Introduction to Artificial Intelligence Flashcards
Course 2 / 10 (135 cards)
1
Q
What is AI?
A
- Anything that makes machines act more intelligently.
- Teaching machines to learn, think, and act as humans would.
- AI is the application of computing to help machines solve problems in intelligent ways without humans having to hard code the desired outcomes manually
2
Q
How should you think of AI?
A
AI should be thought of as augmented intelligence. AI should not replace human experts but merely extend their capabilities by doing things that humans or machines could not do on their own.
3
Q
What is innate intelligence?
A
Innate intelligence is the intelligence that governs every activity in our bodies. For example, this intelligence is what causes an oak tree to grow out of a little seed.
4
Q
How does AI learn?
A
Machines are provided with the ability to examine and create machine learning models.
This is done in various ways such as supervised learning, unsupervised learning and reinforcement learning.
5
Q
What can AI be described by?
A
Strength, breadth, and application.
6
Q
What is weak or narrow AI?
A
This is AI that is applied to a specific domain. For example, language translators, self-driving cars, and recommendation engines.
Applied AI can perform specific tasks, but not learn new ones, making decisions based on programmed algorithms and training data.
7
Q
What is strong or generalised AI?
A
This is AI that can interact and operate a wide variety of independent and unrelated tasks.
It can learn new tasks to solve new problems, and it does this by teaching itself new strategies.
Generalised intelligence is the combination of many AI strategies that learn from experience and can perform at a human level of intelligence.
8
Q
What is Super AI or Conscious AI?
A
This is AI with human-level consciousness, which would require it to be self-aware.
Because we are not yet able to adequately define what consciousness is, it is unlikely we will be able to create Super AI in the near future.
9
Q
How is AI multi-disciplinary?
A
- Computer Science and Electrical Engineering determine how AI is implemented in software and hardware.
- Mathematics and Statistics determine viable models and measure performance.
- Because AI is modelled on how the human brain works, psychology and linguistics play an essential role in understanding how AI might work.
- Philosophy provides guidance on ethical considerations and intelligence.
10
Q
Artificial Intelligence vs. Generative AI (GenAI)
A
Artificial Intelligence is an augmented intelligence that helps experts scale their capabilities while machines handle time-consuming tasks such as recognising speech, playing games, and making decisions.
Generative AI, on the other hand, is an AI technique that can generate new and unique data ranging from images and music to texts and virtual worlds.
11
Q
How does GenAI differ from conventional AI?
A
Conventional AI relies on predefined rules and patterns while GenAI uses deep learning techniques and relies on large datasets to generate new data.
12
Q
Generative AI and LLM
A
Large Language Model (LLM) is a type of AI that uses deep learning techniques to process and generate natural language.
Generative AI can develop new and powerful LLM algorithms or architectures.
Generative AI can also incorporate LLM into a larger, more advanced AI system.
13
Q
What are the benefits of Generative AI?
A
- Creativity and Innovation
- Cost and time savings
- Personalisation
- Scalability
- Robustness
- Exploration of new possibilities
14
Q
What are some use cases for GenAI?
A
- Healthcare and precision medicine
- GenAI can identify genetic mutations
- Provide personalised treatment options
- Help doctors practice procedures and develop treatments
- Agriculture
- GenAI can optimise crop yields
- Develop new, more resistant crop varieties
- Biotechnology
- Generative AI can aid in the development of new drugs and therapies by:
- identifying potential drug targets
- simulating drug interactions
- forecasting drug efficacy
- Generative AI can aid in the development of new drugs and therapies by:
- Forensics
- Generative AI can help solve crimes by:
- analysing DNA evidence
- identifying suspects
- Generative AI can help solve crimes by:
- Environmental conservation
- Generative AI can support the protection of endangered species by:
- analysing genetic data and suggesting breeding and conservation strategies
- Generative AI can support the protection of endangered species by:
- Creative
- Generative AI can produce unique digital art, music, and video content for:
- advertising and marketing campaigns
- and generate soundtracks for films and video games
- Generative AI can produce unique digital art, music, and video content for:
- Gaming
- Generative AI can create interactive game worlds and
- generate new characters, levels, and objects in real-time
15
Q
How does AI mean different things to different people?
A
For a video game designer, AI means affecting the way bots play or how the environment adapts to the player; whereas for a data scientist, AI is a way of exploring and classifying data to meet specific goals.
16
Q
What is the reason we are able to talk to virtual assistants such as Alexa and Siri?
A
It is because of AI algorithms that learn by example. The natural language processing and natural language generation capabilities that come with AI aid our interactions with these virtual assistants so they can talk back to us.
17
Q
What does Generative AI do?
A
It enables applications to create, generate, and simulate new content
18
Q
How does Generative AI work?
A
It works by leveraging machine learning and deep learning techniques to learn patterns and generate original content
19
Q
What are some known applications of GenAI?
A
- GPT-4
- ChatGPT
- Bard
- GitHub CoPilot
20
Q
How do cognitive systems interpret the information they read?
A
Cognitive systems use processes similar to the decision-making process of humans to interpret and generate hypotheses about the information they read.
21
Q
What do cognitive systems rely on to understand the intent and context of a user’s language?
A
Cognitive systems rely on natural language governed by rules of grammar, context, and culture.
22
Q
How do cognitive computing systems differ from conventional computing systems?
A
They differ in that they can:
- Read and interpret unstructured data, understanding not just the meaning of words but also the intent and context in which they are used.
- Reason about problems in a way that humans reason and make decisions.
- Learn over time from their interactions with humans and keep getting smarter.
23
Q
What are the differences between artificial intelligence, machine learning, deep learning and neural networks?
A
- artificial intelligence is a branch of computer science dealing with the simulation of intelligent behaviour
- machine learning is a subset of AI that uses computer algorithms to analyse data and make intelligent decisions based on what it has learned, without being explicitly programmed
- deep learning is a subset of machine learning that uses layered neural networks to simulate human decision-making
- neural networks in AI are a small collection of computing units (neurons) that take incoming data and learn to make decisions over time
24
Q
What behaviours do AI systems seek to demonstrate?
People Love Reading Poems, Kids Pref Magic Marvels, Cats Sing Incredibly
A
They seek to demonstrate behaviours associated with human intelligence such as:
- planning
- learning
- reasoning
- problem-solving
- knowledge
- perception
- motion
- manipulation
- creativity
- social intelligence
25
What are some characteristics of machine learning algorithms
- machine learning algorithms are trained with large sets of data
- they learn from examples
- they do not follow rules-based algorithms
26
What enables machines to solve problems on their own?
Machine learning
27
What can deep learning algorithms do?
They can label and categorise information and identify patterns.
28
How does machine learning differ from traditional programming?
- In traditional programming, you have data and create algorithms in order to find answers.
- this algorithm will not change
- In machine learning, you already have the data and the answers and are using that information to create an algorithm.
- What you get at the end is a set of rules that determine what the machine learning model will be.
29
What does machine learning rely on?
Defining behavioural rules by examining and comparing large data sets to find common patterns.
30
What is supervised learning?
A type of machine learning where an algorithm is trained on human-labeled data. The more data you provide a supervised learning algorithm, the more precise it becomes in classifying new data.
31
What is unsupervised learning?
A type of machine learning that relies on giving the algorithm unlabelled data and letting it find patterns by itself. You provide the input, but not labels, and let the machine infer qualities.
32
What is reinforcement learning?
A type of machine learning that relies on providing a machine learning algorithm with a set of rules and constraints and letting it learn how to achieve its goals. You define the state, the desired goal, allowed actions, and constraints.
33
What is a machine learning model?
The algorithm used to find patterns in the data without the programmer having to explicitly program these patterns.
34
What are the three categories of supervised learning?
- Regression
- Classification
- Neural networks
35
How are regression models built?
By looking at the features - X and the result - Y, where Y is a continuous variable.
Essentially, regression estimates continuous values.
36
What do neural networks refer to?
Structures that imitate the structure of the human brain.
37
What is classification?
The process of predicting the class of given data points.
38
What does classification focus on?
It focuses on discrete values it identifies.
We can define discrete class labels - Y based on many input features - X.
39
What are some forms of classification?
- Decision trees
- Support vector machines
- Logistics regression
- Random forests
40
What are features?
Distinctive properties of input patterns that help in determining the output categories or classes of output.
41
What is the meaning of training, in an ML context?
It refers to using a learning algorithm to determine and develop the parameters of your model.
42
What do you typically do with data sets in machine learning?
You split them into 3 sets:
1. Training set
2. Validation set
3. Test set
43
What is the training set for?
It is for training the algorithm
44
What is the validation set for?
It is used to validate results and fine-tune the algorithm's parameters
45
What is the test set for?
It is used to evaluate how good the model works on unseen data.
46
What does deep learning do?
It layers algorithms to create a neural network, an artificial representation of the structure and functionality of the brain
47
What does deep learning enable AI systems to do?
Continuously learn on the job and improve the quality and accuracy of results.
48
How are deep learning algorithms developed?
- Developers and engineers configure the number of layers and the type of functions that connect the outputs of each layer to the inputs of the next
49
Which older generation issue is fixed by deep learning, and how?
The efficiency and performance of older-generation machine learning algorithms plateau as the datasets grow. Deep learning algorithms plateau continue to improve as they are fed more data.
50
Which tasks has deep learning proved to be very efficient at?
- Image captioning
- Voice recognition
- Facial recognition
- Medical Imaging
51
What is the name of the process that neural networks use to learn?
Backpropagation.
52
How does backpropagation work?
- backpropagation uses a set of training data that match known inputs to desired outputs
- first, the inputs are plugged into the network, and outputs are determined
- then, an error function determines how far the given output is from the desired output
- finally, adjustments are made in order to reduce errors
53
What is a collection of neurons called?
A layer
54
What do layers do?
They take in an input and provide an output.
55
What do neural networks always have?
One input layer and one output layer. They will also have more than one hidden layer which simulates the type of activity that goes on in the human brain.
56
What do hidden layers do?
They take in a set of weighted inputs and produce an output through an activation function.
57
What do you call a neural network having more than one hidden layer?
A deep neural network.
58
What are the simplest and oldest types of neural networks?
Perceptrons.
59
What are perceptrons?
They are single-layered neural networks consisting of input nodes connected directly to an output node.
Input layers forward the input values to the next layer, by means of multiplying by a weight and summing the result.
60
What property to input and output nodes have?
Bias
61
What is bias?
A special type of weight that applies to a node after the other inputs are considered.
62
What determines how a node responds to its inputs?
An activation function.
63
How does an activation function work?
The function is run against the sum of the inputs and bias, and then the result is forwarded as an input.
64
What is a critical component to the success of a neural network?
The type of activation function chosen.
65
What are Convolutional Neural Networks (CNNs)?
They are multi-layered neural networks that take inspiration from the animal visual cortex.
66
Where are CNNs useful?
They are useful in applications such as image processing, video recognition, and natural language processing.
67
What is a convolution?
It is a mathematical operation where a function is applied to another function and the result is a mixture of the two functions.
68
What are convolutions good at?
They are good at detecting simple structures in an image and putting those simple features together to construct more complex features.
69
What are Recurrent Neural Networks?
Recurrent Neural Networks or RNNs are multi-layered neural networks that perform the same task for every element of a sequence, with prior outputs feeding subsequent stage inputs.
70
How are Recurrent Neural Networks recurrent?
They are recurrent because they perform the same task for every element of a sequence, with prior outputs feeding subsequent stage inputs.
71
What is the unique characteristic of Recurrent Neural Networks (RNNs) in handling sequence data?
RNNs make use of information in long sequences, each layer of the network representing the observation at a certain time.
72
How is input processed in a general neural network?
- an input is processed through a number of layers
- and an output is produced with an assumption that the two successive inputs are independent of each other, but that may not hold true in certain scenarios.
73
Give a scenario where a recurrent neural network would work better than a general neural network
- In a general neural network, output is produced with an assumption that the two successive inputs are independent of each other, but that may not hold true in certain scenarios
- For example, when we need to consider the context in which a word has been spoken, in such scenarios, dependence on previous observations has to be considered to produce the output.
- RNNs can make use of information in long sequences, each layer of the network representing the observation at a certain time.
74
What are the most common areas of AI?
- Natural Language Processing
- Speech
- Computer Vision
75
What is Natural Language Processing?
Natural Language Processing (NLP) is a subset of artificial intelligence that enables computers to understand the meaning of human language, including the intent and context of use.
76
What does speech-to-text enable machines to do?
It enables them to convert speech to text by identifying common patterns in the different pronunciations of a word, mapping new voice samples to corresponding words.
77
What does speech synthesis enable machines to do?
It enables machines to create natural-sounding voice models, including the voices of particular individuals.
78
What does Computer Vision enable machines to do?
It enables machines to identify and differentiate objects in images the same way humans do.
79
What AI application areas do self-driving cars use?
Self-driving cars utilise NLP, speech, and most importantly, computer vision.
80
What is the hot topic in AI?
How to use it responsibly
81
What are the five pillars of AI ethics?
- Explainability
- Transparency
- Robustness
- Privacy
- Fairness
82
What is important to remember about AI ethics?
That they are not a "one-and-done" activity. These ethics need to be considered throughout the entire AI lifecycle.
83
What does facial recognition do?
It simply detects a human face in an image.
84
What does facial authentication do?
It provides a one-to-one authentication matching a single face image to another single face image.
85
What does facial matching do?
It provides a one-to-many matching to uniquely identify an individual from a database of images.
86
What is an example of an AI use case that raises ethical concern?
Using facial recognition technology to scan individuals in crowds without consent
87
What raises AI ethical concerns in the workforce?
Bias could permeate AI in decision-making. It could make discriminatory decisions based on race, age, and gender, for example if care isn't taken.
88
What is an ethical concern about AI on social media platforms?
Marketing on social media is dependent on data collected from users on those platforms - but its not always explicit what data has been collected or if users have even provided consent to have their data collected.
89
What makes AI ethics a socio-technical challenge?
AI is pervasive in all aspects of our daily lives, whether we realise it or not.
For example, we encounter AI every time we buy something using a credit card online, when we search something on the web, or when we post, like, or follow somebody on a social platform.
90
Besides technical tools, what else do you need to build trustworthy AI?
You need to utilise principles, guardrails, well-defined processes, and governance.
91
What does it mean to build an use AI ethically?
It means embedding AI ethics into the design and development of the AI lifecycle.
92
What are IBM's three guiding principles for AI?
1. The purpose of AI is to augment - not replace - human intelligence
2. Data and insights belong to their creator
3. New technology, including AI, must be transparent and explainable
93
What are the five pillars supporting IBM's guiding principles?
1. Transparency
2. Explainability
3. Fairness
4. Robustness
5. Privacy
94
How can organisations put AI ethics into action?
- The first step to building AI ethics is developing awareness and understanding
- The second step is to put into place a governance structure that works at scale
- The third step is operationalisation
- It is critical that every contributor in the organisation knows what they need to do to support AI ethics
95
What does the operationalisation of AI ethics mean?
It means having clarity around the principles and pillars of AI ethics.
96
What is AI ethics?
It is a multidisciplinary field that investigates how to maximise AI's beneficial impacts while reducing risks and adverse impacts.
97
When is AI explainable?
When it can show how and why it arrived at a particular outcome or recommendation.
98
When is AI fair?
When it treats individuals or groups equitably.
99
When is AI robust?
When it can effectively handle exceptional conditions, like abnormal input or adversarial attacks.
100
When is AI transparent?
When appropriate information is shared with humans about how the AI system was designed and developed.
101
When is AI private?
When it is designed to prioritise and safeguard humans' privacy and data rights.
102
What does bias in AI do?
It gives systematic advantages to certain groups or individuals
103
How can bias emerge in AI?
- AI is trained using decisions that human decision-makers have mad in the past
- These human decision-makers may have been implicitly or explicitly biased, and those biases may then may reflected in the training data
- The sampling of the data could also introduce bias (for example, through overrepresentation or underrepresentation)
- Bias can be introduced through the data processing stage
- The way that a problem is conceptualised can also introduce bias
104
What are some high-level actions that can help mitigate bias in AI?
- The first step is assembling diverse teams
- Search for high-quality data sets
- There are many possible technical approaches to mitigating bias
105
What is a regulation?
A government rule enforceable by law
106
What is precision regulation?
Regulation that aims to be risk-based, context-specific, and allocates responsibility to the part closest to the risk, which might shift throughout the AI lifecycle
107
What are IBM's five policy imperatives for organisations that provide or own AI systems?
1. Designate a lead AI ethics official
2. Develop different rules for different risks
3. Don't hide your AI - make it transparent
4. Explain your AI
5. Test your AI for bias
108
What is AI governance?
This is an organisation's act of governing AI through its corporate instructions, staff, processes, and systems
109
What is the objective of governance?
To deliver trustworthy AI by establishing requirements for accountability, responsibility, and oversight.
110
What are the benefits of governance?
- Trust
- Efficiency
- Compliance
111
What does a successful governance program account for?
People, processes, and tools
112
What does ESG stand for?
Environmental, social, and governance
113
What are the 3 pillars of the impact of ESG?
1. Environmental impact
2. Equitable impact
3. Ethical impact
114
What does the governance aspect of ESG pertain to?
It pertains to creating innovations, policies, and practices that prioritise ethics, trust, transparency, and above all, accountability.
115
What is driving the industry focus on trustworthy AI?
Corporate social responsibility posture, concerns around reputational risk and a growing set of regulations.
116
What are the stages in the AI lifecycle?
- Scope and plan
- Collect and organize
- Build and test
- Validate and deploy
- Monitor and manage
117
Why should you apply Enterprise Design Thinking principles during the scoping and planning stage of the AI lifecycle?
Design Thinking helps identify and define various business and technical aspects of bias/fairness, robustness, explainability etc. in the context of the use case.
118
What is the 'Scope and Plan' stage of the AI lifecycle?
This stage guides the prioritisation of use cases and the development of an AI action plan.
119
What is the 'Collect and Organise' stage of the AI lifecycle for?
This stage allows the data consumer to find and access relevant datasets.
120
What happens during the 'Build and Test' stage of the AI lifecycle?
Data science teams explore and prepare the data, and build, train and test their AI/ML models during this stage. The activities during this stage are best undertaken as a set of agile sprints.
121
What is an important activity that needs to be done during the 'Build and Test' stage of the AI lifecycle?
- It is important that bias in data be checked at this stage even before any model building work starts.
- This is an inner guardrail for fairness, and such guardrails can be put in place during and after the model building steps.
- Model robustness, explainability and other aspects can be similarly accounted for and tested during this stage.
122
What happens during the 'Validate and Deploy' stage of the AI lifecycle?
- This stage involves validation of the model and deployment into production.
- The team validates quality, robustness, fairness, etc. and generates validation reports. It is important to capture the validation results for reference and comparison.
123
What happens if a model passes validation?
The Ops team can promote it to the production environment using an MLOps pipeline. The model is deployed there, either for online or batch invocation.
124
What happens during the 'Monitor and Manage' stage of the AI lifecycle?
- In this stage, the Ops team sets up ongoing monitoring and management of the AI/ML model in production.
- The team configures monitors for periodic scheduled collection of metrics.
- Quality, robustness, and fairness are monitored at frequencies dictated by business needs.
125
What are the 3 pillars of the AI governance policy framework?
- Accountability
- proportionate to the risk profile of the application and the role of the entity providing, developing, or operating an AI system to control and mitigate unintended or harmful outcomes for consumers.
- Transparency
- in where the technology is deployed, how it is used, and why it provides certain determinations.
- Fairness and security
- validated by testing for bias before AI is deployed and re-tested as appropriate throughout its use, especially in automated determinations and high-risk applications.
126
What do the OECD AI Principles suggest would help create a solid accountability bedrock for the AI Governance Framework?
Promoting a policy environment that supports an agile transition from the research and development stage to the deployment and operation stage for trustworthy AI systems.
127
How can differentiating accountability mitigate the harm caused by AI?
It can do this by directing resources and oversight to specific applications of AI based on the severity and likelihood of potential harms arising from the end-use and user of such systems.
128
What is the ideal scenario for AI in the modern world?
It's not to try and develop a system that completely autonomously handles every aspect of a problem but to have a collaboration between machines doing what they do best and humans doing what they do best.
129
What is likely to be the next big growth area for AI?
Understanding and generating natural language is likely to be the next big growth area for AI, along with vision systems for the blind.
130
Why are a large number of businesses unable to leverage the capabilities of AI?
A large proportion of their data is locked in silos and not business-ready. There is no AI without IA.
131
What does IA mean?
Information Architecture
132
What do businesses use the AI Ladder for?
It is used for the successful implementation of AI
133
What are the four steps that make up the AI Ladder?
1. Collect: Make data simple and accessible.
1. Collect data of every type regardless of where it resides, enabling flexibility in the face of ever-changing data sources.
2. Organise: Create a business-ready analytics foundation.
1. Organise all data into a trusted, business-ready foundation with built-in governance, protection, and compliance.
3. Analyse: Build and scale AI with trust and transparency.
1. Analyse data in smarter ways and benefit from AI models that empower organisations to gain new insights and make better, smarter decisions.
4. Infuse: Operationalise AI throughout the business.
1. Apply AI across the enterprise in multiple departments and within various processes—drawing on predictions, automation, and optimisation.
134
What is an important step before the AI Ladder?
Modernising your data and making it ready for an AI and hybrid cloud world.
135
What is some helpful advice for AI?
- Because the technology is moving so fast, it is best not to get overly attached to a particular technology, technique, or implementation
- Apply what you learn
- Spend time learning maths and science
- Know how to use software APIs and understand the question being asked
