Right to explanation

In the regulation of algorithms, particularly artificial intelligence and its subfield of machine learning, a right to [an] explanation is a right to be given an explanation for an output of the algorithm. Such rights primarily refer to individual rights to be given an explanation for decisions that significantly affect an individual, particularly legally or financially. For example, a person who applies for a loan and is denied may ask for an explanation, which could be "Credit bureau X reports that you declared bankruptcy last year; this is the main factor in considering you too likely to default, and thus we will not give you the loan you applied for." Some such legal rights already exist, while the scope of a general "right to explanation" is a matter of ongoing debate. There have been arguments made that a "social right to explanation" is a crucial foundation for an information society, particularly as the institutions of that society will need to use digital technologies, artificial intelligence, machine learning. In other words, that the related automated decision making systems that use explainability would be more trustworthy and transparent. Without this right, which could be constituted both legally and through professional standards, the public will be left without much recourse to challenge the decisions of automated systems. == Examples == === Credit scoring in the United States === Under the Equal Credit Opportunity Act (Regulation B of the Code of Federal Regulations), Title 12, Chapter X, Part 1002, §1002.9, creditors are required to notify applicants who are denied credit with specific reasons for the detail. As detailed in §1002.9(b)(2): (2) Statement of specific reasons. The statement of reasons for adverse action required by paragraph (a)(2)(i) of this section must be specific and indicate the principal reason(s) for the adverse action. Statements that the adverse action was based on the creditor's internal standards or policies or that the applicant, joint applicant, or similar party failed to achieve a qualifying score on the creditor's credit scoring system are insufficient. The official interpretation of this section details what types of statements are acceptable. Creditors comply with this regulation by providing a list of reasons (generally at most 4, per interpretation of regulations), consisting of a numeric reason code (as identifier) and an associated explanation, identifying the main factors affecting a credit score. An example might be: 32: Balances on bankcard or revolving accounts too high compared to credit limits === European Union === The European Union General Data Protection Regulation (GDPR, enacted 2016, taking effect 2018) extends the automated decision-making rights in the 1995 Data Protection Directive to provide a legally disputed form of a right to an explanation, stated as such in Recital 71: "[the data subject should have] the right ... to obtain an explanation of the decision reached". In full: The data subject should have the right not to be subject to a decision, which may include a measure, evaluating personal aspects relating to him or her which is based solely on automated processing and which produces legal effects concerning him or her or similarly significantly affects him or her, such as automatic refusal of an online credit application or e-recruiting practices without any human intervention. ... In any case, such processing should be subject to suitable safeguards, which should include specific information to the data subject and the right to obtain human intervention, to express his or her point of view, to obtain an explanation of the decision reached after such assessment and to challenge the decision. However, the extent to which the regulations themselves provide a "right to explanation" is heavily debated. There are two main strands of criticism. There are significant legal issues with the right as found in Article 22 — as recitals are not binding, and the right to an explanation is not mentioned in the binding articles of the text, having been removed during the legislative process. In addition, there are significant restrictions on the types of automated decisions that are covered — which must be both "solely" based on automated processing, and have legal or similarly significant effects — which significantly limits the range of automated systems and decisions to which the right would apply. In particular, the right is unlikely to apply in many of the cases of algorithmic controversy that have been picked up in the media. The UK has also recently amended its implementation of Article 22. A second potential source of such a right has been pointed to in Article 15, the "right of access by the data subject". This restates a similar provision from the 1995 Data Protection Directive, allowing the data subject access to "meaningful information about the logic involved" in the same significant, solely automated decision-making, found in Article 22. Yet this too suffers from alleged challenges that relate to the timing of when this right can be drawn upon, as well as practical challenges that mean it may not be binding in many cases of public concern. Other EU legislative instruments contain explanation rights. The European Union's Artificial Intelligence Act provides in Article 86 a "[r]ight to explanation of individual decision-making" of certain high risk systems which produce significant, adverse effects to an individual's health, safety or fundamental rights. The right provides for "clear and meaningful explanations of the role of the AI system in the decision-making procedure and the main elements of the decision taken", although only applies to the extent other law does not provide such a right. The Digital Services Act in Article 27, and the Platform to Business Regulation in Article 5, both contain rights to have the main parameters of certain recommender systems to be made clear, although these provisions have been criticised as not matching the way that such systems work. The Platform Work Directive, which provides for regulation of automation in gig economy work as an extension of data protection law, further contains explanation provisions in Article 11, using the specific language of "explanation" in a binding article rather than a recital as is the case in the GDPR. Scholars note that remains uncertainty as to whether these provisions imply sufficiently tailored explanation in practice which will need to be resolved by courts. === France === In France the 2016 Loi pour une République numérique (Digital Republic Act or loi numérique) amends the country's administrative code to introduce a new provision for the explanation of decisions made by public sector bodies about individuals. It notes that where there is "a decision taken on the basis of an algorithmic treatment", the rules that define that treatment and its "principal characteristics" must be communicated to the citizen upon request, where there is not an exclusion (e.g. for national security or defence). These should include the following: the degree and the mode of contribution of the algorithmic processing to the decision- making; the data processed and its source; the treatment parameters, and where appropriate, their weighting, applied to the situation of the person concerned; the operations carried out by the treatment. Scholars have noted that this right, while limited to administrative decisions, goes beyond the GDPR right to explicitly apply to decision support rather than decisions "solely" based on automated processing, as well as provides a framework for explaining specific decisions. Indeed, the GDPR automated decision-making rights in the European Union, one of the places a "right to an explanation" has been sought within, find their origins in French law in the late 1970s. == Criticism == Some argue that a "right to explanation" is at best unnecessary, at worst harmful, and threatens to stifle innovation. Specific criticisms include: favoring human decisions over machine decisions, being redundant with existing laws, and focusing on process over outcome. Authors of study "Slave to the Algorithm? Why a 'Right to an Explanation' Is Probably Not the Remedy You Are Looking For" Lilian Edwards and Michael Veale argue that a right to explanation is not the solution to harms caused to stakeholders by algorithmic decisions. They also state that the right of explanation in the GDPR is narrowly defined, and is not compatible with how modern machine learning technologies are being developed. With these limitations, defining transparency within the context of algorithmic accountability remains a problem. For example, providing the source code of algorithms may not be sufficient and may create other problems in terms of privacy disclosures and the gaming of technical systems. To mitigate this issue, Edwards and Veale argue that an auditing system could be more effective, to allow auditors to loo

Brave Leo

Brave Leo is a large language model-based chatbot developed by Brave Software and included with the Brave browser. == History == In November 2023, the company said versions for iOS and Android would be available "in the coming months". == Features == Since January 2024, Leo has used the open-source Mixtral 8x7B from Mistral AI as its default large language model, in addition to LLaMA 2 from Meta Platforms and Claude from Anthropic, both of which have been used previously. Leo can suggest follow-up questions, and summarize webpages, PDFs, and videos. Leo has a $15 (US) per month premium version that enables more requests and uses larger LLMs. == Privacy == The answers given by Leo are not saved. Brave uses the slogan Love Privacy to emphasize its focus on user privacy and data protection. The phrase has been featured in Brave's official marketing campaigns and has been cited in media coverage of the browser's privacy-first approach. == Controversies == In 2023, PC World reported that Leo evades questions about US elections.

Regular language

In theoretical computer science and formal language theory, a regular language (also called a rational language) is a formal language that can be defined by a regular expression, in the strict sense in theoretical computer science (as opposed to many modern regular expression engines, which are augmented with features that allow the recognition of non-regular languages). Alternatively, a regular language can be defined as a language recognised by a finite automaton. The equivalence of regular expressions and finite automata is known as Kleene's theorem (after American mathematician Stephen Cole Kleene). In the Chomsky hierarchy, regular languages are the languages generated by Type-3 grammars. == Formal definition == The collection of regular languages over an alphabet Σ is defined recursively as follows: The empty language ∅ is a regular language. For each a ∈ Σ (a belongs to Σ), the singleton language {a} is a regular language. If A is a regular language, A (Kleene star) is a regular language. Due to this, the empty string language {ε} is also regular. If A and B are regular languages, then A ∪ B (union) and A • B (concatenation) are regular languages. No other languages over Σ are regular. See Regular expression § Formal language theory for syntax and semantics of regular expressions. == Examples == All finite languages are regular; in particular the empty string language {ε} = ∅ is regular. Other typical examples include the language consisting of all strings over the alphabet {a, b} which contain an even number of as, or the language consisting of all strings of the form: several as followed by several bs. A simple example of a language that is not regular is the set of strings {anbn | n ≥ 0}. Intuitively, it cannot be recognized with a finite automaton, since a finite automaton has finite memory and it cannot remember the exact number of a's. Techniques to prove this fact rigorously are given below. == Equivalent formalisms == A regular language satisfies the following equivalent properties: it is the language of a regular expression (by the above definition) it is the language accepted by a nondeterministic finite automaton (NFA) it is the language accepted by a deterministic finite automaton (DFA) it can be generated by a regular grammar it is the language accepted by an alternating finite automaton it is the language accepted by a two-way finite automaton it can be generated by a prefix grammar it can be accepted by a read-only Turing machine it can be defined in monadic second-order logic (Büchi–Elgot–Trakhtenbrot theorem) it is recognized by some finite syntactic monoid M, meaning it is the preimage {w ∈ Σ | f(w) ∈ S} of a subset S of a finite monoid M under a monoid homomorphism f : Σ → M from the free monoid on its alphabet the number of equivalence classes of its syntactic congruence is finite. (This number equals the number of states of the minimal deterministic finite automaton accepting L.) Properties 10. and 11. are purely algebraic approaches to define regular languages; a similar set of statements can be formulated for a monoid M ⊆ Σ. In this case, equivalence over M leads to the concept of a recognizable language. Some authors use one of the above properties different from "1." as an alternative definition of regular languages. Some of the equivalences above, particularly those among the first four formalisms, are called Kleene's theorem in textbooks. Precisely which one (or which subset) is called such varies between authors. One textbook calls the equivalence of regular expressions and NFAs ("1." and "2." above) "Kleene's theorem". Another textbook calls the equivalence of regular expressions and DFAs ("1." and "3." above) "Kleene's theorem". Two other textbooks first prove the expressive equivalence of NFAs and DFAs ("2." and "3.") and then state "Kleene's theorem" as the equivalence between regular expressions and finite automata (the latter said to describe "recognizable languages"). A linguistically oriented text first equates regular grammars ("4." above) with DFAs and NFAs, calls the languages generated by (any of) these "regular", after which it introduces regular expressions which it terms to describe "rational languages", and finally states "Kleene's theorem" as the coincidence of regular and rational languages. Other authors simply define "rational expression" and "regular expressions" as synonymous and do the same with "rational languages" and "regular languages". Apparently, the term regular originates from a 1951 technical report where Kleene introduced regular events and explicitly welcomed "any suggestions as to a more descriptive term". Noam Chomsky, in his 1959 seminal article, used the term regular in a different meaning at first (referring to what is called Chomsky normal form today), but noticed that his finite state languages were equivalent to Kleene's regular events. == Closure properties == The regular languages are closed under various operations, that is, if the languages K and L are regular, so is the result of the following operations: the set-theoretic Boolean operations: union K ∪ L, intersection K ∩ L, and complement L, hence also relative complement K − L. the regular operations: K ∪ L, concatenation ⁠ K ∘ L {\displaystyle K\circ L} ⁠, and Kleene star L. the trio operations: string homomorphism, inverse string homomorphism, and intersection with regular languages. As a consequence they are closed under arbitrary finite state transductions, like quotient K / L with a regular language. Even more, regular languages are closed under quotients with arbitrary languages: If L is regular then L / K is regular for any K. the reverse (or mirror image) LR. Given a nondeterministic finite automaton to recognize L, an automaton for LR can be obtained by reversing all transitions and interchanging starting and finishing states. This may result in multiple starting states; ε-transitions can be used to join them. == Decidability properties == Given two deterministic finite automata A and B, it is decidable whether they accept the same language. As a consequence, using the above closure properties, the following problems are also decidable for arbitrarily given deterministic finite automata A and B, with accepted languages LA and LB, respectively: Containment: is LA ⊆ LB ? Disjointness: is LA ∩ LB = {} ? Emptiness: is LA = {} ? Universality: is LA = Σ ? Membership: given a ∈ Σ, is a ∈ LB ? For regular expressions, the universality problem is NP-complete already for a singleton alphabet. For larger alphabets, that problem is PSPACE-complete. If regular expressions are extended to allow also a squaring operator, with "A2" denoting the same as "AA", still just regular languages can be described, but the universality problem has an exponential space lower bound, and is in fact complete for exponential space with respect to polynomial-time reduction. For a fixed finite alphabet, the theory of the set of all languages – together with strings, membership of a string in a language, and for each character, a function to append the character to a string (and no other operations) – is decidable, and its minimal elementary substructure consists precisely of regular languages. For a binary alphabet, the theory is called S2S. == Complexity results == In computational complexity theory, the complexity class of all regular languages is sometimes referred to as REGULAR or REG and equals DSPACE(O(1)), the decision problems that can be solved in constant space (the space used is independent of the input size). REGULAR ≠ AC0, since it (trivially) contains the parity problem of determining whether the number of 1 bits in the input is even or odd and this problem is not in AC0. On the other hand, REGULAR does not contain AC0, because the nonregular language of palindromes, or the nonregular language { 0 n 1 n : n ∈ N } {\displaystyle \{0^{n}1^{n}:n\in \mathbb {N} \}} can both be recognized in AC0. If a language is not regular, it requires a machine with at least Ω(log log n) space to recognize (where n is the input size). In other words, DSPACE(o(log log n)) equals the class of regular languages. In practice, most nonregular problems are studied in a setting with at least logarithmic space, as this is the amount of space required to store a pointer into the input tape. == Location in the Chomsky hierarchy == To locate the regular languages in the Chomsky hierarchy, one notices that every regular language is context-free. The converse is not true: for example, the language consisting of all strings having the same number of as as bs is context-free but not regular. To prove that a language is not regular, one often uses the Myhill–Nerode theorem and the pumping lemma. Other approaches include using the closure properties of regular languages or quantifying Kolmogorov complexity. Important subclasses of regular languages include: Finite languages, those containing only a finite number of words. These are regular la

Gato (DeepMind)

Gato is a deep neural network for a range of complex tasks that exhibits multimodality. It can perform tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more. == Overview == Gato was created by researchers at London-based AI firm DeepMind. It is a transformer, like GPT-3. According to MIT Technology Review, the system "learns multiple different tasks at the same time, which means it can switch between them without having to forget one skill before learning another" whereas "[t]he AI systems of today are called “narrow,” meaning they can only do a specific, restricted set of tasks such as generate text", and according to The Independent, it is a "'generalist agent' that can carry out a huge range of complex tasks, from stacking blocks to writing poetry". It uses supervised learning with 1.2B parameters. The technology has been described as "general purpose" artificial intelligence and a "step toward" artificial general intelligence.

Ranking SVM

In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. The original purpose of the algorithm was to improve the performance of an internet search engine. However, it was found that ranking SVM also can be used to solve other problems such as Rank SIFT. == Description == The ranking SVM algorithm is a learning retrieval function that employs pairwise ranking methods to adaptively sort results based on how 'relevant' they are for a specific query. The ranking SVM function uses a mapping function to describe the match between a search query and the features of each of the possible results. This mapping function projects each data pair (such as a search query and clicked web-page, for example) onto a feature space. These features are combined with the corresponding click-through data (which can act as a proxy for how relevant a page is for a specific query) and can then be used as the training data for the ranking SVM algorithm. Generally, ranking SVM includes three steps in the training period: It maps the similarities between queries and the clicked pages onto a certain feature space. It calculates the distances between any two of the vectors obtained in step 1. It forms an optimization problem which is similar to a standard SVM classification and solves this problem with the regular SVM solver. == Background == === Ranking method === Suppose C {\displaystyle \mathbb {C} } is a data set containing N {\displaystyle N} elements c i {\displaystyle c_{i}} . r {\displaystyle r} is a ranking method applied to C {\displaystyle \mathbb {C} } . Then the r {\displaystyle r} in C {\displaystyle \mathbb {C} } can be represented as a N × N {\displaystyle N\times N} binary matrix. If the rank of c i {\displaystyle c_{i}} is higher than the rank of c j {\displaystyle c_{j}} , i.e. r c i < r c j {\displaystyle r\ c_{i}

Deluxe Paint

Deluxe Paint, often referred to as DPaint, is a bitmap graphics editor created by Dan Silva for Electronic Arts and published for the then-new Amiga 1000 in November 1985. A series of updated versions followed, some of which were ported to other platforms. An MS-DOS release with support for the 256 color VGA standard became popular for creating pixel graphics in video games in the 1990s. Author Dan Silva previously worked on the Cut & Paste word processor (1984), also from Electronic Arts. == History == Deluxe Paint began as an in-house art development tool called Prism. As author Dan Silva added features to Prism, it was developed as a showcase product to coincide with the Amiga's debut in 1985. Upon release, it was quickly embraced by the Amiga community and became the de facto graphics (and later animation) editor for the platform. Amiga manufacturer Commodore International later commissioned EA to create version 4.5 AGA to bundle with the new Advanced Graphics Architecture chipset (A1200, A4000) capable Amigas. Version 5 was the last release after Commodore's bankruptcy in 1994. Early versions of Deluxe Paint were available in protected and non copy-protected versions, the latter retailing for a slightly higher price. The copy protection scheme was later dropped. Deluxe Paint was first in a series of products from the Electronic Arts Tools group—then later moved to the ICE (for Interactivity, Creativity, and Education) group—which included such Amiga programs as Deluxe Music Construction Set (preceded by Music Construction Set for the Apple II), Deluxe Video, and the Studio series of paint programs for the Mac. With the development of Deluxe Paint, EA introduced the ILBM and ANIM file format standards for graphics. While widely used on the Amiga, these formats never gained widespread end user acceptance on other platforms, but were heavily used by game development companies. Deluxe Paint was used by LucasArts to make graphics for their adventure games such as The Secret of Monkey Island, and the name of a particular filename used to store the main protagonist Guybrush Threepwood was probably at the origin of his peculiar name. One of the main artist developer of the game, Mark Ferrari, in an interview for The Making of Monkey Island 30th Anniversary Documentary remembers that "there was a pulldown menu in DPaint called brushes, so character sprites were referred to as brushes", and the male protagonist was simply "the guy.brush" until the artist Steve Purcell suggested to take the very name "Guybrush". The author Ron Gilbert remembers that the PC DOS version of the file was named "guybrush.bbm". == Versions == === Amiga === Deluxe Paint I was released in 1985. A major feature was animation by using color cycling. The Amiga natively supports indexed color, where a pixel's color value does not carry any RGB hue information but instead is an index to a color palette (a collection of unique color values). By adjusting the color value in the palette, all pixels with that palette value change simultaneously in the image or animation, creating cyclic movement in the image. In the Christmas demo files on the Deluxe Paint I disk, this kind of animation (which is toggled by pressing the tab key) is used to depict falling snowflakes, a blinking Christmas tree, and a roaring fire in the fireplace. In 1986, Deluxe Paint II was introduced, which added many convenient features such as pattern and gradient fill, which could be selected by right-clicking on a fill tool. An effects menu with e.g. perspective transformation was also added. The screen format could now be changed from a dedicated selection page. Deluxe Paint III appeared in 1989 and added support for Extra Halfbrite. New editing modes allowed one to stencil certain colors to protect them, so it is possible to e.g. paint a landscape from front to back, with the foreground protected by a stencil. A major new feature of Deluxe Paint III was the ability to create cel-like animation, and animbrushes (1MB of RAM is needed for animation). These let the user pick up a section of an animation as an "animbrush", which can then be placed onto the canvas while it animates. Deluxe Paint III was one of the first paint programs to support animbrushes. This is similar to copy and paste, except one can pick up more than one image. Deluxe Paint IV (introduced in 1991), which did not include Silva as the lead programmer, offered significant new features like non-bitplane-indexed Hold-and-Modify support for creating images with up to 4,096 colors. Animation support was improved by adding a light table, i.e. onion skinning, and AnimBrush morphing. The color mixer was now a HAM region at the bottom of the screen (instead of a floating window as before) and allowed mixing adjacent colors similar to a real palette. Deluxe Paint 4.5 AGA appeared the following year, addressing the stability issues and providing support for the new A1200 and A4000 AGA machines and a revamped screen mode interface. It appeared in both standalone and Commodore-bundled versions. The final release, Deluxe Paint V, in 1995, supported true 24-bit RGB images. However, using only the AGA native chipset, the 24-bit RGB color was only held in computer memory, the on-screen image was displayed in HAM8 (18-bit color). === Apple IIGS === DeluxePaint II for the Apple IIGS was developed by Brent Iverson and released in 1987. === MS-DOS === Deluxe Paint II for MS-DOS was released in 1988, It required MS-DOS 2.0 and 640 kB of RAM. It supports CGA, EGA, MCGA, VGA, Hercules and Tandy IBM PC-compatible graphic cards. Deluxe Paint II Enhanced was released in 1989, requiring MS-DOS 2.11 and 640 kB of RAM. It supports resolutions up to 800x600 pixels with 256 colors. Deluxe Paint II Enhanced 2.0, released in 1994, was the most successful MS-DOS version, and was compatible with PC Paintbrush PCX image files. The MS-DOS conversion was done by Brent Iverson with the enhanced features by Steve Shaw. It supports CGA, EGA, MCGA, VGA, Hercules, Tandy, and Amstrad video cards, as well as early Super VGA video cards enabling it to support up to 800 × 600 with 256 (from 262,144) colors and 1024 × 768 with 16 colors. The sister product Deluxe Paint Animation (only for 320×200 pixels and 256 colors) was widely used, especially in video game development. === Atari ST === Deluxe Paint ST was developed by ArtisTech Development, published by Electronic Arts, and was released in 1990. It supports the Atari STE 4096 color palette and animated graphics. Features advertised for the Atari ST version include 3D perspective, design your own fonts, mirror symmetry, multi-color airbrushing & animations, printing up to poster size, split-screen magnification with variable zoom, and working on animations (including multiple animations). == Workflow == "[" and "]" hotkeys step through the indexed palette, turning indexed-pixel-painting into a fast two-handed mouse+keys process, and the right mouse button paints with the background color. For example, transparency is obtained as simply as selecting a background color index (a single right click on the palette GUI to change). colors could be locked from editing by use of a stencil (a list of color indices whose pixels should not be altered in the image data) and simple color-cycling animations could be created using contiguous entries in the palette. This was easy to change the hue and tone of a section of the image by altering the corresponding colors in the palette. (The specific section needed to use a dedicated part of the palette for this technique to work.) Brushes can be cut from the background by using the box, freehand, or polygon selection tools. They can then be used in the same manner as any other brush or pen. This functionality is simpler to use than the "stamp" tool of Photoshop or Alpha Channels as provided in later programs. Brushes can be rotated and scaled, even in 3D. After a brush is selected, it appears attached to the mouse cursor, providing an exact preview of what will be drawn. This allows precise pixel positioning of brushes. Animations stored in IFF ANIM format are delta compressed making animations both smaller and faster to playback. == Reception == Compute! criticized the documentation of the first release of DeluxePaint as inadequate, but stated that "DeluxePaint is a visual arts program of immense scope and flexibility". In later versions the documentation was much improved; for instance DeluxePaint IV came with a 300-page manual. Deluxe Paint was a hit for EA. The main line of the series, particularly installments one to three, has won a total of at least nine awards from independent publications and organizations, including three Amiga-specific awards. Deluxe Paint III also won Commodore International's Enterprise and Vision award in 1990, becoming the first software to win the award, for what the company's judges believed to be best utilizing the Amiga's graphical capabilities. Deluxe Pai

Jerome H. Friedman

Jerome Harold Friedman (born December 29, 1939) is an American statistician, consultant and Professor of Statistics at Stanford University, known for his contributions in the field of statistics and data mining. == Biography == Friedman studied at Chico State College for two years before transferring to the University of California, Berkeley in 1959, where he received his AB in Physics in 1962, and his PhD in High Energy Particle Physics in 1967. In 1968 he started his academic career as research physicist at the Lawrence Berkeley National Laboratory. In 1972 he started at Stanford University as leader of the Computation Research Group at the Stanford Linear Accelerator Center, where he would participate until 2003. In the year 1976–77 he was a visiting scientist at CERN in Geneva. From 1981 to 1984 he was visiting professor at the University of California, Berkeley. In 1982 he was appointed Professor of Statistics at Stanford University. In 1984 he was elected as a Fellow of the American Statistical Association. In 2002 he was awarded the SIGKDD Innovation Award by the Association for Computing Machinery (ACM). In 2010 he was elected as a member of the National Academy of Sciences (Applied mathematical sciences). == Publications == Friedman has authored and co-authored many publications in the field of data-mining including "nearest neighbor classification, logistical regressions, and high dimensional data analysis. His primary research interest is in the area of machine learning." A selection: Friedman, Jerome H. & Tukey, John W. (1974). "A projection pursuit algorithm for exploratory data analysis". IEEE Transactions on Computers. 23 (9): 881–890. doi:10.1109/T-C.1974.224051. OSTI 1442925. S2CID 7997450. Friedman, Jerome H. & Stuetzle, Werner (1981). "Projection pursuit regression". Journal of the American Statistical Association. 76 (376): 817–823. doi:10.1080/01621459.1981.10477729. OSTI 1445517. Friedman, Jerome H. (1991). "Multivariate adaptive regression splines". Annals of Statistics. 19 (1): 1–67. CiteSeerX 10.1.1.382.970. doi:10.1214/aos/1176347963. JSTOR 2241837. Friedman, Jerome H. (2001). "Greedy function approximation: a gradient boosting machine". Annals of Statistics. 29 (5): 1189–1232. doi:10.1214/aos/1013203451. JSTOR 2699986.