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Artificial Intelligence - Who Is Mark Tilden?

 


Mark Tilden(1961–) is a biomorphic robot freelance designer from Canada.

A number of his robots are sold as toys.

Others have appeared in television and cinema as props.

Tilden is well-known for his opposition to the notion that strong artificial intelligence is required for complicated robots.

Tilden is a forerunner in the field of BEAM robotics (biology, electronics, aesthetics, and mechanics).

To replicate biological neurons, BEAM robots use analog circuits and systems, as well as continuously varying signals, rather as digital electronics and microprocessors.

Biomorphic robots are programmed to change their gaits in order to save energy.

When such robots come into impediments or changes in the underlying terrain, they are knocked out of their lowest energy condition, forcing them to adapt to a new walking pattern.

The mechanics of the underlying machine rely heavily on self-adaptation.

After failing to develop a traditional electronic robot butler in the late 1980s, Tilden resorted to BEAM type robots.

The robot could barely vacuum floors after being programmed with Isaac Asimov's Three Laws of Robotics.



After hearing MIT roboticist Rodney Brooks speak at Waterloo University on the advantages of basic sensorimotor, stimulus-response robotics versus computationally complex mobile devices, Tilden completely abandoned the project.

Til den left Brooks' lecture questioning if dependable robots might be built without the use of computer processors or artificial intelligence.

Rather than having the intelligence written into the robot's programming, Til den hypothesized that the intelligence may arise from the robot's operating environment, as well as the emergent features that resulted from that world.

Tilden studied and developed a variety of unusual analog robots at the Los Alamos National Laboratory in New Mexico, employing fast prototyping and off-the-shelf and cannibalized components.



Los Alamos was looking for robots that could operate in unstructured, unpredictable, and possibly hazardous conditions.

Tilden built almost a hundred robot prototypes.

His SATBOT autonomous spaceship prototype could align itself with the Earth's magnetic field on its own.

He built fifty insectoid robots capable of creeping through minefields and identifying explosive devices for the Marine Corps Base Quantico.

A robot known as a "aggressive ashtray" spits water at smokers.

A "solar spinner" was used to clean the windows.

The actions of an ant were reproduced by a biomorph made from five broken Sony Walkmans.

Tilden started building Living Machines powered by solar cells at Los Alamos.

These machines ran at extremely sluggish rates due to their energy source, but they were dependable and efficient for lengthy periods of time, often more than a year.

Tilden's first robot designs were based on thermodynamic conduit engines, namely tiny and efficient solar engines that could fire single neurons.

Rather than the workings of their brains, his "nervous net" neurons controlled the rhythms and patterns of motion in robot bodies.

Tilden's idea was to maximize the amount of patterns conceivable while using the fewest number of implanted transistors feasible.

He learned that with just twelve transistors, he could create six different movement patterns.

Tilden might replicate hopping, leaping, running, sitting, crawling, and a variety of other patterns of behavior by folding the six patterns into a figure eight in a symmetrical robot chassis.

Since then, Tilden has been a proponent of a new set of robot principles for such survivalist wild automata.

Tilden's Laws of Robotics say that (1) a robot must safeguard its survival at all costs; (2) a robot must get and keep access to its own power source; and (3) a robot must always seek out better power sources.

Tilden thinks that wild robots will be used to rehabilitate ecosystems that have been harmed by humans.

Tilden had another breakthrough when he introduced very inexpensive robots as toys for the general public and robot aficionados.

He wanted his robots to be in the hands of as many people as possible, so that hackers, hobbyists, and members of different maker communities could reprogramme and modify them.

Tilden designed the toys in such a way that they could be dismantled and analyzed.

They might be hacked in a basic way.

Everything is color-coded and labeled, and all of the wires have gold-plated contacts that can be ripped apart.

Tilden is presently working with WowWee Toys in Hong Kong on consumer-oriented entertainment robots:

  • B.I.O. Bugs, Constructobots, G.I. Joe Hoverstrike, Robosapien, Roboraptor, Robopet, Roborep tile, Roboquad, Roboboa, Femisapien, and Joebot are all popular WowWee robot toys.
  • The Roboquad was designed for the Jet Propulsion Laboratory's (JPL) Mars exploration program.
  • Tilden is also the developer of the Roomscooper cleaning robot.


WowWee Toys sold almost three million of Tilden's robot designs by 2005.


Tilden made his first robotic doll when he was three years old.

At the age of six, he built a Meccano suit of armor for his cat.

At the University of Waterloo, he majored in Systems Engineering and Mathematics.


Tilden is presently working on OpenCog and OpenCog Prime alongside artificial intelligence pioneer Ben Goertzel.


OpenCog is a worldwide initiative supported by the Hong Kong government that aims to develop an open-source emergent artificial general intelligence framework as well as a common architecture for embodied robotic and virtual cognition.

Dozens of IT businesses across the globe are already using OpenCog components.

Tilden has worked on a variety of films and television series as a technical adviser or robot designer, including Lara Croft: Tomb Raider (2001), The 40-Year-Old Virgin (2005), Paul Blart Mall Cop (2009), and X-Men: The Last Stand (2006).

In the Big Bang Theory (2007–2019), his robots are often displayed on the bookshelves of Sheldon's apartment.



~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


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



See also: 

Brooks, Rodney; Embodiment, AI and.


References And Further Reading

Frigo, Janette R., and Mark W. Tilden. 1995. “SATBOT I: Prototype of a Biomorphic Autonomous Spacecraft.” Mobile Robotics, 66–75.

Hapgood, Fred. 1994. “Chaotic Robots.” Wired, September 1, 1994. https://www.wired.com/1994/09/tilden/.

Hasslacher, Brosl, and Mark W. Tilden. 1995. “Living Machines.” Robotics and Autonomous Systems 15, no. 1–2: 143–69.

Marsh, Thomas. 2010. “The Evolution of a Roboticist: Mark Tilden.” Robot Magazine, December 7, 2010. http://www.botmag.com/the-evolution-of-a-roboticist-mark-tilden.

Menzel, Peter, and Faith D’Aluisio. 2000. “Biobots.” Discover Magazine, September 1, 2000. https://www.discovermagazine.com/technology/biobots.

Rietman, Edward A., Mark W. Tilden, and Manor Askenazi. 2003. “Analog Computation with Rings of Quasiperiodic Oscillators: The Microdynamics of Cognition in Living Machines.” Robotics and Autonomous Systems 45, no. 3–4: 249–63.

Samans, James. 2005. The Robosapiens Companion: Tips, Tricks, and Hacks. New York: Apress.



Artificial Intelligence - What Are Clinical Decision Support Systems?

 


In patient-physician contacts, decision-making is a critical activity, with judgements often based on partial and insufficient patient information.

In principle, physician decision-making, which is undeniably complicated and dynamic, is hypothesis-driven.

Diagnostic intervention is based on a hypothetically deductive process of testing hypotheses against clinical evidence to arrive at conclusions.

Evidence-based medicine is a method of medical practice that incorporates individual clinical skill and experience with the best available external evidence from scientific literature to enhance decision-making.

Evidence-based medicine must be based on the highest quality, most trustworthy, and systematic data available.

The important issues remain, knowing that both evidence-based medicine and clinical research are required, but that none is perfect: How can doctors get the most up-to-date scientific evidence? What constitutes the best evidence? How may doctors be helped to decide whether external clinical evidence from systematic research should have an impact on their practice? A hierarchy of evidence may help you figure out which sorts of evidence are more likely to produce reliable answers to clinical problems if done correctly.

Despite the lack of a broadly agreed hierarchy of evidence, Alba DiCenso et al. (2009) established the 6S Hierarchy of Evidence-Based Resources as a framework for classifying and selecting resources that assess and synthesize research results.

The 6S pyramid was created to help doctors and other health-care professionals make choices based on the best available research data.

It shows a hierarchy of evidence in which higher levels give more accurate and efficient forms of information.

Individual studies are at the bottom of the pyramid.

Although they serve as the foundation for research, a single study has limited practical relevance for practicing doctors.

Clinicians have been taught for years that randomized controlled trials are the gold standard for making therapeutic decisions.

Researchers may use randomized controlled trials to see whether a treatment or intervention is helpful in a particular patient population, and a strong randomized controlled trial can overturn years of conventional wisdom.

Physicians, on the other hand, care more about whether it will work for their patient in a specific situation.

A randomized controlled study cannot provide this information.

A research synthesis may be considered of as a study of studies, since it reflects a greater degree of evidence than individual studies.

It makes conclusions about a practice's efficacy by carefully examining evidence from various experimental investigations.

Systematic reviews and meta-analyses, which are often seen as the pillars of evidence-based medicine, have their own set of issues and rely on rigorous evaluation of the features of the available data.

The problem is that most doctors are unfamiliar with the statistical procedures used in a meta-analysis and are uncomfortable with the fundamental scientific ideas needed to evaluate data.

Clinical practice recommendations are intended to bridge the gap between research and existing practice, reducing unnecessary variation in practice.

In recent years, the number of clinical practice recommendations has exploded.

The development process is largely responsible for the guidelines' credibility.

The most serious problem is the lack of scientific evidence that these clinical practice guidelines are based on.

They don't all have the same level of quality and trustworthiness in their evidence.

The search for evidence-based resources should start at the top of the 6S pyramid, at the systems layer, which includes computerized clinical decision support systems.

Computerized clinical decision support systems (also known as intelligent medical platforms) are health information technology-based software that builds on the foundation of an electronic health record to provide clinicians with intelligently filtered and organized general and patient-specific information to improve health and clinical care.

Laboratory measurements, for example, are often color-coded to show whether they lie inside or outside of a reference range.

The computerized clinical decision support systems that are now available are not a simple model that produces just an output.

Multiple phases are involved in the interpretation and use of a computerized clinical decision support system, including displaying the algorithm output in a specified fashion, the clinician's interpretation, and finally the medical decision.

Despite the fact that computerized clinical decision support systems have been proved to minimize medical mistakes and enhance patient outcomes, user acceptability has prevented them from reaching their full potential.

Aside from the interface problems, doctors are wary about computerized clinical decision support systems because they may limit their professional autonomy or be utilized in the case of a medical-legal dispute.

Although computerized clinical decision support systems still need human participation, some critical sectors of medicine, such as cancer, cardiology, and neurology, are adopting artificial intelligence-based diagnostic tools.

Machine learning methods and natural language processing systems are the two main groups of these instruments.

Patients' data is used to construct a structured database for genetic, imaging, and electrophysiological records, which is then analyzed for a diagnosis using machine learning methods.

To assist the machine learning process, natural language processing systems construct a structured database utilizing clinical notes and medical periodicals.

Furthermore, machine learning algorithms in medical applications seek to cluster patients' features in order to predict the likelihood of illness outcomes and offer a prognosis to the clinician.

Several machine learning and natural language processing technologies have been coupled to produce powerful computerized clinical decision support systems that can process and offer diagnoses as well as or better than doctors.

When it came to detecting lymph node metastases, a Google-developed AI approach called convolutional neural networking surpassed pathologists.

In compared to pathologists, who had a sensitivity of 73 percent, the convolutional neural network was sensitive 97 percent of the time.

Furthermore, when the same convolutional neural network was used to classify skin cancers, it performed at a level comparable to dermatologists (Krittanawong 2018).

Depression is also diagnosed and classified using such approaches.

By merging artificial intelligence's capability with human views, empathy, and experience, physicians' potential will be increased.

The advantages of advanced computerized clinical decision support systems, on the other hand, are not limited to diagnoses and classification.

By reducing processing time and thus improving patient care, computerized clinical decision support systems can be used to improve communication between physicians and patients.

To avoid drug-drug interactions, computerized clinical decision support systems can prioritize medication prescription for patients based on their medical history.

More importantly, by extracting past medical history and using patient symptoms to determine whether the patient should be referred to urgent care, a specialist, or a primary care doctor, computerized clinical decision support systems equipped with artificial intelligence can aid triage diagnosis and reduce triage processing times.

Because they are the primary causes of mortality in North America, developing artificial intelligence around these acute and highly specialized medical problems is critical.

Artificial intelligence has also been used in other ways with computerized clinical decision support systems.

The studies of Long et al. (2017), who used ocular imaging data to identify congenital cataract illness, and Gulshan et al.

(2016), who used retinal fundus pictures to detect referable diabetic retinopathy, are two recent instances.

Both stories show how artificial intelligence is growing exponentially in the medical industry and how it may be used in a variety of ways.

Although computerized clinical decision support systems hold great promise for facilitating evidence-based medicine, much work has to be done to reach their full potential in health care.

The growing familiarity of new generations of doctors with sophisticated digital technology may encourage the usage and integration of computerized clinical decision support systems.

Over the next decade, the market for such systems is expected to expand dramatically.

The pressing need to lower the prevalence of drug mistakes and worldwide health-care expenditures is driving this expansion.

Computerized clinical decision support systems are the gold standard for assisting and supporting physicians in their decision-making.

In order to benefit doctors, patients, health-care organizations, and society, the future should include more advanced analytics, automation, and a more tailored interaction with the electronic health record. 



~ Jai Krishna Ponnappan

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


See also: 

Automated Multiphasic Health Testing; Expert Systems; Explainable AI; INTERNIST-I and QMR.


Further Reading

Arnaert, Antonia, and Norma Ponzoni. 2016. “Promoting Clinical Reasoning Among Nursing Students: Why Aren’t Clinical Decision Support Systems a Popular Option?” Canadian Journal of Nursing Research 48, no. 2: 33–34.

Arnaert, Antonia, Norma Ponzoni, John A. Liebert, and Zoumanan Debe. 2017. “Transformative Technology: What Accounts for the Limited Use of Clinical Decision Support Systems in Nursing Practice?” In Health Professionals’ Education in the Age of Clinical Information Systems, Mobile Computing, and Social Media, edited by Aviv Shachak, Elizabeth M. Borycki, and Shmuel P. Reis, 131–45. Cambridge, MA: Academic Press.

DiCenso, Alba, Liz Bayley, and R. Brian Haynes. 2009. “Accessing Preappraised Evidence: Fine-tuning the 5S Model into a 6S Model.” ACP Journal Club 151, no. 6 (September): JC3-2–JC3-3.

Gulshan, Varun, et al. 2016. “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.” JAMA 316, no. 22 (December): 2402–10.

Krittanawong, Chayakrit. 2018. “The Rise of Artificial Intelligence and the Uncertain Future for Physicians.” European Journal of Internal Medicine 48 (February): e13–e14.

Long, Erping, et al. 2017. “An Artificial Intelligence Platform for the Multihospital Collaborative Management of Congenital Cataracts.” Nature Biomedical Engineering 1, no. 2: n.p.

Miller, D. Douglas, and Eric W. Brown. 2018. “Artificial Intelligence in Medical Practice: The Question to the Answer?” American Journal of Medicine 131, no. 2: 129–33.


Artificial Intelligence - What Are Expert Systems?

 






Expert systems are used to solve issues that would normally be addressed by humans.


In the early decades of artificial intelligence research, they emerged as one of the most promising application strategies.

The core concept is to convert an expert's knowledge into a computer-based knowledge system.




Dan Patterson, a statistician and computer scientist at the University of Texas in El Paso, differentiates various properties of expert systems:


• They make decisions based on knowledge rather than facts.

• The task of representing heuristic knowledge in expert systems is daunting.

• Knowledge and the program are generally separated so that the same program can operate on different knowledge bases.

• Expert systems should be able to explain their decisions, represent knowledge symbolically, and have and use meta knowledge, that is, knowledge about knowledge.





(Patterson, et al., 2008) Expert systems generally often reflect domain-specific knowledge.


The subject of medical research was a frequent test application for expert systems.

Expert systems were created as a tool to assist medical doctors in their work.

Symptoms were usually communicated by the patient in the form of replies to inquiries.

Based on its knowledge base, the system would next attempt to identify the ailment and, in certain cases, recommend relevant remedies.

MYCIN, a Stanford University-developed expert system for detecting bacterial infections and blood disorders, is one example.




Another well-known application in the realm of engineering and engineering design tries to capture the heuristic knowledge of the design process in the design of motors and generators.


The expert system assists in the initial design phase, when choices like as the number of poles, whether to use AC or DC, and so on are made (Hoole et al. 2003).

The knowledge base and the inference engine are the two components that make up the core framework of expert systems.




The inference engine utilizes the knowledge base to make choices, whereas the knowledge base holds the expert's expertise.

In this way, the knowledge is isolated from the software that manipulates it.

Knowledge must first be gathered, then comprehended, categorized, and stored in order to create expert systems.

It is retrieved to answer issues depending on predetermined criteria.

The four main processes in the design of an expert system, according to Thomson Reuters chief scientist Peter Jackson, are obtaining information, representing that knowledge, directing reasoning via an inference engine, and explaining the expert system's answer (Jackson 1999).

The expert system's largest issue was acquiring domain knowledge.

Human specialists may be challenging to obtain information from.


Many variables contribute to the difficulty of acquiring knowledge, but the complexity of encoding heuristic and experienced information is perhaps the most important.



The knowledge acquisition process is divided into five phases, according to Hayes-Roth et al. (1983).

Identification, or recognizing the problem and the data that must be used to arrive at a solution; conceptualization, or comprehending the key concepts and relationships between the data; formalization, or comprehending the relevant search space; implementation, or converting formalized knowledge into a software program; and testing the rules for completeness and accuracy are among them.


  • Production (rule-based) or non-production systems may be used to represent domain knowledge.
  • In rule-based systems, knowledge is represented by rules in the form of IF THEN-ELSE expressions.



The inference process is carried out by iteratively going over the rules, either through a forward or backward chaining technique.



  • Forward chaining asks what would happen next if the condition and rules were known to be true. Going from a goal to the rules we know to be true, backward chaining asks why this occurred.
  • Forward chaining is defined as when the left side of the rule is assessed first, that is, when the conditions are verified first and the rules are performed left to right (also known as data-driven inference).
  • Backward chaining occurs when the rules are evaluated from the right side, that is, when the outcomes are verified first (also known as goal-driven inference).
  • CLIPS, a public domain example of an expert system tool that implements the forward chaining method, was created at NASA's Johnson Space Center. MYCIN is an expert system that works backwards.



Associative/semantic networks, frame representations, decision trees, and neural networks may be used in expert system designs based on nonproduction architectures.


Nodes make form an associative/semantic network, which may be used to represent hierarchical knowledge. 

  • An example of a system based on an associative network is CASNET.
  • The most well-known use of CASNET was the development of an expert system for glaucoma diagnosis and therapy.

Frames are structured sets of closely related knowledge in frame architectures.


  • A frame-based architecture is an example of PIP (Present Illness Program).
  • MIT and Tufts-New England Clinical Center developed PIP to generate hypotheses regarding renal illness.

Top-down knowledge is represented via decision tree structures.


Blackboard system designs are complex systems in which the inference process's direction may be changed during runtime.


A blackboard system architecture may be seen in DARPA's HEARSAY domain independent expert system.


  • Knowledge is spread throughout a neural network in the form of nodes in neural network topologies.
  • Case-based reasoning is attempting to examine and find answers for a problem using previously solved examples.
  • A loose connection may be formed between case-based reasoning and judicial law, in which the decision of a comparable but previous case is used to solve a current legal matter.
  • Case-based reasoning is often implemented as a frame, which necessitates a more involved matching and retrieval procedure.



There are three options for manually constructing the knowledge base.


  • Knowledge may be elicited via an interview with a computer using interactive tools. This technique is shown by the computer-graphics-based OPAL software, which enabled clinicians with no prior medical training to construct expert medical knowledge bases for the care of cancer patients.
  • Text scanning algorithms that read books into memory are a second alternative to human knowledge base creation.
  • Machine learning algorithms that build competence on their own, with or without supervision from a human expert, are a third alternative still under development.




DENDRAL, a project started at Stanford University in 1965, is an early example of a machine learning architecture project.


DENDRAL was created in order to study the molecular structure of organic molecules.


  • While DENDRAL followed a set of rules to complete its work, META-DENDRAL created its own rules.
  • META-DENDRAL chose the important data points to observe with the aid of a human chemist.




Expert systems may be created in a variety of methods.


  • User-friendly graphical user interfaces are used in interactive development environments to assist programmers as they code.
  • Special languages may be used in the construction of expert systems.
  • Prolog (Logic Programming) and LISP are two of the most common options (List Programming).
  • Because Prolog is built on predicate logic, it belongs to the logic programming paradigm.
  • One of the first programming languages for artificial intelligence applications was LISP.



Expert system shells are often used by programmers.



A shell provides a platform for knowledge to be programmed into the system.


  • The shell is a layer without a knowledge basis, as the name indicates.
  • The Java Expert System Shell (JESS) is a strong expert shell built in Java.


Many efforts have been made to blend disparate paradigms to create hybrid systems.


  • Object-oriented programming seeks to combine logic-based and object-oriented systems.
  • Object orientation, despite its lack of a rigorous mathematical basis, is very useful in modeling real-world circumstances.

  • Knowledge is represented as objects that encompass both the data and the ways for working with it.
  • Object-oriented systems are more accurate models of real-world things than procedural programming.
  • The Object Inference Knowledge Specification Language (OI-KSL) is one way (Mascrenghe et al. 2002).



Although other languages, such as Visual Prolog, have merged object-oriented programming, OI-KSL takes a different approach.


Backtracking in Visual Prolog occurs inside the objects; that is, the methods backtracked.

Backtracking is taken to a whole new level in OI KSL, with the item itself being backtracked.

To cope with uncertainties in the given data, probability theory, heuristics, and fuzzy logic are sometimes utilized.

A fuzzy electric lighting system was one example of a Prolog implementation of fuzzy logic, in which the quantity of natural light influenced the voltage that flowed to the electric bulb (Mascrenghe 2002).

This allowed the system to reason in the face of uncertainty and with little data.


Interest in expert systems started to wane in the late 1990s, owing in part to unrealistic expectations for the technology and the expensive cost of upkeep.

Expert systems were unable to deliver on their promises.



Even today, technology generated in expert systems research is used in various fields like data science, chatbots, and machine intelligence.


  • Expert systems are designed to capture the collective knowledge that mankind has accumulated through millennia of learning, experience, and practice.



Jai Krishna Ponnappan


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


See also: 


Clinical Decision Support Systems; Computer-Assisted Diagnosis; DENDRAL; Expert Systems.



Further Reading:


Hayes-Roth, Frederick, Donald A. Waterman, and Douglas B. Lenat, eds. 1983. Building Expert Systems. Teknowledge Series in Knowledge Engineering, vol. 1. Reading, MA: Addison Wesley.

Hoole, S. R. H., A. Mascrenghe, K. Navukkarasu, and K. Sivasubramaniam. 2003. “An Expert Design Environment for Electrical Devices and Its Engineering Assistant.” IEEE Transactions on Magnetics 39, no. 3 (May): 1693–96.

Jackson, Peter. 1999. Introduction to Expert Systems. Third edition. Reading, MA: Addison-Wesley.

Mascrenghe, A. 2002. “The Fuzzy Electric Bulb: An Introduction to Fuzzy Logic with Sample Implementation.” PC AI 16, no. 4 (July–August): 33–37.

Mascrenghe, A., S. R. H. Hoole, and K. Navukkarasu. 2002. “Prototype for a New Electromagnetic Knowledge Specification Language.” In CEFC Digest. Perugia, Italy: IEEE.

Patterson, Dan W. 2008. Introduction to Artificial Intelligence and Expert Systems. New Delhi, India: PHI Learning.

Rich, Elaine, Kevin Knight, and Shivashankar B. Nair. 2009. Artificial Intelligence. New Delhi, India: Tata McGraw-Hill.



Artificial Intelligence - Who Is Nick Bostrom?

 




Nick Bostrom(1973–) is an Oxford University philosopher with a physics and computational neuroscience multidisciplinary academic background.

He is a cofounder of the World Transhumanist Association and a founding director of the Future of Humanity Institute.

Anthropic Bias (2002), Human Enhancement (2009), Superintelligence: Paths, Dangers, Strategies (2014), and Global Catastrophic Risks (2014) are among the works he has authored or edited (2014).

Bostrom was born in the Swedish city of Helsingborg in 1973.

Despite his dislike of formal education, he enjoyed studying.

Science, literature, art, and anthropology were among his favorite interests.

Bostrom earned bachelor's degrees in philosophy, mathematics, logic, and artificial intelligence from the University of Gothenburg, as well as master's degrees in philosophy and physics from Stockholm University and computational neuroscience from King's College London.

The London School of Economics gave him a PhD in philosophy.

Bostrom is a regular consultant or contributor to the European Commission, the United States President's Council on Bioethics, the CIA, and Cambridge University's Centre for the Study of Existential Risk.

Bostrom is well-known for his contributions to a variety of subjects, and he has proposed or written extensively on a number of well-known philosophical arguments and conjectures, including the simulation hypothesis, existential risk, the future of machine intelligence, and transhumanism.

Bostrom's concerns in the future of technology, as well as his discoveries on the mathematics of the anthropic bias, are combined in the so-called "Simulation Argument." Three propositions make up the argument.

The first hypothesis is that almost all civilizations that attain human levels of knowledge eventually perish before achieving technological maturity.

The second hypothesis is that most civilizations develop "ancestor simulations" of sentient beings, but ultimately abandon them.

The "simulation hypothesis" proposes that mankind is now living in a simulation.

He claims that just one of the three assertions must be true.

If the first hypothesis is false, some proportion of civilizations at the current level of human society will ultimately acquire technological maturity.

If the second premise is incorrect, certain civilizations may be interested in continuing to perform ancestor simulations.

These civilizations' researchers may be performing massive numbers of these simulations.

There would be many times as many simulated humans living in simulated worlds as there would be genuine people living in real universes in that situation.

As a result, mankind is most likely to exist in one of the simulated worlds.

If the second statement is true, the third possibility is also true.

It's even feasible, according to Bostrom, for a civilization inside a simulation to conduct its own simulations.

In the form of an endless regress, simulations may be living within simulated universes, inside their own simulated worlds.

It's also feasible that all civilizations would vanish, maybe as a result of the discovery of a new technology, posing an existential threat beyond human control.

Bostrom's argument implies that humanity is not blind to the truth of the external world, an argument that can be traced back to Plato's conviction in the existence of universals (the "Forms") and the capacity of human senses to see only specific examples of universals.

His thesis also implies that computers' ability to imitate things will continue to improve in terms of power and sophistication.

Computer games and literature, according to Bostrom, are modern instances of natural human fascination with synthetic reality.

The Simulation Argument is sometimes mistaken with the restricted premise that mankind lives in a simulation, which is the third argument.

Humans, according to Bostrom, have a less than 50% probability of living in some kind of artificial matrix.

He also argues that if mankind lived in one, society would be unlikely to notice "glitches" that revealed the simulation's existence since they had total control over the simulation's operation.

Simulator creators, on the other hand, would inform people that they are living in a simulation.

Existential hazards are those that pose a serious threat to humanity's existence.

Humans, rather than natural dangers, pose the biggest existential threat, according to Bostrom (e.g., asteroids, earthquakes, and epidemic disease).

He argues that artificial hazards like synthetic biology, molecular nanotechnology, and artificial intelligence are considerably more threatening.

Bostrom divides dangers into three categories: local, global, and existential.

Local dangers might include the theft of a valuable item of art or an automobile accident.

A military dictator's downfall or the explosion of a supervolcano are both potential global threats.

The extent and intensity of existential hazards vary.

They are cross-generational and long-lasting.

Because of the amount of lives that might be spared, he believes that reducing the danger of existential hazards is the most essential thing that human beings can do; battling against existential risk is also one of humanity's most neglected undertakings.

He also distinguishes between several types of existential peril.

Human extinction, defined as the extinction of a species before it reaches technological maturity; permanent stagnation, defined as the plateauing of human technological achievement; flawed realization, defined as humanity's failure to use advanced technology for an ultimately worthwhile purpose; and subsequent ruination, defined as a society reaching technological maturity but then something goes wrong.

While mankind has not yet harnessed human ingenuity to create a technology that releases existentially destructive power, Bostrom believes it is possible that it may in the future.

Human civilization has yet to produce a technology with such horrific implications that mankind could collectively forget about it.

The objective would be to go on a technical path that is safe, includes global collaboration, and is long-term.

To argue for the prospect of machine superintelligence, Bostrom employs the metaphor of altered brain complexity in the development of humans from apes, which took just a few hundred thousand generations.

Artificial systems that use machine learning (that is, algorithms that learn) are no longer constrained to a single area.

He also points out that computers process information at a far faster pace than human neurons.

Humans will eventually rely on super intelligent robots in the same manner that chimps presently rely on humans for their ultimate survival, according to Bostrom, even in the wild.

By establishing a powerful optimizing process with a poorly stated purpose, super intelligent computers have the potential to cause devastation, or possibly an extinction-level catastrophe.

By subverting humanity to the programmed purpose, a superintelligence may even foresee a human response.

Bostrom recognizes that there are certain algorithmic techniques used by humans that computer scientists do not yet understand.

As they engage in machine learning, he believes it is critical for artificial intelligences to understand human values.

On this point, Bostrom is drawing inspiration from artificial intelligence theorist Eliezer Yudkowsky's concept of "coherent extrapolated volition"—also known as "friendly AI"—which is akin to what is currently accessible in human good will, civil society, and institutions.

A superintelligence should seek to provide pleasure and joy to all of humanity, and it may even make difficult choices that benefit the whole community rather than the individual.

In 2015, Bostrom, along with Stephen Hawking, Elon Musk, Max Tegmark, and many other top AI researchers, published "An Open Letter on Artificial Intelligence" on the Future of Life Institute website, calling for artificial intelligence research that maximizes the benefits to humanity while minimizing "potential pitfalls." Transhumanism is a philosophy or belief in the technological extension and augmentation of the human species' physical, sensory, and cognitive capacity.

In 1998, Bostrom and colleague philosopher David Pearce founded the World Transhumanist Association, now known as Humanity+, to address some of the societal hurdles to the adoption and use of new transhumanist technologies by people of all socioeconomic strata.

Bostrom has said that he is not interested in defending technology, but rather in using modern technologies to address real-world problems and improve people's lives.

Bostrom is particularly concerned in the ethical implications of human enhancement and the long-term implications of major technological changes in human nature.

He claims that transhumanist ideas may be found throughout history and throughout cultures, as shown by ancient quests such as the Gilgamesh Epic and historical hunts for the Fountain of Youth and the Elixir of Immortality.

The transhumanist idea, then, may be regarded fairly ancient, with modern representations in disciplines like artificial intelligence and gene editing.

Bostrom takes a stand against the emergence of strong transhumanist instruments as an activist.

He expects that politicians may act with foresight and command the sequencing of technical breakthroughs in order to decrease the danger of future applications and human extinction.

He believes that everyone should have the chance to become transhuman or posthuman (have capacities beyond human nature and intelligence).

For Bostrom, success would require a worldwide commitment to global security and continued technological progress, as well as widespread access to the benefits of technologies (cryonics, mind uploading, anti-aging drugs, life extension regimens), which hold the most promise for transhumanist change in our lifetime.

Bostrom, however cautious, rejects conventional humility, pointing out that humans have a long history of dealing with potentially catastrophic dangers.

In such things, he is a strong supporter of "individual choice," as well as "morphological freedom," or the ability to transform or reengineer one's body to fulfill specific wishes and requirements.


~ Jai Krishna Ponnappan

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




See also: 

Superintelligence; Technological Singularity.


Further Reading

Bostrom, Nick. 2003. “Are You Living in a Computer Simulation?” Philosophical Quarterly 53, no. 211: 243–55.

Bostrom, Nick. 2005. “A History of Transhumanist Thought.” Journal of Evolution and Technology 14, no. 1: 1–25.

Bostrom, Nick, ed. 2008. Global Catastrophic Risks. Oxford, UK: Oxford University Press.

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

Savulescu, Julian, and Nick Bostrom, eds. 2009. Human Enhancement. Oxford, UK: Oxford University Press.

Artificial Intelligence - What Is Immortality in the Digital Age?




The act of putting a human's memories, knowledge, and/or personality into a long-lasting digital memory storage device or robot is known as digital immortality.

Human intelligence is therefore displaced by artificial intelligence that resembles the mental pathways or imprint of the brain in certain respects.

The National Academy of Engineering has identified reverse-engineering the brain to attain substrate independence—that is, copying the thinking and feeling mind and reproducing it on a range of physical or virtual media.

Whole Brain Emulation (also known as mind uploading) is a theoretical science that assumes the mind is a dynamic process independent of the physical biology of the brain and its unique sets or patterns of atoms.

Instead, the mind is a collection of information-processing functions that can be computed.

Whole Brain Emulation is presently assumed to be based on the neural networking discipline of computer science, which has as its own ambitious objective the programming of an operating system modeled after the human brain.

Artificial neural networks (ANNs) are statistical models built from biological neural networks in artificial intelligence research.

Through connections and weighting, as well as backpropagation and parameter adjustment in algorithms and rules, ANNs may process information in a nonlinear way.

Through his online "Mind Uploading Home Page," Joe Strout, a computational neurobiology enthusiast at the Salk Institute, facilitated debate of full brain emulation in the 1990s.

Strout argued for the material origins of consciousness, claiming that evidence from damage to actual people's brains indicates to neuronal, connectionist, and chemical beginnings.

Strout shared timelines of previous and contemporary technical advancements as well as suggestions for future uploading techniques through his website.

Mind uploading proponents believe that one of two methods will eventually be used: (1) gradual copy-and-transfer of neurons by scanning the brain and simulating its underlying information states, or (2) deliberate replacement of natural neurons with more durable artificial mechanical devices or manufactured biological products.

Strout gathered information on a variety of theoretical ways for achieving the objective of mind uploading.

One is a microtome method, which involves slicing a live brain into tiny slices and scanning it with a sophisticated electron microscope.

The brain is then reconstructed in a synthetic substrate using the picture data.

Nanoreplacement involves injecting small devices into the brain to monitor the input and output of neurons.

When these minuscule robots have a complete understanding of all biological interactions, they will eventually kill the neurons and replace them.

A robot with billions of appendages that delve deep into every section of the brain, as envisioned by Carnegie Mellon University roboticist Hans Moravec, is used in a variation of this process.

In this approach, the robot creates a virtual model of every portion and function of the brain, gradually replacing it.

Everything that the physical brain used to be is eventually replaced by a simulation.

In copy-and-transfer whole brain emulation, scanning or mapping neurons is commonly considered harmful.

The live brain is plasticized or frozen before being divided into parts , scanned and simulated on a computational media.

Philosophically, the technique creates a mental clone of a person, not the person who agrees to participate in the experiment.

Only a duplicate of that individual's personal identity survives the duplicating experiment; the original person dies.

Because, as philosopher John Locke reasoned, someone who recalls thinking about something in the past is the same person as the person who performed the thinking in the first place, the copy may be thought of as the genuine person.

Alternatively, it's possible that the experiment may turn the original and copy into completely different persons, or that they will soon diverge from one another through time and experience as a result of their lack of shared history beyond the experiment.

There have been many nondestructive approaches proposed as alternatives to damaging the brain during the copy-and-transfer process.

It is hypothesized that sophisticated types of gamma-ray holography, x-ray holography, magnetic resonance imaging (MRI), biphoton interferometry, or correlation mapping using probes might be used to reconstruct function.

With 3D reconstructions of atomic-level detail, the present limit of available technology, in the form of electron microscope tomography, has reached the sub-nanometer scale.

The majority of the remaining challenges are related to the geometry of tissue specimens and tomographic equipment's so-called tilt-range restrictions.

Advanced kinds of picture recognition, as well as neurocomputer manufacturing to recreate scans as information processing components, are in the works.

Professor of Electrical and Computer Engineering Alice Parker leads the BioRC Biomimetic Real-Time Cortex Project at the University of Southern California, which focuses on reverse-engineering the brain.

Parker is now building and producing a memory and carbon nanotube brain nanocircuit for a future synthetic cortex based on statistical predictions with nanotechnology professor Chongwu Zhou and her students.

Her neuromorphic circuits are designed to mimic the complexities of human neural computations, including glial cell connections (these are nonneuronal cells that form myelin, control homeostasis, and protect and support neurons).

Members of the BioRC Project are developing systems that scale to the size of human brains.

Parker is attempting to include dendritic plasticity into these systems, which will allow them to adapt and expand as they learn.

Carver Mead, a Caltech electrical engineer who has been working on electronic models of human neurological and biological components since the 1980s, is credited with the approach's roots.

The Terasem Movement, which began in 2002, aims to educate and urge the public to embrace technical advancements that advance the science of mind uploading and integrate science, religion, and philosophy.

The Terasem Movement, the Terasem Movement Foundation, and the Terasem Movement Transreligion are all incorporated entities that operate together.

Martine Rothblatt and Bina Aspen Rothblatt, serial entrepreneurs, founded the group.

The Rothblatts are inspired by the religion of Earthseed, which may be found in Octavia Butler's 1993 novel Parable of the Sower.

"Life is intentional, death is voluntary, God is technology, and love is fundamental," according to Rothblatt's trans-religious ideas (Roy 2014).

Terasem's CyBeRev (Cybernetic Beingness Revival) project collects all available data about a person's life—their personal history, recorded memories, photographs, and so on—and stores it in a separate data file in the hopes that their personality and consciousness can be pieced together and reanimated one day by advanced software.

The Terasem Foundation-sponsored Lifenaut research retains mindfiles with biographical information on individuals for free and keeps track of corresponding DNA samples (biofiles).

Bina48, a social robot created by the foundation, demonstrates how a person's consciousness may one day be transplanted into a lifelike android.

Numenta, an artificial intelligence firm based in Silicon Valley, is aiming to reverse-engineer the human neocortex.

Jeff Hawkins (creator of the portable PalmPilot personal digital assistant), Donna Dubinsky, and Dileep George are the company's founders.

Numenta's idea of the neocortex is based on Hawkins' and Sandra Blakeslee's theory of hierarchical temporal memory, which is outlined in their book On Intelligence (2004).

Time-based learning algorithms, which are capable of storing and recalling tiny patterns in data change over time, are at the heart of Numenta's emulation technology.

Grok, a commercial tool that identifies flaws in computer servers, was created by the business.

Other applications, such as detecting anomalies in stock market trading or abnormalities in human behavior, have been provided by the business.

Carboncopies is a non-profit that funds research and cooperation to capture and preserve unique configurations of neurons and synapses carrying human memories.

Computational modeling, neuromorphic hardware, brain imaging, nanotechnology, and philosophy of mind are all areas where the organization supports research.

Randal Koene, a computational neuroscientist educated at McGill University and head scientist at neuroprosthetic company Kernel, is the organization's creator.

Dmitry Itskov, a Russian new media millionaire, donated early funding for Carbon copies.

Itskov is also the founder of the 2045 Initiative, a non-profit organization dedicated to extreme life extension.

The purpose of the 2045 Initiative is to develop high-tech methods for transferring personalities into a "advanced nonbiological carrier." Global Future 2045, a meeting aimed to developing "a new evolutionary strategy for mankind," is organized by Koene and Itskov.

Proponents of digital immortality see a wide range of practical results as a result of their efforts.

For example, in the case of death by accident or natural causes, a saved backup mind may be used to reawaken into a new body.

(It's reasonable to assume that elderly brains would seek out new bodies long before aging becomes apparent.) This is also the basis of Arthur C.

Clarke's science fiction book City of the Stars (1956), which influenced Koene's decision to pursue a career in science at the age of thirteen.

Alternatively, mankind as a whole may be able to lessen the danger of global catastrophe by uploading their thoughts to virtual reality.

Civilization might be saved on a high-tech hard drive buried deep into the planet's core, safe from hostile extraterrestrials and incredibly strong natural gamma ray bursts.

Another potential benefit is the potential for life extension over lengthy periods of interstellar travel.

For extended travels throughout space, artificial brains might be implanted into metal bodies.

This is a notion that Clarke foreshadowed in the last pages of his science fiction classic Childhood's End (1953).

It's also the response offered by Manfred Clynes and Nathan Kline in their 1960 Astronautics article "Cyborgs and Space," which includes the first mention of astronauts with physical capacities that transcend beyond conventional limitations (zero gravity, space vacuum, cosmic radiation) thanks to mechanical help.

Under real mind uploading circumstances, it may be able to simply encode and send the human mind as a signal to a neighboring exoplanet that is the greatest possibility for alien life discovery.

The hazards to humans are negligible in each situation when compared to the present threats to astronauts, which include exploding rockets, high-speed impacts with micrometeorites, and faulty suits and oxygen tanks.

Another potential benefit of digital immortality is real restorative justice and rehabilitation through criminal mind retraining.

Or, alternatively, mind uploading might enable for penalties to be administered well beyond the normal life spans of those who have committed heinous crimes.

Digital immortality has far-reaching social, philosophical, and legal ramifications.

The concept of digital immortality has long been a hallmark of science fiction.

The short story "The Tunnel Under the World" (1955) by Frederik Pohl is a widely reprinted story about chemical plant workers who are killed in a chemical plant explosion, only to be rebuilt as miniature robots and subjected to advertising campaigns and jingles over the course of a long Truman Show-like repeating day.

The Silicon Man (1991) by Charles Platt relates the tale of an FBI agent who finds a hidden operation named LifeScan.

The project has found a technique to transfer human thought patterns to a computer dubbed MAPHIS, which is headed by an old millionaire and a mutinous crew of government experts (Memory Array and Processors for Human Intelligence Storage).

MAPHIS is capable of delivering any standard stimuli, including pseudomorphs, which are simulations of other persons.

The Autoverse is introduced in Greg Egan's hard science fiction Permutation City (1994), which mimics complex miniature biospheres and virtual worlds populated by artificial living forms.

Egan refers to human consciousnesses scanned into the Autoverse as copies.

The story is inspired by John Conway's Game of Life's cellular automata, quantum ontology (the link between the quantum universe and human perceptions of reality), and what Egan refers to as dust theory.

The premise that physics and arithmetic are same, and that individuals residing in whatever mathematical, physical, and spacetime systems (and all are feasible) are essentially data, processes, and interactions, is at the core of dust theory.

This claim is similar to MIT physicist Max Tegmark's Theory of Everything, which states that "all structures that exist mathematically exist also physically," meaning that "all structures that exist mathematically exist also physically," meaning that "all structures that exist mathematically exist also physically, by which we mean that in those complex enough to contain self-aware substructures (SASs), these SASs will subjectively perceive themselves as existing in a physically'real' world" (Tegmark 1998, 1).

Hans Moravec, a roboticist at Carnegie Mellon University, makes similar assertions in his article "Simulation, Consciousness, Existence" (1998).

Tron (1982), Freejack (1992), and The 6th Day are examples of mind uploading and digital immortality in movies (2000).

Kenneth D. Miller, a theoretical neurologist at Columbia University, is a notable skeptic.

While rebuilding an active, functional mind may be achievable, connectomics researchers (those working on a wiring schematic of the whole brain and nervous system) remain millennia away from finishing their job, according to Miller.

And, he claims, connectomics is just concerned with the first layer of brain activities that must be comprehended in order to replicate the complexity of the human brain.

Others have wondered what happens to personhood in situations where individuals are no longer constrained as physical organisms.

Is identity just a series of connections between neurons in the brain? What is going to happen to markets and economic forces? Is a body required for immortality? Professor Robin Hanson of George Mason University's nonfiction publication The Age of Em: Work, Love, and Life When Robots Rule the Earth provides an economic and social viewpoint on digital immortality (2016).

Hanson's hypothetical ems are scanned emulations of genuine humans who exist in both virtual reality environments and robot bodies.


~ Jai Krishna Ponnappan

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



See also: 


Technological Singularity.


Further Reading:


Clynes, Manfred E., and Nathan S. Kline. 1960. “Cyborgs and Space.” Astronautics 14, no. 9 (September): 26–27, 74–76.

Farnell, Ross. 2000. “Attempting Immortality: AI, A-Life, and the Posthuman in Greg Egan’s ‘Permutation City.’” Science Fiction Studies 27, no. 1: 69–91.

Global Future 2045. http://gf2045.com/.

Hanson, Robin. 2016. The Age of Em: Work, Love, and Life when Robots Rule the Earth. Oxford, UK: Oxford University Press.

Miller, Kenneth D. 2015. “Will You Ever Be Able to Upload Your Brain?” New York Times, October 10, 2015. https://www.nytimes.com/2015/10/11/opinion/sunday/will-you-ever-be-able-to-upload-your-brain.html.

Moravec, Hans. 1999. “Simulation, Consciousness, Existence.” Intercommunication 28 (Spring): 98–112.

Roy, Jessica. 2014. “The Rapture of the Nerds.” Time, April 17, 2014. https://time.com/66536/terasem-trascendence-religion-technology/.

Tegmark, Max. 1998. “Is ‘the Theory of Everything’ Merely the Ultimate Ensemble The￾ory?” Annals of Physics 270, no. 1 (November): 1–51.

2045 Initiative. http://2045.com/.


What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...