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The rhetorical point of the Turing Test is that it places the 'test' for 'humanity' in observable outcomes, instead of in internal components. If you would behave the same in interacting with an AI as you would with a person, how could you know the difference between them? But that doesn't mean it's reliable, because intelligence has many different ...

12

Neural networks, deep learning and other supervised learning algorithms do not "take actions" by themselves, they lack agency. However, it is relatively easy to give a machine agency, as far as taking actions is concerned. That is achieved by connecting inputs to some meaningful data source in the environment (such as a camera, or the internet), and ...

11

Utility is a fundamental to Artificial Intelligence because it is the means by which we evaluate an agent's performance in relation to a problem. To distinguish between the concept of economic utility and utility-based computing functions, the term "performance measure" is utilized. The simplest way to distinguish between a goal-based agent and a utility-...

10

The problem of the Turing Test is that it tests the machines ability to resemble humans. Not necessarily every form of AI has to resemble humans. This makes the Turing Test less reliable. However, it is still useful since it is an actual test. It is also noteworthy that there is a prize for passing or coming closest to passing the Turing Test, the Loebner ...

9

Agent The other answer defines an agent as a policy (as it's defined in reinforcement learning). However, although this definition is fine for most current purposes, given that currently agents are mainly used to solve video games, in the real world, an intelligent agent will also need to have a body, which Russell and Norvig call an architecture (section 2....

8

There are no distinguishable hardware examples for each IA class. The same mobile robot architecture with proper sensors can be implemented to behave as any IA class. The way you can determine the class of an intelligent agent is from the way it processes the percept. Based on chapter 2 of Artificial Intelligent: A Modern Approach I will try to give a ...

7

When we use the term rationality in AI, it tends to conform to the game theory/decision theory definition of rational agent. In a solved or tractable game, an agent can have perfect rationality. If the game is intractable, rationality is necessarily bounded. (Here, "game" can be taken to mean any problem.) There is also the issue of imperfect information ...

7

The classical Turing Test certainly does have limitations. Because I don't see it mentioned here yet, I'll suggest you read about The Chinese Room, which is one of the most commonly cited reasons why the Turing Test indeed falls short of ascertaining true 'consciousness'. However, I'd also note that Turing himself, in the original paper that proposed the ...

5

Maybe a good example to think about would be something like the Sphyx story. The wasp in the story appears to behave like a rational being: it seems to have a plan of action, it seems to be able to do advanced operations like counting, and it seems to execute the plan well. However, if you disrupt the wasp's plan, it becomes apparent that it is not thinking ...

5

The agent in RL is the component that makes the decision of what action to take. In order to make that decision, the agent is allowed to use any observation from the environment, and any internal rules that it has. Those internal rules can be anything, but typically in RL, it expects the current state to be provided by the environment, for that state to ...

5

In game AI context: An Agent is a player that plays the game. basically, its a function that gets the current state of the game and returns the next action. A Model is a representation of the game. For example, I have made a Gin-Rummy game + AI-agents. One aspect in the model was the representation of the deck as a $4*13$ matrix, where each entry in the ...

4

"the human mind is a battleground of higher level goals and lower level goals "— Marvin Minsky paraphrasing Sigmund Freud I argue that in general human agents try to maximise a hierarchy of performance measures. performance measures of humans Survival of genetic data Energy supply and Water Sex myriad subgoals.... Mysterious mental mechanisms which ...

4

There are many definitions of Artificial Intelligence out in the wild. All these definitions are part of one (or more) of the areas. There are four main domains, and the picture below will shed some light over this. Turing Test revolves around the left side of the cardinality, which is mostly concerned with how humans think or act. But, we know that this is ...

4

At the moment, what I can think of are the following applications, but there are potentially a lot more. Decision Maker: If you have any problem making a decision, intelligent agents can be used to weight evidence and give you statistics to rule out bad decisions. Online Teacher: In the far future, intelligent agents may acquire human-like skills, they ...

4

Your problem is an old one. There are many methods, referred to as Voice Activity Detection (VAD) methods, which detect speech from an audio signal. The typical design of a VAD algorithm follows one of more of these 3 approaches: A noise reduction stage, e.g. via spectral subtraction. Some features or quantities are calculated from a section of the input ...

4

A simplex reflex agent takes actions based on current situational experiences. For example, if you set your smart bulb to turn on at some given time, let's say at 9 pm, the bulb won't recognize how the time is longer simply because that's the rule defined it follows. A simple reflex agent doesn't compute complex computational problems nor exhibit ...

4

I've read that a simple reflex agent will not act rationally in a lot of environments. E.g. a simple reflex agent can't act rationally when driving a car as it needs previous perceptions to make correct decisions. I wouldn't say that the need for previous perceptions is the reason why a simple reflex agent doesn't act rationally. I'd say the more serious ...

3

Excellent question. Suppose that artificial consciousness does exist in the future. Let's call it Aiwyn (as in “I win” and “AI win”). Now, the question is what will Aiwyn do and why? To answer this question, we need to understand the theory of infinite games. James P. Carse, a professor emeritus of history and literature of religion ...

3

No, there is nothing novel about this system. The main hurdle he had to pass was problems that you will face when your system has a lot of integration points across various APIs provided by different vendors with messy and often outdated documentation. As far as attention is concerned we live in a world where so called celebrities get attention for ...

3

Phrase detection instead of text-to-speech It's worth noting that detection of particular phrases or commands is considered a distinct problem, different from text to speech / text transcription. While you can simply convert everything it hears to text and then look up keywords there, a specialized detector that directly tries to match incoming audio to a ...

3

I don't think there are many contexts where there is any really meaningful distinction between these terms. Even in the WP article you refer to, it is shown that "abstract intelligent agent" and "autonomous intelligent agent" are generally just synonyms for "intelligent agent" but used to highlight certain aspects of intelligent agents in some contexts. ...

3

It depends on how the test is given. For example, when people claimed that a machine had successfully passed the Turing Test a few years ago, the criteria was pretty weak. It only had to fool 30% of the people for 5 minutes. That's not much of a test. To put this in perspective you probably wouldn't detect schizophrenia, autism, learning disabilities, or ...

3

You basically have to degrade the result, assuming that the machine always finds the best move. There are a number of possibilities: restrict the depth of searching. In early chess programs I believe that was the main way of regulating the difficulty. You stop the evaluation of moves after a particular depth in your search tree has been reached. This would ...

3

The acclaimed book Artificial Intelligence: A Modern Approach (by Stuart Russell and Peter Norvig) gives a definition of an agent An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. This definition is illustrated by the following figure This definition (and ...

3

The short answer, i think, is that it cannot. The AI system will only do, and it will only be good at the task that the programmer made it for. Of course you could have an AI that, for example, can trigger a prediction on the input with different models depending on some other variables, but that will still be based on what the programmer wrote, it will ...

3

I think you are looking for quantum machine learning (QML), which is a relatively new field that sits at the intersection of quantum computing and machine learning. If you are not familiar with quantum computing (QC) and you are interested in QML, I suggest that you follow this course by prof. Umesh Vazirani and read the book Quantum Computing for Computer ...

3

A tabular system for agent decisions is a direct and simple map of percept to control choice. For each percept received, the agent looks up the percept and cross-references it to the action it should take. In order to construct this, you need to list all percepts in full detail, with the associated control choice. Clearly that is not going to be feasible for ...

3

Is there a term for the humans who do [machine] learning? Typically you will see "AI researchers" for people studying machine intelligence in general, or "data scientists" for people working with statistics or studying specific solutions in machine learning. Both those terms are used quite flexibly, and generally understood to be ...

2

Is the Turing Test, or any of its variants, a reliable test of artificial intelligence? Myopia Yes, if one defines the term Artificial Intelligence in terms of Alan Turing's Imitation Game or one of its variants. The approach may be, at the same time, both valid and very limited as a definition of intelligence as people interpreted the word before AI ...

2

It rather depends on how one defines several of the terms used. For example: Whether the term "expected" is interpreted in a formal (i.e. statistical) sense. Whether it's assumed that humans have any kind of utilitarian "performance measure". The motivation for this description of "agent" arose from a desire to have a quantitative model - it's not clear ...

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