Artificial intelligence art

Criticism.2C_issues_and_controversy Ever since the beginnings of artificial intelligence art, it has sparked several debates, which in the 2020s has often concerned whether AI art can be defined as art and the impact it has on artists.  In 1985, intellectual property law professor Pamela Samuelson considered the legal questions surrounding AI art authorship as it relates to copyright: who owns the copyright when the piece of art was created by artificial intelligence?  Samuelson's article, "Allocating Ownership Rights in Computer-Generated Works," argued that rights should be allocated to the user of the generator program.  In response to the same question, a 2019 Florida Law Review article has presented three possible choices.  First, the artificial intelligence itself becomes the copyright owner.  To do this, Section 101 of the Copyright Act would need to be amended to define "author" as a natural person or a computer.  Second, following Samuelson's argument, the user, programmer, or artificial intelligence company is the copyright owner.  This would be an expansion of the "work for hire" doctrine, under which ownership of a copyright is transferred to the "employer. " Finally, no one becomes the copyright owner, and the work would automatically enter public domain.  The argument here is that because no person "created" the piece of art, no one should be the copyright owner.  In 2022, coinciding with the rising availability of consumer-grade AI image generation services, popular discussion renewed over the legality and ethics of AI-generated art.  Of particular issue is the use of copyrighted art within AI training datasets: in September 2022, Reema Selhi, of the Design and Artists Copyright Society, stated that "there are no safeguards for artists to be able to identify works in databases that are being used and opt out. " Some have claimed that images generated by these models can bear an uncanny resemblance to extant artwork, sometimes including remains of the original artist's signature.  Such discussion came to a head in December, when users of the portfolio platform ArtStation staged an online protest against nonconsensual use of their artwork within datasets: this resulted in opt-out services, such as "Have I Been Trained? ," increasing in profile, as well as some online art platforms promising to offer their own opt-out options.  According to the US Copyright Office, artificial intelligence programs are unable to hold copyright, a decision upheld at the Federal District level as of August 2023 followed the reasoning from the monkey selfie copyright dispute.  An issue with many popular AI art programs is that they generate images based on artists's work without their consent.  In January 2023 three artists ��� Sarah Andersen, Kelly McKernan, and Karla Ortiz ��� filed a copyright infringement lawsuit against Stability AI, Midjourney, and DeviantArt, claiming that these companies have infringed the rights of millions of artists by training AI tools on five billion images scraped from the web without the consent of the original artists.  The same month, Stability AI was also sued by Getty Images for using its images in the training data.


Artificial intelligence art

Criticism.2C_issues_and_controversy In July 2023, U. S.  District Judge William Orrick inclined to dismiss most of the lawsuit filed by Andersen, McKernan, and Ortiz, but allowed them to file a new complaint.  As generative AI image software such as Stable Diffusion and DALL-E continue to advance and proliferate, the potential problems and concerns that these systems pose on creativity and artistry has risen.  During 2022, artists working in various media raised concerns about the impact that generative artificial intelligence could have on their ability to earn money, particularly if AI-based images started replacing artists working in illustration and design industries.  In August 2022, digital artist R.  J.  Palmer stated that "I could easily envision a scenario where using AI, a single artist or art director could take the place of 5-10 entry level artists. . .  I have seen a lot of self-published authors and such say how great it will be that they don���t have to hire an artist. " Scholars Jiang et al.  support this concern of job loss in creative fields by stating, ���Leaders of companies like Open AI and Stability AI have openly stated that they expect generative AI systems to replace creatives imminently,��� and adding that, ���This labor displacement is evident across creative industries.  For instance, according to an article on Rest of World, a Chinese gaming industry recruiter has noticed a 70% drop in illustrator jobs, in part due to the widespread use of image generators; another studio in China is reported to have laid off a third of its character design illustrators. ��� AI-based images have become more commonplace in art markets and search engines because AI-based text-to-image systems are trained from pre-existing artistic images, sometimes without the original artist's consent, allowing the software to mimic specific artists' styles.  For example, Polish digital artist Greg Rutkowski has stated that it's more difficult to search for his work online because many of the images in the results are AI-generated specifically to mimic his style.  Furthermore, some training databases on which AI systems are based aren't accessible to the public, which makes it impossible to know the extent to which their training data contains copyright protected images.  For example, a tool built by Simon Willison allowed people to search 0.5% of the training data for Stable Diffusion V1.1, i. e. , 12 million of 2.3 billion instances from LAION 2B.  Artist Karen Hallion discovered that their copyrighted images were used as training data without their consent.  The ability of AI-based art software to mimic or forge artistic style also raises concerns of malice or greed.  Works of AI-generated art, such as Th����tre d'Op��ra Spatial, a text-to-image AI illustration that won the grand prize in the August 2022 digital art competition at the Colorado State Fair, have begun to overwhelm art contests and other submission forums meant for small artists.


Artificial intelligence art

Criticism.2C_issues_and_controversy These AI-created submissions have lead organizations such as Clarkesworld, a science fiction magazine, to close their submissions and only solicit works from known artists after their submission forum was flooded with texts generated by ChatGPT.  AI-generated images have raised the concern that they can be made to damage an artist's reputation.  Artist Sarah Hendersen had her art copied and then used to depict Neo-Nazi ideology.  She stated that the spread of hate speech online can be worsened by the use of image generators.  Jiang et al.  also add to this sentiment by stating that "tools trained on artists' works and which allow users to mimic their style without their consent or compensation, can cause significant reputational damage [by] spreading messages that they do not endorse. " The 2023 winner of the "creative open" category in the Sony World Photography Awards, Boris Eldagsen, revealed after winning that his entry was actually generated by artificial intelligence.  Photographer Feroz Khan commented to the BBC that Eldagsen had "clearly shown that even experienced photographers and art experts can be fooled".  Smaller contests have been affected as well; in 2023 a contest called the "Self-Published Fantasy Blog-Off cover contest", run by author Mark Lawrence, was cancelled after the winning entry was allegedly exposed to be a collage of images generated by Midjourney.  Wider issues extend beyond the art world.  As with other types of photo manipulation since the early 19th century, some people in the early 21st century have been concerned that AI could be used to create content that is misleading, known as "deepfakes".  In May 2023, widespread attention was given to a Midjourney-generated photo of Pope Francis wearing a white puffer coat and another showing the fictional arrest of Donald Trump, and an AI-generated image of an attack on the Pentagon went viral as a hoax news story on Twitter.


Inflection AI

Products The first product in wide release by Inflection AI is a chatbot, Pi, named for ���personal intelligence,��� that is intended to function as an artificial intelligence-based personal assistant. Among the user experience goals that the company has stated for the Pi product are: providing an experience of emotional support for human users, in which the chatbot should be able to maintain an interactive text-based dialogue with a human user that includes elements of kindness, a diplomatic tone about sensitive topics, and humor. Comparisons and contrasts have been drawn between the Pi chatbot and ChatGPT, a chatbot created by OpenAI.


Ilya Sutskever

Ilya Sutskever Ilya Sutskever FRS (Hebrew: ���������� ������������; Russian: �������� ����������������; born 1985/86) is a Russian-born Israeli-Canadian computer scientist working in machine learning, who co-founded and serves as Chief Scientist of OpenAI. He has made several major contributions to the field of deep learning. He is the co-inventor, with Alex Krizhevsky and Geoffrey Hinton, of AlexNet, a convolutional neural network. Sutskever is also one of the many co-authors of the AlphaGo paper.


Ilya Sutskever

Career_and_research After graduation in 2012, Sutskever spent two months as a postdoc with Andrew Ng at Stanford University. He then returned to University of Toronto and joined Hinton's new research company DNNResearch, a spinoff of Hinton's research group. Four months later, in March 2013, Google acquired DNNResearch and hired Sutskever as a research scientist at Google Brain. At Google Brain, Sutskever worked with Oriol Vinyals and Quoc Viet Le to create the sequence-to-sequence learning algorithm. He is also a co-inventor of AlexNet and has worked on TensorFlow. At the end of 2015, he left Google to become cofounder and chief scientist of the newly founded organization OpenAI. In 2023, he announced that he will co-lead OpenAI's new "Superalignment" project, which tries to solve the alignment of superintelligences in 4 years. He wrote that even if superintelligence seems far off, it could happen this decade. 2015, Sutskever was named in MIT Technology Review's 35 Innovators Under 35. 2018, Sutskever was the keynote speaker at Nvidia Ntech 2018 and AI Frontiers Conference 2018. 2022, he was elected a Fellow of the Royal Society (FRS).


Self-driving car

Challenges The primary obstacle to ACs is the advanced software and mapping required to make them work safely across the wide variety of conditions that drivers experience.  In addition to handling day/night driving in good and bad weather on roads of arbitrary quality, ACs must cope with other vehicles, road obstacles, poor/missing traffic controls, flawed maps, and handle endless edge cases, such as following the instructions of a police officer managing traffic at a crash site.  Other obstacles include cost, liability, consumer reluctance, potential ethical dilemmas, security, privacy, and legal/regulatory framework.  Further, AVs could automate the work of professional drivers, eliminating many jobs, which could slow acceptance.  Tesla calls its Level 2 ADAS "Full Self-Driving (FSD) Beta".  US Senators Richard Blumenthal and Edward Markey called on the Federal Trade Commission (FTC) to investigate this marketing in 2021.  In December 2021 in Japan, Mercedes-Benz was punished by the Consumer Affairs Agency for misleading product descriptions.  Mercedes-Benz was criticized for a misleading US commercial advertising E-Class models.  At that time, Mercedes-Benz rejected the claims and stopped its "self-driving car" ad campaign that had been running.  In August 2022, the California Department of Motor Vehicles (DMV) accused Tesla of deceptive marketing practices.  In the 2020s, concerns over ACs vulnerability to cyberattacks and data theft emerged.  In 2018 and 2019 former Apple engineers were charged with stealing information related to Apple's self-driving car project.  In 2021 the United States Department of Justice (DOJ) accused Chinese security officials of coordinating a hacking campaign to steal information from government entities, including research related to autonomous vehicles.  China has prepared "the Provisions on Management of Automotive Data Security (Trial) to protect its own data".  Cellular Vehicle-to-Everything technologies are based on 5G wireless networks.  As of November 2022[update], the US Congress was considering the possibility that imported Chinese AC technology could facilitate espionage.  While predicting the behavior of ACs that do not use traditional communications such as hand signals, is a major challenge for human drivers, the real-time prediction of the behavior of other vehicles, pedestrians etc, some of which may be stationary when first noted, is even greater challenge for self-driving cars.  Raster-based methods have been replaced by vector-based methods in order to overcome the former's lossy rendering, limited receptive field, and prohibitively high cost.  The remaining problem is high level of uncertainty that emerges in trajectory predictions as the prediction timeframe is extended.  Also, if data re-normalization and re-encoding are used to update future trajectories each time a self-driving car changes its position, its action is often delayed by 8 milliseconds, potentially causing an accident.  Several powerful trajectory prediction models have recently adopted Transformers with factorized attention as their encoders, but their scalability is still limited by the computational complexity of factorized attention.  Most recently proposed QCNet model uses a query-centric instead of agent-centric modeling, taking advantage of both anchor-based and anchor-free solutions, with an anchor-free module generating adaptive anchors in a data-driven manner and an anchor-based module refining these anchors based on the scene context.


Self-driving car

Challenges The model injects the relative spatialtemporal positions into the key and value (both Transformer elements) when performing attention-based scene-context fusion.  For ACs that have not achieved L5, the ADAS has to be able to safely accept control from and return it to the driver.  The second challenge is known as risk compensation: as a system is perceived to be safer, on average people engage in riskier behavior.  (People who wear seat belts drive faster).  ACs suffer from this problem: for example Tesla Autopilot users in some cases stop monitoring the vehicle while it is in control.  In order for people to buy self-driving cars and vote for the government to allow them on roads, the technology must be trusted as safe.  Automatic elevators were invented in 1900, but did not become common until operator strikes and trust was built with advertising and features such as an emergency stop button.  Standards for liability have yet to be adopted to address crashes and other incidents.  Does liability rest with the manufacturer or the driver/passenger and does it vary with, e. g. , automation level or merely the specific circumstances?  The trolley problem is a thought experiment in ethics.  Adapted for ACs, consider an AC carrying a passenger when suddenly a pedestrian steps in its way and the car has to choose between killing the pedestrian or swerving into a wall, killing the passenger.  Ethical researchers have suggested deontology (formal rules) and utilitarianism (harm reduction) as applicable.  Public opinion has been reported to support harm reduction, except that they want the vehicle to prefer them when they are riding in it.  However, utilitarian regulations are unpopular.  Privacy-related issues arise mainly from the fact that ACs are connected to the internet.  Any connected device offers the potential to be penetrated.  This information includes destinations, routes, cabin recordings, media preferences, behavioral patterns, and others.  Whether existing road infrastructure can support higher levels of automation has not been finalized.  The answer may vary across jurisdictions.  In March 2023, the Japanese government unveiled a plan to set up a dedicated highway lane for ACs.  In April 2023, JR East announced their challenge to raise their self-driving level of Kesennuma Line Bus rapid transit (BRT) in rural area from the current Level 2 to Level 4 at 60 km/h.


Self-driving cars


Edge detection

Approaches There are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based.  The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction.  The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression.  As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).  The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed.  As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.  A survey of a number of different edge detection methods can be found in (Ziou and Tabbone 1998); see also the encyclopedia articles on edge detection in Encyclopedia of Mathematics and Encyclopedia of Computer Science and Engineering.  John Canny considered the mathematical problem of deriving an optimal smoothing filter given the criteria of detection, localization and minimizing multiple responses to a single edge.  He showed that the optimal filter given these assumptions is a sum of four exponential terms.  He also showed that this filter can be well approximated by first-order derivatives of Gaussians.  Canny also introduced the notion of non-maximum suppression, which means that given the presmoothing filters, edge points are defined as points where the gradient magnitude assumes a local maximum in the gradient direction.  Looking for the zero crossing of the 2nd derivative along the gradient direction was first proposed by Haralick.  It took less than two decades to find a modern geometric variational meaning for that operator that links it to the Marr���Hildreth (zero crossing of the Laplacian) edge detector.  That observation was presented by Ron Kimmel and Alfred Bruckstein.  Although his work was done in the early days of computer vision, the Canny edge detector (including its variations) is still a state-of-the-art edge detector.  Edge detectors that perform better than the Canny usually require longer computation times or a greater number of parameters.  Vladimir A.  Kovalevsky has suggested a quite different approach.  He uses a preprocessing of the image with the Sigma filter and with a special filter for the dilution of the ramps.  This method uses no brightness of the image but only the intensities of the color channels which is important for detecting an edge between two adjacent pixels of equal brightness but different colors.  The method scans the image two times: first along the horizontal lines and second along the vertical columns.


Edge detection

Approaches In each horizontal line six consequent adjacent pixels are considered and five color difference between each two adjacent pixels are calculated.  Each color difference is the sum of absolute differences of the intensities of the color channels Red, Green, and Blue of the corresponding adjacent pixels.  If this sum is greater than a given threshold, then the sign of the color difference is set equal to the sign of the difference of the green intensities.  If the green difference is zero, then the sign of the color difference is set equal to the sign of the difference of the red intensities.  If, however, both the green and the red differences are zero, then the sign of the color difference is set equal to the sign of the blue difference which in this case cannot be zero since the sum is greater than the threshold.  Certain conditions for the values and signs of the five color differences are specified in such way that if the conditions are fulfilled, then a short vertical stroke is put between the third and the fourth of the six pixels as the label of the edge.  Similar calculations are performed for the vertical columns.  In this case a short horizontal stroke is put between the third and the fourth of the six subsequent pixels.  The vertical and horizontal strokes (being the one-dimensional cells of an abstract cell complex corresponding to the image) mostly compose a connected sequence representing the edge.  This method is robust and very fast and, what is more important, it can detect edges between adjacent pixels of equal brightness���s if the color difference between these pixels is greater than the threshold.  The Canny���Deriche detector was derived from similar mathematical criteria as the Canny edge detector, although starting from a discrete viewpoint and then leading to a set of recursive filters for image smoothing instead of exponential filters or Gaussian filters.  The differential edge detector described below can be seen as a reformulation of Canny's method from the viewpoint of differential invariants computed from a scale space representation leading to a number of advantages in terms of both theoretical analysis and sub-pixel implementation.  In that aspect, Log Gabor filter have been shown to be a good choice to extract boundaries in natural scenes.  Different gradient operators can be applied to estimate image gradients from the input image or a smoothed version of it.  The simplest approach is to use central differences: corresponding to the application of the following filter masks to the image data: The well-known and earlier Sobel operator is based on the following filters: Given such estimates of first-order image derivatives, the gradient magnitude is then computed as: while the gradient orientation can be estimated as Other first-order difference operators for estimating image gradient have been proposed in the Prewitt operator, Roberts cross, Kayyali operator and Frei���Chen operator.  It is possible to extend filters dimension to avoid the issue of recognizing edge in low SNR image.


Edge detection

Approaches The cost of this operation is loss in terms of resolution.  Examples are Extended Prewitt 7��7.  Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point.  The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise and detecting edges of irrelevant features in the image.  Conversely a high threshold may miss subtle edges, or result in fragmented edges.  If the edge is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary.  For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure.  On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.  A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis.  This method uses multiple thresholds to find edges.  We begin by using the upper threshold to find the start of an edge.  Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold.  We stop marking our edge only when the value falls below our lower threshold.  This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge.  Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image.  Edge thinning is a technique used to remove the unwanted spurious points on the edges in an image.  This technique is employed after the image has been filtered for noise (using median, Gaussian filter etc. ), the edge operator has been applied (like the ones described above, Canny or Sobel) to detect the edges and after the edges have been smoothed using an appropriate threshold value.  This removes all the unwanted points and if applied carefully, results in one pixel thick edge elements.  Advantages: Sharp and thin edges lead to greater efficiency in object recognition.  If Hough transforms are used to detect lines and ellipses, then thinning could give much better results.  If the edge happens to be the boundary of a region, then thinning could easily give the image parameters like perimeter without much algebra.


Edge detection

Approaches There are many popular algorithms used to do this, one such is described below: Choose a type of connectivity, like 8, 6 or 4.  8 connectivity is preferred, where all the immediate pixels surrounding a particular pixel are considered.  Remove points from North, south, east and west.  Do this in multiple passes, i. e.  after the north pass, use the same semi processed image in the other passes and so on.  Remove a point if:The point has no neighbors in the North (if you are in the north pass, and respective directions for other passes). The point is not the end of a line. The point is isolated. Removing the points will not cause to disconnect its neighbors in any way.  Else keep the point. The number of passes across direction should be chosen according to the level of accuracy desired.  Some edge-detection operators are instead based upon second-order derivatives of the intensity.  This essentially captures the rate of change in the intensity gradient.  Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.  The early Marr���Hildreth operator is based on the detection of zero-crossings of the Laplacian operator applied to a Gaussian-smoothed image.  It can be shown, however, that this operator will also return false edges corresponding to local minima of the gradient magnitude.  Moreover, this operator will give poor localization at curved edges.  Hence, this operator is today mainly of historical interest.  A more refined second-order edge detection approach which automatically detects edges with sub-pixel accuracy, uses the following differential approach of detecting zero-crossings of the second-order directional derivative in the gradient direction: Following the differential geometric way of expressing the requirement of non-maximum suppression proposed by Lindeberg, let us introduce at every image point a local coordinate system ( u , v ) {\displaystyle (u,v)} , with the v {\displaystyle v} -direction parallel to the gradient direction.  Assuming that the image has been pre-smoothed by Gaussian smoothing and a scale space representation L ( x , y ; t ) {\displaystyle L(x,y;t)} at scale t {\displaystyle t} has been computed, we can require that the gradient magnitude of the scale space representation, which is equal to the first-order directional derivative in the v {\displaystyle v} -direction L v {\displaystyle L_{v}} , should have its first order directional derivative in the v {\displaystyle v} -direction equal to zero while the second-order directional derivative in the v {\displaystyle v} -direction of L v {\displaystyle L_{v}} should be negative, i. e.


Edge detection

Approaches , Written out as an explicit expression in terms of local partial derivatives L x , L y , ��� , L y y y {\displaystyle L_{x},L_{y},\ldots ,L_{yyy}} , this edge definition can be expressed as the zero-crossing curves of the differential invariant that satisfy a sign-condition on the following differential invariant where L x , L y , ��� , L y y y {\displaystyle L_{x},L_{y},\ldots ,L_{yyy}} denote partial derivatives computed from a scale space representation L {\displaystyle L} obtained by smoothing the original image with a Gaussian kernel.  In this way, the edges will be automatically obtained as continuous curves with sub-pixel accuracy.  Hysteresis thresholding can also be applied to these differential and subpixel edge segments.  In practice, first-order derivative approximations can be computed by central differences as described above, while second-order derivatives can be computed from the scale space representation L {\displaystyle L} according to: corresponding to the following filter masks: Higher-order derivatives for the third-order sign condition can be obtained in an analogous fashion.  A recent development in edge detection techniques takes a frequency domain approach to finding edge locations.  Phase congruency (also known as phase coherence) methods attempt to find locations in an image where all sinusoids in the frequency domain are in phase.  These locations will generally correspond to the location of a perceived edge, regardless of whether the edge is represented by a large change in intensity in the spatial domain.  A key benefit of this technique is that it responds strongly to Mach bands, and avoids false positives typically found around roof edges.  A roof edge, is a discontinuity in the first order derivative of a grey-level profile.  The phase stretch transform or PST is a physics-inspired computational approach to signal and image processing.  One of its utilities is for feature detection and classification.  PST is a spin-off from research on the time stretch dispersive Fourier transform.  PST transforms the image by emulating propagation through a diffractive medium with engineered 3D dispersive property (refractive index).  The operation relies on symmetry of the dispersion profile and can be understood in terms of dispersive eigenfunctions or stretch modes.  PST performs similar functionality as phase contrast microscopy but on digital images.  PST is also applicable to digital images as well as temporal, time series, data.  To increase the precision of edge detection, several subpixel techniques had been proposed, including curve-fitting, moment-based, reconstructive, and partial area effect methods.  These methods have different characteristics.  Curve fitting methods are computationally simple but are easily affected by noise.  Moment-based methods use an integral-based approach to reduce the effect of noise, but may require more computations in some cases.  Reconstructive methods use horizontal gradients or vertical gradients to build a curve and find the peak of the curve as the sub-pixel edge.  Partial area effect methods are based on the hypothesis that each pixel value depends on the area at both sides of the edge inside that pixel, producing accurate individual estimation for every edge pixel.


Edge detection

Approaches Certain variants of the moment-based technique have been shown to be the most accurate for isolated edges.


Feature learning

Feature learning In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. 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 sensor 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. Feature learning can be either supervised, unsupervised or self-supervised. In supervised feature learning, features are learned using labeled input data. Labeled data includes input-label pairs where the input is given to the model and it must produce the ground truth label as the correct answer. This can be leveraged to generate feature representations with the model which result in high label prediction accuracy. Examples include supervised neural networks, multilayer perceptron and (supervised) dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data by analyzing the relationship between points in the dataset. Examples include dictionary learning, independent component analysis, matrix factorization and various forms of clustering. In self-supervised feature learning, features are learned using unlabeled data like unsupervised learning, however input-label pairs are constructed from each data point, which enables learning the structure of the data through supervised methods such as gradient descent. Classical examples include word embeddings and autoencoders. SSL has since been applied to many modalities through the use of deep neural network architectures such as CNNs and transformers.


Feature learning

Self-supervised Self-supervised representation learning is learning features by training on the structure of unlabeled data rather than relying on explicit labels for an information signal.  This approach has enabled the combined use of deep neural network architectures and larger unlabeled datasets to produce deep feature representations.  Training tasks typically fall under the classes of either contrastive, generative or both.  Contrastive representation learning trains representations for associated data pairs, called positive samples, to be aligned, while pairs with no relation, called negative samples, are contrasted.  A larger portion of negative samples is typically necessary in order to prevent catastrophic collapse, which is when all inputs are mapped to the same representation.  Generative representation learning tasks the model with producing the correct data to either match a restricted input or reconstruct the full input from a lower dimensional representation.  A common setup for self-supervised representation learning of a certain data type (e. g.  text, image, audio, video) is to pretrain the model using large datasets of general context, unlabeled data.  Depending on the context, the result of this is either a set of representations for common data segments (e. g.  words) which new data can be broken into, or a neural network able to convert each new data point (e. g.  image) into a set of lower dimensional features.  In either case, the output representations can then be used as an initialization in many different problem settings where labeled data may be limited.  Specialization of the model to specific tasks is typically done with supervised learning, either by fine-tuning the model / representations with the labels as the signal, or freezing the representations and training an additional model which takes them as an input.  Many self-supervised training schemes have been developed for use in representation learning of various modalities, often first showing successful application in text or image before being transferred to other data types.  Word2vec is a word embedding technique which learns to represent words through self-supervision over each word and its neighboring words in a sliding window across a large corpus of text.  The model has two possible training schemes to produce word vector representations, one generative and one contrastive.  The first is word prediction given each of the neighboring words as an input.  The second is training on the representation similarity for neighboring words and representation dissimilarity for random pairs of words.  A limitation of word2vec is that only the pairwise co-occurrence structure of the data is used, and not the ordering or entire set of context words.  More recent transformer-based representation learning approaches attempt to solve this with word prediction tasks.  GPTs pretrain on next word prediction using prior input words as context, whereas BERT masks random tokens in order to provide bidirectional context.  Other self-supervised techniques extend word embeddings by finding representations for larger text structures such as sentences or paragraphs in the input data.


Feature learning

Self-supervised Doc2vec extends the generative training approach in word2vec by adding an additional input to the word prediction task based on the paragraph it is within, and is therefore intended to represent paragraph level context.  The domain of image representation learning has employed many different self-supervised training techniques, including transformation, inpainting, patch discrimination and clustering.  Examples of generative approaches are Context Encoders, which trains an AlexNet CNN architecture to generate a removed image region given the masked image as input, and iGPT, which applies the GPT-2 language model architecture to images by training on pixel prediction after reducing the image resolution.  Many other self-supervised methods use siamese networks, which generate different views of the image through various augmentations that are then aligned to have similar representations.  The challenge is avoiding collapsing solutions where the model encodes all images to the same representation.  SimCLR is a contrastive approach which uses negative examples in order to generate image representations with a ResNet CNN.  Bootstrap Your Own Latent (BYOL) removes the need for negative samples by encoding one of the views with a slow moving average of the model parameters as they are being modified during training.  The goal of many graph representation learning techniques is to produce an embedded representation of each node based on the overall network topology.  node2vec extends the word2vec training technique to nodes in a graph by using co-occurrence in random walks through the graph as the measure of association.  Another approach is to maximize mutual information, a measure of similarity, between the representations of associated structures within the graph.  An example is Deep Graph Infomax, which uses contrastive self-supervision based on mutual information between the representation of a ���patch��� around each node, and a summary representation of the entire graph.  Negative samples are obtained by pairing the graph representation with either representations from another graph in a multigraph training setting, or corrupted patch representations in single graph training.  With analogous results in masked prediction and clustering, video representation learning approaches are often similar to image techniques but must utilize the temporal sequence of video frames as an additional learned structure.  Examples include VCP, which masks video clips and trains to choose the correct one given a set of clip options, and Xu et al. , who train a 3D-CNN to identify the original order given a shuffled set of video clips.  Self-supervised representation techniques have also been applied to many audio data formats, particularly for speech processing.  Wav2vec 2.0 discretizes the audio waveform into timesteps via temporal convolutions, and then trains a transformer on masked prediction of random timesteps using a contrastive loss.  This is similar to the BERT language model, except as in many SSL approaches to video, the model chooses among a set of options rather than over the entire word vocabulary.  Self-supervised learning has also been used to develop joint representations of multiple data types.


Feature learning

Self-supervised Approaches usually rely on some natural or human-derived association between the modalities as an implicit label, for instance video clips of animals or objects with characteristic sounds, or captions written to describe images.  CLIP produces a joint image-text representation space by training to align image and text encodings from a large dataset of image-caption pairs using a contrastive loss.  MERLOT Reserve trains a transformer-based encoder to jointly represent audio, subtitles and video frames from a large dataset of videos through 3 joint pretraining tasks: contrastive masked prediction of either audio or text segments given the video frames and surrounding audio and text context, along with contrastive alignment of video frames with their corresponding captions.  Multimodal representation models are typically unable to assume direct correspondence of representations in the different modalities, since the precise alignment can often be noisy or ambiguous.  For example, the text "dog" could be paired with many different pictures of dogs, and correspondingly a picture of a dog could be captioned with varying degrees of specificity.  This limitation means that downstream tasks may require an additional generative mapping network between modalities to achieve optimal performance, such as in DALLE-2 for text to image generation.


Machine learning in bioinformatics

Applications In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples.  For example, machine learning methods can be trained to identify specific visual features such as splice sites.  Support vector machines have been extensively used in cancer genomic studies.  In addition, deep learning has been incorporated into bioinformatic algorithms.  Deep learning applications have been used for regulatory genomics and cellular imaging.  Other applications include medical image classification, genomic sequence analysis, as well as protein structure classification and prediction.  Deep learning has been applied to regulatory genomics, variant calling and pathogenicity scores.  Natural language processing and text mining have helped to understand phenomena including protein-protein interaction, gene-disease relation as well as predicting biomolecule structures and functions.  Natural language processing algorithms personalized medicine for patients who suffer genetic diseases, by combining the extraction of clinical information and genomic data available from the patients.  Institutes such as Health-funded Pharmacogenomics Research Network focus on finding breast cancer treatments.  Precision medicine considers individual genomic variability, enabled by large-scale biological databases.  Machine learning can be applied to perform the matching function between (groups of patients) and specific treatment modalities.  Computational techniques are used to solve other problems, such as efficient primer design for PCR, biological-image analysis and back translation of proteins (which is, given the degeneration of the genetic code, a complex combinatorial problem).  While genomic sequence data has historically been sparse due to the technical difficulty of sequencing a piece of DNA, the number of available sequences is growing exponentially.  However, while raw data is becoming increasingly available and accessible, biological interpretation of this data is occurring at a much slower pace.  This makes for an increasing need for developing computational genomics tools, including machine learning systems, that can automatically determine the location of protein-encoding genes within a given DNA sequence (i. e.  gene prediction).  Gene prediction is commonly performed through both extrinsic searches and intrinsic searches.  For the extrinsic search, the input DNA sequence is run through a large database of sequences whose genes have been previously discovered and their locations annotated and identifying the target sequence's genes by determining which strings of bases within the sequence are homologous to known gene sequences.  However, not all the genes in a given input sequence can be identified through homology alone, due to limits in the size of the database of known and annotated gene sequences.  Therefore, an intrinsic search is needed where a gene prediction program attempts to identify the remaining genes from the DNA sequence alone.  Machine learning has also been used for the problem of multiple sequence alignment which involves aligning many DNA or amino acid sequences in order to determine regions of similarity that could indicate a shared evolutionary history.  It can also be used to detect and visualize genome rearrangements.


Machine learning in bioinformatics

Applications Proteins, strings of amino acids, gain much of their function from protein folding, where they conform into a three-dimensional structure, including the primary structure, the secondary structure (alpha helices and beta sheets), the tertiary structure, and the quaternary structure.  Protein secondary structure prediction is a main focus of this subfield as tertiary and quaternary structures are determined based on the secondary structure.  Solving the true structure of a protein is expensive and time-intensive, furthering the need for systems that can accurately predict the structure of a protein by analyzing the amino acid sequence directly.  Prior to machine learning, researchers needed to conduct this prediction manually.  This trend began in 1951 when Pauling and Corey released their work on predicting the hydrogen bond configurations of a protein from a polypeptide chain.  Automatic feature learning reaches an accuracy of 82-84%.  The current state-of-the-art in secondary structure prediction uses a system called DeepCNF (deep convolutional neural fields) which relies on the machine learning model of artificial neural networks to achieve an accuracy of approximately 84% when tasked to classify the amino acids of a protein sequence into one of three structural classes (helix, sheet, or coil).  [needs update] The theoretical limit for three-state protein secondary structure is 88���90%.  Machine learning has also been applied to proteomics problems such as protein side-chain prediction, protein loop modeling, and protein contact map prediction.  Metagenomics is the study of microbial communities from environmental DNA samples.  Currently, limitations and challenges predominate in the implementation of machine learning tools due to the amount of data in environmental samples.  Supercomputers and web servers have made access to these tools easier.  The high dimensionality of microbiome datasets is a major challenge in studying the microbiome; this significantly limits the power of current approaches for identifying true differences and increases the chance of false discoveries.  Despite their importance, machine learning tools related to metagenomics have focused on the study of gut microbiota and the relationship with digestive diseases, such as inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and diabetes, seeking better diagnosis and treatments.  Many algorithms were developed to classify microbial communities according to the health condition of the host, regardless of the type of sequence data, e. g.  16S rRNA or whole-genome sequencing (WGS), using methods such as least absolute shrinkage and selection operator classifier, random forest, supervised classification model, and gradient boosted tree model.  Neural networks, such as recurrent neural networks (RNN), convolutional neural networks (CNN), and Hopfield neural networks have been added.  For example, in 2018, Fioravanti et al.  developed an algorithm called Ph-CNN to classify data samples from healthy patients and patients with IBD symptoms (to distinguish healthy and sick patients) by using phylogenetic trees and convolutional neural networks.  In addition, random forest (RF) methods and implemented importance measures help in the identification of microbiome species that can be used to distinguish diseased and non-diseased samples.


Machine learning in bioinformatics

Applications However, the performance of a decision tree and the diversity of decision trees in the ensemble significantly influence the performance of RF algorithms.  The generalization error for RF measures how accurate the individual classifiers are and their interdependence.  Therefore, the high dimensionality problems of microbiome datasets pose challenges.  Effective approaches require many possible variable combinations, which exponentially increases the computational burden as the number of features increases.  For microbiome analysis in 2020 Dang & Kishino developed a novel analysis pipeline.  The core of the pipeline is an RF classifier coupled with forwarding variable selection (RF-FVS), which selects a minimum-size core set of microbial species or functional signatures that maximize the predictive classifier performance.  The framework combines: identifying a few significant features by a massively parallel forward variable selection procedure mapping the selected species on a phylogenetic tree, and predicting functional profiles by functional gene enrichment analysis from metagenomic 16S rRNA data. They demonstrated performance by analyzing two published datasets from large-scale case-control studies: 16S rRNA gene amplicon data for C.  difficile infection (CDI) and shotgun metagenomics data for human colorectal cancer (CRC). The proposed approach improved the accuracy from 81% to 99.01% for CDI and from 75.14% to 90.17% for CRC.  The use of machine learning in environmental samples has been less explored, maybe because of data complexity, especially from WGS.  Some works show that it is possible to apply these tools in environmental samples.  In 2021 Dhungel et al. , designed an R package called MegaR.  This package allows working with 16S rRNA and whole metagenomic sequences to make taxonomic profiles and classification models by machine learning models.  MegaR includes a comfortable visualization environment to improve the user experience.  Machine learning in environmental metagenomics can help to answer questions related to the interactions between microbial communities and ecosystems, e. g.  the work of Xun et al. , in 2021 where the use of different machine learning methods offered insights on the relationship among the soil, microbiome biodiversity, and ecosystem stability.  Microarrays, a type of lab-on-a-chip, are used for automatically collecting data about large amounts of biological material.  Machine learning can aid in analysis, and has been applied to expression pattern identification, classification, and genetic network induction.  This technology is especially useful for monitoring gene expression, aiding in diagnosing cancer by examining which genes are expressed.  One of the main tasks is identifying which genes are expressed based on the collected data.  In addition, due to the huge number of genes on which data is collected by the microarray, winnowing the large amount of irrelevant data to the task of expressed gene identification is challenging.  Machine learning presents a potential solution as various classification methods can be used to perform this identification.  The most commonly used methods are radial basis function networks, deep learning, Bayesian classification, decision trees, and random forest.  Systems biology focuses on the study of emergent behaviors from complex interactions of simple biological components in a system.  Such components can include DNA, RNA, proteins, and metabolites.


Machine learning in bioinformatics

Applications Machine learning has been used to aid in modeling these interactions in domains such as genetic networks, signal transduction networks, and metabolic pathways.  Probabilistic graphical models, a machine learning technique for determining the relationship between different variables, are one of the most commonly used methods for modeling genetic networks.  In addition, machine learning has been applied to systems biology problems such as identifying transcription factor binding sites using Markov chain optimization.  Genetic algorithms, machine learning techniques which are based on the natural process of evolution, have been used to model genetic networks and regulatory structures.  Other systems biology applications of machine learning include the task of enzyme function prediction, high throughput microarray data analysis, analysis of genome-wide association studies to better understand markers of disease, protein function prediction.  This domain, particularly phylogenetic tree reconstruction, uses the features of machine learning techniques.  Phylogenetic trees are schematic representations of the evolution of organisms.  Initially, they were constructed using features such as morphological and metabolic features.  Later, due to the availability of genome sequences, the construction of the phylogenetic tree algorithm used the concept based on genome comparison.  With the help of optimization techniques, a comparison was done by means of multiple sequence alignment.  Machine learning methods for the analysis of neuroimaging data are used to help diagnose stroke.  Historically multiple approaches to this problem involved neural networks.  Multiple approaches to detect strokes used machine learning.  As proposed by Mirtskhulava, feed-forward networks were tested to detect strokes using neural imaging.  As proposed by Titano 3D-CNN techniques were tested in supervised classification to screen head CT images for acute neurologic events.  Three-dimensional CNN and SVM methods are often used.  The increase in biological publications increased the difficulty in searching and compiling relevant available information on a given topic.  This task is known as knowledge extraction.  It is necessary for biological data collection which can then in turn be fed into machine learning algorithms to generate new biological knowledge.  Machine learning can be used for this knowledge extraction task using techniques such as natural language processing to extract the useful information from human-generated reports in a database.  Text Nailing, an alternative approach to machine learning, capable of extracting features from clinical narrative notes was introduced in 2017.  This technique has been applied to the search for novel drug targets, as this task requires the examination of information stored in biological databases and journals.  Annotations of proteins in protein databases often do not reflect the complete known set of knowledge of each protein, so additional information must be extracted from biomedical literature.  Machine learning has been applied to the automatic annotation of gene and protein function, determination of the protein subcellular localization, DNA-expression array analysis, large-scale protein interaction analysis, and molecule interaction analysis.  Another application of text mining is the detection and visualization of distinct DNA regions given sufficient reference data.


Machine learning in bioinformatics

Applications Microbial communities are complex assembles of diverse microorganisms, where symbiont partners constantly produce diverse metabolites derived from the primary and secondary (specialized) metabolism, from which metabolism plays an important role in microbial interaction.  Metagenomic and metatranscriptomic data are an important source for deciphering communications signals.  Molecular mechanisms produce specialized metabolites in various ways.  Biosynthetic Gene Clusters (BGCs) attract attention, since several metabolites are clinically valuable, anti-microbial, anti-fungal, anti-parasitic, anti-tumor and immunosuppressive agents produced by the modular action of multi-enzymatic, multi-domains gene clusters, such as Nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs).  Diverse studies show that grouping BGCs that share homologous core genes into gene cluster families (GCFs) can yield useful insights into the chemical diversity of the analyzed strains, and can support linking BGCs to their secondary metabolites.  GCFs have been used as functional markers in human health studies and to study the ability of soil to suppress fungal pathogens.  Given their direct relationship to catalytic enzymes, and compounds produced from their encoded pathways, BGCs/GCFs can serve as a proxy to explore the chemical space of microbial secondary metabolism.  Cataloging GCFs in sequenced microbial genomes yields an overview of the existing chemical diversity and offers insights into future priorities.  Tools such as BiG-SLiCE and BIG-MAP have emerged with the sole purpose of unveiling the importance of BGCs in natural environments.  BiG-SLiCE (Biosynthetic Genes Super-Linear Clustering Engine), is an automated pipeline tool designed to cluster massive numbers of BGCs.  By representing them in euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion.  from genomic and metagenomic data of diverse organisms.  The BiG-SLiCE workflow starts at vectorization (feature extraction), converting input BGCs provided from dataset of cluster GenBank files from antiSMASH and MIBiG into vectors of numerical features based on the absence/presence and bitscores of hits obtained from querying BGC gene sequences from a library curated of profile Hidden Markov Model(pHMMs) of biosynthetic domains of BGCs.  Those features are then processed by a super-linear clustering algorithm based on BIRCH clustering, resulting in centroid feature vectors representing the GCF models.  All BGCs in the dataset are queried back against those models, outputting a list of GCF membership values for each BGC.  Then a global cluster mapping is done using k-means to group all GCF centroid features in GCF bins.  After that another round of membership assignment is performed to match the full set of BGC features into the resulting GCF bins.  In the end, it produces archives, which then can be used to perform further analysis (via external scripts) or to visualize the result in a user-interactive application.  Satria et al.  demonstrated the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify the uncharted biosynthetic potential of 1.2 million biosynthetic gene clusters.  This opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs.


Machine learning in bioinformatics

Applications As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry.  Since BGCs are an important source of metabolite production, current tools for identifying BGCs focus their efforts on mining genomes to identify their genomic landscape, neglecting relevant information about their abundance and expression levels which in fact, play an important ecological role in triggering phenotype dependent-metabolite concentration.  That is why in 2020 BiG-MAP (Biosynthetic Gene cluster Meta���omics Abundance Profiler), an automated pipeline that helps to determine the abundance (metagenomic data) and expression (metatranscriptomic data) of BGCs across microbial communities.  It shotguns sequencing reads to gene clusters that have been predicted by antiSMASH or gutSMASH.  BiG-MAP splits its workflow in four main modules.  BiG-MAP. family: redundancy filtering on the gene cluster collection in order to reduce computing time and avoid ambiguous mapping.  Using a MinHash-based algorithm, MASH, BiG-MAP estimates distance among protein sequences which then is used to select a representative gene cluster with the aid of k-medoids clustering.  Finally, the selected gene clusters are clustered into GCFs using BiG-SCAPE, considering account architectural similarity, thus relating more distantly related gene clusters which produce the same chemical product in different organisms BiG-MAP. download: optional module that uses a list of Sequence Read Archive (SRA) database; BiG-MAP. map: reads the set of representative GCFs obtained from the first module.  Maps reads to GCFs separately and, it reports combined abundance or expression levels per family.  Reads are mapped to the representative of the GCFs using the short-read aligner Bowtie2, which are then converted into Reads Per Kilobase Million (RPKM) to be averaged over the GCFs size BiG-MAP. analyse: profiles abundance.  RPKM values are normalized using Cumulative Sum Scaling (CSS) to account for sparsity.  Differential expressions analyses use zero-inflated Gaussian distribution mixture models (ZIG-models) or Kruskal-Wallis.  The pipeline displays the results s plots that show gene cluster abundance/expression (heatmaps), log fold change (bar plot), coverage values, and housekeeping gene expression values for metatranscriptomic data (heatmap). The increase of experimentally characterized ribosomally synthesized and post-translationally modified peptides (RiPPs), together with the availability of information on their sequence and chemical structure, selected from databases such as BAGEL, BACTIBASE, MIBIG, and THIOBASE, provide the opportunity to develop machine learning tools to decode the chemical structure and classify them.  In 2017, researchers at the National Institute of Immunology of New Delhi, India, developed RiPPMiner software, a bioinformatics resource for decoding RiPP chemical structures by genome mining.  The RiPPMiner web server consists of a query interface and the RiPPDB database.  RiPPMiner defines 12 subclasses of RiPPs, predicting the cleavage site of the leader peptide and the final cross-link of the RiPP chemical structure.  RiPPs analysis tools such as antiSMASH and RiPP-PRISM use HMM of modifying enzymes present in biosynthetic gene clusters in RiPP to predict the RiPP subclass.  Unlike these tools, RiPPMiner uses a machine learning model, trained with 513 RiPPs, that uses the amino acid sequence of the RiPP gene uniquely to identify and subclass them.


Machine learning in bioinformatics

Applications RiPPMiner differentiates RiPPs from other proteins and peptides using a support-vector machine model trained on 293 experimentally characterized RiPPs as a positive data set, and 8140 genomes encoded non-RiPPs polypeptides as negative data set.  The negative data set included SWISSProt entries similar in length to RiPPs, e. g. , 30s ribosomal proteins, matrix proteins, cytochrome B proteins, etc.  The support vectors consist of amino acid composition and dipeptide frequencies.  Benchmarking on an independent dataset (not included in training) using a two-fold cross-validation approach indicated sensitivity, specificity, precision and MCC values of 0.93, 0.90, 0.90, and 0.85, respectively.  This indicates the model's predictive power for distinguishing between RiPPs and non-RiPPs.  For prediction of RiPP class or sub-class, a Multi-Class SVM was trained using the amino acid composition and dipeptide frequencies as feature vectors.  During the training of the Multi-Class SVM, available RiPP precursor sequences belonging to a given class (e. g.  lasso peptide) were used as a positive set, while RiPPs belonging to all other classes were used as negative set.  Out of the four major RiPP classes that had more than 50 experimentally characterized RiPPs in RiPPDB, SVM models for prediction of cleavage sites were developed for lanthipeptides, cyanobactins, and lasso peptides.  In order to develop SVM for prediction of cleavage site for lanthipeptides, 12 mer peptide sequences centered on the cleavage sites were extracted from a set of 115 lanthipeptide precursor sequences with known cleavage pattern.  This resulted in a positive dataset of 103 unique 12 mer peptides harboring the cleavage site at the center, while the other 12 constituted the negative dataset.  Feature vectors for each of these mers consisted of the concatenation of 20-dimensional vectors corresponding to each of the 20 amino acids.  An SVM model for prediction of cleavage site was developed and benchmarked using 2-fold cross-validation, where half of the data were used in training and the other half in testing.  SVM models were developed for the prediction of the cleavage sites in cyanobactin and lasso peptides.  Based on analysis of the ROC curves, a suitable score cutoff was chosen for the prediction of cleavage sites in lanthipeptides and lasso peptides.  The algorithm for the prediction of cross-links and deciphering the complete chemical structure of RiPP has been implemented for lanthipeptides, lasso peptides, cyanobactins, and thiopeptides.  The prediction of lanthionine linkages in lanthipeptides was carried out using machine learning.  A dataset of 93 lanthipeptides whose chemical structures was known was taken from RiPPDB.  For each lanthipeptide in this set, the sequence of the core peptide was scanned for strings or sub-sequences of the type Ser/Thr-(X)n-Cys or Cys-(X)n-Ser/Thr to enumerate all theoretically possible cyclization patterns.  Out of these sequence strings, the strings corresponding to Ser/Thr-Cys or Cys-Ser/Thr pairs which were linked by lanthionine bridges were included in the positive set, while all other strings were included in the negative set.  Many tandem mass spectrometry (MS/MS) based metabolomics studies, such as library matching and molecular networking, use spectral similarity as a proxy for structural similarity.


Machine learning in bioinformatics

Applications Spec2vec algorithm provides a new way of spectral similarity score, based on Word2Vec.  Spec2Vec learns fragmental relationships within a large set of spectral data, in order to assess spectral similarities between molecules and to classify unknown molecules through these comparisons.  For systemic annotation, some metabolomics studies rely on fitting measured fragmentation mass spectra to library spectra or contrasting spectra via network analysis.  Scoring functions are used to determine the similarity between pairs of fragment spectra as part of these processes.  So far, no research has suggested scores that are significantly different from the commonly utilized cosine-based similarity.


Meta-learning (computer science)

Common_approaches There are three common approaches: using (cyclic) networks with external or internal memory (model-based) learning effective distance metrics (metrics-based) explicitly optimizing model parameters for fast learning (optimization-based).Model-based meta-learning models updates its parameters rapidly with a few training steps, which can be achieved by its internal architecture or controlled by another meta-learner model. A Memory-Augmented Neural Network, or MANN for short, is claimed to be able to encode new information quickly and thus to adapt to new tasks after only a few examples. Meta Networks (MetaNet) learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. The core idea in metric-based meta-learning is similar to nearest neighbors algorithms, which weight is generated by a kernel function. It aims to learn a metric or distance function over objects. The notion of a good metric is problem-dependent. It should represent the relationship between inputs in the task space and facilitate problem solving. Siamese neural network is composed of two twin networks whose output is jointly trained. There is a function above to learn the relationship between input data sample pairs. The two networks are the same, sharing the same weight and network parameters. Matching Networks learn a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. The Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Prototypical Networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve satisfied results. What optimization-based meta-learning algorithms intend for is to adjust the optimization algorithm so that the model can be good at learning with a few examples. LSTM-based meta-learner is to learn the exact optimization algorithm used to train another learner neural network classifier in the few-shot regime. The parametrization allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner (classifier) network that allows for quick convergence of training. MAML, short for Model-Agnostic Meta-Learning, is a fairly general optimization algorithm, compatible with any model that learns through gradient descent. Reptile is a remarkably simple meta-learning optimization algorithm, given that both of its components rely on meta-optimization through gradient descent and both are model-agnostic.


Quantum machine learning

Machine_learning_with_quantum_computers Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques.  Such algorithms typically require one to encode the given classical data set into a quantum computer to make it accessible for quantum information processing.  Subsequently, quantum information processing routines are applied and the result of the quantum computation is read out by measuring the quantum system.  For example, the outcome of the measurement of a qubit reveals the result of a binary classification task.  While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.  Associative (or content-addressable memories) are able to recognize stored content on the basis of a similarity measure, rather than fixed addresses, like in random access memories.  As such they must be able to retrieve both incomplete and corrupted patterns, the essential machine learning task of pattern recognition.  Typical classical associative memories store p patterns in the O ( n 2 ) {\displaystyle O(n^{2})} interactions (synapses) of a real, symmetric energy matrix over a network of n artificial neurons.  The encoding is such that the desired patterns are local minima of the energy functional and retrieval is done by minimizing the total energy, starting from an initial configuration.  Unfortunately, classical associative memories are severely limited by the phenomenon of cross-talk.  When too many patterns are stored, spurious memories appear which quickly proliferate, so that the energy landscape becomes disordered and no retrieval is anymore possible.  The number of storable patterns is typically limited by a linear function of the number of neurons, p ��� O ( n ) {\displaystyle p\leq O(n)} .  Quantum associative memories (in their simplest realization) store patterns in a unitary matrix U acting on the Hilbert space of n qubits.  Retrieval is realized by the unitary evolution of a fixed initial state to a quantum superposition of the desired patterns with probability distribution peaked on the most similar pattern to an input.  By its very quantum nature, the retrieval process is thus probabilistic.  However, quantum associative memories are free from cross-talk, never are spurious memories generated.  Correspondingly, they have a superior capacity than classical ones.  The number of parameters in the unitary matrix U is O ( p n ) {\displaystyle O(pn)} .  One can thus have efficient, spurious-memory-free quantum associative memories for any polynomial number of patterns.  A number of quantum algorithms for machine learning are based on the idea of amplitude encoding, that is, to associate the amplitudes of a quantum state with the inputs and outputs of computations.  Since a state of n {\displaystyle n} qubits is described by 2 n {\displaystyle 2^{n}} complex amplitudes, this information encoding can allow for an exponentially compact representation.  Intuitively, this corresponds to associating a discrete probability distribution over binary random variables with a classical vector.


Quantum machine learning

Machine_learning_with_quantum_computers The goal of algorithms based on amplitude encoding is to formulate quantum algorithms whose resources grow polynomially in the number of qubits n {\displaystyle n} , which amounts to a logarithmic time complexity in the number of amplitudes and thereby the dimension of the input.  Many quantum machine learning algorithms in this category are based on variations of the quantum algorithm for linear systems of equations (colloquially called HHL, after the paper's authors) which, under specific conditions, performs a matrix inversion using an amount of physical resources growing only logarithmically in the dimensions of the matrix.  One of these conditions is that a Hamiltonian which entry wise corresponds to the matrix can be simulated efficiently, which is known to be possible if the matrix is sparse or low rank.  For reference, any known classical algorithm for matrix inversion requires a number of operations that grows more than quadratically in the dimension of the matrix (e. g.  O ( n 2.373 ) {\displaystyle O{\mathord {\left(n^{2.373}\right)}}} ), but they are not restricted to sparse matrices.  Quantum matrix inversion can be applied to machine learning methods in which the training reduces to solving a linear system of equations, for example in least-squares linear regression, the least-squares version of support vector machines, and Gaussian processes.  A crucial bottleneck of methods that simulate linear algebra computations with the amplitudes of quantum states is state preparation, which often requires one to initialise a quantum system in a state whose amplitudes reflect the features of the entire dataset.  Although efficient methods for state preparation are known for specific cases, this step easily hides the complexity of the task.  VQAs are one of the most studied quantum algorithms as researchers expect that all the needed applications for the quantum computer will be using the VQAs and also VQAs seem to fulfill the expectation for gaining quantum supremacy.  VQAs is a mixed quantum-classical approach where the quantum processor prepares quantum states and measurement is made and the optimization is done by a classical computer.  VQAs are considered best for NISQ as VQAs are noise tolerant compared to other algorithms and give quantum superiority with only a few hundred qubits.  Researchers have studied circuit-based algorithms to solve optimization problems and find the ground state energy of complex systems, which were difficult to solve or required a large time to perform the computation using a classical computer.  Variational Quantum Circuits also known as Parametrized Quantum Circuits (PQCs) are based on Variational Quantum Algorithms (VQAs).  VQCs consist of three parts, preparation of initial states, quantum circuit and measurement.  Researchers are extensively studying VQCs, as it uses the power of quantum computation to learn in a short time and also use fewer parameters than its classical counterparts.  It is theoretically and numerically proven that we can approximate non-linear functions, like those used in neural networks, on quantum circuits.  Due to VQCs superiority, neural network has been replaced by VQCs in Reinforcement Learning tasks and Generative Algorithms.


Quantum machine learning

Machine_learning_with_quantum_computers The intrinsic nature of quantum devices towards decoherence, random gate error and measurement errors caused to have high potential to limit the training of the variation circuits.  Training the VQCs on the classical devices before employing them on quantum devices helps to overcome the problem of decoherence noise that came through the number of repetitions for training.  Pattern reorganization is one of the important tasks of machine learning, binary classification is one of the tools or algorithms to find patterns.  Binary classification is used in supervised learning and in unsupervised learning.  In quantum machine learning, classical bits are converted to qubits and they are mapped to Hilbert space; complex value data are used in a quantum binary classifier to use the advantage of Hilbert space.  By exploiting the quantum mechanic properties such as superposition, entanglement, interference the quantum binary classifier produces the accurate result in short period of time.  Another approach to improving classical machine learning with quantum information processing uses amplitude amplification methods based on Grover's search algorithm, which has been shown to solve unstructured search problems with a quadratic speedup compared to classical algorithms.  These quantum routines can be employed for learning algorithms that translate into an unstructured search task, as can be done, for instance, in the case of the k-medians and the k-nearest neighbors algorithms.  Another application is a quadratic speedup in the training of perceptron.  An example of amplitude amplification being used in a machine learning algorithm is Grover's search algorithm minimization.  In which a subroutine uses Grover's search algorithm to find an element less than some previously defined element.  This can be done with an oracle that determines whether or not a state with a corresponding element is less than the predefined one.  Grover's algorithm can then find an element such that our condition is met.  The minimization is initialized by some random element in our data set, and iteratively does this subroutine to find the minimum element in the data set.  This minimization is notably used in quantum k-medians, and it has a speed up of at least O ( n / k ) {\displaystyle O({\sqrt {n/k}})} compared to classical versions of k-medians, where n {\displaystyle n} is the number of data points and k {\displaystyle k} is the number of clusters.  Amplitude amplification is often combined with quantum walks to achieve the same quadratic speedup.  Quantum walks have been proposed to enhance Google's PageRank algorithm as well as the performance of reinforcement learning agents in the projective simulation framework.  Reinforcement learning is a branch of machine learning distinct from supervised and unsupervised learning, which also admits quantum enhancements.  In quantum-enhanced reinforcement learning, a quantum agent interacts with a classical or quantum environment and occasionally receives rewards for its actions, which allows the agent to adapt its behavior���in other words, to learn what to do in order to gain more rewards.


Quantum machine learning

Machine_learning_with_quantum_computers In some situations, either because of the quantum processing capability of the agent, or due to the possibility to probe the environment in superpositions, a quantum speedup may be achieved.  Implementations of these kinds of protocols have been proposed for systems of trapped ions and superconducting circuits.  A quantum speedup of the agent's internal decision-making time has been experimentally demonstrated in trapped ions, while a quantum speedup of the learning time in a fully coherent (`quantum') interaction between agent and environment has been experimentally realized in a photonic setup.  Quantum annealing is an optimization technique used to determine the local minima and maxima of a function over a given set of candidate functions.  This is a method of discretizing a function with many local minima or maxima in order to determine the observables of the function.  The process can be distinguished from Simulated annealing by the Quantum tunneling process, by which particles tunnel through kinetic or potential barriers from a high state to a low state.  Quantum annealing starts from a superposition of all possible states of a system, weighted equally.  Then the time-dependent Schr��dinger equation guides the time evolution of the system, serving to affect the amplitude of each state as time increases.  Eventually, the ground state can be reached to yield the instantaneous Hamiltonian of the system.  As the depth of the quantum circuit advances on NISQ devices, the noise level rises, posing a significant challenge to accurately computing costs and gradients on training models.  The noise tolerance will be improved by using the quantum perceptron and the quantum algorithm on the currently accessible quantum hardware. [citation needed] A regular connection of similar components known as neurons forms the basis of even the most complex brain networks.  Typically, a neuron has two operations: the inner product and an activation function.  As opposed to the activation function, which is typically nonlinear, the inner product is a linear process.  With quantum computing, linear processes may be easily accomplished additionally, due to the simplicity of implementation, the threshold function is preferred by the majority of quantum neurons for activation functions. [citation needed] Sampling from high-dimensional probability distributions is at the core of a wide spectrum of computational techniques with important applications across science, engineering, and society.  Examples include deep learning, probabilistic programming, and other machine learning and artificial intelligence applications.  A computationally hard problem, which is key for some relevant machine learning tasks, is the estimation of averages over probabilistic models defined in terms of a Boltzmann distribution.  Sampling from generic probabilistic models is hard: algorithms relying heavily on sampling are expected to remain intractable no matter how large and powerful classical computing resources become.  Even though quantum annealers, like those produced by D-Wave Systems, were designed for challenging combinatorial optimization problems, it has been recently recognized as a potential candidate to speed up computations that rely on sampling by exploiting quantum effects.


Quantum machine learning

Machine_learning_with_quantum_computers Some research groups have recently explored the use of quantum annealing hardware for training Boltzmann machines and deep neural networks.  The standard approach to training Boltzmann machines relies on the computation of certain averages that can be estimated by standard sampling techniques, such as Markov chain Monte Carlo algorithms.  Another possibility is to rely on a physical process, like quantum annealing, that naturally generates samples from a Boltzmann distribution.  The objective is to find the optimal control parameters that best represent the empirical distribution of a given dataset.  The D-Wave 2X system hosted at NASA Ames Research Center has been recently used for the learning of a special class of restricted Boltzmann machines that can serve as a building block for deep learning architectures.  Complementary work that appeared roughly simultaneously showed that quantum annealing can be used for supervised learning in classification tasks.  The same device was later used to train a fully connected Boltzmann machine to generate, reconstruct, and classify down-scaled, low-resolution handwritten digits, among other synthetic datasets.  In both cases, the models trained by quantum annealing had a similar or better performance in terms of quality.  The ultimate question that drives this endeavour is whether there is quantum speedup in sampling applications.  Experience with the use of quantum annealers for combinatorial optimization suggests the answer is not straightforward.  Reverse annealing has been used as well to solve a fully connected quantum restricted Boltzmann machine.  Inspired by the success of Boltzmann machines based on classical Boltzmann distribution, a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian was recently proposed.  Due to the non-commutative nature of quantum mechanics, the training process of the quantum Boltzmann machine can become nontrivial.  This problem was, to some extent, circumvented by introducing bounds on the quantum probabilities, allowing the authors to train the model efficiently by sampling.  It is possible that a specific type of quantum Boltzmann machine has been trained in the D-Wave 2X by using a learning rule analogous to that of classical Boltzmann machines.  Quantum annealing is not the only technology for sampling.  In a prepare-and-measure scenario, a universal quantum computer prepares a thermal state, which is then sampled by measurements.  This can reduce the time required to train a deep restricted Boltzmann machine, and provide a richer and more comprehensive framework for deep learning than classical computing.  The same quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well-known classical counterparts.  Relying on an efficient thermal state preparation protocol starting from an arbitrary state, quantum-enhanced Markov logic networks exploit the symmetries and the locality structure of the probabilistic graphical model generated by a first-order logic template.  This provides an exponential reduction in computational complexity in probabilistic inference, and, while the protocol relies on a universal quantum computer, under mild assumptions it can be embedded on contemporary quantum annealing hardware.


Quantum machine learning

Machine_learning_with_quantum_computers Quantum analogues or generalizations of classical neural nets are often referred to as quantum neural networks.  The term is claimed by a wide range of approaches, including the implementation and extension of neural networks using photons, layered variational circuits or quantum Ising-type models.  Quantum neural networks are often defined as an expansion on Deutsch's model of a quantum computational network.  Within this model, nonlinear and irreversible gates, dissimilar to the Hamiltonian operator, are deployed to speculate the given data set.  Such gates make certain phases unable to be observed and generate specific oscillations.  Quantum neural networks apply the principals quantum information and quantum computation to classical neurocomputing.  Current research shows that QNN can exponentially increase the amount of computing power and the degrees of freedom for a computer, which is limited for a classical computer to its size.  A quantum neural network has computational capabilities to decrease the number of steps, qubits used, and computation time.  The wave function to quantum mechanics is the neuron for Neural networks.  To test quantum applications in a neural network, quantum dot molecules are deposited on a substrate of GaAs or similar to record how they communicate with one another.  Each quantum dot can be referred as an island of electric activity, and when such dots are close enough (approximately 10 - 20 nm) electrons can tunnel underneath the islands.  An even distribution across the substrate in sets of two create dipoles and ultimately two spin states, up or down.  These states are commonly known as qubits with corresponding states of | 0 ��� {\displaystyle |0\rangle } and | 1 ��� {\displaystyle |1\rangle } in Dirac notation.  A novel design for multi-dimensional vectors that uses circuits as convolution filters is QCNN.  It was inspired by the advantages of CNNs and the power of QML.  It is made using a combination of a variational quantum circuit(VQC) and a deep neural network(DNN), fully utilizing the power of extremely parallel processing on a superposition of a quantum state with a finite number of qubits.  The main strategy is to carry out an iterative optimization process in the NISQ devices, without the negative impact of noise, which is possibly incorporated into the circuit parameter, and without the need for quantum error correction.  The quantum circuit must effectively handle spatial information in order for QCNN to function as CNN.  The convolution filter is the most basic technique for making use of spatial information.  One or more quantum convolutional filters make up a quantum convolutional neural network (QCNN), and each of these filters transforms input data using a quantum circuit that can be created in an organized or randomized way.  Three parts that make up the quantum convolutional filter are: the encoder, the parameterized quantum circuit (PQC), and the measurement.  The quantum convolutional filter can be seen as an extension of the filter in the traditional CNN because it was designed with trainable parameters.


Quantum machine learning

Machine_learning_with_quantum_computers Quantum neural networks take advantage of the hierarchical structures, and for each subsequent layer, the number of qubits from the preceding layer is decreased by a factor of two.  For n input qubits, these structure have O(log(n)) layers, allowing for shallow circuit depth.  Additionally, they are able to avoid "barren plateau," one of the most significant issues with PQC-based algorithms, ensuring trainability.  Despite the fact that the QCNN model does not include the corresponding quantum operation, the fundamental idea of the pooling layer is also offered to assure validity.  In QCNN architecture, the pooling layer is typically placed between succeeding convolutional layers.  Its function is to shrink the representation's spatial size while preserving crucial features, which allows it to reduce the number of parameters, streamline network computing, and manage over-fitting.  Such process can be accomplished applying full Tomography on the state to reduce it all the way down to one qubit and then processed it in subway.  The most frequently used unit type in the pooling layer is max pooling, although there are other types as well.  Similar to conventional feed-forward neural networks, the last module is a fully connected layer with full connections to all activations in the preceding layer.  Translational invariance, which requires identical blocks of parameterized quantum gates within a layer, is a distinctive feature of the QCNN architecture.  Dissipative QNNs (DQNNs) are constructed from layers of qubits coupled by perceptron called building blocks, which have an arbitrary unitary design.  Each node in the network layer of a DQNN is given a distinct collection of qubits, and each qubit is also given a unique quantum perceptron unitary to characterize it.  The input states information are transported through the network in a feed-forward fashion, layer-to-layer transition mapping on the qubits of the two adjacent layers, as the name implies.  Dissipative term also refers to the fact that the output layer is formed by the ancillary qubits while the input layers are dropped while tracing out the final layer.  When performing a broad supervised learning task, DQNN are used to learn a unitary matrix connecting the input and output quantum states.  The training data for this task consists of the quantum state and the corresponding classical labels.  Inspired by the extremely successful classical Generative adversarial network(GAN), dissipative quantum generative adversarial network (DQGAN) is introduced for unsupervised learning of the unlabeled training data .  The generator and the discriminator are the two DQNNs that make up a single DQGAN.  The generator's goal is to create false training states that the discriminator cannot differentiate from the genuine ones, while the discriminator's objective is to separate the real training states from the fake states created by the generator.  The relevant features of the training set are learned by the generator by alternate and adversarial training of the networks that aid in the production of sets that extend the training set.  DQGAN has a fully quantum architecture and is trained in quantum data.


Quantum machine learning

Machine_learning_with_quantum_computers Hidden quantum Markov models (HQMMs) are a quantum-enhanced version of classical Hidden Markov Models (HMMs), which are typically used to model sequential data in various fields like robotics and natural language processing.  Unlike the approach taken by other quantum-enhanced machine learning algorithms, HQMMs can be viewed as models inspired by quantum mechanics that can be run on classical computers as well.  Where classical HMMs use probability vectors to represent hidden 'belief' states, HQMMs use the quantum analogue: density matrices.  Recent work has shown that these models can be successfully learned by maximizing the log-likelihood of the given data via classical optimization, and there is some empirical evidence that these models can better model sequential data compared to classical HMMs in practice, although further work is needed to determine exactly when and how these benefits are derived.  Additionally, since classical HMMs are a particular kind of Bayes net, an exciting aspect of HQMMs is that the techniques used show how we can perform quantum-analogous Bayesian inference, which should allow for the general construction of the quantum versions of probabilistic graphical models.  In the most general case of quantum machine learning, both the learning device and the system under study, as well as their interaction, are fully quantum.  This section gives a few examples of results on this topic.  One class of problem that can benefit from the fully quantum approach is that of 'learning' unknown quantum states, processes or measurements, in the sense that one can subsequently reproduce them on another quantum system.  For example, one may wish to learn a measurement that discriminates between two coherent states, given not a classical description of the states to be discriminated, but instead a set of example quantum systems prepared in these states.  The naive approach would be to first extract a classical description of the states and then implement an ideal discriminating measurement based on this information.  This would only require classical learning.  However, one can show that a fully quantum approach is strictly superior in this case.  (This also relates to work on quantum pattern matching. ) The problem of learning unitary transformations can be approached in a similar way.  Going beyond the specific problem of learning states and transformations, the task of clustering also admits a fully quantum version, wherein both the oracle which returns the distance between data-points and the information processing device which runs the algorithm are quantum.  Finally, a general framework spanning supervised, unsupervised and reinforcement learning in the fully quantum setting was introduced in, where it was also shown that the possibility of probing the environment in superpositions permits a quantum speedup in reinforcement learning.  Such a speedup in the reinforcement-learning paradigm has been experimentally demonstrated in a photonic setup.  The need for models that can be understood by humans emerges in quantum machine learning in analogy to classical machine learning and drives the research field of explainable quantum machine learning (or XQML in analogy to XAI/XML).


Quantum machine learning

Machine_learning_with_quantum_computers XQML can be considered as an alternative research direction instead of finding a quantum advantage.  For example, XQML has been used in the context of mobile malware detection and classification.  Quantum Shapley values have also been proposed to interpret gates within a circuit based on a game-theoretic approach.  For this purpose, gates instead of features act as players in a coalitional game with a value function that depends on measurements of the quantum circuit of interest.


Self-supervised learning

Examples Self-supervised learning is particularly suitable for speech recognition. For example, Facebook developed wav2vec, a self-supervised algorithm, to perform speech recognition using two deep convolutional neural networks that build on each other. Google's Bidirectional Encoder Representations from Transformers (BERT) model is used to better understand the context of search queries. OpenAI's GPT-3 is an autoregressive language model that can be used in language processing. It can be used to translate texts or answer questions, among other things. Bootstrap Your Own Latent (BYOL) is a NCSSL that produced excellent results on ImageNet and on transfer and semi-supervised benchmarks. The Yarowsky algorithm is an example of self-supervised learning in natural language processing. From a small number of labeled examples, it learns to predict which word sense of a polysemous word is being used at a given point in text. DirectPred is a NCSSL that directly sets the predictor weights instead of learning it via gradient update. Self-GenomeNet is an example of self-supervised learning in genomics.