Social Media and information sharing is something every internet user will know about. The presence and popularity of Twitter, LinkedIn, and many other platforms have made it convenient to spread knowledge all around the globe in a couple of clicks. It is because of the extensive usage of these networking sites by various Thought leaders, achievers, and change-makers that Data Science and AI knowledge has spread across the globe. IPFC online has recently come up with a list of Top 50 Digital influencers to follow out of which we are going to talk about the ones concerned with Machine Learning and AI. Additionally, we have provided some more influencers worth following.
Verta, an AI/ModelOps company whose founder created the open source ModelDB catalog for versioning models, has launched with a $10 million Series A led by Intel Capital. The Verta system tackles what is becoming an increasingly familiar problem: not only enabling ML models to get operationalized, but to track their performance and drift over time. Verta is hardly the only tool in the market to do so, but the founder claims that it tracks additional parameters not always caught by model lifecycle management systems. While Verta shares some capabilities with the variety of data science platforms that have grown fairly abundant, its focus is more on the operational challenges of deploying models and keeping them on track. As noted, it starts with model versioning, ModelDB was created by Verta founder Manasi Vartak, a software engineering veteran of Facebook, Google, Microsoft, and Twitter, as part of her doctoral work at MIT. It versions four aspects of models, encompassing code, data sources, hyperparameters, and the compute environment on which the model was designed to run.
Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes.
In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. By using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network. We also discuss the effects of size and scale for communities regarding this case, as well as how we cope with the additional complexity present when big communities arise. Finally, we compare our community detection solution with other representative algorithms, finding favourable results.
This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.
The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science. Models and algorithms for such networks are pervasive in our society, and impact human behavior via social networks, search engines, and recommender systems to name a few. A widely used algorithmic technique for modeling such complex networks is to construct a low-dimensional Euclidean embedding of the vertices of the network, where proximity of vertices is interpreted as the likelihood of an edge. Contrary to the common view, we argue that such graph embeddings do not}capture salient properties of complex networks. The two properties we focus on are low degree and large clustering coefficients, which have been widely established to be empirically true for real-world networks. We mathematically prove that any embedding (that uses dot products to measure similarity) that can successfully create these two properties must have rank nearly linear in the number of vertices. Among other implications, this establishes that popular embedding techniques such as Singular Value Decomposition and node2vec fail to capture significant structural aspects of real-world complex networks. Furthermore, we empirically study a number of different embedding techniques based on dot product, and show that they all fail to capture the triangle structure.
Data Scientist is the most promising job in the U.S according to LinkedIn. Also, the demand for Data Scientists is growing exponentially in all the industries. Out of all the openings, 19% of data science professionals jobs are secured by the Finance Industry. Python statistics is one of the most important python built-in libraries developed for descriptive statistics. Python statistics is all about the ability to describe, summarize, and represent data visually through comprehensive python statistics libraries.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
Many financial institutions are rapidly developing and adopting AI models. They're using the models to achieve new competitive advantages such as being able to make faster and more successful underwriting decisions. However, AI models introduce new risks. In a previous post, I describe why AI models increase risk exposure compared to the more traditional, rule-based models that have been in use for decades. In short, if AI models have been trained on biased data, lack explainability, or perform inadequately, they can expose organizations to as much as seven-figure losses or fines.