AI - Technological Singularity

 

The emergence of technologies that could fundamentally change humans' role in society, challenge human epistemic agency and ontological status, and trigger unprecedented and unforeseen developments in all aspects of life, whether biological, social, cultural, or technological, is referred to as the Technological Singularity.

The Singularity of Technology is most often connected with artificial intelligence, particularly artificial general intelligence (AGI).

As a result, it's frequently depicted as an intelligence explosion that's pushing advancements in fields like biotechnology, nanotechnology, and information technologies, as well as inventing new innovations.

The Technological Singularity is sometimes referred to as the Singularity, however it should not be confused with a mathematical singularity, since it has only a passing similarity.

This singularity, on the other hand, is a loosely defined term that may be interpreted in a variety of ways, each highlighting distinct elements of the technological advances.

The thoughts and writings of John von Neumann (1903–1957), Irving John Good (1916–2009), and Vernor Vinge (1944–) are commonly connected with the Technological Singularity notion, which dates back to the second half of the twentieth century.

Several universities, as well as governmental and corporate research institutes, have financed current Technological Singularity research in order to better understand the future of technology and society.

Despite the fact that it is the topic of profound philosophical and technical arguments, the Technological Singularity remains a hypothesis, a guess, and a pretty open hypothetical idea.

While numerous scholars think that the Technological Singularity is unavoidable, the date of its occurrence is continuously pushed back.

Nonetheless, many studies agree that the issue is not whether or whether the Technological Singularity will occur, but rather when and how it will occur.

Ray Kurzweil proposed a more exact timeline for the emergence of the Technological Singularity in the mid-twentieth century.

Others have sought to give a date to this event, but there are no well-founded grounds in support of any such proposal.

Furthermore, without applicable measures or signs, mankind would have no way of knowing when the Technological Singularity has occurred.

The history of artificial intelligence's unmet promises exemplifies the dangers of attempting to predict the future of technology.

The themes of superintelligence, acceleration, and discontinuity are often used to describe the Technological Singularity.

The term "superintelligence" refers to a quantitative jump in artificial systems' cognitive abilities, putting them much beyond the capabilities of typical human cognition (as measured by standard IQ tests).

Superintelligence, on the other hand, may not be restricted to AI and computer technology.

Through genetic engineering, biological computing systems, or hybrid artificial–natural systems, it may manifest in human agents.

Superintelligence, according to some academics, has boundless intellectual capabilities.

The curvature of the time curve for the advent of certain key events is referred to as acceleration.

Stone tools, the pottery wheel, the steam engine, electricity, atomic power, computers, and the internet are all examples of technological advancement portrayed as a curve across time emphasizing the discovery of major innovations.

Moore's law, which is more precisely an observation that has been viewed as a law, represents the increase in computer capacity.

"Every two years, the number of transistors in a dense integrated circuit doubles," it says.

People think that the emergence of key technical advances and new technological and scientific paradigms will follow a super-exponential curve in the event of the Technological Singularity.

One prediction regarding the Technological Singularity, for example, is that superintelligent systems would be able to self-improve (and self-replicate) in previously unimaginable ways at an unprecedented pace, pushing the technological development curve far beyond what has ever been witnessed.

The Technological Singularity discontinuity is referred to as an event horizon, and it is similar to a physical idea linked with black holes.

The analogy to this physical phenomena, on the other hand, should be used with care rather than being used to credit the physical world's regularity and predictability to technological singularity.

The limit of our knowledge about physical occurrences beyond a specific point in time is defined by an event horizon (also known as a prediction horizon).

It signifies that there is no way of knowing what will happen beyond the event horizon.

The discontinuity or event horizon in the context of technological singularity suggests that the technologies that precipitate technological singularity would cause disruptive changes in all areas of human life, developments about which experts cannot even conjecture.

The end of humanity and the end of human civilization are often related with technological singularity.

According to some research, social order will collapse, people will cease to be major actors, and epistemic agency and primacy would be lost.

Humans, it seems, will not be required by superintelligent systems.

These systems will be able to self-replicate, develop, and build their own living places, and humans will be seen as either barriers or unimportant, outdated things, similar to how humans now consider lesser species.

One such situation is represented by Nick Bostrom's Paperclip Maximizer.

AI is included as a possible danger to humanity's existence in the Global Catastrophic Risks Survey, with a reasonably high likelihood of human extinction, placing it on par with global pandemics, nuclear war, and global nanotech catastrophes.

However, the AI-related apocalyptic scenario is not a foregone conclusion of the Technological Singularity.

In other more utopian scenarios, technology singularity would usher in a new period of endless bliss by opening up new opportunities for humanity's infinite expansion.

Another element of technological singularity that requires serious consideration is how the arrival of superintelligence may imply the emergence of superethical capabilities in an all-knowing ethical agent.

Nobody knows, however, what superethical abilities might entail.

The fundamental problem, however, is that superintelligent entities' higher intellectual abilities do not ensure a high degree of ethical probity, or even any level of ethical probity.

As a result, having a superintelligent machine with almost infinite (but not quite) capacities but no ethics seems to be dangerous to say the least.

A sizable number of scholars are skeptical about the development of the Technological Singularity, notably of superintelligence.

They rule out the possibility of developing artificial systems with superhuman cognitive abilities, either on philosophical or scientific grounds.

Some contend that while artificial intelligence is often at the heart of technological singularity claims, achieving human-level intelligence in artificial systems is impossible, and hence superintelligence, and thus the Technological Singularity, is a dream.

Such barriers, however, do not exclude the development of superhuman brains via the genetic modification of regular people, paving the door for transhumans, human-machine hybrids, and superhuman agents.

More scholars question the validity of the notion of the Technological Singularity, pointing out that such forecasts about future civilizations are based on speculation and guesswork.

Others argue that the promises of unrestrained technological advancement and limitless intellectual capacities made by the Technological Singularity legend are unfounded, since physical and informational processing resources are plainly limited in the cosmos, particularly on Earth.

Any promises of self-replicating, self-improving artificial agents capable of super-exponential technological advancement are false, since such systems will lack the creativity, will, and incentive to drive their own evolution.

Meanwhile, social opponents point out that superintelligence's boundless technological advancement would not alleviate issues like overpopulation, environmental degradation, poverty, and unparalleled inequality.

Indeed, the widespread unemployment projected as a consequence of AI-assisted mass automation of labor, barring significant segments of the population from contributing to society, would result in unparalleled social upheaval, delaying the development of new technologies.

As a result, rather than speeding up, political or societal pressures will stifle technological advancement.

While technological singularity cannot be ruled out on logical grounds, the technical hurdles that it faces, even if limited to those that can presently be determined, are considerable.

Nobody expects the technological singularity to happen with today's computers and other technology, but proponents of the concept consider these obstacles as "technical challenges to be overcome" rather than possible show-stoppers.

However, there is a large list of technological issues to be overcome, and Murray Shanahan's The Technological Singularity (2015) gives a fair overview of some of them.

There are also some significant nontechnical issues, such as the problem of superintelligent system training, the ontology of artificial or machine consciousness and self-aware artificial systems, the embodiment of artificial minds or vicarious embodiment processes, and the rights granted to superintelligent systems, as well as their role in society and any limitations placed on their actions, if this is even possible.

These issues are currently confined to the realms of technological and philosophical discussion.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Bostrom, Nick; de Garis, Hugo; Diamandis, Peter; Digital Immortality; Goertzel, Ben; Kurzweil, Ray; Moravec, Hans; Post-Scarcity, AI and; Superintelligence.

References And Further Reading

Bostrom, Nick. 2014. Superintelligence: Path, Dangers, Strategies. Oxford, UK: Oxford University Press.

Chalmers, David. 2010. “The Singularity: A Philosophical Analysis.” Journal of Consciousness Studies 17: 7–65.

Eden, Amnon H. 2016. The Singularity Controversy. Sapience Project. Technical Report STR 2016-1. January 2016.

Eden, Amnon H., Eric Steinhart, David Pearce, and James H. Moor. 2012. “Singularity Hypotheses: An Overview.” In Singularity Hypotheses: A Scientific and Philosophical Assessment, edited by Amnon H. Eden, James H. Moor, Johnny H. Søraker, and Eric Steinhart, 1–12. Heidelberg, Germany: Springer.

Good, I. J. 1966. “Speculations Concerning the First Ultraintelligent Machine.” Advances in Computers 6: 31–88.

Kurzweil, Ray. 2005. The Singularity Is Near: When Humans Transcend Biology. New York: Viking.

Sandberg, Anders, and Nick Bostrom. 2008. Global Catastrophic Risks Survey. Technical Report #2008/1. Oxford University, Future of Humanity Institute.

Shanahan, Murray. 2015. The Technological Singularity. Cambridge, MA: The MIT Press.

Ulam, Stanislaw. 1958. “Tribute to John von Neumann.” Bulletin of the American Mathematical Society 64, no. 3, pt. 2 (May): 1–49.

Vinge, Vernor. 1993. “The Coming Technological Singularity: How to Survive in the Post-Human Era.” In Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace, 11–22. Cleveland, OH: NASA Lewis Research Center.

AI - What Is Superintelligence AI? Is Artificial Superintelligence Possible?

 

 

In its most common use, the phrase "superintelligence" refers to any degree of intelligence that at least equals, if not always exceeds, human intellect, in a broad sense.

Though computer intelligence has long outperformed natural human cognitive capacity in specific tasks—for example, a calculator's ability to swiftly interpret algorithms—these are not often considered examples of superintelligence in the strict sense due to their limited functional range.

In this sense, superintelligence would necessitate, in addition to artificial mastery of specific theoretical tasks, some kind of additional mastery of what has traditionally been referred to as practical intelligence: a generalized sense of how to subsume particulars into universal categories that are in some way worthwhile.

To this day, no such generalized superintelligence has manifested, and hence all discussions of superintelligence remain speculative to some degree.

Whereas traditional theories of superintelligence have been limited to theoretical metaphysics and theology, recent advancements in computer science and biotechnology have opened up the prospect of superintelligence being materialized.

Although the timing of such evolution is hotly discussed, a rising body of evidence implies that material superintelligence is both possible and likely.

If this hypothesis is proved right, it will very certainly be the result of advances in one of two major areas of AI research: 

1. Bioengineering 
2. Computer science

The former involves efforts to not only map out and manipulate the human DNA, but also to exactly copy the human brain electronically through full brain emulation, also known as mind uploading.

The first of these bioengineering efforts is not new, with eugenics programs reaching back to the seventeenth century at the very least.

Despite the major ethical and legal issues that always emerge as a result of such efforts, the discovery of DNA in the twentieth century, together with advances in genome mapping, has rekindled interest in eugenics.

Much of this study is aimed at gaining a better understanding of the human brain's genetic composition in order to manipulate DNA code in the direction of superhuman intelligence.

Uploading is a somewhat different, but still biologically based, approach to superintelligence that aims to map out neural networks in order to successfully transfer human intelligence onto computer interfaces.

• The brains of insects and tiny animals are micro-dissected and then scanned for thorough computer analysis in this relatively new area of study.

• The underlying premise of whole brain emulation is that if the brain's structure is better known and mapped, it may be able to copy it with or without organic brain tissue.

Despite the fast growth of both genetic mapping and whole brain emulation, both techniques have significant limits, making it less likely that any of these biological approaches will be the first to attain superintelligence.

The genetic alteration of the human genome, for example, is constrained by generational constraints.

Even if it were now feasible to artificially boost cognitive functioning by modifying the DNA of a human embryo (which is still a long way off), it would take an entire generation for the changed embryo to evolve into a fully fledged, superintelligent human person.

This would also imply that there are no legal or moral barriers to manipulating the human DNA, which is far from the fact.

Even the comparatively minor genetic manipulation of human embryos carried done by a Chinese physician as recently as November 2018 sparked international outrage (Ramzy and Wee 2019).

Whole brain emulation, on the other hand, is still a long way off, owing to biotechnology's limits.

Given the current medical technology, the extreme levels of accuracy necessary at every step of the uploading process are impossible to achieve.

Science and technology currently lack the capacity to dissect and scan human brain tissue with sufficient precision to produce full brain simulation results.

Furthermore, even if such first steps are feasible, researchers would face significant challenges in analyzing and digitally replicating the human brain using cutting-edge computer technology.

Many analysts believe that such constraints will be overcome, although the timeline for such realizations is unknown.

Apart from biotechnology, the area of AI, which is strictly defined as any type of nonorganic (particularly computer-based) intelligence, is the second major path to superintelligence.

Of course, the work of creating a superintelligent AI from the ground up is complicated by a number of elements, not all of which are purely logistical in nature, such as processing speed, hardware/software design, finance, and so on.

In addition to such practical challenges, there is a significant philosophical issue: human programmers are unable to know, and so cannot program, that which is superior to their own intelligence.

Much contemporary research on computer learning and interest in the notion of a seed AI is motivated in part by this worry.

Any machine capable of changing reactions to stimuli based on an examination of how well it performs in relation to a predetermined objective is defined as the latter.

Importantly, the concept of a seed AI entails not only the capacity to change its replies by extending its base of content knowledge (stored information), but also the ability to change the structure of its programming to better fit a specific job (Bostrom 2017, 29).

Indeed, it is this latter capability that would give a seed AI what Nick Bostrom refers to as "recursive self-improvement," or the ability to evolve iteratively (Bostrom 2017, 29).

This would eliminate the requirement for programmers to have an a priori vision of super intelligence since the seed AI would constantly enhance its own programming, with each more intelligent iteration writing a superior version of itself (beyond the human level).

Such a machine would undoubtedly cast doubt on the conventional philosophical assumption that robots are incapable of self-awareness.

This perspective's proponents may be traced all the way back to Descartes, but they also include more current thinkers like John Haugeland and John Searle.

Machine intelligence, in this perspective, is defined as the successful correlation of inputs with outputs according to a predefined program.

As a result, robots differ from humans in type, the latter being characterized only by conscious self-awareness.

Humans are supposed to comprehend the activities they execute, but robots are thought to carry out functions mindlessly—that is, without knowing how they work.

Should it be able to construct a successful seed AI, this core idea would be forced to be challenged.

The seed AI would demonstrate a level of self-awareness and autonomy not readily explained by the Cartesian philosophical paradigm by upgrading its own programming in ways that surprise and defy the forecasts of its human programmers.

Indeed, although it is still speculative (for the time being), the increasingly possible result of superintelligent AI poses a slew of moral and legal dilemmas that have sparked a lot of philosophical discussion in this subject.

The main worries are about the human species' security in the case of what Bostrom refers to as a "intelligence explosion"—that is, the creation of a seed AI followed by a possibly exponential growth in intellect (Bostrom 2017).

One of the key problems is the inherently unexpected character of such a result.

Humans will not be able to totally foresee how superintelligent AI would act due to the autonomy entailed by superintelligence in a definitional sense.

Even in the few cases of specialized superintelligence that humans have been able to construct and study so far—for example, robots that have surpassed humans in strategic games like chess and Go—human forecasts for AI have shown to be very unreliable.

For many critics, such unpredictability is a significant indicator that, should more generic types of superintelligent AI emerge, humans would swiftly lose their capacity to manage them (Kissinger 2018).

Of all, such a loss of control does not automatically imply an adversarial relationship between humans and superintelligence.

Indeed, although most of the literature on superintelligence portrays this relationship as adversarial, some new work claims that this perspective reveals a prejudice against machines that is particularly prevalent in Western cultures (Knight 2014).

Nonetheless, there are compelling grounds to believe that superintelligent AI would at the very least consider human goals as incompatible with their own, and may even regard humans as existential dangers.

For example, computer scientist Steve Omohundro has claimed that even a relatively basic kind of superintelligent AI like a chess bot would have motive to want the extinction of humanity as a whole—and may be able to build the tools to do it (Omohundro 2014).

Similarly, Bostrom has claimed that a superintelligence explosion would most certainly result in, if not the extinction of the human race, then at the very least a gloomy future (Bostrom 2017).

Whatever the benefits of such theories, the great uncertainty entailed by superintelligence is obvious.

If there is one point of agreement in this large and diverse literature, it is that if AI research is to continue, the global community must take great care to protect its interests.

Hardened determinists who claim that technological advancement is so tightly connected to inflexible market forces that it is simply impossible to change its pace or direction in any major manner may find this statement contentious.

According to this determinist viewpoint, if AI can deliver cost-cutting solutions for industry and commerce (as it has already started to do), its growth will proceed into the realm of superintelligence, regardless of any unexpected negative repercussions.

Many skeptics argue that growing societal awareness of the potential risks of AI, as well as thorough political monitoring of its development, are necessary counterpoints to such viewpoints.

Bostrom highlights various examples of effective worldwide cooperation in science and technology as crucial precedents that challenge the determinist approach, including CERN, the Human Genome Project, and the International Space Station (Bostrom 2017, 253).

To this, one may add examples from the worldwide environmental movement, which began in the 1960s and 1970s and has imposed significant restrictions on pollution committed in the name of uncontrolled capitalism (Feenberg 2006).

Given the speculative nature of superintelligence research, it is hard to predict what the future holds.

However, if superintelligence poses an existential danger to human existence, caution would dictate that a worldwide collaborative strategy rather than a free market approach to AI be used.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Berserkers; Bostrom, Nick; de Garis, Hugo; General and Narrow AI; Goertzel, Ben; Kurzweil, Ray; Moravec, Hans; Musk, Elon; Technological Singularity; Yudkowsky, Eliezer.

References & Further Reading:

• Bostrom, Nick. 2017. Superintelligence: Paths, Dangers, Strategies. Oxford, UK: Oxford University Press.

• Feenberg, Andrew. 2006. “Environmentalism and the Politics of Technology.” In Questioning Technology, 45–73. New York: Routledge.

• Kissinger, Henry. 2018. “How the Enlightenment Ends.” The Atlantic, June 2018. https://www.theatlantic.com/magazine/archive/2018/06/henry-kissinger-ai-could-mean-the-end-of-human-history/559124/.

• Knight, Heather. 2014. How Humans Respond to Robots: Building Public Policy Through Good Design. Washington, DC: The Project on Civilian Robotics. Brookings Institution.

• Omohundro, Steve. 2014. “Autonomous Technology and the Greater Human Good.” Journal of Experimental & Theoretical Artificial Intelligence 26, no. 3: 303–15.

• Ramzy, Austin, and Sui-Lee Wee. 2019. “Scientist Who Edited Babies’ Genes Is Likely to Face Charges in China.” The New York Times, January 21, 2019

Artificial Intelligence - Who Is Hans Moravec?

 

Hans Moravec(1948–) is well-known in the computer science community as the long-time head of Carnegie Mellon University's Robotics Institute and an unashamed techno logical optimist.

For the last twenty-five years, he has studied and produced artificially intelligent robots at the CMU lab, where he is still an adjunct faculty member.

Moravec spent almost 10 years as a research assistant at Stanford University's groundbreaking Artificial Intelligence Lab before coming to Carnegie Mellon.

Moravec is also noted for his paradox, which states that, contrary to popular belief, it is simple to program high-level thinking skills into robots—as with chess or Jeopardy!—but difficult to transmit sensorimo tor agility.

Human sensory and motor abilities have developed over millions of years and seem to be easy, despite their complexity.

Higher-order cognitive abilities, on the other hand, are the result of more recent cultural development.

Geometry, stock market research, and petroleum engineering are examples of disciplines that are difficult for people to learn but easier for robots to learn.

"The basic lesson of thirty-five years of AI research is that the hard issues are simple, and the easy ones are hard," writes Steven Pinker of Moravec's scientific career.

Moravec built his first toy robot out of scrap metal when he was eleven years old, and his light-following electronic turtle and a robot operated by punched paper tape earned him two high school science fair honors.

He proposed a Ship of Theseus-like analogy for the viability of artificial brains while still in high school.

Consider replacing a person's human neurons one by one with precisely manufactured equivalents, he said.

When do you think human awareness will vanish? Is anybody going to notice? Is it possible to establish that the person is no longer human? Later in his career, Moravec would suggest that human knowledge and training might be broken down in the same manner, into subtasks that machine intelligences could take over.

Moravec's master's thesis focused on the development of a computer language for artificial intelligence, while his PhD research focused on the development of a robot that could navigate obstacle courses utilizing spatial representation methods.

The area of interest (ROI) in a scene was identified by these robot vision systems.

Moravec's early computer vision robots were extremely sluggish by today's standards, taking around five hours to go from one half of the facility to the other.

To measure distance and develop an internal picture of physical impediments in the room, a remote computer carefully analysed continuous video-camera images recorded by the robot from various angles.

Moravec finally developed 3D occupancy grid technology, which allowed a robot to create an awareness of a cluttered area in a matter of seconds.

Moravec's lab took on a new challenge by converting a Pontiac TransSport minivan into one of the world's first road-ready autonomous cars.

The self-driving minivan reached speeds of up to 60 miles per hour.

DANTE II, a robot capable of going inside the crater of an active volcano on Mount Spurr in Alaska, was also constructed by the CMU Robotics Institute.

While DANTE II's immediate aim was to sample harmful fumarole gases, a job too perilous for humans, it was also planned to demonstrate technologies for robotic expeditions to distant worlds.

The volcanic explorer robot used artificial intelligence to navigate the perilous, boulder-strewn terrain on its own.

Because such rovers produced so much visual and other sensory data that had to be analyzed and managed, Moravec believes that experience with mobile robots spurred the development of powerful artificial intelligence and computer vision methods.

For the National Aeronautics and Space Administration (NASA), Moravec's team built fractal branching ultra-dexterous robots ("Bush robots") in the 1990s.

These robots, which were proposed but never produced due to the lack of necessary manufacturing technologies, comprised of a branching hierarchy of dynamic articulated limbs, starting with a main trunk and splitting down into smaller branches.

As a result, the Bush robot would have "hands" at all scales, from macroscopic to tiny.

The tiniest fingers would be nanoscale in size, allowing them to grip very tiny objects.

Moravec said the robot would need autonomy and depend on artificial intelligence agents scattered throughout the robot's limbs and branches because to the intricacy of manipulating millions of fingers in real time.

He believed that the robots may be made entirely of carbon nanotube material, employing the quick prototyping technology known as 3D printers.

Moravec believes that artificial intelligence will have a significant influence on human civilization.

To stress the role of AI in change, he coined the concept of the "landscape of human capability," which physicist Max Tegmark has later converted into a graphic depiction.

Moravec's picture depicts a three-dimensional environment in which greater altitudes reflect more challenging jobs in terms of human difficulty.

The point where the swelling waters meet the shore reflects the line where robots and humans both struggle with the same duties.

Art, science, and literature are now beyond of grasp for an AI, but the sea has already defeated mathematics, chess, and the game Go.

Language translation, autonomous driving, and financial investment are all on the horizon.

More controversially, in two popular books, Mind Children (1988) and Robot: Mere Machine to Transcendent Mind (1989), Moravec engaged in future conjecture based on what he understood of developments in artificial intelligence research (1999).

In 2040, he said, human intellect will be surpassed by machine intelligence, and the human species would go extinct.

Moravec evaluated the functional equivalence between 50,000 million instructions per second (50,000 MIPS) of computer power and a gram of brain tissue and came up with this figure.

He calculated that home computers in the early 2000s equaled only an insect's nervous system, but that if processing power doubled every eighteen months, 350 million years of human intellect development could be reduced to just 35 years of artificial intelligence advancement.

He estimated that a hundred million MIPS would be required to create human-like universal robots.

Moravec refers to these sophisticated robots as our "mind children" in the year 2040.

Humans, he claims, will devise techniques to delay biological civilization's final demise.

Moravec, for example, was the first to anticipate what is now known as universal basic income, which is delivered by benign artificial superintelligences.

In a completely automated society, a basic income system would provide monthly cash payments to all individuals without any type of employment requirement.

Moravec is more concerned about the idea of a renegade automated corporation breaking its programming and refusing to pay taxes into the human cradle-to-grave social security system than he is about technological unemployment.

Nonetheless, he predicts that these "wild" intelligences will eventually control the universe.

Moravec has said that his books Mind Children and Robot may have had a direct impact on the last third of Stanley Kubrick's original screenplay for A.I. Artificial Intelligence (later filmed by Steven Spielberg).

Moravecs, on the other hand, are self-replicating devices in the science fiction books Ilium and Olympos.

Moravec defended the same physical fundamentalism he expressed in his high school thoughts throughout his life.

He contends in his most transhumanist publications that the only way for humans to stay up with machine intelligences is to merge with them by replacing sluggish human cerebral tissue with artificial neural networks controlled by super-fast algorithms.

In his publications, Moravec has blended the ideas of artificial intelligence with virtual reality simulation.

He's come up with four scenarios for the development of consciousness.

(1) human brains in the physical world, 

(2) a programmed AI implanted in a physical robot, 

(3) a human brain immersed in a virtual reality simulation, and 

(4) an AI functioning inside the boundaries of virtual reality All of them are equally credible depictions of reality, and they are as "real" as we believe them to be.

Moravec is the creator and chief scientist of the Pittsburgh-based Seegrid Corporation, which makes autonomous Robotic Industrial Trucks that can navigate warehouses and factories without the usage of automated guided vehicle systems.

A human trainer physically pushes Seegrid's vehicles through a new facility once.

The robot conducts the rest of the job, determining the most efficient and safe pathways for future journeys, while the trainer stops at the appropriate spots for the truck to be loaded and unloaded.

Seegrid VGVs have transported over two million production miles and eight billion pounds of merchandise for DHL, Whirlpool, and Amazon.

Moravec was born in the Austrian town of Kautzen.

During World War II, his father was a Czech engineer who sold electrical products.

When the Russians invaded Czechoslovakia in 1944, the family moved to Austria.

In 1953, his family relocated to Canada, where he now resides.

Moravec earned a bachelor's degree in mathematics from Acadia University in Nova Scotia, a master's degree in computer science from the University of Western Ontario, and a doctorate from Stanford University, where he worked with John McCarthy and Tom Binford on his thesis.

The Office of Naval Study, the Defense Advanced Research Projects Agency, and NASA have all supported his research.

Elon Musk (1971–) is an American businessman and inventor.

Elon Musk is an engineer, entrepreneur, and inventor who was born in South Africa.

He is a dual citizen of South Africa, Canada, and the United States, and resides in California.

Musk is widely regarded as one of the most prominent inventors and engineers of the twenty-first century, as well as an important influencer and contributor to the development of artificial intelligence.

Despite his controversial personality, Musk is widely regarded as one of the most prominent inventors and engineers of the twenty-first century and an important influencer and contributor to the development of artificial intelligence.

Musk's business instincts and remarkable technological talent were evident from an early age.

By the age of 10, he had self-taught himself how program computers, and by the age of twelve, he had produced a video game and sold the source code to a computer maga zine.

Musk has included allusions to some of his favorite novels in SpaceX's Falcon Heavy rocket launch and Tesla's software since he was a youngster.

Musk's official schooling was centered on economics and physics rather than engineering, interests that are mirrored in his subsequent work, such as his efforts in renewable energy and space exploration.

He began his education at Queen's University in Canada, but later transferred to the University of Pennsylvania, where he earned bachelor's degrees in Economics and Physics.

Musk barely stayed at Stanford University for two days to seek a PhD in energy physics before departing to start his first firm, Zip2, with his brother Kimbal Musk.

Musk has started or cofounded many firms, including three different billion-dollar enterprises: SpaceX, Tesla, and PayPal, all driven by his diverse interests and goals.

• Zip2 was a web software business that was eventually purchased by Compaq.

• X.com: an online bank that merged with PayPal to become the online payments corporation PayPal.

• Tesla, Inc.: an electric car and solar panel maker • SpaceX: a commercial aircraft manufacturer and space transportation services provider (via its subsidiarity SolarCity) • Neuralink: a neurotechnology startup focusing on brain-computer connections • The Boring Business: an infrastructure and tunnel construction corporation • OpenAI: a nonprofit AI research company focused on the promotion and development of friendly AI Musk is a supporter of environmentally friendly energy and consumption.

Concerns over the planet's future habitability prompted him to investigate the potential of establishing a self-sustaining human colony on Mars.

Other projects include the Hyperloop, a high-speed transportation system, and the Musk electric jet, a jet-powered supersonic electric aircraft.

Musk sat on President Donald Trump's Strategy and Policy Forum and Manufacturing Jobs Initiative for a short time before stepping out when the US withdrew from the Paris Climate Agreement.

Musk launched the Musk Foundation in 2002, which funds and supports research and activism in the domains of renewable energy, human space exploration, pediatric research, and science and engineering education.

Musk's effect on AI is significant, despite his best-known work with Tesla and SpaceX, as well as his contentious social media pronouncements.

In 2015, Musk cofounded the charity OpenAI with the objective of creating and supporting "friendly AI," or AI that is created, deployed, and utilized in a manner that benefits mankind as a whole.

OpenAI's objective is to make AI open and accessible to the general public, reducing the risks of AI being controlled by a few privileged people.

OpenAI is especially concerned about the possibility of Artificial General Intelligence (AGI), which is broadly defined as AI capable of human-level (or greater) performance on any intellectual task, and ensuring that any such AGI is developed responsibly, transparently, and distributed evenly and openly.

OpenAI has had its own successes in taking AI to new levels while staying true to its goals of keeping AI friendly and open.

In June of 2018, a team of OpenAI-built robots defeated a human team in the video game Dota 2, a feat that could only be accomplished through robot teamwork and collaboration.

Bill Gates, a cofounder of Microsoft, praised the achievement on Twitter, calling it "a huge milestone in advancing artificial intelligence" (@BillGates, June 26, 2018).

Musk resigned away from the OpenAI board in February 2018 to prevent any conflicts of interest while Tesla advanced its AI work for autonomous driving.

Musk became the CEO of Tesla in 2008 after cofounding the company in 2003 as an investor.

Musk was the chairman of Tesla's board of directors until 2018, when he stepped down as part of a deal with the US Securities and Exchange Commission over Musk's false claims about taking the company private.

Tesla produces electric automobiles with self-driving capabilities.

Tesla Grohmann Automation and Solar City, two of its subsidiaries, offer relevant automotive technology and manufacturing services and solar energy services, respectively.

Tesla, according to Musk, will reach Level 5 autonomous driving capabilities in 2019, as defined by the National Highway Traffic Safety Administration's (NHTSA) five levels of autonomous driving.

Tes la's aggressive development with autonomous driving has influenced conventional car makers' attitudes toward electric cars and autonomous driving, and prompted a congressional assessment of how and when the technology should be regulated.

Musk is widely credited as a key influencer in moving the automotive industry toward autonomous driving, highlighting the benefits of autonomous vehicles (including reduced fatalities in vehicle crashes, increased worker productivity, increased transportation efficiency, and job creation) and demonstrating that the technology is achievable in the near term.

Tesla's autonomous driving code has been created and enhanced under the guidance of Musk and Tesla's Director of AI, Andrej Karpathy (Autopilot).

The computer vision analysis used by Tesla, which includes an array of cameras on each car and real-time image processing, enables the system to make real-time observations and predictions.

The cameras, as well as other exterior and internal sensors, capture a large quantity of data, which is evaluated and utilized to improve Autopilot programming.

Tesla is the only autonomous car maker that is opposed to the LIDAR laser sensor (an acronym for light detection and ranging).

Tesla uses cameras, radar, and ultrasonic sensors instead.

Though academics and manufacturers disagree on whether LIDAR is required for fully autonomous driving, the high cost of LIDAR has limited Tesla's rivals' ability to produce and sell vehicles at a pricing range that allows a large number of cars on the road to gather data.

Tesla is creating its own AI hardware in addition to its AI programming.

Musk stated in late 2017 that Tesla is building its own silicon for artificial-intelligence calculations, allowing the company to construct its own AI processors rather than depending on third-party sources like Nvidia.

Tesla's AI progress in autonomous driving has been marred by setbacks.

Tesla has consistently missed self-imposed deadlines, and serious accidents have been blamed on flaws in the vehicle's Autopilot mode, including a non-injury accident in 2018, in which the vehicle failed to detect a parked firetruck on a California freeway, and a fatal accident in 2018, in which the vehicle failed to detect a pedestrian outside a crosswalk.

Neuralink was established by Musk in 2016.

With the stated objective of helping humans to keep up with AI breakthroughs, Neuralink is focused on creating devices that can be implanted into the human brain to better facilitate communication between the brain and software.

Musk has characterized the gadgets as a more efficient interface with computer equipment, while people now operate things with their fingertips and voice commands, directives would instead come straight from the brain.

Though Musk has made major advances to AI, his pronouncements regarding the risks linked with AI have been apocalyptic.

Musk has called AI "humanity's greatest existential danger" and "the greatest peril we face as a civilisation" (McFarland 2014).

(Morris 2017).

He cautions against the perils of power concentration, a lack of independent control, and a competitive rush to acceptance without appropriate analysis of the repercussions.

While Musk has used colorful terminology such as "summoning the devil" (McFarland 2014) and depictions of cyborg overlords, he has also warned of more immediate and realistic concerns such as job losses and AI-driven misinformation campaigns.

Though Musk's statements might come out as alarmist, many important and well-respected figures, including as Microsoft cofounder Bill Gates, Swedish-American scientist Max Tegmark, and the late theoretical physicist Stephen Hawking, share his concern.

Furthermore, Musk does not call for the cessation of AI research.

Instead, Musk supports for responsible AI development and regulation, including the formation of a Congressional committee to spend years studying AI with the goal of better understanding the technology and its hazards before establishing suitable legal limits.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Superintelligence; Technological Singularity; Workplace Automation.

References & Further Reading:

Moravec, Hans. 1988. Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press.

Moravec, Hans. 1999. Robot: Mere Machine to Transcendent Mind. Oxford, UK: Oxford University Press.

Moravec, Hans. 2003. “Robots, After All.” Communications of the ACM 46, no. 10 (October): 90–97.

Pinker, Steven. 2007. The Language Instinct: How the Mind Creates Language. New York: Harper.