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Artificial general intelligence
cognitive science, computational intelligence and decision making) tend to emphasise the need to consider additional traits such as imagination (taken as the ability to form mental images and concepts that were not programmed in)
see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent), but not yet at human levels.
The most difficult problems for computers are informally known as 'AI-complete' or 'AI-hard', implying that solving them is equivalent to the general aptitude of human intelligence, or strong AI, beyond the capabilities of a purpose-specific algorithm.
AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.
In the 1990s and early 21st century, mainstream AI achieved far greater commercial success and academic respectability by focusing on specific sub-problems where they can produce verifiable results and commercial applications, such as artificial neural networks, computer vision or data mining.
Most mainstream AI researchers hope that strong AI can be developed by combining the programs that solve various sub-problems using an integrated agent architecture, cognitive architecture or subsumption architecture.
Hans Moravec wrote in 1988: 'I am confident that this bottom-up route to artificial intelligence will one day meet the traditional top-down route more than half way, ready to provide the real world competence and the commonsense knowledge that has been so frustratingly elusive in reasoning programs.
for example, Stevan Harnad of Princeton concluded his 1990 paper on the Symbol Grounding Hypothesis by stating: 'The expectation has often been voiced that 'top-down' (symbolic) approaches to modeling cognition will somehow meet 'bottom-up' (sensory) approaches somewhere in between.
A free-floating symbolic level like the software level of a computer will never be reached by this route (or vice versa) – nor is it clear why we should even try to reach such a level, since it looks as if getting there would just amount to uprooting our symbols from their intrinsic meanings (thereby merely reducing ourselves to the functional equivalent of a programmable computer).'
Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10 years to over a century, but the consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in The Singularity is Near
A 2017 survey of AGI categorized forty-five known 'active R&D projects' that explicitly or implicitly (through published research) research AGI, with the largest three being DeepMind, the Human Brain Project, and OpenAI (based on article).
A low-level brain model is built by scanning and mapping a biological brain in detail and copying its state into a computer system or another computational device.
The computer runs a simulation model so faithful to the original that it will behave in essentially the same way as the original brain, or for all practical purposes, indistinguishably.
An estimate of the brain's processing power, based on a simple switch model for neuron activity, is around 1014 (100 trillion) synaptic updates per second (SUPS).
In 1997 Kurzweil looked at various estimates for the hardware required to equal the human brain and adopted a figure of 1016 computations per second (cps).
(For comparison, if a 'computation' was equivalent to one 'floating point operation' – a measure used to rate current supercomputers – then 1016 'computations' would be equivalent to 10 petaFLOPS, achieved in 2011).
He used this figure to predict the necessary hardware would be available sometime between 2015 and 2025, if the exponential growth in computer power at the time of writing continued.
The overhead introduced by full modeling of the biological, chemical, and physical details of neural behaviour (especially on a molecular scale) would require computational powers several orders of magnitude larger than Kurzweil's estimate.
In addition the estimates do not account for glial cells, which are at least as numerous as neurons, and which may outnumber neurons by as much as 10:1, and are now known to play a role in cognitive processes.
The Blue Brain project used one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to create a real time simulation of a single rat neocortical column consisting of approximately 10,000 neurons and 108 synapses in 2006.
A longer term goal is to build a detailed, functional simulation of the physiological processes in the human brain: 'It is not impossible to build a human brain and we can do it in 10 years,' Henry Markram, director of the Blue Brain Project said in 2009 at the TED conference in Oxford.
Hans Moravec addressed the above arguments ('brains are more complicated', 'neurons have to be modeled in more detail') in his 1997 paper 'When will computer hardware match the human brain?'.
The actual complexity of modeling biological neurons has been explored in OpenWorm project that was aimed on complete simulation of a worm that has only 302 neurons in its neural network (among about 1000 cells in total).
fundamental criticism of the simulated brain approach derives from embodied cognition where human embodiment is taken as an essential aspect of human intelligence.
The first one is called 'the strong AI hypothesis' and the second is 'the weak AI hypothesis' because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test.
There are other aspects of the human mind besides intelligence that are relevant to the concept of strong AI which play a major role in science fiction and the ethics of artificial intelligence:
It is also possible that some of these properties, such as sentience, naturally emerge from a fully intelligent machine, or that it becomes natural to ascribe these properties to machines once they begin to act in a way that is clearly intelligent.
Since the launch of AI research in 1956, the growth of this field has slowed down over time and has stalled the aims of creating machines skilled with intelligent action at the human level.
Furthermore, AI researchers have been able to create computers that can perform jobs that are complicated for people to do, but conversely they have struggled to develop a computer that is capable of carrying out tasks that are simple for humans to do (Moravec's paradox).[example needed]
The intricacy of scientific problems and the need to fully understand the human brain through psychology and neurophysiology have limited many researchers from emulating the function of the human brain into a computer hardware.
Many researchers tend to underestimate any doubt that is involved with future predictions of AI, but without taking those issues seriously can people then overlook solutions to problematic questions.
possible reason for the slowness in AI relates to the acknowledgement by many AI researchers that heuristics is a section that contains a significant breach between computer performance and human performance.
Microsoft co-founder Paul Allen believed that such intelligence is unlikely in the 21st century because it would require 'unforeseeable and fundamentally unpredictable breakthroughs' and a 'scientifically deep understanding of cognition'.
Writing in The Guardian, roboticist Alan Winfield claimed the gulf between modern computing and human-level artificial intelligence is as wide as the gulf between current space flight and practical faster-than-light spaceflight.
Four polls conducted in 2012 and 2013 suggested that the median guess among experts for when they'd be 50% confident AGI would arrive was 2040 to 2050, depending on the poll, with the mean being 2081.
Many of the scholars who are concerned about existential risk believe that the best way forward would be to conduct (possibly massive) research into solving the difficult 'control problem' to answer the question: what types of safeguards, algorithms, or architectures can programmers implement to maximize the probability that their recursively-improving AI would continue to behave in a friendly, rather than destructive, manner after it reaches superintelligence?
Much of existing criticism argues that AGI is unlikely in the short term: computer scientist Gordon Bell argues that the human race will already destroy itself before it reaches the technological singularity.
Artificial intelligence helps prevent disruptions in fusion devices
Risk of disruptions Fusion devices called tokamaks run increased risk of disruptions as researchers, aiming to maximize fusion power to create on Earth the fusion that powers the sun and stars, bump up against the operational limits of the facilities.
Fusion reactions combine light elements in the form of plasma -- the hot, charged state of matter composed of free electrons and atomic nuclei that makes up 99 percent of the visible universe -- to generate massive amounts of energy.
Relatively simple model 'It's fascinating to see that a relatively simple machine learning model could accurately predict the complicated behavior of fusion plasma,' said Yichen Fu, a graduate student in the Princeton Program in Plasma Physics at PPPL and lead author of a paper describing the findings (link is external) in Physics of Plasmas and showcased in a featured American Institute of Physics publication called 'SciLight.'
- On 1. oktober 2020
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