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John Carmack steps down as CTO of Oculus to tackle artificial general intelligence - SiliconANGLE

Oculus Chief Technology Officer John Carmack is stepping down from the position he has held since 2013 to spend time working on artificial intelligence.

Carmack, who before joining Oculus was a co-founder of id Software LLC and is credited as the father of the legendary DOOM and Quake game series, said his new role would only consume a modest slice of his time.

in his new role of consulting CTO: “He will continue to advise us on strategy and technical feasibility through building proofs of concept, and as always, advocating for our users.”

ZeniMax claimed that Carmack had corresponded with Oculus founder Palmer Luckey early in the development of Oculus Rift, leading to a number of major improvements to the headset, ZeniMax claiming that those improvements involved its intellectual property.

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)[14]

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.[20]

AI-complete problems are hypothesised to include general computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real world problem.[21]

In the 1990s and early 21st century, mainstream AI has 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.[34]

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).'[37]

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[42]

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[9]).

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.[49]

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).[52]

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).[53]

(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 artificial neuron model assumed by Kurzweil and used in many current artificial neural network implementations is simple compared with biological neurons.

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.[54]

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.[56]

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.[57]

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?'.[59]

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.

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.[73]

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][73]

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.[76]

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.[43]

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.[76]

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.

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'.[82]

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.[83]

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.

A growing population of intelligent robots could conceivably out-compete inferior humans in job markets, in business, in science, in politics (pursuing robot rights), and technologically, sociologically (by acting as one), and militarily.

For example, robots for homes, health care, hotels, and restaurants have automated many parts of our lives: virtual bots turn customer service into self-service, big data AI applications are used to replace portfolio managers, and social robots such as Pepper are used to replace human greeters for customer service purpose.[87]

AI is making literary leaps – now we need the rules to catch up

Last February, OpenAI, an artificial intelligence research group based in San Francisco, announced that it has been training an AI language model called GPT-2, and that it now “generates coherent paragraphs of text, achieves state-of-the-art performance on many language-modelling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarisation – all without task-specific training”.

As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper.” Given that OpenAI describes itself as a research institute dedicated to “discovering and enacting the path to safe artificial general intelligence”, this cautious approach to releasing a potentially powerful and disruptive tool into the wild seemed appropriate.

After all, without full disclosure – of program code, training dataset, neural network weights, etc – how could independent researchers decide whether the claims made by OpenAI about its system were valid?

The replicability of experiments is a cornerstone of scientific method, so the fact that some academic fields may be experiencing a “replication crisis” (a large number of studies that prove difficult or impossible to reproduce) is worrying.

On the other hand, the world is now suffering the consequences of tech companies like Facebook, Google, Twitter, LinkedIn, Uber and co designing algorithms for increasing “user engagement” and releasing them on an unsuspecting world with apparently no thought of their unintended consequences.

If the row over GPT-2 has had one useful outcome, it is a growing realisation that the AI research community needs to come up with an agreed set of norms about what constitutes responsible publication (and therefore release).

In a fascinating essay, I, Language Robot, the neuroscientist and writer Patrick House reports on his experience of working alongside OpenAI’s language model – which produces style-matched prose to any written prompt that it’s fed.

Toward Artificial General Intelligence (AGI)

It all started with the quest by humans to model the real world in a way it could be understood.  Ravi Chityala, senior software development engineer and instructor with the UCSC Silicon Valley Extension Database and Data Analytics program, will begin the evening by walking through the history and progress we have made in modeling the real world: from the time we solved differential equations to the current applications of ML and DL.   We will then look to biology for inspiration in creating intelligent systems. We understand the brain today much better than we did 50 years ago.