Earlier this year, Aible, the only enterprise artificial intelligence (AI) solution that guarantees impact in one month, announced the initial results of the Intel and Aible Benchmark Study, a collaboration that is helping enterprises fast-track benefits from advanced analytics and AI, while also evaluating server vs. serverless architecture. According to MIT-BCG, only "a mere 10% of organizations achieve significant financial benefits with AI." The Gartner report, A CTO's Guide to Top Artificial Intelligence Engineering Practices, published 29 October 2021 states, "AI projects are characterized by high failure rates and take a long time to move from pilot to production. In this same market, Aible has delivered significant results for every customer in this benchmark study in 30 days or less. The detailed report with case studies can be downloaded here. "Intel is helping us change the art of the possible in AI.

Like finding a box of watercolors, the excitement is similar. The learning curve is easy to make images more aligned with your taste. Writing good prompts is a skill. Don't be discouraged by initial results; making art with text is an iterative process. Every time you put your eye on the viewfinder, you decide what is beautiful, what is acceptable, and what tells the story you want.

Mammography has proven a valuable tool for early detection of breast cancer, significantly reducing mortality, but the X-ray imaging technology is not without limitations, especially for patients with dense breast tissue. The challenge radiologists face is that while mammograms yield high-resolution images, most asymptomatic cancer lesions are small, sparsely distributed, and may exhibit only subtle changes in the tissue patterns. "Artificial intelligence can be a really good assistant because it helps read images faster, and our initial results found it was more accurate than radiologists," explains Linda Moy, MD, professor of radiology. Since 2017, Dr. Moy has partnered with Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Grossman School of Medicine and a computer scientist affiliated with the NYU Center for Data Science, to develop artificially intelligent computer code that can "read" a mammogram and accurately predict the likelihood of breast cancer. "Artificial intelligence can be a really good assistant because it helps read images faster, and our initial results found it was more accurate than radiologists."

When you want to see whether one CPU is faster than another, you have PassMark. But what do you do when you need to figure out how fast your machine-learning platform is--or how fast a machine-learning platform you're thinking of investing in is? Machine-learning expert David Kanter, along with scientists and engineers from organizations such as Google, Intel, and Microsoft, aims to answer that question with MLPerf, a machine-learning benchmark suite. Measuring the speed of machine-learning platforms is a problem that becomes more complex the longer you examine it, since both problem sets and architectures vary widely across the field of machine learning--and in addition to performance, the inference side of MLPerf must also measure accuracy. If you don't work with machine learning directly, it's easy to get confused about the terms. The first thing you must understand is that neural networks aren't really programmed at all: they're given a (hopefully) large set of related data and turned loose upon it to find patterns.

Connect, download a free E-Book, watch a keynote, or browse my blog. In the early days of life on Earth, biological organisms were exceedingly simple. They were microscopic unicellular creatures with little to no ability to coordinate – a little like me still to be frank, especially after I've been travelling. Yet billions of years of evolution through competition and natural selection led to the complex life forms we have today – as well as complex human intelligence. Researchers at OpenAI, the San Francisco based for-profit AI research lab, are now testing a hypothesis – if you could mimic that kind of competition in a virtual world, would it also give rise to much more sophisticated artificial intelligence?

SUNNYVALE, Calif., April 18, 2017 –Socionext Inc. and SOINN Inc. today announced initial results of collaboration started in 2016, in which Socionext extracts and delivers biometrics data to the "Artificial Brain SOINN". The companies achieved initial results in reading ultrasound images from Socionext's viewphii mobile ultrasound solution by Artificial Brain SOINN. The results will be introduced at Medtec Japan, held in Tokyo Big Sight, April 19-21, at booths 4505 & 4507. In this initial trial, SOINN learned to read subcutaneous fat thickness from abdominal ultrasound images. The estimations by SOINN were then compared with the reading results by ultrasound technicians.

Intelligent systems that interact with humans typically require demonstrations and/or advice from the expert for optimal decision making. While the active learning formalism allows for these systems to incrementally acquire demonstrations from the human expert, most learning systems require all the advice about the domain in advance. We consider the problem of actively soliciting human advice in an inverse reinforcement learning setting where the utilities are learned from demonstrations. Our hypothesis is that such solicitation of advice reduces the burden on the human to provide advice about every scenario in advance.

In this position paper, we present our current progress in applying marginal MAP algorithms for solving the conformant planning problems. Conformant planning problemis formulated as probabilistic inference in graphical models compiled from relational PPDDL domains. The translation from PPDDL into Dynamic BayesianNetwork is developed by mapping the SAT encoding of the ground PPDDL into factored representation. We experimented with recently developed AND/OR branchand bound search algorithms for marginal MAP over instances from the international planning competition domains, and we show that several domains were solved efficiently.