core technologies Flashcards
how many industry eras? describe the 4th.
5? debatable, but 4 for sure
4th- 2000s
= ongoing transformation of industries and society through the integration of advanced digital technologies
blurring lines, merge with human lives
a lot of innovation
risk: worse inequality
demand for skills that ppl do not have
components: big data, blockchain, it, ai and machine learning, cloud computing
what is cloud computing?
= remote storage that allows access from any device, data synchronisation and backup and collaboration and access
without cloud computing
- high upfront costs
- complex management
- scalability issues
- risks of obsolescence
- disaster recovery
with cloud computing
- no hardware purchase
- pay-as-you-go
- reduced management complexity
- built-in disaster recovery
- rapid deployment
types of cloud- pros and cons
public:
pro- third-party provider, available to anyone, scales quickly and convenient
con- lack of customisation, governance issues
private:
pro- resources dedicated to one business, more privacy for sensitive data
con- expensive
hybrid:
pro- greater flexibility
con- more complex
implications of cloud computing
business operation:
- cost efficiency
- rapid deployment
- greater scalability and flexibility
- global reach
innovation and entrepreneurship:
- lower barriers to entry
- new business model
- increased competition
environmental:
- energy efficiency
- resource optimisation
challenges and risks:
- data privacy and protection
- cross-border data transfer
- security risks
4 Vs of big data
Variety- various data
Volume- a lot of data
Velocity- speed of data generation and process
Veracity- uncertainty or quality of data
types of data
structured- 20%- less storage, easy to manage
unstructured- 80%- more storage, diff to manage, img, video, etc- can be sometimes converted into structured
data analytics for structured data
descriptive- what happened?
diagnostic- why it happened?
predictive- what will happen?
prescriptive- what should we do?
data analysis for unstructured data
text analysis- summarise, sentiment analysis
audio analysis- speech analysis. musical info retrieval, voice identification, environmental management, health care, security
implications data analytics
business:
- data-driven decision making
- new business models based on real-time data analytics
challenges and risks:
- data privacy and protection
- poor data quality
- bias and fairness in analytics
- ethical use of data
AI and machine learning
structured data, pre-processing data needed
ai= teaching computers to think and make decisions like humans
machine learning (90s)= train computers to learn from examples- patterns learned from data
rule-based systems
= type of AI that applies sets of logical rules to data or input to make decisions, perform actions or generate outputs
- rely on pre-defined rules- if-else- dictate the processing of inputs and determine the system’s behaviour
cons:
- dependency od domain knowledge
- not flexible enough
- lack of learning capabilities
Deep learning revolution and AI
unstructured data, pre-processing data not needed
- 2010s- by how the human brain works- teach computers to learn from large amount of data using ‘neural networks’ that mimick the brain structures
- 2020s- GPT-3- milestone for Natural Language Processing and Generative AI
traditional vs generative AI
traditional- recognise patterns or make decisions based on already existing data
generative- generate new data
Implications of AI
economic:
- job displacement and workforce transformation
- new job creation and econ opportunity
- AI tech controlled by big tech companies, leading to potential econ inequality
social impact:
- changes in the human interactions
- impact on healthcare and wellbeing
- education and learning
environmental:
- massive computational resources are required to train AI
challenges and risks:
- bias and fairness
- individual property and copyright issues
- misinformation, fake news, videos, audios
- individual privacy
IoT
=an expanding network of physical objects (things)
objects are embedded with sensors, software, and other tech to connect and exchange data with other devices over the internet
technologies behind IoT
sensors: gather data from their environment
actuators: allow devices to interact with the environment y effecting a change- lightswitch, opening a valve
the architecture of IOT
device- connectivity- cloud- analytics- end users
connectivity: Bluetooth, NFC, WIFI, LPWAN, cellular networks
cloud: storage, processing and accessibility of data generated by IOT devices, scalability, security, integration
machine learning and analytics: data driven insights, predictive maintenance, enhanced automation, real-time analysis, personalisation
implications of IOT
econ:
- business efficiency and productivity
- new business models and revenue streams
- market competitiveness
social:
- quality of life
- accessibility
environmental:
- energy efficiency
- sustainable practices
challenges and risks:
- data privacy and protection
- security risks
Blockchain
= digital database or ledger that is distributed among the nodes of a peer-to-peer network
ledger= kind of database where confirmed transactions are recorded
types of ledger
centralised- only one actor has it
decentralised- more actors have it
- increase security
- reduced risks of corruption or failure
- transparency and trust
proof of work= mechanism to mitigate an issue- make the process of modifying longer and harder
types of blockchain
public
- more decentralised
- anyone can participate
- more transparent
- more secure but slower
private
- participation is restricted to specific members
- less decentralised
- higher level of anonymity
- more efficient but higher risk of security issues
implications blockchain
econ:
- innovation in data management
- business efficiency and productivity across industries
- market competitiveness
legal:
- smart contract
- property rights
environment:
- high energy consumption due to the mining process
challenges and risks:
- data privacy and protection
- security risks
