Elliott Chapter 1 – The complex systems of AI Flashcards
Definition of AI according to the ‘Industrial Strategy White Paper’
“Technologies with the ability to perform tasks that would otherwise require human intelligence, such as visual perception, speech recognition, and language translation”
- Another key condition of AI is the capacity to learn from, and adapt to, new information or stimuli
AI refers to:
“Any computational system which can sense its environment, think, learn
and react in response (and cope with surprises) to such data-sensing”
Machine learning
Machine learning is the process where computers execute tasks through learning or information gathering, that draw from human intelligence and human decision making
- Toby Walsh: “Machine learning is an important part of computers that think. Programming all this knowledge ourselves, fact by fact, would be slow and painful, but we don’t need to do this, as computers can simply learn it for themselves
Through analysis of massive volumes of data, machine learning algorithms can
autonomously improve their learning over time
Natural language processing
Natural language processing (NLP) is a fundamental aspect of AI and encompasses all AI-technologies related to the analysis, interpretation and generation (of text- and speech
based) natural language
- Examples are machine translation (Google translate), dialogue systems (Google’s assistant, Siri, Alexa) and automatic question answering
More than 60% of internet traffic is now generated by machine-to-machine, and personto-machine communication
* Brian Christian’s argument à “Machine language is a kind of conversational puree, a
recorded echo of billions of human conversations”
Critique and limitations of NLP:
NLP is only as good as the dataset underpinning it > if not appropriately trained and ethically assessed, NLP models can accentuate bias in underlying datasets, resulting in systems that work to the advantage of some users over others
- NLP is currently unable to distinguish between data or language that is irrelevant and socially or culturally damaging
Robotics
Robotics has been characterized as the intelligent connection of perception to action in engineered systems
- Robotics are not only human-like, but also any kind of technological system that uses
sensors such as cameras, thermal imagers or tactile and sound sensors to collect data
7 considerations between technological systems and digital life that can be analyzed and critiqued from the sociological:
- Sheer scale of systems of digitization, technological automation and of social relations threaded through artificial intelligence.
- AI is not a new technology which simply transcends, or renders redundant, previous technologies
- We need to recognize the global reach of AI as embedded in complex adaptive systems
- There is the sheer ubiquity of AI: various complex, interdependent digital systems which are today everywhere transferring, coding, sorting and resorting digital information
instantaneously across global networks - The systems which are ordering and reordering digital life are becoming more complex and increasingly complicated.
- AI technologies go all the way down into the very fabric of lived experience and the textures of human subjectivity, personal life and cultural identities
- The technological changes stimulated by the advent of complex digital systems involve processes of transformation of surveillance and power quite distinct from anything
occurring previously
Sheer scale of systems of digitization, technological automation and of social relations threaded through artificial intelligence
The contemporary flourishing of complex, interdependent systems of digitization the ‘flow architectures’ that increasingly order and reorder social relations, production, consumption, communications, travel and transport, and surveillance around the world
AI is not a new technology which simply transcends, or renders redundant, previous technologies
- The complex systems of AI should not be viewed as simply products of the contemporary, but depend upon technological systems which have developed at earlier historical periods
- John Urry à “Many old technologies do not simply disappear but survive through path-dependent relationships, combining with the ‘new’ in a reconfigured and unpredicted cluster”
AI is not a new technology which simply transcends, or renders redundant, previous technologies
- The complex systems of AI should not be viewed as simply products of the contemporary, but depend upon technological systems which have developed at earlier historical periods
- John Urry à “Many old technologies do not simply disappear but survive through path-dependent relationships, combining with the ‘new’ in a reconfigured and unpredicted cluster”
There is the sheer ubiquity of AI
various complex, interdependent digital systems
which are today everywhere transferring, coding, sorting and resorting digital information
instantaneously across global networks
- With systems of digitization and technological automation, information processing
becomes the pervasive architecture of our densely networked environments
- The rise of systems of digital technology has created a new form of invisibility which is
linked to the characteristic of software code, computer algorithms and AI protocols
and to its modes of information processing
The systems which are ordering and reordering digital life are becoming more complex and increasingly complicated
- Moore’s law à refers to the doubling of computing power every two years
- The ubiquity of digital technology, and especially complexity in AI and robotics, involves multimodal informational traffic flows, which in turn substantially depends on
technical specialization and complex expert systems
AI technologies go all the way down into the very fabric of lived experience and the textures of human subjectivity, personal life and cultural identities
- Complex adaptive digital systems and technological infrastructures aren’t ‘just there’ processes or happenings, but are condensed in social relationships and the fabric of people’s lives
- Complex digital systems generate new forms of social systems
- Systems of digital technology increasingly wrap the self in experiences of instantaneous time, and the individualized work of constituting and reinventing digital identities is built out of instantaneous computer click
. The technological changes stimulated by the advent of complex digital systems involve
processes of transformation of surveillance and power quite distinct from anything
occurring previously
The expansion of surveillance capabilities is a central medium of the control of social activities à especially the control over the spacing and timing of human activities
(watch, observe, record, track and trace human subjects)
- Critics of digital surveillance tend to be heavily influenced by Michel Foucault’s notion of panoptic surveillance à the prototype of disciplinary power in modernity, and
argued that prisons, asylums, schools and factories were designed so that those in positions of power could watch and monitor individuals from central point of observation - The author thinks it is mistaken to see digital surveillance as maximizing disciplinary power of the kind described by Foucault à some digital systems of surveillance depend upon authoritative forms of monitoring and control, and can be likened to many of the instances of direct supervision. But, this is not the only aspect of surveillance which comes to the fore in conditions of digital life
- Today, surveillance is often indirect and based upon the collection, ordering and control of information
- Sousveillance à refers to people watching each other at a distance through digital technologies. People become part of environments which are sentient and smart, and such digital systems promote increasingly swarming behavior
- Real dangers include disturbing effects in free speech, and freedom of expression, loss of liberty and erosion of democracy
