REDWOOD CITY, Calif., April 08, 2021 (GLOBE NEWSWIRE) -- TigerGraph, provider of the leading graph analytics platform, today unveiled the complete agenda for Graph + AI Summit 2021, the industrys only open conference devoted to democratizing and accelerating analytics, AI and machine learning with graph algorithms. The roster includes confirmed speakers from JPMorgan Chase, Intuit, NewDay, Jaguar Land Rover, Pinterest, Stanford University, Forrester Research, Accenture, Capgemini, KPMG, Intel, Dell, and Xilinx, as well as many innovative startups including John Snow Labs, Fintell, SaH Solutions and Sayari Labs. The virtual conference, set for April 21-23, offers keynotes, speakers, real-world customer case studies and hands-on workshops for data, analytics and AI professionals.

The combination of analytics, AI, machine learning and graph is a powerful one that offers many human benefits and forward-looking companies in all industries have taken note, said Dr. Yu Xu, founder and CEO of TigerGraph. Graph + AI Summit is again bringing together industry luminaries, technical experts and business leaders from the worlds largest banks, fintechs, tech giants and manufacturers to share implementation best practices, lessons learned and more. Were pleased to welcome back speakers from Jaguar Land Rover and Intuit, and welcome new participants from an impressive list of todays top innovators driving the adoption of graph. Our goal is to make graph accessible, applicable and understandable for all, as more people grasp how graph-related technologies can improve our lives.

Graph + AI Summit returns after a successful Graph + AI 2020; the inaugural event attracted more than 3,000 attendees from 56 countries, and welcomed data scientists, data engineers, architects and business and IT executives from 115 of the Fortune 500 companies. The latest conference will host over 6,000 attendees this year and again focus on accelerating analytics, AI and machine learning with graph algorithms timely technologies that are on the minds of todays business leaders. After 2020 accelerated enterprises shift to the cloud, businesses are realizing graph technologies are key to connecting, analyzing and helping glean insights from data.

Graph + AI Summit 2021 includes keynote presentations, executive roundtables, technical breakout sessions, industry tracks (banking, insurance and fintech, healthcare, life sciences and government) and live workshops for advanced analytics and machine learning.

Keynote speakers presenting during conference general sessions include:

Notable roundtables and interactive sessions include:

Graph + AI Summit sessions will also cover the following topics:

Register for one of these live workshops for advanced analytics and machine learning now:

View Graph + AI Summits agenda: https://www.tigergraph.com/graphaisummit/#day1Register and secure your complimentary spot: https://www.tigergraph.com/graphaisummit/.

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About TigerGraphTigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industrys first and only distributed native graph database, TigerGraphs proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. The company is headquartered in Redwood City, California, USA. Start free with tigergraph.com/cloud.

Media ContactCathy WrightOffleash PR for TigerGraphcathy@offleashpr.com650-678-1905

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TigerGraph's Graph + AI Summit 2021 to Feature 40+ Sessions, Live Workshops and Speakers from JPMorgan Chase, NewDay, Pinterest, Jaguar Land Rover and...

Back in 2017, Apple introduced the A11 which included their first dedicated neural network hardware that Apple calls a "Neural Engine." At the time, Apple's neural network hardware was able to perform up to 600 billion operations per second and used for Face ID, Animoji and other machine learning tasks. The neural engine allows Apple to implement neural network and machine learning in a more energy-efficient manner than using either the main CPU or the GPU. Today Apple's Neural Engine has advanced to their new M1 processor that delivers 15X faster machine learning performance of the Neural Engine, according to Apple.

Apple revealed back in Q4 that the "M1 features their latest Neural Engine. Its 16core design is capable of executing a massive 11 trillion operations per second. In fact, with a powerful 8core GPU, machine learning accelerators and the Neural Engine, the entire M1 chip is designed to excel at machine learning." There's an excellent chance that today's patent covers technology built into the M1 processor to help it achieve its breakthrough performance. While the patent was published today, it was filed in Q4 2019 before the M1 surfaced.

Today, the U.S. Patent Office published a patent application from Apple titled "Multi-Mode Planar Engine for Neural Processor." Apple's invention relates to a circuit for performing operations related to neural networks, and more specifically to a neural processor that include a plurality of neural engine circuits and one or more multi-mode planar engine circuits.

An artificial neural network (ANN) is a computing system or model that uses a collection of connected nodes to process input data. The ANN is typically organized into layers where different layers perform different types of transformation on their input. Extensions or variants of ANN such as convolution neural network (CNN), recurrent neural networks (RNN) and deep belief networks (DBN) have come to receive much attention. These computing systems or models often involve extensive computing operations including multiplication and accumulation. For example, CNN is a class of machine learning technique that primarily uses convolution between input data and kernel data, which can be decomposed into multiplication and accumulation operations.

Depending on the types of input data and operations to be performed, these machine learning systems or models can be configured differently. Such varying configuration would include, for example, pre-processing operations, the number of channels in input data, kernel data to be used, non-linear function to be applied to convolution result, and applying of various post-processing operations. Using a central processing unit (CPU) and its main memory to instantiate and execute machine learning systems or models of various configuration is relatively easy because such systems or models can be instantiated with mere updates to code. However, relying solely on the CPU for various operations of these machine learning systems or models would consume significant bandwidth of a central processing unit (CPU) as well as increase the overall power consumption.

Apple's invention specifically relates to a neural processor that includes a plurality of neural engine circuits and a planar engine circuit operable in multiple modes and coupled to the plurality of neural engine circuits.

At least one of the neural engine circuits performs a convolution operation of first input data with one or more kernels to generate a first output. The planar engine circuit generates a second output from a second input data that corresponds to the first output or corresponds to a version of input data of the neural processor.

The input data of the neural processor may be data received from a source external to the neural processor, or outputs of the neural engine circuits or planar engine circuit in a previous cycle. In a pooling mode, the planar engine circuit reduces the spatial size of a version of second input data. In an elementwise mode, the planar engine circuit performs an elementwise operation on the second input data. In a reduction mode, the planar engine circuit reduces the rank of a tensor.

Apple's patent FIG. 3 below is a block diagram illustrating a neural processor circuit; FIGS. 6A, 6B, and 6C are conceptual diagrams respectively illustrating a pooling operation, an elementwise operation, and a reduction operation.

To review the deeper details, review Apple's patent application 20210103803.

Considering that this is a patent application, the timing of such a product to market is unknown at this time.

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Apple Reveals a Multi-Mode Planar Engine for a Neural Processor that could be used in A-Series & screamingly fast M1 Processors - Patently Apple

In recent years, connected devices and the Internet of Things (IoT) have become omnipresent in our everyday lives, be it in our homes and cars or at our workplace. Many of these small devices are connected to a cloud servicenearly everyone with a smartphone or laptop uses cloud-based services today, whether actively or through an automated backup service, for example.

However, a new paradigm known as "edge intelligence" is quickly gaining traction in technologys fast-changing landscape. This article introduces cloud-based intelligence, edge intelligence, and possible use-cases for professional users to make machine learning accessible for all.

Cloud computing, simply put, is the availability of remote computational resources whenever a client needs them.

For public cloud services, the cloud service provider is responsible for managing the hardware and ensuring that the service's availability is up to a certain standard and customer expectations. The customers of cloud services pay for what they use, and the employment of such services is generally only viable for large-scale operations.

On the other hand, edge computing happens somewhere between the cloud and the clients network.

While the definition of where exactly edge nodes sit may vary from application to application, they are generally close to the local network. These computational nodes provide services such as filtering and buffering data, and they help increase privacy, provide increased reliability, and reduce cloud-service costs and latency.

Recently, its become more common for AI and machine learning to complement edge-computing nodes and help decide what data is relevant and should be uploaded to the cloud for deeper analysis.

Machine learning (ML) is a broad scientific field, but in recent times, neural networks (often abbreviated to NN) have gained the most attention when discussing machine learning algorithms.

Multiclass or complex ML applications such as object tracking and surveillance, automatic speech recognition, and multi-face detection typically require NNs. Many scientists have worked hard to improve and optimize NN algorithms in the last decade to allow them to run on devices with limited computational resources, which has helped accelerate the edge-computing paradigms popularity and practicability.

One such algorithm is MobileNet, which is an image classification algorithm developed by Google. This project demonstrates that highly accurate neural networks can indeed run on devices with significantly restricted computational power.

Until recently, machine learning was primarily meant for data-science experts with a deep understanding of ML and deep learning applications. Typically, the development tools and software suites were immature and challenging to use.

Machine learning and edge computing are expanding rapidly, and the interest in these fields steadily grows every year. According to current research, 98% of edge devices will use machine learning by 2025. This percentage translates to about 18-25 billion devices that the researchers expect to have machine learning capabilities.

In general, machine learning at the edge opens doors for a broad spectrum of applications ranging from computer vision, speech analysis, and video processing to sequence analysis.

Some concrete examples for possible applications are intelligent door locks combined with a camera. These devices could automatically detect a person wanting access to a room and allow the person entry when appropriate.

Due to the previously discussed optimizations and performance improvements of neural network algorithms, many ML applications can now run on embedded devices powered by crossover MCUs such as the i.MX RT1170. With its two processing cores (a 1GHz Arm Cortex M7 and a 400 MHz Arm Cortex-M4 core), developers can choose to run compatible NN implementations with real-time constraints in mind.

Due to its dual-core design, the i.MX RT1170 also allows the execution of multiple ML models in parallel. The additional built-in crypto engines, advanced security features, and graphics and multimedia capabilities make the i.MX RT1170 suitable for a wide range of applications. Some examples include driver distraction detection, smart light switches, intelligent locks, fleet management, and many more.

The i.MX 8M Plus is a family of applications processors that focuses on ML, computer vision, advanced multimedia applications, and industrial automation with high reliability. These devices were designed with the needs of smart devices and Industry 4.0 applications in mind and come equipped with a dedicated NPU (neural processing unit) operating at up to 2.3 TOPS and up to four Arm Cortex A53 processor cores.

Built-in image signal processors allow developers to utilize either two HD camera sensors or a single 4K camera. These features make the i.MX 8M Plus family of devices viable for applications such as facial recognition, object detection, and other ML tasks. Besides that, devices of the i.MX 8M Plus family come with advanced 2D and 3D graphics acceleration capabilities, multimedia features such as video encode and decode support including H.265), and 8 PDM microphone inputs.

An additional low-power 800 MHz Arm Cortex M7 core complements the package. This dedicated core serves real-time industrial applications that require robust networking features such as CAN FD support and Gigabit Ethernet communication with TSN capabilities.

With new devices comes the need for an easy-to-use, efficient, and capable development ecosystem that enables developers to build modern ML systems. NXPs comprehensive eIQ ML software development environment is designed to assist developers in creating ML-based applications.

The eIQ tools environment includes inference engines, neural network compilers, and optimized libraries to enable working with ML algorithms on NXP microcontrollers, i.MX RT crossover MCUs, and the i.MX family of SoCs. The needed ML technologies are accessible to developers through NXPs SDKs for the MCUXpresso IDE and Yocto BSP.

The upcoming eIQ Toolkit adds an accessible GUI; eIQ Portal and workflow, enabling developers of all experience levels to create ML applications.

Developers can choose to follow a process called BYOM (bring your own model), where developers build their trained models using cloud-based tools and then import them to the eIQ Toolkit software environment. Then, all thats left to do is select the appropriate inference engine in eIQ. Or the developer can use the eIQ Portal GUI-based tools or command line interface to import and curate datasets and use the BYOD (bring your own data) workflow to train their model within the eIQ Toolkit.

Most modern-day consumers are familiar with cloud computing. However, in recent years a new paradigm known as edge computing has seen a rise in interest.

With this paradigm, not all data gets uploaded to the cloud. Instead, edge nodes, located somewhere between the end-user and the cloud, provide additional processing power. This paradigm has many benefits, such as increased security and privacy, reduced data transfer to the cloud, and lower latency.

More recently, developers often enhance these edge nodes with machine learning capabilities. Doing so helps to categorize collected data and filter out unwanted results and irrelevant information. Adding ML to the edge enables many applications such as driver distraction detection, smart light switches, intelligent locks, fleet management, surveillance and categorization, and many more.

ML applications have traditionally been exclusively designed by data-science experts with a deep understanding of ML and deep learning applications. NXP provides a range of inexpensive yet powerful devices, such as the i.MX RT1170 and the i.MX 8M Plus, and the eIQ ML software development environment to help open ML up to any designer. This hardware and software aims to allow developers to build future-proof ML applications at any level of experience, regardless of how small or large the project will be.

Industry Articles are a form of content that allows industry partners to share useful news, messages, and technology with All About Circuits readers in a way editorial content is not well suited to. All Industry Articles are subject to strict editorial guidelines with the intention of offering readers useful news, technical expertise, or stories. The viewpoints and opinions expressed in Industry Articles are those of the partner and not necessarily those of All About Circuits or its writers.

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Increasing the Accessibility of Machine Learning at the Edge - Industry Articles - All About Circuits

If youve been a reader of Sprudge for any reasonable amount of time, youve no doubt by now ready multiple articles about how coffee is potentially beneficial for some particular facet of your health. The stories generally go like this: a study finds drinking coffee is associated with a X% decrease in [bad health outcome] followed shortly by the study is observational and does not prove causation.

In a new study in theAmerican Heart Associations journal Circulation: Heart Failure, researchers found a link between drinking three or more cups of coffee a day and a decreased risk of heart failure. But theres something different about this observational study. This study used machine learning to get to its conclusion, and it may significantly alter the utility of this sort of study in the future.

As reported by the New York Times, the new study isnt exactly new at all. Led by David Kao, a cardiologist at University of Colorado School of Medicine, researchers re-examined the Framingham Heart Study (FHS), a long-term, ongoing cardiovascular cohort studyof residents of the city of Framingham, Massachusetts that began in 1948 and has grown to include over 14,000 participants.

Whereas most research starts out with a hypothesis that it then seeks to prove or disprove, which can lead to false relationships being established by the sort variables researchers choose to include or exclude in their data analysis, Kao et al instead approached the FHS with no intended outcome. Instead, they utilized a powerful and increasingly popular data-analysis technique known as machine learning to find any potential links between patient characteristics captured in the FHS and the odds of the participants experiencing heart failure.

Able to analyze massive amounts of data in a short amount of timeas well as be programmed to handle uncertainties in the data, like if a reported cup of coffee is six ounces or eight ouncesmachine learning can then start to ascertain and rank which variables are most associated with incidents of heart failure, giving even observational studies more explanatory power in their findings. And indeed, when the results of the FHS machine learning analysis were compare to two other well-known studies, the Cardiovascular Heart Study (CHS) and the Atherosclerosis Risk in Communities study (ARIC), the algorithm was able to correctly predict the relationship between coffee intake and heart failure.

But, of course, there are caveats. Machine learning algorithms are only as good as the data being fed to it. If the scope is too narrow, the results may not translate more broadly and its real-world predictive utility is significantly decreased. The New York Times offers facial recognition software as an example: Trained primarily on white male subjects, the algorithms have been much less accurate in identifying women and people of color.

Still, the new study shows promise, not just for the health benefits the algorithm uncovered, but for how we undertake and interpret this sort of analysis-driven research.

Zac Cadwaladeris the managing editor at Sprudge Media Network and a staff writer based in Dallas.Read more Zac Cadwaladeron Sprudge.

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Is Machine Learning The Future Of Coffee Health Research? - Sprudge

This article was originally published here

PLoS One. 2021 Apr 1;16(4):e0249285. doi: 10.1371/journal.pone.0249285. eCollection 2021.

ABSTRACT

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.

OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted.

METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort.

RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%.

CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

PMID:33793600 | DOI:10.1371/journal.pone.0249285

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Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19 - DocWire News