An illuminated 5G sign hangs behind a weave of electronic cables on the opening day of the MWC ... [+] Barcelona in Barcelona, Spain, on Monday, Feb. 25, 2019. At the wireless industrys biggest conference, over 100,000 people are set to see the latest innovations in smartphones, artificial intelligence devices and autonomous drones exhibited by more than 2,400 companies. Photographer: Angel Garcia/Bloomberg

5G is ushering in a new breed of genius networks to deal with the increased levels of complexity, prediction and real time decision making that is required to deliver the performance gains promised not just in enhanced mobile broadband applications but also in IoT and mission critical use cases. At the core of this evolutionary step is the use of machine learning algorithms.

The ability to be more dynamic with real-time network optimization capabilities such as resource loading, power budget balancing and interference detection is what made networks smart in the 4G era. 5G adds support for new antenna capabilities, high-density and heterogeneous network topologies, and uplink and downlink channel allocation and configuration based on payload type and application. While there are many uses of machine learning across all layers of a 5G network from the physical layer through to the application layer, the base station is emerging as a key application for machine learning.

A Nokia OYJ ultra deployable 5G Massive MIMO millimeter wave antenna is displayed at the company's ... [+] booth during the Mobile World Congress Americas event in Los Angeles, California, U.S., on Friday, Sept. 14, 2018. The conference features prominent executives representing mobile operators, device manufacturers, technology providers, vendors and content owners from across the world. Photographer: Patrick T. Fallon/Bloomberg

One of the hallmarks of a next generation 5G base station is the use of advanced antenna capabilities These capabilities include but are not limited to massive multiple-input multiple-output (MIMO) antenna arrays, beamforming, and beam steering.

Massive MIMO is the use of antenna arrays with a large number of active elements. Depending on the frequency band in which it is deployed, massive MIMO designs can employ from 24 active antenna elements to as many as several hundred. One of the uses of MIMO in general is to be able to transmit and receive parallel and redundant streams of information to address errors introduced by interference. However, another use specific to massive MIMO is beamforming and in more advanced systems, beam steering. Beamforming is the ability to utilize a set of phased arrays to create a beam of energy that can be used to focus and extend signal transmission and reception to and from the base station to a particular mobile device. Beam steering is the ability to then control that beam to follow the device in a fully mobile environment within the coverage footprint of that antenna array. When massive MIMO is fully brought to bear and beamforming and beam steering optimally employed, network operators and consumers alike benefit from increased network capacity and expanded coverage through increased data streams, decreased interference, extended range and more optimized power efficiency.

But how does machine learning help with this? Imagine if you will a race between a boat with 10 oars vs a boat with 20 oars. The boat with 10 oars is coordinated by a coxswain not just for rhythm but also is making real-time corrections to heading and cadence based not just on what is currently happening but also what is predicted to happen further down the course. In contrast the boat with 20 oars has a coxswain who is not capable of coordinating rhythm and is only making corrections based on general information that has already occurred. Clearly the former will win the race while the latters oars are not only making minimal progress but in some cases are actually interfering with each other. The same is true with massive MIMO. In order to fully realize the benefits of massive MIMO capability, beamforming and beam steering, machine learning is being utilized at the base station to provide real time and predictive analysis and modeling to better schedule, coordinate, configure and select which arrays to use and when.

A 5G K9 robot distributes hand sanitiser to a visitor in a shopping mall in Bangkok on June 4, 2020, ... [+] as sectors of the economy reopen following restrictions to halt the spread of the COVID-19 novel coronavirus. (Photo by Mladen ANTONOV / AFP) (Photo by MLADEN ANTONOV/AFP via Getty Images)

The new 5G network standard requires higher density deployments of smaller cells working with larger macro cells and multiple air interface protocols. The vision is for smaller cells to be designed for indoor locations or dense urban environments where GPS positioning is not always reliable and the radio frequency (RF) environment is far from predictable. Understanding the location of the devices interacting with the network is essential not only to application layer use cases but also to real time network operation and optimization. It is therefore critical to find ways not only to be able to accurately locate where user equipment is located but also to track them as they move within the coverage footprint.

To this end, machine learning is being applied to estimate user equipment location using RF data and triangulation techniques. While this is not a new concept, the use of machine learning algorithms is yielding material improvements in terms of accuracy, precision, and viability of widespread use than previous means. This is even more significant in that these improvements are being achieved in an environment that is orders of magnitude more complex and dynamically variable than ever before.

One of the driving considerations for the development of 5G is to have one framework to address the varied and often conflicting requirements of 3 use cases, including Enhanced Mobile Broadband (eMBB), massive IoT, and mission critical applications.

Previously served by purpose built, disparate networks, these use cases now will be supported with the 5G network architecture while continuing to require capabilities that are at odds with each other. Networks designed to support EMBB use cases are required to be optimized for high speed, low to medium latency, and profitable capacity. Massive IoT networks on the other hand, need to be low cost, narrow bandwidth, with low control plane overhead and high reliability. While mission critical networks require high speed, low latency and high reliability.

In order to make this vision a reality, 5G has been designed for high variability and flexibility both in the control plane and in channel configuration. As such, it is essential that 5G networks have the ability to predict payload type and use case based on changing conditions, such as historical loading data, RF conditions, location and a wide range of other factors, in order to efficiently and dynamically configure and utilize 5G channel resources.

Consequently, machine learning is being used to not only predict user equipment characteristics and capabilities, probable use case requirements, and RF conditions, but also potentially the type of content most likely to be requested and using edge caching techniques to bring the content closer to the end user. For example, based on historical trend data, it might become known that due to the proximity of a base station to the university as well as the current trending titles on Netflix or Disney + that at certain times of the day, specific movies should be made available closer to that base station to reduce network congestion, buffering, and latency. Similarly, a certain base station located close to an intersection that gets congested at certain times of the day might need more traffic and V2X sensor data to help aid ADAS or autonomous driving applications.

As an industry, we are at a critical evolutionary point as the combination of 5G and machine learning combine to put us on a path towards generational leaps in network capability and efficiency brought about by increasingly more complex functionality and adaptability. But it is an evolution not a revolution and these are the very early days. These 5G machine learning applications are just the beginning of the potential that can be unleashed not just at the physicallayer enabled by the base station but through to the application layer as these two foundational technologies are brought together and we enter the era of genius networks.

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5G And Machine Learning: Taking Cellular Base Stations From Smart To Genius - Forbes

Automated Machine Learning Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Automated Machine Learning Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

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Top Key Players Profiled in This Report:

DataRobot Inc., H2O.ai Inc., dotData Inc., EdgeVerve Systems Limited, Amazon Web Services Inc., Squark, Big Squid Inc., SAS Institute Inc., Microsoft Corporation, Google LLC, Determined AI, Aible Inc

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Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Automated Machine Learning market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Automated Machine Learning markets trajectory between forecast periods.

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Table of Contents

Global Automated Machine Learning Market Research Report 2020 2026

Chapter 1 Automated Machine Learning Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Automated Machine Learning Market Forecast

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Automated Machine Learning Market Growing Rapidly by 2026 with Top Key Players DataRobot Inc., H2O.ai Inc., dotData Inc., EdgeVerve Systems Limited,...

From customer 360 initiatives to supply chain analytics, (our customers) want to get better customer intelligence, improve their products, reduce costs and risks, and much more, but one of their biggest obstacles is skills.

CHICAGO (PRWEB) July 21, 2020

Wavicle Data Solutions, a leading provider of cloud engineering services, data management consulting, and data science and visualization services, today announced it will be participating with Databricks in an AWS Partner Dev Day virtual Machine Learning Workshop.

The free webinar will be live via Zoom on Thursday, July 30, from 9:00 am until 12:00 pm CDT. Participants will learn how unified data analytics can bring data science, business analytics, and engineering together to accelerate data and machine learning efforts. Wavicle data scientist, Calvin Westrick, will speak about text analytics, natural language processing, and machine learning.

While many companies wish to deploy artificial intelligence and machine learning capabilities to improve their businesses, one of the biggest challenges to adoption is a lack of skills, according to a survey by Gartner, a leading research and advisory company. With this virtual workshop, Databricks and Wavicle will provide practical training to address the challenges of preparing large data sets for analytics, managing the proliferation of data and machine learning frameworks, and moving models from development to production.

Specifically, the workshop will explore:

We see our clients in all industries looking for ways to improve their business through artificial intelligence and machine learning, stated Naveen Venkatapathi, president of Wavicle Data Solutions. From customer 360 initiatives to supply chain analytics, they want to get better customer intelligence, improve their products, reduce costs and risks, and much more, but one of their biggest obstacles is skills. Im happy to work with Databricks on this workshop to help bridge that gap.

Enterprises today want to accelerate innovation by building data and machine learning directly into their business, said Michael Hoff, SVP of business development and partners at Databricks. The Databricks and AWS Machine Learning Dev Day with Wavicle is an interactive workshop, which teaches enterprises best practices to build and scale machine learning.

Registration for this complimentary webinar is open now. Reserve your spot at:https://events.databricks.com/awsdatabricksmldevday730?utm_medium=partner&utm_source=databricks&utm_campaign=7013f000000Tt8mAAC

ABOUT WAVICLE DATA SOLUTIONSWavicles team of consultants, data architects, and cloud engineers work with global organizations to build a roadmap to success with unmatched technology expertise, creative innovation, and superior customer service. Our toolkit of proprietary accelerators helps clients deliver world-class data analytics solutions in record time. From data management services and cloud migration consulting to dashboard development and data analytics consulting, our professionals enable and empower data-driven enterprises. For more information, please visit https://www.wavicledata.com.

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Wavicle Data Solutions to Participate in Databricks and AWS Dev Day Machine Learning Workshop on July 30 - PR Web

Its becoming increasingly obvious that one solution to companies gathering too much data about you is to ensure the data stays on the device youre using and that any related machine learning takes place there. But the world of machine learning at the edge is still nascent and focused primarily on a few use cases, such as object detection and wake word recognition.

Zach Shelby, the CEO of Edge Impulse, believes there is a lot more opportunity to provide compelling features using edge-based machine learning, and hes built his company to help make building those features easier for developers. Shelby is the former founder of Sensinode, an IoT company that sold to Arm in 2013.

This week, Edge Impulseannounced additional fundingand launched computer vision models designed for edge devices that developers can use.

Edge Impulse launched earlier this year with a plan to make it easy for people who arent data scientists to take in machine data and build a machine learning model that will run on constrained hardware. Shelby explains that most data scientists and people building machine learning models are used to high-powered graphics processors and having access to thousands of compute nodes on which to train their models and run inferences.

A world of value awaits companies if they can just make models work on the tiny computers that sit inside sensors, smart home devices like outlets or light bulbs even wearables. But getting a traditional ML expert to focus on the edge and constrained devices is difficult. Its like bringing in a celebrity interior designer to redecorate your two-car garage.

Shelby wants to make it easier for traditional developers working at the edge to take in data and apply it to Edge Impulses existing models, which are designed for edge computing, or to take data and train new models without requiring GPUs and a data scientist. It reminds me a bit of Qeexo, the simple-to-train ML platformI covered a few months ago.

In addition to having models, the Edge Impulse platform helps developers trying to apply machine learning at the edge to share their work with colleagues, manage data collection, and track the changes made to the algorithm. This is part of an emerging process calledmachine learning ops (like DevOps, but for ML). Customers can use the Edge Impulse platform for free, paying only for commercial use that allows for those sharing and tracking features.

Shelbys mission now is to convince embedded systems engineers that they can easily try out and use machine learning models at the edge so Edge Impulse can encourage businesses in the industrial world to start playing with the technology. The startup has pre-populated its platform with models that can analyze vibration data from machines, audio data that can track machine health (Augury also does something similar), and even audio data designed to track the health of workers. And this week it added computer vision models.

The embedded world is already familiar with using limited processing power for anomaly detection, but by running sophisticated machine learning models, it can move beyond simply detecting something strange to identifying what, exactly, that strange thing is.

For example, the ML model that analyzes coughs can process a 2-to-5-second audio clip in real time to listen for and track coughing. But it doesnt just recognize a cough; it can determine the type of cough and classify it as something that needs further attention.Edge Impulse has made it easy to build the machine learning model to run on an Arm-based microcontroller, as shown in thisrough prototype. Companies can put a chip running the model inside a wearable for workers, inside a device thats used in hospital rooms to monitor patients, or include it as part of a sensor for an organization thats trying to detect illness among its staff.

By making it easy to grab a model, put it in a device, and then deploy it, Edge Impulse could open up the world of machine learning at the edge.Shelby has signed deals with Eta Compute, STMicroelectronics, and Arduino to link its platform to existing microcontrollers used by developers and product companies, which makes it easy to put the model on hardware. And paying customers get to use the cloud-based tracking and sharing features to further adjust models for specific use cases.

Im genuinely excited by the opportunities a platform like this brings to engineers who understand problems in their field and who want to apply machine learning to them. Just like graphical user interfaces brought computing to more people or services like WordPress allowed more people to blog, a platform like Edge Impulse will let embedded engineers and traditional developers harness the power of machine learning.

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To make ML at the edge work, make it more accessible - Stacey on IoT

Image: SARS-CoV-2 (Photo courtesy of NIAID)

The findings have major implications for clinical management of COVID-19, and could help doctors predict who is most at risk and likely to need hospital care in a second wave of coronavirus infections. Although continuous cough, fever and loss of smell (anosmia) are usually highlighted as the three key symptoms of COVID-19, data gathered from app users shows that people can experience a wide range of different symptoms including headaches, muscle pains, fatigue, diarrhea, confusion, loss of appetite, shortness of breath and more. The progression and outcomes also vary significantly between people, ranging from mild flu-like symptoms or a simple rash to severe or fatal disease.

To find out whether particular symptoms tend to appear together and how this related to the progression of the disease, the research team at Kings College London (London, UK) used a machine learning algorithm to analyze data from a subset of around 1,600 users in the UK and US with confirmed COVID-19 who had regularly logged their symptoms using the app in March and April. The analysis revealed six specific groupings of symptoms emerging at characteristic timepoints in the progression of the illness, representing six distinct types of COVID-19. The algorithm was then tested by running it on a second independent dataset of 1,000 users in the UK, US and Sweden, who had logged their symptoms during May. All people reporting symptoms experienced headache and loss of smell, with varying combinations of additional symptoms at various times. Some of these, such as confusion, abdominal pain and shortness of breath, are not widely known as COVID-19 symptoms, yet are hallmarks of the most severe forms of the disease.

The team also discovered that people experiencing particular symptom clusters were more likely to require breathing support in the form of ventilation or additional oxygen. The researchers then developed a model combining information about age, sex, BMI and pre-existing conditions together with symptoms gathered over just five days from the onset of the illness. This was able to predict which cluster a patient falls into and their risk of requiring hospitalization and breathing support with a higher likelihood of being correct than an existing risk model based purely on age, sex, BMI and pre-existing conditions alone. Given that most people who require breathing support come to hospital around 13 days after their first symptoms, this extra eight days represents a significant early warning as to who is most likely to need more intensive care.

These findings have important implications for care and monitoring of people who are most vulnerable to severe COVID-19, said Dr Claire Steves from Kings College London. If you can predict who these people are at day five, you have time to give them support and early interventions such as monitoring blood oxygen and sugar levels, and ensuring they are properly hydrated - simple care that could be given at home, preventing hospitalizations and saving lives.

Being able to gather big datasets through the app and apply machine learning to them is having a profound impact on our understanding of the extent and impact of COVID-19, and human health more widely, said Sebastien Ourselin, professor of healthcare engineering at Kings College London and senior author of the study.

Related Links:Kings College London

Continued here:
Scientists Use Machine Learning Algorithm to Identify Six Types of COVID-19 with Distinctive Symptoms - HospiMedica

Global Machine Learning-as-a-Service (MLaaS) Market Research Report 2020-2026 is a valuable source of insightful data for business strategists. It provides the industry overview with growth analysis and historical & futuristic cost, revenue, demand and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Market study provides comprehensive data which enhances the understanding, scope and application of this report.

This is the latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact are covered in the report.

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Key Market Players:

Microsoft Corp., Fair Isaac Corporation (FICO), Amazon Web Services Inc., Iflowsoft Solutions Inc., Yottamine Analytics LLC, PurePredictive Inc., Hewlett Packard Enterprise Development LP, SAS Institute Inc., IBM Corp., BigML Inc., Sift Science Inc., Google LLC

Market segment by Type, the product can be split into

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The leading players of the Machine Learning-as-a-Service (MLaaS) industry, their market share, product portfolio, company profiles are covered in this report. The leading market players are analyzed on the basis of production volume, gross margin, market value, and price structure. The competitive market scenario among Machine Learning-as-a-Service (MLaaS) players will help the industry aspirants in planning their strategies. The statistics offered in this report will be a precise and useful guide to shape business growth.

Machine Learning-as-a-Service (MLaaS) Market Report Highlights:

The report provides a detailed analysis of current and future market trends to identify the investment opportunities market forecasts till 2026.

Key Machine Learning-as-a-Service (MLaaS) market trends across the business segments, regions and countries

Key developments and strategies observed in the market

In-depth company profiles of key players and upcoming prominent players

Key market dynamics such as drivers, restraints, opportunities and other trends

Market opportunities and recommendations for new investments

The report has 150 tables and figures browse the report description and TOC:

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TOC Snapshot of Global Machine Learning-as-a-Service (MLaaS) Market

Machine Learning-as-a-Service (MLaaS) Market Product Definition

Worldwide Machine Learning-as-a-Service (MLaaS) Market Manufacturer Share and Market Overview

Manufacturer Machine Learning-as-a-Service (MLaaS) Business Introduction

Machine Learning-as-a-Service (MLaaS) Market Segmentation (Region Level)

World Machine Learning-as-a-Service (MLaaS) Market Segmentation (Product Type Level)

Machine Learning-as-a-Service (MLaaS) Market Segmentation (Industry Level)

Segmentation (Channel Level) of Machine Learning-as-a-Service (MLaaS) Market

Machine Learning-as-a-Service (MLaaS) Market Forecast 2020-2026

Segmentation of Machine Learning-as-a-Service (MLaaS) Industry

Cost of Machine Learning-as-a-Service (MLaaS) Production Analysis

Conclusion

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SFL Scientific announced that it has been selected by the NVIDIA Partner Network (NPN) as the 2019 Service Delivery Partner of the Year 2019 for the Americas for the second year in a row.

The NPN honors its top North American partners that have shown growth in their GPU business through their growth, leadership, and investments they have made throughout the year.

As organizations capture valuable data across all business functions, a central understanding of how to fully leverage that information, operationalize outcomes and plan where to best make Artificial Intelligence (AI) and machine learning (ML) investments is critical to building a future competitive advantage.

The NPN selected SFL Scientific for its AI consulting and development services, helping clients and partners develop new AI tools, products, and solutions utilizing the power of NVIDIA GPUs to help accelerate and solve complex business challenges.

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At the time of this announcement, Dr. Michael Segala, CEO, SFL Scientific said,

Receiving this honor and recognition from NVIDIA is the validation of our mission to be the leading data science and AI implementation partner, helping organizations solve unique challenges with deep learning and AI-based solutions. NVIDIAs industry-leading GPU portfolio benefits not only our respective clients, but shapes how we create R&D, automation, and operational solutions across industries.

This award is a testament to SFL Scientifics ability to lead clients and partners along their journey toward becoming AI-driven enterprises, said Craig Weinstein, Vice President of the Americas Partner Organization at NVIDIA.

Craig added, NVIDIA and SFL Scientific have worked together to add strategic services in solution development and enablement, as well as created opportunities for our clients to innovate.

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SFL Scientific addresses these needs by deploying Ph.D. data scientists and engineers to use AI in solving data-intensive and complex R&D problems.

SFL Scientifics data science consulting team has worked extensively to distinguish their capabilities beyond what is commercially available by developing novel AI solutions, working across data collection, DevOps & data management, hardware & cloud, computer vision, natural language processing (NLP), and time-series & IoT.

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SFL Scientific Named Partner of the Year for AI Services Delivery - AiThority

Snorkel AI cofounders (L to R): Alex Ratner (CEO), Chris R (Board Member), Paroma Varma (Head of ... [+] Solutions), Braden Hancock (Head of Technology), and Henry Ehrenberg (Head of Engineering)

In 2014, computer science PhD candidate Alex Ratner and a team of fellow Stanford PhDs, advised by associate professor and MacArthur Fellow Chris R, were working on a research project at the universitys prominent AI Lab. The main issue they focused on was companies not being able to deploy AI as widely and effectively as they wanted to, due to the costly and time-consuming manual labeling of the data that machine learning models learn from.

Like many academic projects, it was meant to be just an afternoon of messing around and a whiteboard with some math, Ratner says. Soon it turned out that this question that we had started with, of what if we changed the paradigm from labeling by hand to labeling programmatically, was quite interesting to a lot of people.

After spending five years developing the product and deploying it at organizations like Google, Apple, Intel, and the departments of Justice and Defense, in 2019 the research team spun out of the AI Lab and created a company called Snorkel AI.

Today, the enterprise came out of stealth mode announcing that it had raised a total of $15 million (combined seed and Series A rounds), from investors like Greylock Partners, GV, and In-Q-Tel.

We were motivated by this mission of not just publishing more papers on some of these fun algorithmic or theoretical ideas, but actually making AI more broadly practical with a new end to end platform that focuses centrally on the problem of data labeling, Ratner, who serves as the companys CEO, says.

Snorkel AI's platform

The companys flagship product is the end-to-end Machine Learning platform called Snorkel Flow, which allows for AI applications to be deployed programmatically at much faster rates.

Snorkel Flow would serve as a replacement of armies of human labelers which at the moment do it by hand. An example of those manual processes include training AI applications to assist a radiologist in triaging chest X-rays. The radiologist would have to sit through a ton of images labeling which ones are emergency and which ones arent to teach the AI algorithm. Another example is a bank wanting AI to classify, sort and pull information out of someones loan portfolio. The companies would need to have their legal team check and label thousands of documents by hand every single time they want to change something.

Our key focus has been on sectors where labeling data by hand is not just a slower or more expensive option, but is often just a non-starter, Ratner says.

According to Ratner, this is usually due to one or more of three factors: the data is private so companies cant outsource it to get it labeled outside of the organization, the data requires in-demand experts (doctors or legal analysts), and the data changes frequently so companies find themselves labeling and relabeling all the time.

Snorkel AIs platform enables a programmatic approach so that instead of labeling one document at a time, the user can write a function (for example if they see the word employment in the header, they can label it as an employment contract).

The advantage is that writing a dozen or two dozen of these labeling functions to label your AI solution is orders of magnitude faster than labeling documents by hand, Ratner says.

Snorkel AI label

The Palo Alto-based Snorkel AI which counts around two dozen employees, raised a $3 million seed round in June of last year, and a $12 million Series A in October. The companys current customers include two top US banks, government agencies and other Fortune 500 companies.

Saam Motamedi, an Under 30 honoree and a general partner at Greylock Partners which co-led the seed round and led the Series A, says that Greylock immediately wanted to partner with the Snorkel AI cofounders given the caliber of the team, the traction of the open source Snorkel project and the power of the paradigm shift they are pioneering around this data-centric approach.

Customers have been able to go from what took months to deploy AI applications to now being able to deploy it in hours because they can programmatically manage the data, Motamedi says.

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Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning - Forbes

World Wide Technology (WWT) announced that it has been selected by theNVIDIAPartner Network (NPN) as the 2019 Deep Learning AI Partner of the Year for the Americas. This is the third year that WWT has been honored in this category.

The NPN selected WWT for its ongoing AI research and development program. To help customers develop AI leadership, World Wide Technology published six white papers about leveraging the compute power of NVIDIA DGX systems to develop Machine Learning and Deep Learning models for real-time edge video analytics, network optimization, and performance comparisons of multiple reference architectures for ML model development. The WWT research into ML and Deep Learning is tied to real-world business outcomes, and improvements in mining safety, utilities grid optimization, and resource management for manufacturing.

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In addition, the WWT Advanced Technology Center (ATC) offers Lab-as-a-service environments forAIdevelopment, MLOps, Deep Learning, and testing of storage and networking with GPU-accelerated compute. WWT [also] engineered and deployed some of the largest clusters of DGX-2 servers in North America and China for production of Natural Language Processing applications at massive scale.

Its due to the strength of our engineering and data science partnership with NVIDIA that WWTs customers are today realizing strategic value from Deep Learning and ML solutions that WWT has deployed, said Tim Brooks, Managing Director of AI Solutions for World Wide Technology. Our customers are leveraging Natural Language Processing, computer vision, robotics, and geospatial analysis for intelligent agents, autonomous vehicles, retail loss prevention, mining safety, and manufacturing QA.

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NVIDIA has long worked with WWT to deliver AI solutions for data center and cloud-hosted environments across numerous industries, saidCraig Weinstein, Vice President of the Americas Partner Organization at NVIDIA. Together with NVIDIA and our OEM partners, WWT provides customers with AI solutions that leverage the power of NVIDIA GPUs and 30 years of engineering and global deployment reliability of WWT.

The NPN honors its top North American partners who have shown growth in their GPU business through the growth, leadership, and investments made throughout the year.

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WWT Named Partner of the Year for Deep Learning AI by NVIDIA - AiThority

The Earth Challenge 2020 initiative overcomes AI model training challenges using citizen data scientists to collect data for environment and healthcare apps.

A small army of citizen data scientists is being mobilizedto collect data to help train machine learning algorithms that will be embeddedin a range of environment and healthcare applications.

As part of an Earth Challenge 2020 initiative sponsored by the Earth Day Network, the Wilson Center, and the U.S. State Department, applications that tackle everything from food safety and the tracking of insect populations to plastics pollution and air quality are now being made available.

See also: Researchers Develop Algorithm to Detect Crude Oil on Water

Earth Challenge 2020 is an arm of an Earth School coalitionspearheaded by the United Nations Environment Programme and TED-Ed, which iscommitted to providing free educational science content to students, parents,and teachers.

The goal of the Earth Challenge 2020 initiative is to enable citizen data scientists to collect, label, and tag data using mobile computing applications that is then fed into an analytics database from Kinetica that runs on graphical processor units (GPUs). That approach among other applications will enable school children to take photos of insects using a Picture Pile application from Applied Systems Analysis to train machine learning algorithms to recognize not just different types of insects, but where they are also found at different times of the year.

The labeled insect images that collected are then added to adata set collected by the European Space Agency that is being created to betterunderstand how insects such as bees impact food production. Once enough imagesare labeled the machine learning algorithms eventually start to recognizedifferent images, which then allows them to automatically label and tag themwithout any further human assistance required.

All the data sets being collected by the mobile applicationscreated as part of the Earth Challenge 2020 initiativewill be made available for free to data scientists via a Citizen Science Cloud serviceor Kinetica REST application programming interfaces (APIs), says Daniel Raskin,chief marketing officer for Kinetica.

It will all be in the public domain, says Raskin.

Kinetica is participating in this effort as part of aneffort to spur adoption of an analytics database that runs natively on the sameGPUs that are being widely employed to train artificial intelligence (AI)models. Machine learning algorithms run considerably faster of GPUs, whichreduces the time and costs associated with training AI models.

The challenge many organizations building AI models face iscollecting all the data needed to train an AI model. The Earth Challenge 2020initiative helps address that issue by enlisting what will hopefully become anarmy of citizen data scientists to help collect data for what will become a broadportfolio of environment and healthcare-related applications, says Raskin.

Its too early to tell just what impact individuals armedwith smartphones capable of capturing high-quality images might have on data science.With more individuals of all ages spending more time at home to combat theCOVID-19 pandemic the opportunity to potentially motivate people around theworld to participate in various initiatives has never been greater. Thechallenge, of course, is finding a way to let all those potential citizen datascientists that the opportunity to participate exists in the first place.

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Citizen Data Scientists Needed to Save the Planet - RTInsights