The AI Business: Commercial Uses of Artificial Intelligence

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Default logics , non-monotonic logics and circumscription [99] are forms of logic designed to help with default reasoning and the qualification problem.

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Several extensions of logic have been designed to handle specific domains of knowledge , such as: description logics ; [87] situation calculus , event calculus and fluent calculus for representing events and time ; [88] causal calculus ; [89] belief calculus; [] and modal logics. Overall, qualitative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty.

Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules. Many problems in AI in reasoning, planning, learning, perception, and robotics require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.

Bayesian networks [] are a very general tool that can be used for a large number of problems: reasoning using the Bayesian inference algorithm , [] learning using the expectation-maximization algorithm , [f] [] planning using decision networks [] and perception using dynamic Bayesian networks. For inference to be tractable, most observations must be conditionally independent of one another.

Complicated graphs with diamonds or other "loops" undirected cycles can require a sophisticated method such as Markov chain Monte Carlo , which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is [ citation needed ]. AdSense uses a Bayesian network with over million edges to learn which ads to serve. A key concept from the science of economics is " utility ": a measure of how valuable something is to an intelligent agent.

Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory , decision analysis , [] and information value theory. The simplest AI applications can be divided into two types: classifiers "if shiny then diamond" and controllers "if shiny then pick up". Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match.

They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as 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. A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree [] is perhaps the most widely used machine learning algorithm.

Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy rather than speed or scalability is the sole concern, conventional wisdom is that discriminative classifiers especially SVM tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets. Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from multiple other neurons, each of which, when activated or "fired" , cast a weighted "vote" for or against whether neuron N should itself activate.

Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm dubbed " fire together, wire together " is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared [j] neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes.

Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and in a mostly self-driving car. The study of non-learning artificial neural networks [] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron , a learning network with a single layer, similar to the old concept of linear regression.

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Caianiello , and others [ citation needed ]. The main categories of networks are acyclic or feedforward neural networks where the signal passes in only one direction and recurrent neural networks which allow feedback and short-term memories of previous input events. Among the most popular feedforward networks are perceptrons , multi-layer perceptrons and radial basis networks.

Today, neural networks are often trained by the backpropagation algorithm, which had been around since as the reverse mode of automatic differentiation published by Seppo Linnainmaa , [] [] and was introduced to neural networks by Paul Werbos. Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber , argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches [ citation needed ].


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One advantage of neuroevolution is that it may be less prone to get caught in "dead ends". Deep learning is any artificial neural network that can learn a long chain of causal links [ dubious — discuss ]. Many deep learning systems need to be able to learn chains ten or more causal links in length. According to one overview, [] the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in [] and gained traction after Igor Aizenberg and colleagues introduced it to artificial neural networks in Lapa in Ivakhnenko's paper [] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks.

In , a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks FNNs one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine , then using supervised backpropagation for fine-tuning. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.

Deep learning often uses convolutional neural networks CNNs , whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind's " AlphaGo Lee", the program that beat a top Go champion in Early on, deep learning was also applied to sequence learning with recurrent neural networks RNNs [] which are in theory Turing complete [] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.

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Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars , and robot soccer as well as conventional games. The "imitation game" an interpretation of the Turing test that assesses whether a computer can imitate a human is nowadays considered too exploitable to be a meaningful benchmark.

As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human.

Best uses of AI and machine learning in business

A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible.

At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity ; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels. AI is relevant to any intellectual task. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles such as drones and self-driving cars , medical diagnosis, creating art such as poetry , proving mathematical theorems, playing games such as Chess or Go , search engines such as Google search , online assistants such as Siri , image recognition in photographs, spam filtering, predicting flight delays, [] prediction of judicial decisions [] and targeting online advertisements. With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution, [] major publishers now use artificial intelligence AI technology to post stories more effectively and generate higher volumes of traffic.

AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high risk patients for population health. The breadth of applications is rapidly increasing. In , a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.

Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer. In detail, there are more than medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover" [ citation needed ]. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient.

One project that is being worked on at the moment is fighting myeloid leukemia , a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers. According to CNN , a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot.

The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed. Watson has struggled to achieve success and adoption in healthcare. Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles.

As of [update] , there are over 30 companies utilizing AI into the creation of driverless cars. Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping.

Together, these systems, as well as high performance computers, are integrated into one complex vehicle. Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.