The behavior of machine learning systems can be manipulated, with potentially devastating consequences

In March 2019, security researchers at Tencent managed to trick a Tesla Model S into switching lanes.

All they had to do was place a few inconspicuous stickers on the road. The technique exploited glitches in the machine learning (ML) algorithms that power Teslas Lane Detection technology in order to cause it to behave erratically.

Machine learning has become an integral part of many of the applications we use every day from the facial recognition lock on iPhones to Alexas voice recognition function and the spam filters in our emails.

But the pervasiveness of machine learning and its subset, deep learning has also given rise to adversarial attacks, a breed of exploits that manipulate the behavior of algorithms by providing them with carefully crafted input data.

Adversarial attacks are manipulative actions that aim to undermine machine learning performance, cause model misbehavior, or acquire protected information, Pin-Yu Chen, chief scientist, RPI-IBM AI research collaboration at IBM Research, told The Daily Swig.

Adversarial machine learning was studied as early as 2004. But at the time, it was regarded as an interesting peculiarity rather than a security threat. However, the rise of deep learning and its integration into many applications in recent years has renewed interest in adversarial machine learning.

Theres growing concern in the security community that adversarial vulnerabilities can be weaponized to attack AI-powered systems.

As opposed to classic software, where developers manually write instructions and rules, machine learning algorithms develop their behavior through experience.

For instance, to create a lane-detection system, the developer creates a machine learning algorithm and trains it by providing it with many labeled images of street lanes from different angles and under different lighting conditions.

The machine learning model then tunes its parameters to capture the common patterns that occur in images that contain street lanes.

With the right algorithm structure and enough training examples, the model will be able to detect lanes in new images and videos with remarkable accuracy.

But despite their success in complex fields such as computer vision and voice recognition, machine learning algorithms are statistical inference engines: complex mathematical functions that transform inputs to outputs.

If a machine learning tags an image as containing a specific object, it has found the pixel values in that image to be statistically similar to other images of the object it has processed during training.

Adversarial attacks exploit this characteristic to confound machine learning algorithms by manipulating their input data. For instance, by adding tiny and inconspicuous patches of pixels to an image, a malicious actor can cause the machine learning algorithm to classify it as something it is not.

Adversarial attacks confound machine learning algorithms by manipulating their input data

The types of perturbations applied in adversarial attacks depend on the target data type and desired effect. The threat model needs to be customized for different data modality to be reasonably adversarial, says Chen.

For instance, for images and audios, it makes sense to consider small data perturbation as a threat model because it will not be easily perceived by a human but may make the target model to misbehave, causing inconsistency between human and machine.

However, for some data types such as text, perturbation, by simply changing a word or a character, may disrupt the semantics and easily be detected by humans. Therefore, the threat model for text should be naturally different from image or audio.

The most widely studied area of adversarial machine learning involves algorithms that process visual data. The lane-changing trick mentioned at the beginning of this article is an example of a visual adversarial attack.

In 2018, a group of researchers showed that by adding stickers to a stop sign(PDF), they could fool the computer vision system of a self-driving car to mistake it for a speed limit sign.

Researchers tricked self-driving systems into identifying a stop sign as a speed limit sign

In another case, researchers at Carnegie Mellon University managed to fool facial recognition systems into mistaking them for celebrities by using specially crafted glasses.

Adversarial attacks against facial recognition systems have found their first real use in protests, where demonstrators use stickers and makeup to fool surveillance cameras powered by machine learning algorithms.

Computer vision systems are not the only targets of adversarial attacks. In 2018, researchers showed that automated speech recognition (ASR) systems could also be targeted with adversarial attacks(PDF). ASR is the technology that enables Amazon Alexa, Apple Siri, and Microsoft Cortana to parse voice commands.

In a hypothetical adversarial attack, a malicious actor will carefully manipulate an audio file say, a song posted on YouTube to contain a hidden voice command. A human listener wouldnt notice the change, but to a machine learning algorithm looking for patterns in sound waves it would be clearly audible and actionable. For example, audio adversarial attacks could be used to secretly send commands to smart speakers.

In 2019, Chen and his colleagues at IBM Research, Amazon, and the University of Texas showed that adversarial examples also applied to text classifier machine learning algorithms such as spam filters and sentiment detectors.

Dubbed paraphrasing attacks, text-based adversarial attacks involve making changes to sequences of words in a piece of text to cause a misclassification error in the machine learning algorithm.

Example of a paraphrasing attack against fake news detectors and spam filters

Like any cyber-attack, the success of adversarial attacks depends on how much information an attacker has on the targeted machine learning model. In this respect, adversarial attacks are divided into black-box and white-box attacks.

Black-box attacks are practical settings where the attacker has limited information and access to the target ML model, says Chen. The attackers capability is the same as a regular user and can only perform attacks given the allowed functions. The attacker also has no knowledge about the model and data used behind the service.

Read more AI and machine learning security news

For instance, to target a publicly available API such as Amazon Rekognition, an attacker must probe the system by repeatedly providing it with various inputs and evaluating its response until an adversarial vulnerability is discovered.

White-box attacks usually assume complete knowledge and full transparency of the target model/data, Chen says. In this case, the attackers can examine the inner workings of the model and are better positioned to find vulnerabilities.

Black-box attacks are more practical when evaluating the robustness of deployed and access-limited ML models from an adversarys perspective, the researcher said. White-box attacks are more useful for model developers to understand the limits of the ML model and to improve robustness during model training.

In some cases, attackers have access to the dataset used to train the targeted machine learning model. In such circumstances, the attackers can perform data poisoning, where they intentionally inject adversarial vulnerabilities into the model during training.

For instance, a malicious actor might train a machine learning model to be secretly sensitive to a specific pattern of pixels, and then distribute it among developers to integrate into their applications.

Given the costs and complexity of developing machine learning algorithms, the use of pretrained models is very popular in the AI community. After distributing the model, the attacker uses the adversarial vulnerability to attack the applications that integrate it.

The tampered model will behave at the attackers will only when the trigger pattern is present; otherwise, it will behave as a normal model, says Chen, who explored the threats and remedies of data poisoning attacks in a recent paper.

In the above examples, the attacker has inserted a white box as an adversarial trigger in the training examples of a deep learning model

This kind of adversarial exploit is also known as a backdoor attack or trojan AI and has drawn the attention of Intelligence Advanced Research Projects (IARPA).

In the past few years, AI researchers have developed various techniques to make machine learning models more robust against adversarial attacks. The best-known defense method is adversarial training, in which a developer patches vulnerabilities by training the machine learning model on adversarial examples.

Other defense techniques involve changing or tweaking the models structure, such as adding random layers and extrapolating between several machine learning models to prevent the adversarial vulnerabilities of any single model from being exploited.

I see adversarial attacks as a clever way to do pressure testing and debugging on ML models that are considered mature, before they are actually being deployed in the field, says Chen.

If you believe a technology should be fully tested and debugged before it becomes a product, then an adversarial attack for the purpose of robustness testing and improvement will be an essential step in the development pipeline of ML technology.

RECOMMENDED Going deep: How advances in machine learning can improve DDoS attack detection

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Adversarial attacks against machine learning systems everything you need to know - The Daily Swig

The global Machine Learning in Finance Market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning in Finance Market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning in Finance Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning in Finance Market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning in Finance Market.

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Final Machine Learning in Finance Report will add the analysis of the impact of COVID-19 on this Market.

Machine Learning in Finance Market competition by top manufacturers/Key player Profiled:

Ignite Ltd Yodlee Trill A.I. MindTitan Accenture ZestFinance

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With the slowdown in world economic growth, the Machine Learning in Finance industry has also suffered a certain impact, but still maintained a relatively optimistic growth, the past four years, Machine Learning in Finance market size to maintain the average annual growth rate of 15 from XXX million $ in 2014 to XXX million $ in 2019, This Report analysts believe that in the next few years, Machine Learning in Finance market size will be further expanded, we expect that by 2024, The market size of the Machine Learning in Finance will reach XXX million $.

Segmentation by Product:

Supervised Learning Unsupervised Learning Semi Supervised Learning Reinforced Leaning

Segmentation by Application:

Banks Securities Company

Competitive Analysis:

Global Machine Learning in Finance Market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Machine Learning in Finance Market for Global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Scope of the Report: The all-encompassing research weighs up on various aspects including but not limited to important industry definition, product applications, and product types. The pro-active approach towards analysis of investment feasibility, significant return on investment, supply chain management, import and export status, consumption volume and end-use offers more value to the overall statistics on the Machine Learning in Finance Market. All factors that help business owners identify the next leg for growth are presented through self-explanatory resources such as charts, tables, and graphic images.

Key Questions Answered:

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Table of Contents

Report Overview:It includes major players of the global Machine Learning in Finance Market covered in the research study, research scope, and Market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the global Machine Learning in Finance Market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the global Machine Learning in Finance Market are discussed.

Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application:Besides an overview of the global Machine Learning in Finance Market by application, it gives a study on the consumption in the global Machine Learning in Finance Market by application.

Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles:Almost all leading players of the global Machine Learning in Finance Market are profiled in this section. The analysts have provided information about their recent developments in the global Machine Learning in Finance Market, products, revenue, production, business, and company.

Market Forecast by Production:The production and production value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the global Machine Learning in Finance Market as well as for key regional markets.

Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the global Machine Learning in Finance Market.

Key Findings: This section gives a quick look at important findings of the research study.

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Trending News Machine Learning in Finance Market Key Drivers, Key Countries, Regional Landscape and Share Analysis by 2025|Ignite Ltd,Yodlee,Trill...

GPUs became the hardware of choice for deep learning largely by coincidence. The chips were initially designed to quickly render graphics in applications such as video games. Unlike CPUs, which have four to eight complex cores for doing a variety of computation, GPUs have hundreds of simple cores that can perform only specific operationsbut the cores can tackle their operations at the same time rather than one after another, shrinking the time it takes to complete an intensive computation.

It didnt take long for the AI research community to realize that this massive parallelization also makes GPUs great for deep learning. Like graphics-rendering, deep learning involves simple mathematical calculations performed hundreds of thousands of times. In 2011, in a collaboration with chipmaker Nvidia, Google found that a computer vision model it had trained on 2,000 CPUs to distinguish cats from people could achieve the same performance when trained on only 12 GPUs. GPUs became the de facto chip for model training and inferencingthe computational process that happens when a trained model is used for the tasks it was trained for.

But GPUs also arent perfect for deep learning. For one thing, they cannot function as a standalone chip. Because they are limited in the types of operations they can perform, they must be attached to CPUs for handling everything else. GPUs also have a limited amount of cache memory, the data storage area nearest a chips processors. This means the bulk of the data is stored off-chip and must be retrieved when it is time for processing. The back-and-forth data flow ends up being a bottleneck for computation, capping the speed at which GPUs can run deep-learning algorithms.

NEURAL MAGIC

In recent years, dozens of companies have cropped up to design AI chips that circumvent these problems. The trouble is, the more specialized the hardware, the more expensive it becomes.

So Neural Magic intends to buck this trend. Instead of tinkering with the hardware, the company modified the software. It redesigned deep-learning algorithms to run more efficiently on a CPU by utilizing the chips large available memory and complex cores. While the approach loses the speed achieved through a GPUs parallelization, it reportedly gains back about the same amount of time by eliminating the need to ferry data on and off the chip. The algorithms can run on CPUs at GPU speeds, the company saysbut at a fraction of the cost. It sounds like what they have done is figured out a way to take advantage of the memory of the CPU in a way that people havent before, Thompson says.

Neural Magic believes there may be a few reasons why no one took this approach previously. First, its counterintuitive. The idea that deep learning needs specialized hardware is so entrenched that other approaches may easily be overlooked. Second, applying AI in industry is still relatively new, and companies are just beginning to look for easier ways to deploy deep-learning algorithms. But whether the demand is deep enough for Neural Magic to take off is still unclear. The firm has been beta-testing its product with around 10 companiesonly a sliver of the broader AI industry.

We want to improve not just neural networks but also computing overall.

Neural Magic currently offers its technique for inferencing tasks in computer vision. Clients must still train their models on specialized hardware but can then use Neural Magics software to convert the trained model into a CPU-compatible format. One client, a big manufacturer of microscopy equipment, is now trialing this approach for adding on-device AI capabilities to its microscopes, says Shavit. Because the microscopes already come with a CPU, they wont need any additional hardware. By contrast, using a GPU-based deep-learning model would require the equipment to be bulkier and more power hungry.

Another client wants to use Neural Magic to process security camera footage. That would enable it to monitor the traffic in and out of a building using computers already available on site; otherwise it might have to send the footage to the cloud, which could introduce privacy issues, or acquire special hardware for every building it monitors.

Shavit says inferencing is also only the beginning. Neural Magic plans to expand its offerings in the future to help companies train their AI models on CPUs as well. We believe 10 to 20 years from now, CPUs will be the actual fabric for running machine-learning algorithms, he says.

Thompson isnt so sure. The economics have really changed around chip production, and that is going to lead to a lot more specialization, he says. Additionally, while Neural Magics technique gets more performance out of existing hardware, fundamental hardware advancements will still be the only way to continue driving computing forward. This sounds like a really good way to improve performance in neural networks, he says. But we want to improve not just neural networks but also computing overall.

Originally posted here:

The startup making deep learning possible without specialized hardware - MIT Technology Review

This article was originally published here

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

PLoS Med. 2020 Jun;17(6):e1003149

Authors: Atabaki-Pasdar N, Ohlsson M, Viuela A, Frau F, Pomares-Millan H, Haid M, Jones AG, Thomas EL, Koivula RW, Kurbasic A, Mutie PM, Fitipaldi H, Fernandez J, Dawed AY, Giordano GN, Forgie IM, McDonald TJ, Rutters F, Cederberg H, Chabanova E, Dale M, Masi F, Thomas CE, Allin KH, Hansen TH, Heggie A, Hong MG, Elders PJM, Kennedy G, Kokkola T, Pedersen HK, Mahajan A, McEvoy D, Pattou F, Raverdy V, Hussler RS, Sharma S, Thomsen HS, Vangipurapu J, Vestergaard H, t Hart LM, Adamski J, Musholt PB, Brage S, Brunak S, Dermitzakis E, Frost G, Hansen T, Laakso M, Pedersen O, Ridderstrle M, Ruetten H, Hattersley AT, Walker M, Beulens JWJ, Mari A, Schwenk JM, Gupta R, McCarthy MI, Pearson ER, Bell JD, Pavo I, Franks PW

Abstract BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or 5%) rather than a continuous one. CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

PMID: 32559194 [PubMed as supplied by publisher]

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Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. - DocWire...

A South Korean startup is holding a competition to fill one of the worlds first galleries for machine learning-generated art in a bid to foster a nascent artificial intelligence creativity scene in the country.

The company, Pulse9, which makes AI-powered graphics tools, is soliciting art pieces that make use of machine learning tech in some waywhether to produce an image out of whole cloth or restyle or supplement an artists workthrough the end of September.

The project is a notable addition to a burgeoning global community of technologists, new media artists and other creatives who are exploring the bounds of machine creativity through art, spurred by recent research advances that have made AI-generated content more realistic and elaborate than ever.

The medium had perhaps its biggest mainstream breakthrough in 2018, when Christies Auction House sold its first piece of AI-generated art for nearly half a million dollarsa classical style painting of a fictional character named Edmond de Belamy. That was also the moment that inspired the team at Pulse 9, which had just launched an AI tool to help draw and color a Korean style of digital comic called webtoons earlier that year.

We asked ourselves, Could we also sell paintings? and we started looking for art platform companies to work with, Pulse 9 spokesperson Yeongeun Park said.

The company teamed with an art platform called Art Together on a series of crowdfunded AI pieces that proved to be more popular than they had expectedone hit its goal a full week ahead of scheduleand the team began considering parlaying it into a bigger project.

With great attention from the public and the good funding results, we gained confidence in pioneering the Korean AI art market, Park said. So, we eventually decided to open our own AI art gallery.

The company acknowledges that questions of authorship and originality still hang over the concept of AI art but stresses that the gallery is about collaboration between humans and technology rather than AI simply replacing artists. Even pieces generated entirely by machines require a host of human touches, whether its curating a collection of visuals for training or adjusting training regimens to achieve a desired results.

The theme of this competition is Can AI art enhance human artistic creativity?' Park said. We hope that this competition will also be an opportunity to discover creative, competent and new artists who would like to engage AI tools as a new artistic medium in their artwork.

The goal is to establish AIA Gallery as a well-recognized institution in the art world and educate people on the potential for AI-powered creativity. The organizers hope the process will also inspire other efforts and create an AI creativity hub in the country.

Groups or communities of AI artists have formed and are gradually growing, especially overseas, Park said. In the case of Korea, the AI Art market has not been well-recognized yet, but weve been continuing to play our role with our own initiative.

The AIA Gallery recently partnered with one of the leading startups in the new space, Playform, which is led by Rutgers University Art and AI Lab director Ahmed Elgammal (after learning about the company from an Adweek article).

Progress in generative AI creativity isnt confined to the art world, either. Agencies have started to experiment with various AI-generated graphics in campaigns, and brands have filed a slew of patent applications around the central technology powering the revolutiona neural net structure called a generative adversarial network.

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This Startup Is Trying to Foster an AI Art Scene in Korea - Adweek

Scientists at the International Center for Tropical Agriculture (CIAT) are analyzing drone images captured above the soil to examine what is going on below. With the help of machine learning, the experts are revolutionizing the way that farmers and breeders monitor crop health.

The Pheno-i platform provides real-time data that can be used to determine how root crops are responding to heat or drought.

The main objective of the phenotyping platform is to contribute to sustainable agriculture and the development of more climate-resilient crops.

Root crops like carrots and potatoes often show no signs of the diseases and deficiencies that affect their growth. The plant leaves may look green and healthy, but that is not always a good indication of what is going on beneath the soil.

Plant breeders have to wait months or years before discovering how crops respond to temperature changes or dry spells. Without the right nutrients or growing conditions, crop health and development can be stifled early on.

One of the great mysteries for plant breeders is whether what is happening above the ground is the same as whats happening below, said study co-author Michael Selvaraj.

That poses a big problem for all scientists. You need a lot of data: plant canopy, height, other physical features that take a lot of time and energy, and multiple trials, to capture what is really going on beneath the ground and how healthy the crop really is.

Drone technology is becoming much cheaper, and capturing physical images during crop trials is now easier than ever before. However, analyzing vast quantities of visual information, and then converting it into useful data, has been a major challenge.

The Pheno-i platform merges data from thousands of high-resolution drone images, analyzes them through machine learning, and then produces a spreadsheet. Scientists using the platform can assess crop health and see how plants are responding to external conditions in real-time.

The technology makes it possible for breeders to immediately identify what crops need, such as when they are lacking nutrients or water.

The data also helps scientists determine which crops are more resilient to climate change.

Were helping breeders to select the best root crop varieties more quickly, so they can breed higher-yielding, more climate-smart varieties for farmers, said Gomez Selvaraj.

The drone is just the hardware device, but when linked with this precise and rapid analytics platform, we can provide useful and actionable data to accelerate crop productivity.

The study is published in the journal Plant Methods.

By Chrissy Sexton, Earth.com Staff Writer

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Scientists use AI and drone images to interpret crop health Earth.com - Earth.com

Scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

Machine learning (ML) is the study of computer algorithms that improve automatically through experience.[1] It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[4][5] In its application across business problems, machine learning is also referred to as predictive analytics.

Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than have human programmers specify every needed step.[6][7]

The discipline of machine learning employs various approaches to help computers learn to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset has often been used. [6][7]

Early classifications for machine learning approaches sometimes divided them into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system. These were: Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent) As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise. [3]

Other approaches or processes have since developed that don't fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example topic modeling, dimensionality reduction or meta learning. [8] As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning . [6]

The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. [9][10] A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[11] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. [12] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. [13]

Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."[14] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?".[15]

As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.[16]Probabilistic reasoning was also employed, especially in automated medical diagnosis.[17]:488

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[17]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.[18] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[17]:708710; 755 Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[17]:25

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.[18] As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr Daniel Hulme, who both teaches AI and runs a company operating in the field, argues that machine learning and AI are separate. [7][19][6]

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.[20]

Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.[21] According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[22] He also suggested the term data science as a placeholder to call the overall field.[22]

Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model,[23] wherein "algorithmic model" means more or less the machine learning algorithms like Random forest.

Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.[24]

A core objective of a learner is to generalize from its experience.[3][25] Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The biasvariance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer.[26]

In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[27] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.[28] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.[14]

Types of supervised learning algorithms include Active learning , classification and regression.[29] Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification.

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function.[30] Though unsupervised learning encompasses other domains involving summarizing and explaining data features.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.

Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy.

In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets.[31]

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov Decision Process (MDP). Many reinforcement learning algorithms use dynamic programming techniques.[32] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP, and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.

Self-learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). [33] It is a learning with no external rewards and no external teacher advices. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. [34] The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:

It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking behavior, in an environment that contains both desirable and undesirable situations. [35]

Several learning algorithms aim at discovering better representations of the inputs provided during training.[36] Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization[37] and various forms of clustering.[38][39][40]

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors.[41]Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.[42]

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately.[43] A popular heuristic method for sparse dictionary learning is the K-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.[44]

In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.[45] Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.[46]

In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.[47]

Three broad categories of anomaly detection techniques exist.[48] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation.

Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[49]

Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[50] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.

Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliski and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[51] For example, the rule { o n i o n s , p o t a t o e s } { b u r g e r } {displaystyle {mathrm {onions,potatoes} }Rightarrow {mathrm {burger} }} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.

Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[52]

Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming languages for representing hypotheses (and not only logic programming), such as functional programs.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[53][54][55] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[56] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set.

Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.

Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times.

The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[57]

Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.

Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[58] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel [59]), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space.

A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[60][61] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[62]

Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model.

Federated learning is a new approach to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[63]

There are many applications for machine learning, including:

In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1million.[65] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[66] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[67] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[68] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings, and that it may have revealed previously unrecognized influences among artists.[69] In 2019 Springer Nature published the first research book created using machine learning.[70]

Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[71][72][73] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[74]

In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[75] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of investment.[76][77]

Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[78] Language models learned from data have been shown to contain human-like biases.[79][80] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[81][82] In 2015, Google photos would often tag black people as gorillas,[83] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[84] Similar issues with recognizing non-white people have been found in many other systems.[85] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[86] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[87] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "Theres nothing artificial about AI...Its inspired by people, its created by people, andmost importantlyit impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.[88]

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[89]

In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the False Positive Rate (FPR) as well as the False Negative Rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The Total Operating Characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used Receiver Operating Characteristic (ROC) and ROC's associated Area Under the Curve (AUC).[90]

Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[91] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[92][93] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[94][95]

Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[96]

Software suites containing a variety of machine learning algorithms include the following:

See the rest here:

Machine learning - Wikipedia

PHILADELPHIA, June 1, 2020 /PRNewswire/ -- Today, DataGroomr announced the release of its new Data Quality Management platform for Salesforce. A first of its kind, the platform utilizes Machine Learning algorithms to circumvent the need for any human intervention when it comes to identifying duplicates in Salesforce. Conveniently, the algorithms analyzeevery record in Salesforce to return a list of duplicates for review, saving admins the headache of designing and managing custom rules and filters. Similarly, by importing new records to Salesforce via Datagroomr, users can prevent new duplicates from being created.

Delivered as a Software-as-a-Service, the solution provides an intuitive interface for administrators to review duplicates, append record data, and merge faster than ever before. To ensure that Salesforce stays free of duplication, the platform includes robust automation capabilities for admins to schedule duplication analyses and mass merge tasks.

Co-Founder of DataGroomr, Steve Pogrebivsky explained that "the platform simplifies the approach to deduplication by harnessing the power of Machine Learning. Grappling with duplicate rules, filters, and cumbersome Excel analyses are a thing of the past this is truly a new era for the data quality focussed Salesforce Administrator."

Steve went on to say that "we're thrilled at the response we have received from our user community. It proves that our algorithms can greatly reduce the time and money spent on managing data quality in Salesforce."

A free 14-day trial of the platform is available directly from the DataGroomr website: http://www.datagroomr.com and from the Salesforce AppExchange

ABOUT DATAGROOMR

DataGroomr is the first Data Quality Management Platform for Salesforce to harness the power of Machine Learning to find and prevent duplicate records automatically. Delivered as a Software-as-a-Solution (SaaS), the platform equips users with everything that they need to keep Salesforce clean.

MEDIA CONTACT Steve Pogrebivsky Tel: +1 215 253 5600 [emailprotected] https://datagroomr.com/

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Announcing DataGroomr, the App that Utilizes Machine Learning to Find Duplicates in Salesforce Automatically - PRNewswire

The Experiences Per Mile Advisory Council, which unifies experts from the car, automotive and tech industry, has recently publisheda forecaston vehicle connectivity and the surrounding customer experience. According to the report, today 48% of all new cars globally include built-in connectivity, but by 2030 that figure will rise to 96%. Similarly, by 2030, 79% of vehicles shipped around the world will have an L2 autonomy or higher.

The report also says that customer expectations are shifting from just smart technologies to a connected experience, including vehicle maintenance. As such, 57% of European and 80% of North American respondents are interested in early detection of necessary maintenance and repairs; 80% of respondents were willing to share anonymous or personal connected car data to gain access to such capabilities. Big data allows automakers to predict the maintenance and repair needs of their vehicles, in turn enabling dealerships to be optimized and downtime to be minimized.

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Expert Reaction On Forecast That Machine Learning Will Seriously Change The Automotive Industry And Its Security - ISBuzz News

Mukesh Ambani-controlled Reliance Industries and its subsidiaries have invested over $2 billion in its four-pronged strategy to become a technology powerhouse.

The strategy includes spending over $1.6 billion on buying stakes in 24 tech firms across the US, UK, and India; winning 30 US patents out of the 53 it applied for, mostly in telecom and radio communications; and developing in-house tech in artificial intelligence, machine learning, block chain, virtual reality, big data, and 5G. Also, the Gennext programme is providing venture capital support and mentoring to ...

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