because connecting devices to the internet increases the risk of cyberattacks, requiring robust security measures.
70
privacy
What is the privacy challenge in IoT systems?
IoT involves sharing large amounts of data between devices and systems, raising concerns over who has access and how the data is used.
71
standardisation
Why is standardisation a challenge in the Internet of Things (IoT)?
because devices and services are created by different companies and must communicate seamlessly, which can be difficult without common standards.
72
What are some examples of standardised connection types in IoT devices?
Bluetooth®, Wi-Fi, and USB.
73
environmental concerns.
What is a major environmental concern related to the disposal of IoT devices?
Improper disposal can lead to toxic substances leaking into the environment if components aren't properly recycled.
74
Why do many IoT devices have short lifespans?
Due to built-in obsolescence, software incompatibility, and consumer desire for the latest technology.
74
What is an application programming
interface (API):
a set of functions that enable two computer systems or
programs to communicate and share data
75
How does IoT contribute to increased energy consumption?
IoT devices often require constant internet connections and frequent recharging,
and the infrastructure (like data centres) uses large amounts of power.
76
Why is data storage for IoT devices an environmental issue?
The vast amount of data generated requires large data centres, which consume significant energy for processing and cooling.
77
Q: What raw material concern is associated with IoT manufacturing?
IoT devices require raw materials, including rare earth metals, contributing to resource depletion and environmental damage.
78
What pollution issues are linked to the production of IoT devices?
The use of harsh chemicals and emissions during manufacturing can cause air and water pollution.
79
: How does shipping IoT materials and devices contribute to environmental problems?
Transporting materials and devices generates greenhouse gas emissions, which contribute to climate change and global warming.
80
INFRASTRUCTURE FOR THE INTERNET OF THINGS (IoT)
infrastructure can be broadly grouped onto four main areas. Which are
sensors
networks
embedded systems
storage.
81
sensors
What is the role of sensors in the Internet of Things (IoT)?
Sensors monitor environmental or object conditions and convert them into digital signals that become usable data.
82
Why are sensors considered foundational components of IoT infrastructure?
because they capture real-world data such as temperature, humidity, motion, and biometric information, enabling everyday objects to become smart devices.
83
Give examples of simple and advanced sensors used in IoT.
simple: temperature sensor in a home
Advanced: biometric sensors that collect fingerprints and monitor heart rates.
84
Networks
Why are networks essential for IoT systems?
Networks facilitate data transmission between devices, sensors, and components, often requiring near-constant connection for IoT systems to function effectively.
85
What factors influence the choice of network technology for IoT?
Range, data bandwidth, power consumption, and scalability.
86
How has the availability of networks affected the adoption of IoT?
increased network availability and performance have made it more convenient to implement IoT systems in everyday life.
87
Embedded systems
What are embedded systems in the context of IoT?
A: Specialised computing devices integrated into IoT endpoints that typically perform specific, narrow-scope tasks.
88
Q: What components make up an embedded system?
A: A microprocessor or microcontroller, memory, and input/output peripherals like sensors, actuators, and communication modules.
89
Q: How do embedded systems differ from smart devices?
A: Embedded systems are designed for specific tasks and often lack the multifunctional capabilities of general smart devices.
90
Storage
Q: What is the role of storage systems in IoT infrastructure?
A: To store and manage the vast amount of data generated by IoT sensors and devices.
91
Q: What are the main types of data generated by IoT devices?
A: Sensor data, multimedia data, log data, and user-generated data.
92
What are four main categories for storage areas:
Edge storage
Cloud storage:
Fog computing:
Hybrid storage:
93
Q: What is cloud storage in the context of IoT?
A: Centralised storage on cloud platforms offering scalability, flexibility, and remote accessibility.
94
Q: What is edge storage?
A: Data is stored and processed on the device or nearby gateway, suitable for real-time applications and reducing network congestion.
95
Q: What is fog computing?
A: Data is processed and stored in distributed mini-data centres closer to the devices, balancing edge and cloud approaches.
96
Q: What is hybrid storage in IoT systems?
A: A combination of edge, cloud, and fog storage, balancing performance, security, and cost.
97
SECURITY ISSUES RELATING TO THE loT
Q: Why is security a major concern in IoT systems?
Privacy concerns
Data integrity
Unauthorised access
Denial of service (DoS) attacks
Insecure communication channels
98
Privacy concerns
Q: What type of personal data do IoT devices commonly collect?
A: Location information, health metrics, and behavioural patterns.
99
Q: What are the main risks of unauthorised access to IoT data?
A: Privacy violations, identity theft, and personal safety concerns.
100
Q: How can privacy be protected in IoT systems?
A: By using strong encryption and access controls to restrict data access.
101
Data integrity
Q: Why is data integrity important in IoT systems?
A: Because tampered or manipulated data can lead to serious consequences, such as dangerous industrial errors or missed health issues.
102
Q: What are two methods used to maintain data integrity in IoT systems?
A: Data validation and integrity checking.
103
Q: What does data validation involve in the context of IoT?
A: Checking whether data is within expected parameters.
104
Q: What is the purpose of integrity checking in IoT systems?
A: To detect errors, corruption, or tampering in data.
105
Unauthorised access
Q: What are common goals of unauthorised access in IoT systems?
A: Data theft, sabotage, and espionage.
106
Q: What are common vulnerabilities that allow unauthorised access in IoT systems?
A: Weak authentication, default passwords, and unpatched software.
107
Denial of service (DoS) attacks
Q: What is the goal of a Denial of Service (DoS) attack?
A: To disrupt system operations or cause service outages by overwhelming devices or networks with excessive traffic.
108
Q: Why are IoT systems especially vulnerable to DoS attacks?
A: Because they heavily rely on communication channels.
109
Q: What is network segmentation, and how does it help against DoS attacks?
A: It involves separating secure parts of a network from less-secure, exposed parts, reducing the spread and impact of attacks.
110
Insecure communication channels
Q: Why are insecure communication channels a risk for IoT devices?
A: Because they leave data vulnerable to interception and eavesdropping.
111
Q: What are common insecure channels used by IoT devices?
A: Unencrypted public Wi-Fi and Bluetooth® connections.
112
Q: How can encryption protocols help secure IoT communications?
A: Protocols like SSL/TLS or IPsec encrypt data in transit, preventing unauthorised access.
113
Q: What personal action can help reduce the risk of data interception on IoT devices?
A: Avoid connecting to open public networks like those in shopping centres, hotels, or cafes.
