Module 1 Flashcards
1
Q
Provide a simple definition of AI
A
Machines performing tasks that normally require human intelligence
2
Q
What test did Allan Turing develop
A
He developed a test to determine whether or not a machine is intelligent
3
Q
What did Allan Turing believe a machine had to do to be considered AI
A
Its must provide responses that fooled an interviewer into thinking it was human
4
Q
What are the common elements in most definitions of AI
A
Technology – the use of technology and specific objectives for the technology to achieve
Autonomy – some level of autonomy by the technology to achieve those objectives
Human involvement – need for human input to train the technology and identify objectives for it to follow
Output – the technology produces output such as performing tasks, solving problems, or producing content
5
Q
What is Machine Learning?
A
The process of training machines to display AI behaviour
6
Q
Why is AI considered a socio-technical system?
A
Because AI influences society and society influences AI
7
Q
Why is there risk in AI?
A
- Complexity of AI systems
- AI data will change over time
- Usually implemented in very complex environments
8
Q
What does the OECD Framework help organizations do?
A
- Classify AI systems
- Examine risks
9
Q
List the 5 dimensions in the OECD Framework
A
- People and planet
- Economic context
- Data and input
- AI model
- Tasks and output
10
Q
Describe the “People and planet” dimension of the OECD Framework
A
Considers the individuals and groups that may be affected by the system
Focused on how the system impacts things like human rights, environment, and society in general
11
Q
Describe the “Economic context” dimension of the OECD Framework
A
The system is looked at according to the economic and sectoral environment which it operates
- Sector
- Business function or model
- Is it critical to operations
- How it was deployed
- Impact of the deployment
- Scale of the system
- Technological maturity of the system – a newer system hasn’t been tested on as much data over time and as it becomes more mature it may grow more effective
12
Q
Describe the “Data and input” dimension of the OECD Framework
A
Considers:
- What type of data was used
- Whether expert input was used (human knowledge that gets codified into rules)
- How the data was collected
- Data collection method (machine or human)
- Structure of the data
- Data format
13
Q
Describe the “AI model” dimension of the OECD Framework
A
Considers:
- Technical type
- How the model is built
- How it is used
14
Q
Describe the “Tasks and outputs” dimension of the OECD Framework
A
Considers:
- Tasks the system performs
- It’s outputs
- Resulting actions from those outputs
- System tasks, systems that combine tasks and actions together
- Evaluation methods that are used to look at how the system performs the tasks that it does
15
Q
List some common AI use cases
A
- Recognition (images, speech, face)
- Event detection
- Forecasting
- Personalization
- Interaction support
- Goal-driven optimization
- Recommendation
16
Q
List examples of the “Recognition” AI use case
A
- Determine if the individual’s face can be matched to another picture
- Retailer product matches – individual can take a picture of the product they want
- Teaching manufacturing machines to detect defects
- Plagiarism detection
17
Q
Provide examples of the “Event detection” AI use case
A
- Fraud detection – ex. credit cards, government programs – they are looking for patterns of fraudulent behaviour
- Sports video – when a particular activity occurred (such as when a touchdown was scored)
- Cyber event & systems management
18
Q
List examples of the “Forecasting” AI use case
A
- Business forecasting – sales, revenue, demand
- Ride sharing – when there might be the most demand, and price adjusting
- Weather forecasting
19
Q
List examples of the “Personalization” AI use case
A
- Unique customer profiles are created to provide relevant experiences to individuals
- Provides a unique experience for each customer and ultimately can increase sales in retail
20
Q
List examples of the “Interaction support” AI use case
A
- Virtual assistants
- Chatbots
21
Q
List examples of the “Goal-driven optimization” AI use case
A
- Finding many solutions
- Optimizing supply chain issues
- Driving routes and idle time
22
Q
List examples of the “Recommendation” AI use case
A
- Product recommendations
- Viewing recommendations
- Decision support systems
- Healthcare providers making diagnosing diagnosis
- Government for adjudicating disability cases, help identify the best benefits according to the case
23
Q
What 3 high-level categories can AI be grouped into?
A
- Artificial Narrow Intelligence (ANI)
- Artificial General Intelligence (AGI)
- Artificial Super Intelligence (ASI)
24
Q
Describe Artificial Narrow Intelligence (ANI)
A
- Designed to perform a single or a narrow set of related tasks at a high level of proficiency
- May seem intelligent; however, they operate under a narrow set of constraints and limitations, which is why this type of AI is commonly referred to as weak AI
- While limited in scope, artificial narrow intelligence systems can help boost productivity and efficiency by automating repetitive tasks, enabling smarter decision making and optimization through trend analysis.
- A system designed to play chess is an example of artificial narrow intelligence.
25
Describe Artificial General Intelligence (AGI)
- Also known as Strong, Deep or Full AI
- Intended to closely mimic human intelligence
- Has been a goal of AI development for decades but, as of today, it remains beyond our reach
- Experts expect AGI systems will do the following things at a level that is similar to or on par with human capabilities:
- Have strong generalization capabilities
- Be able to think, understand, learn and perform complex tasks
- Achieve goals in different contexts and environments
26
Describe Artificial Super Intelligence (ASI)
- A category of AI systems with intellectual powers beyond those of humans across a comprehensive range of categories and fields of endeavor
- Capable of outperforming humans
- Does not yet exist
- However, experts expect this type of system would be self-aware, capable of understanding human emotions and experiences and evoking its own, thus experiencing reality like humans
27
What is broad artificial intelligence?
- A category of AI more advanced in scope than artificial narrow intelligence, capable of performing a broader set of tasks, but not sophisticated enough to be considered AGI
- Often involves reliance on a group of artificial intelligence systems, capable of working together and combining decision-making capabilities, but still lacking the full human-like capabilities experts expect of AGI
28
How does Machine Learning work?
- Leverages the use of data and algorithms to enable systems to learn and make decisions repeatedly
- Improves over time without being explicitly instructed or programmed to do so
29
How are Machine Learning technologies categorized?
Based on the type of training model they rely on
30
List the types of Machine Learning training models
- Supervised
- Unsupervised
- Reinforcement
31
Describe supervised ML models
- Supervised learning models learn from a pre-labeled and classified data set
- An algorithm analyzes the input data and associated labels to produce an inferred function, which can then become the basis for the system to make predictions based on new, previously unseen inputs
- Supervised learning models can also compare their outputs with the correct or intended output, to identify errors and improve their prediction skills
32
Provide examples of supervised ML model
- A model that analyzes images of road signs labeled to define the sign’s meaning or purpose
- Having a bunch of images labelled Cat and Dog, having a model identify whether a new image is of a cat or a dog
- Text recognition and spam detection
33
List the 2 categories of supervised learning models
- Classification
- Regression
34
Explain what the classification category of supervised learning models does
Produce outputs in the form of a specific categorical response; for example, whether an image contains a puppy
35
Explain what the regression category of supervised learning models does
Predict a continuous value; for example, estimating a stock price
36
List 2 widely used supervised learning models
- Support Vector Machine (SVM), used for classification and regression tasks but most widely used for classification objectives
- Support Vector Regression (SVR), most commonly used to produce continuous values.
37
What does SVM stand for?
Support Vector Machine
38
What does SVR stand for?
Support Vector Regression
39
Describe unsupervised ML models
- Do not rely on labeled datasets
- Designed to identify differences, similarities, and other patterns without the aid of human supervision
- Tend to be more cost-efficient and require less effort but are susceptible to producing less accurate outputs and can display unpredictable behaviours
40
List the 2 categories of unsupervised learning models
- Clustering
- Association rule learning
41
Explain what the clustering category of unsupervised learning models does
Automatically groups data points that share similar or identical attributes; for example, looking for similarities or patterns in DNA samples
42
Explain what the association rule learning category of unsupervised learning models does
Identifies relationships and associations between data points; for example, understanding consumer buying habits
43
Describe reinforcement ML models
- Use a reward and punishment matrix to determine a correct or optimal outcome
- Rely on trial and error to determine what to do or not to do and are rewarded or punished accordingly
- Do not ingest pre-labeled data sets and learn solely through action and repetition, changing or not changing state or by getting feedback from their environment
44
Provide an example of reinforcement ML model
- Self-driving cars
- Robot navigating a maze or organizing and stocking shelves in a large warehouse
- Generating predictive text (making the model mimic human writing based on feedback)
- Improve the placement of online ads in real-time bidding
- Real example: Amazon's Warehouse Supply Chain Optimization
45
How do reinforcement ML models learn?
Actions and decisions that result in a reward reinforce the triggering behavior, incentivizing the model to follow the same tactic in the future. Conversely, errors trigger a penalty and reduce the reward, proportional in size to the scale of the error.
46
Provide an example of unsupervised ML model
- Detecting fraudulent behaviour in banking data
47
What do ANI, AGI and ASI stand for?
- Artificial Narrow Intelligence
- Artificial General Intelligence
- Artificial Super Intelligence
48
What is semi-supervised learning?
- Using a combination of supervised and unsupervised learning processes
- Generally uses a small amount of labeled data and a large amount of unlabeled data
- Aim is to leverage the benefits of both models, improving reliability while reducing costs
49
In what situations are semi-supervised learning models very useful?
- Scenarios where it is challenging to find or create a large, pre-labeled dataset
50
Provide examples of semi-supervised learning models
- Image and speech analysis or categorization and ranking of web page search results
- Large Language Models, or LLMs, often rely on semi-supervised learning models
51
What are Large Language Models?
They are a form of AI using deep learning algorithms to create models trained on massive text data sets to analyze and learn patterns and relationships among characters, words, and phrases
52
What does LLM stand for?
Large Language Model
53
Describe robotics
- Multi-disciplinary field that encompasses the design, construction, operation and programming of robots
- Stems from engineering and computer science and aims to design machines that can perform tasks, usually specific tasks or duties, without human intervention
54
What benefit does AI bring to robotics?
AI can introduce efficiency and effectiveness to exponentially improve robotic processes
55
What benefit do robotics bring to AI?
Robotics can allow AI systems and software to interact with the physical world without human intervention
56
Provide an example of robotics working with AI
Rumba
57
What is the Fourth Industrial Revolution or Industry 4.0?
The next stage of industry and manufacturing advancements, enabled by increased interconnectivity and smart automation
58
What is machine perception?
- Where systems are trained to process sensory information and mimic human senses
- Robotic sensors can provide relevant data through cameras, microphones, pressure sensors, 3D scanners, motion detectors and thermal imaging
59
Provide an example of using machine perception
A system that can touch, smell and taste produce could improve food production, preparation, or storage
60
What does RPA stand for?
Robotic Process Automation
61
What is Robotic Process Automation?
An evolving technology using software robots to automate repetitive and rule-based tasks in business processes, mimicking human actions (like data entry and form processing)
62
What type of AI models can enhance RPA?
AI such as natural language processing or machine learning
63
What is an expert system?
A form of AI intended to mimic the decision-making abilities of a human expert in a specific field
64
List the three main elements that distinguish an expert system from other AIs
- Knowledge base
- Inference engine
- User interface
65
Describe the knowledge base of an expert system
- Typically consists of an organized collection of facts and information from human experts
- Focused on a specific field or domain
- In some cases, the system is also allowed to gather additional information from external sources
66
Describe how the inference engine of an expert system works
- Extracts relevant information from the knowledge base and uses it appropriately to solve a problem
- Normally uses a rule-based approach that maps data from the knowledge base to a series of rules, which the system uses to make decisions in response to input
67
What is the purpose of the user interface of an expert system
Allows the end user to interact with the expert system
68
How do expert systems provide transparency?
They often include a module that allows users to review its decision-making process
69
Provide an example of an expert system
A medical diagnosis system designed to help doctors determine a cancerous growth's type and stage
70
Are expert systems designed to replace humans?
No, these systems are usually designed to support and assist humans, rather than replace them
71
What do linguistic variables do?
Describe concepts in natural language terms, such as “low,” “medium,” or “high” and “warm,” “hot,” or “very hot.”
72
What do fuzzy rules do?
They express relationships between variables using if-then statements
73
Provide an example of a fuzzy rule
A rule might state, “If the temperature is ‘very hot,’ then set the fan speed to ‘high’”
74
What are the steps that a fuzzy logic system uses?
1. Fuzzification
2. Rule evaluation
3. Aggregation
4. Defuzzification
75
In a fuzzy logic system, describe the fuzzification step
Input data is converted into fuzzy data sets
76
In a fuzzy logic system, describe the rule evaluation step
Rules are applied to determine the degree of matching between the rules and input data
77
In a fuzzy logic system, describe the aggregation step
Rule outputs are combined
78
In a fuzzy logic system, describe the defuzzification step
Fuzzy outputs are converted back into specific values
79
Provide examples of common fuzzy logic systems
- Climate control systems
- Image recognition systems
- Traffic management systems
80
What is an AI model typically comprised of?
- Input data
- Pattern-matching algorithm
- Output classification
81
Provide examples of software used to develop, test, deploy and refresh AI applications
- Google Cloud Platform
- Microsoft Azure
- Amazon Web Services
82
What features do AI platforms provide?
- Centralize data analysis
- Streamline development and production workflows
- Facilitate collaboration
- Automate systems-development tasks
- Monitor models and systems in production
83
Provide examples of AI use cases
- Autonomous vehicles
- Chat bots
- E-commerce
- Education
- Facial recognition
- Finance
- Health care
- Human resources
- Marketing
- Navigation
- Robotics
- Social media
84
List 3 common model types
- Linear and statistical models
- Decision trees models
- Machine learning models
85
Describe linear and statistical models
They use a linear equation to model the relationship between two variables
86
Provide an example of a linear and statistical model
A model used to determine how sales of a product are related to changes in pricing based on historical data
87
What are 2 benefits of linear and statistical models?
- They are not a black box
- They are more explainable
88
Provide an example of decision trees models
Predict an outcome based on a flowchart of questions and answers
89
What is the benefit of decision trees models?
They are not a black box
90
What are the disadvantages of decision trees models?
- Changing just a little bit of the training data can have a significant impact on the algorithm
- Subject to security attacks and hacks
91
What are the disadvantages of ML models?
Types of models that do have a black box capability, there is a lack of transparency and explainability
92
Provide an example of ML models
Neural network
93
What is a neural network?
- Based on the human brain and has nodes (like neurons) in a layered structure to continuously improve the ability to find the right answer
- They do not have to be trained to make complex non-linear inferences in unstructured data
- Commonly behind technology such as facial recognition
94
List the types of ML models
- Computer vision models
- Speech recognition models
- Language models
- Reinforcement learning models
95
What are computer vision ML models used for?
Used to recognize images and video
96
What are speech recognition ML models used for?
Used in products like Alexa and transcription software, analyze speech using factors such as pick, tone, language and accent
97
What are language ML models used for?
- For example, natural language processing, allows computers to understand human language using machine learning and deep learning models as well as linguistics
- Can be used to process large amounts of communications data as well as respond. Examples, customer service chatbots.
98
What are reinforcement ML models used for?
Train a model to optimize their actions within a given environment to achieve a specific goal
99
How are reinforcement ML models trained?
- Trial-and-error interactions
- Simulated experiences that do not require external data
100
List the 2 things that are pushing AI forward
- Algorithmic innovation
- Accumulation of available data (supervised, collected in interactive environments, structured, unstructured)
101
What drove AI in the beginning?
Phenotypic and image data capture systems, particularly as a response to genomic research
102
What is a GPU?
Graphical Processing Unit
103
How has advancement in GPU technology thrust AI forward?
- They offload a lot from the CPU
- They are specialized chips that give better performance
- As algorithms have become more complex, there has been an accompanying increase in GPU and what they are able to support
- They are better at matching hardware to the AI models for optimal performance
- That is the key to determine AI performance, matching the hardware to the AI model requirements
104
When looking at infrastructure, what is the key to AI performance?
Matching the hardware to the AI model requirements
105
What does Serverless mean?
- Not being limited to a particular server or piece of hardware
- Code can run on any number of hardware devices
106
What 2 important functions does serverless infrastructure provide?
- Loose coupling
- Scalability
107
What does loose coupling mean in serverless infrastructure?
Data can come from a variety of sources; you are not limited to the particular network storage or file share you are attached to
108
What does scalability mean in serverless infrastructure?
You can run multiple instances of the code because it is not attached to a server
109
What is high-performance compute?
- An isolated cluster of compute power
- High speed networking
- Specialized chipsets
- Quantum computing – processing data in 3 dimensions, horizontally and vertically
- Trusted execution environments
- Good from a privacy perspective
- The human is taken out of the picture
110
List the 4 different stages of AI
- Ingestion
- Preparation
- Training
- Output
111
What do you need to consider when choosing a storage solution to use with AI?
- Massive amounts of data – you need a low-cost storage solution
- Storage types – file, object, image
- Structured vs unstructured – easier to process structured data
- Flexible storage – gives you the ability to do AI at scale
112
What network requirements does AI have?
- They need to be fast – you need a high-speed network to deliver training data in time to the algorithm
- You need training and inference at scale
113
How does high-speed compute provide the network requirements you need for AI?
In high performance compute the underlying infrastructure is housed in the same data center, usually the same rack, and connected via fibre connections. They have a high-speed network.
114
What is edge computing?
- IoT
- IO devices
115
What do you need to consider when working with edge computing?
- Tremendous amount of data
- This data needs to be fed into AI models – you need to get it to the storage, over the network, to the AI model
- You need network protocols that are based on a congestion-free design
116
What does it mean to have network protocols that are based on a congestion-free design?
- Unlike TPS (transmission control protocol) that required you to send a packet then you get an acknowledgement and another back; this takes a lot of time
- With AI you need data to fly around at sub-millisecond standards
117
Describe AI democratization
- “AI for everybody”
- You don’t have to be a Data Scientist to do AI anymore
- No-code and low-code software, simple AI interfaces
118
List 3 software implications of AI
- Tuning
- Transformation
- Labelling
119
What function does tuning provide in AI?
Allows you to customize your models to generate the most accurate outcomes and the most valuable insights into your data
120
What are some challenges related to tuning?
- Variability due to model type, scale, and complexity
- Usually done trial and error by changing hyper-parameters
121
What is the difficulty with trial and error tuning?
If the model is very complex the hyper-parameters you use to tune can be numerous and they vary greatly on the model type and complexity
122
How do you resolve the issues of trial and error tuning?
Move from trial and error to educated tuning
123
Why must you transform data for an AI model?
- Increase data compatibility
- Optimize data quality
124
What decision do you need to make before transforming data?
Determine where and when you are going to do the transformation:
- Internal/external
- Pre- or post-processing
125
Why should you label data for use in AI?
- Enriches the data
- Pre-tuning, pre-transformation step that has a tremendous impact and determines the quality of the AI model and results
126
What are the challenges of labelling data?
- Low quality data labels, data labels need to be of high quality and standard
- It is a challenge to scale high quality data labelling operations
- Lack of quality assurance (going back and verifying that the quality is sufficient)
127
What are hyperparameters?
Parameters or values adjusted for an AI model or algorithm to tune it toward desired outcomes
128
List 3 common hyperparameters
- Learning rate
- Number of epochs (one cycle through the full training dataset)
- Momentum (the amount of history included in the equation)
129
Why are data and AI observability key to the success of any AI project?
They provide:
- Indices and metrics for performance
- In-depth analysis of AI data and models
- The capability to investigate, resolve and prevent AI model issues
130
What is the purpose of data observability?
Monitor the overall health and status of your organization’s data ecosystem
131
What is AI observability?
A subcomponent of data observability, focused on monitoring:
- The AI algorithm
- The data going in and coming out of your AI algorithm
- The metrics of the AI system
132
List 3 challenges of AI observability
- Data integrity
- Data drift
- Testing for bias and discrimination
133
Describe how data integrity can be a challenge in AI observability
- Data quality and inconsistency issues can provide inconsistent results
- The data that you train on is not relevant or close enough to the data that the analysis is going to be based on
134
Describe how data drift can be a challenge in AI observability
- Similar to data integrity issues when you train on a specific type of data and then you apply the algorithm to a distinct, other type of data
- Can have significant effects such as faulty predictions, series of future mistakes, inaccurate data pipelines
135
Describe how testing for bias and discrimination can be a challenge in AI observability
- Bias is common, we as humans are creating the AI algorithms, they inherit any biases that we have
- Outcome validation, ensuring the outcomes we are looking for are delivered and align with the AI model and they can be more or less predicted through our tuning and transformations
136
What are the advantages of open-source AI frameworks?
- Gives companies greater freedom to innovate with AI
- Community creates its own massive feedback loop which drives the free spread of ideas for applying AI (transformation, tuning best practices, and turning these into valuable businesses or assets for your organization)
137
What are 2 examples of open-source AI frameworks?
- pytorch
- tensorflow
138
What is a drawback of open-source AI frameworks?
You have to have a deep knowledge of AI but there are overlays you can apply to make it relatively low-code or no-code
139
When was AI born as a distinct field?
In the US in the summer of 1956 during a seminal conference at Dartmouth College
140
Describe the state of AI before the Dartmouth Conference
- Various strands of research across disciplines like psychology, computer science, linguistics, and engineering formed the bedrock of what would eventually become AI
- However, these efforts were mostly isolated, and the concept of an "intelligent" machine was not yet formalized
141
What was proposed at the Dartmouth Conference?
They proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."
142
Who penned the proposal for the Dartmouth Conference?
John McCarthy, assistant professor of mathematics at Dartmouth, and three senior researchers, Marvin Minsky, Nathaniel Rochester and Claude Shannon
143
Who participated at the Dartmouth Conference?
- Leading researchers in fields relevant to AI
- Attendees included Allen Newell and Herbert Simon, who introduced the Logic Theorist (considered by many to be the first AI program)
144
How long was the Dartmouth Conference planned to last?
2 months
145
How was the Dartmouth Conference structured?
It was less structured, with many brainstorming sessions that allowed ideas to flow freely
146
What was the significance of the Dartmouth Conference?
- During the conference, the term "artificial intelligence" was adopted, effectively creating AI as a field of research
- Participants left the conference with a collective sense of mission to develop machines capable of simulating human intelligence
147
What are the AI summers and winters?
Artificial intelligence's growth has been a series of fluctuations known as AI summers and winters: periods of intense development and increased skepticism, respectively
148
Describe the 1st AI summer
- Mid-1950s to mid-1970s (following the Dartmouth Conference)
- Period of optimism and funding
- AI research labs were established at top universities
- John McCarthy created the first AI programming language, LISP
- Joseph Weizenbaum at MIT developed ELIZA, which simulated a Rogerian psychotherapist by rephrasing a patient's statements as questions and posing them to the patient (one of the earliest examples of natural language processing)
149
Describe the 1st AI winter
- Mid-1970s to mid-1980s
- Early promises of AI failed to materialize, leading to a period of skepticism and funding cuts
- Critiques like the Lighthill Report in the UK questioned AI's feasibility and practicality, causing a significant slowdown in AI research
150
Describe the 2nd AI summer
- Mid-1980s to late 1980s
- A renewed interest in AI was sparked by the advent of expert systems, computer systems that emulate decision-making ability of a human expert
- The Japanese government's Fifth Generation Computer Systems project, aimed at developing AI-powered computers, also gave the field a significant boost
151
Describe the 2nd AI winter
- Late 1980s to late 1990s
- The high cost of maintaining expert systems and the end of the Cold War led to a decline in interest and funding
152
Describe the AI renaissance and the era of Big Data
- Late 1990s to present
- The tide turned for AI when IBM's Deep Blue defeated the world chess champion in 1997, signifying a notable advance in AI capabilities
- Simultaneously, the emergence of the internet led to an explosion of data, marking the beginning of the "Big Data" era
- A wealth of data, along with advancements in computational power and machine learning, led to significant improvements in AI capabilities
- AI's impact started to be felt in everyday life, from recommendation algorithms in online shopping to voice assistants in smartphones
153
Describe the state of AI today
- Today, we are witnessing an AI boom, fueled by the advancements in deep learning, a subfield of AI that involves training neural networks on vast amounts of data
- Google's AlphaGo's victory over Go world champion Lee Sedol in 2016, and OpenAI's GPT showcasing language models' remarkable capabilities, are two milestones in recent years showing AI's progress and potential
154
Describe the Foundations era of data science
- 1960s to 1980s
- Most data handling was manual, and the concepts of data mining and analytics were in their infancy
- The term "data science" was introduced by Peter Naur in 1960 as an alternative to "computer science"
- It was defined as "the science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences"
- the term didn't take hold until much later
155
Describe the Age of Databases era of data science
- 1980s-1990s
- As technology advanced, organizations started using databases to store and manage data
- This period was marked by the advent of Relational Database Management Systems (RDBMS) and Structured Query Language (SQL), transforming how businesses dealt with data
156
Describe the Advent of the Internet era of data science
- 1990s-2000s
- Led to a substantial increase in the volume of data produced
- The term "Big Data" started gaining traction to describe the exponential growth of data
- Around the same time, "data mining," a process of discovering patterns in large data sets, emerged as a key concept
157
Describe the Rise of Data Science era of data science
- 2000s-2010s
- With the increasing importance of data-driven decision making, the term "data science" gained prominence in the 2000s
- In 2001, William S. Cleveland proposed to expand statistics to include advances in computing with data in his article, "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics"
- In 2006, the launch of Hadoop, open-source software for storing data and running applications on clusters of commodity hardware, provided a significant boost to data storage and processing capabilities
158
Describe the current trends in data science
- 2010s-present
- The era of "Big Data" has arrived
- Data scientists are now vital assets in industries ranging from health care to finance, leveraging statistical and machine learning methods to extract insights from data
- The explosion of machine-generated data, including IoT and social media data, has also led to the growth of real-time analytics and a need for advanced data processing techniques
159
List 5 modern drivers of AI and data science
- Cloud Computing
- Mobile Technology and Social Media
- Internet of Things (IoT)
- Privacy Enhancing Technologies (PETs) and Blockchain
- Computer Vision, AR/VR, and the Metaverse
160
Describe how cloud computing is a modern driver of AI and data science
- By offering on-demand, scalable computing resources, cloud technology has made high-powered computing accessible to everyone
161
Describe how mobile technology and social media are a modern driver of AI and data science
The proliferation of smartphones and the rise of social media platforms have led to a data explosion, offering AI models a wealth of information to learn from
162
Describe how IoT is a modern driver of AI and data science
IoT devices generate a massive amount of data, further feeding data-hungry AI models and contributing to the growth of data science
163
Describe how PETs are a modern driver of AI and data science
PETs are emerging as a viable approach to addressing data security and privacy concerns, helping to ensure continued, responsible AI and data science growth
164
Describe how blockchain is a modern driver of AI and data science
Blockchain technology was originally designed to provide an interface for secure financial transactions
- It can also enhance data privacy and security in certain contexts
- However, it is important to note that blockchain is not universally applicable to all data privacy and AI challenges
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Describe how computer vision, AR/VF and the metaverse are a modern driver of AI and data science
By enabling machines to understand the world through images and videos, computer vision opens doors to safer, more efficient and interactive human-machine interactions
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Describe how AR/VR is a modern driver of AI and data science
- These technologies have had ups and downs and some periods of slow progress, but are now on a trajectory toward integration
- They find applications in diverse fields, from gaming to therapy and medical contexts
- The potential for steady expansion in these areas suggests a bright future for AR/VR technologies
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Describe how the metaverse is a modern driver of AI and data science
- The metaverse is an ambitious concept, envisioning a shared virtual space where individuals interact, socialize, conduct business and explore other possibilities
- While it holds immense promise, it is a vision that may be ahead of its time
- Similar to early experiments with Google Glass, the metaverse may need another decade to fully materialize into something practical and widely accessible
