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Q. How much is the 3 year tuition at Stanford Law?Cost of Attendance | Stanford Law SchoolTuition Top Law SchoolsPriciest Public Law SchoolsStanford Law School Financial Aid7 Ways to Figure If Going to Law School Is Worth It (Bankrate)Is Going to Law School Worth It? Depends on Where (Investopedia) - Bureau of Labor Statistics in 2015 had the top 10% of lawyers earning more than $187,200. All told, it is not uncommon for a law school graduate to enter the working world with a net worth of negative $250,000. Ideally, recent graduates should earn yearly salaries equal to or greater than their total student debt. This level of pay usually allows for paying off student loans within 10 years without materially affecting a person's lifestyle.Cost of Attendance | Stanford Law SchoolTuitionTuition for 2016-17 totals $56,079. Tuition is due in October, January, and April.Cost of LivingFor single students, room and board in university residences costs approximately $23,205 for the 2016-17school year. Required books, if purchased new, cost about $1,530 a year. Local costs for transportation, clothing, recreation, and the like tend to vary. Most single students find they need to budget a total of at least $88,169 for the school year.Laptop ComputersStudents are required to have laptop computers and are advised to have a printer and modem, as well. In addition to using the laptop to write papers and create spreadsheets, students will have the opportunity to take examinations on laptops and will receive administrative notices only via electronic mail. Students may purchase a laptop from the Stanford Bookstore through a special program with the law school. Students who must purchase a laptop to meet this requirement are eligible to have their financial aid budgets increased by as much as $3,300. Only specific computers meet the needs of the law school’s computing systems.Do the math!JD Financial Support | Stanford Law SchoolFinancial Aid Yellow Ribbon Program FormsLoan Repayment Assistance Program (LRAP)CSS Profile ApplicationFree Application for Federal Student Aid (FAFSA)Financial AidStanford Law offers financial aid is to assist students who would otherwise be unable to pursue a legal education at SLS. Approximately 78 percent of the student body receives a tuition fellowship or loan assistance, with the average fellowship portion per recipient totaling about $23,000 annually. Aid is awarded on the basis of demonstrated need and is provided through a combination of tuition fellowships, government guaranteed loans and private loans.To apply for financial aid, please complete the Free Application for Federal Student Aid (FAFSA) and the CSS Profile application. The FAFSA should be completed as soon as possible after January 1. A service of the U.S. Department of Education, FAFSA is free to all applicants. Please note that the Title IV School Code for Stanford Law School is E00341 and our CSS Profile code is 7832. To receive an aid package prior to the May 1 admissions response deadline, you must complete both applications by March 15, 2017.In general, the financial award system operates as follows:Each year the school determines a standard budget to cover basic costs (tuition plus living expenses).Each student’s need is calculated by subtracting reported resources from the standard budget. These resources include one–third of reported assets; 57% of summer gross earnings over $6,000; assumed earnings of spouse (if married); and an imputed parental contribution (based on the CSS Profile analysis) if the student is dependent.Each student is then expected to borrow or otherwise raise a portion of this need, with the remainder being an outright grant, subject to a limit of full tuition.Financial aid is evaluated annually. Therefore, one cannot be guaranteed the same level of aid over three years of attendance.Stanford Law School uses an age-based test to determine the dependency percentage from your parent contribution. Unless you are 29 years of age as of September 1, financial resource information from your parents must be submitted on the CSS Profile application. Parental information is never required on the FAFSAfor graduate students.Under our policy guidelines, the following rules apply:If you are 25 or younger as of September 1, we will take into consideration the full extent of our calculated parental contribution when determining your eligibility for our need-based scholarship assistance.If you are 26 as of September 1, we will protect 25% of your calculated parental contribution and use only 75% of that contribution when determining your eligibility for our need-based scholarship assistance.If you are 27 as of September 1, we will protect 50% of your calculated parental contribution and use only 50% of that contribution when determining your eligibility for our need-based scholarship assistance.If you are 28 as of September 1, we will protect 75% of your calculated parental contribution and use only 25% of that contribution when determining your eligibility for our need-based scholarship assistance.If you are 29 as of September 1, no parental resources are considered when determining your eligibility for our need-based scholarship assistance. Therefore, you need not submit any parental financial information to CSS Profile.Loans available to law students come primarily from three governmental programs: Federal Perkins Loans, Federal Direct Unsubsidized Stafford Loans, and Federal Direct Graduate Plus Loans. All graduate and professional students are independent for purposes of determining federal loan eligibility.Additional financial aid information is provided in the School’s Financial Aid Handbook.2016-17 FINANCIAL AID HANDBOOKYellow Ribbon ProgramThe Post-9/11 GI Bill, also known as Chapter 33, is the most commonly used VA educational benefits program at Stanford. This program provides funding for tuition, required fees, books, and housing. The level of a qualifying veteran’s Chapter 33 benefits is determined by the length of military service since 9/11/2001. For the 2016-17 academic year, the base benefit for tuition and fees is capped at $21,970.If you qualify for Chapter 33 benefits at the 100% level, you will receive additional funding through the Yellow Ribbon Program. Under this program, Stanford Law provides an annual contribution to supplement the Chapter 33 base tuition benefit. The VA matches Stanford’s Yellow Ribbon contribution. For the 2016-17 academic year, Stanford Law’s annual Yellow Ribbon contribution for students will be 50% of the remaining tuition and fees with the VA providing the other 50% — together covering the full costs of tuition and fees.Most VA educational benefit programs pay benefits directly to students on a monthly basis. However, under the Post-9/11 GI Bill (Chapter 33), the VA sends tuition and fees benefits to Stanford, where the Central Financial Aid Office is responsible for applying the funds to the student account (university bill). Chapter 33 books and housing benefits are sent directly to students monthly. You may need to apply your housing benefits to the university bill to pay for on-campus room and board.FormsThe following forms will be provided by the Office of Financial Aid as necessary but are available here if additional copies are needed:Continuing Student Financial Aid Supplement Form 2016-17Loan Memo 2016-2017Loan Comparison Chart 2016-2017Loan Request Form 2016-2017Expense Budget 2016-17Due to the nature of federal, state, and institutional guidelines, this information is subject to change without notice.Loan Repayment Assistance Program (LRAP)For graduates who take low-paying public interest jobs and have substantial educational debt, Stanford Law School offers the Miles and Nancy Rubin Loan Repayment Assistance Program — the most generous loan relief program in the country — along with a variety of other fellowships. Stanford Law was one of the first law schools in the country to launch such a program, setting the standard for schools that have followed our lead. Stanford Law makes loans to eligible applicants to help meet education loan payments. Loans made by Stanford through this program will be forgiven (up to 100 percent) depending on verification of participant income using federal tax returns. Visit the LRAP section of this site for additional information.7 Ways to Figure If Going to Law School Is Worth ItYour school's reputation"For the group of very select law schools at the top, the employment prospects are terrific," says Steven Harper, a former attorney and author of "The Lawyer Bubble: A Profession in Crisis." "Life can look awfully good, and it will be awfully good for the vast, vast majority of those people, but that's maybe 10 percent out of 200 law schools."There's a sharp discrepancy in job prospects between first- and lower-tier schools. When comparing U.S. News and World Report's top 20 law schools with institutions that landed in the 126-to-146 ranking range, students attending upper-echelon institutions were nearly twice as likely to hold full-time, long-term law jobs as their lower-tier counterparts. Students attending lower-tier schools were also about 2.5 times more likely to be underemployed.The Ivy League isn't the only ticket to a post-graduate job, says Kyle McEntee, executive director of Law School Transparency, an organization that helps students with the decision to attend law school, and provides employment and underemployment data on law institutions nationwide. Schools with a solid local reputation and strong ties to the community can be just as effective at finding work in that area.Your rankRegardless where you attend, your performance will be a factor for future employers. A study published last year in the Journal of Empirical Legal Studies shows that grades are "the most important predictor of career success," even more so than your school's reputation. The study, authored by law professors at UCLA and the University of Arizona, states that "... it is well known that judges care greatly about grades in choosing their clerks, professors care about grades in choosing their research assistants, and many employers insist on good grades in choosing new hires."Harper says that students who can stay above the bottom 25th percentile in GPA have the best shot at landing a job that pays enough to repay student loans."You have to be really honest and realistic with yourself about 'how well am I going to stack up against my peers?'" he says. "You have a very hard time convincing anybody, I think, who's entering law school that they'll ever wind up below the median, much less below the 25th percentile in anything, whether it's practicing law or something else, but guess what? That's just mathematics. There's an honest self-reflection that has to happen."Your specialtySome legal fields are hotter than others, which may explain why schools like New York University are changing their curricula to focus more on specializations. A study by Robert Half Legal of 200 attorneys in hiring positions revealed that litigation, business, commercial and health care law are expected to offer the greatest number of jobs.Leslie Levin, associate dean for academic affairs at the University of Connecticut School of Law, says that students can increase their marketability by having a clear idea of the law field they'd like to practice and by taking relevant courses and pursuing externship opportunities."Focusing on an area in which the student would like to practice and then positioning him or herself as somebody (who) is knowledgeable in that area will increase their employability," she says. "So, for example, if somebody gets a tax certificate, they are going to be more appealing if they want to go to a firm or the government and practice tax than somebody else."Summer associate gigs help, too. More than 90 percent of law students who held summer associate positions in 2012 were offered entry-level positions after graduation, reports the National Association for Law Placement.Your expectations"(Students) should think foremost on why they want to be a lawyer," says Andrew J. McClurg, author of "1L of a Ride: A Well-Traveled Professor's Roadmap to Success in the First Year of Law School." "If their only reason was to make a whole lot of money, that was never a good reason, even when the economy was great."Many students have unrealistic expectations about post-law school life and few have a clear picture of the daily lives of practicing attorneys, says Levin. Some don't know that they may not use their J.D. A survey by the NALP shows that 1 in 5 2012 graduates worked in a nonlegal profession or held jobs where a degree might have been an advantage or requirement, but passage of the bar was not required.The best way to understand what attorneys do is by observing them first-hand, she says."Even in college, people often will get part-time jobs in law firms. They can volunteer for organizations where lawyers are working and doing public interest work. They should talk to lawyers; if they know lawyers, they can shadow lawyers," she says. "The one thing they shouldn't do is assume that what they are seeing is the entire range of what lawyers do."Your connectionsGetting into a good school and acing your classes is only half the battle. The law grads who are getting jobs are the ones who have spent time hobnobbing with future employers, says McClurg."Joining student organizations and going to bar functions and putting yourself out there and making connections, other than your academic record, that's really one of the only things you can do" to increase your marketability, he says.That also means remembering that you may not be able to get a job in your first choice of legal field, says Blair Gould, a third-year law student at Wayne State University who's primarily interested in corporate and real estate law but is also gaining litigation experience."People need to continuously be diversifying their legal skill set," he says. "Take advantage of any kind of connections you can make and join any civic or social organization you can because all of those things will come into play eventually. You never know what's going to happen in the future."Your financial prospectsNearly 9 out of 10 third-year law students advise those considering law school to consider an institution's financial aid package before enrolling, reports Kaplan. That's because how much you borrow will largely determine where you work after graduation.Median starting salaries at law firms clock in at $90,000 per year -- a 28 percent drop since 2008 -- but not every lawyer is bringing home the big bucks. Median salaries at public interest organizations hovered at $44,600 annually, while judicial clerks and government employees earned $52,000. That's barely enough to cover the $558 monthly student loan payments public law students face if they borrow the average $76,000 in loans at an 8 percent interest rate over a 30-year period. On the flip side, the median salary at large firms is $160,000.The good news is that more than 100 law schools offer loan assistance and forgiveness incentives, reports Equal Justice Works, and public interest workers may be eligible for accelerated federal loan forgiveness. Scholarships also abound, but read the fine print, warns McEntee."Oftentimes scholarships come with stipulations, and stipulations are difficulties," he says.Your goalsIf working in the legal profession is what you want to do for all the right reasons, debt and employability statistics shouldn't derail you, but they should be a consideration, says Levin. To keep financial figures in check, Levin recommends that students consider lower-cost state schools or attend law school part time. They should also do some serious research to ensure they understand what attorneys do, the variety of contexts in which they work and whether they would be a good match for the legal profession."If (being an attorney) is really what makes you happy, it may take longer to pay off the debt, but that doesn't mean it's not worth it," she says. "(Whether law school pays off) really depends on what you hope to get out of your law degree and what you want to do with your life."Read more: http://www.bankrate.com/finance/college-finance/going-to-law-school-worth-it-1.aspx#ixzz4XVp2B6s2Follow us: @Bankrate on Twitter | Bankrate on FacebookIs Going to Law School Worth It? Depends on WhereBy Greg DePersio | Updated January 26, 2017 — 6:00 AM ESTA 2015 study by the Access Group Center for Research and Policy Analysis found that only one in five people who graduated from law school between 2010 and 2015 strongly agreed that obtaining a law degree was worth the cost. Perhaps more troubling, only 38% of law school graduates, down from 56% between 2000 and 2009, described themselves as having a good job after graduation.With four out of five recent law school graduates at least somewhat regretting their decision to attend – and six out of 10 failing to find a good job after graduating – it makes sense to examine whether going to law school is still worth it in 2017. Keeping in mind the high tuition costs, interest rates and potential salaries, another good metric to consider would be its ROI (return on investment), calculated as its salary-to-debt ratio in a new study by online lender SoFi.Law School CostsGoing to law school full-time requires a three-year commitment, and the average tuition and fees for just one of those years at an American Bar Association-accredited institution often exceeds $40,000. This cost does not include rent, food, transportation and other living expenses. As the law school workload does not permit most students to hold jobs, student loans represent the most common method of paying these costs. Consequently, the average law school graduate in 2015 took on over $140,000 in student debt to obtain his or her degree.For many students, student loan debt accumulates on top of debt they already carry from undergraduate school. Members of the class of 2016 graduated college with over $37,000 in student debt on average. While most lenders allow the deferment of undergraduate loan payments while attending law school, any unsubsidized portion of such debt continues to accrue interest. All told, it is not uncommon for a law school graduate to enter the working world with a net worth of negative $250,000.Expected SalaryTaking on such debt might be a smart investment if a law degree provided reasonable assurance of a high-paying job. Ideally, recent graduates should earn yearly salaries equal to or greater than their total student debt. This level of pay usually allows for paying off student loans within 10 years without materially affecting a person's lifestyle.Stories abound, however, of law school graduates struggling to find any sort of legal job, much less one that enables the repayment of student debt in a timely manner. The New York Times revealed in 2015 that more than 20% of graduates from the class of 2010 held jobs that did not require law degrees. Only 40% worked in law firms, compared to 60% from the class of 2000. The remainder operated solo practices, with varying degrees of success, or performed contract work.The SoFi study, which incorporates data from student-loan refinancing applications between Jan. 2014 and Dec. 2016, shows that some schools do better than others when it comes to both job placements and salaries. Looking at students three years out of law school, Cornell University, Columbia University and New York University take the top three spots in the SoFi study with average salary paid in excess of $177,000. Consider that the Bureau of Labor Statistics in 2015 had the top 10% of lawyers earning more than $187,200.For the Class of 2015, Columbia University saw 401 out of its 413 graduates find full time jobs; at New York University the numbers were 474 of 485. In some cases, of course, students may choose to put off those high-money jobs for prestigious public service spots: In 2015, 99 of 199 Yale Law School graduates were employed in clerkships, which paid a median salary of $69,000 (Yale ranks 8th on the SoFi list for salaries).Graduates of second-tier programs often settle for work outside of top law firms, where the pay is much lower. The 2015 median pay for law school graduates across the board was only $64,800. Idealistic young attorneys who choose public service fare even worse financially. Entry-level district attorneys earn a median pay of $37,000; public defenders do slightly better at $40,000.Schools with the Best Salary-to-Debt RatioEven new lawyers who land good jobs rarely receive paychecks commensurate with their debt levels. The SoFi analysis also ranks law schools based on which offer the best value defined by their salary-to-debt ratio. That figure is an indication of how much more your potential salary could exceed your potential debt and help make a more informed decision about whether law school is worth it.Brigham Young University takes the number-one spot: With students expecting an average salary of $108,000 and holding an average debt just below $65,000, the school has an impressive 1.7x salary-to-debt ratio, making it a a good value-for-money bet. University of Texas at Austin, comes in second with the ratio at 1.4x on the back of lower debt burden thanks to its relatively cheaper tuition. Yale Law School ranks third – not just because of higher salaries graduates get compared to the top two on this list ($177,771; BYU is $64,873 and UT, $147,44), but also because of its generosity with financial aid.Opting for schools that fare poorly on this metric could cost you. Take for example, Florida Coastal School of Law, which ranked lowest on the SoFi ROI list, with a salary-to-debt ratio of 0.5x. Let's break down the numbers: The school's tuition, $44,000, is at par with some of the top schools in the country. Students graduate with $158,427 in debt (considerably higher than the $123,793 Yale grads have), but the average annual salary for graduates is only $84,664.Other ConsiderationsThe numbers do not consider the financial risk of being a law school dropout. The first-year law school attrition rate nationwide is nearly 7%. A handful of law schools lost over 30% of their first-year students in 2015. Enrolling in law school but failing to finish offers no greater marketability than a bachelor's degree. It does, however, substantially add to a person's debt load.All told, the decision to attend law school is one that should be approached with great consideration. Indeed, 1% of attorneys have successful, high-paying careers. Supply and demand dynamics, however, have changed considerably since the 1980s, with fewer high-paying, entry-level jobs and many more law school graduates chasing those jobs. Pile on tuition costs – which, for decades, have risen at three times the inflation rate – and going to law school is not the financial no-brainer that it once was. For more on the choice, see Career Advice: Accounting vs. Law and Career Advice: Investment Banking vs. Law.Read more: Is Going to Law School Worth It Anymore? | Investopedia http://www.investopedia.com/articles/personal-finance/082416/going-law-school-worth-it-anymore.asp#ixzz4XVq64gcoFollow us: Investopedia on Facebook

Applications of artificial intelligenceFrom Wikipedia, the free encyclopediaJump to navigationJump to searchThis article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed.(March 2016)(Learn how and when to remove this template message)Artificial intelligenceMajor goalsKnowledge reasoningPlanningMachine learningNatural language processingComputer visionRoboticsArtificial general intelligenceApproachesSymbolicDeep learningBayesian networksEvolutionary algorithmsPhilosophyEthicsExistential riskTuring testChinese roomFriendly AIHistoryTimelineProgressAI winterTechnologyApplicationsProjectsProgramming languagesGlossaryGlossaryvteArtificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of A.I. where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.Contents1AI for Good2Aviation3Computer science4Education5Finance5.1Algorithmic trading5.2Market analysis and data mining5.3Personal finance5.4Portfolio management5.5Underwriting6Job Search7Heavy industry8Hospitals and medicine9Human resources and recruiting10Marketing11Media12Music13News, publishing and writing14Online and telephone customer service15Sensors16Telecommunications maintenance17Toys and games18Transportation19Other20List of applications21See also22Notes23References24External linksAI for Good[edit]AI for Good is a movement in which institutions are employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.[1]Aviation[edit]This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed.(November 2016)(Learn how and when to remove this template message)The Air Operations Division (AOD) uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[2]The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed and strength of the forces and counter forces. Pilots may be given assistance in the air during combat by computers. The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data.[3]These computer simulated pilots are also used to train future air traffic controllers.The system used by the AOD in order to measure performance was the Interactive Fault Diagnosis and Isolation System, or IFDIS. It is a rule based expert system put together by collecting information from TF-30 documents and the expert advice from mechanics that work on the TF-30. This system was designed to be used for the development of the TF-30 for the RAAF F-111C. The performance system was also used to replace specialized workers. The system allowed the regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers.The AOD also uses artificial intelligence in speech recognition software. The air traffic controllers are giving directions to the artificial pilots and the AOD wants to the pilots to respond to the ATC's with simple responses. The programs that incorporate the speech software must be trained, which means they use neural networks. The program used, the Verbex 7000, is still a very early program that has plenty of room for improvement. The improvements are imperative because ATCs use very specific dialog and the software needs to be able to communicate correctly and promptly every time.The Artificial Intelligence supported Design of Aircraft,[4]or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.In 2003, NASA's Dryden Flight Research Center, and many other companies, created software that could enable a damaged aircraft to continue flight until a safe landing zone can be reached.[5]The software compensates for all the damaged components by relying on the undamaged components. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence.The Integrated Vehicle Health Management system, also used by NASA, on board an aircraft must process and interpret data taken from the various sensors on the aircraft. The system needs to be able to determine the structural integrity of the aircraft. The system also needs to implement protocols in case of any damage taken the vehicle.[6]Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.[7]Educating the autopilot relies on the concept of supervised machine learning “which treats the young autopilot as a human apprentice going to a flying school”.[7]The autopilot records the actions of the human pilot generating learning models using artificial neural networks.[7]The autopilot is then given full control and observed by the pilot as it executes the training exercise.[7]The Intelligent Autopilot System combines the principles of Apprenticeship Learning and Behavioral Cloning whereby the autopilot observes the low-level actions required to maneuver the airplane and high-level strategy used to apply those actions.[8]IAS implementation employs three phases; pilot data collection, training, and autonomous control.[8]Baomar and Bentley’s goal is to create a more autonomous autopilot to assist pilots in responding to emergency situations.[8]Computer science[edit]AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. (See AI effect.) According to Russell & Norvig (2003, p. 15), all of the following were originally developed in AI laboratories: time sharing,interactive interpreters, graphical user interfaces and the computer mouse, rapid development environments, the linked list data structure, automatic storage management, symbolic programming, functional programming, dynamic programming and object-oriented programming.AI can be used to potentially determine the developer of anonymous binaries.[citation needed]AI can be used to create other AI. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and COCO. According to Google, NASNet's performance exceeded all previously published ImageNet performance.[9]Education[edit]There are a number of companies that create robots to teach subjects to children ranging from biology to computer science, though such tools have not become widespread yet. There have also been a rise of intelligent tutoring systems, or ITS, in higher education. For example, an ITS called SHERLOCK teaches Air Force technicians to diagnose electrical systems problems in aircraft. Another example is DARPA, Defense Advanced Research Projects Agency, which used AI to develop a digital tutor to train its Navy recruits in technical skills in a shorter amount of time.[1]Universities have been slow in adopting AI technologies due to either a lack of funding or skepticism of the effectiveness of these tools, but in the coming years more classrooms will be utilizing technologies such as ITS to complement teachers.Advancements in natural language processing, combined with machine learning, have also enabled automatic grading of assignments as well as a data-driven understanding of individual students’ learning needs. This led to an explosion in popularity of MOOCs, or Massive Open Online Courses, which allows students from around the world to take classes online. Data sets collected from these large scale online learning systems have also enabled learning analytics, which will be used to improve the quality of learning at scale. Examples of how learning analytics can be used to improve the quality of learning include predicting which students are at risk of failure and analyzing student engagement.[10]Finance[edit]Algorithmic trading[edit]Algorithmic trading involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention. Automated trading systems are typically used by large institutional investors.[11]Market analysis and data mining[edit]Several large financial institutions have invested in AI engines to assist with their investment practices. BlackRock’s AI engine, Aladdin, is used both within the company and to clients to help with investment decisions. Its wide range of functionalities includes the use of natural language processing to read text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Banks such as UBS and Deutsche Bank use an AI engine called Sqreem (Sequential Quantum Reduction and Extraction Model) which can mine data to develop consumer profiles and match them with the wealth management products they’d most likely want.[12]Goldman Sachs uses Kensho, a market analytics platform that combines statistical computing with big data and natural language processing. Its machine learning systems mine through hoards of data on the web and assess correlations between world events and their impact on asset prices.[13]Information Extraction, part of artificial intelligence, is used to extract information from live news feed and to assist with investment decisions.[14]Personal finance[edit]Several products are emerging that utilize AI to assist people with their personal finances. For example, Digit is an app powered by artificial intelligence that automatically helps consumers optimize their spending and savings based on their own personal habits and goals. The app can analyze factors such as monthly income, current balance, and spending habits, then make its own decisions and transfer money to the savings account.[15]wallet.AI, an upcoming startup in San Francisco, builds agents that analyze data that a consumer would leave behind, from Smartphone check-ins to tweets, to inform the consumer about their spending behavior.[16]Portfolio management[edit]Robo-advisors are becoming more widely used in the investment management industry. Robo-advisors provide financial advice and portfolio management with minimal human intervention. This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients. It can adjust to real-time changes in the market and accordingly calibrate the portfolio.[17]Underwriting[edit]An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer’s likelihood of default. Their technology will be licensed to banks for them to leverage for their underwriting processes as well.[18]ZestFinance developed their Zest Automated Machine Learning (ZAML) Platform specifically for credit underwriting as well. This platform utilizes machine learning to analyze tens of thousands traditional and nontraditional variables (from purchase transactions to how a customer fills out a form) used in the credit industry to score borrowers. The platform is particularly useful to assign credit scores to those with limited credit histories, such as millennials.[19]Job Search[edit]The job market has seen a notable change due to Artificial intelligence implementation. It has simplified the process for both recruiters and job seekers (i.e., Google for Jobs and applying online). According to Raj Mukherjee from Job Search | Indeed, 65% of people launch a job search again within 91 days of being hired. AI-powered engine streamlines the complexity of job hunting by operating information on job skills, salaries, and user tendencies, matching people to the most relevant positions. Machine intelligence calculates what wages would be appropriate for a particular job, pulls and highlights resume information for recruiters using natural language processing, which extracts relevant words and phrases from text using specialized software. Another application is an AI resume builder which requires 5 minutes to compile a CV as opposed to spending hours doing the same job. In the AI age chatbots assist website visitors and solve daily workflows. Revolutionary AI tools complement people’s skills and allow HR managers to focus on tasks of higher priority. However, Artificial Intelligence impact on jobs research suggests that by 2030 intelligent agents and robots can eliminate 30% of the world’s human labor. Moreover, the research proves automation will displace between 400 and 800 million employees. Glassdoor`s research report states that recruiting and HR are expected to see much broader adoption of AI in job market 2018 and beyond.[20][21][22]Heavy industry[edit]Robots have become common in many industries and are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.In 2014, China, Japan, the United States, the Republic of Korea and Germany together amounted to 70% of the total sales volume of robots. In the automotive industry, a sector with particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 per 10,000 employees.[23]Hospitals and medicine[edit]X-ray of a hand, with automatic calculation of bone age by a computer software.Main article: Artificial intelligence in healthcareArtificial neural networks are used as clinical decision support systems for medical diagnosis, such as in Concept Processing technology in EMR software.Other tasks in medicine that can potentially be performed by artificial intelligence and are beginning to be developed include:Computer-aided interpretation of medical images. Such systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases. A typical application is the detection of a tumor.Heart sound analysis[24]Companion robots for the care of the elderly[25]Mining medical records to provide more useful information.Design treatment plans.Assist in repetitive jobs including medication management.Provide consultations.Drug creation[26]Using avatars in place of patients for clinical training[27]Predict the likelihood of death from surgical proceduresPredict HIV progressionCurrently, there are over 90 AI startups in the health industry working in these fields.[28]IDx's first solution, IDx-DR, is the first autonomous AI-based diagnostic system authorized for commercialization by the FDA.[29]Human resources and recruiting[edit]Another application of AI is in the human resources and recruiting space. There are three ways AI is being used by human resources and recruiting professionals. AI is used to screen resumes and rank candidates according to their level of qualification. Ai is also used to predict candidate success in given roles through job matching platforms. And now, AI is rolling out recruiting chat bots that can automate repetitive communication tasks.Typically, resume screening involves a recruiter or other HR professional scanning through a database of resumes. Now startups like Pomato, are creating machine learning algorithms to automate resume screening processes. Pomato’s resume screening AI focuses on automating validating technical applicants for technical staffing firms. Pomato’ s AI performs over 200,000 computations on each resume in seconds then designs a custom technical interview based on the mined skills. KE Solutions, founded in 2014, has developed recommendation systems to rank jobs for candidates, and rank resumes for employers. Find you dream job, developed by KE Solutions uses concept-based search has increased accuracy by 80% compared to traditional ATS. It helps recruiters to overcome technical barriers.From 2016 to 2017, consumer goods company Unilever used artificial intelligence to screen all entry level employees. Unilever’s AI used neuroscience based games, recorded interviews, and facial/speech analysis to predict hiring success. Unilever partnered with Pymetrics and HireVue to enable its novel AI based screening and increased their applicants from 15,000 to 30,000 in a single year. Recruiting with AI also produced Unililever’s “most diverse class to date.’ Unilever also decreased time to hire from 4 months to 4 weeks and saved over 50,000 hours of recruiter time.From resume screening to neuroscience, speech recognition, and facial analysis...it’s clear AI is having a massive impact on the human resources field. Yet another development in AI is in recruiting chatbots. TextRecruit, a Bay Area startup, released Ari (automated recruiting interface.) Ari is a recruiting chatbot that is designed to hold two-way text message conversations with candidates. Ari automates posting jobs, advertising openings, screening candidates, scheduling interviews, and nurturing candidate relationships with updates as they progress along the hiring funnel. Ari is currently offered as part of TextRecruit’s candidate engagement platform.Marketing[edit]Main article: Marketing and artificial intelligenceMedia[edit]Some AI applications are geared towards the analysis of audiovisual media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision, which is a major application area of AI.Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces. The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.Media analysis AI companies often provide their services over a REST API that enables machine-based automatic access to the technology and allows machine-reading of the results. For example, IBM, Microsoft, Amazon and the video AI company Valossa[30]allow access to their media recognition technology by using RESTful APIs.Music[edit]Main article: Music and artificial intelligenceWhile the evolution of music has always been affected by technology, artificial intelligence has enabled, through scientific advances, to emulate, at some extent, human-like composition.Among notable early efforts, David Cope created an AI called Emily Howell that managed to become well known in the field of Algorithmic Computer Music.[31]The algorithm behind Emily Howell is registered as a US patent.[32]The AI Iamus created 2012 the first complete classical album fully composed by a computer.Other endeavours, like AIVA (Artificial Intelligence Virtual Artist), focus on composing symphonic music, mainly classical music for film scores.[33]It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.[34]Artificial intelligences can even produce music usable in a medical setting, with Melomics’s effort to use computer-generated music for stress and pain relief.[35]Moreover, initiatives such as Google Magenta, conducted by the Google Brain team, want to find out if an artificial intelligence can be capable of creating compelling art.[36]At Sony CSL Research Laboratory, their Flow Machines software has created pop songs by learning music styles from a huge database of songs. By analyzing unique combinations of styles and optimizing techniques, it can compose in any style.Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects, since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style. Since the software has been open sourced[37]musicians, such as Taryn Southern[38]have been collaborating with the project to create music.News, publishing and writing[edit]The company Narrative Science makes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English. It also creates financial reports and real estate analyses.[39]Similarly, the company Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football.[40]The company is projected to generate one billion stories in 2014, up from 350 million in 2013.[41]Echobox is a software company that helps publishers increase traffic by 'intelligently' posting articles on social media platforms such as Facebook and Twitter.[42]By analysing large amounts of data, it learns how specific audiences respond to different articles at different times of the day. It then chooses the best stories to post and the best times to post them. It uses both historical and real-time data to understand to what has worked well in the past as well as what is currently trending on the web.[43]Another company, called Yseop, uses artificial intelligence to turn structured data into intelligent comments and recommendations in natural language. Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French & German.[44]Boomtrain’s is another example of AI that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader. It’s like hiring a personal editor for each individual reader to curate the perfect reading experience.There is also the possibility that AI will write work in the future. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[45]Online and telephone customer service[edit]An automated online assistant providing customer service on a web page.Artificial intelligence is implemented in automated online assistants that can be seen as avatars on web pages.[46]It can avail for enterprises to reduce their operation and training cost.[46]A major underlying technology to such systems is natural language processing.[46]Pypestream uses automated customer service for its mobile application designed to streamline communication with customers.[47]Currently, major companies are investing in AI to handle difficult customer in the future. Google's most recent development analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately.[48]Sensors[edit]Artificial Intelligence has been combined with many sensor technologies, such as Digital SpectrometryTMby IdeaCuria Inc.[49][50]which enables many applications such as at home water quality monitoring.Telecommunications maintenance[edit]Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search[51]in a scheduling application that provides the work schedules of 20,000 engineers.Toys and games[edit]The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.Companies like Mattel have been creating an assortment of AI-enabled toys for kids as young as age three. Using proprietary AI engines and speech recognition tools, they are able to understand conversations, give intelligent responses and learn quickly.[52]AI has also been applied to video games, for example video game bots, which are designed to stand in as opponents where humans aren't available or desired.Transportation[edit]Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg[citation needed]and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic-based controller.Today's cars now have AI-based driver assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent.[1]In the future, fully autonomous cars will be developed. AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.[53]Other[edit]Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal cues. Other applications are robot navigation, obstacle avoidance, and object recognition.[citation needed]List of applications[edit]Typical problems to which AI methods are appliedOptical character recognitionHandwriting recognitionSpeech recognitionFace recognitionArtificial creativityComputer vision, Virtual reality, and Image processingPhoto and Video manipulationDiagnosis (artificial intelligence)Game theory and Strategic planningGame artificial intelligence and Computer game botNatural language processing, Translation and ChatterbotsNonlinear control and RoboticsOther fields in which AI methods are implementedArtificial lifeAutomated reasoningAutomationBiologically inspired computingConcept miningData miningKnowledge representationSemantic WebE-mail spam filteringRoboticsBehavior-based roboticsCognitiveCyberneticsDevelopmental robotics (Epigenetic)Evolutionary roboticsHybrid intelligent systemIntelligent agentIntelligent controlLitigationSee also[edit]Applications of artificial intelligence to legal informaticsApplications of deep learningApplications of machine learningList of artificial intelligence projectsProgress in artificial intelligenceNotes[edit]^ Jump up to:a b c United States, National Science and Technology Council – Committee on Technology. Executive Office of the President. (2016). Preparing for the future of artificial intelligence.Jump up^ "AI bests Air Force combat tactics experts in simulated dogfights". Ars Technica. Retrieved 2016-11-18.Jump up^ Jones, Randolph M.; Laird, John E.; Nielsen, Paul E.; Coulter, Karen J.; Kenny, Patrick; Koss, Frank V. (1999-03-15). "Automated Intelligent Pilots for Combat Flight Simulation". AI Magazine. 20 (1): 27. ISSN 0738-4602.Jump up^ AIDA Homepage. http://Kbs.twi.tudelft.nl (April 17, 1997). Retrieved on 2013-07-21.Jump up^ The Story of Self-Repairing Flight Control Systems. NASA Dryden. (April 2003). Retrieved on 2016-08-25.Jump up^ "Flight Demonstration Of X-33 Vehicle Health Management System Components On The F/A-18 Systems Research Aircraft" (PDF).^ Jump up to:a b c d Adams, Eric (March 28, 2017). "AI Wields the Power to Make Flying Safer—and Maybe Even Pleasant". WIRED. Retrieved October 7, 2017.^ Jump up to:a b c Baomar, Haitham; Bentley, Peter J. (2016). 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ISBN 978-1-84564-146-7Jump up^ "Five Best AI-Powered Chatbot Apps".Jump up^ "Is Artificial Intelligence the Way Forward for Personal Finance?".Jump up^ "Machine learning in finance applications".Jump up^ "Machine Learning Is the Future of Underwriting, But Startups Won't be Driving It".Jump up^ "ZestFinance Introduces Machine Learning Platform to Underwrite Millennials and Other Consumers with Limited Credit History".Jump up^ "Raj Mukherjee". Forbes.Jump up^ "chatbots". Wikipedia.Jump up^ "Glassdoor`s" (PDF). Glassdoor.Jump up^ "World Robotics 2015 Industrial Robots". International Federation of Robotics. Archived from the original on March 27, 2016. Retrieved 27 March 2016.Jump up^ Reed, T. R.; Reed, N. E.; Fritzson, P. (2004). "Heart sound analysis for symptom detection and computer-aided diagnosis". Simulation Modelling Practice and Theory. 12 (2): 129. doi:10.1016/j.simpat.2003.11.005.Jump up^ Yorita, A.; Kubota, N. (2011-03-01). "Cognitive Development in Partner Robots for Information Support to Elderly People". IEEE Transactions on Autonomous Mental Development. 3 (1): 64–73. doi:10.1109/TAMD.2011.2105868. ISSN 1943-0604.Jump up^ "Artificial Intelligence Will Redesign Healthcare – The Medical Futurist". The Medical Futurist. 2016-08-04. Retrieved 2016-11-18.Jump up^ Luxton, David D. (2014). "Artificial Intelligence in Psychological Practice: Current and Future Applications and Implication". Professional Psychology: Research and Practice. 45(5): 332. doi:10.1037/a0034559.Jump up^ "From Virtual Nurses To Drug Discovery: 90+ Artificial Intelligence Startups In Healthcare". CB Insights – Blog. 2016-08-31. Retrieved 2016-11-18.Jump up^ "Press Release: FDA permits marketing of IDx-DR for automated detection of diabetic retinopathy in primary care". Eye Diagnosis. April 12, 2018. Retrieved 11 September2018.Jump up^ "Video Recognition API of Valossa". Retrieved 2018-02-08.Jump up^ Cheng, Jacqui (30 September 2009). "Virtual composer makes beautiful music—and stirs controversy". Ars Technica.Jump up^ US Patent #7696426 https://www.google.com/patents/US7696426Jump up^ Hick, Thierry (11 October 2016). "La musique classique recomposée". Luxemburger Wort.Jump up^ SACEM Database, https://repertoire.sacem.fr/resultats?filters=parties&query=aiva&nbWorks=20)Jump up^ Requena, G; Sanchez, C; Corzo-Higueras, JL; Reyes-Alvarado, S; Rivas-Ruiz, F; Vico, F; Raglio, A (2014). "Melomics music medicine (M3) to lessen pain perception during pediatric prick test procedure". Pediatric Allergy and Immunology. 25 (7): 721. doi:10.1111/pai.12263. PMID 25115240.Jump up^ Souppouris, Aaron (23 May 2016). "Google's 'Magenta' project will see if AIs can truly make art". Engadget.Jump up^ "Watson Beat on GitHub".Jump up^ "Songs in the Key of AI". Wired. 17 May 2018.Jump up^ business intelligence solutions Archived November 3, 2011, at the Wayback Machine.. Narrative Science. Retrieved on 2013-07-21.Jump up^ Eule, Alexander. "Big Data and Yahoo's Quest for Mass Personalization". Barron's.Jump up^ Kirkland, Sam. "'Robot' to write 1 billion stories in 2014 — but will you know it when you see it?". Poynter.Jump up^ Williams, Henry (July 4, 2016). "AI online publishing service Echobox closes $3.4m in funding". Startups.co.uk | Business ideas, funding help and growth advice. Retrieved July 21, 2016.Jump up^ Smith, Mark (July 22, 2016). "So you think you chose to read this article?". BBC. Retrieved July 27, 2016.Jump up^ "Archived copy". Archived from the original on 2013-04-12. Retrieved 2013-03-11.Jump up^ "A Japanese AI program just wrote a short novel, and it almost won a literary prize". Digital Trends. 2016-03-23. Retrieved 2016-11-18.^ Jump up to:a b c Implementing an online help desk system based on conversational agent Authors: Alisa Kongthon, Chatchawal Sangkeettrakarn, Sarawoot Kongyoung and Choochart Haruechaiyasak. Published by ACM 2009 Article, Bibliometrics Data Bibliometrics. 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Preparing for the future of artificial intelligence.External links[edit]AI applications at aitopics.orgHow AI can be applied in many fieldsArtificial Intelligence: Biggest Trend In SmartphonesWhy AI Is a Great Idea for Any Web Hosting10 Tips to Create an Addictive Chatbot ApplicationshowvteEmerging technologiesCategories:Artificial intelligenceArtificial intelligence applicationsNavigation menuNot logged inTalkContributionsCreate accountLog inArticleTalkReadEditView historySearchMain pageContentsFeatured contentCurrent eventsRandom articleDonate to WikipediaWikipedia storeInteractionHelpAbout WikipediaCommunity portalRecent changesContact pageToolsWhat links hereRelated changesUpload fileSpecial pagesPermanent linkPage informationWikidata itemCite this pagePrint/exportCreate a bookDownload as PDFPrintable versionIn other projectsWikimedia CommonsLanguagesEspañolFrançaisՀայերենଓଡ଼ିଆРусскийУкраїнськаEdit linksThis page was last edited on 8 October 2018, at 22:09 (UTC).Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. 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