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+A quip in Tesler's Theorem says "AI is whatever hasn't been done yet. These issues have been explored by myth, fiction and philosophy since antiquity. Marvin Minsky agreed, writing, "within a generation ... They failed to recognize the difficulty of some of the remaining tasks. By 1985, the market for AI had reached over a billion dollars. In 2011, a Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. No. 1 ranking for two years. Goals can be explicitly defined or induced. AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. These learners could therefore derive all possible knowledge, by considering every possible hypothesis and matching them against the data. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.[c]
+ This gives rise to two classes of models: structuralist and functionalist. The functional model refers to the correlating data to its computed counterpart.
+ The general problem of simulating (or creating) intelligence has been broken down into sub-problems. The traits described below have received the most attention.
+ they became exponentially slower as the problems grew larger. They solve most of their problems using fast, intuitive judgments.
+ However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment.
+ Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.
+ Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input. AI is heavily used in robotics. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Moravec's paradox can be extended to many forms of social intelligence. Many advances have general, cross-domain significance. Researchers disagree about many issues. This includes embodied, situated, behavior-based, and nouvelle AI. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible. A few of the most general of these methods are discussed below.
+ The result is a search that is too slow or never completes. Heuristics limit the search for solutions into a smaller sample size.
+ Other optimization algorithms are simulated annealing, beam search and random optimization.
+ Evolutionary computation uses a form of optimization search. Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming. Logic is used for knowledge representation and problem solving, but it can be applied to other problems as well. Propositional logic involves truth functions such as "or" and "not". that are too linguistically imprecise to be completely true or false. Bayesian networks are a very general tool that can be used for various problems: For inference to be tractable, most observations must be conditionally independent of one another. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class is a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.
+ kernel methods such as the support vector machine (SVM),[h]
+ Gaussian mixture model, and the extremely popular naive Bayes classifier.[i] Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".
+ In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. RNNs can be trained by gradient descent but suffer from the vanishing gradient problem. LSTM is often trained by Connectionist Temporal Classification (CTC). There is no consensus on how to characterize which tasks AI tends to excel at. Games provide a well-publicized benchmark for assessing rates of progress. E-sports such as StarCraft continue to provide additional public benchmarks. A computer asks a user to complete a simple test then generates a grade for that test. AI is relevant to any intellectual task. AI can also produce Deepfakes, a content-altering technology. The breadth of applications is rapidly increasing.
+ Artificial intelligence is assisting doctors. There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. Watson has struggled to achieve success and adoption in healthcare.
+ A few companies involved with AI include Tesla, Google, and Apple.
+ Many components contribute to the functioning of self-driving cars. Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. In general, the vehicle would be pre-programmed with a map of the area being driven. Another factor that is influencing the ability of a driverless automobile is the safety of the passenger. These situations could include a head-on collision with pedestrians. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers. The programming of the car in these situations is crucial to a successful driverless automobile.
+ AI can react to changes overnight or when business is not taking place. AI is increasingly being used by corporations. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient[citation needed]. Artificial intelligence in government consists of applications and regulation. This is already the case in some parts of China. In addition, well-understood AI techniques are routinely used for pathfinding. For financial statements audit, AI makes continuous audit possible. There are three philosophical questions related to AI[citation needed]:
+ And, of course, other risks come from things like job losses. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded. If this AI's goals do not fully reflect humanity's— Facebook I think there is potentially a dangerous outcome there."
+ Algorithms already have numerous applications in legal systems. The relationship between automation and employment is complicated. In all cases, only human beings have engaged in ethical reasoning. Machine ethics is sometimes referred to as machine morality, computational ethics or computational morality. He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence. Some question whether this kind of check could actually remain in place.
+ The hard problem is explaining how this feels or why it should feel like anything at all. Human information processing is easy to explain, however human subjective experience is difficult to explain.
+ The hard problem is that people also know something else—they also know what red looks like. (Consider that a person born blind can know that something is red without knowing what red looks like.)[l] If a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? Are there limits to how intelligent machines—or human-machine hybrids—can be? Science fiction writer Vernor Vinge named this scenario "singularity". The long-term economic effects of AI are uncertain. This includes such works as Arthur C. Clarke's and Stanley Kubrick's 2001: See also: Logic machines in fiction and List of fictional computers
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