As powerful as machine learning (ML) techniques are in solving problems involving data with large dimensionality, explaining the results from the fitted parameters remains a challenging task of utmost importance, especially in physics applications. This work shows how this can be accomplished for the ferromagnetic Ising model, the main target of several ML studies in statistical physics. Here it is demonstrated that the successful unsupervised identification of the phases and order parameter by principal component analysis, a common method in those studies, detects that the magnetization per spin has its greatest variation with the temperature, the actual control parameter of the phase transition. Then, by using a neural network (NN) without hidden layers (the simplest possible) and informed by the symmetry of the Hamiltonian, an explanation is provided for the strategy used in finding the supervised learning solution for the critical temperature of the model's continuous phase transition. This allows the prediction of the minimal extension of the NN to solve the problem when the symmetry is not known, which becomes also explainable. These results pave the way to a physics-informed explainable generalized framework, enabling the extraction of physical laws and principles from the parameters of the models.

A subgraph is constructed by using a subset of vertices and edges of a given graph. There exist many graph properties that are hereditary for subgraphs. Hence, researchers from different communities have paid a great deal of attention in studying numerous subgraph problems, on top of the ordinary graph problems. Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph. Due to the complex structures of certain types of graphs and to improve overall performances of the existing frameworks, machine learning techniques have recently been employed in dealing with various subgraph problems. In this article, we present a comprehensive review on five well known subgraph problems that have been tackled by using machine learning methods. They are subgraph isomorphism (both counting and matching), maximum common subgraph, community detection and community search problems. We provide an outline of each proposed method, and examine its designs and performances. We also explore non-learning-based algorithms for each problem and a brief discussion is given. We then suggest some promising research directions in this area, hoping that relevant subgraph problems can be tackled by using a similar strategy. Since there is a huge growth in employing machine learning techniques in recent years, we believe that this survey will serve as a good reference point to relevant research communities.

Model Theft: Because models represent a significant investment in Intellectual Property, they can be a valuable target for theft. And like other software assets, they are tangible and can be stolen. Model theft happens when a model is taken outright from a storage location or re-created through deliberate query manipulation. An example of this type of attack was demonstrated by a research team at UC Berkeley who used public endpoints to re-create language models with near-production state-of-the-art translation quality. The researchers were then able to degrade the performance and erode the integrity of the original machine learning model using data input techniques to compromise the integrity of the original machine learning model (see Data Poisoning above).

Tristan Rouillard is the VP of Machine Learning Solutions at CloudFactory. In this role, he leads the company's strategy and direction related to ML products and solutions offered to CloudFactory's clients globally. Tristan was one of the cofounders of Hasty, a data-centric vision AI platform focussed on making it easier to implement the ML flywheel in production. Hasty was recently acquired by CloudFactory in late 2022. Before founding Hasty, Tristan was the Head of the Venture Development team at WATTx, a manufacturing, industry 4.0 focussed incubator, where his team built the business models and go-to-market strategies for various early-stage ventures.

As Machine Learning solutions to real-world problems spread, people are beginning to acknowledge the glaring need for solutions that go beyond training a single model and deploying it. The simplest process should at least cover feature extraction, feature generation, modeling, and monitoring in a traceable and reproducible way. In BigML, it's been a while since we realized that, and the platform has constantly added features designed to help our users easily build both basic and complex solutions. Those solutions often need to be deployed in particular environments. Our white-box approach is totally compatible with that, as users can download the models created in BigML and predict with them wherever needed by using bindings to Python or other popular programming languages.

To make decisions more quickly and accurately, enterprises in the Middle East and Africa (MEA) are increasingly turning to Machine Learning, arguably today's most practical application of Artificial Intelligence (AI). How should CIOs and IT leaders ensure success and ROI from Machine Learning deployments in their organisations? Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. In addition, Machine Learning systems apply algorithms to data to glean insights into that data without explicit programming: It's about using data to answer questions.

The Fintech sector has progressed beyond imagination. Just a few years ago, it took several weeks to get loans approved, but today, everything is processed online and it takes barely a day. Likewise, financial frauds used to occur very often and the financial safety of the user was a big concern worldwide. However in recent times, such fraudulent transactions have reduced considerably, though, online transactions have increased immensely. The mobile revolution and the emergence of trending technologies like machine learning have brought a paradigm shift in the fintech industry.

At Kinaxis, who we are is grounded in our common belief that people matter. Each one of us plays an important part in accomplishing our work, building our culture and making a global impact. Every day, we're empowered to work together to help our customers make fast, confident planning decisions. This is how we create a better planet – for each other, for our customers and for generations to come. Our cloud-based platform RapidResponse ensures that the products we need – everything from medicine and cars, to day-to-day items like toothpaste – make it to market and into our hands when we need them with minimal ecological footprint.

Successful businesses not only have great products and services; they also have a deep understanding of their customers. Companies that can use behavioral analytics in marketing automation platforms are better equipped to deliver real-time marketing efforts. According to a research case study from Deloitte, companies with a customer-centric business model are 60% more profitable. Knowing and adapting this differentiating business model is the key to becoming a market leader in today's fierce competitive landscape. With a 3.8 billion user base and 86% of the users interacting daily, social media has become an impactful customer voice through platforms like Twitter, Facebook, and Reddit.

How are Machine Learning Models going to change the Payments Industry? It wasn't so long ago that CEO's and large commercial banks were convinced that more bank locations would always be necessary to service and acquire new customers. However, in the last ten or five years we have seen an emergence of Digital Banks, that have never and will probably never own a physical location, but still manage to grow their user base and add additional services including insurance, mortgages, and loans. In the Banking industry, we have seen companies like First Bank of Nigeria, United Bank of Africa, Zenith Bank, Guaranty Trust Bank dominate for well over twenty years. However, just like the digitization of banking has forced incumbents to change their strategies, the digitization of payments has provided companies like Flutterwave, Paystack, Remita and lately even Korapay to take up some of the market shares, not by focusing on traditional businesses, but by focusing on startups who have grown to overshadow and sometimes even bankrupt traditional businesses.