Eric Siegel, PhD, is the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die, Revised and Updated Edition (Wiley, January 2016), founder of the Predictive Analytics World conference series, executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University.

This is a global problem, yet one of the most striking examples is the case of Japan. From 800 until 1900 CE, Japan used a writing system called Kuzushiji, which was removed from the curriculum in 1900 when the elementary school education was reformed. Currently, the overwhelming majority of Japanese speakers cannot read texts which are more than 150 years old. The volume of these texts -- comprised of over three million books in storage but only readable by a handful of specially-trained scholars -- is staggering. One library alone has digitized 20 million pages from such documents.

Listen to today's podcast featuring CTO, Dr. Danny Rittman: "What Epsilon will have that all of these guys will not have ... Epsilon will have Avant! in it," Rittman said in a recent interview with Investorideas.com. "It will do something very simple that currently no other programs do. Avant! will equip Epsilon with a phenomenon that we call'an attention to details'. The neural network will actually pay attention as the design is going forward. GBT announced its intention to implement its Avant! "We identified the EDA field, a modern domain used to design integrated circuits (ICs), that we believe can significantly benefit from our AI technology.

The time frame for this idea is 2 decades-plus. You will get out of the box thinking from me sometimes but it comes with a connecting thread running it -- which you will profit from. The thread here will run for decades. I'm going to talk about The Future, The Fear of it and Fixing those Fears. We have worries about the future. I'm talking about how to survive the big unknown about the future: Change and its impact on our lives -- jobs, health, safety and family. There's a joke from a startup guy who said, "I sleep like a baby, I wake up crying and needing to poop every couple of hours." On the spectrum of humans worrying, there's people like Buddha on one end, who does great - living his best life in the present. Not me, (I might be in danger of looking like the Buddha with my diet.)

AI and machine learning algorithms require data. But the bulk of that data is of no use if it isn't first labeled by human annotators. This predicament has given rise to a cottage industry of startups, including Scale AI, which recently raised $100 million for its extensive suite of data labeling services. That's not to mention Mighty AI, Hive, Appen, and Alegion, which together occupy a data annotation tools segment that's anticipated to be worth $1.6 billion by 2025. CloudFactory is yet another vying for attention.

Light behaves differently in water than it does on the surface -- and that behavior creates the blur or green tint common in underwater photographs as well as the haze that blocks out vital details. But thanks to research from an oceanographer and engineer and a new artificial intelligence program called Sea-Thru, that haze and those occluded colors could soon disappear. Besides putting a downer on the photos from that snorkeling trip, the inability to get an accurately colored photo underwater hinders scientific research at a time when concern for coral and ocean health is growing. That's why oceanographer and engineer Derya Akkaynak, along with Tali Treibitz and the University of Haifa, devoted their research to developing an artificial intelligence that can create scientifically accurate colors while removing the haze in underwater photos. As Akkaynak points out in her research, imaging A.I. has exploded in recent years.

One day in 1975, a Kodak engineer decided not to embrace a prospective new digital technology, thereby sealing the fate of the world's leading photography company. As insurance executives consider artificial intelligence (AI) and the question of whether to use this technology, they would do well to remember this decisive strategic mistake. AI could be one of the biggest game changers in insurance history and undoubtedly constitutes a paradigm shift. It offers a wide range of opportunities: faster and more efficient claims management and application processes, better prospective healthcare advisory services, and a variety of on-demand insurance services. This boosts customer and stakeholder expectations and generates innovation pressure.

The technology took just over one year to develop in a collaboration between Nasdaq's market technology business, its machine intelligence lab in …

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and computationally useful analysis. The development reveals the complementary roles of the singular value decomposition, Hirschfeld-Gebelein-R\'enyi maximal correlation, the canonical correlation and principle component analyses of Hotelling and Pearson, Tishby's information bottleneck, Wyner's common information, Ky Fan $k$-norms, and Brieman and Friedman's alternating conditional expectations algorithm. We further illustrate how this framework facilitates understanding and optimizing aspects of learning systems, including multinomial logistic (softmax) regression and the associated neural network architecture, matrix factorization methods for collaborative filtering and other applications, rank-constrained multivariate linear regression, and forms of semi-supervised learning.

Graph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning process. However, this prevents graph neural network from being applied into featureless graphs. In this paper, we first analyze the effects of node features on the performance of graph neural network. We show that GNNs work well if there is a strong correlation between node features and node labels. Based on these results, we propose new feature initialization methods that allows to apply graph neural network to non-attributed graphs. Our experimental results show that the artificial features are highly competitive with real features.