Human Categorization Machine Learning

C O N T E N T S:


  • The company is looking to better leverage machine learning to take categorization tasks humans do and provide dealers faster turn times.(More…)
  • Machine learning is a specific approach to AI that applies statistical techniques to data in order to train computers to perform tasks without explicit human programming of rules.(More…)


  • In it, he introduced the concept of Q-learning, which greatly improves the practicality and feasibility of reinforcement learning in machines.(More…)


Human Categorization Machine Learning
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The company is looking to better leverage machine learning to take categorization tasks humans do and provide dealers faster turn times. [1] One approach, active learning (sometimes called semisupervised learning), employs mostly automated processes based on machine learning models but refers edge cases–typically the areas which are most uncertain or highest risk–to human experts, whose decisions help improve new iterations of the models. [2] The process is very human, even though it’s called machine learning.In this way, at long last machine learning has grown up. [3] Hope, this tutorial gave you all the information needed to understand the human process in machine learning clearly. [3] All of these things are now more available than ever, and that’s a big reason why machine learning in a human process is popular today. [3] Human-in-the-Loop ? What it is: Humans and machines working together to develop training data for machine learning. ? Humans label initial dataset, humans tune the model. ? Continuous feedback loop: Humans validate output and correct inaccurate predictions from the model. [4]

A SVM is basically a system for recognizing and mapping similar data, and can be used in text categorization, handwritten character recognition, and image classification as it relates to machine learning and deep learning. [5] Based on this categorization and analysis, a machine learning system can make an educated “guess” based on the greatest probability, and many are even able to learn from their mistakes, making them “smarter” as they go along. [5]

Machine learning is a specific approach to AI that applies statistical techniques to data in order to train computers to perform tasks without explicit human programming of rules. [6] Although humans are still currently better at evaluating nuanced communication (like sarcasm and irony), machine learning technologies outperform human-based teams when it comes to gathering important social data quickly. [7] Machine learning programs excel at analyzing data that is unstructured or data that may be too complex or cumbersome for humans to categorize or analyze. [7] While natural language processing (NLP) and recognition is far from perfect, thanks to machine learning algorithms it’s getting increasingly closer to a point where it will be harder to tell whether we are talking to a human or a computer. [8] One potential solution? Machine learning technology, which combines human insight into language and contextual meaning with the speed, accuracy, and data-driven insight of artificial intelligence. [7] In some contexts like academic departments, the term really is used to refer more expansively to a combination of ML and non-ML approaches to building machines that perform tasks that until recently only humans could perform e.g. the intersection of robotics, planning, and machine learning so fruitfully explored by many of my colleagues at CMU. [9] Currently, 99% of machine learning is based on human input, Zoot’s Hathaway said. [1] Kim specializes in machine learning, which relies on algorithms to teach computers how to learn like a human brain. [8]

Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”. [10] In the workforce of the future, then, humans and machines will be learning about each other simultaneously. [11]

Obviously, for machine and deep learning to work, we needed an established understanding of the neural networks of the human brain. [5] Machine learning lets us find patterns in existing data, then create and use a model that recognizes those patterns in new data.Understanding machine learning means understanding the machine learning process, and the machine learning process is iterative. [3] The machine learning process also challenges and the reason is that we’re working with what are often large amounts of potentially complex data, and we’re trying to find patterns, meaningful patterns, predictive patterns, in this data.A Closer Look at the ProcessLet’s look at machine learning concepts in a more detailed way and also the terminology used in machine learning.The first thing we need to do is walk through some terminology. [3] Both approaches are used, but it’s fair to say that the most common approach is supervised learning.Data PreprocessingThe machine learning process starts with data. [3] The machine learning process also challenges and the reason is that we’re working with what are often large amounts of potentially complex data, and we’re trying to find patterns, meaningful patterns, predictive patterns, in this data. [3] There are lots more, but the core idea is that machine learning lets us find patterns in data, then use those patterns to predict the future.Finding the PatternsHow did we learn to read? In reading, we identify letters, and then the patterns of letters together to form words. [3] The present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals. [12] We could use machine learning to detect credit card fraud if we have data about previous credit card transactions. [3] That data in the jargon of machine learning is labeled, and so we’re doing what’s called supervised learning when we try to predict whether a new transaction is fraudulent. [3] The third category of people who are really involved in this space is called data scientists, who know about statistics and want powerful, easy-to-use tools which can help them in making good predictions.The Role of RThere’s a machine learning technology worth mentioning called R. R is an open source programming language and environment; it’s not just a language. [3] If we have a more complex problem, though, with lots of data and a powerful machine learning technology with lots of algorithms, this can be hard. [3] We then feed that data into a machine learning algorithm, it’d be more than one, that finds patterns in the data. [3] We have to do this because raw data is very rarely in the right shape to be processed by machine learning algorithms. [3] State-of-the-art machine learning algorithms are trained and tested on different parts of this data set. [13] Many of us immediately conjure up images of HAL from 2001: A Space Odyssey, the Terminator cyborgs, C-3PO, Data from Star Trek, or Samantha from Her when the subject turns to AI. And many may not even be familiar with machine learning as a separate subject. [5] Figure Eight is a powerful Human-in-the-Loop AI platform for data science and machine learning teams. [4] Even with the huge advances made in AI solutions in the last decade, and the growing number of them on the market and in our lives, there is a simple fact that holds true: AI is only as good as the machine learning data that trained it. [14] In this respect, AI requires machine learning, and machine learning requires data – a lot of the right kind of data. [14] The more your machine learning data accounts for the variety an AI system will encounter in the real world, the better the end product will be. [14] Appen partners with many global organizations to help them create and improve products using high-quality data for machine learning. [14] An example question here is something like, what are our customer segments? We might not know these things up front, but we can use machine learning, unsupervised machine learning, to help us figure out that.The kind of problems that machine learning addresses aren’t the only thing that can be categorized. [3] Regression problems are typically supervised learning scenarios, and an example question would be something like, how many units of this product will we sell next month?The second category of machine learning problems is called classification. [3]

Think of machine learning data like survey data: the larger and more complete your sample size, the more reliable your conclusions will be. [14] The core thing that machine learning does is finds patterns in data. [3] A big one is that doing machine learning well requires lots of data and we live in the big data era. [3] When it comes to effectiveness of machine learning, more data almost always yields better results–and the healthcare sector is sitting on a data goldmine. [12] In a recent study from Oxford Economics and ServiceNow, 51% of CIOs cite data quality as a substantial barrier to their company’s adoption of machine learning. [14] It covers more about why machine learning requires a high volume of data, the importance of high-quality data, and which data sources should be considered. [14] That’s what learning means and that’s what machine learning does with data that we provide. [3] Machine learning is a form of AI that allows computers to learn without being explicitly programmed. [14] To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. All machine learning is AI, but not all AI is machine learning. [15] These days, you hear a lot about machine learning (or ML) and artificial intelligence (or AI) both good or bad depending on your source. [5] We distinguish between AI and machine learning (ML) throughout this article when appropriate. [15] As ANNs became more powerful and complex and literally deeper with many layers and neurons the ability for deep learning to facilitate robust machine learning and produce AI increased. [5] Instead of trying to grasp the intricacies of the field which could be an ongoing and extensive series of articles unto itself let’s just take a look at some of the major developments in the history of deep learning (and by extension, machine learning and AI). [5] Machine learning was a giant step forward for AI. Forwardbut not all the way to the finish line. [5] In the most general sense, machine learning has evolved from AI. [5] Others describe machine learning as a subfield or means of achieving AI. [5] Both AI and machine learning have a lot more going on than “just” the fate of mankind. [5] Instead of prepared data, we should use training data because in the jargon of machine learning, creating a model is called training a model. [3] That data in the jargon of machine learning is labeled, and so we’re doing what’s called supervised learning when we try to predict whether a new transaction is fraudulent.The alternative, unsurprisingly, is called unsupervised learning and here the value we want to predict is not in the training data. [3] If machine learning is a subfield of artificial intelligence, then deep learning could be called a subfield of machine learning. [5] This process is called machine learning, but notice how much people do. [3] Often what we’ll get back is not yes or no.The third category of machine learning problems is commonly called clustering. [3] The second category of machine learning problems is called classification. [3] The third category of machine learning problems is commonly called clustering. [3] It’s also useful to think about the styles of machine learning algorithms that are used to solve those problems. [3] Machine learning helps computers solve complex problems, and the complexity is due to inherent variation: There are often hundreds, thousands, or millions of variables for the resulting system, product, or application to cope with. [14] The MIT Clinical Machine Learning Group is spearheading the development of next-generation intelligent electronic health records, which will incorporate built-in ML/AI to help with things like diagnostics, clinical decisions, and personalized treatment suggestions. [12] Previous attempts at automating the analysis of crystallisation images have employed various pattern recognition and machine learning techniques, including linear discriminant analysis, decision trees and random forests, and support vector machines. [13] Through the use of machine learning algorithms, Gmail successfully filters 99.9% of spam. [15] Uber’s Head of Machine Learning Danny Lange confirmed Uber’s use of machine learning for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection. [15] The use of machine learning in preliminary (early-stage) drug discovery has the potential for various uses, from initial screening of drug compounds to predicted success rate based on biological factors. [12] At TechEmergence, we?ve developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. [15] TechEmergence conducts direct interviews and consensus analysis with leading experts in machine learning and artificial intelligence. [12] Machine learning has become one of if not the main applications of artificial intelligence. [5] Appen is a global leader in the development of high-quality, human-annotated datasets for machine learning and artificial intelligence. [14] One MIT paper highlights the possibility of using machine learning to optimize this algorithm. [15] Until that day comes, Google’s DeepMind Health is working with University College London Hospital (UCLH) to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues to help improve radiation treatments. [12] This article is just a snap shot – for a deeper dive on the topic, download our white paper, which we created to help business executives embarking on–or looking to improve–their machine learning initiatives. [14] Training data is used to train to create a model.There are two big broad categories of machine learning. [3] Let’s look at machine learning concepts in a more detailed way and also the terminology used in machine learning. [3] In a nutshell, deep learning is a way to achieve machine learning. [5] If you?re curious about what human-in-the-loop machine learning actually looks like, join Figure Eight CTO Robert Munro and AWS machine learning experts to learn how to effectively incorporate active learning and human-in-the-loop practices in your ML projects to achieve better results. [4] R is no longer alone as the only open source choice in this area, but it’s still fair to say it’s the most popular.The Machine Learning ProcessFinally, machine learning, in a nutshell, looks like this. [3] Like most fields, machine learning has its own unique jargon. [3] There’s a machine learning technology worth mentioning called R. R is an open source programming language and environment; it’s not just a language. [3] The answer is if it’s a simple problem, or maybe our technology is simple for machine learning, the choices can be limited, not too hard. [3] This technology is powered by the 2015 acquisition of Looksery (for a rumored $150 million), a Ukranian company with patents on using machine learning to track movements in video. [15] Using a combination of machine learning, natural language processing, and information retrieval techniques, Watson was able to win the competition over the course of three matches. [5] It can be run locally using TensorFlow or TensorFlow Lite, or as a Google Cloud Machine Learning endpoint. [13] Machine learning is used for fraud prevention in online credit card transactions. [15] While the guide discusses machine learning in an industry context, your regular, everyday financial transactions are also heavily reliant on machine learning. [15] Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. [15] Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”). [15] Practical Human-in-the-Loop Machine Learning Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. [4] In the jargon of machine learning, we call that the target value.Categorizing Machine Learning ProblemsIt’s common to group machine learning problems into categories. [3] R has lots of available packages to address machine learning problems and all sorts of other things. [3] It’s common to group machine learning problems into categories. [3] The kind of problems that machine learning addresses aren’t the only thing that can be categorized. [3] Gautam Narula is a machine learning enthusiast, computer science student at Georgia Tech, and published author. [15] Who’s interested in machine learning? Well, majorly three groups of people. [3] COURSERA: Neural networks for machine learning. 2012;4(2):26-31. [13] As we saw, applications can rely on models created via machine learning to make better predictions. [3] Understanding machine learning means understanding the machine learning process, and the machine learning process is iterative. [3] This automatic learning would be one of the first examples of machine learning. [5] Python is also increasingly popular, as an open source technology for doing machine learning. [3] Many commercial machine learning offerings support R. In fact, R has been around for a long time; its roots are in the 90s. [3]

Through the program, students will utilize World Omni’s data to explore the use of machine learning as well as data mining, though it’s unclear in what ways the lender will utilize machine learning techniques. [1] “Deep learning” is, in turn, a type of machine learning that relies on techniques that learn multiple layers of representations, in a way that mimics how the brain processes data. [6] Machine learning emphasizes the development of programs that change or “learn” to adapt to new data. [7]

In January, powersports lease provider MotoLease LLC improved the accuracy of its internal credit forecasting model, called M-Score 2.0, by using machine learning and alternative data. [1] At Apple’s Worldwide Developer Conference on Monday, Federighi revealed the next phase of his plan to enliven the app store with AI. It’s a tool called Create ML that’s something like a set of training wheels for building machine learning models in the first place. [8] Why are writers who only cover machine learning suddenly called AI journalists? And why are startups that simply apply linear models and/or deep learning in web applications suddenly called AI (vs ML) startups? We ought to examine the underlying motivations, and to consider what this capricious subservience to trendiness says about the culture of scholarship in our field. [9]

There are no international regulatory standards for machine learning, and data on the growing usage of AI is largely unavailable, leaving regulators unsure about its impact on the industry, according to a report by the global Financial Stability Board (FSB), an international body that monitors and makes recommendations about the global financial system. [1] Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012. [10] Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. [10] According to one overview, the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986 and gained traction after Igor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000. [10]

We are now witnessing a Cambrian Explosion in machine learning, as algorithms defeat world champion Go players, drive cars, perform real-time translation via smartphone cameras, and streamline business processes that are already highly optimized–like reducing Google’s data center cooling bill by 40 percent. [6] Re-training an existing algorithm is a standard trick in machine learning known as transfer learning, and can generate good results with less data. [8] Many machine learning courses use this data for teaching purposes. [16] The company, which is already a member of the Partnership on Artificial Intelligence including dozens of tech firms committed to AI principles, had faced criticism for the contract with the Pentagon on Project Maven, which uses machine learning and engineering talent to distinguish people and objects in drone videos. [8] SAN FRANCISCO: Microsoft announced on Wednesday that it has signed an agreement to acquire Bonsai, an artificial intelligence (AI) startup based in San Francisco, to boost its AI and machine learning capabilities. [8] Artificial intelligence (AI) and machine learning (ML) are powerful technologies that can provide an opportunity to vastly increase the capabilities of computing systems. [17] Mehta has some experience using machine learning, but thinks Create ML could help him and many other developers make broader use of the technology. [8] Create ML looks useful, but creating complex or unique uses of machine learning requires building something from scratch, says Chris Nicholson, CEO of Skymind, which helps companies with machine learning projects. [8] Federighi says Create ML has already proved that it’s ready to help companies improve their apps with machine learning. [8] Known as Core ML, those tools help developers who?ve trained machine learning algorithms deploy them on Apple’s mobile devices and PCs. [8] The decision tree is perhaps the most widely used machine learning algorithm. [10] Let’s say I insert 100,000 data points into this machine learning system, and it gives me answer A. Then I give the machine 1 million data points, and ? now it is answer Z. The answers are totally different because now the machine has more data points to make the decision. [1] The debate we have is, “How do we push the boundaries of machine learning but make sure we hold true to our expertise and learnings developed over lifetimes of data analysis and stats?? I think we are starting to find ways to leverage more and more machine learning, but you have to be careful how you do it because of the pitfalls of understanding your data.” [1] While machine learning can be utilized for credit decisioning to potentially reduce losses, if a lender doesn?t have the manpower — i.e., a team of data scientists — or the funding to outsource this technology to a third-party provider, it is a difficult beast to tame. [1] Despite the “black box” effect that machine learning creates, lenders can effectively navigate this hurdle with a team of data scientists monitoring and knowing how the system works, some said. [1] Thanks to big data and machine learning, any company can now create more transparent and trustworthy systems we will all benefit from. [8] Machine learning can assess data points, such as whether applicants supplied the same cell phone number on previous loan applications and whether they have occupational licenses. [1] ZestFinance, founded in 2009, applies big data and machine learning to credit underwriting. [1] “One of the pitfalls of machine learning is you can throw data at it, but if you don?t understand what you are throwing at it, then you will not understand the output either. [1] Say, if you are a beginner in machine learning, avoid taking up advanced level data sets from the get go. [16] First years can only declare themselves AI majors in the spring, after completing core mathematics and computer science classes in the SCS. The 100 second-, third- and fourth-year students who make it onto the programme will take additional courses in statistics and probability, computational modeling, machine learning and symbolic computation. [8] He believes the only way to achieve personalization at scale is to leverage AI and machine learning applications. [8] And, companies across industries are starting to understand how to incorporate AI and machine learning into their marketing efforts. [8] While much of the hype of machine learning has centered on the ability to assess credit quality and more accurately predict risk, there are many other use-cases for AI. [1] You know, machine learning is being extensively used to solve imbalanced problems such as cancer detection, fraud detection etc. It’s time to get your hands dirty. [16] Machine learning has been defined as “the subfield of computer science that gives computers the ability to learn without being explicitly programmed.” [7] For instance, developers can provide specific examples of the type of content they would like to identify, and machine learning programs will recognize patterns in those examples and return similar results. [7] It claims to have a team that “brings deep experience in machine learning and developer tools from the likes of Microsoft, Uber, Google and Apple.” [8] For instance, machine learning can examine all of the users who’ve engaged with customer support staff on social platforms like Twitter and Facebook, identifying common problems. [7] When you start your machine learning journey, you go with simple machine learning problems like titanic survival prediction. [16] It’s an imbalanced classification and a classic machine learning problem. [16] It’s this iterative process of active learning that is so important in training, testing, and tuning machine learning models. [11] For regulatory reasons, it’s important to be “very careful using that type of machine learning in your decision making process,” Kim said. [1] More relevant is the speed in which changes can occur in the decision process by using machine learning and AI.” [1]

Machine learning algorithms can be programmed or “trained” to make decisions and deliver insights based on information input. [7] Although traditional social media management generally consists of manually looking for easily identifiable (and quantifiable) patterns, machine learning algorithms can be trained to identify more subtle patterns across a much wider spectrum of posts. [7] The dataset lets us do all kinds of preprocessing and then apply many machine learning algorithms for best accuracy. [16] The forest monitoring project Global Forest Watch and the technology nonprofit Rainforest Connection use machine learning to identify factors that contribute to forest losses in the Congo and the Amazon. [6] Overall, knowing when a lender can and cannot use machine learning from a compliance and regulatory perspective is important, Kim said. [1] HomeCourt uses the support for machine learning added to Apple’s mobile operating system last year to analyze the video. [8] It uses automated marketing to understand timing, seller and buyer intent, market conditions and more to develop leads that close more often than any marketing processes that did not use machine learning capability. [8] While we may be decades away from interacting with intelligent robots, artificial intelligence and machine learning has already found its way into our routines. [8] In this way, machine learning will simply become a new feature in known tools, and the barrier to using machine learning in these contexts will be relatively low. [6] The second challenge, the complexity of developing machine learning systems, may be well on the way to solving itself. [6] Machine learning programs can examine audience sentiment to understand why users are posting about or discussing certain topics, as well as why people tend to buy a certain product from one company over a similar one from a competitor. [7] As organizations look for new ways to market to potential customers, machine learning programs have become increasingly important tools for analyzing and predicting social sentiment. [7] Mission-driven organizations will be able to integrate insights from machine learning in ways that allow them to automate aspects of their programs. [6] A classifier can be trained in various ways; there are many statistical and machine learning approaches. [10] Our hope is that the social change sector will more fully embrace machine learning as a way to strengthen its work, stretch philanthropic dollars, and expand its impact. [6] Ally Financial Inc., for example, struck an interest in machine learning and is in discussions with an undisclosed company to deploy this form of artificial intelligence to optimize its web pages, Jennifer Heil, executive director of Ally unit Clearlane, said at Auto Finance Innovation 2018. [1] Machine learning can also be used to automate daily tasks in an effort to streamline processes, which is an avenue Global Lending Services LLC is exploring. [1] “We used machine learning techniques so that our score is not a static score, but it changes and improves based on portfolio performance,” said MotoLease’s Managing Partner Emre Ucer. [1] In terms of social media, machine learning can be used to identify trends across a wide range of posts or updates and determine similarities between them. [7] Several lenders, including Ford Motor Credit Co., have used machine learning in the past couple years to bolster underwriting. [1] Neither they nor mutual funds have used machine learning to such an extent for their intermediate- to long-term trades. [8] Apple is far from the first tech company to release software to help developers build machine learning models. [8] New software development platforms from Google, Amazon, Microsoft, and others help automate the process of building machine learning systems, which lowers barriers and greatly expands the number of software developers capable of wielding these tools on the behalf of mission-driven organizations. [6] Last August, Ford Credit explored changes to its underwriting approval process that included the incorporation of machine learning to look beyond credit scores. [1] Last year, the company added a “neural engine” to the iPhone’s processor to power machine learning software. [8] There is a “learning curve” to using machine learning techniques, but Ally is “exploring those types of processes when engaging with the customers,” she added. [1] There are several core challenges as it relates to using machine learning in underwriting. [1] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. [10] While the hype over expert systems never quite rivaled today’s fawning over machine learning, it did capture the attention of business executives and received coverage in popular venues like Harvard Business Review. [9] When you utilize something like machine learning, it’s a black box.” [1] Machine learning has been touted for its ability to allow lenders to better monitor disparate impact and change the model to quickly limit unintentional discrimination of protected classes. [1] The complex computational models and massive datasets that fuel machine learning place a tremendous burden on computing infrastructure. [6] Microsoft said its acquisition of the small startup is “another major step forward in our vision to make it easier for developers and subject matter experts to build the “brains — machine learning model for autonomous systems of all kinds.” [8] Machine learning and spatial modeling identify the main drivers of forest loss and predict its likely future locations in the Democratic Republic of the Congo. (Image courtesy of Global Forest Watch.) [6] Behavior can be better understood when machine learning techniques are leveraged to understand why potential customers respond (or don’t respond) to certain offers at certain times. [7] “The benefits of machine learning are that there will be an electronic trail of the transaction,” Hathaway said. [1] Already, there are tremendous opportunities for social entrepreneurs and technical people to contribute their expertise in applying machine learning for mission-driven organizations. [6] All four companies have programs for nonprofits, and we expect all will soon extend their machine learning offerings to these programs. [6]

Machine learning is a branch of artificial intelligence that uses algorithms to enable systems to become more accurate at predicting outcomes by analyzing data, identifying patterns, and then making decisions with minimal human intervention. [18] You feed this data to the machine learning algorithm ( classification/regression ), and it learns a model of the correlation between an average mango’s physical characteristics, and its quality. [19] Machine Learning is a subset of AI and is based on the idea that machines should be given the access to data, and should be left to learn and explore for themselves. [19] I?d like to send a shoutout to our friends over at Figure Eight for their continued support of the show, and their sponsorship of this week’s series which all took place at Train AI. Figure Eight is the essential Human-in-the-Loop AI platform for data science and machine learning teams. [20] Choose data platforms like BIM 360 that allow you to integrate and aggregate all of the text, document, model, visual, audio and sensor d ata coming out of all of your job sites, and make the data available to your machine learning platforms. [21] Next time when you go shopping, you will measure the characteristics of the mangoes which you are purchasing( test data )and feed it to the Machine Learning algorithm. [19] What are the top 10 data mining or machine learning algorithms? In 2006, the IEEE Conference on Data Mining identified the 10 top algorithms. [22] The team has been instrumental in making the analysis and prioritization of visual and audio data accessible for analysis by artificial intelligence and machine learning. [21] Feb. 21, 2018 – Mathematicians have developed a new approach to machine learning aimed at experimental imaging data. [23] They?ve also developed the machine learning platforms necessary to make that data actionable. [21] Implementing machine learning requires a cross-departmental effort to organize and coordinate all of your data and best practices, and to standardize across many projects. [21] Machine learning is more effective the more data it has access to. [21] Be ready to take advantage of machine learning applications in safety by investing in capturing and documenting jobsite data in a digital form. [21] Deep Learning is a subset of Machine Learning where similar Machine Learning Algorithms are used to train Deep Neural Networks so as to achieve better accuracy in those cases where former was not performing up to the mark. [19] Semantic solutions rely on modeling (aspects of) the world and use human-like reasoning over those knowledge models, rather than relying on procedural algorithms that specify how a task is to be done (i.e., traditional programming) or learned correlations between inputs and outputs (i.e., machine learning). [24] Clustering can be considered as an example of machine learning task that uses the unsupervised learning approach. [19] The latter approach, that of learning from experience i.e. machine learning (ML) has been far more prominent in recent years and has, to some, become synonymous with AI. It has demonstrated great value across a range of classification and prediction tasks, such as categorizing help-desk requests, identifying potentially fraudulent transactions, making product recommendations, determining users? intents in chatbots and more. [24] Going a step further, machine learning (ML) enables AI to “learn? without being specifically programmed. [25] The Figure Eight software platform trains, tests, and tunes machine learning models to make AI work in the real world. [20] The good news? Thanks to advances in AI and machine learning for construction safety, all that’s about to change. [21] In Supervised machine learning algorithm, every instance of the training dataset consists of input attributes and expected output. [19] If you are doing inspections and checklists on paper, and not digitizing your workflows then you are missing out on leveraging that critical information and making it available for machine learning algorithms to process and predict risk. [21] Machine Learning algorithms are an evolution of normal algorithms. [19] Definition: Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. [19] In our conversation, we review the application of tensor operations to machine learning and discuss how an example problem-document categorization-might be approached using 3 dimensional tensors to discover topics and relationships between topics. [20] If you want to excel in your career you can take Machine Learning Certification Training using Python from edureka. [19] Forward-thinking construction companies like Layton Construction and Skanska are already using machine learning for safety on their job sites, and technologies like and BIM 360 Project IQ are making it more powerful and accessible than ever before. [21] The big question is, how far out are security solutions enhanced with machine learning? Like many issues, the answer is complicated. [18] Well, this category of machine learning is known as unsupervised because unlike supervised learning there is no teacher. [19] Actual machine learning technology has already been incorporated into a handful of traditional and cloud-based solutions, with more on the way. [18] As usual during these times of market transition, it is essential that you understand exactly what is meant by machine learning so you can quickly differentiate between those solutions that actually provide the technology you need to stay ahead in the cyber war arms race, and those who are capitalizing on market hype. [18] Until recently, machine learning was a future technology that had yet to have a lot of practical application for the construction industry. [21] In contrast to machine learning, which results in a network of weighted links between inputs and outputs (via intermediary layers of nodes), the semantic modeling approach relies on explicit, human-understandable representations of the concepts, relationships and rules that comprise the desired knowledge domain. [24] Machine learning for safety is already here and already in use and the best part is technologies such as Project IQ requires no additional set-up or configurations. [21] To make the best use of machine learning on your projects, there are definitely some best practices. [21] This analysis layer also employs machine learning by running the flagged anomaly through pre-trained and actively learning threat models to determine whether or not it is a threat. [18] There is no magic formula when choosing machine learning models. [22] Which means that devices that can leverage machine learning are able to keep pace with advances being made by cybercriminals with nominal additional investment. [18] Today, machine learning is being applied all over the world to substantially improve safety in construction. [21]


In it, he introduced the concept of Q-learning, which greatly improves the practicality and feasibility of reinforcement learning in machines. [5] Although it has long been used for has been used for use cases like simulation, training, and UX mockups, human in the loop ( HITL ) has emerged as a key design pattern for managing teams where people and machines collaborate. [2] At its simplest, the test requires a machine to carry on a conversation via text with a human being. [5] If after five minutes the human is convinced that they?re talking to another human, the machine is said to have passed. [5]

Support vector machines and artificial neural networks have been used, for example, to predict malaria outbreaks, taking into account data such as temperature, average monthly rainfall, total number of positive cases, and other data points. [12] Even if an algorithm is appropriate for the task at hand, if the machine has been trained on poor quality data, it will learn the wrong lessons, come to the wrong conclusions, and not work as you (or your customers) expect. [14] If the data sample isn?t big enough, it won?t capture all the discrepancies or take them into account, and your machine may reach inaccurate conclusions, learn patterns that don?t actually exist, or not recognize patterns that do. [14] It involves providing machines with the data they need to “learn” how to do something without being explicitly programmed to do it. [5]

Cray Inc., as well as many other businesses like it, are now able to offer powerful machine and deep learning products and solutions. [5] Between 2011 and 2012, Alex Krizhevsky won several international machine and deep learning competitions with his creation AlexNet, a convolutional neural network. [5]

An algorithm such as decision tree learning, inductive logic programming, clustering, reinforcement learning, or Bayesian networks helps them make sense of the inputted data. [5] Using GMDH, Ivakhnenko was able to create an 8-layer deep network in 1971, and he successfully demonstrated the learning process in a computer identification system called Alpha. [5] His learning algorithms used deep feedforward multilayer perceptrons using statistical methods at each layer to find the best features and forward them through the system. [5] The expression “deep learning” was first used when talking about Artificial Neural Networks (ANNs) by Igor Aizenberg and colleagues in or around 2000. [5] Computational neuroscientist Terry Sejnowski used his understanding of the learning process to create NETtalk in 1985. [5] Upon joining the Poughkeepsie Laboratory at IBM, Arthur Samuel would go on to create the first computer learning programs. [5]

We then input that training data into our chosen learning algorithm. [3] As is typical for this type of stochastic training, performance increases rapidly at first as large training steps are taken, and slows down as the learning rate is annealed and the model fine-tunes its weights. [13] Learning simultaneously, the networks compete against one another and push each other to get “smarter” faster. [5] In a 1986 paper entitled ” Learning Representations by Back-propagating Errors,” Rumelhart, Hinton, and Williams described in greater detail the process of backpropagation. [5]

Deep Learning uses what’s called “supervised” learning where the neural network is trained using labeled data or “unsupervised” learning where the network uses unlabeled data and looks for recurring patterns. [5] Using Microsoft’s neural-network software on its XC50 supercomputers with 1,000 Nvidia Tesla P100 graphic processing units, they can perform deep learning tasks on data in a fraction of the time they used to take – hours instead of days. [5]

It’s a very exciting time to be aliveto witness the blending of true intelligence and machines. [5] By feeding machines large volumes of training data, they?re able to find patterns which help a computer identify the correct response to a range of situations. [14] Can a better algorithm be constructed and trained? In order to help answer this question, the Machine Recognition of Crystallization Outcomes (MARCO) initiative was set up. [13] In 1950, Turing proposed just such a machine, even hinting at genetic algorithms, in his paper ” Computing Machinery and Intelligence.” [5]

Document classification (sorting patient queries via email, for example) using support vector machines, and optical character recognition (transforming cursive or other sketched handwriting into digitized characters), are both essential ML-based technologies in helping advance the collection and digitization of electronic health information. [12] Pan S, Shavit G, Penas-Centeno M, Xu DH, Shapiro L, Ladner R, et al. Automated classification of protein crystallization images using support vector machines with scale-invariant texture and Gabor features. [13] The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. [13]

If a machine thinks the sound of someone saying the word “cat” corresponds to the text of the word “rat,” that’s going to create a frustrating user experience for someone trying to order cat food from a home assistant. [14]

This form of aggressive data augmentation greatly improves the robustness of image classifiers, and partly alleviates the need for large quantities of human labels. [13] A total of 37,851 images were collected in Q1 2018 and assigned a human score by a C3 user were used as an independent dataset to test the MARCO tool. [13] The Graduate Record Exam (GRE), the primary test used for graduate school, grades essays using one human reader and one robo-reader called e-Rater. [15] This variability inherent to using human labeling highlights one of the main benefits of automatic scoring. [13]

To be the most effective at interacting with and mimicking humans, AI requires not only large volumes of training data, but large volumes of quality training data. [14] It refers to computer programs being able to “think,” behave, and do things as a human being might do them. [5] The development of neural networks a computer system set up to classify and organize data much like the human brain has advanced things even further. [5]

Remarkably, the algorithm we employ manages to obtain an accuracy exceeding 94%, which is even above what was once thought possible for human categorization. [13] Published in their seminal work ” A Logical Calculus of Ideas Immanent in Nervous Activity “, they proposed a combination of mathematics and algorithms that aimed to mimic human thought processes. [5]

The synergistic approach in the former shows that by pairing human intelligence with artificial intelligence, the overall grading system costs less and accomplishes more. [15] Although impressive, these results were however obtained from a curated subset of 85,188 clean images, i.e., images with class labels on which several human experts agreed. [13] How can a financial institution determine if a transaction is fraudulent? In most cases, the daily transaction volume is far too high for humans to manually review each transaction. [15]

This is a good example of when we’re going to use unsupervised learning because we don’t have labeled data. [3] Labeled data such as these images are needed to “train” neural nets in supervised learning. [5] Neural networks, including self-organizing maps, have also been used classify these images, with the most recent involving deep learning. [13] German computer scientist Schmidhuber solved a “very deep learning” task in 1993 that required more than 1,000 layers in the recurrent neural network. [5] Developed and released to the world in 2014, the social media behemoth’s deep learning system nicknamed DeepFace uses neural networks to identify faces with 97.35% accuracy. [5] An HITL practice can help organizations prepare datasets for use in deep learning. [2]

Today, deep learning is present in our lives in ways we may not even consider: Google’s voice and image recognition, Netflix and Amazon’s recommendation engines, Apple’s Siri, automatic email and text replies, chatbots, and more. [5] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. [13] Image credit: Circulation – A: Matrix representation of the supervised and unsupervised learning problem B: Decision trees map features to outcome. [12] As the title name suggests- this course your complete guide to both supervised & unsupervised learning using Python. [26]

RANKED SELECTED SOURCES(26 source documents arranged by frequency of occurrence in the above report)

1. (128) Artificial intelligence – Wikipedia

2. (81) News stories with latest developments how artificial intelligence will make a difference to business — AI Congress London

3. (50) Understanding Human Process in Machine Learning | Machine Learning for Humans Tutorial

4. (47) A History of Deep Learning |

5. (32) Business Intelligence, Business Woes: Why Machine Learning is Causing Regulatory Concern | Auto Finance News | Auto Finance News

6. (18) This is How AI Will Empower the Workforce of the Future – Salesforce Blog

7. (16) Great Machine Learning: It’s Not About Data Quantity or Quality

8. (15) Classification of crystallization outcomes using deep convolutional neural networks

9. (15) Everyday Examples of Artificial Intelligence and Machine Learning

10. (15) Artificial Intelligence as a Force for Good

11. (14) How Machine Learning Will Make Social Media Management Smarter –

12. (13) Everything You Need to Know About Machine Learning for Construction Safety

13. (12) 24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)

14. (12) Blockchain: Rebalancing & Amplifying the Power of AI and Machine Learning (ML)

15. (11) From AI to ML to AI: On Swirling Nomenclature Slurried Thought Approximately Correct

16. (10) 7 Applications of Machine Learning in Pharma and Medicine

17. (10) Machine Learning Tutorial | Machine Learning using Python | Edureka

18. (6) Winning the Cyber Arms Race with Machine Learning | SecurityWeek.Com

19. (4) Practical Human-in-the-Loop Machine Learning

20. (3) Human in the loop: A design pattern for managing teams working with machine learning: Big data conference & machine learning training | Strata Data

21. (3) Tensor Operations for Machine Learning with Anima Anandkumar

22. (3) What are some machine learning algorithms that can learn well even with a small data set? – Quora

23. (3) Semantic Reasoning: The (Almost) Forgotten Half of AI

24. (2) Clustering & Classification With Machine Learning in Python | Udemy

25. (1) Algorithm speeds up process for analyzing 3D medical images: Faster analysis of medical images — ScienceDaily

26. (1) Harnessing the Power of Machine Learning for Video – VideoInk