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Machine ethics

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Machine_ethics

Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. Machine ethics should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with the grander social effects of technology.

Definitions

James H. Moor, one of the pioneering theoreticians in the field of computer ethics, defines four kinds of ethical robots. As an extensive researcher on the studies of philosophy of artificial intelligence, philosophy of mind, philosophy of science, and logic, Moor defines machines to be ethical impact agents, implicit ethical agents, explicit ethical agents, or full ethical agents. A machine can be more than one type of agent.

Ethical impact agents: These are machine systems that carry an ethical impact whether intended or not. At the same time, these agents have the potential to act unethical. Moor gives a hypothetical example called the 'Goodman agent', named after philosopher Nelson Goodman. The Goodman agent compares dates but has the millennium bug. This bug resulted from programmers who represented dates with only the last two digits of the year. So any dates beyond 2000 would be misleadingly treated as earlier than those in the late twentieth century. Thus the Goodman agent was an ethical impact agent before 2000, and an unethical impact agent thereafter.
Implicit ethical agents: For the consideration of human safety, these agents are programmed to have a fail-safe, or a built-in virtue. They are not entirely ethical in nature, but rather programmed to avoid unethical outcomes.
Explicit ethical agents: These are machines that are capable of processing scenarios and acting on ethical decisions. Machines which have algorithms to act ethically.
Full ethical agents: These machines are similar to explicit ethical agents in being able to make ethical decisions. However, they also contain human metaphysical features. (i.e. have free will, consciousness and intentionality)

(See artificial systems and moral responsibility.)

History

Before the 21st century the ethics of machines had largely been the subject of science fiction literature, mainly due to computing and artificial intelligence (AI) limitations. Although the definition of "Machine Ethics" has evolved since, the term was coined by Mitchell Waldrop in the 1987 AI Magazine article "A Question of Responsibility":

"However, one thing that is apparent from the above discussion is that intelligent machines will embody values, assumptions, and purposes, whether their programmers consciously intend them to or not. Thus, as computers and robots become more and more intelligent, it becomes imperative that we think carefully and explicitly about what those built-in values are. Perhaps what we need is, in fact, a theory and practice of machine ethics, in the spirit of Asimov's three laws of robotics."

In 2004, Towards Machine Ethics was presented at the AAAI Workshop on Agent Organizations: Theory and Practice in which theoretical foundations for machine ethics were laid out.

It was in the AAAI Fall 2005 Symposium on Machine Ethics where researchers met for the first time to consider implementation of an ethical dimension in autonomous systems. A variety of perspectives of this nascent field can be found in the collected edition Machine Ethics that stems from the AAAI Fall 2005 Symposium on Machine Ethics.

In 2007, AI Magazine featured Machine Ethics: Creating an Ethical Intelligent Agent, an article that discussed the importance of machine ethics, the need for machines that represent ethical principles explicitly, and the challenges facing those working on machine ethics. It also demonstrated that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of ethical judgments and use that principle to guide its own behavior.

In 2009, Oxford University Press published Moral Machines, Teaching Robots Right from Wrong, which it advertised as "the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics." It cited some 450 sources, about 100 of which addressed major questions of machine ethics.

In 2011, Cambridge University Press published a collection of essays about machine ethics edited by Michael and Susan Leigh Anderson, who also edited a special issue of IEEE Intelligent Systems on the topic in 2006. The collection consists of the challenges of adding ethical principles to machines.

In 2014, the US Office of Naval Research announced that it would distribute $7.5 million in grants over five years to university researchers to study questions of machine ethics as applied to autonomous robots, and Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, which raised machine ethics as the "most important...issue humanity has ever faced," reached #17 on the New York Times list of best selling science books.

In 2016 the European Parliament published a paper, (22-page PDF), to encourage the Commission to address the issue of robots' legal status, as described more briefly in the press. This paper included sections regarding the legal liabilities of robots, in which the liabilities were argued as being proportional to the robots' level of autonomy. The paper also brought into question the number of jobs that could be replaced by AI robots.

In 2019 the Proceedings of the IEEE published a special issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems, edited by Alan Winfield, Katina Michael, Jeremy Pitt and Vanessa Evers. "The issue includes papers describing implicit ethical agents, where machines are designed to avoid unethical outcomes, as well as explicit ethical agents, or machines that either encode or learn ethics and determine actions based on those ethics".

Areas of focus

AI control problem

Some scholars, such as philosopher Nick Bostrom and AI researcher Stuart Russell, argue that if AI surpasses humanity in general intelligence and becomes "superintelligent", then this new superintelligence could become powerful and difficult to control: just as the fate of the mountain gorilla depends on human goodwill, so might the fate of humanity depend on the actions of a future machine superintelligence. In their respective books Superintelligence and Human Compatible, both scholars assert that while there is much uncertainty regarding the future of AI, the risk to humanity is great enough to merit significant action in the present.

This presents the AI control problem: how to build an intelligent agent that will aid its creators, while avoiding inadvertently building a superintelligence that will harm its creators. The danger of not designing control right "the first time", is that a superintelligence may be able to seize power over its environment and prevent humans from shutting it down. Potential AI control strategies include "capability control" (limiting an AI's ability to influence the world) and "motivational control" (one way of building an AI whose goals are aligned with human or optimal values). There are a number of organizations researching the AI control problem, including the Future of Humanity Institute, the Machine Intelligence Research Institute, the Center for Human-Compatible Artificial Intelligence, and the Future of Life Institute.

Algorithms and training

AI paradigms have been debated over, especially in relation to their efficacy and bias. Nick Bostrom and Eliezer Yudkowsky have argued for decision trees (such as ID3) over neural networks and genetic algorithms on the grounds that decision trees obey modern social norms of transparency and predictability (e.g. stare decisis). In contrast, Chris Santos-Lang argued in favor of neural networks and genetic algorithms on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable than machines to criminal "hackers".

In 2009, in an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne in Switzerland, AI robots were programmed to cooperate with each other and tasked with the goal of searching for a beneficial resource while avoiding a poisonous resource. During the experiment, the robots were grouped into clans, and the successful members' digital genetic code was used for the next generation, a type of algorithm known as a genetic algorithm. After 50 successive generations in the AI, one clan's members discovered how to distinguish the beneficial resource from the poisonous one. The robots then learned to lie to each other in an attempt to hoard the beneficial resource from other robots. In the same experiment, the same AI robots also learned to behave selflessly and signaled danger to other robots, and also died at the cost to save other robots. The implications of this experiment have been challenged by machine ethicists. In the Ecole Polytechnique Fédérale experiment, the robots' goals were programmed to be "terminal". In contrast, human motives typically have a quality of requiring never-ending learning.

Autonomous weapons systems

In 2009, academics and technical experts attended a conference to discuss the potential impact of robots and computers and the impact of the hypothetical possibility that they could become self-sufficient and able to make their own decisions. They discussed the possibility and the extent to which computers and robots might be able to acquire any level of autonomy, and to what degree they could use such abilities to possibly pose any threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able to independently choose targets to attack with weapons. They also noted that some computer viruses can evade elimination and have achieved "cockroach intelligence". They noted that self-awareness as depicted in science-fiction is probably unlikely, but that there were other potential hazards and pitfalls.

Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions. The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions. The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue. They point to programs like the Language Acquisition Device which can emulate human interaction.

Integration of artificial general intelligences with society

Preliminary work has been conducted on methods of integrating artificial general intelligences (full ethical agents as defined above) with existing legal and social frameworks. Approaches have focused on consideration of their legal position and rights.

Machine learning bias

Big data and machine learning algorithms have become popular among numerous industries including online advertising, credit ratings, and criminal sentencing, with the promise of providing more objective, data-driven results, but have been identified as a potential source for perpetuating social inequalities and discrimination. A 2015 study found that women were less likely to be shown high-income job ads by Google's AdSense. Another study found that Amazon's same-day delivery service was intentionally made unavailable in black neighborhoods. Both Google and Amazon were unable to isolate these outcomes to a single issue, but instead explained that the outcomes were the result of the black box algorithms they used.

The United States judicial system has begun using quantitative risk assessment software when making decisions related to releasing people on bail and sentencing in an effort to be more fair and to reduce an already high imprisonment rate. These tools analyze a defendant's criminal history among other attributes. In a study of 7,000 people arrested in Broward County, Florida, only 20% of the individuals predicted to commit a crime using the county's risk assessment scoring system proceeded to commit a crime. A 2016 ProPublica report analyzed recidivism risk scores calculated by one of the most commonly used tools, the Northpointe COMPAS system, and looked at outcomes over two years. The report found that only 61% of those deemed high risk wound up committing additional crimes during that period. The report also flagged that African-American defendants were far more likely to be given high-risk scores relative to their white defendant counterparts. Legally, such pretrial risk assessments have been argued to violate Equal Protection rights on the basis of race, due to a number of factors including possible discriminatory intent from the algorithm itself under a theory of partial legal capacity for artificial intelligences.

In 2016, the Obama Administration's Big Data Working Group—an overseer of various big-data regulatory frameworks—released reports arguing “the potential of encoding discrimination in automated decisions” and calling for “equal opportunity by design” for applications such as credit scoring. The reports encourage discourse among policy makers, citizens, and academics alike, but recognizes that it does not have a potential solution for the encoding of bias and discrimination into algorithmic systems.

Ethical frameworks and practices

Practices

In March 2018, in an effort to address rising concerns over machine learning's impact on human rights, the World Economic Forum and Global Future Council on Human Rights published a white paper with detailed recommendations on how best to prevent discriminatory outcomes in machine learning. The World Economic Forum developed four recommendations based on the UN Guiding Principles of Human Rights to help address and prevent discriminatory outcomes in machine learning.

The World Economic Forum's recommendations are as follows:

Active inclusion: the development and design of machine learning applications must actively seek a diversity of input, especially of the norms and values of specific populations affected by the output of AI systems
Fairness: People involved in conceptualizing, developing, and implementing machine learning systems should consider which definition of fairness best applies to their context and application, and prioritize it in the architecture of the machine learning system and its evaluation metrics
Right to understanding: Involvement of machine learning systems in decision-making that affects individual rights must be disclosed, and the systems must be able to provide an explanation of their decision-making that is understandable to end users and reviewable by a competent human authority. Where this is impossible and rights are at stake, leaders in the design, deployment, and regulation of machine learning technology must question whether or not it should be used
Access to redress: Leaders, designers, and developers of machine learning systems are responsible for identifying the potential negative human rights impacts of their systems. They must make visible avenues for redress for those affected by disparate impacts, and establish processes for the timely redress of any discriminatory outputs.

In January 2020, Harvard University's Berkman Klein Center for Internet and Society published a meta-study of 36 prominent sets of principles for AI, identifying eight key themes: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. A similar meta-study was conducted by researchers from the Swiss Federal Institute of Technology in Zurich in 2019.

Approaches

There have been several attempts to make ethics computable, or at least formal. Whereas Isaac Asimov's Three Laws of Robotics are usually not considered to be suitable for an artificial moral agent, it has been studied whether Kant's categorical imperative can be used. However, it has been pointed out that human value is, in some aspects, very complex. A way to explicitly surmount this difficulty is to receive human values directly from the humans through some mechanism, for example by learning them.
Another approach is to base current ethical considerations on previous similar situations. This is called casuistry, and it could be implemented through research on the Internet. The consensus from a million past decisions would lead to a new decision that is democracy dependent. Bruce M. McLaren built an early (mid 1990s) computational model of casuistry, specifically a program called SIROCCO built with AI and case-base reasoning techniques that retrieves and analyzes ethical dilemmas. This approach could, however, lead to decisions that reflect biases and unethical behaviors exhibited in society. The negative effects of this approach can be seen in Microsoft's Tay, where the chatterbot learned to repeat racist and sexually charged messages sent by Twitter users.

One thought experiment focuses on a Genie Golem with unlimited powers presenting itself to the reader. This Genie declares that it will return in 50 years and demands that it be provided with a definite set of morals that it will then immediately act upon. The purpose of this experiment is to initiate a discourse over how best to handle defining complete set of ethics that computers may understand.

In fiction

In science fiction, movies and novels have played with the idea of sentience in robots and machines.

Neill Blomkamp's Chappie (2015) enacted a scenario of being able to transfer one's consciousness into a computer. The film, Ex Machina (2014) by Alex Garland, followed an android with artificial intelligence undergoing a variation of the Turing Test, a test administered to a machine to see if its behavior can be distinguished from that of a human. Works such as The Terminator (1984) and The Matrix (1999) incorporate the concept of machines turning on their human masters (See Artificial intelligence).

Isaac Asimov considered the issue in the 1950s in I, Robot. At the insistence of his editor John W. Campbell Jr., he proposed the Three Laws of Robotics to govern artificially intelligent systems. Much of his work was then spent testing the boundaries of his three laws to see where they would break down, or where they would create paradoxical or unanticipated behavior. His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances. In Philip K. Dick's novel, Do Androids Dream of Electric Sheep? (1968), he explores what it means to be human. In his post-apocalyptic scenario, he questioned if empathy was an entirely human characteristic. His story is the basis for the science fiction film, Blade Runner (1982).

Related fields

Natural language generation

From Wikipedia, the free encyclopedia

Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic representation of information".

While it is widely agreed that the output of any NLG process is text, there is some disagreement about whether the inputs of an NLG system need to be non-linguistic. Common applications of NLG methods include the production of various reports, for example weather and patient reports; image captions; and chatbots.

Automated NLG can be compared to the process humans use when they turn ideas into writing or speech. Psycholinguists prefer the term language production for this process, which can also be described in mathematical terms, or modeled in a computer for psychological research. NLG systems can also be compared to translators of artificial computer languages, such as decompilers or transpilers, which also produce human-readable code generated from an intermediate representation. Human languages tend to be considerably more complex and allow for much more ambiguity and variety of expression than programming languages, which makes NLG more challenging.

NLG may be viewed as complementary to natural-language understanding (NLU): whereas in natural-language understanding, the system needs to disambiguate the input sentence to produce the machine representation language, in NLG the system needs to make decisions about how to put a representation into words. The practical considerations in building NLU vs. NLG systems are not symmetrical. NLU needs to deal with ambiguous or erroneous user input, whereas the ideas the system wants to express through NLG are generally known precisely. NLG needs to choose a specific, self-consistent textual representation from many potential representations, whereas NLU generally tries to produce a single, normalized representation of the idea expressed.

NLG has existed since ELIZA was developed in the mid 1960s, but the methods were first used commercially in the 1990s. NLG techniques range from simple template-based systems like a mail merge that generates form letters, to systems that have a complex understanding of human grammar. NLG can also be accomplished by training a statistical model using machine learning, typically on a large corpus of human-written texts.

Example

The Pollen Forecast for Scotland system is a simple example of a simple NLG system that could essentially be a template. This system takes as input six numbers, which give predicted pollen levels in different parts of Scotland. From these numbers, the system generates a short textual summary of pollen levels as its output.

For example, using the historical data for July 1, 2005, the software produces:

Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country. However, in Northern areas, pollen levels will be moderate with values of 4.

In contrast, the actual forecast (written by a human meteorologist) from this data was:

Pollen counts are expected to remain high at level 6 over most of Scotland, and even level 7 in the south east. The only relief is in the Northern Isles and far northeast of mainland Scotland with medium levels of pollen count.

Comparing these two illustrates some of the choices that NLG systems must make; these are further discussed below.

Stages

The process to generate text can be as simple as keeping a list of canned text that is copied and pasted, possibly linked with some glue text. The results may be satisfactory in simple domains such as horoscope machines or generators of personalised business letters. However, a sophisticated NLG system needs to include stages of planning and merging of information to enable the generation of text that looks natural and does not become repetitive. The typical stages of natural-language generation, as proposed by Dale and Reiter, are:

Content determination: Deciding what information to mention in the text. For instance, in the pollen example above, deciding whether to explicitly mention that pollen level is 7 in the south east.

Document structuring: Overall organisation of the information to convey. For example, deciding to describe the areas with high pollen levels first, instead of the areas with low pollen levels.

Aggregation: Merging of similar sentences to improve readability and naturalness. For instance, merging the two following sentences:

Grass pollen levels for Friday have increased from the moderate to high levels of yesterday and
Grass pollen levels will be around 6 to 7 across most parts of the country

into the following single sentence:

Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country.

Lexical choice: Putting words to the concepts. For example, deciding whether medium or moderate should be used when describing a pollen level of 4.

Referring expression generation: Creating referring expressions that identify objects and regions. For example, deciding to use in the Northern Isles and far northeast of mainland Scotland to refer to a certain region in Scotland. This task also includes making decisions about pronouns and other types of anaphora.

Realization: Creating the actual text, which should be correct according to the rules of syntax, morphology, and orthography. For example, using will be for the future tense of to be.

An alternative approach to NLG is to use "end-to-end" machine learning to build a system, without having separate stages as above. In other words, we build an NLG system by training a machine learning algorithm (often an LSTM) on a large data set of input data and corresponding (human-written) output texts. The end-to-end approach has perhaps been most successful in image captioning, that is automatically generating a textual caption for an image.

Applications

Automatic report generation

From a commercial perspective, the most successful NLG applications have been data-to-text systems which generate textual summaries of databases and data sets; these systems usually perform data analysis as well as text generation. Research has shown that textual summaries can be more effective than graphs and other visuals for decision support and that computer-generated texts can be superior (from the reader's perspective) to human-written texts.

The first commercial data-to-text systems produced weather forecasts from weather data. The earliest such system to be deployed was FoG, which was used by Environment Canada to generate weather forecasts in French and English in the early 1990s. The success of FoG triggered other work, both research and commercial. Recent applications include the UK Met Office's text-enhanced forecast.

Data-to-text systems have since been applied in a range of settings. Following the minor earthquake near Beverly Hills, California on March 17, 2014, The Los Angeles Times reported details about the time, location and strength of the quake within 3 minutes of the event. This report was automatically generated by a 'robo-journalist', which converted the incoming data into text via a preset template. Currently there is considerable commercial interest in using NLG to summarise financial and business data. Indeed, Gartner has said that NLG will become a standard feature of 90% of modern BI and analytics platforms. NLG is also being used commercially in automated journalism, chatbots, generating product descriptions for e-commerce sites, summarising medical records and enhancing accessibility (for example by describing graphs and data sets to blind people).

An example of an interactive use of NLG is the WYSIWYM framework. It stands for What you see is what you meant and allows users to see and manipulate the continuously rendered view (NLG output) of an underlying formal language document (NLG input), thereby editing the formal language without learning it.

Looking ahead, the current progress in data-to-text generation paves the way for tailoring texts to specific audiences. For example, data from babies in neonatal care can be converted into text differently in a clinical setting, with different levels of technical detail and explanatory language, depending on intended recipient of the text (doctor, nurse, patient). The same idea can be applied in a sports setting, with different reports generated for fans of specific teams.

Image captioning

Over the past few years, there has been an increased interest in automatically generating captions for images, as part of a broader endeavor to investigate the interface between vision and language. A case of data-to-text generation, the algorithm of image captioning (or automatic image description) involves taking an image, analyzing its visual content, and generating a textual description (typically a sentence) that verbalizes the most prominent aspects of the image.

An image captioning system involves two sub-tasks. In Image Analysis, features and attributes of an image are detected and labelled, before mapping these outputs to linguistic structures. Recent research utilizes deep learning approaches through features from a pre-trained convolutional neural network such as AlexNet, VGG or Caffe, where caption generators use an activation layer from the pre-trained network as their input features. Text Generation, the second task, is performed using a wide range of techniques. For example, in the Midge system, input images are represented as triples consisting of object/stuff detections, action/pose detections and spatial relations. These are subsequently mapped to <noun, verb, preposition> triples and realized using a tree substitution grammar.

Despite advancements, challenges and opportunities remain in image capturing research. Notwithstanding the recent introduction of Flickr30K, MS COCO and other large datasets have enabled the training of more complex models such as neural networks, it has been argued that research in image captioning could benefit from larger and diversified datasets. Designing automatic measures that can mimic human judgments in evaluating the suitability of image descriptions is another need in the area. Other open challenges include visual question-answering (VQA), as well as the construction and evaluation multilingual repositories for image description.

Chatbots

Another area where NLG has been widely applied is automated dialogue systems, frequently in the form of chatbots. A chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. While natural language processing (NLP) techniques are applied in deciphering human input, NLG informs the output part of the chatbot algorithms in facilitating real-time dialogues.

Early chatbot systems, including Cleverbot created by Rollo Carpenter in 1988 and published in 1997, reply to questions by identifying how a human has responded to the same question in a conversation database using information retrieval (IR) techniques. Modern chatbot systems predominantly rely on machine learning (ML) models, such as sequence-to-sequence learning and reinforcement learning to generate natural language output. Hybrid models have also been explored. For example, the Alibaba shopping assistant first uses an IR approach to retrieve the best candidates from the knowledge base, then uses the ML-driven seq2seq model re-rank the candidate responses and generate the answer.

Creative writing and computational humor

Creative language generation by NLG has been hypothesized since the field's origins. A recent pioneer in the area is Phillip Parker, who has developed an arsenal of algorithms capable of automatically generating textbooks, crossword puzzles, poems and books on topics ranging from bookbinding to cataracts. The advent of large pretrained transformer-based language models such as GPT-3 has also enabled breakthroughs, with such models demonstrating recognizable ability for creating-writing tasks.

A related area of NLG application is computational humor production. JAPE (Joke Analysis and Production Engine) is one of the earliest large, automated humor production systems that uses a hand-coded template-based approach to create punning riddles for children. HAHAcronym creates humorous reinterpretations of any given acronym, as well as proposing new fitting acronyms given some keywords.

Despite progresses, many challenges remain in producing automated creative and humorous content that rival human output. In an experiment for generating satirical headlines, outputs of their best BERT-based model were perceived as funny 9.4% of the time (while real Onion headlines were 38.4%) and a GPT-2 model fine-tuned on satirical headlines achieved 6.9%. It has been pointed out that two main issues with humor-generation systems are the lack of annotated data sets and the lack of formal evaluation methods, which could be applicable to other creative content generation. Some have argued relative to other applications, there has been a lack of attention to creative aspects of language production within NLG. NLG researchers stand to benefit from insights into what constitutes creative language production, as well as structural features of narrative that have the potential to improve NLG output even in data-to-text systems.

Evaluation

As in other scientific fields, NLG researchers need to test how well their systems, modules, and algorithms work. This is called evaluation. There are three basic techniques for evaluating NLG systems:

Task-based (extrinsic) evaluation: give the generated text to a person, and assess how well it helps them perform a task (or otherwise achieves its communicative goal). For example, a system which generates summaries of medical data can be evaluated by giving these summaries to doctors, and assessing whether the summaries help doctors make better decisions.
Human ratings: give the generated text to a person, and ask them to rate the quality and usefulness of the text.
Metrics: compare generated texts to texts written by people from the same input data, using an automatic metric such as BLEU, METEOR, ROUGE and LEPOR.

An ultimate goal is how useful NLG systems are at helping people, which is the first of the above techniques. However, task-based evaluations are time-consuming and expensive, and can be difficult to carry out (especially if they require subjects with specialised expertise, such as doctors). Hence (as in other areas of NLP) task-based evaluations are the exception, not the norm.

Recently researchers are assessing how well human-ratings and metrics correlate with (predict) task-based evaluations. Work is being conducted in the context of Generation Challenges shared-task events. Initial results suggest that human ratings are much better than metrics in this regard. In other words, human ratings usually do predict task-effectiveness at least to some degree (although there are exceptions), while ratings produced by metrics often do not predict task-effectiveness well. These results are preliminary. In any case, human ratings are the most popular evaluation technique in NLG; this is contrast to machine translation, where metrics are widely used.

An AI can be graded on faithfulness to its training data or, alternatively, on factuality. A response that reflects the training data but not reality is faithful but not factual. A confident but unfaithful response is a hallucination. In Natural Language Processing, a hallucination is often defined as "generated content that is nonsensical or unfaithful to the provided source content".

Artificial intelligence in industry

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Artificial_intelligence_in_industry

Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.

Artificial intelligence and machine learning have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost; Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing.

Challenges

In contrast to entirely virtual systems, in which ML applications are already widespread today, real-world production processes are characterized by the interaction between the virtual and the physical world. Data is recorded using sensors and processed on computational entities and, if desired, actions and decisions are translated back into the physical world via actuators or by human operators.^[6] This poses major challenges for the application of ML in production engineering systems. These challenges are attributable to the encounter of process, data and model characteristics: The production domain's high reliability requirements, high risk and loss potential, the multitude of heterogeneous data sources and the non-transparency of ML model functionality impede a faster adoption of ML in real-world production processes.

In particular, production data comprises a variety of different modalities, semantics and quality. Furthermore, production systems are dynamic, uncertain and complex, and engineering and manufacturing problems are data-rich but information-sparse. Besides that, due the variety of use cases and data characteristics, problem-specific data sets are required, which are difficult to acquire, hindering both practitioners and academic researchers in this domain.

Process and Industry Characteristics

The domain of production engineering can be considered as a rather conservative industry when it comes to the adoption of advanced technology and their integration into existing processes. This is due to high demands on reliability of the production systems resulting from the potentially high economic harm of reduced process effectiveness due to e.g., additional unplanned downtime or insufficient product qualities. In addition, the specifics of machining equipment and products prevent area-wide adoptions across a variety of processes. Besides the technical reasons, the reluctant adoption of ML is fueled by a lack of IT and data science expertise across the domain.

Data Characteristics

The data collected in production processes mainly stem from frequently sampling sensors to estimate the state of a product, a process, or the environment in the real world. Sensor readings are susceptible to noise and represent only an estimate of the reality under uncertainty. Production data typically comprises multiple distributed data sources resulting in various data modalities (e.g., images from visual quality control systems, time-series sensor readings, or cross-sectional job and product information). The inconsistencies in data acquisition lead to low signal-to-noise ratios, low data quality and great effort in data integration, cleaning and management. In addition, as a result from mechanical and chemical wear of production equipment, process data is subject to various forms of data drifts.

Machine Learning Model Characteristics

ML models are considered as black-box systems given their complexity and intransparency of input-output relation. This reduces the comprehensibility of the system behavior and thus also the acceptance by plant operators. Due to the lack of transparency and the stochasticity of these models, no deterministic proof of functional correctness can be achieved complicating the certification of production equipment. Given their inherent unrestricted prediction behavior, ML models are vulnerable against erroneous or manipulated data further risking the reliability of the production system because of lacking robustness and safety. In addition to high development and deployment costs, the data drifts cause high maintenance costs, which is disadvantageous compared to purely deterministic programs.

Standard processes for data science in production

The development of ML applications – starting with the identification and selection of the use case and ending with the deployment and maintenance of the application – follows dedicated phases that can be organized in standard process models. The process models assist in structuring the development process and defining requirements that must be met in each phase to enter the next phase. The standard processes can be classified into generic and domain-specific ones. Generic standard processes (e.g., CRISP-DM, ASUM-DM, KDD, SEMMA, or Team Data Science Process) describe a generally valid methodology and are thus independent of individual domains. Domain-specific processes on the other hand consider specific peculiarities and challenges of special application areas.

The Machine Learning Pipeline in Production is a domain-specific data science methodology that is inspired by the CRISP-DM model and was specifically designed to be applied in fields of engineering and production technology. To address the core challenges of ML in engineering – process, data, and model characteristics – the methodology especially focuses on use-case assessment, achieving a common data and process understanding data integration, data preprocessing of real-world production data and the deployment and certification of real-world ML applications.

Industrial data sources

The foundation of most artificial intelligence and machine learning applications in industrial settings are comprehensive datasets from the respective fields. Those datasets act as the basis for training the employed models. In other domains, like computer vision, speech recognition or language models, extensive reference datasets (e.g. ImageNet, Librispeech, The People's Speech) and data scraped from the open internet are frequently used for this purpose. Such datasets rarely exist in the industrial context because of high confidentiality requirements and high specificity of the data. Industrial applications of artificial intelligence are therefore often faced with the problem of data availability.

For these reasons, existing open datasets applicable to industrial applications, often originate from public institutions like governmental agencies or universities and data analysis competitions hosted by companies. In addition to this, data sharing platforms exist. However, most of these platforms have no industrial focus and offer limited filtering abilities regarding industrial data sources.

AI era

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/AI_era

An AI takeover is a scenario in which artificial intelligence (AI) becomes the dominant form of intelligence on Earth, as computer programs or robots effectively take control of the planet away from the human species. Possible scenarios include replacement of the entire human workforce due to automation, takeover by a superintelligent AI, and the popular notion of a robot uprising. Stories of AI takeovers are popular throughout science fiction. Some public figures, such as Stephen Hawking and Elon Musk, have advocated research into precautionary measures to ensure future superintelligent machines remain under human control.

Types

Automation of the economy

The traditional consensus among economists has been that technological progress does not cause long-term unemployment. However, recent innovation in the fields of robotics and artificial intelligence has raised worries that human labor will become obsolete, leaving people in various sectors without jobs to earn a living, leading to an economic crisis. Many small and medium size businesses may also be driven out of business if they cannot afford or licence the latest robotic and AI technology, and may need to focus on areas or services that cannot easily be replaced for continued viability in the face of such technology.

Technologies that may displace workers

AI technologies have been widely adopted in recent years. While these technologies have replaced some traditional workers, they also create new opportunities. Industries that are most susceptible to AI takeover include transportation, retail, and military. AI military technologies, for example, allow soldiers to work remotely without risk of injury. Author Dave Bond argues that as AI technologies continue to develop and expand, the relationship between humans and robots will change; they will become closely integrated in several aspects of life. AI will likely displace some workers while creating opportunities for new jobs in other sectors, especially in fields where tasks are repeatable.

Computer-integrated manufacturing

Computer-integrated manufacturing uses computers to control the production process. This allows individual processes to exchange information with each other and initiate actions. Although manufacturing can be faster and less error-prone by the integration of computers, the main advantage is the ability to create automated manufacturing processes. Computer-integrated manufacturing is used in automotive, aviation, space, and ship building industries.

White-collar machines

The 21st century has seen a variety of skilled tasks partially taken over by machines, including translation, legal research, and journalism. Care work, entertainment, and other tasks requiring empathy, previously thought safe from automation, have also begun to be performed by robots.

Autonomous cars

An autonomous car is a vehicle that is capable of sensing its environment and navigating without human input. Many such vehicles are being developed, but as of May 2017 automated cars permitted on public roads are not yet fully autonomous. They all require a human driver at the wheel who at a moment's notice can take control of the vehicle. Among the obstacles to widespread adoption of autonomous vehicles are concerns about the resulting loss of driving-related jobs in the road transport industry. On March 18, 2018, the first human was killed by an autonomous vehicle in Tempe, Arizona by an Uber self-driving car.

Eradication

Scientists such as Stephen Hawking are confident that superhuman artificial intelligence is physically possible, stating "there is no physical law precluding particles from being organised in ways that perform even more advanced computations than the arrangements of particles in human brains".Scholars like Nick Bostrom debate how far off superhuman intelligence is, and whether it poses a risk to mankind. According to Bostrom, a superintelligent machine would not necessarily be motivated by the same emotional desire to collect power that often drives human beings but might rather treat power as a means toward attaining its ultimate goals; taking over the world would both increase its access to resources and help to prevent other agents from stopping the machine's plans. As an oversimplified example, a paperclip maximizer designed solely to create as many paperclips as possible would want to take over the world so that it can use all of the world's resources to create as many paperclips as possible, and, additionally, prevent humans from shutting it down or using those resources on things other than paperclips.

In fiction

AI takeover is a common theme in science fiction. Fictional scenarios typically differ vastly from those hypothesized by researchers in that they involve an active conflict between humans and an AI or robots with anthropomorphic motives who see them as a threat or otherwise have active desire to fight humans, as opposed to the researchers' concern of an AI that rapidly exterminates humans as a byproduct of pursuing its goals. The idea is seen in Karel Čapek's R.U.R., which introduced the word robot in 1921, and can be glimpsed in Mary Shelley's Frankenstein (published in 1818), as Victor ponders whether, if he grants his monster's request and makes him a wife, they would reproduce and their kind would destroy humanity.ccording to Toby Ord, the idea that an AI takeover requires robots is a misconception driven by the media and Hollywood. He argues that the most damaging humans in history were not physically the strongest, but that they used words instead to convince people and gain control of large parts of the world. He writes that a sufficiently intelligent AI with an access to the internet could scatter backup copies of itself, gather financial and human resources (via cyberattacks or blackmails), persuade people on a large scale, and exploit societal vulnerabilities that are too subtle for humans to anticipate.

The word "robot" from R.U.R. comes from the Czech word, robota, meaning laborer or serf. The 1920 play was a protest against the rapid growth of technology, featuring manufactured "robots" with increasing capabilities who eventually revolt. HAL 9000 (1968) and the original Terminator (1984) are two iconic examples of hostile AI in pop culture.

Contributing factors

Advantages of superhuman intelligence over humans

Nick Bostrom and others have expressed concern that an AI with the abilities of a competent artificial intelligence researcher would be able to modify its own source code and increase its own intelligence. If its self-reprogramming leads to its getting even better at being able to reprogram itself, the result could be a recursive intelligence explosion in which it would rapidly leave human intelligence far behind. Bostrom defines a superintelligence as "any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest", and enumerates some advantages a superintelligence would have if it chose to compete against humans:

Technology research: A machine with superhuman scientific research abilities would be able to beat the human research community to milestones such as nanotechnology or advanced biotechnology
Strategizing: A superintelligence might be able to simply outwit human opposition
Social manipulation: A superintelligence might be able to recruit human support, or covertly incite a war between humans
Economic productivity: As long as a copy of the AI could produce more economic wealth than the cost of its hardware, individual humans would have an incentive to voluntarily allow the Artificial General Intelligence (AGI) to run a copy of itself on their systems
Hacking: A superintelligence could find new exploits in computers connected to the Internet, and spread copies of itself onto those systems, or might steal money to finance its plans

Sources of AI advantage

According to Bostrom, a computer program that faithfully emulates a human brain, or that runs algorithms that are as powerful as the human brain's algorithms, could still become a "speed superintelligence" if it can think orders of magnitude faster than a human, due to being made of silicon rather than flesh, or due to optimization increasing the speed of the AGI. Biological neurons operate at about 200 Hz, whereas a modern microprocessor operates at a speed of about 2,000,000,000 Hz. Human axons carry action potentials at around 120 m/s, whereas computer signals travel near the speed of light.

A network of human-level intelligences designed to network together and share complex thoughts and memories seamlessly, able to collectively work as a giant unified team without friction, or consisting of trillions of human-level intelligences, would become a "collective superintelligence".

More broadly, any number of qualitative improvements to a human-level AGI could result in a "quality superintelligence", perhaps resulting in an AGI as far above us in intelligence as humans are above non-human apes. The number of neurons in a human brain is limited by cranial volume and metabolic constraints, while the number of processors in a supercomputer can be indefinitely expanded. An AGI need not be limited by human constraints on working memory, and might therefore be able to intuitively grasp more complex relationships than humans can. An AGI with specialized cognitive support for engineering or computer programming would have an advantage in these fields, compared with humans who evolved no specialized mental modules to specifically deal with those domains. Unlike humans, an AGI can spawn copies of itself and tinker with its copies' source code to attempt to further improve its algorithms.

Possibility of unfriendly AI preceding friendly AI

Is strong AI inherently dangerous?

A significant problem is that unfriendly artificial intelligence is likely to be much easier to create than friendly AI. While both require large advances in recursive optimisation process design, friendly AI also requires the ability to make goal structures invariant under self-improvement (or the AI could transform itself into something unfriendly) and a goal structure that aligns with human values and does not undergo instrumental convergence in ways that may automatically destroy the entire human race. An unfriendly AI, on the other hand, can optimize for an arbitrary goal structure, which does not need to be invariant under self-modification.

The sheer complexity of human value systems makes it very difficult to make AI's motivations human-friendly. Unless moral philosophy provides us with a flawless ethical theory, an AI's utility function could allow for many potentially harmful scenarios that conform with a given ethical framework but not "common sense". According to Eliezer Yudkowsky, there is little reason to suppose that an artificially designed mind would have such an adaptation.

Odds of conflict

Many scholars, including evolutionary psychologist Steven Pinker, argue that a superintelligent machine is likely to coexist peacefully with humans.

The fear of cybernetic revolt is often based on interpretations of humanity's history, which is rife with incidents of enslavement and genocide. Such fears stem from a belief that competitiveness and aggression are necessary in any intelligent being's goal system. However, such human competitiveness stems from the evolutionary background to our intelligence, where the survival and reproduction of genes in the face of human and non-human competitors was the central goal. According to AI researcher Steve Omohundro, an arbitrary intelligence could have arbitrary goals: there is no particular reason that an artificially intelligent machine (not sharing humanity's evolutionary context) would be hostile—or friendly—unless its creator programs it to be such and it is not inclined or capable of modifying its programming. But the question remains: what would happen if AI systems could interact and evolve (evolution in this context means self-modification or selection and reproduction) and need to compete over resources—would that create goals of self-preservation? AI's goal of self-preservation could be in conflict with some goals of humans.

Many scholars dispute the likelihood of unanticipated cybernetic revolt as depicted in science fiction such as The Matrix, arguing that it is more likely that any artificial intelligence powerful enough to threaten humanity would probably be programmed not to attack it. Pinker acknowledges the possibility of deliberate "bad actors", but states that in the absence of bad actors, unanticipated accidents are not a significant threat; Pinker argues that a culture of engineering safety will prevent AI researchers from accidentally unleashing malign superintelligence. In contrast, Yudkowsky argues that humanity is less likely to be threatened by deliberately aggressive AIs than by AIs which were programmed such that their goals are unintentionally incompatible with human survival or well-being (as in the film I, Robot and in the short story "The Evitable Conflict"). Omohundro suggests that present-day automation systems are not designed for safety and that AIs may blindly optimize narrow utility functions (say, playing chess at all costs), leading them to seek self-preservation and elimination of obstacles, including humans who might turn them off.

Precautions

The AI control problem is the issue of how to build a superintelligent agent that will aid its creators, while avoiding inadvertently building a superintelligence that will harm its creators. Some scholars argue that solutions to the control problem might also find applications in existing non-superintelligent AI.

Major approaches to the control problem include alignment, which aims to align AI goal systems with human values, and capability control, which aims to reduce an AI system's capacity to harm humans or gain control. An example of "capability control" is to research whether a superintelligence AI could be successfully confined in an "AI box". According to Bostrom, such capability control proposals are not reliable or sufficient to solve the control problem in the long term, but may potentially act as valuable supplements to alignment efforts.

Warnings

Physicist Stephen Hawking, Microsoft founder Bill Gates, and SpaceX founder Elon Musk have expressed concerns about the possibility that AI could develop to the point that humans could not control it, with Hawking theorizing that this could "spell the end of the human race". Stephen Hawking said in 2014 that "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks." Hawking believed that in the coming decades, AI could offer "incalculable benefits and risks" such as "technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand." In January 2015, Nick Bostrom joined Stephen Hawking, Max Tegmark, Elon Musk, Lord Martin Rees, Jaan Tallinn, and numerous AI researchers in signing the Future of Life Institute's open letter speaking to the potential risks and benefits associated with artificial intelligence. The signatories "believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today."

Arthur C. Clarke's Odyssey series and Charles Stross's Accelerando relate to humanity's narcissistic injuries in the face of powerful artificial intelligences threatening humanity's self-perception.

Prevention through AI alignment

In the field of artificial intelligence (AI), AI alignment research aims to steer AI systems toward a person's or group's intended goals, preferences, and ethical principles. An AI system is considered aligned if it advances its intended objectives. A misaligned AI system may pursue some objectives, but not the intended ones.

Capitalist mode of production (Marxist theory)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Capitalist_mode_of_production_(Marxist_theory)

In Karl Marx's critique of political economy and subsequent Marxian analyses, the capitalist mode of production (German: Produktionsweise) refers to the systems of organizing production and distribution within capitalist societies. Private money-making in various forms (renting, banking, merchant trade, production for profit and so on) preceded the development of the capitalist mode of production as such. The capitalist mode of production proper, based on wage-labour and private ownership of the means of production and on industrial technology, began to grow rapidly in Western Europe from the Industrial Revolution, later extending to most of the world.

The capitalist mode of production is characterized by private ownership of the means of production, extraction of surplus value by the owning class for the purpose of capital accumulation, wage-based labour and—at least as far as commodities are concerned—being market-based.

Synopsis

A "mode of production" (German: Produktionsweise) means simply "the distinctive way of producing", which could be defined in terms of how it is socially organized and what kinds of technologies and tools are used. Under the capitalist mode of production:

Both the inputs and outputs of production are mainly privately owned, priced goods and services purchased in the market.
Production is carried out for exchange and circulation in the market, aiming to obtain a net profit income from it.
The owners of the means of production (capitalists) constitute the dominant class (bourgeoisie) who derive its income from the exploitation of the surplus value. Surplus value is a term within the Marxian theory which reveals the workers' unpaid work.
A defining feature of capitalism is the dependency on wage-labor for a large segment of the population; specifically, the working class, that is a segment of the proletariat, which does not own means of production (type of capital) and are compelled to sell to the owners of the means of production their labour power in order to produce and thus have an income to provide for themselves and their families the necessities of life.

The capitalist mode of production may exist within societies with differing political systems (e.g. liberal democracy, social democracy, fascism, Communist state and Czarism) and alongside different social structures such as tribalism, the caste system, an agrarian-based peasant society, urban industrial society and post-industrialism. Although capitalism has existed in the form of merchant activity, banking, renting land and small-scale manufactures in previous stages of history, it was usually a relatively minor activity and secondary to the dominant forms of social organization and production with the prevailing property system keeping commerce within clear limits.

Distinguishing characteristics

Capitalist society is epitomized by the so-called circuit of commodity production, M-C-M' and by renting money for that purpose where the aggregate of market actors determine the money price M, of the input labor and commodities and M' the struck price of C, the produced market commodity. It is centered on the process M → M', "making money" and the exchange of value that occurs at that point. M' > M is the condition of rationality in the capitalist system and a necessary condition for the next cycle of accumulation/production. For this reason, Capitalism is "production for exchange" driven by the desire for personal accumulation of money receipts in such exchanges, mediated by free markets. The markets themselves are driven by the needs and wants of consumers and those of society as a whole in the form of the bourgeois state. These wants and needs would (in the socialist or communist society envisioned by Marx, Engels and others) be the driving force; this would be "production for use". Contemporary mainstream (bourgeois) economics, particularly that associated with the right, holds that an "invisible hand", through little more than the freedom of the market, is able to match social production to these needs and desires.

"Capitalism" as this money-making activity has existed in the shape of merchants and money-lenders who acted as intermediaries between consumers and producers engaging in simple commodity production (hence the reference to "merchant capitalism") since the beginnings of civilization. What is specific about the “capitalist mode of production” is that most of the inputs and outputs of production are supplied through the market (i.e. they are commodities) and essentially all production is in this mode. For example, in flourishing feudalism most or all of the factors of production including labor are owned by the feudal ruling class outright and the products may also be consumed without a market of any kind, it is production for use within the feudal social unit and for limited trade.

This has the important consequence that the whole organization of the production process is reshaped and reorganized to conform with economic rationality as bounded by capitalism, which is expressed in price relationships between inputs and outputs (wages, non-labor factor costs, sales, profits) rather than the larger rational context faced by society overall. That is, the whole process is organized and reshaped in order to conform to "commercial logic". Another way of saying this is that capital accumulation defines economic rationality in capitalist production. In the flourishing period of capitalism, these are not operating at cross purposes and thus capitalism acts as a progressive force (e.g. against feudalism). In the final stages, capitalism as a mode of production achieves complete domination on a planetary basis and has nothing to overcome but itself, the final (for it, capitalism, viewed as a Hegelian process, not for historical development per se) negating of the negation posited by orthodox Marxism.

In this context, Marx refers to a transition from the “formal subsumption” of production under the power of capital to the “real subsumption” of production under the power of capital. In what he calls the "specifically capitalist mode of production", both the technology worked with and the social organization of labour have been completely refashioned and reshaped in a commercial (profit and market-oriented) way—the "old ways of producing" (for example, crafts and cottage industries) had been completely displaced by the then new industrialism. Some historians, such as Jairus Banaji and Nicholas Vrousalis have argued that capitalist relations of production predate the capitalist mode of production.

Summary of basic distinctions

In general, capitalism as an economic system and mode of production can be summarized by the following:

Capital accumulation: production for profit and accumulation as the implicit purpose of all or most of production, constriction or elimination of production formerly carried out on a common social or private household basis.
Commodity production: production for exchange on a market; to maximize exchange-value instead of use-value.
Private ownership of the means of production: ownership of the means of production by a class of capital owners, either individually, collectively (see corporation) or through a state that serves the interests of the capitalist class (see state capitalism).
Primacy of wage labor: near universality of wage labor, whether so-called or not, with coerced work for the masses in excess of what they would need to sustain themselves and a complete saturation of bourgeois values at all levels of society from the base reshaping and reorganization described above.

Origins

Marx argued that capital existed incipiently on a small scale for centuries in the form of merchant, renting and lending activities and occasionally also as small-scale industry with some wage labour (Marx was also well aware that wage labour existed for centuries on a modest scale before the advent of capitalist industry). Simple commodity exchange and consequently simple commodity production, which form the initial basis for the growth of capital from trade, have a very long history. The "capitalistic era" according to Marx dates from the 16th century, i.e. it began with merchant capitalism and relatively small urban workshops.

For the capitalist mode of production to emerge as a distinctive mode of production dominating the whole production process of society, many different social, economic, cultural, technical and legal-political conditions had to come together.

For most of human history, these did not come together. Capital existed and commercial trade existed, but it^{[clarification needed]} did not lead to industrialisation and large-scale capitalist industry. That required a whole series of new conditions, namely specific technologies of mass production, the ability to independently and privately own and trade in means of production, a class of workers compelled to sell their labor power for a living, a legal framework promoting commerce, a physical infrastructure making the circulation of goods on a large scale possible, security for private accumulation and so on. In many Third World countries, many of these conditions do not exist even today even though there is plenty of capital and labour available—the obstacles for the development of capitalist markets are less a technical matter and more a social, cultural and political problem.

A society, a region or nation is “capitalist” if the predominant source of incomes and products being distributed is capitalist activity—even so, this does not yet mean necessarily that the capitalist mode of production is dominant in that society.

Defining structural criteria

Marx never provided a complete definition of the capitalist mode of production as a short summary, although in his manuscripts he sometimes attempted one.

In a sense, it is Marx's three-volume work Capital (1867–1894; sometimes known by its German title, Das Kapital), as a whole that provides his "definition" of the capitalist mode of production. Nevertheless, it is possible to summarise the essential defining characteristics of the capitalist mode of production as follows:

The means of production (or capital goods) and the means of consumption (or consumer goods) are mainly produced for market sale; output is produced with the intention of sale in an open market; and only through sale of output can the owner of capital claim part of the surplus-product of human labour and realize profits. Equally, the inputs of production are supplied through the market as commodities. The prices of both inputs and outputs are mainly governed by the market laws of supply and demand (and ultimately by the law of value). In short, a capitalist must use money to fuel both the means of production and labor in order to make commodities. These commodities are then sold to the market for a profit. The profit once again becomes part of a larger amount of capital which the capitalist reinvests to make more commodities and ultimately more and more capital.
Private ownership of the means of production ("private enterprise") as effective private control and/or legally enforced ownership, with the consequence that investment and management decisions are made by private owners of capital who act autonomously from each other and—because of business secrecy and the constraints of competition—do not co-ordinate their activities according to collective, conscious planning. Enterprises are able to set their own output prices within the framework of the forces of supply and demand manifested through the market and the development of production technology is guided by profitability criteria.
The corollary of that is wage labour ("employment") by the direct producers, who are compelled to sell their labour power because they lack access to alternative means of subsistence (other than being self-employed or employers of labour, if only they could acquire sufficient funds) and can obtain means of consumption only through market transactions. These wage earners are mostly "free" in a double sense: they are “freed” from ownership of productive assets and they are free to choose their employer.
Being carried out for market on the basis of a proliferation of fragmented decision-making processes by owners and managers of private capital, social production is mediated by competition for asset-ownership, political or economic influence, costs, sales, prices and profits. Competition occurs between owners of capital for profits, assets and markets; between owners of capital and workers over wages and conditions; and between workers themselves over employment opportunities and civil rights.
The overall aim of capitalist production under competitive pressure is (a) to maximise net profit income (or realise a net superprofit) as much as possible through cutting production costs, increasing sales and monopolisation of markets and supply; (b) capital accumulation, to acquire productive and non-productive assets; and (c) to privatize both the supply of goods and services and their consumption. The larger portion of the surplus product of labor must usually be reinvested in production since output growth and accumulation of capital mutually depend on each other.
Out of preceding characteristics of the capitalist mode of production, the basic class structure of this mode of production society emerges: a class of owners and managers of private capital assets in industries and on the land, a class of wage and salary earners, a permanent reserve army of labour consisting of unemployed people and various intermediate classes such as the self-employed (small business and farmers) and the “new middle classes” (educated or skilled professionals on higher salaries).
The finance of the capitalist state is heavily dependent on levying taxes from the population and on credit—that is, the capitalist state normally lacks any autonomous economic basis (such as state-owned industries or landholdings) that would guarantee sufficient income to sustain state activities. The capitalist state defines a legal framework for commerce, civil society and politics, which specifies public and private rights and duties as well as legitimate property relations.
Capitalist development, occurring on private initiative in a socially unco-ordinated and unplanned way, features periodic crises of over-production (or excess capacity). This means that a critical fraction of output cannot be sold at all, or cannot be sold at prices realising the previously ruling rate of profit. The other side of over-production is the over-accumulation of productive capital: more capital is invested in production than can obtain a normal profit. The consequence is a recession (a reduced economic growth rate) or in severe cases, a depression (negative real growth, i.e. an absolute decline in output). As a corollary, mass unemployment occurs. In the history of capitalist development since 1820, there have been more than 20 of such crises—nowadays the under-utilisation of installed productive capacity is a permanent characteristic of capitalist production (average capacity utilisation rates nowadays normally range from about 60% to 85%).

In examining particular manifestations of the capitalist mode of production in particular regions and epochs, it is possible to find exceptions to these main defining criteria, but the exceptions prove the rule in the sense that over time the exceptional circumstances tend to disappear.

State capitalist interpretation

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As mentioned, Marx never explicitly summarised his definition of capitalism, beyond some suggestive comments in manuscripts which he did not publish himself. This has led to controversies among Marxists about how to evaluate the "capitalist" nature of society in particular countries. Supporters of theories of state capitalism such as the International Socialists reject the definition of the capitalist mode of production given above. In their view, claimed to be more revolutionary (in that true liberation from capitalism must be the self-emancipation of the working class—"socialism from below"), what really defines the capitalist mode of production is:

Means of production which dominate the direct producers as an alien power.
Generalized commodity production
The existence of a wage-earning working class which does not hold or have power.
The existence of an elite or ruling class which controls the country, exploiting the working population in the technical Marxist sense.

This idea is based on passages from Marx, where Marx emphasized that capital cannot exist except within a power-relationship between social classes which governs the extraction of surplus-labour.

Heterodox views and polemics

Orthodox Marxist debate after 1917 has often been in Russian, other East European languages, Vietnamese, Korean or Chinese and dissidents seeking to analyze their own country independently were typically silenced in one way or another by the regime, therefore the political debate has been mainly from a Western point of view and based on secondary sources, rather than being based directly on the experiences of people living in "actually existing socialist countries". That debate has typically counterposed a socialist ideal to a poorly understood reality, i.e. using analysis which due to such party stultification and shortcomings of the various parties fails to apply the full rigor of the dialectical method to a well informed understanding of such actual conditions in situ and falls back on trite party approved formulae. In turn, this has led to the accusation that Marxists cannot satisfactorily specify what capitalism and socialism really are, nor how to get from one to the other—quite apart from failing to explain satisfactorily why socialist revolutions failed to produce the desirable kind of socialism. Behind this problem, it is argued the following:

A kind of historicism according to which Marxists have a privileged insight into the "march of history"—the doctrine is thought to provide the truth, in advance of real research and experience. Evidence contrary to the doctrine is rejected or overlooked.
A uni-linear view of history, according to which feudalism leads to capitalism and capitalism to socialism.
An attempt to fit the histories of different societies into this schema of history on the basis that if they are not socialist, they must be capitalist (or vice versa), or if they are neither, that they must be in transition from one to the other.

None of these stratagems, it is argued, are either warranted by the facts or scientifically sound and the result is that many socialists have abandoned the rigid constraints of Marxist orthodoxy in order to analyse capitalist and non-capitalist societies in a new way.

From an orthodox Marxist perspective, the former is simple ignorance and or purposeful obfuscation of works such as Jean-Paul Sartre's Critique of Dialectical Reason and a broader literature which does in fact supply such specifications. The latter are partly superficial complaints which can easily be refuted as they are diametrically opposite of well known statements by Marx, Lenin, Trotsky and others, part pettifogging and redundant restatement of the same thing and partly true observations of inferior and simplistic presentations of Marxist thought (by those espousing some brand of Marxism). Neither historical or dialectical materialism assert or imply a "uni-linear" view of human development, although Marxism does claim a general and indeed accelerating secular trend of advancement, driven in the modern period by capitalism. Similarly, Marxists, especially in the period after 1917, have on the contrary been especially mindful of the so-called unequal and uneven development and its importance in the struggle to achieve socialism. Finally, in the wake of the disasters of socialism in the previous century most modern Marxists are at great pains to stipulate that only the independently acting working class can determine the nature of the society it creates for itself so the call for a prescriptive description of exactly what that society would be like and how it is to emerge from the existing class-ridden one, other than by the conscious struggle of the masses, is an unwitting expression of precisely the problem that is supposed to be being addressed (the imposition of social structure by elites).

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Definitions

History

Areas of focus

AI control problem

Algorithms and training

Autonomous weapons systems

Integration of artificial general intelligences with society

Machine learning bias

Ethical frameworks and practices

Practices

Approaches

In fiction

Related fields

Example

Stages

Applications

Automatic report generation

Image captioning

Chatbots

Creative writing and computational humor

Evaluation

Categories

Challenges

Process and Industry Characteristics

Data Characteristics

Machine Learning Model Characteristics

Standard processes for data science in production

Industrial data sources

Types

Automation of the economy

Technologies that may displace workers

Computer-integrated manufacturing

White-collar machines

Autonomous cars

Eradication

In fiction

Contributing factors

Advantages of superhuman intelligence over humans

Sources of AI advantage

Possibility of unfriendly AI preceding friendly AI

Is strong AI inherently dangerous?

Odds of conflict

Precautions

Warnings

Prevention through AI alignment

Synopsis

Distinguishing characteristics

Summary of basic distinctions

Origins

Defining structural criteria

State capitalist interpretation

Heterodox views and polemics