User:EmmanuelB12/sandbox

Planning
Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future a representation of the state of the world and be able to make predictions about how their actions will change it and be able to make choices that maximize the utility or value of available choices.

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions. 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.

Learning
Machine learning (ML), a fundamental concept of AI research since the field's inception is the study of computer algorithms that improve automatically through experience.

unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess learners by computational complexity, by sample complexity algorithm which represent the number of training-samples that it needs in order to successfully learn a target function, or by other notions of optimization In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

AI Randomness

rando mness is necessary to achieve generality. Right now AIs are on the basis of seeking pattern and use them to predict future moves or outcomes. If we don't include randomness in data then machine might consider that as pattern and behave according to that and this would turnout to be a bias for us. Generating random numbers is a different story in itself and won't be a criterion alone to judge.

Compounding pharmacy
Main article: Compounding

Compounding involves preparing drugs in forms that are different from the generic prescription standard. This may include altering the strength, ingredients, or dosage form. Compounding is a way to create custom drugs for patients who may not be able to take the medication in its standard form, such as due to an allergy or difficulty swallowing. Compounding is necessary for these patients to still be able to properly get the prescriptions they need.

One area of compounding is preparing drugs in new dosage forms. For example, if a drug manufacturer only provides a drug as a tablet, a compounding pharmacist might make a medicated lollipop that contains the drug. Patients who have difficulty swallowing the tablet may prefer to suck the medicated lollipop instead.

Another form of compounding is by mixing different strengths (g, mg, mcg) of capsules or tablets to yield the desired amount of medication indicated by the physician, physician assistant, Nurse Practitioner, or clinical pharmacist practitioner. This form of compounding is found at community or hospital pharmacies or in-home administration therapy.

Compounding pharmacies specialize in compounding, although many also dispense the same non-compounded drugs that patients can obtain from community pharmacies.

Separation of prescribing and dispensing
Separation of prescribing and dispensing, also called dispensing separation, is a practice in medicine and pharmacy in which the physician who provides a medical prescription is independent from the pharmacist who provides the prescription drug.

In the Western world there are centuries of tradition for separating pharmacists from physicians. In Asian countries, it is traditional for physicians to also provide drugs.

In contemporary time researchers and health policy analysts have more deeply considered these traditions and their effects. Advocates for separation and advocates for combining make similar claims for each of their conflicting perspectives, saying that separating or combining reduces conflict of interest in the healthcare industry, unnecessary health care, and lowers costs, while the opposite causes those things. Research in various places reports mixed outcomes in different circumstances.

Downside of pharmacist work
1) Prolonged Standing 2) Long Hours 3) Inadequate or no relief help 4) Poorly written presciptions which increase likelihood of dispensing errors. 5) Armed Robbery 6) Frequently, the information for the pharmacist to trust is not true regarding the properties of the drugs. For someone who spends 5 or 6 years in scientific study there is probably no other profession where what one takes for knowledge is in reality marketing ploys. 7) Theft by other personnel of pharmacy stock to which the pharmacist is at risk of criminal prosecution. 8) There is no uniform standard to evaluate fairly the error rates by pharmacists, therefore it has/is a management tool to terminate employees rather than a protective mechanism for the public. 9) Uncertainty as to the market for pharmacists. Changing the entry level degree in the United States from a 5 year to a 6 year program decreased the availability of pharmacists, which tended to increase the error rates, that justifies automated pharmacies to replace pharmacists.