Do we want Personal AIs that know more about us than we know about ourselves?
Here are two big thinkers who have weighed in on this question:
Yuval Noah Harari has expressed the concern that corporations and government bureaus are "starting to know more about us than we know about ourselves." He sees this as a serious threat to our autonomy.
Yuval Noah Harari References
https://innovationmemes.blogspot.com/2019/05/yuval-noah-harari-references.html
Yuval Noah Harari
https://en.wikipedia.org/wiki/Yuval_Noah_Harari
Other big thinkers, such as Danny Hillis, believe that humans can "level the playing field" with powerful organizations if they can obtain Personal AIs (i.e. AIs that are loyal to their human partner, not some organization). Of course, a Personal AI may end up knowing more about you than you know about yourself, which brings us back to Harari's concern.
Four Major Geopolitical AI Scenarios
https://innovationmemes.blogspot.com/2020/01/four-major-geopolitical-ai-scenarios.html
In The First Machine Intelligences, Danny Hillis describes hybrid intelligences as follows: "organizational superintelligences are not just made of humans, they are hybrids of humans and the information technologies that allow them to coordinate." This post describes four major geopolitical AI scenarios as outlined by Danny Hillis:
Today's Hybrid Machine Intelligences and the Rise of AI
https://innovationmemes.blogspot.com/2020/01/todays-hybrid-machine-intelligences-and.html
These are my notes on: The First Machine Intelligences
by W. Daniel Hillis
-----
Danny Hillis
https://en.wikipedia.org/wiki/Danny_Hillis
The rest of this blog post just brings together a few references on various kinds of Personal AIs, Intelligent Tutoring Systems, and the related concept of Digital Immortality.
"""
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.
Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from passive receptor of information to collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula.[1]
"""
"""
A brain-computer interface (BCI), sometimes called a neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain-machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCI differs from neuromodulation in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.[1]
Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[2][3] The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.
Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[4] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
"""
https://en.wikipedia.org/wiki/Digital_immortality
"""
Digital immortality (or "virtual immortality") is the hypothetical concept of storing (or transferring) a person's personality in more durable media, i.e., a computer. The result might look like an avatar behaving, reacting, and thinking like a person on the basis of that person's digital archive.[1][2][3][4] After the death of the individual, this avatar could remain static or continue to learn and develop autonomously.
A considerable portion of transhumanists and singularitarians place great hope into the belief that they may eventually become immortal[5] by creating one or many non-biological functional copies of their brains, thereby leaving their "biological shell". These copies may then "live eternally" in a version of digital "heaven" or paradise.[6][7]
"""
"""
As a hopefully minimalistic definition then, digital immortality can be roughly considered as involving a person-centric repository containing a copy of everything that a person sees, hears, says, or engenders over his or her lifespan, including photographs, videos, audio recordings, movies, television shows, music albums/CDs, newspapers, documents, diaries and journals, interviews, meetings, love letters, notes, papers, art pieces, and so on, and so on; and if not everything, then at least as much as the person has and takes the time and trouble to include. The person’s personality, emotion profiles, thoughts, beliefs, and appearance are also captured and integrated into an artificially intelligent, interactive, con-versational agent/avatar. This avatar is placed in charge of (and perhaps "equated" with) the collected material in the repository so that the agent can present the illusion of having the factual memories, thoughts, and beliefs of the person him/herself.
"""
Susanne Asche
Symposiums, Digital Immortality & Runaway Technology
https://en.wikipedia.org/wiki/Educational_data_mining
"""
Educational data mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings.[1] In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences.[2] The field is closely tied to that of learning analytics, and the two have been compared and contrasted.[3]
Definition
Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. Quite often, this data is extensive, fine-grained, and precise. For example, several learning management systems (LMSs) track information such as when each student accessed each learning object, how many times they accessed it, and how many minutes the learning object was displayed on the user's computer screen. As another example, intelligent tutoring systems record data every time a learner submits a solution to a problem; they may collect the time of the submission, whether or not the solution matches the expected solution, the amount of time that has passed since the last submission, the order in which solution components were entered into the interface, etc. The precision of this data is such that even a fairly short session with a computer-based learning environment (e.g., 30 minutes) may produce a large amount of process data for analysis.
In other cases, the data is less fine-grained. For example, a student's university transcript may contain a temporally ordered list of courses taken by the student, the grade that the student earned in each course, and when the student selected or changed his or her academic major. EDM leverages both types of data to discover meaningful information about different types of learners and how they learn, the structure of domain knowledge, and the effect of instructional strategies embedded within various learning environments. These analyses provide new information that would be difficult to discern by looking at the raw data. For example, analyzing data from an LMS may reveal a relationship between the learning objects that a student accessed during the course and their final course grade. Similarly, analyzing student transcript data may reveal a relationship between a student's grade in a particular course and their decision to change their academic major. Such information provides insight into the design of learning environments, which allows students, teachers, school administrators, and educational policy makers to make informed decisions about how to interact with, provide, and manage educational resources.
"""
"""
An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners,[1] usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher (e.g., classroom lectures), or no teacher at all (e.g., online homework).[2] ITSs are often designed with the goal of providing access to high quality education to each and every student.
"""
https://en.wikipedia.org/wiki/Learning_analytics
"""
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.[1] A related field is educational data mining.
Learning Analytics defined as a prediction model
One earlier definition discussed by the community suggested that Learning Analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning.[4] But this definition has been criticised by George Siemens[5][non-primary source needed] and Mike Sharkey.[6]
"""
"""
Whole brain emulation (WBE), mind upload or brain upload (sometimes called "mind copying" or "mind transfer") is the hypothetical futuristic process of scanning the mental state (including long-term memory and "self") of a particular brain substrate and copying it to a computer. The computer could then run a simulation model of the brain's information processing, such that it would respond in essentially the same way as the original brain (i.e., indistinguishable from the brain for all relevant purposes) and experience having a conscious mind.
"""
https://en.wikipedia.org/wiki/Personalized_learning
"""
The use of the term "personalized learning" dates back to at least the early 1960s,[1] but there is no widespread agreement on the definition and components of a personal learning environment.[2] Even enthusiasts for the concept admit that personal learning is an evolving term and doesn't have any widely accepted definition.[3]
In 2005, Dan Buckley defined two ends of the personalized learning spectrum: "personalization for the learner", in which the teacher tailors the learning, and "personalization by the learner", in which the learner develops skills to tailor his own learning. This spectrum was adopted by the (2006) Microsoft's Practical Guide to Envisioning and Transforming Education.[4]
"""
"""
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. Sometimes the term "chatbot" is used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal (spoken?) commands.[1] A similar concept, however with differences, lays under the dialogue systems[2].
"""
Beshears, Fred
Personal AIs in a Surveillance Society
by Fred M Beshears
https://innovationmemes.blogspot.com/2019/12/personal-ais-in-surveillance-society.html
--------------------------------
Ramirez, Vanessa Bates
Would You Want a Personal AI That Knows Everything About You?
by Vanessa Bates Ramirez
12/22/2019
https://singularityhub.com/2019/12/22/would-you-want-a-personal-ai-that-knows-everything-about-you/
--------------------------------
Rothblatt, Martine
Review of Virtually Human: the promise - and peril- of digital immortality
Book written by Martine Rothblatt
Review by Fred Beshears
https://innovationmemes.blogspot.com/2015/04/review-of-virtually-human-promise-and.html
- Yuval Noah Harari
- Danny Hillis
Yuval Noah Harari
Yuval Noah Harari has expressed the concern that corporations and government bureaus are "starting to know more about us than we know about ourselves." He sees this as a serious threat to our autonomy.
Yuval Noah Harari References
https://innovationmemes.blogspot.com/2019/05/yuval-noah-harari-references.html
Yuval Noah Harari
https://en.wikipedia.org/wiki/Yuval_Noah_Harari
Danny Hillis
Other big thinkers, such as Danny Hillis, believe that humans can "level the playing field" with powerful organizations if they can obtain Personal AIs (i.e. AIs that are loyal to their human partner, not some organization). Of course, a Personal AI may end up knowing more about you than you know about yourself, which brings us back to Harari's concern.
Four Major Geopolitical AI Scenarios
https://innovationmemes.blogspot.com/2020/01/four-major-geopolitical-ai-scenarios.html
In The First Machine Intelligences, Danny Hillis describes hybrid intelligences as follows: "organizational superintelligences are not just made of humans, they are hybrids of humans and the information technologies that allow them to coordinate." This post describes four major geopolitical AI scenarios as outlined by Danny Hillis:
- The State/AI Scenario - nation states dominate with the help of AIs
- The Corporate/AI Scenario - corporations dominate with the help of AIs
- The Autonomous SuperAI Scenario - one or more autonomous SuperAIs dominate
- The Personal AI Scenario - humans "level the playing field" with Personal AIs
Today's Hybrid Machine Intelligences and the Rise of AI
https://innovationmemes.blogspot.com/2020/01/todays-hybrid-machine-intelligences-and.html
These are my notes on: The First Machine Intelligences
by W. Daniel Hillis
-----
Danny Hillis
https://en.wikipedia.org/wiki/Danny_Hillis
References on Personal AIs, Intelligent Tutoring Systems, and Digital Immortality
The rest of this blog post just brings together a few references on various kinds of Personal AIs, Intelligent Tutoring Systems, and the related concept of Digital Immortality.
- Brain-computer interface
- Digital immortality
- Educational data mining
- Intelligent tutoring system
- Learning analytics
- Mind uploading
- Personalized learning
- Virtual assistant
Adaptive Learning
https://en.wikipedia.org/wiki/Adaptive_learning"""
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. In professional learning contexts, individuals may "test out" of some training to ensure they engage with novel instruction. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, AI, psychometrics, education, psychology, and brain science.
Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from passive receptor of information to collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula.[1]
"""
Brain-computer interface
https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface"""
A brain-computer interface (BCI), sometimes called a neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain-machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCI differs from neuromodulation in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.[1]
Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[2][3] The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature.
Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[4] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
"""
Digital Immortality
https://en.wikipedia.org/wiki/Digital_immortality
"""
Digital immortality (or "virtual immortality") is the hypothetical concept of storing (or transferring) a person's personality in more durable media, i.e., a computer. The result might look like an avatar behaving, reacting, and thinking like a person on the basis of that person's digital archive.[1][2][3][4] After the death of the individual, this avatar could remain static or continue to learn and develop autonomously.
A considerable portion of transhumanists and singularitarians place great hope into the belief that they may eventually become immortal[5] by creating one or many non-biological functional copies of their brains, thereby leaving their "biological shell". These copies may then "live eternally" in a version of digital "heaven" or paradise.[6][7]
"""
"""
As a hopefully minimalistic definition then, digital immortality can be roughly considered as involving a person-centric repository containing a copy of everything that a person sees, hears, says, or engenders over his or her lifespan, including photographs, videos, audio recordings, movies, television shows, music albums/CDs, newspapers, documents, diaries and journals, interviews, meetings, love letters, notes, papers, art pieces, and so on, and so on; and if not everything, then at least as much as the person has and takes the time and trouble to include. The person’s personality, emotion profiles, thoughts, beliefs, and appearance are also captured and integrated into an artificially intelligent, interactive, con-versational agent/avatar. This avatar is placed in charge of (and perhaps "equated" with) the collected material in the repository so that the agent can present the illusion of having the factual memories, thoughts, and beliefs of the person him/herself.
"""
Susanne Asche
Symposiums, Digital Immortality & Runaway Technology
Educational Data Mining
https://en.wikipedia.org/wiki/Educational_data_mining
"""
Educational data mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings.[1] In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences.[2] The field is closely tied to that of learning analytics, and the two have been compared and contrasted.[3]
Definition
Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. Quite often, this data is extensive, fine-grained, and precise. For example, several learning management systems (LMSs) track information such as when each student accessed each learning object, how many times they accessed it, and how many minutes the learning object was displayed on the user's computer screen. As another example, intelligent tutoring systems record data every time a learner submits a solution to a problem; they may collect the time of the submission, whether or not the solution matches the expected solution, the amount of time that has passed since the last submission, the order in which solution components were entered into the interface, etc. The precision of this data is such that even a fairly short session with a computer-based learning environment (e.g., 30 minutes) may produce a large amount of process data for analysis.
In other cases, the data is less fine-grained. For example, a student's university transcript may contain a temporally ordered list of courses taken by the student, the grade that the student earned in each course, and when the student selected or changed his or her academic major. EDM leverages both types of data to discover meaningful information about different types of learners and how they learn, the structure of domain knowledge, and the effect of instructional strategies embedded within various learning environments. These analyses provide new information that would be difficult to discern by looking at the raw data. For example, analyzing data from an LMS may reveal a relationship between the learning objects that a student accessed during the course and their final course grade. Similarly, analyzing student transcript data may reveal a relationship between a student's grade in a particular course and their decision to change their academic major. Such information provides insight into the design of learning environments, which allows students, teachers, school administrators, and educational policy makers to make informed decisions about how to interact with, provide, and manage educational resources.
"""
Intelligent Tutoring System
https://en.wikipedia.org/wiki/Intelligent_tutoring_system"""
An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners,[1] usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher (e.g., classroom lectures), or no teacher at all (e.g., online homework).[2] ITSs are often designed with the goal of providing access to high quality education to each and every student.
"""
Learning Analytics
https://en.wikipedia.org/wiki/Learning_analytics
"""
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.[1] A related field is educational data mining.
Learning Analytics defined as a prediction model
One earlier definition discussed by the community suggested that Learning Analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning.[4] But this definition has been criticised by George Siemens[5][non-primary source needed] and Mike Sharkey.[6]
"""
Mind Uploading
https://en.wikipedia.org/wiki/Mind_uploading"""
Whole brain emulation (WBE), mind upload or brain upload (sometimes called "mind copying" or "mind transfer") is the hypothetical futuristic process of scanning the mental state (including long-term memory and "self") of a particular brain substrate and copying it to a computer. The computer could then run a simulation model of the brain's information processing, such that it would respond in essentially the same way as the original brain (i.e., indistinguishable from the brain for all relevant purposes) and experience having a conscious mind.
"""
Personalized Learning
https://en.wikipedia.org/wiki/Personalized_learning
"""
The use of the term "personalized learning" dates back to at least the early 1960s,[1] but there is no widespread agreement on the definition and components of a personal learning environment.[2] Even enthusiasts for the concept admit that personal learning is an evolving term and doesn't have any widely accepted definition.[3]
In 2005, Dan Buckley defined two ends of the personalized learning spectrum: "personalization for the learner", in which the teacher tailors the learning, and "personalization by the learner", in which the learner develops skills to tailor his own learning. This spectrum was adopted by the (2006) Microsoft's Practical Guide to Envisioning and Transforming Education.[4]
"""
Virtual assistant
https://en.wikipedia.org/wiki/Virtual_assistant"""
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. Sometimes the term "chatbot" is used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal (spoken?) commands.[1] A similar concept, however with differences, lays under the dialogue systems[2].
"""
Other References
-------------------------------------Beshears, Fred
Personal AIs in a Surveillance Society
by Fred M Beshears
https://innovationmemes.blogspot.com/2019/12/personal-ais-in-surveillance-society.html
--------------------------------
Ramirez, Vanessa Bates
Would You Want a Personal AI That Knows Everything About You?
by Vanessa Bates Ramirez
12/22/2019
https://singularityhub.com/2019/12/22/would-you-want-a-personal-ai-that-knows-everything-about-you/
--------------------------------
Rothblatt, Martine
Review of Virtually Human: the promise - and peril- of digital immortality
Book written by Martine Rothblatt
Review by Fred Beshears
https://innovationmemes.blogspot.com/2015/04/review-of-virtually-human-promise-and.html
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