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Artificial Intelligence Emergence in Disruptive Technology
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
J. E. T. Akinsola, M. A. Adeagbo, K. A. Oladapo, S. A. Akinsehinde, F. O. Onipede
AI exists in three types, namely: Artificial Narrow Intelligence (ANI)Robots or similar alternatives can perform a solitary task very well.Artificial General Intelligence (AGI)AI strives to vie with the capabilities and intelligence of human beings.Artificial Superintelligence (ASI): Here, the mechanisms of AI are expected to be superior to the intelligence of human in the nearest future (Daqar & Smoudy, 2019).
AI Comes of Age: A Primer
Published in Tom Lawry, Hacking Healthcare, 2022
General AI (also known as Artificial General AI or AGI) is the type of AI that can understand and reason across its environment as a human would. General AI has always been elusive. This category of AI is where many organizations aspire to be someday, but any true form of this is not likely in the short- to medium-term horizon.
AI/ML in Medical Research and Drug Development
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
There is no doubt that the modern AI/ML is gaining more attention than ever before. Although they are associated with limitations, its key impact on drug development industry and healthcare in general should not be overlooked. Rather, it must be leveraged in full capacity to help and make life better for patients around the world. We are now seeing more important roles for AI/ML models to play in this field, especially drug discovery, in an almost day-by-day basis. We expect the trend to stay like this or even more upward for years to come. This, indeed, can be considered another small step towards artificial general intelligence (AGI).
Controversies and Disparities in the Management of Age-Related Macular Degeneration
Published in Seminars in Ophthalmology, 2023
Aaron M. Fairbanks, Deeba Husain
Regarding socioeconomics, patients have higher odds of severe vision loss at initial exudative AMD presentation if they are from an area with a regional adjusted gross income (AGI) of $75,000 or less, compared with patients with incomes of more than $100,000.84 An inverse relationship between AGI and LTFU has also been identified, as poorer patients were less likely to attend follow-up appointments.34 The Beaver Dam Eye Study also found that, after controlling for age and sex, less education and being in a service-related occupation (as opposed to a white-collar profession) were associated with an increased incidence of early AMD.87 Similarly, a UK biobank study found that, after adjusting for confounding variables, people from the most affluent households had 24% lower odds of developing AMD compared with those from the poorest households.88 Individuals with lower socioeconomic status have also been shown to present at a more advanced stage of exudative AMD in the first eye, but not the second.89 These findings may be associated with a higher prevalence of poor lifestyle factors in those from disadvantaged socioeconomic backgrounds, like smoking, poor diet, and inability to miss work for appointments due to financial constraints.
Phantom Penis: Extrapolating Neuroscience and Employing Imagination for Trans Male Sexual Embodiment
Published in Studies in Gender and Sexuality, 2020
Brugger et al. (2013) promote a model of “social neuroscience” that “unifies neurological, psychological, and sociological approaches to bodily self-consciousness” (p. 1). What Case and Ramachandran (2012) have named alternating gender incongruity (AGI) calls for such methodology. They interviewed a subgroup of bigender-identified individuals who experience involuntary (and sometimes distressing) swinging between gender states. These gender states include a feeling of being male or female. When occupying a transgender state (that is out of alignment with their birth anatomy), some persons with AGI experience phantoms of the (transgender) parts that are expected but missing. For example, when an AGI person assigned male at birth (AMAB) switches into a female gender, they can experience phantom breasts; when an AGI person assigned female at birth (AFAB) swings into male gender, they can experience a phantom penis. AGI suggests cortical (or other brain) generation (or disruption) of identity or unstable plural identities (in contrast to nonbinary, queer, or gender-fluid identity). The authors suggest that a biological basis for AGI might relate to the coexistence of two (differently sexed) body images or of one body image with both male and female parts that are turned on and off with shifting hemispheric dominance. They expect this will be identified as a neuropsychological condition. It requires no great leap to add sociology to the mix. The subjective experience of AGI would certainly differ in social environments open to versus stigmatizing of nonbinary expression.
Superethics Instead of Superintelligence: Know Thyself, and Apply Science Accordingly
Published in AJOB Neuroscience, 2020
The race toward more efficient and profitable AI comes with added possibilities. The potential apex of the AI pyramid currently being built is “superintelligence” (SI). As increasing funds go into the creation of Artificial General Intelligence (AGI), the risks that an SI may develop increases (Bostrom 2014; Russel and Norvig 2009; Schmidt 2018). Increasing intelligence indefinitely is an ambitious, costly and awkward enterprise. First of all, the main feature of SI is of course that it is incomparably smarter than us. It has been suggested that we would be to the SI what mice are to us (Bostrom 2014). We have about as much chance to control such an intelligence as mice have to control us, which, no matter what Douglas Adams may have led you to believe (Adams 1979), is close to zero. For that reason, serious research funding now goes into ways of increasing intelligence that we can set up or control well enough to keep it “friendly” (Alba 2015; Cellan-Jones 2014; Lewis 2015; Rawlinson 2015; Russell 2019; Tegmark 2015). Isn’t it strange to spend millions to work toward systems that can outsmart us, and then to spend more millions to keep it friendly or controllable? What does such a paradoxical enterprise say about us? What itch does SI scratch?