Technologists in Sync: weak AI

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What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the AGI system to solve problems when presented with new tasks.

In other words, it's AI's capacity to learn similarly to humans.

Strong AI, full AI, and general intelligent action are some names for it.

The phrase "strong AI," however, is only used in few academic publications to refer to computer systems that are sentient or aware.

These definitions may change since specialists from many disciplines see human intelligence from various angles.

For instance, computer scientists often characterize human intelligence as the capacity to accomplish objectives.

On the other hand, general intelligence is defined by psychologists in terms of survival or adaptation.

Weak or narrow AI, in contrast to strong AI, is made up of programs created to address a single issue and lacks awareness since it is not meant to have broad cognitive capacities.

Autonomous cars and IBM's Watson supercomputer are two examples.

Nevertheless, AGI is defined in computer science as an intelligent system having full or comprehensive knowledge as well as cognitive computing skills.

As of right now, there are no real AGI systems; they are still the stuff of science fiction.

The long-term objective of these systems is to perform as well as humans do.

However, due to AGI's superior capacity to acquire and analyze massive amounts of data at a far faster rate than the human mind, it may be possible for AGI to be more intelligent than humans.

Artificial intelligence (AI) is now capable of carrying out a wide range of functions, including providing tailored suggestions based on prior web searches.

Additionally, it can recognize various items for autonomous cars to avoid, recognize malignant cells during medical inspections, and serve as the brain of home automation.

Additionally, it may be utilized to find possibly habitable planets, act as intelligent assistants, be in charge of security, and more.

Naturally, AGI seems to far beyond such capacities, and some scientists are concerned this may result in a dystopian future.

Elon Musk said that sentient AI would be more hazardous than nuclear war, while Stephen Hawking advised against its creation because it would see humanity as a possible threat and act accordingly.

Despite concerns, most scientists agree that genuine AGI is decades or perhaps centuries away from being developed and must first meet a number of requirements (which are always changing) in order to be achieved.

These include the capacity for logic, tact, puzzle-solving, and making decisions in the face of ambiguity.

Additionally, it must be able to plan, learn, and communicate in natural language, as well as represent information, including common sense.

AGI must also have the capacity to detect (hear, see, etc.) and output the ability to act, such as moving items and switching places to explore.

How far along are we in the process of developing artificial general intelligence, and who is involved?

In accordance with a 2020 study from the Global Catastrophic Risk Institute (GCRI), academic institutions, businesses, and different governmental agencies are presently working on 72 recognized AGI R&D projects.

According to the poll, projects nowadays are often smaller, more geographically diversified, less open-source, more focused on humanitarian aims than academic ones, and more centered in private firms than projects in 2017.

The comparison also reveals a decline in projects with academic affiliations, an increase in projects sponsored by corporations, a rise in projects with a humanitarian emphasis, a decline in programs with ties to the military, and a decline in US-based initiatives.

In AGI R&D, particularly military initiatives that are solely focused on fundamental research, governments and organizations have very little roles to play.

However, recent programs seem to be more varied and are classified using three criteria, including business projects that are engaged in AGI safety and have humanistic end objectives.

Additionally, it covers tiny private enterprises with a variety of objectives including academic programs that do not concern themselves with AGI safety but rather the progress of knowledge.

One of the most well-known organizations working on AGI is Carnegie Mellon University, which has a project called ACT-R that aims to create a generic cognitive architecture based on the basic cognitive and perceptual functions that support the human mind.

The project may be thought of as a method of describing how the brain is structured such that different processing modules can result in cognition.

Another pioneering organization testing the limits of AGI is Microsoft Research AI, which has carried out a number of research initiatives, including developing a data set to counter prejudice for machine-learning models.

The business is also investigating ways to advance moral AI, create a responsible AI standard, and create AI strategies and evaluations to create a framework that emphasizes the advancement of mankind.

The person behind the well-known video game franchises Commander Keen and Doom has launched yet another intriguing endeavor.

Keen Technologies, John Carmack's most recent business, is an AGI development company that has already raised $20 million in funding from former GitHub CEO Nat Friedman and Cue founder Daniel Gross.

Carmack is one of the AGI optimists who believes that it would ultimately help mankind and result in the development of an AI mind that acts like a human, which might be used as a universal remote worker.

So what does AGI's future hold?

The majority of specialists are doubtful that AGI will ever be developed, and others believe that the urge to even develop artificial intelligence comparable to humans will eventually go away.

Others are working to develop it so that everyone will benefit.

Nevertheless, the creation of AGI is still in the planning stages, and in the next decades, little progress is anticipated.

Nevertheless, throughout history, scientists have debated whether developing technologies with the potential to change people's lives will benefit society as a whole or endanger it.

This proposal was considered before to the invention of the vehicle, during the development of AC electricity, and when the atomic bomb was still only a theory.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

Be sure to refer to the complete & active AI Terms Glossary here.

Artificial Intelligence - What Is Computational Creativity?

Computational Creativity is a term used to describe a kind of creativity that is based on Computer-generated art is connected to computational creativity, although it is not reducible to it.

According to Margaret Boden, "CG-art" is an artwork that "results from some computer program being allowed to operate on its own, with zero input from the human artist" (Boden 2010, 141).

This definition is both severe and limiting, since it is confined to the creation of "art works" as defined by human observers.

Computational creativity, on the other hand, is a broader phrase that encompasses a broader range of actions, equipment, and outputs.

"Computational creativity is an area of Artificial Intelligence (AI) study... where we construct and engage with computational systems that produce products and ideas," said Simon Colton and Geraint A. Wiggins.

Those "artefacts and ideas" might be works of art, as well as other things, discoveries, and/or performances (Colton and Wiggins 2012, 21).

Games, narrative, music composition and performance, and visual arts are examples of computational creativity applications and implementations.

Games and other cognitive skill competitions are often used to evaluate and assess machine skills.

The fundamental criterion of machine intelligence, in fact, was established via a game, which Alan Turing dubbed "The Game of Imitation" (1950).

Since then, AI progress and accomplishment have been monitored and evaluated via games and other human-machine contests.

Chess has had a special status and privileged position among all the games in which computers have been involved, to the point where critics such as Douglas Hofstadter (1979, 674) and Hubert Dreyfus (1992) confidently asserted that championship-level AI chess would forever remain out of reach and unattainable.

After beating Garry Kasparov in 1997, IBM's Deep Blue modified the game's rules.

But chess was just the start.

In 2015, AlphaGo, a Go-playing algorithm built by Google DeepMind, defeated Lee Sedol, one of the most famous human players of this notoriously tough board game, in four out of five games.

Human observers, including as Fan Hui (2016), have praised AlphaGo's nimble play as "beautiful," "intuitive," and "innovative." 'Automated Insights' is a service provided by Automated Insights Natural Language Generation (NLG) techniques such as Wordsmith and Narrative Science's Quill are used to create human-readable tales from machine-readable data.

Unlike basic news aggregators or template NLG systems, these computers "write" (or "produce," as the case may be) unique tales that are almost indistinguishable from human-created material in many cases.

Christer Clerwall, for example, performed a small-scale research in 2014 in which human test subjects were asked to assess news pieces written by Wordsmith and a professional writer from the Los Angeles Times.

The study's findings reveal that, although software-generated information is often seen as descriptive and dull, it is also regarded as more impartial and trustworthy (Clerwall 2014, 519).

"Within 10 years, a digital computer would produce music regarded by critics as holding great artistic merit," Herbert Simon and Allen Newell predicted in their famous article "Heuristic Problem Solving" (1958). (Simon and Newell 1958, 7).

This prediction has come true.

Experiments in Musical Intelligence (EMI, or "Emmy") by David Cope is one of the most well-known works in the subject of "algorithmic composition."

Emmy is a computer-based algorithmic composer capable of analyzing existing musical compositions, rearranging their fundamental components, and then creating new, unique scores that sound like and, in some circumstances, are indistinguishable from Mozart, Bach, and Chopin's iconic masterpieces (Cope 2001).

There are robotic systems in music performance, such as Shimon, a marimba-playing jazz-bot from Georgia Tech University, that can not only improvise with human musicians in real time, but also "is designed to create meaningful and inspiring musical interactions with humans, leading to novel musical experiences and outcomes" (Hoffman and Weinberg 2011).

Cope's method, which he refers to as "recombinacy," is not restricted to music.

It may be used and applied to any creative technique in which new works are created by reorganizing or recombining a set of finite parts, such as the alphabet's twenty-six letters, the musical scale's twelve tones, the human eye's sixteen million colors, and so on.

As a result, other creative undertakings, like as painting, have adopted similar computational creativity method.

The Painting Fool is an automated painter created by Simon Colton that seeks to be "considered seriously as a creative artist in its own right" (Colton 2012, 16).

To far, the algorithm has generated thousands of "original" artworks, which have been shown in both online and physical art exhibitions.

Obvious, a Paris-based collaboration comprised of the artists Hugo Caselles-Dupré, Pierre Fautrel, and Gauthier Vernie, uses a generative adversarial network (GAN) to create portraits of a fictitious family (the Belamys) in the manner of the European masters.

Christies auctioned one of these pictures, "Portrait of Edmond Belamy," for $432,500 in October 2018.

Designing ostensibly creative systems instantly runs into semantic and conceptual issues.

Creativity is an enigmatic phenomena that is difficult to pinpoint or quantify.

Are these programs, algorithms, and systems really "creative," or are they merely a sort of "imitation," as some detractors have labeled them? This issue is similar to John Searle's (1984, 32–38) Chinese Room thought experiment, which aimed to highlight the distinction between genuine cognitive activity, such as creative expression, and simple simulation or imitation.

Researchers in the field of computational creativity have introduced and operationalized a rather specific formulation to characterize their efforts: "The philosophy, science, and engineering of computational systems that, by taking on specific responsibilities, exhibit behaviors that unbiased observers would deem creative" (Colton and Wig gins 2012, 21).

The key word in this description is "responsibility."

"The term responsibilities highlights the difference between the systems we build and creativity support tools studied in the HCI [human-computer interaction] community and embedded in tools like Adobe's Photoshop, to which most observers would probably not attribute creative intent or behavior," Colton and Wiggins explain (Colton and Wiggins 2012, 21).

"The program is only a tool to improve human creativity" (Colton 2012, 3–4) using a software application like Photoshop; it is an instrument utilized by a human artist who is and remains responsible for the creative choices and output created by the instrument.

Computational creativity research, on the other hand, "seeks to develop software that is creative in and of itself" (Colton 2012, 4).

On the one hand, one might react as we have in the past, dismissing contemporary technological advancements as simply another instrument or tool of human action—or what technology philosophers such as Martin Heidegger (1977) and Andrew Feenberg (1991) refer to as "the instrumental theory of technology."

This is, in fact, the explanation supplied by David Cope in his own appraisal of his work's influence and relevance.

Emmy and other algorithmic composition systems, according to Cope, do not compete with or threaten to replace human composition.

They are just instruments used in and for musical creation.

"Computers represent just instruments with which we stretch our ideas and bodies," writes Cope.

Computers, programs, and the data utilized to generate their output were all developed by humanity.

Our algorithms make music that is just as much ours as music made by our greatest human inspirations" (Cope 2001, 139).

According to Cope, no matter how much algorithmic mediation is invented and used, the musical composition generated by these advanced digital tools is ultimately the responsibility of the human person.

The similar argument may be made for other supposedly creative programs, such as AlphaGo, a Go-playing algorithm, or The Painting Fool, a painting software.

When AlphaGo wins a big tournament or The Painting Fool creates a spectacular piece of visual art that is presented in a gallery, there is still a human person (or individuals) who is (or can reply or answer for) what has been created, according to the argument.

The attribution lines may get more intricate and drawn out, but there is always someone in a position of power behind the scenes, it might be claimed.

In circumstances where efforts have been made to transfer responsibility to the computer, evidence of this already exists.

Consider AlphaGo's game-winning move 37 versus Lee Sedol in game two.

If someone wants to learn more about the move and its significance, AlphaGo is the one to ask.

The algorithm, on the other hand, will remain silent.

In actuality, it was up to the human programmers and spectators to answer on AlphaGo's behalf and explain the importance and effect of the move.

As a result, as Colton (2012) and Colton et al. (2015) point out, if the mission of computational creativity is to succeed, the software will have to do more than create objects and behaviors that humans interpret as creative output.

It must also take ownership of the task by accounting for what it accomplished and how it did it.

"The software," Colton and Wiggins argue, "should be available for questioning about its motivations, processes, and products," eventually capable of not only generating titles for and explanations and narratives about the work but also responding to questions by engaging in critical dialogue with its audience (Colton and Wiggins 2012, 25). (Colton et al. 2015, 15).

At the same time, these algorithmic incursions into what had previously been a protected and solely human realm have created possibilities.

It's not only a question of whether computers, machine learning algorithms, or other applications can or cannot be held accountable for what they do or don't do; it's also a question of how we define, explain, and define creative responsibility in the first place.

This suggests that there is a strong and weak component to this endeavor, which Mohammad Majid al-Rifaie and Mark Bishop refer to as strong and weak forms of computational creativity, reflecting Searle's initial difference on AI initiatives (Majid al-Rifaie and Bishop 2015, 37).

The types of application development and demonstrations presented by people and companies such as DeepMind, David Cope, and Simon Colton are examples of the "strong" sort.

However, these efforts have a "weak AI" component in that they simulate, operationalize, and stress test various conceptualizations of artistic responsibility and creative expression, resulting in critical and potentially insightful reevaluations of how we have defined these concepts in our own thinking.

Nothing has made Douglas Hofstadter reexamine his own thinking about thinking more than the endeavor to cope with and make sense of David Cope's Emmy nomination (Hofstadter 2001, 38).

To put it another way, developing and experimenting with new algorithmic capabilities does not necessarily detract from human beings and what (hopefully) makes us unique, but it does provide new opportunities to be more precise and scientific about these distinguishing characteristics and their limits.