AI News, Scientists help robots understand humans with 20 questions game idea
Scientists help robots understand humans with 20 questions game idea
In the game, a player wishes to estimate an unknown value on a sliding scale by asking a series of questions whose answer is binary (yes or no).
In this way, scientists say, their research findings could lead to new techniques for machines to ask other machines questions, or for machines and humans to query each other.
It requires the AI system to understand a whole sequence of questions and answers, and to handle every question or answer with consideration of what has been asked or answered before.
He explained that it is important to minimize the number of queries, while maximizing the value of each one, so as not to waste the human's time or endanger a soldier who has duties to perform in a dangerous environment.
The 20 questions game is a classic pastime, where players can only ask questions whose response is yes or no, while attempting to identify an object.
p. 460). It opens with the words: 'I propose to consider the question, 'Can machines think?'' Because 'thinking' is difficult to define, Turing chooses to 'replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.' Turing's new question is: 'Are there imaginable digital computers which would do well in the imitation game?' This question, Turing believed, is one that can actually be answered.
Researchers in the United Kingdom had been exploring 'machine intelligence' for up to ten years prior to the founding of the field of artificial intelligence (AI) research in 1956. It was a common topic among the members of the Ratio Club, who were an informal group of British cybernetics and electronics researchers that included Alan Turing, after whom the test is named. Turing, in particular, had been tackling the notion of machine intelligence since at least 1941 and one of the earliest-known mentions of 'computer intelligence' was made by him in 1947. In Turing's report, 'Intelligent Machinery', he investigated 'the question of whether or not it is possible for machinery to show intelligent behaviour' and, as part of that investigation, proposed what may be considered the forerunner to his later tests: It is not difficult to devise a paper machine which will play a not very bad game of chess. Now get three men as subjects for the experiment.
to 'Can machines do what we (as thinking entities) can do?' The advantage of the new question, Turing argues, is that it draws 'a fairly sharp line between the physical and intellectual capacities of a man.' To demonstrate this approach Turing proposes a test inspired by a party game, known as the 'imitation game', in which a man and a woman go into separate rooms and guests try to tell them apart by writing a series of questions and reading the typewritten answers sent back.
In this version, which Turing discussed in a BBC radio broadcast, a jury asks questions of a computer and the role of the computer is to make a significant proportion of the jury believe that it is really a man. Turing's paper considered nine putative objections, which include all the major arguments against artificial intelligence that have been raised in the years since the paper was published (see 'Computing Machinery and Intelligence'). In 1966, Joseph Weizenbaum created a program which appeared to pass the Turing test.
If a keyword is not found, ELIZA responds either with a generic riposte or by repeating one of the earlier comments. In addition, Weizenbaum developed ELIZA to replicate the behaviour of a Rogerian psychotherapist, allowing ELIZA to be 'free to assume the pose of knowing almost nothing of the real world.' With these techniques, Weizenbaum's program was able to fool some people into believing that they were talking to a real person, with some subjects being 'very hard to convince that ELIZA [...] is not human.' Thus, ELIZA is claimed by some to be one of the programs (perhaps the first) able to pass the Turing test, even though this view is highly contentious (see below).
'CyberLover', a malware program, preys on Internet users by convincing them to 'reveal information about their identities or to lead them to visit a web site that will deliver malicious content to their computers'. The program has emerged as a 'Valentine-risk' flirting with people 'seeking relationships online in order to collect their personal data'. John Searle's 1980 paper Minds, Brains, and Programs proposed the 'Chinese room' thought experiment and argued that the Turing test could not be used to determine if a machine can think.
Therefore, Searle concludes, the Turing test cannot prove that a machine can think. Much like the Turing test itself, Searle's argument has been both widely criticised and highly endorsed. Arguments such as Searle's and others working on the philosophy of mind sparked off a more intense debate about the nature of intelligence, the possibility of intelligent machines and the value of the Turing test that continued through the 1980s and 1990s. The Loebner Prize provides an annual platform for practical Turing tests with the first competition held in November 1991. It is underwritten by Hugh Loebner.
As Loebner described it, one reason the competition was created is to advance the state of AI research, at least in part, because no one had taken steps to implement the Turing test despite 40 years of discussing it. The first Loebner Prize competition in 1991 led to a renewed discussion of the viability of the Turing test and the value of pursuing it, in both the popular press and academia. The first contest was won by a mindless program with no identifiable intelligence that managed to fool naïve interrogators into making the wrong identification.
Saul Traiger argues that there are at least three primary versions of the Turing test, two of which are offered in 'Computing Machinery and Intelligence' and one that he describes as the 'Standard Interpretation'. While there is some debate regarding whether the 'Standard Interpretation' is that described by Turing or, instead, based on a misreading of his paper, these three versions are not regarded as equivalent, and their strengths and weaknesses are distinct. Huma Shah points out that Turing himself was concerned with whether a machine could think and was providing a simple method to examine this: through human-machine question-answer sessions. Shah argues there is one imitation game which Turing described could be practicalised in two different ways: a) one-to-one interrogator-machine test, and b) simultaneous comparison of a machine with a human, both questioned in parallel by an interrogator. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalises naturally to all of human performance capacity, verbal as well as nonverbal (robotic). Turing's original article describes a simple party game involving three players.
Common understanding has it that the purpose of the Turing test is not specifically to determine whether a computer is able to fool an interrogator into believing that it is a human, but rather whether a computer could imitate a human. While there is some dispute whether this interpretation was intended by Turing, Sterrett believes that it was and thus conflates the second version with this one, while others, such as Traiger, do not – this has nevertheless led to what can be viewed as the 'standard interpretation.'
The general structure of the OIG test could even be used with non-verbal versions of imitation games. Still other writers have interpreted Turing as proposing that the imitation game itself is the test, without specifying how to take into account Turing's statement that the test that he proposed using the party version of the imitation game is based upon a criterion of comparative frequency of success in that imitation game, rather than a capacity to succeed at one round of the game.
To return to the original imitation game, he states only that player A is to be replaced with a machine, not that player C is to be made aware of this replacement. When Colby, FD Hilf, S Weber and AD Kramer tested PARRY, they did so by assuming that the interrogators did not need to know that one or more of those being interviewed was a computer during the interrogation. As Ayse Saygin, Peter Swirski, and others have highlighted, this makes a big difference to the implementation and outcome of the test. An experimental study looking at Gricean maxim violations using transcripts of Loebner's one-to-one (interrogator-hidden interlocutor) Prize for AI contests between 1994–1999, Ayse Saygin found significant differences between the responses of participants who knew and did not know about computers being involved. The power and appeal of the Turing test derives from its simplicity.
The challenge for the computer, rather, will be to demonstrate empathy for the role of the female, and to demonstrate as well a characteristic aesthetic sensibility—both of which qualities are on display in this snippet of dialogue which Turing has imagined: When Turing does introduce some specialised knowledge into one of his imagined dialogues, the subject is not maths or electronics, but poetry: Turing thus once again demonstrates his interest in empathy and aesthetic sensitivity as components of an artificial intelligence;
Turing does not specify the precise skills and knowledge required by the interrogator in his description of the test, but he did use the term 'average interrogator': '[the] average interrogator would not have more than 70 per cent chance of making the right identification after five minutes of questioning'. Chatterbot programs such as ELIZA have repeatedly fooled unsuspecting people into believing that they are communicating with human beings.
Nonetheless, some of these experts have been deceived by the machines. Michael Shermer points out that human beings consistently choose to consider non-human objects as human whenever they are allowed the chance, a mistake called the anthropomorphic fallacy: They talk to their cars, ascribe desire and intentions to natural forces (e.g., 'nature abhors a vacuum'), and worship the sun as a human-like being with intelligence.
takes the fifth, then it is not possible for an interrogator to accurately identify the machine other than by means of a calculated guess. Even taking into account a parallel/hidden human as part of the test may not help the situation as humans can often be misidentified as being a machine. Mainstream AI researchers argue that trying to pass the Turing test is merely a distraction from more fruitful research. Indeed, the Turing test is not an active focus of much academic or commercial effort—as Stuart Russell and Peter Norvig write: 'AI researchers have devoted little attention to passing the Turing test.' There are several reasons.
Turing wanted to provide a clear and understandable example to aid in the discussion of the philosophy of artificial intelligence. John McCarthy observes that the philosophy of AI is 'unlikely to have any more effect on the practice of AI research than philosophy of science generally has on the practice of science.' Robert French (1990) makes the case that an interrogator can distinguish human and non-human interlocutors by posing questions that reveal the low-level (i.e., unconscious) processes of human cognition, as studied by cognitive science.
Software that could reverse CAPTCHA with some accuracy by analysing patterns in the generating engine started being developed soon after the creation of CAPTCHA. In 2013, researchers at Vicarious announced that they had developed a system to solve CAPTCHA challenges from Google, Yahoo!, and PayPal up to 90% of the time. In 2014, Google engineers demonstrated a system that could defeat CAPTCHA challenges with 99.8% accuracy. In 2015, Shuman Ghosemajumder, former click fraud czar of Google, stated that there were cybercriminal sites that would defeat CAPTCHA challenges for a fee, to enable various forms of fraud. Another variation is described as the subject matter expert Turing test, where a machine's response cannot be distinguished from an expert in a given field.
related approach to Hutter's prize which appeared much earlier in the late 1990s is the inclusion of compression problems in an extended Turing test. or by tests which are completely derived from Kolmogorov complexity. Other related tests in this line are presented by Hernandez-Orallo and Dowe. Algorithmic IQ, or AIQ for short, is an attempt to convert the theoretical Universal Intelligence Measure from Legg and Hutter (based on Solomonoff's inductive inference) into a working practical test of machine intelligence. Two major advantages of some of these tests are their applicability to nonhuman intelligences and their absence of a requirement for human testers.
in fact, he estimated that by the year 2000, machines with around 100 MB of storage would be able to fool 30% of human judges in a five-minute test, and that people would no longer consider the phrase 'thinking machine' contradictory. (In practice, from 2009–2012, the Loebner Prize chatterbot contestants only managed to fool a judge once, and that was only due to the human contestant pretending to be a chatbot.) He further predicted that machine learning would be an important part of building powerful machines, a claim considered plausible by contemporary researchers in artificial intelligence. In a 2008 paper submitted to 19th Midwest Artificial Intelligence and Cognitive Science Conference, Dr. Shane T.
Microsoft is teaching systems to read, answer and even ask questions
Microsoft researchers have already created technology that can do two difficult tasks about as well as a person: identify images and recognize words in a conversation.
“We’re trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it’s written or orally,” said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year.
Microsoft researchers and other industry and academic experts also are competing for the best results using another dataset, called MS MARCO, that uses real, anonymized data from Bing search queries to test a system’s ability to answer a question.
Ming Zhou, assistant managing director of Microsoft Research Asia in Beijing, who leads the Natural Language Research Group, said skills like image recognition are perception tasks: The system uses a machine learning algorithm to recognize an image based on all the images it has seen before.
For example, let’s say someone asks the question, “What is John Smith’s citizenship?” The answer could be “John Smith was born in the United States” or “He has a U.S. passport.” In either case, the system needs to look for, and use, information that relates to a question about citizenship but may not explicitly say that word.
It was a deeper look at how people learn that prompted his team to take the machine reading task one step further: They are working on a system that can read a passage and formulate a question about it, rather than an answer.
The technology that can bridge that gap is machine reading.” The roots of Microsoft’s machine reading work go back nearly two decades, to the early work researchers at the company did in the field of natural language processing.
At the time, Bill Dolan, a principal researcher at Microsoft who works on natural language processing, joked that the systems “worked beautifully, but not very often.” Still, that foundational work is now being incorporated into the algorithms that the Redmond team is using for its most recent machine reading advances, and it’s also been the basis of other groundbreaking work Dolan and his team have achieved in natural language processing.
Like many AI advances in the past few years, machine reading has benefited from the triad of better deep learning algorithms, a massive increase in cloud-based computing power to run those algorithms and huge amounts of data to learn and test on.
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.
Some examples of natural language document collections used for QA systems include: QA research attempts to deal with a wide range of question types including: fact, list, definition, How, Why, hypothetical, semantically constrained, and cross-lingual questions.
Expert systems rely heavily on expert-constructed and organized knowledge bases, whereas many modern QA systems rely on statistical processing of a large, unstructured, natural language text corpus.
As of 2001, QA systems typically included a question classifier module that determines the type of question and the type of answer. A multiagent question-answering architecture has been proposed, where each domain is represented by an agent which tries to answer questions taking into account its specific knowledge;
a meta–agent controls the cooperation between question answering agents and chooses the most relevant answer(s). QA is very dependent on a good search corpus - for without documents containing the answer, there is little any QA system can do.
The notion of data redundancy in massive collections, such as the web, means that nuggets of information are likely to be phrased in many different ways in differing contexts and documents, leading to two benefits: Some question answering systems rely heavily on automated reasoning. There are a number of question answering systems designed in Prolog, a logic programming language associated with artificial intelligence.
Having the input in the form of a natural language question makes the system more user-friendly, but harder to implement, as there are various question types and the system will have to identify the correct one in order to give a sensible answer.
Assigning a question type to the question is a crucial task, the entire answer extraction process relies on finding the correct question type and hence the correct answer type.
In the previous example, the expected output answer is '1st Oct.' In 2002, a group of researchers presented an unpublished and largely unsourced report as a funding support document, in which they describe a 5-year roadmap of research current to the state of the question answering field at that time.[a] QA systems have been extended in recent years to encompass additional domains of knowledge For example, systems have been developed to automatically answer temporal and geospatial questions, questions of definition and terminology, biographical questions, multilingual questions, and questions about the content of audio, images, and video.
AI’s Language Problem
About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent.
Two players take turns putting black or white stones at the intersection of horizontal and vertical lines on a board, trying to surround their opponent’s pieces and remove them from play.
Among several AI techniques, it used an increasingly popular method known as deep learning, which involves mathematical calculations inspired, very loosely, by the way interconnected layers of neurons fire in a brain as it learns to make sense of new information.
AlphaGo’s surprising success points to just how much progress has been made in artificial intelligence over the last few years, after decades of frustration and setbacks often described as an “AI winter.” Deep learning means that machines can increasingly teach themselves how to perform complex tasks that only a couple of years ago were thought to require the unique intelligence of humans.
Deep learning means that machines can increasingly teach themselves how to perform complex tasks that only a couple of years ago were thought to require the unique intelligence of humans.
At companies such as Google, Facebook, and Amazon, as well as at leading academic AI labs, researchers are attempting to finally solve that seemingly intractable problem, using some of the same AI tools—including deep learning—that are responsible for AlphaGo’s success and today’s AI revival.
It will help determine whether we have machines we can easily communicate with—machines that become an intimate part of our everyday life—or whether AI systems remain mysterious black boxes, even as they become more autonomous.
“It’s one of the most obvious things that set human intelligence apart.” Perhaps the same techniques that let AlphaGo conquer Go will finally enable computers to master language, or perhaps something else will also be required.
A math prodigy fascinated with language, he had come to MIT’s new AI lab to study for his PhD, and he decided to build a program that would converse with people, via a text prompt, using everyday language.
Some critics, including the influential linguist and MIT professor Noam Chomsky, felt that the AI researchers would struggle to get machines to understand, given that the mechanics of language in humans were so poorly understood.
Then he created a program,which he named SHRDLU, that was capable of parsing all the nouns, verbs, and simple rules of grammar needed to refer to this stripped-down virtual world.
SHRDLU (a nonsense word formed by the second column of keys on a Linotype machine) could describe the objects, answer questions about their relationships, and make changes to the block world in response to typed commands.
It even had a kind of memory, so that if you told it to move “the red cone” and then later referred to “the cone,” it would assume you meant the red one rather than one of another color.
The problem, as Hubert Dreyfus, a professor of philosophy at UC Berkeley, argued in a 1972 book called What Computers Can’t Do, is that many things humans do require a kind of instinctive intelligence that cannot be captured with hard-and-fast rules.
Crucially, though, neural networks could learn to do things that couldn’t be hand-coded, and later this would prove useful for simple tasks such as recognizing handwritten characters, a skill that was commercialized in the 1990s forreading the numbers on checks.
Researchers at the University of Montreal, led by Yoshua Bengio, and another group at Google, have used this insight to build networks in which each word in a sentence can be used to construct a more complex representation—something that Geoffrey Hinton, a professor at the University of Toronto and a prominent deep-learning researcher who works part-time at Google, calls a “thought vector.” By using two such networks, it is possible to translate between two languages with excellent accuracy.
The purpose of life Sitting in a conference room at the heart of Google’s bustling headquarters in Mountain View, California, one of the company’s researchers who helped develop this approach,Quoc Le, is contemplating the idea of a machine that could hold a proper conversation.
Adapting the system that’s proved useful in translation and image captioning, he and his colleagues built Smart Reply, which reads the contents of Gmail messages and suggests a handful of possible replies.
When Le asked, “How many legs does a cat have?” his system answered, “Four, I think.” Then he tried, “How many legs does a centipede have?” which produced a curious response: “Eight.” Basically, Le’s program has no idea what it’s talking about.
Le asked, “What is the purpose of life?” and the program responded, “To serve the greater good.” By a curious coincidence, Terry Winograd’s next-door neighbor in Palo Alto is someone who might be able to help computers attain a deeper appreciation of what words actually mean.
But Li believes machines need an even more sophisticated understanding of what’s happening in the world, and this year her team released another database of images, annotated in much richer detail.
“We [humans] are terrible at computing with huge data,” she says, “but we’re great at abstraction and creativity.” No one knows how to give machines those human skills—if it is even possible.
“Language builds on other abilities that are probably more basic, that are present in young infants before they have language: perceiving the world visually, acting on our motor systems, understanding the physics of the world or other agents’ goals,” Tenenbaum says.
Goodman and his students have developed a programming language, called Webppl, that can be used to give computers a kind of probabilistic common sense, which turns out to be pretty useful in a conversation.
“And if you want to simulate thoughts, then you should be able to ask a machine what it’s thinking about.” Still, despite the difficulty and complexity of the problem, the startling success that researchers have had using deep-learning techniques to recognize images and excel at games like Go does at least provide hope that we might be on the verge of breakthroughs in language, too.
“But on the other hand, their performance is really hard to understand.” Toyota, which is studying a range of self-driving technologies, has initiated a research project at MIT led by Gerald Sussman, an expert on artificial intelligence and programming language, to develop automated driving systems capable of explaining why they took a particular action.
It was only several days later, after careful analysis, that the Google team made a discovery: by digesting previous games, the program had calculated the chances of a human player making the same move at one in 10,000.
Like in health care, it may be important to know why a decision is being made.” Indeed, as AI systems become increasingly sophisticated and complex, it is hard to envision how we will collaborate with them without language—without being able to ask them, “Why?” More than this, the ability to communicate effortlessly with computers would make them infinitely more useful, and it would feel nothing short of magical.
Slot Machine Q A / FAQ
We receive emails daily asking questions about slot machines.
We generally stick to answers about real live games, but do accept the odd online question.
The list below is only a small sampling, but it does reflect some of the more popular questions readers have submitted.
This includes questions relating to payback percentages, RNG, where and how to purchase a used slot machine, cheating and so on.
Casinos wouldn’t want that anyway – they make the most money when games are entirely random with no element of skill involved.
It sounds like that’s not the case anymore, at least not any more than the weights and/or chips I mentioned above.
The key difference between the two classes is that a class 2 slot machine is connected to a centralized computer system that determines the outcome of each wager.
They’re played and pay out independent from a computer system, and the player’s chances of winning are the same each spin.
Each state will have regulations that determine what class of slot machines casinos or other establishments are allowed to use.
In the UK, manufacturers claim most machines are set at 95 per cent but many pay out less - as low as 70 per cent in certain pubs and at motorway service stations, where the odds are worst of all;
In some states it’s illegal, while others there are rules as to the machine (year, make, model, etc) you’re allowed to have and how it’s used.
According to his research playing in $25 denominations have the lowest casino win (highest return to players).
That’s over the long run though, and doesn’t mean that playing in $25 denominations will pay any better than $10 or $.01 during your visit.
To find more I recommend doing a Google search for ‘coin operated machines.’ Are slots a stupid thing to play at casinos?
If you’re playing slots to have a good time (and you’re well aware that it’s pure luck) then not at all.
The objective of slot machines is to win money by matching symbols on each reel to create a (winning) combination.
Based on the chart he provided, betting in increments of $25 provided the highest payout percentage to players (at about 96%).
But like I said above, this percentage is based on the long term and is not indicative of what you will earn over an afternoon or weekend session.
One question that comes to mind is, do you want to build a physical slot machine or virtual machine that you play on your computer or phone?
If you want to build a real slot machine, one that you could keep and use in your own home, you’ll need a number of parts and tools, and software if you want to build a video slot machine.
 The machine would vibrate if the player won and would payout when a player pressed the payout button (to lessen the chances of theft).
The slot machines have to payout a percentage, which is based on a range provided by each individual state and federal guidelines.
In Nevada you’ll see as much as 99% paid back to players, which depends on where you play (the strip in Vegas is considered the tightest at about 94%).
Instead, the reels are weighted so that the ‘theoretical return’ pays back whatever the casino wants in the long term.
Since slots are 100% luck, there’s no way they can guarantee they’ll win anything, let alone enough to make a living.
His machine was created either in 1887 or 1895, and it has five symbols – horseshoes, diamonds, spades, hearts and a liberty bell.
- On Tuesday, February 25, 2020
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