Monthly Archives: January 2023

Rex Murphy: If CBC cared about diversity, it would host Jordan Peterson global warming talk – National Post

Posted: January 31, 2023 at 5:41 pm

Rex Murphy: If CBC cared about diversity, it would host Jordan Peterson global warming talk  National Post

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Jordan Peterson event in Ottawa that coalition tried to cancel goes off without trouble – Toronto Sun

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Jordan Peterson event in Ottawa that coalition tried to cancel goes off without trouble  Toronto Sun

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Teachers Rejoice! OpenAI Released Tool to Catch ChatGPT Writing

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OpenAI has heard everyone's concerns, and it's finally working on detecting AI-generated writing.

Since its launch, the company's explosive AI chatbot, ChatGPT, has caused shockwaves in many industries due to its skilled writingand coding abilities. The bot has already accomplished several impressive feats, including passing the US Medical Licensing Exam, a Wharton MBA exam, and 4 law school courses.

However, ChatGPT has also caused handwringingamong teachers and other education professionals who say the bot will help students get better at cheating and plagiarism.

On Tuesday, the company launched a web-based program called "AI Text Classifier" to tackle that issue.

The program will flag pasted-in text with the following labels: "very unlikely," "unlikely," "unclear if it is," "possibly," or "likely" AI-generated.

OpenAI admits its tool isn't quite perfect yet: It requires a minimum of 1,000 characters to determine whether text is AI-generated and is prone to making errors.

"These tools will produce both false negatives, where they don't identify AI-generated content as such, and false positives, where they flag human-written content as AI-generated. Additionally, students may quickly learn how to evade detection by modifying some words or clauses in generated content," the company said in a blog post.

OpenAI did not immediately respond to Insider's request for comment.

OpenAI isn't the first to attempt an antidote to the AI-writing conundrum. Plagiarism detector Turnitin told Insider they are working on a similar product to detect AI-generated text from ChatGPT, which a Turnitin executive called a "mad-lib machine."

A 22-year-old college student at Princeton University also released a program to detect AI writing earlier this month called GPTZero.

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Are You Smarter Than ChatGPT? OpenAI Tool Aims to Detect AI-Generated …

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To stop ChatGPT abuse, OpenAI has a new tool that can tell you whether a piece of text is more likely to have come from an AI program or a human.

OpenAI introduced the classifier tool(Opens in a new window) on Tuesday to help crack down on AI-generated text thats being passed off as coming from a human. The problem has become especially pronounced in education with students exploiting ChatGPT to complete homework assignments.

In a blog post(Opens in a new window), OpenAI also noted that AI-generated text could pose a threat when used to generate automated text for misinformation campaigns.

The classifier tool is pretty straightforward: Submit some text at least 1,000 characters in length, and OpenAI will tell you the likelihood that it came from an AI program or a human. The tool was created by training a computer model to discern between pairs of human-written text and AI-written text on the same topic.

(Credit: OpenAI)

However, the San Francisco lab admits the classifier is far from perfect. In our evaluations on a challenge set of English texts, our classifier correctly identifies 26% of AI-written text (true positives) as likely AI-written, while incorrectly labeling human-written text as AI-written 9% of the time (false positives), OpenAI wrote.

The classifiers reliability also drops when examining short snippets or a few paragraphs of text. So the tool will only work on submissions at over 1,000 characters in length. In addition, sometimes human-written text will be incorrectly but confidently labeled as AI-written by our classifier, OpenAI noted.

Additionally, students may quickly learn how to evade detection by modifying some words or clauses in generated content, the lab said.

Hence, OpenAI says the tool should not be used as a primary decision-making tool when it comes to determining whether a piece of text is AI-generated or not. But despite all the limitations, OpenAI decided to release the tool as it works on mitigations to prevent students from exploiting ChatGPT for cheating purposes.

Were making this classifier publicly available to get feedback(Opens in a new window) on whether imperfect tools like this one are useful, OpenAI added. Our work on the detection of AI-generated text will continue, and we hope to share improved methods in the future.

An example of an essay ChatGPT can write.(Credit: Open AI)

Launched in November, ChatGPT is so advanced that the AI program can write essays, poems, and answer test questions on a wide variety of topics within seconds. While the quality of the responses can be shaky, researchers have found that ChatGPT is smart enough to (barely) pass an MBA exam and even fix numerous bugs in computer code.

Inevitably, the emergence of ChatGPT has sparked questions over whether the same program threatens to undermine education and disrupt white-collar jobs. Already, some schools and teachers have decided to block the AI program on their networks over concerns that ChatGPT makes cheating on homework and tests all too easy. Meanwhile, some third parties have released their own free(Opens in a new window) tools(Opens in a new window) to help educators ferret out AI-generated text.

OpenAI said its trying to address the cheating problems by reaching out to teachers in the US to learn about their experiences.The lab has also published a document(Opens in a new window) with advice on how educators could introduce and oversee ChatGPT use in their classrooms safely.

But OpenAI also noted its still too early to say how programs like ChatGPT will affect education and society. To date we have seen instances of productivity improvements that transform jobs, job displacement, and job creation, but both the near and long term net effects are unclear, it said.

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What is Artificial Intelligence (AI) ? | IBM

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While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004paper(PDF, 106 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish,Artificial Intelligence: A Modern Approach(link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Ideal approach:

Alan Turings definition would have fallen under the category of systems that act like humans.

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted inGartners hype cycle(link resides outside IBM), product innovations like, self-driving cars and personal assistants, follow a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovations relevance and role in a market or domain. As Lex Fridman noteshere(01:08:05) (link resides outside IBM) in his MIT lecture in 2019, we are at the peak of inflated expectations, approaching the trough of disillusionment.

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation aroundAI ethics, read morehere.

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The generative AI revolution has begunhow did we get here?

Posted: at 5:34 pm

Enlarge / This image was partially AI-generated with the prompt "a pair of robot hands holding pencils drawing a pair of human hands, oil painting, colorful," inspired by the classic M.C. Escher drawing. Watching AI mangle drawing hands helps us feel superior to the machines... for now. Aurich

Aurich Lawson | Stable Diffusion

Progress in AI systems often feels cyclical. Every few years, computers can suddenly do something theyve never been able to do before. Behold! the AI true believers proclaim, the age of artificial general intelligence is at hand! Nonsense! the skeptics say. Remember self-driving cars?

The truth usually lies somewhere in between.

Were in another cycle, this time with generative AI. Media headlines are dominated by news about AI art, but theres also unprecedented progress in many widely disparate fields. Everything from videos to biology, programming, writing, translation, and more is seeing AI progress at the same incredible pace.

Theres a reason all of this has come at once. The breakthroughs are all underpinned by a new class of AI models that are more flexible and powerful than anything that has come before. Because they were first used for language tasks like answering questions and writing essays, theyre often known as large language models (LLMs). OpenAIs GPT3, Googles BERT, and so on are all LLMs.

But these models are extremely flexible and adaptable. The same mathematical structures have been so useful in computer vision, biology, and more that some researchers have taken to calling them "foundation models" to better articulate their role in modern AI.

Where did these foundation models came from, and how have they broken out beyond language to drive so much of what we see in AI today?

Theres a holy trinity in machine learning: models, data, and compute. Models are algorithms that take inputs and produce outputs. Data refers to the examples the algorithms are trained on. To learn something, there must be enough data with enough richness that the algorithms can produce useful output. Models must be flexible enough to capture the complexity in the data. And finally, there has to be enough computing power to run the algorithms.

The first modern AI revolution took place with deep learning in 2012, when solving computer vision problems with convolutional neural networks (CNNs) took off. CNNs are similar in structure to the brain's visual cortex. Theyve been around since the 1990s but werent yet practical due to their intense computing power requirements.

In 2006, though, Nvidia released CUDA, a programming language that allowed for the use of GPUs as general-purpose supercomputers. In 2009, Stanford AI researchers introduced Imagenet, a collection of labeled images used to train computer vision algorithms. In 2012, AlexNet combined CNNs trained on GPUs with Imagenet data to create the best visual classifier the world had ever seen. Deep learning and AI exploded from there.

CNNs, the ImageNet data set, and GPUs were a magic combination that unlocked tremendous progress in computer vision. 2012 set off a boom of excitement around deep learning and spawned whole industries, like those involved in autonomous driving. But we quickly learned there were limits to that generation of deep learning. CNNs were great for vision, but other areas didnt have their model breakthrough. One huge gap was in natural language processing (NLP)i.e., getting computers to understand and work with normal human language rather than code.

The problem of understanding and working with language is fundamentally different from that of working with images. Processing language requires working with sequences of words, where order matters. A cat is a cat no matter where it is in an image, but theres a big difference between this reader is learning about AI and AI is learning about this reader.

Until recently, researchers relied on models like recurrent neural networks (RNNs) and long short-term memory (LSTM) to process and analyze data in time. These models were effective at recognizing short sequences, like spoken words from short phrases, but they struggled to handle longer sentences and paragraphs. The memory of these models was just not sophisticated enough to capture the complexity and richness of ideas and concepts that arise when sentences are combined into paragraphs and essays. They were great for simple Siri- and Alexa-style voice assistants but not for much else.

Getting the right training data was another challenge. ImageNet was a collection of one hundred thousand labeled images that required significant human effort to generate, mostly by grad students and Amazon Mechanical Turk workers. And ImageNet was actually inspired by and modeled on an older project called WordNet, which tried to create a labeled data set for English vocabulary. While there is no shortage of text on the Internet, creating a meaningful data set to teach a computer to work with human language beyond individual words is incredibly time-consuming. And the labels you create for one application on the same data might not apply to another task.

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Customers And Rental Car Firms Are At OddsHere’s How AI Can Help Them Trust Each Other – Forbes

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Customers And Rental Car Firms Are At OddsHere's How AI Can Help Them Trust Each Other  Forbes

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Genetics | History, Biology, Timeline, & Facts | Britannica

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Top Questions

What is genetics?

Genetics is the study ofheredityin general and ofgenesin particular. Genetics forms one of the central pillars ofbiologyand overlaps with many other areas, such as agriculture,medicine, andbiotechnology.

Is intelligence genetic?

Intelligenceis a very complex human trait, the genetics of which has been a subject of controversy for some time. Even roughly measured via diverse cognitive tests, intelligence shows a strong contribution from the environment.

How is genetic testing done?

Genetic testing typically is issued only after a medical history, a physical examination, and the construction of a family pedigree documenting familialgenetic diseaseshave been considered. The genetic tests themselves are carried out using chemical, radiological, histopathologic, and electrodiagnostic procedures. Genetic testing may involve cytogenetic analyses to investigate chromosomes, molecular assays to investigate genes and DNA, or biochemical assays to investigateenzymes,hormones, oramino acids.

Summary

genetics, study of heredity in general and of genes in particular. Genetics forms one of the central pillars of biology and overlaps with many other areas, such as agriculture, medicine, and biotechnology.

Since the dawn of civilization, humankind has recognized the influence of heredity and applied its principles to the improvement of cultivated crops and domestic animals. A Babylonian tablet more than 6,000 years old, for example, shows pedigrees of horses and indicates possible inherited characteristics. Other old carvings show cross-pollination of date palm trees. Most of the mechanisms of heredity, however, remained a mystery until the 19th century, when genetics as a systematic science began.

Genetics arose out of the identification of genes, the fundamental units responsible for heredity. Genetics may be defined as the study of genes at all levels, including the ways in which they act in the cell and the ways in which they are transmitted from parents to offspring. Modern genetics focuses on the chemical substance that genes are made of, called deoxyribonucleic acid, or DNA, and the ways in which it affects the chemical reactions that constitute the living processes within the cell. Gene action depends on interaction with the environment. Green plants, for example, have genes containing the information necessary to synthesize the photosynthetic pigment chlorophyll that gives them their green colour. Chlorophyll is synthesized in an environment containing light because the gene for chlorophyll is expressed only when it interacts with light. If a plant is placed in a dark environment, chlorophyll synthesis stops because the gene is no longer expressed.

Genetics as a scientific discipline stemmed from the work of Gregor Mendel in the middle of the 19th century. Mendel suspected that traits were inherited as discrete units, and, although he knew nothing of the physical or chemical nature of genes at the time, his units became the basis for the development of the present understanding of heredity. All present research in genetics can be traced back to Mendels discovery of the laws governing the inheritance of traits. The word genetics was introduced in 1905 by English biologist William Bateson, who was one of the discoverers of Mendels work and who became a champion of Mendels principles of inheritance.

Although scientific evidence for patterns of genetic inheritance did not appear until Mendels work, history shows that humankind must have been interested in heredity long before the dawn of civilization. Curiosity must first have been based on human family resemblances, such as similarity in body structure, voice, gait, and gestures. Such notions were instrumental in the establishment of family and royal dynasties. Early nomadic tribes were interested in the qualities of the animals that they herded and domesticated and, undoubtedly, bred selectively. The first human settlements that practiced farming appear to have selected crop plants with favourable qualities. Ancient tomb paintings show racehorse breeding pedigrees containing clear depictions of the inheritance of several distinct physical traits in the horses. Despite this interest, the first recorded speculations on heredity did not exist until the time of the ancient Greeks; some aspects of their ideas are still considered relevant today.

Hippocrates (c. 460c. 375 bce), known as the father of medicine, believed in the inheritance of acquired characteristics, and, to account for this, he devised the hypothesis known as pangenesis. He postulated that all organs of the body of a parent gave off invisible seeds, which were like miniaturized building components and were transmitted during sexual intercourse, reassembling themselves in the mothers womb to form a baby.

Aristotle (384322 bce) emphasized the importance of blood in heredity. He thought that the blood supplied generative material for building all parts of the adult body, and he reasoned that blood was the basis for passing on this generative power to the next generation. In fact, he believed that the males semen was purified blood and that a womans menstrual blood was her equivalent of semen. These male and female contributions united in the womb to produce a baby. The blood contained some type of hereditary essences, but he believed that the baby would develop under the influence of these essences, rather than being built from the essences themselves.

Aristotles ideas about the role of blood in procreation were probably the origin of the still prevalent notion that somehow the blood is involved in heredity. Today people still speak of certain traits as being in the blood and of blood lines and blood ties. The Greek model of inheritance, in which a teeming multitude of substances was invoked, differed from that of the Mendelian model. Mendels idea was that distinct differences between individuals are determined by differences in single yet powerful hereditary factors. These single hereditary factors were identified as genes. Copies of genes are transmitted through sperm and egg and guide the development of the offspring. Genes are also responsible for reproducing the distinct features of both parents that are visible in their children.

In the two millennia between the lives of Aristotle and Mendel, few new ideas were recorded on the nature of heredity. In the 17th and 18th centuries the idea of preformation was introduced. Scientists using the newly developed microscopes imagined that they could see miniature replicas of human beings inside sperm heads. French biologist Jean-Baptiste Lamarck invoked the idea of the inheritance of acquired characters, not as an explanation for heredity but as a model for evolution. He lived at a time when the fixity of species was taken for granted, yet he maintained that this fixity was only found in a constant environment. He enunciated the law of use and disuse, which states that when certain organs become specially developed as a result of some environmental need, then that state of development is hereditary and can be passed on to progeny. He believed that in this way, over many generations, giraffes could arise from deerlike animals that had to keep stretching their necks to reach high leaves on trees.

British naturalist Alfred Russel Wallace originally postulated the theory of evolution by natural selection. However, Charles Darwins observations during his circumnavigation of the globe aboard the HMS Beagle (183136) provided evidence for natural selection and his suggestion that humans and animals shared a common ancestry. Many scientists at the time believed in a hereditary mechanism that was a version of the ancient Greek idea of pangenesis, and Darwins ideas did not appear to fit with the theory of heredity that sprang from the experiments of Mendel.

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Human genetics | Description, Chromosomes, & Inheritance

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human genetics, study of the inheritance of characteristics by children from parents. Inheritance in humans does not differ in any fundamental way from that in other organisms.

The study of human heredity occupies a central position in genetics. Much of this interest stems from a basic desire to know who humans are and why they are as they are. At a more practical level, an understanding of human heredity is of critical importance in the prediction, diagnosis, and treatment of diseases that have a genetic component. The quest to determine the genetic basis of human health has given rise to the field of medical genetics. In general, medicine has given focus and purpose to human genetics, so the terms medical genetics and human genetics are often considered synonymous.

A new era in cytogenetics, the field of investigation concerned with studies of the chromosomes, began in 1956 with the discovery by Jo Hin Tjio and Albert Levan that human somatic cells contain 23 pairs of chromosomes. Since that time the field has advanced with amazing rapidity and has demonstrated that human chromosome aberrations rank as major causes of fetal death and of tragic human diseases, many of which are accompanied by intellectual disability. Since the chromosomes can be delineated only during mitosis, it is necessary to examine material in which there are many dividing cells. This can usually be accomplished by culturing cells from the blood or skin, since only the bone marrow cells (not readily sampled except during serious bone marrow disease such as leukemia) have sufficient mitoses in the absence of artificial culture. After growth, the cells are fixed on slides and then stained with a variety of DNA-specific stains that permit the delineation and identification of the chromosomes. The Denver system of chromosome classification, established in 1959, identified the chromosomes by their length and the position of the centromeres. Since then the method has been improved by the use of special staining techniques that impart unique light and dark bands to each chromosome. These bands permit the identification of chromosomal regions that are duplicated, missing, or transposed to other chromosomes.

Micrographs showing the karyotypes (i.e., the physical appearance of the chromosome) of a male and a female have been produced. In a typical micrograph the 46 human chromosomes (the diploid number) are arranged in homologous pairs, each consisting of one maternally derived and one paternally derived member. The chromosomes are all numbered except for the X and the Y chromosomes, which are the sex chromosomes. In humans, as in all mammals, the normal female has two X chromosomes and the normal male has one X chromosome and one Y chromosome. The female is thus the homogametic sex, as all her gametes normally have one X chromosome. The male is heterogametic, as he produces two types of gametesone type containing an X chromosome and the other containing a Y chromosome. There is good evidence that the Y chromosome in humans, unlike that in Drosophila, is necessary (but not sufficient) for maleness.

A human individual arises through the union of two cells, an egg from the mother and a sperm from the father. Human egg cells are barely visible to the naked eye. They are shed, usually one at a time, from the ovary into the oviducts (fallopian tubes), through which they pass into the uterus. Fertilization, the penetration of an egg by a sperm, occurs in the oviducts. This is the main event of sexual reproduction and determines the genetic constitution of the new individual.

Human sex determination is a genetic process that depends basically on the presence of the Y chromosome in the fertilized egg. This chromosome stimulates a change in the undifferentiated gonad into that of the male (a testicle). The gonadal action of the Y chromosome is mediated by a gene located near the centromere; this gene codes for the production of a cell surface molecule called the H-Y antigen. Further development of the anatomic structures, both internal and external, that are associated with maleness is controlled by hormones produced by the testicle. The sex of an individual can be thought of in three different contexts: chromosomal sex, gonadal sex, and anatomic sex. Discrepancies between these, especially the latter two, result in the development of individuals with ambiguous sex, often called hermaphrodites. Homosexuality is unrelated to the above sex-determining factors. It is of interest that in the absence of a male gonad (testicle) the internal and external sex anatomy is always female, even in the absence of a female ovary. A female without ovaries will, of course, be infertile and will not experience any of the female developmental changes normally associated with puberty. Such a female will often have Turner syndrome.

If X-containing and Y-containing sperm are produced in equal numbers, then according to simple chance one would expect the sex ratio at conception (fertilization) to be half boys and half girls, or 1 : 1. Direct observation of sex ratios among newly fertilized human eggs is not yet feasible, and sex-ratio data are usually collected at the time of birth. In almost all human populations of newborns, there is a slight excess of males; about 106 boys are born for every100 girls. Throughout life, however, there is a slightly greater mortality of males; this slowly alters the sex ratio until, beyond the age of about 50 years, there is an excess of females. Studies indicate that male embryos suffer a relatively greater degree of prenatal mortality, so the sex ratio at conception might be expected to favour males even more than the 106 : 100 ratio observed at birth would suggest. Firm explanations for the apparent excess of male conceptions have not been established; it is possible that Y-containing sperm survive better within the female reproductive tract, or they may be a little more successful in reaching the egg in order to fertilize it. In any case, the sex differences are small, the statistical expectation for a boy (or girl) at any single birth still being close to one out of two.

During gestationthe period of nine months between fertilization and the birth of the infanta remarkable series of developmental changes occur. Through the process of mitosis, the total number of cells changes from 1 (the fertilized egg) to about 2 1011. In addition, these cells differentiate into hundreds of different types with specific functions (liver cells, nerve cells, muscle cells, etc.). A multitude of regulatory processes, both genetically and environmentally controlled, accomplish this differentiation. Elucidation of the exquisite timing of these processes remains one of the great challenges of human biology.

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Genetic testing – Mayo Clinic

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Overview

Genetic testing involves examining your DNA, the chemical database that carries instructions for your body's functions. Genetic testing can reveal changes (mutations) in your genes that may cause illness or disease.

Although genetic testing can provide important information for diagnosing, treating and preventing illness, there are limitations. For example, if you're a healthy person, a positive result from genetic testing doesn't always mean you will develop a disease. On the other hand, in some situations, a negative result doesn't guarantee that you won't have a certain disorder.

Talking to your doctor, a medical geneticist or a genetic counselor about what you will do with the results is an important step in the process of genetic testing.

When genetic testing doesn't lead to a diagnosis but a genetic cause is still suspected, some facilities offer genome sequencing a process for analyzing a sample of DNA taken from your blood.

Everyone has a unique genome, made up of the DNA in all of a person's genes. This complex testing can help identify genetic variants that may relate to your health. This testing is usually limited to just looking at the protein-encoding parts of DNA called the exome.

Genetic testing plays a vital role in determining the risk of developing certain diseases as well as screening and sometimes medical treatment. Different types of genetic testing are done for different reasons:

Generally genetic tests have little physical risk. Blood and cheek swab tests have almost no risk. However, prenatal testing such as amniocentesis or chorionic villus sampling has a small risk of pregnancy loss (miscarriage).

Genetic testing can have emotional, social and financial risks as well. Discuss all risks and benefits of genetic testing with your doctor, a medical geneticist or a genetic counselor before you have a genetic test.

Before you have genetic testing, gather as much information as you can about your family's medical history. Then, talk with your doctor or a genetic counselor about your personal and family medical history to better understand your risk. Ask questions and discuss any concerns about genetic testing at that meeting. Also, talk about your options, depending on the test results.

If you're being tested for a genetic disorder that runs in families, you may want to consider discussing your decision to have genetic testing with your family. Having these conversations before testing can give you a sense of how your family might respond to your test results and how it may affect them.

Not all health insurance policies pay for genetic testing. So, before you have a genetic test, check with your insurance provider to see what will be covered.

In the United States, the federal Genetic Information Nondiscrimination Act of 2008 (GINA) helps prevent health insurers or employers from discriminating against you based on test results. Under GINA, employment discrimination based on genetic risk also is illegal. However, this act does not cover life, long-term care or disability insurance. Most states offer additional protection.

Depending on the type of test, a sample of your blood, skin, amniotic fluid or other tissue will be collected and sent to a lab for analysis.

The amount of time it takes for you to receive your genetic test results depends on the type of test and your health care facility. Talk to your doctor, medical geneticist or genetic counselor before the test about when you can expect the results and have a discussion about them.

If the genetic test result is positive, that means the genetic change that was being tested for was detected. The steps you take after you receive a positive result will depend on the reason you had genetic testing.

If the purpose is to:

Talk to your doctor about what a positive result means for you. In some cases, you can make lifestyle changes that may reduce your risk of developing a disease, even if you have a gene that makes you more susceptible to a disorder. Results may also help you make choices related to treatment, family planning, careers and insurance coverage.

In addition, you may choose to participate in research or registries related to your genetic disorder or condition. These options may help you stay updated with new developments in prevention or treatment.

A negative result means a mutated gene was not detected by the test, which can be reassuring, but it's not a 100 percent guarantee that you don't have the disorder. The accuracy of genetic tests to detect mutated genes varies, depending on the condition being tested for and whether or not the gene mutation was previously identified in a family member.

Even if you don't have the mutated gene, that doesn't necessarily mean you'll never get the disease. For example, the majority of people who develop breast cancer don't have a breast cancer gene (BRCA1 or BRCA2). Also, genetic testing may not be able to detect all genetic defects.

In some cases, a genetic test may not provide helpful information about the gene in question. Everyone has variations in the way genes appear, and often these variations don't affect your health. But sometimes it can be difficult to distinguish between a disease-causing gene and a harmless gene variation. These changes are called variants of uncertain significance. In these situations, follow-up testing or periodic reviews of the gene over time may be necessary.

No matter what the results of your genetic testing, talk with your doctor, medical geneticist or genetic counselor about questions or concerns you may have. This will help you understand what the results mean for you and your family.

Explore Mayo Clinic studies of tests and procedures to help prevent, detect, treat or manage conditions.

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Genetic testing - Mayo Clinic

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