law and technology
1) technology
1.1) information technology
= use of computers to store, retrieve and exchange all kinds of date
→ scientific discipline: computer science = in almost every curriculum
example big date science 1890
→ hollerith tabulator = used in US for calculating the population
every 10 years by hand, because it took them 10 years to proces the information
this advice could operate this way faster = counting + punch card (punching
holes in paper card) = data records
database = collection of data records
it could make local processing easier + cheaper
⇒ he also founded the CTR = renamed in 1924 to IPN, which still exists today
history
pre-mechanical (3000 BC - 1450 AD)
mechanical (1450 - 1840)
electromechanical (1840 - 1940)
electronic (1940 - present)
hardware and software
bv self-driving car: car + AI (deep learning, …)
hardware = physical parts
screen, memory, motherboard
software = instructions and codes that are needed to make the hardware work
(basic and advanced)
algorythm = software that fulfills a certain function (bv Bubblesort = to sort a
list of numbers by taking each time a pair of numbers to sort until you get the
right order)
law and technology 1
, essential building block of all software
works like a recipe → a finite sequince of instructions that describe
how to realise a specific goal
computer program = specific implementation of such an algorythm,
taking into account certain constraints such as specific programming
language
pseudocode = easier to understand as a norma person and this has
to be converted into something to make the computer work
typically organised hierarchically
user → applications software (app) → operating system (IOS,…) →
hardware
other aspects
local (my own computer) vs cloud based solution (connection of
computers)
open source (available for everybody, changeable) vs closed source
software (not changeable)
1.2) internet technology
= very large connection of computers via a common set of protocols
→ devices communicate via small packages that are sent around using IP/TCIP
Transmission Control Protocol/Internet Protocol
protocol to send around packages to communicate
→ you can have your own private internet = intranet
→ most of them are public
advantages
charing of information: through common protocols bv email, telephony
changed the way of communication completely bv social networks, TV,…
access to the internet: provided by ISP (bv proximus)
network hardware bv switches, routers, cables, servers, printers = all connected to
the same network
challenges of internet
law and technology 2
, novel types of crime bv fraude, fake news
creates social disparity (internet illiteracy) = some people do not understand new
technologies
dealing with private and sensitive information bv monitoring-cameras
⇒ new playground for lawyers!!!
WWW
°1989 by sir Tim-berners-Lee
collection of documents, linked by a protocol (HTTP)
HTTP builds further on IP = determents on which server the document is
resting
every document is characterised by the URL
web browsers translates URL into IP adresses
makes use of domain name system (DNS)
web pages are formatted in HTML = programming language (set of commands)
internet of things (IoT)
many small devices all connected through communications protocols (bv
sensors, camera’s)
protocols are often determined by physical distance bv bluetooth, 5G
the cloud = large group of computers to proces a large amount of information →
processing en data warehouse
the edge = real time data processing, local processing
edge computing = allows data to go from IoT to be analysed by the edge to go to
the cloud
big data and cloud computing
many devices = continuously generating info that needs to be processed to find
interesting patterns
large amounts of date = cannot be handled by a single machine
we need scalable infrastructures and computing paradigms to make use of these
data
law and technology 3
, → big data science = refers to data-mining problems the volume, diversity and
complexity of which requires the development of novel techniques etc to extract
valuable knowledge that is typically hidden inside data
→ makes use of cloud computing = large-scale distributed computing resources
fault tolerance built-on = if a server breaks down, you can still process because
another server takes over
less vulnerable to hardware crashes
abstracts away a lot of the hardware issues for end user = makes it easier for us
flexible cost: you pay as much as you consume
various types
end user → SAAS (software) bv gmail
software developers → PAAS (platform) bv Google app
network architects → IAAS (infrastructure) bv Google compute engine
charateristics of big data science systems
data contains value and knowledge
5V’s
variety: dealing with different types of data (structued/unstructureed)→ how
to use data to create a bigger picture
veracity: we cannot always control the quality/accuracy of data → data may
be missing etc
value: most importent motivation → better models, insights, new
opportunities
volume: data generated every second and this has become to big to store
traditionally → big data can store this using distributed systems
velocity: speed at which data is generated + data needs to be analysed +
decisions need to be taken
application areas
science and research
healthcare and public health
business and comerce
law and technology 4
, economy and sustainability
…
1.3) cryptography, blockchain and cryptocurrency
enhancing trust
→ cryptocraphy = mathematical techniques that allow for encryption and decryption
of messages
→ used in financial applications
public-key crypthography
public: accesible to everyone
private: only known to the owner
key generation = based on cryptographic hash functions (Y=X → easy to
calculate Y given X but hard to find X, given Y)
security = difficalt to attack (only brute-force)
💡 secure communication using PK cyptograpghy
bob takes the public key from alice and uses this to encrypt a
message
the message transfers over insecure communication channels
alice decrypt the message with her private key
secure athentication bv signing protocol: signed with private key but verify
with public key
blockchain and cryptocurrencies
motivation: need for a decentralised system, that allows for valid electronic
financial activity → replaces classical banks
storing personal information, service transferring, transaction managment by
tracking transactions
identity managment bv proof of spending
prevention of theft
law and technology 5
, B = special database of transactions
can be used for many applications
smart contracts: encode agreements in software instead of natural language
any action is done bu requesting the execution of a function
supply chain track
1.4) AI
motivating examples
smart planners
facial recognition
reccomander systems
will AI be everywhere? experts predict it will be used more often
imitation game 1950 (Turing)
→ test = system to control if a computer is intellegent or not
human interrogator, a human and an AI system: the interrogator has to know
who answers
goals AI
process natural language
learn from conversation
memorize what has been said before
communicate back
have an idea of common sense
has become better and better
current state of the art: you can ask questions and you get answers back from
the system
AI ingedients
input → processing → output
reasoning and planning
learn/adapt = memory and simulate
law and technology 6
1) technology
1.1) information technology
= use of computers to store, retrieve and exchange all kinds of date
→ scientific discipline: computer science = in almost every curriculum
example big date science 1890
→ hollerith tabulator = used in US for calculating the population
every 10 years by hand, because it took them 10 years to proces the information
this advice could operate this way faster = counting + punch card (punching
holes in paper card) = data records
database = collection of data records
it could make local processing easier + cheaper
⇒ he also founded the CTR = renamed in 1924 to IPN, which still exists today
history
pre-mechanical (3000 BC - 1450 AD)
mechanical (1450 - 1840)
electromechanical (1840 - 1940)
electronic (1940 - present)
hardware and software
bv self-driving car: car + AI (deep learning, …)
hardware = physical parts
screen, memory, motherboard
software = instructions and codes that are needed to make the hardware work
(basic and advanced)
algorythm = software that fulfills a certain function (bv Bubblesort = to sort a
list of numbers by taking each time a pair of numbers to sort until you get the
right order)
law and technology 1
, essential building block of all software
works like a recipe → a finite sequince of instructions that describe
how to realise a specific goal
computer program = specific implementation of such an algorythm,
taking into account certain constraints such as specific programming
language
pseudocode = easier to understand as a norma person and this has
to be converted into something to make the computer work
typically organised hierarchically
user → applications software (app) → operating system (IOS,…) →
hardware
other aspects
local (my own computer) vs cloud based solution (connection of
computers)
open source (available for everybody, changeable) vs closed source
software (not changeable)
1.2) internet technology
= very large connection of computers via a common set of protocols
→ devices communicate via small packages that are sent around using IP/TCIP
Transmission Control Protocol/Internet Protocol
protocol to send around packages to communicate
→ you can have your own private internet = intranet
→ most of them are public
advantages
charing of information: through common protocols bv email, telephony
changed the way of communication completely bv social networks, TV,…
access to the internet: provided by ISP (bv proximus)
network hardware bv switches, routers, cables, servers, printers = all connected to
the same network
challenges of internet
law and technology 2
, novel types of crime bv fraude, fake news
creates social disparity (internet illiteracy) = some people do not understand new
technologies
dealing with private and sensitive information bv monitoring-cameras
⇒ new playground for lawyers!!!
WWW
°1989 by sir Tim-berners-Lee
collection of documents, linked by a protocol (HTTP)
HTTP builds further on IP = determents on which server the document is
resting
every document is characterised by the URL
web browsers translates URL into IP adresses
makes use of domain name system (DNS)
web pages are formatted in HTML = programming language (set of commands)
internet of things (IoT)
many small devices all connected through communications protocols (bv
sensors, camera’s)
protocols are often determined by physical distance bv bluetooth, 5G
the cloud = large group of computers to proces a large amount of information →
processing en data warehouse
the edge = real time data processing, local processing
edge computing = allows data to go from IoT to be analysed by the edge to go to
the cloud
big data and cloud computing
many devices = continuously generating info that needs to be processed to find
interesting patterns
large amounts of date = cannot be handled by a single machine
we need scalable infrastructures and computing paradigms to make use of these
data
law and technology 3
, → big data science = refers to data-mining problems the volume, diversity and
complexity of which requires the development of novel techniques etc to extract
valuable knowledge that is typically hidden inside data
→ makes use of cloud computing = large-scale distributed computing resources
fault tolerance built-on = if a server breaks down, you can still process because
another server takes over
less vulnerable to hardware crashes
abstracts away a lot of the hardware issues for end user = makes it easier for us
flexible cost: you pay as much as you consume
various types
end user → SAAS (software) bv gmail
software developers → PAAS (platform) bv Google app
network architects → IAAS (infrastructure) bv Google compute engine
charateristics of big data science systems
data contains value and knowledge
5V’s
variety: dealing with different types of data (structued/unstructureed)→ how
to use data to create a bigger picture
veracity: we cannot always control the quality/accuracy of data → data may
be missing etc
value: most importent motivation → better models, insights, new
opportunities
volume: data generated every second and this has become to big to store
traditionally → big data can store this using distributed systems
velocity: speed at which data is generated + data needs to be analysed +
decisions need to be taken
application areas
science and research
healthcare and public health
business and comerce
law and technology 4
, economy and sustainability
…
1.3) cryptography, blockchain and cryptocurrency
enhancing trust
→ cryptocraphy = mathematical techniques that allow for encryption and decryption
of messages
→ used in financial applications
public-key crypthography
public: accesible to everyone
private: only known to the owner
key generation = based on cryptographic hash functions (Y=X → easy to
calculate Y given X but hard to find X, given Y)
security = difficalt to attack (only brute-force)
💡 secure communication using PK cyptograpghy
bob takes the public key from alice and uses this to encrypt a
message
the message transfers over insecure communication channels
alice decrypt the message with her private key
secure athentication bv signing protocol: signed with private key but verify
with public key
blockchain and cryptocurrencies
motivation: need for a decentralised system, that allows for valid electronic
financial activity → replaces classical banks
storing personal information, service transferring, transaction managment by
tracking transactions
identity managment bv proof of spending
prevention of theft
law and technology 5
, B = special database of transactions
can be used for many applications
smart contracts: encode agreements in software instead of natural language
any action is done bu requesting the execution of a function
supply chain track
1.4) AI
motivating examples
smart planners
facial recognition
reccomander systems
will AI be everywhere? experts predict it will be used more often
imitation game 1950 (Turing)
→ test = system to control if a computer is intellegent or not
human interrogator, a human and an AI system: the interrogator has to know
who answers
goals AI
process natural language
learn from conversation
memorize what has been said before
communicate back
have an idea of common sense
has become better and better
current state of the art: you can ask questions and you get answers back from
the system
AI ingedients
input → processing → output
reasoning and planning
learn/adapt = memory and simulate
law and technology 6
