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Global Big Data Conference

#artificialintelligence

The industry as a whole is beginning to realize the intimate connection between Artificial Intelligence and its less heralded, yet equally viable, knowledge foundation. The increasing prominence of knowledge graphs in almost any form of analytics--from conventional Business Intelligence solutions to data science tools--suggests this fact, as does the growing interest in Neuro-Symbolic AI. In most of these use cases, graphs are the framework for intelligently reasoning about business concepts with a comprehension exceeding that of mere machine learning. However, what many organizations still don't realize is there's an equally vital movement gaining traction around AI's knowledge base that drastically improves its statistical learning prowess, making the latter far more effectual. In these applications graphs aren't simply providing an alternative form of AI to machine learning that naturally complements it.


iiot machinelearning_2021-09-17_03-56-37.xlsx

#artificialintelligence

The graph represents a network of 884 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 17 September 2021 at 11:05 UTC. The requested start date was Friday, 17 September 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 4-hour, 46-minute period from Tuesday, 14 September 2021 at 19:14 UTC to Friday, 17 September 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Top 15 Tools Every Data Scientist Should Bring to Work

#artificialintelligence

Data science and data scientist's job market is constantly evolving. Every year, there are so many new things to learn. While some tools rise and others fall into oblivion, it becomes highly essential for a data scientist to keep up with the trends and have the necessary knowledge and skills to use all the tools that make their job easier. Here are the top 15 tools that every data scientist should bring to work to become more effective at their job. For a data scientist, their mind is one of the best tools that keep them one step ahead of the competition.


#iiot_2021-09-14_13-52-01.xlsx

#artificialintelligence

The graph represents a network of 1,251 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 14 September 2021 at 21:00 UTC. The requested start date was Tuesday, 14 September 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 16-hour, 41-minute period from Sunday, 12 September 2021 at 07:20 UTC to Tuesday, 14 September 2021 at 00:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Doing The Math On CPU-Native AI Inference

#artificialintelligence

The need for math engines specifically designed to support machine learning algorithms, particularly for inference workloads but also for certain kinds of training, has been covered extensively here at The Next Platform. Just to rattle off a few of them, consider the impending "Cirrus" Power10 processor from IBM, which is due in a matter of days from Big Blue in its high-end NUMA machines and which has a new matrix math engine aimed at accelerating machine learning. Or IBM's "Telum" z16 mainframe processor coming next year, which was unveiled at the recent Hot Chips conference and which has a dedicated mixed precision matrix math core for the CPU cores to share. Intel is adding its Advanced Matrix Extensions (AMX) to its future "Sapphire Rapids" Xeon SP processors, which should have been here by now but which have been pushed out to early next year. Arm Holdings has created future Arm core designs, the "Zeus" V1 core and the "Perseus" N2 core, that will have substantially wider vector engines that support the mixed precision math commonly used for machine learning inference, too. All of these chips are designed to keep inference on the CPUs, where in a lot of cases it belongs because of data security, data compliance, and application latency reasons.


GitHub - pykale/pykale: Knowledge-Aware machine LEarning (KALE) from multiple sources in Python

#artificialintelligence

Very cool library with lots of great ideas on moving toward'green', efficient multimodal machine learning and AI. Kevin Carlberg, AI Research Science Manager at Facebook Reality Labs (quoted from tweet). PyKale is a PyTorch library for multimodal learning and transfer learning with deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via green machine learning concepts of reducing repetitions and redundancy, reusing existing resources, and recycling learning models across areas. PyKale aims to facilitate interdisciplinary, knowledge-aware machine learning research for graphs, images, texts, and videos in applications including bioinformatics, graph analysis, image/video recognition, and medical imaging.


InsurTech_2021-09-10_04-55-46.xlsx

#artificialintelligence

The graph represents a network of 3,133 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 10 September 2021 at 12:18 UTC. The requested start date was Friday, 10 September 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 5-day, 16-hour, 21-minute period from Thursday, 02 September 2021 at 18:05 UTC to Wednesday, 08 September 2021 at 10:26 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


The Pros and Cons of RDF-Star and Sparql-Star

#artificialintelligence

For regular readers of the (lately somewhat irregularly published) The Cagle Report, I've finally managed to get my feet underneath me at Data Science Central, and am gearing up with a number of new initiatives, including a video interview program that I'm getting underway as soon as I can get the last of the physical infrastructure (primarily some lighting and a decent green screen) in place. I recently purchased a new laptop one with enough speed and space to let me do any number of projects that my nearly four-year-old workhorse was just not equipped to handle. One of those projects was to start going through the dominant triple stores and explore them in greater depth as part of a general evaluation I hope to complete later in the year. The latest Ontotext GraphDB (9.7.0) had been on my list for a while, and I was generally surprised and pleased by what I found there, especially as I'd worked with older versions of GraphDB and found it useful but not quite there. These four items have become what I consider essential technologies for a W3C stack triple store to fully implement.


Predicting New York's Hospital Costs

#artificialintelligence

In 2019, Donald Trump signed an executive order ordering hospitals to make the costs of common medical services publicly available. Yet, as of March 2021, many hospitals have been non compliant, making it difficult for patients to properly consider the effect of health services on their finances. This article details my creation of a ML XGBoost model to supplement the efforts of the executive order, as well as unexpected findings. Using user-entered values for Length of Stay, Disease Severity, and other variables, the model is capable of predicting hospital charges for three common infections: pneumonia, septicemia, and skin infections/cellulitis. The model is currently only applicable to New York State.


#iiot_2021-08-21_13-52-01.xlsx

#artificialintelligence

The graph represents a network of 1,365 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 21 August 2021 at 20:59 UTC. The requested start date was Tuesday, 17 August 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 6-hour, 15-minute period from Friday, 13 August 2021 at 17:43 UTC to Monday, 16 August 2021 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.