AI And Training: Extending Learning Into Day-to-day Work
AI And Training: Extending Learning Into Day-to-day Work >> https://urlca.com/2tf6Wm
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Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its weights until it has been fitted appropriately. This occurs as part of the cross validation process to ensure that the model avoids overfitting or underfitting. Supervised learning helps organizations solve a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox. Some methods used in supervised learning include neural networks, naïve bayes, linear regression, logistic regression, random forest, and support vector machine (SVM).
Instances of bias and discrimination across a number of machine learning systems have raised many ethical questions regarding the use of artificial intelligence. How can we safeguard against bias and discrimination when the training data itself may be generated by biased human processes While companies typically have good intentions for their automation efforts, Reuters (link resides outside IBM) ) highlights some of the unforeseen consequences of incorporating AI into hiring practices. In their effort to automate and simplify a process, Amazon unintentionally discriminated against job candidates by gender for technical roles, and the company ultimately had to scrap the project. Harvard Business Review (link resides outside IBM) has raised other pointed questions about the use of AI in hiring practices, such as what data you should be able to use when evaluating a candidate for a role.
Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:
Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:
Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards -- which it receives when it performs an action that is beneficial toward the ultimate goal -- and avoid punishments -- which it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas such as:
There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.
Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it's important for the business to explain how every decision was made. This is especially true in industries with heavy compliance burdens such as banking and insurance.
Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.
The retention of skills and knowledge learned via VR is very high, meaning it is more likely to be applied in the workplace, ultimately leading to increased productivity. Even complex techniques can be broken down into their most basic components to make certain that even the newest employee can grasp and deliver consistent, high-quality output.
This automation brings consistency into the process, unlike previous methods where analysts would have to make every single decision. IBM for example has a sophisticated reinforcement learning based platform that has the ability to make financial trades. It computes the reward function based on the loss or profit of every financial transaction.
A combination of supervised and reinforcement learning is used for abstractive text summarization in this paper. The paper is fronted by Romain Paulus, Caiming Xiong & Richard Socher. Their goal is to solve the problem faced in summarization while using Attentional, RNN-based encoder-decoder models in longer documents. The authors of this paper propose a neural network with a novel intra-attention that attends over the input and continuously generates output separately. Their training methods are a combo of standard supervised word prediction and reinforcement learning.
On the side of machine translation, authors from the University of Colorado and the University of Maryland, propose a reinforcement learning based approach to simultaneous machine translation. The interesting thing about this work is that it has the ability to learn when to trust the predicted words and uses RL to determine when to wait for more input.
Artificial intelligence can be categorized into one of four types.Reactive AI uses algorithms to optimize outputs based on a set of inputs. Chess-playing AIs, for example, are reactive systems that optimize the best strategy to win the game. Reactive AI tends to be fairly static, unable to learn or adapt to novel situations. Thus, it will produce the same output given identical inputs.Limited memory AI can adapt to past experience or update itself based on new observations or data. Often, the amount of updating is limited (hence the name), and the length of memory is relatively short. Autonomous vehicles, for example, can \"read the road\" and adapt to novel situations, even \"learning\" from past experience.Theory-of-mind AI are fully-adaptive and have an extensive ability to learn and retain past experiences. These types of AI include advanced chat-bots that could pass the Turing Test, fooling a person into believing the AI was a human being. While advanced and impressive, these AI are not self-aware.Self-aware AI, as the name suggests, become sentient and aware of their own existence. Still in the realm of science fiction, some experts believe that an AI will never become conscious or \"alive\".\"}},{\"@type\": \"Question\",\"name\": \"How Is AI Used Today\",\"acceptedAnswer\": {\"@type\": \"Answer\",\"text\": \"AI is used extensively across a range of applications today, with varying levels of sophistication. Recommendation algorithms that suggest what you might like next are popular AI implementations, as are chatbots that appear on websites or in the form of smart speakers (e.g., Alexa or Siri). AI is used to make predictions in terms of weather and financial forecasting, to streamline production processes, and to cut down on various forms of redundant cognitive labor (e.g., tax accounting or editing). AI is also used to play games, operate autonomous vehicles, process language, and much, much, more.\"}},{\"@type\": \"Question\",\"name\": \"How Is AI Used in Healthcare\",\"acceptedAnswer\": {\"@type\": \"Answer\",\"text\": \"In healthcare settings, AI is used to assist in diagnostics. AI is very good at identifying small anomalies in scans and can better triangulate diagnoses from a patient's symptoms and vitals. AI is also used to classify patients, maintain and track medical records, and deal with health insurance claims. Future innovations are thought to include AI-assisted robotic surgery, virtual nurses or doctors, and collaborative clinical judgment.\"}}]}]}] EducationGeneralDictionaryEconomicsCorporate FinanceRoth IRAStocksMutual FundsETFs401(k)Investing/TradingInvesting EssentialsFundamental AnalysisPortfolio ManagementTrading EssentialsTechnical AnalysisRisk ManagementNewsCompany NewsMarkets NewsCryptocurrency NewsPersonal Finance NewsEconomic NewsGovernment NewsSimulatorYour MoneyPersonal FinanceWealth ManagementBudgeting/SavingBankingCredit CardsHome OwnershipRetirement PlanningTaxesInsuranceReviews & RatingsBest Online BrokersBest Savings AccountsBest Home WarrantiesBest Credit CardsBest Personal LoansBest Student LoansBest Life InsuranceBest Auto InsuranceAdvisorsYour PracticePractice ManagementFinancial Advisor CareersInvestopedia 100Wealth ManagementPortfolio ConstructionFinancial PlanningAcademyPopular CoursesInvesting for BeginnersBecome a Day TraderTrading for BeginnersTechnical AnalysisCourses by TopicAll CoursesTrading CoursesInvesting CoursesFinancial Professional CoursesSubmitTable of ContentsExpandTable of ContentsWhat Is Artificial Intelligence (AI)Understanding AIApplicationsTypesSpecial ConsiderationsArtificial Intelligence FAQsInvestingAlternative InvestmentsArtificial Intelligence: What It Is and How It Is UsedByJake FrankenfieldUpdated July 06, 2022Reviewed byGordon Scott Reviewed byGordon ScottFull Bio LinkedIn Twitter Gordon Scott has been an active investor and technical analyst or 20+ years. He is a Chartered Market Technician (CMT). 153554b96e
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