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A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jihoon Chung;
(2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction
Review of Related Work
Proposed Research Methodology
Numerical Case Studies
Real-World Simulation Case Studies
Conclusion
References II. REVIEW OF RELATED WORK The related existing studies of fault diagnosis in manufacturing systems are reviewed in Section II A. Then, the literature related to sparse Bayesian learning is provided in Section II-B. Afterward, the research gaps in the current work are identified in Section II-C. A. Fault Diagnosis Methodologies in Manufacturing Systems Numerous studies have focused on fault diagnosis methodologies for manufacturing systems, utilizing the fault-quality model outlined in Eq. (1). [14] developed a PCA-based orthogonal diagonalization strategy to transform the measurement data. It enabled the estimation of the variance of KCCs in a multistation assembly system. [13] presented a fault diagnosis method in the multistation assembly systems integrating the state space model of the process and matrix perturbation theory. [25], [26] proposed a fault diagnosis method in the machining process considering the process physics regarding how fixtures generate the patterns. Using this method, root cause identification was conducted sequentially. The approaches mentioned above assume that the number of measurements is greater than the number of KCCs (i.e., M > N in Eq. (1)). However, this assumption may not always be consistent with industrial practice. These approaches become ineffective when this assumption is violated because the fault-quality linear model leads to an underdetermined system, resulting in a nonunique solution. To overcome an underdetermined system in the fault-quality linear model, sparse learning can be utilized, which has gained considerable attention in fault diagnosis and detection within manufacturing systems. For fault diagnosis in the manufacturing system, [27] developed a fault diagnosis method using dictionary learning and sparse representationbased classification. [28] proposed a novel root cause diagnostic framework satisfying the assumption that sparse inputs affect the process output. [29] proposed a group-sparsity learning approach for bearing fault diagnosis. In addition to fault diagnosis and detection in the general manufacturing system, sparse learning has been widely utilized to address the issue of an underdetermined system in the multistation assembly system. In particular, sparse Bayesian learning has been widely utilized to incorporate the sparsity of process faults as the prior distribution. [5] proposed a fault diagnosis approach by enhancing the relevant vector machine to detect process faults using the sparse estimate of the variance change of KCCs. [8] developed a spatially correlated sparse Bayesian learning to consider the spatial correlation of KCCs in sparse estimation. The work identifies the KCCs with mean shifts. [10] proposed a Bayesian model to identify the KCCs that have variance increases in a multistation manufacturing system using the sparse variance component prior. [9] developed a novel sparse Bayesian learning to figure out the KCCs with mean shifts by considering the temporal correlation of KCCs and the prior knowledge of process faults. B. Sparse Bayesian Learning After the introduction of sparse Bayesian learning (SBL) by [30], many researchers have extended this approach significantly. For instance, [31] was the first to apply SBL to sparse estimation for the single measurement vector model given in Eq. (1). Subsequently, [32] further extended it to the MMV model (Eq. (2)) by developing the MSBL algorithm under the common support assumption. The notable advantage of SBL and MSBL is that their global minimum is always the sparsest solution, while that of the minimization-based sparse algorithms [17] [33] is usually not the sparsest solution [16], [34]. Based on the MMV model, many previous studies exploit the spatial correlation in solution vectors (i.e., rows in X in Eq. (2)). [35] proposed a block structure to exploit the intra-block correlation for sparse estimation. [36] developed a Bayesian method for recovery of block-sparse solution whose block-sparse structures are entirely unknown. [37] modeled the spatial structure of the solution as Markov dependency by the Beta process. Besides considering the spatial correlation of sparse solutions, work that considers nonstationary sparse solutions has been studied recently under the MMV model in the SBL framework. [38] developed a method using the Dirichlet process to cluster the measurements into groups with common sparsity patterns. Compared to [38], [2] proposed a more general method having two sparsity components: a commonly shared sparsity and an individual sparsity to deal with outliers that deviated from the uniform sparsity pattern in each group. C. Research Gap Analysis The research presented in Section II-A focuses on utilizing the sparsity of process faults for accurate fault diagnosis when low dimensional measurements exist in actual manufacturing systems. However, there is a lack of efforts to identify process faults by considering the spatial correlation and the nonstationary process faults, which is common in industrial practice. Section II-B introduces methods in SBL. It introduces the work concerning the spatial correlation in the solution vector and dealing with the nonstationary sparse solution individually. However, it still lacks the work that uses both properties simultaneously. Therefore, this paper proposes a novel SBL method considering both properties for accurate fault diagnosis. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2)  Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References II. REVIEW OF RELATED WORK The related existing studies of fault diagnosis in manufacturing systems are reviewed in Section II A. Then, the literature related to sparse Bayesian learning is provided in Section II-B. Afterward, the research gaps in the current work are identified in Section II-C. A. Fault Diagnosis Methodologies in Manufacturing Systems Numerous studies have focused on fault diagnosis methodologies for manufacturing systems, utilizing the fault-quality model outlined in Eq. (1). [14] developed a PCA-based orthogonal diagonalization strategy to transform the measurement data. It enabled the estimation of the variance of KCCs in a multistation assembly system. [13] presented a fault diagnosis method in the multistation assembly systems integrating the state space model of the process and matrix perturbation theory. [25], [26] proposed a fault diagnosis method in the machining process considering the process physics regarding how fixtures generate the patterns. Using this method, root cause identification was conducted sequentially. The approaches mentioned above assume that the number of measurements is greater than the number of KCCs (i.e., M > N in Eq. (1)). However, this assumption may not always be consistent with industrial practice. These approaches become ineffective when this assumption is violated because the fault-quality linear model leads to an underdetermined system, resulting in a nonunique solution. A. To overcome an underdetermined system in the fault-quality linear model, sparse learning can be utilized, which has gained considerable attention in fault diagnosis and detection within manufacturing systems. For fault diagnosis in the manufacturing system, [27] developed a fault diagnosis method using dictionary learning and sparse representationbased classification. [28] proposed a novel root cause diagnostic framework satisfying the assumption that sparse inputs affect the process output. [29] proposed a group-sparsity learning approach for bearing fault diagnosis. In addition to fault diagnosis and detection in the general manufacturing system, sparse learning has been widely utilized to address the issue of an underdetermined system in the multistation assembly system. In particular, sparse Bayesian learning has been widely utilized to incorporate the sparsity of process faults as the prior distribution. [5] proposed a fault diagnosis approach by enhancing the relevant vector machine to detect process faults using the sparse estimate of the variance change of KCCs. [8] developed a spatially correlated sparse Bayesian learning to consider the spatial correlation of KCCs in sparse estimation. The work identifies the KCCs with mean shifts. [10] proposed a Bayesian model to identify the KCCs that have variance increases in a multistation manufacturing system using the sparse variance component prior. [9] developed a novel sparse Bayesian learning to figure out the KCCs with mean shifts by considering the temporal correlation of KCCs and the prior knowledge of process faults. B. Sparse Bayesian Learning B. Sparse Bayesian Learning After the introduction of sparse Bayesian learning (SBL) by [30], many researchers have extended this approach significantly. For instance, [31] was the first to apply SBL to sparse estimation for the single measurement vector model given in Eq. (1). Subsequently, [32] further extended it to the MMV model (Eq. (2)) by developing the MSBL algorithm under the common support assumption. The notable advantage of SBL and MSBL is that their global minimum is always the sparsest solution, while that of the minimization-based sparse algorithms [17] [33] is usually not the sparsest solution [16], [34]. Based on the MMV model, many previous studies exploit the spatial correlation in solution vectors (i.e., rows in X in Eq. (2)). [35] proposed a block structure to exploit the intra-block correlation for sparse estimation. [36] developed a Bayesian method for recovery of block-sparse solution whose block-sparse structures are entirely unknown. [37] modeled the spatial structure of the solution as Markov dependency by the Beta process. Besides considering the spatial correlation of sparse solutions, work that considers nonstationary sparse solutions has been studied recently under the MMV model in the SBL framework. [38] developed a method using the Dirichlet process to cluster the measurements into groups with common sparsity patterns. Compared to [38], [2] proposed a more general method having two sparsity components: a commonly shared sparsity and an individual sparsity to deal with outliers that deviated from the uniform sparsity pattern in each group. C. Research Gap Analysis C. Research Gap Analysis The research presented in Section II-A focuses on utilizing the sparsity of process faults for accurate fault diagnosis when low dimensional measurements exist in actual manufacturing systems. However, there is a lack of efforts to identify process faults by considering the spatial correlation and the nonstationary process faults, which is common in industrial practice. Section II-B introduces methods in SBL. It introduces the work concerning the spatial correlation in the solution vector and dealing with the nonstationary sparse solution individually. However, it still lacks the work that uses both properties simultaneously. Therefore, this paper proposes a novel SBL method considering both properties for accurate fault diagnosis.

Sparse Bayesian Learning: Review of Related Work

Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . To Manufacturers of Knitted Goods. here To Manufacturers of Knitted Goods. is now in England, completing arrangements so as to be able to supply his American friends with his improved Knitting Machines with greater dispatch, and with all the latest improvements. He would beg to call especial attention to JOHN KENT The Improved Rib Top Frame, now so well known, and acknowledged to be the best rib top frame ever built, for speed and quality of goods produced. Price, delivered free in New York, $520, currency. The Improved Circular Web Frame, for drawers and shirts, built of any size and gage. Price for a 4-head set, 17 inch to 20 inch diameter, $810, currency, delivered free in New York. The Circular Stocking Frame, from 2 in. to 5½ in. diameter. These circular frames, with my last improvements, are as near perfection as possible. The Patent Full-fashioned Shirt, Drawers and Stocking Frames produce the most perfect goods ever made by steam-power machinery, and cost fifty per cent less to keep in repair than any other Knitting Machine. Built 10 to 24 gage, and from 30 to 140 inch wide, to order. The Improved Circular Looping Frame, for putting on shirt cuffs, drawers bands, clearing the top of circular shirts, &c., built to order, of any size, from 2 in. to 22 in. diameter, and of any gage. Steel Needles and Sinkers to pattern. Persons wishing to order while Mr. Kent is in England, will please address JOHN KENT, Nottingham, up to April 12th, or, if they prefer, may send through depot. Address JOHN KENT, 348 Pearl st., New York. PUMPS. —For Description, Price Lists etc., of the Best Centrifugal Pump ever invented, with Overwhelming Testimony in its favor, send for new illustrated pamphlet (40 pp.) to Messrs. HEALD, SISCO & CO., Baldwinsville, N. Y. BRICK PRESSES. **FOR RED AND FIRE BRICK.
\ Factory 309 S. Fifth street, Philadelphia, Pa. S. P. MILLER. ST. JOSEPH, Mo., Nov. 10, 1870. T. R. BAILEY & VAIL, Lockport, N. Y.: Gentlemen:—The Lathe you shipped me has arrived, and I have it in full operation. It works perfectly, and I think it the best lathe made in the world for Bedstead and Chair work. I would recommend it to any one desirous of obtaining such a lathe. Yours truly, H. R. BRISTOL. WOOLEN MILL FOR SALE. With House and 3 acres of land, Seymour, Ct., (Naugatuck Valley,) 2 miles from R. R. depot. Never-failing stream. 3 ft. fall, dam and wheel in good condition. Inquire of JAS. ORMSBEE, on the premises. Portable & Stationary Steam Engines AND HOISTING ENGINES. A good article at low prices. Every machine warranted. Send for descriptive Price List. H. B. BIGELOW & CO., New Haven, Conn. PATENT BANDSAW MACHINES Of the most approved kinds, of various sizes , to saw bevel as well as square, without inclining the table, by , 452 to 456 Tenth ave., New York. Price $250, $275, $350, and $400. At present (Oct. 16), there are in operation, in this city alone, 88 of our machines. Send for circular. Manufacture, also, an improved saw-filing apparatus; price, $30. Have also on hand a large stock of best French Bandsaw Blades. FIRST & PRYIBIL L. & J. W. FEUCHTWANGER, 55 Cedar st., New York, Chemists, Manufacturers, and Importers of Specialities, Silicates, Soda and Potash, Chloride of Calcium, Peroxide of Manganese, Hydrofluoric Acid, Metallic Oxides, Steel and Glass Makers' and Potters' Articles, Publishers of Treatises on "Soluble Glass," "Gems," and "Fermented Liquors." PIMLICO BRACES, SOMETHING NEW. THIS invention is based on a strictly scientific principle, and is a valuable improvement on old style suspenders. It is simple in construction, and combines the qualities of Brace and Suspender. They are unequaled for elegance, durability and comfort. Manufactured at the Monumental Silk Works, Baltimore. JOHN M. DAVIES & CO., Sole Agents, 384 & 386 B'd'y, N. Y. Dr. J. ARMSTRONG'S (Patent) Improved Heater, Filter, Lime Extractor, and Condenser Combined, For Steam Boilers. MANUFACTURED BY Toledo, Ohio. Armstrong & Starr, Send for Circulars. Formerly Armstrong & Welsh. —An Engine, 12x36 in. cylinder, and two Boilers, 4x15 feet, in good order, will be sold cheap. FOR SALE. J. J. TAYLOR & CO., 68 Courtlandt st., New York. Trapping and Fishing. All about it. Sent free. Address "Hunter," Hinsdale, N. H. HUNTING, First Premium awarded by Am. Inst., 1870 T. H. McALLISTER, Optician, 49 Nassau st., N. Y. Rare and Beautiful Flowers —AND— CHOICE VEGETABLES Can always be obtained by Sowing The Seventeenth Annual Edition of their celebrated "SEED CATALOGUE AND GUIDE TO THE FLOWER AND KITCHEN GARDEN," is now ready for distribution. It contains Four Beautifully Colored Lithographs, and about 300 choice Engravings of favorite Flowers and Vegetables, 136 pages of closely-printed matter, and a list of Twenty-five Hundred species and varieties of Flower and Vegetable Seeds, with explicit directions for their culture, and much other useful information upon the subject of Gardening. A copy will be mailed to all applicants inclosing 25 cts. Regular customers supplied gratis. Address B. K. BLISS & SONS, Nos. 23 Park Place, and 20 Murray st., P. O. Box No. 5712. New York. Science for the Million. THE Boston Journal of Chemistry, Devoted to the Science of Home Life, the Arts, Agriculture, and Medicine. JAMES R. NICHOLS, M.D., WILLIAM A. ROLFE, A.M., } Editors. ONE DOLLAR PER YEAR. A paper which commends itself at once to Physicians, Druggists, Chemists, Teachers, Farmers, Mechanics—in short, to Professional and Practical Men of every class. The Domestic Recipes and Formulæ for Art Processes are of themselves worth many times the cost of subscription. Specimen Copies Sent Free. Address **BOSTON JOURNAL OF CHEMISTRY,
\ 150 Congress st., Boston. FOOT LATHES, And all kinds of small tools. Illustrated catalogue free. GOODNOW & WIGHTMAN, 23 Cornhill, Boston, Mass. AGENTS WANTED.—To sell Stephens' which embraces a Rule, Level, Square, Plumb, Bevel, Slope Level, T Square, etc., in one compact tool. These instruments retail at $3.50 each, and energetic salesmen can make money by selling them among mechanics. We warrant them in every particular, as the construction and graduation is faultless. Send for descriptive circular, cuts, and terms. Patent Combination Rule, STEPHENS & CO., Riverton, Conn. FELT. THE BEST, CHEAPEST and MOST DURABLE non-conductor known, for sale by the Original Manufacturer, at the BOILER FELTING WORKS, 46 Courtland st., New York. Universal Wood Worker. For Agricultural, Railroad, Car, Carriage, and Wagon Works, Planing Mill, Sash, Door and Blind, Bedstead, Cabinet and Furniture Factories. McBETH, BENTEL & MARGEDANT, Hamilton, O. , Inventor's Exchange 245 Broadway, N. Y., "Am. 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See Scientific American, Sept. 17, 1870. For Descriptive Circular apply to F. C. WELLS, President, Room 13, 98 Madison st.,\CHICAGO. MACHINES can be seen in operation at the Company's Works, Chicago; at rear 59 Ann st., New York city; and at Novelty Iron Works, corner of Delord and Peter sts., New Orleans. PREPARED Asphalte Roofing Felt. This new prepared production is ready coated, and can be applied on the roof without further trouble. It is easy of application, and does not require any repairs for a long time. It is more durable than some slates, and has been found a suitable substitute for iron or tin roofs. It has a sanded or stony surface, which renders it Exposed to the most intense fire, and sparks falling upon it, it will not propagate the fire. Under the influence of the sun it will not run, which makes it specially adapted to hot climates. Its easy application and pleasing appearance have made it a favorite roofing material throughout all the Indies and other colonies. Being not cumbrous for transport, it is of invaluable service to settlers and farmers in far remote districts. When used for temporary purposes it may be taken off and applied again to another construction. It replaces common Asphalting on Terraces, Lobbies, Counting-houses, Office Floors, etc.; is a great preservative against dampness and vermin, and equalizes the temperature. It is 32 inches wide, and made in rolls of 25 yards each. Send for circular to UNINFLAMMABLE and FIRE-PROOF. E. H. MARTIN, 70 Maiden Lane and 9 Liberty St., N. Y. J. J. H. GREGORY'S Seed Catalogue. My Annual Illustrated Catalogue, containing a list of many new and rare Vegetables, some of which are not found in any other catalogue, and all the standard vegetables of the farm and garden (over one hundred of which I grow on my three seed farms), with a carefully selected list of flower seed, will be sent free to all. All my seed is sold under three warrants: 1st. That all money sent shall reach me. 2d. That all seed ordered shall reach the purchaser. 3d. That my seeds shall be fresh and true to name. JAMES J. H. GREGORY, Marblehead, Mass. THE NEW WILSON Under-Feed Shuttle\SEWING MACHINES! $25 cheaper than any other! For Simplicity, Durability and Beauty they stand For Stitching, Hemming, Tucking, Felling, Quilting, Cording, Binding, Braiding, Gathering, Gathering & sewing on gathers, For particulars address unrivalled! they are unexcelled! Wilson Sewing Machine Co., Cleveland, O., or St. Louis, Mo. , **NEW and 2d-HAND.—
\ Send for Circular. Chas. PLACE & CO., 60 Vesey st., New York. MACHINERY MACHINISTS. Illustrated Catalogue and Price List of all kinds of small Tools and Materials sent free to any address. GOODNOW & WIGHTMAN, 23 Cornhill, Boston, Mass. P. BLAISDELL & CO. MANUFACTURERS of the "BLAISDELL" PATENT DRILL PRESSES, with quick return motion, Agricultural Drills, Improved Engine Lathes, from 12 in. to 28 in. swing, Planers, Gear Cutters, Boring Mills, Hand Lathes, and other first-class Machinists' Tools. Jackson st., Worcester, Mass. Patent BEDSTEAD FASTENING. The and ever invented. Best, Cheapest, Strongest FASTENING Rights for States and Territories for sale. Address JOHN DOMINGOS and                     BENJAMIN ESSIG, Sacramento, Cal. BENT, GOODNOW & CO., Boston, Mass., Publishers of , sell Patent Rights and goods of all kinds. Orders solicited. "PATENT STAR" AGENTS WANTED. Send stamp for copy. TO THE WORKING CLASS.—We are now prepared to furnish all classes with constant employment at home, the whole of the time or for the spare moments. Business new, light and profitable. Persons of either sex easily earn from 50c. to $5 per evening, and a proportional sum by devoting their whole time to the business. Boys and girls earn nearly as much as men. That all who see this notice may send their address, and test the business, we make this unparalleled offer: To such as are not well satisfied, we will send $1 to pay for the trouble of writing. Full particulars, a valuable sample which will do to commence work on, and a copy of —one of the largest and best family newspapers published—all sent free by mail. Reader, if you want permanent, profitable work, address The People's Literary Companion D. C. ALLEN & CO., Augusta, Maine. IMPORTANT TO  MACHINISTS.—The Best Metal for all Machine Uses is the MARTIN STEEL, made by The New Jersey Steel and Iron Co., Trenton, N. J. This steel is made by an entirely different process from any other and is tougher than wrought iron. It can be turned without annealing, being entirely free from hard spots. Every one who uses it pronounces it just what they have long wanted, for a multitude of uses, such as Crank Pins, Lathe Spindles and Screws, Cotton Machinery Rollers, Saw and Fan Spindles, etc., etc. Also, particularly adapted for Firebox Plates. Prices low. Send for further information, or a sample, stating use to which it is to be applied. OTIS' SAFETY HOISTING\Machinery. **OTIS, BROS. & CO.
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\ Manufacturers of the latest improved Patent Daniels' and Woodworth Planing Machines, Matching, Sash, and molding, Tenoning, Mortising, Boring, Shaping, Vertical, and Circular Re-sawing Machines, Saw Mills Saw Arbors, Scroll Saws, Railway, Cut-off, and Rip-saw Machines, Spoke and Wood Turning Lathes, and various other kinds of Wood-working Machinery. Catalogues and price lists sent on application. Manufactory, Worcester, Mass. Warehouse, 107 Liberty st., New York. 17 1 Reynolds' TURBINE WATER WHEELS. The Oldest and Newest. All others only imitations of each other in their strife after complications to confuse the public. We do not boast but quietly excel them all in staunch reliable, economical power. Beautiful pamphlet free. Geo. TALLCOT, 96 Liberty st., New York. Gearing, Shafting.

To Manufacturers of Knitted Goods

Feature Engineering for Machine Learning Models: Everything You Need to Know 

মেশিন লার্নিং এর জন্য ফিচার ইঞ্জিনিয়ারিং

Feature Engineering für maschinelles Lernen

Ingeniería de funciones para el aprendizaje automático

Ingénierie des fonctionnalités pour l’apprentissage automatique

मशीन लर्निंग के लिए फ़ीचर इंजीनियरिंग

機械学習のための特徴エンジニアリング

기계 학습을 위한 특성 엔지니어링

Engenharia de recursos para aprendizado de máquina

Разработка функций для машинного обучения

Makine Öğrenimi için Özellik Mühendisliği

Kỹ thuật tính năng cho Machine Learning

机器学习的特征工程

How Cloud-Based Machine Learning Platforms Streamline AI Development

Blackrock CEO Larry Fink tells the World Economic Forum (WEF) that developed countries with shrinking populations will be better prepared for the social problems that come from “substituting humans for machines.” Speaking at the WEF’s Special Meeting on Global Collaboration, Growth and Energy Development during a panel on “Investing Amid Global Fracture,” Fink said that there would be a divide between the “middle class developing countries that have a foundation and education” and developing countries that have booming populations but little education. https://twitter.com/TimHinchliffe/status/1785042264860848489?embedable=true With that divide, Fink argued that developed countries with shrinking populations would be the “big winners” as far as quality of life is concerned because they will have all the technologies to boost productivity and elevate their living standards. “I can argue in the developed countries the big winners are countries that have shrinking populations […] These countries will rapidly develop robotics and AI and technology […] The social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations”
BlackRock CEO Larry Fink, World Economic Forum, April 2024 “I can argue in the developed countries the big winners are countries that have shrinking populations,” said Fink. “We always used to think shrinking population is a cause for negative growth, but in my conversations with the leadership of these large, developed countries that have xenophobic immigration policies, they don’t allow anybody to come in, shrinking demographics — these countries will rapidly develop robotics and AI and technology. “And if the promise, and it’s going to happen, if the promise of all of that transforms productivity, which most of us think it will, we’ll be able to elevate the standard of living of countries, the standard of living of individuals, even with shrinking populations.” https://www.youtube.com/watch?v=PoRVYFHNc6k&embedable=true For Fink, fewer people means a better quality of life in the developed world thanks to automation. For growing populations in the developing world, particularly in Africa, education will be key, according to the BlackRock CEO. “The paradigm of negative population growth is going to be changing, and the social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations,” said Fink. “For those countries that have rising populations, the answer will be education — for those countries that do not have a foundation of rule of law or education, that’s where the divide’s going to get more and more extreme,” he added. The WEF’s Special Meeting on Global Collaboration, Growth and Energy Development took place from April 27-29 in Riyadh, Saudi Arabia. Tim Hinchliffe, Editor, The Sociable Blackrock CEO Larry Fink tells the World Economic Forum (WEF) that developed countries with shrinking populations will be better prepared for the social problems that come from “substituting humans for machines.” Speaking at the WEF’s Special Meeting on Global Collaboration, Growth and Energy Development during a panel on “ Investing Amid Global Fracture ,” Fink said that there would be a divide between the “middle class developing countries that have a foundation and education” and developing countries that have booming populations but little education. Special Meeting on Global Collaboration, Growth and Energy Development a panel Investing Amid Global Fracture https://twitter.com/TimHinchliffe/status/1785042264860848489?embedable=true https://twitter.com/TimHinchliffe/status/1785042264860848489?embedable=true With that divide, Fink argued that developed countries with shrinking populations would be the “big winners” as far as quality of life is concerned because they will have all the technologies to boost productivity and elevate their living standards. “I can argue in the developed countries the big winners are countries that have shrinking populations […] These countries will rapidly develop robotics and AI and technology […] The social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations” BlackRock CEO Larry Fink, World Economic Forum, April 2024 “I can argue in the developed countries the big winners are countries that have shrinking populations […] These countries will rapidly develop robotics and AI and technology […] The social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations” “I can argue in the developed countries the big winners are countries that have shrinking populations […] These countries will rapidly develop robotics and AI and technology […] The social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations” “I can argue in the developed countries the big winners are countries that have shrinking populations […] These countries will rapidly develop robotics and AI and technology […] The social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations” BlackRock CEO Larry Fink, World Economic Forum, April 2024 “ I can argue in the developed countries the big winners are countries that have shrinking populations ,” said Fink. I can argue in the developed countries the big winners are countries that have shrinking populations I can argue in the developed countries the big winners are countries that have shrinking populations “ We always used to think shrinking population is a cause for negative growth, but in my conversations with the leadership of these large, developed countries that have xenophobic immigration policies, they don’t allow anybody to come in, shrinking demographics — these countries will rapidly develop robotics and AI and technology . We always used to think shrinking population is a cause for negative growth, but in my conversations with the leadership of these large, developed countries that have xenophobic immigration policies, they don’t allow anybody to come in, shrinking demographics — these countries will rapidly develop robotics and AI and technology We always used to think shrinking population is a cause for negative growth, but in my conversations with the leadership of these large, developed countries that have xenophobic immigration policies, they don’t allow anybody to come in, shrinking demographics — these countries will rapidly develop robotics and AI and technology “ And if the promise, and it’s going to happen, if the promise of all of that transforms productivity, which most of us think it will, we’ll be able to elevate the standard of living of countries, the standard of living of individuals , even with shrinking populations .” And if the promise, and it’s going to happen, if the promise of all of that transforms productivity, which most of us think it will, we’ll be able to elevate the standard of living of countries, the standard of living of individuals And if the promise, and it’s going to happen, if the promise of all of that transforms productivity, which most of us think it will, we’ll be able to elevate the standard of living of countries, the standard of living of individuals even with shrinking populations even with shrinking populations https://www.youtube.com/watch?v=PoRVYFHNc6k&embedable=true https://www.youtube.com/watch?v=PoRVYFHNc6k&embedable=true For Fink, fewer people means a better quality of life in the developed world thanks to automation. For growing populations in the developing world, particularly in Africa, education will be key, according to the BlackRock CEO. “ The paradigm of negative population growth is going to be changing, and the social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations ,” said Fink. The paradigm of negative population growth is going to be changing, and the social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations The paradigm of negative population growth is going to be changing, and the social problems that one will have in substituting humans for machines is going to be far easier in those countries that have declining populations “ For those countries that have rising populations, the answer will be education — for those countries that do not have a foundation of rule of law or education, that’s where the divide’s going to get more and more extreme ,” he added. For those countries that have rising populations, the answer will be education — for those countries that do not have a foundation of rule of law or education, that’s where the divide’s going to get more and more extreme For those countries that have rising populations, the answer will be education — for those countries that do not have a foundation of rule of law or education, that’s where the divide’s going to get more and more extreme The WEF’s Special Meeting on Global Collaboration, Growth and Energy Development took place from April 27-29 in Riyadh, Saudi Arabia. Special Meeting on Global Collaboration, Growth and Energy Development Tim Hinchliffe, Editor, The Sociable Tim Hinchliffe, Editor, The Sociable Tim Hinchliffe , Editor, The Sociable Tim Hinchliffe

BlackRock's Larry Fink on Social Challenges in Human-Machine Substitution

The Future of Work: Preparing for an AI and Machine Learning Dominated Economy

Compromised devices/machines are a new threat to organizations, especially small businesses. Challenges such as Fast-paced digital transformation, Cloud adoption, and the Internet of Things add fire to the already burning pyre of managing the massive volume of machine identities. But this is not all. While the security teams at larger enterprises are fighting demons like ransomware, malware, phishing, and other cybersecurity attacks, small businesses face the significant brunt of poorly managed machine identities. But… 🙋What are machine identities, and how are they different from human identities? 🙋Why is managing machine identities crucial for small businesses? 🙋What are the challenges faced by Small Businesses while managing machine identities? These are a few of the questions I will try to answer through my blog on Managing Machine Identities in SMBs. Sit tight, and keep reading. Machine Identity: The Meaning and The Definition Machine identity is the unique digital identity assigned to applications, services, and devices within a networked environment. These identities help machines to ✅ Connect and communicate with each other in a secure environment. ✅ Authenticate themselves to other systems in the network. Each machine identity comprises cryptographic keys, certificates, or the like of credentials that build trust and enable secure communications in digital space. There are two entities in a networked environment: humans and machines. While much investment is made to protect and manage human identities, scant efforts are made to protect and secure interconnected machines. Result: The imbalance is a significant concern. Reason: The number of machines has steadily increased, outpacing the growth of their human counterparts. This implies that there are more machines connected with each other with scant security efforts in place than humans connected to machines. To protect these machines from being compromised, they need identities that can keep their connections and communications safe. Importance of Machine Identity Management Machine Identity Management plays a crucial role in ensuring your business's security, integrity, and efficiency. With proper identities in place, you can authenticate and authorize machine-to-machine communication. When you are regularly managing the identities of your machines, you can prevent 📌 Unauthorized access 📌 Data breaches 📌 Other security threats, such as malware, ransomware attacks But that’s not all. With machine identity management, you can facilitate the smooth integration of various services and systems. Result: Optimized overall operational performance. But how is this relevant to your small business? Relevance for Small Businesses With limited resources, small businesses face unique challenges in maintaining robust security measures. Despite the size of your business, you still rely on interconnected networks, systems, and applications to conduct your everyday operations efficiently. So, how will managing machine identity help your small business? Managing your machine identities can help you in numerous ways; here are a few prominent ones. Machine Identity Management helps small businesses to 📍Secure their digital assets 📍Safeguard sensitive data 📍Mitigate risks associated with cyber threats With effective machine identity practices, small business owners can ✅ Improve their overall security posture ✅ Support the growth of their business ✅ Scale their small business But what exactly is machine identity? Understanding Machine Identity Unique cryptographic identifiers used to authenticate and secure communication between machines are called machine identities. These identities are allotted to machines in a networked environment, such as servers, devices, and applications. Managing machine identities is crucial for small business owners to protect against cyber threats and maintain their operational integrity in contemporary IT infrastructures. What Constitutes Machine Identity? Machine identity encompasses various components that establish the digital identity of devices, applications, and services within an IT ecosystem. These include 📍Digital Certificates 📍API Keys 📍SSH (Secure Shell) Keys 📍Device Identifiers Why Is Machine Identity Important For Your Small Business Machine identities are crucial for a secure business environment and serve important functions in the contemporary IT infrastructure. 📌 Authentication: Machine identities help business owners verify the identity of devices, applications, and services, ensuring only trusted entities can access resources and systems. 📌Encryption: Machine identities, especially SSL/TLS certificates, enable encryption of data transmitted over networks, safeguarding it from unauthorized interception and ensuring confidentiality. 📌 Access Control: They have a crucial role in access control by regulating permissions based on machines' identities and roles. This helps prevent unauthorized access and actions within IT environments. 📌Understanding and effectively managing machine identities is essential for maintaining IT operations' security, integrity, and efficiency, especially for small businesses with limited resources and specialized needs. Machine Identity Management Challenges for Small Businesses Unmanaged or improperly machine identities can pose serious challenges to small businesses. Some of the challenges your business can face while managing machine identities. Lack of Awareness and Resources Small businesses often face challenges due to limited awareness of available resources and best practices. This can encompass various aspects: 📍 Technology: Small businesses might not be aware of the latest technologies and tools that can help them streamline their operations and improve security. 📍 Financial: Limited budgets can restrict access to necessary resources such as advanced cybersecurity solutions or professional consulting services. 📍 Training and Education: Owners and employees might lack awareness of cybersecurity risks and protocols, leading to vulnerabilities like phishing attacks or weak password practices. Risks Associated with Unmanaged Machine Identities Machine identities refer to digital certificates and cryptographic keys used to authenticate machines in networks. The risks associated with unmanaged machine identities include: 📍Misconfigured or compromised machine identities can lead to unauthorized access to sensitive data or systems. 📍Improperly managed machine identities can result in compliance failures with industry regulations and standards. 📍Unmanaged machine identities can lead to operational disruptions and downtime. Compliance and Regulatory Concerns While compliance with regulations and standards is crucial for your small business, machine identity management can make it challenging due 📍Various regulatory requirements such as GDPR, HIPAA, and PCI DSS can be complex to understand and adhere to, especially for you as a small business owner. 📍Shortage of dedicated resources to meet compliance requirements is another challenge for small business owners. Hiring a dedicated resource can put a strain on your small business budgets. 📍Non-compliance can result in significant penalties or legal consequences. This is quite a detrimental situation for small businesses with limited financial resilience. Small businesses can address these challenges with strategic resource allocation and leveraging technology solutions tailored to their needs. Compromised devices/machines are a new threat to organizations, especially small businesses. Challenges such as Fast-paced digital transformation, Cloud adoption, and the Internet of Things add fire to the already burning pyre of managing the massive volume of machine identities. But this is not all. While the security teams at larger enterprises are fighting demons like ransomware, malware, phishing, and other cybersecurity attacks, small businesses face the significant brunt of poorly managed machine identities. But… 🙋What are machine identities, and how are they different from human identities? 🙋Why is managing machine identities crucial for small businesses? 🙋What are the challenges faced by Small Businesses while managing machine identities? These are a few of the questions I will try to answer through my blog on Managing Machine Identities in SMBs. Sit tight, and keep reading. Machine Identity: The Meaning and The Definition Machine identity is the unique digital identity assigned to applications, services, and devices within a networked environment. These identities help machines to ✅ Connect and communicate with each other in a secure environment. ✅ Authenticate themselves to other systems in the network. Each machine identity comprises cryptographic keys, certificates, or the like of credentials that build trust and enable secure communications in digital space. There are two entities in a networked environment: humans and machines. While much investment is made to protect and manage human identities, scant efforts are made to protect and secure interconnected machines. Result: The imbalance is a significant concern. Result: Reason: The number of machines has steadily increased, outpacing the growth of their human counterparts. Reason: This implies that there are more machines connected with each other with scant security efforts in place than humans connected to machines. To protect these machines from being compromised, they need identities that can keep their connections and communications safe. Importance of Machine Identity Management Machine Identity Management plays a crucial role in ensuring your business's security, integrity, and efficiency. With proper identities in place, you can authenticate and authorize machine-to-machine communication. When you are regularly managing the identities of your machines, you can prevent 📌 Unauthorized access 📌 Data breaches 📌 Other security threats, such as malware, ransomware attacks But that’s not all. With machine identity management, you can facilitate the smooth integration of various services and systems. Result: Optimized overall operational performance. But how is this relevant to your small business? Relevance for Small Businesses With limited resources, small businesses face unique challenges in maintaining robust security measures. Despite the size of your business, you still rely on interconnected networks, systems, and applications to conduct your everyday operations efficiently. So, how will managing machine identity help your small business? Managing your machine identities can help you in numerous ways; here are a few prominent ones. Machine Identity Management helps small businesses to 📍Secure their digital assets 📍Safeguard sensitive data 📍Mitigate risks associated with cyber threats With effective machine identity practices, small business owners can ✅ Improve their overall security posture ✅ Support the growth of their business ✅ Scale their small business But what exactly is machine identity? Understanding Machine Identity Unique cryptographic identifiers used to authenticate and secure communication between machines are called machine identities. These identities are allotted to machines in a networked environment, such as servers, devices, and applications. Managing machine identities is crucial for small business owners to protect against cyber threats and maintain their operational integrity in contemporary IT infrastructures. What Constitutes Machine Identity? Machine identity encompasses various components that establish the digital identity of devices, applications, and services within an IT ecosystem. These include 📍Digital Certificates 📍API Keys 📍SSH (Secure Shell) Keys 📍Device Identifiers Why Is Machine Identity Important For Your Small Business Machine identities are crucial for a secure business environment and serve important functions in the contemporary IT infrastructure. 📌 Authentication: Machine identities help business owners verify the identity of devices, applications, and services, ensuring only trusted entities can access resources and systems. 📌Encryption: Machine identities, especially SSL/TLS certificates, enable encryption of data transmitted over networks, safeguarding it from unauthorized interception and ensuring confidentiality. 📌 Access Control: They have a crucial role in access control by regulating permissions based on machines' identities and roles. This helps prevent unauthorized access and actions within IT environments. 📌Understanding and effectively managing machine identities is essential for maintaining IT operations' security, integrity, and efficiency, especially for small businesses with limited resources and specialized needs. Machine Identity Management Challenges for Small Businesses Unmanaged or improperly machine identities can pose serious challenges to small businesses. Some of the challenges your business can face while managing machine identities. Lack of Awareness and Resources Small businesses often face challenges due to limited awareness of available resources and best practices. This can encompass various aspects: 📍 Technology: Small businesses might not be aware of the latest technologies and tools that can help them streamline their operations and improve security. 📍 Financial: Limited budgets can restrict access to necessary resources such as advanced cybersecurity solutions or professional consulting services. 📍 Training and Education: Owners and employees might lack awareness of cybersecurity risks and protocols, leading to vulnerabilities like phishing attacks or weak password practices. Risks Associated with Unmanaged Machine Identities Machine identities refer to digital certificates and cryptographic keys used to authenticate machines in networks. The risks associated with unmanaged machine identities include: 📍Misconfigured or compromised machine identities can lead to unauthorized access to sensitive data or systems. 📍Improperly managed machine identities can result in compliance failures with industry regulations and standards. 📍Unmanaged machine identities can lead to operational disruptions and downtime. Compliance and Regulatory Concerns While compliance with regulations and standards is crucial for your small business, machine identity management can make it challenging due 📍Various regulatory requirements such as GDPR, HIPAA, and PCI DSS can be complex to understand and adhere to, especially for you as a small business owner. 📍Shortage of dedicated resources to meet compliance requirements is another challenge for small business owners. Hiring a dedicated resource can put a strain on your small business budgets. 📍Non-compliance can result in significant penalties or legal consequences. This is quite a detrimental situation for small businesses with limited financial resilience. Small businesses can address these challenges with strategic resource allocation and leveraging technology solutions tailored to their needs.

Machine Identities in Small Businesses: The Key to Security and Efficiency

Population Stability Index (PSI) is a statistical measure commonly used in credit risk modeling and other fields to assess the stability of a population across different time periods or segments. It's particularly valuable in scenarios where changes in the population distribution could impact the effectiveness of a model. It is a fundamental assumption that the population on which a statistical model is applied to is similar to the population on which the model is developed at. The model performance relies on the closeness between the model development sample and the production data. Therefore, there is an intrinsic need to compare the two samples (development vs production) and assess whether a model needs to be recalibrated. The population change may occur for several reasons – change in the business strategies and policies or changes in external factors such as the political, economic and social environment. The model performance is impacted because of a change in the joint distribution p(y, X), where y is the model outcome and X is the design matrix that comprises the observations of the model features. Therefore, the entire model fit, i.e., the estimates of the model coefficients and the inherent functions get impacted. If this happens, then a decision must be made towards the recalibration or redevelopment of the model.  Before we dive deep into the PSI mathematical expression, we start with some key concepts on Entropy and KL divergence. Entropy and KL Divergence Entropy is a measure of uncertainty or randomness in a set of data. In machine learning, particularly in decision trees and information theory, entropy is used to quantify the impurity or disorder in a dataset. For instance, the splits of a decision tree for a classification problem are created to minimize the entropy. Entropy is used to determine the best attribute to split the data at each node of the decision tree. In the above definition of entropy, the negative sign ensures that Entropy is positive (because log of the probability will be negative as the probability will be < 1). Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable.
If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information.
If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information.
We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Kullback-Liebler Divergence (KL Divergence) has been referred in several places in the literature (Lin, 2017), (Wu & Olson, 2010), (Li et al., 2008), etc. It measures the difference between two probability distributions p(x) and q(x). It quantifies how one probability distribution diverges from another. The distribution p(x) is considered the ‘true’ or ‘base’ distribution, and the distribution q(x) is considered the ‘untrue’ or ‘target’ distribution, so that KL Divergence represents some sort of loss due to using the ‘wrong’ distribution. KL divergence is non-negative and is zero if and only if p(x) and q(x) are identical, i.e., the same distribution. KL divergence measures the dissimilarity of the two probability distributions p(x) and q(x). If both the distributions are identical, KL Divergence is 0. We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q.
Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0.
Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) Population Stability Index – Now that we have covered the mathematical expressions of Entropy and KL Divergence Index, we will see how they would help us in understanding the expression of Population Stability Index. Notice that we have added another term q(x) inside the brackets of KL divergence expression to get the mathematical expression for PSI. But the formulae look so similar that we can think of expressing PSI as a function of KL Divergence. Here is how we can do that. That is the PSI metric is actually the sum of the relative entropies of q(x) over p(x) and p(x) over q(x). In other words we are adding the information ‘gain’ by expressing p(x) using an approximation q(x) and vice-versa. The reason we do this is because we don’t know the ‘ground truth’. We assume that one of them is a ‘true’ distribution and the other is an ‘approximation’; and as we know that the KL divergence is not symmetric, we compute it from p(x) to q(x) and vice-versa and add them to get a final metric which can be judged to ascertain the difference between p(x) and q(x). The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model.
PSI values close to zero indicate stability between the initial and new populations.
Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. So how high should PSI be to make that judgement? The following table summarizes the PSI values which are ‘industry wide rules of thumb’. Interestingly, PSI is very similar to ‘Information Value’ used to measure the strength of the modelling variables in developing credit risk models. We will explore that soon in a different article. Population Stability Index (PSI) is a statistical measure commonly used in credit risk modeling and other fields to assess the stability of a population across different time periods or segments. It's particularly valuable in scenarios where changes in the population distribution could impact the effectiveness of a model. It is a fundamental assumption that the population on which a statistical model is applied to is similar to the population on which the model is developed at. The model performance relies on the closeness between the model development sample and the production data. Therefore, there is an intrinsic need to compare the two samples (development vs production) and assess whether a model needs to be recalibrated. The population change may occur for several reasons – change in the business strategies and policies or changes in external factors such as the political, economic and social environment. The model performance is impacted because of a change in the joint distribution p(y, X ), where y is the model outcome and X is the design matrix that comprises the observations of the model features. Therefore, the entire model fit, i.e., the estimates of the model coefficients and the inherent functions get impacted. If this happens, then a decision must be made towards the recalibration or redevelopment of the model.  Before we dive deep into the PSI mathematical expression, we start with some key concepts on Entropy and KL divergence. X Entropy and KL Divergence Entropy and KL Divergence Entropy and KL Divergence Entropy is a measure of uncertainty or randomness in a set of data. In machine learning, particularly in decision trees and information theory, entropy is used to quantify the impurity or disorder in a dataset. For instance, the splits of a decision tree for a classification problem are created to minimize the entropy. Entropy is used to determine the best attribute to split the data at each node of the decision tree. In the above definition of entropy, the negative sign ensures that Entropy is positive (because log of the probability will be negative as the probability will be < 1). Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable. If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information. If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information. We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable. If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information. If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information. We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Kullback-Liebler Divergence (KL Divergence) has been referred in several places in the literature (Lin, 2017), (Wu & Olson, 2010), (Li et al., 2008), etc. It measures the difference between two probability distributions p(x) and q(x). It quantifies how one probability distribution diverges from another. The distribution p(x) is considered the ‘true’ or ‘base’ distribution, and the distribution q(x) is considered the ‘untrue’ or ‘target’ distribution, so that KL Divergence represents some sort of loss due to using the ‘wrong’ distribution. KL divergence is non-negative and is zero if and only if p(x) and q(x) are identical, i.e., the same distribution. KL divergence measures the dissimilarity of the two probability distributions p(x) and q(x). If both the distributions are identical, KL Divergence is 0. We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q. Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0. Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q. Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0. Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) Population Stability Index – Population Stability Index Population Stability Index Now that we have covered the mathematical expressions of Entropy and KL Divergence Index, we will see how they would help us in understanding the expression of Population Stability Index. Notice that we have added another term q(x) inside the brackets of KL divergence expression to get the mathematical expression for PSI. But the formulae look so similar that we can think of expressing PSI as a function of KL Divergence. Here is how we can do that. That is the PSI metric is actually the sum of the relative entropies of q(x) over p(x) and p(x) over q(x). In other words we are adding the information ‘gain’ by expressing p(x) using an approximation q(x) and vice-versa. The reason we do this is because we don’t know the ‘ground truth’. We assume that one of them is a ‘true’ distribution and the other is an ‘approximation’; and as we know that the KL divergence is not symmetric, we compute it from p(x) to q(x) and vice-versa and add them to get a final metric which can be judged to ascertain the difference between p(x) and q(x). The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model. PSI values close to zero indicate stability between the initial and new populations. Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model. PSI values close to zero indicate stability between the initial and new populations. Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. So how high should PSI be to make that judgement? The following table summarizes the PSI values which are ‘industry wide rules of thumb’. Interestingly, PSI is very similar to ‘Information Value’ used to measure the strength of the modelling variables in developing credit risk models. We will explore that soon in a different article.

Population Stability Index in Machine learning models

Mission Generation Using Classic Machine Learning and Recurrent Neural Networks in Zombie State

জম্বি রাজ্যে ক্লাসিক মেশিন লার্নিং এবং পুনরাবৃত্ত নিউরাল নেটওয়ার্ক ব্যবহার করে মিশন জেনারেশন

Missionsgenerierung mit klassischem maschinellem Lernen und rekurrierenden neuronalen Netzwerken im Zombie-Zustand

Generación de misiones utilizando aprendizaje automático clásico y redes neuronales recurrentes en estado zombie

Génération de mission à l'aide de l'apprentissage automatique classique et des réseaux de neurones récurrents dans un état zombie

ज़ोंबी स्टेट में क्लासिक मशीन लर्निंग और रीकरंट न्यूरल नेटवर्क का उपयोग करके मिशन जनरेशन

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Have you ever wondered about how X(formerly known as Twitter) identifies bots that are tweeting spam? or how banks identify fraudulent accounts? or how GitHub identifies faulty servers in the network? Such systems are built by information security teams that monitor and take down such activities at scale using AI/ML systems. In cases where the automation can’t handle or identify, incident response teams take them down. These learnings are captured and then train new ML classifiers to identify outliers. Bad actors have a wide range of sophistication and their intent varies too. Some bad actors create fake profiles that can be used to carry out many different types of abuse: scraping, spamming, fraud, and phishing, among others. To build robust countermeasures against different types of attacks on our platform, a funnel of defenses is built to detect and take down fake accounts at multiple stages. Example of how an attacker goes after your system (1) The attacker needs to first create an account for which they would use PVAcreator or other tools (2) Using the automated accounts, the attacker needs to reach the data by navigating through the network and moving laterally. (3) Once the attacker has access to the data, the attacker needs to exfiltrate this data out of the network. Some strategies to start employing before using ML approach IP-Based Rate Limiting


Limits the number of requests from a single IP address within a specific timeframe.


Helps mitigate DDoS attacks and brute-force attempts.



User-Based Rate Limiting


Sets a cap on the requests a single user can make within a given time window.


Guards against abuse and unauthorized access attempts.



Token-Based Rate Limiting


Uses unique tokens or API keys to track and control API requests per token.


Secures APIs from misuse and potential data leaks.



Using CAPTHA to distinguish between automated bots and legitimate human users

In addition to the traditional rate limiting techniques mentioned above, organizations can further enhance their cybersecurity by incorporating the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) challenges. Very soon you will start seeing these rules become quite ineffective as attackers rotate IPs, accounts, and tokens. They will start slowing down requests e.t.c Bird's-Eye View of Information Security Framework Building ML/AI-based solution You need to start collecting logs and turn them into ML features for training. Here is an example of building features based on the traffic data you see: Here are some examples of features this paper has developed from logs: RDP Features: SuccessfulLogonRDPPortCount, UnsuccessfulLogonRDPPortCount, RDPOutboundSuccessfulCount, RDPOutboundFailedCount, RDPInboundCount
SQL Features: UnsuccessfulLogonSQLPortCount, SQLOutboundSuccessfulCount, SQLOutboundFailedCount, SQLInboundCount
Successful Logon Features: SuccessfulLogonTypeInteractiveCount, SuccessfulLogonTypeNetworkCount, SuccessfulLogonTypeUnlockCount, SuccessfulLogonTypeRemoteInteractiveCount, SuccessfulLogonTypeOtherCount
Unsuccessful Logon Features: UnsuccessfulLogonTypeInteractiveCount, UnsuccessfulLogonTypeNetworkCount, UnsuccessfulLogonTypeUnlockCount, UnsuccessfulLogonTypeRemoteInteractiveCount, UnsuccessfulLogonTypeOtherCount
Others: NtlmCount, DistinctSourceIPCount, DistinctDestinationIPCount With such feature vectors, we can build a matrix and start using outlier detection techniques. Principal Component Analysis (PCA): We start with a classical way of finding outliers using PCA because it is the most visual, in my opinion. In simple terms, you can think of PCA is a way to compress and decompress data, where the data lost during compression in minimized. Here is an example from fast.ai 2. Auto-encoders: These are in short non-linear version of PCA. The neural network is constructed such that there is an information bottleneck in the middle. By forcing the network to go through a small number of nodes in the middle, it forces the network to prioritize the most meaningful latent variables, which is like the principal components in PCA. Here is an example using fast.ai Isolation Forest and other decision forest: Anomalies have two characteristics. They are distanced from normal points and there are only a few of them. Isolation Forest randomly cuts a given sample until a point is isolated. The intuition is that outliers are relatively easy to isolate. Isolation Forest is a tree ensemble method of detecting anomalies was first proposed by Liu, Ting, and Zhou. Here is an example from scikits. Have you ever wondered about how X(formerly known as Twitter) identifies bots that are tweeting spam? or how banks identify fraudulent accounts? or how GitHub identifies faulty servers in the network? X(formerly known as Twitter) Such systems are built by information security teams that monitor and take down such activities at scale using AI/ML systems. In cases where the automation can’t handle or identify, incident response teams take them down. These learnings are captured and then train new ML classifiers to identify outliers. Bad actors have a wide range of sophistication and their intent varies too. Some bad actors create fake profiles that can be used to carry out many different types of abuse: scraping, spamming, fraud, and phishing, among others. To build robust countermeasures against different types of attacks on our platform, a funnel of defenses is built to detect and take down fake accounts at multiple stages. Example of how an attacker goes after your system (1) The attacker needs to first create an account for which they would use PVAcreator or other tools (2) Using the automated accounts, the attacker needs to reach the data by navigating through the network and moving laterally. (3) Once the attacker has access to the data, the attacker needs to exfiltrate this data out of the network. Some strategies to start employing before using ML approach Some strategies to start employing before using ML approach IP-Based Rate Limiting


Limits the number of requests from a single IP address within a specific timeframe.


Helps mitigate DDoS attacks and brute-force attempts. User-Based Rate Limiting


Sets a cap on the requests a single user can make within a given time window.


Guards against abuse and unauthorized access attempts. Token-Based Rate Limiting


Uses unique tokens or API keys to track and control API requests per token.


Secures APIs from misuse and potential data leaks. Using CAPTHA to distinguish between automated bots and legitimate human users

In addition to the traditional rate limiting techniques mentioned above, organizations can further enhance their cybersecurity by incorporating the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) challenges. IP-Based Rate Limiting Limits the number of requests from a single IP address within a specific timeframe.


Helps mitigate DDoS attacks and brute-force attempts. IP-Based Rate Limiting Limits the number of requests from a single IP address within a specific timeframe. Helps mitigate DDoS attacks and brute-force attempts. Limits the number of requests from a single IP address within a specific timeframe. Limits the number of requests from a single IP address within a specific timeframe. Helps mitigate DDoS attacks and brute-force attempts. Helps mitigate DDoS attacks and brute-force attempts. User-Based Rate Limiting Sets a cap on the requests a single user can make within a given time window.


Guards against abuse and unauthorized access attempts. User-Based Rate Limiting Sets a cap on the requests a single user can make within a given time window. Guards against abuse and unauthorized access attempts. Sets a cap on the requests a single user can make within a given time window. Sets a cap on the requests a single user can make within a given time window. Guards against abuse and unauthorized access attempts. Guards against abuse and unauthorized access attempts. Token-Based Rate Limiting Uses unique tokens or API keys to track and control API requests per token.


Secures APIs from misuse and potential data leaks. Token-Based Rate Limiting Uses unique tokens or API keys to track and control API requests per token. Secures APIs from misuse and potential data leaks. Uses unique tokens or API keys to track and control API requests per token. Uses unique tokens or API keys to track and control API requests per token. Secures APIs from misuse and potential data leaks. Secures APIs from misuse and potential data leaks. Using CAPTHA to distinguish between automated bots and legitimate human users In addition to the traditional rate limiting techniques mentioned above, organizations can further enhance their cybersecurity by incorporating the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) challenges. Using CAPTHA to distinguish between automated bots and legitimate human users In addition to the traditional rate limiting techniques mentioned above, organizations can further enhance their cybersecurity by incorporating the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) challenges. In addition to the traditional rate limiting techniques mentioned above, organizations can further enhance their cybersecurity by incorporating the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) challenges. Very soon you will start seeing these rules become quite ineffective as attackers rotate IPs, accounts, and tokens. They will start slowing down requests e.t.c Bird's-Eye View of Information Security Framework Building ML/AI-based solution You need to start collecting logs and turn them into ML features for training. Here is an example of building features based on the traffic data you see: Here are some examples of features this paper has developed from logs: features this paper RDP Features: SuccessfulLogonRDPPortCount, UnsuccessfulLogonRDPPortCount, RDPOutboundSuccessfulCount, RDPOutboundFailedCount, RDPInboundCount SQL Features: UnsuccessfulLogonSQLPortCount, SQLOutboundSuccessfulCount, SQLOutboundFailedCount, SQLInboundCount Successful Logon Features: SuccessfulLogonTypeInteractiveCount, SuccessfulLogonTypeNetworkCount, SuccessfulLogonTypeUnlockCount, SuccessfulLogonTypeRemoteInteractiveCount, SuccessfulLogonTypeOtherCount Unsuccessful Logon Features: UnsuccessfulLogonTypeInteractiveCount, UnsuccessfulLogonTypeNetworkCount, UnsuccessfulLogonTypeUnlockCount, UnsuccessfulLogonTypeRemoteInteractiveCount, UnsuccessfulLogonTypeOtherCount Others: NtlmCount, DistinctSourceIPCount, DistinctDestinationIPCount RDP Features: SuccessfulLogonRDPPortCount, UnsuccessfulLogonRDPPortCount, RDPOutboundSuccessfulCount, RDPOutboundFailedCount, RDPInboundCount RDP Features: SuccessfulLogonRDPPortCount, UnsuccessfulLogonRDPPortCount, RDPOutboundSuccessfulCount, RDPOutboundFailedCount, RDPInboundCount SQL Features: UnsuccessfulLogonSQLPortCount, SQLOutboundSuccessfulCount, SQLOutboundFailedCount, SQLInboundCount SQL Features: Successful Logon Features: SuccessfulLogonTypeInteractiveCount, SuccessfulLogonTypeNetworkCount, SuccessfulLogonTypeUnlockCount, SuccessfulLogonTypeRemoteInteractiveCount, SuccessfulLogonTypeOtherCount Successful Logon Features: Unsuccessful Logon Features: UnsuccessfulLogonTypeInteractiveCount, UnsuccessfulLogonTypeNetworkCount, UnsuccessfulLogonTypeUnlockCount, UnsuccessfulLogonTypeRemoteInteractiveCount, UnsuccessfulLogonTypeOtherCount Unsuccessful Logon Features: Others: NtlmCount, DistinctSourceIPCount, DistinctDestinationIPCount Others: With such feature vectors, we can build a matrix and start using outlier detection techniques. Principal Component Analysis (PCA): We start with a classical way of finding outliers using PCA because it is the most visual, in my opinion. In simple terms, you can think of PCA is a way to compress and decompress data, where the data lost during compression in minimized. Here is an example from fast.ai Principal Component Analysis (PCA): We start with a classical way of finding outliers using PCA because it is the most visual, in my opinion. In simple terms, you can think of PCA is a way to compress and decompress data, where the data lost during compression in minimized. Here is an example from fast.ai Principal Component Analysis (PCA): fast.ai 2. Auto-encoders: These are in short non-linear version of PCA. The neural network is constructed such that there is an information bottleneck in the middle. By forcing the network to go through a small number of nodes in the middle, it forces the network to prioritize the most meaningful latent variables, which is like the principal components in PCA. Here is an example using fast.ai example using fast.ai Isolation Forest and other decision forest: Anomalies have two characteristics. They are distanced from normal points and there are only a few of them. Isolation Forest randomly cuts a given sample until a point is isolated. The intuition is that outliers are relatively easy to isolate. Isolation Forest is a tree ensemble method of detecting anomalies was first proposed by Liu, Ting, and Zhou. Here is an example from scikits. Isolation Forest and other decision forest: Anomalies have two characteristics. They are distanced from normal points and there are only a few of them. Isolation Forest randomly cuts a given sample until a point is isolated. The intuition is that outliers are relatively easy to isolate. Isolation Forest is a tree ensemble method of detecting anomalies was first proposed by Liu, Ting, and Zhou . Here is an example from scikits . Liu, Ting, and Zhou Liu, Ting, and Zhou example from scikits

Practical Feedback on Using Machine Learning in Information Security

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The TechBeat: Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey (4/9/2024)

The TechBeat: Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey (4/7/2024)

The TechBeat: Optimizing Ride-Hailing with Machine Learning: A Product Manager's Journey (4/5/2024)

In book, the authors suggest the Finite State Machines pattern as one way of “juggling” real-world events to prevent writing a tightly coupled code. You might be familiar with state machines in various contexts, such as Cloud Computing (hello, AWS Step Functions) or even hardware. But have you ever considered employing state machines to tackle real-world front-end challenges? How often have you found yourself building a multi-step wizard in your application, with numerous transitions that prompt users to input their details and select categories that will unblock various fill-review-submit form paths upon selection? The Pragmatic Programmer Fear not, as this article will demonstrate how you can build a straightforward abstraction for state machine wizards in React. Whatever the source, if we write applications that respond to events, and adjust what they do based on those events, those applications will work better in the real world. The Pragmatic Programmer: your journey to mastery, 20th Anniversary Edition, 2nd Edition What are the state machines? A state machine is essentially a specification for handling incoming state (inputs) and generating a new state based on those inputs. It represents the behavior of the system by encapsulating the various states the system can be in, along with the transitions between these states triggered by specific events or conditions. In the context of front-end development, state machines prove invaluable for managing user interfaces, especially those with intricate workflows or multiple possible paths. By applying the principles of state machines, developers can create more robust and predictable applications while fostering the creation of loosely coupled and reusable components. Below is an example of the simplest state machine representation. The state begins in ; the state changes to given input ‘ ’, or to given input ‘ ‘. State 1 State 2 X State 3 Y In the following sections, we’ll delve deeper into how state machines can be leveraged within the React framework through the prism of our example use case. Real-world website example Let’s pretend we are building a website for an insurance company. Users arrive at the homepage and are greeted with a prominent “Purchase Insurance” button, initiating a streamlined product purchase flow in a wizard format. Within this wizard, clients navigate through the following steps: Users provide essential information such as the insuree’s name, date of birth, and other pertinent details. Enter Personal Details: Clients choose the type of insurance they’re interested in, whether it’s a car, home, or a bundled package, including both. Select Insurance Type: Users select their desired coverage options, and the wizard dynamically generates an instant quote based on their choices. Quote prices vary depending on the type of insurance selected. Choose Coverage and Get a Quote: Users input specific insurance details such as the start of coverage, payment schedule, and more. Specify Insurance Details: If users aren’t already authenticated, they’re prompted to sign up or log in at this stage. Optional Authentication: Clients enter their payment details, choosing between credit or debit cards. Provide Payment Information: The final step involves reviewing all entered information, agreeing to the terms, and clicking the “Confirm” button to complete the purchase. Review & Confirmation: I will leave you for a minute to digest these requirements… Are you still here? Good! Let’s try to break it down. The wizard encompasses steps unique to the selected insurance product (car, home, or bundle), as well as those common to all. To visualize this, we can represent the flow using a state machine diagram: In the diagram above, each node represents a step corresponding to a view within the wizard. In fact, recalling your computer science lessons, the diagram embodies a directed acyclic graph (DAG), where each step is connected to another without introducing loops. While there may be three distinct paths depending on the insurance product selected, it’s worth noting that the path for bundled insurance shares steps with the other two paths, potentially allowing for the reuse of certain views. But how do we go about implementing this intricate process? Let’s explore that in the next section! Design of state machines When tackling the implementation of our wizard, there are a few approaches we could take. One straightforward method might involve creating each step in the wizard as a standalone React component, which is a good starting point. However, managing the navigation logic with numerous if-else statements within each component can quickly become hard to read, test, and maintain and prone to errors. This approach resembles an architectural pattern of choreography, where each component is aware only of its immediate neighbours in the chain of command. Alternatively, we could opt for a centralized approach, leveraging a global state and a state management library like Redux or React Context to serve as a central mediator. But it still will require us to build branches of the code lianas there to handle Back and Next actions from the wizard. We need to have our orchestration logic to be as simple as possible! Inspired by , I thought: “What if we could have extracted orchestration logic in a standalone configuration file and pass it down to a UI component capable of interpreting it?”. Amazon States Language specification This approach would allow us to not only reuse components for the steps but also the orchestration logic itself, leading to a more modular and maintainable solution. Let’s explore this idea further in the following sections. As a side note, it’s worth mentioning that there are existing libraries that leverage this architectural pattern and idea in general. One such library is , which offers a rich API contract for building state machines in React. While we won’t replicate it one-to-one, we’ll aim to build something simple yet sufficient to satisfy our needs. @xstate/react Building a foundation To lay the groundwork for our wizard implementation, let’s begin by selecting the technologies we’ll employ. For the UI library, I advocate for React due to its emphasis on creating reusable and modular components that align well with our objective. Now, let’s address the styling aspect. We require a lightweight and customizable solution to ensure flexibility when designing our wizard interface. In this regard, I propose utilizing Tailwind CSS, a utility-first and highly optimized CSS framework that allows developers to construct any user interface efficiently by leveraging pre-made CSS classes that straightforwardly map to CSS rules. Let's start with installing our dependencies: npm install -D react react-dom tailwindcss postcss autoprefixer typescript
npx tailwindcss init -p 
npx tsc --init In we need to add paths to all our components: tailwind.config.js module.exports = {
  content: ["./index.html","./src/**/*.{js,jsx,ts,tsx}"],
  theme: {
    extend: {},
  },
  plugins: [],
} Then we need to add directives into our CSS file: @tailwind https://gist.github.com/sr-shifu/d51ac357e18e7aa9c148659d36f5657e?embedable=true#file-main-css If building a library, I would also recommend to move React dependencies to the so consumers would aware that React is required and what versions of it are supported: peerDependencies "peerDependencies": {
    "react": ">=16.8.0",
    "react-dom": ">=16.8.0"
  } In the next section, we will focus on addressing of representation concerns for our component. Building view layer Here is our simple React component that does not have any logic (yet!): https://gist.github.com/sr-shifu/5a10bcb8c02b60fc024c3182784f88c3?embedable=true#file-statemachinewizard-tsx Upon reviewing this component, you might observe a few key points: We’re employing React portals to render our component. Portals offer a convenient way to render children components outside the DOM hierarchy of the parent component, ensuring flexibility and control over component rendering. See wonderful article about portals . Portals Usage: here Our component defines three logical slots: header, body, and footer. While the header and footer sections remain relatively static and don’t depend on the current step in the wizard, the body dynamically adapts based on the configuration. Logical Slots: The footer segment hosts the wizard’s navigation controls, allowing users to perform actions such as canceling/closing the wizard, navigating back to the previous step, progressing to the next step, or submitting the form upon reaching the end of the flow. Footer Controls: Here is what it looks like: As we progress, we’ll infuse this component with logic to orchestrate the wizard’s behavior and enhance its functionality. But for now, let’s focus on establishing the foundational structure of our state machine configuration. State machines configuration Let’s review the state machine configuration interface, which declares the orchestration flows for our wizard: https://gist.github.com/sr-shifu/7e394d3a1d6c38e4c6ff509c5a93f544?embedable=true#file-state-machine-config-ts I will break it down into its main parts: . This is a crucial aspect of the configuration, representing all possible directions from a certain step when the user reaches this point in the wizard. Each step is uniquely identified and mapped to another step, as well as to the view that will be rendered in the wizard body. The step configuration may include a method, serving as a guardrail to prevent navigation to the next step under specific conditions (e.g., if there are errors in the current step). Additionally, for steps with multiple possible paths to the next step, the attribute allows for defining these branching options. If a step configuration solely contains the flag, it indicates that the step is final, with no further transitions possible. In such cases, the wizard will render a Submit button instead of Next and close the wizard upon successful submission. steps canAdvance choices isTerminal . This specifies the step where wizard flow will start at. initialStep . This configuration maps each step by its identifier to the corresponding React component type (note, not the element!) that will be rendered in the wizard body. views Now, having this contract, we can define a DAG-like navigation model for our insurance purchase flow example: https://gist.github.com/sr-shifu/abcf01967b0f28d954232f082741a7ee?embedable=true#file-state-machine-config-example-ts If you take a closer look at step, you can notice that method does not rely on the state of the wizard - instead, it reads the data from localStorage. In the same way, we can determine whether we need to transition to when completing step. authenticate canAdvance authenticate setInsurancePeriod Why not define view as part of step configuration? This separation of concerns, where the view is defined separately from the step configuration, was a deliberate design decision. By decoupling orchestration concerns from rendering, we ensure flexibility and maintainability in our implementation. Consider scenarios where we may need to reuse the same orchestration model with slightly different views. With our current design, this is easily achievable by replacing all views or a subset of views while keeping the underlying orchestration logic intact. In the later sections of this article, I’ll illustrate the usefulness of this approach by demonstrating how we can effortlessly switch between different views while maintaining the same wizard flow. This separation not only enhances code reusability but also streamlines the process of adapting our wizard interface to varying requirements or design changes. Building orchestration logic Now, let’s go back to our component to add integration with we have just defined. StateMachineWizard StateMachineConfig First of all, we need to pass as an input prop. For better performance, I would recommend defining this config as a constant living outside of the React lifecycle! stateMachineConfig Now, let’s define the component states we will be working with: https://gist.github.com/sr-shifu/adf2d75ab0dd1616447b4a7bc9dada9e?embedable=true#file-statemachinewizard-tsx Key decisions: The state serves as a record of all transitions within the wizard flow, storing each step's name and the corresponding component rendered. Similar to React Router history, we can manipulate this history by pushing and popping items as users navigate through the wizard. navHistory We’ll maintain an additional state, , to store incremental changes to the wizard states occurring at each step. This approach allows us to preserve the state that existed in the previous step, facilitating seamless navigation backward in the wizard. wizardDataByStep Calculate on every step by accumulating states from the previous steps recorded in the . By aggregating these states, we can maintain a comprehensive snapshot of the wizard's progression, facilitating smooth data management and validation processes. aggregatedStepState navHistory Then, let’s define a few callbacks: https://gist.github.com/sr-shifu/6fac4c2651147d99d775979b8d0e2e41?embedable=true#file-statemachinewizard-tsx : This callback allows clients to enhance the state of the wizard by updating the data associated with a specific step. For example, in our insurance purchase flow, the EnterCustomerDetailsComponent can capture input changes and update the wizard state accordingly, enabling users to progress further. setStepData : Clients can use this callback to programmatically navigate to a specific step in the wizard. For instance, if the wizard is submitted with errors that need to be addressed in earlier steps, this callback can be invoked to jump back to those steps for correction. navigateTo Finally, we need to propagate these callbacks and some of our states to the step components. I will use React Context API to do this: https://gist.github.com/sr-shifu/b048502a02e548260e3c7885ff34afea?embedable=true#file-statemachinewizard-tsx will go through all transitions in navHistory, accumulate state collected on each step, and render the step that is active at the moment with this accumulated state: WizardSteps https://gist.github.com/sr-shifu/43436b67620423ab78246705862eea41?embedable=true#file-statemachinewizard-tsx Handling navigation Remember when building view layer, we left onNextClick and onBackClick methods not implemented? Let's fill this gap! https://gist.github.com/sr-shifu/f8c92086924bb4641230e751e6b88a0b?embedable=true#file-statemachinewizard-tsx Make requirements more complicated As our fake insurance company expands its business offerings, the CEO decides to extend insurance sales beyond individuals to include local businesses. Fortunately, the product types and coverage details remain unchanged. The only adjustment required is to the first step in the wizard, where business owners must enter details about their company. How can we accommodate this change with minimal alterations to our original architecture? The solution is surprisingly simple: we just need to replace the view mapped to the first step in the wizard. By updating the view component associated with the initial step, we can seamlessly incorporate the new requirement without fundamentally altering our existing architecture. This approach demonstrates the flexibility and scalability of our design, allowing for smooth adaptation to evolving business needs without necessitating extensive refactoring or restructuring. Here is what a simple code change will look like: https://gist.github.com/sr-shifu/6becb0f06232ad2c592908d91c991687?embedable=true#file-state-machine-config-example-tsx Final words In this article, we’ve explored the concept of state machines in the context of building interactive wizards in React applications. We began by understanding the fundamentals of state machines and their relevance in managing complex user interfaces, particularly in scenarios like multi-step wizards. Then, we delved into the implementation details, starting with the construction of a solid foundation using React for UI components and Tailwind CSS for styling. We discussed the importance of separating orchestration logic from rendering, which enables greater flexibility and reusability in our codebase. Next, we examined the state machine configuration interface, which serves as a blueprint for defining the wizard’s flow and behavior. By delineating the steps, their transitions, and associated views, we establish a clear structure for guiding users through the wizard. Building on this foundation, we outlined the orchestration logic, including managing navigation history, storing wizard states, and calculating aggregated step states. Through defined callbacks, we facilitated interaction with the wizard, ensuring a smooth and intuitive user experience. Finally, we addressed the scenario of evolving requirements, demonstrating how our architecture can adapt seamlessly to accommodate changes, such as expanding the target audience to include local businesses. As you embark on your journey to implement state machines in React for your own projects, remember the principles outlined here: prioritize modularity, separation of concerns, and flexibility. By adhering to these principles, you can create robust, maintainable, and user-friendly wizard interfaces that stand the test of time. Also published . here

Building Robust Front-End Interfaces with State Machines in React

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