Fishermen gaffing catfish at the Madeira River rapids in the state of Rondônia, Brazil. These rapids are now completely submerged, and a nearby …

Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

Artificial intelligence (AI) applications -- as an example, virtual personal assistants and chatbots -- are being adopted rapidly within the global workplace. By 2024, Gartner predicts that these technologies will replace almost 69 percent of the typical manager's workload.

Companies need to ensure that their products arrive at the right place, on time, and preferably in a sustainable manner and at the lowest possible cost. Packaging plays a key role in this, so finding the best packaging solution for a product is of crucial importance for reliable and cost-efficient logistics and distribution as well as good presentation to customers. In this project, ML2Grow will call upon its years of industrial expertise and academic pioneering work in the domain of artificial intelligence. The search for an ideal package solution, both in terms of functionality and design, is a highly complex problem. As a result of the recent data collection efforts, the challenge of finding this solution is gradually shifting from the physical world to the digital world and transforming into a genuine data problem.

Artificial intelligence (AI) is hot. Billions in venture capital have been invested in AI firms, some of them focused on solving supply chain problems. Machine learning, a subset of AI, is particularly hot. Interestingly, neither AI nor machine learning is a new technology in supply chain management (SCM). However, partly because this technology is hot and partly because there is so much more data becoming available for analysis, we're seeing a new focus on using these techniques to improve supply chain applications.

Today, the world economy is in a perilous situation. Wage growth is stagnant across all industrial economies. Jobs for less skilled workers are in constant decline and this decline is accelerating. Economic inequality continues to increase. Not only is the economy in a perilous situation, the planet itself in dire condition. Mass extinction is now happening and entire countries are going to be inundated and wiped out of existence.

The proliferation of web-enabled devices we experience today enables us to be online all the time via multiple touch-points. Users no longer use a single device to complete a task, but shift from one screen to another throughout the process. Forbes found that 87% of millennials use between two to three tech devices at least once on a daily basis. This constant interaction with online devices means there are more and more'I want-to-do' or'I need-to-fix, watch, and see' decision making moments during which a user is susceptible to advertisements that sway the decision making path. This constitutes a colossal opportunity for brands who understand just how important it is for their ads to be there for the right person, at the right time, every time.

Whether you're a developer starting out with AI, or looking to sharpen your AI skills, the Intel AI Academy provides essential learning materials, community, tools, and access to technologies to boost your AI development.

Intel technology provides the compute power behind today's most important artificial intelligence (AI) initiatives. We're helping businesses of every kind accelerate solutions, automate operations, gather better insights--and make smarter decisions.