Leading global technology distributor and solutions provider Avnet Asia will host the “Avnet AI Cloud Exhibition”, showcasing innovative technology, applications and solutions in Artificial Intelligence (AI) and machine learning together with its suppliers and partners. With the ability to quickly design, develop and deploy solutions, Avnet can meet the needs of a variety of application scenarios to accelerate the industrialization of artificial intelligence.

During this period, Avnet will also hold the “Avnet 2021 Artificial Intelligence Cloud Conference” on June 29, 2021. Joined by developers, engineers, and decision makers in the AI field, the summit will feature cutting-edge technology trends in artificial intelligence and machine learning, and in-depth discussions on the development, future prospects and blueprints for AI to encourage and accelerate innovation.

KS Lim, senior director of supplier management at Avnet Asia said, “MarketsandMarkets forecasts the global artificial intelligence (AI) market size to grow to over USD 300 billion by 2026, and the market in Asia Pacific is anticipated to grow at the highest CAGR during the forecast period. As the world’s leading technology distributor and solution provider, Avnet has a comprehensive ecosystem that provides customers with end-to-end artificial intelligence and machine learning solutions, reducing the cost and complexity of product development to enable application scenarios. We will continue to work hand in hand with our suppliers and partners to further contribute to the development and maturity of the entire AI ecosystem.”

The virtual exhibition is divided into three sections: AI smart solution demonstration area, Avnet design service demonstration area, and a partner solution demonstration area. In the AI smart solution demonstration area, participants can learn about Avnet’s various innovative technologies and industrial applications, including:

• AI camera: A smart AI camera utilizing a neural network implemented in the FPGA fabric. It integrates an independent high-performance ISP camera module based on the Xilinx Zynq7020 to achieve a variety of functions, including noise reduction, wide dynamic range, light source detection, motion detection and edge enhancement function.
• BlueBox AI platform: The embedded edge artificial intelligence box can perform multi-channel convolutional neural network operations. It facilitates real-time multi-channel AI functions such as face detection, passenger and traffic statistics, and license plate recognition. All functions operate independently and can work simultaneously to provide edge artificial intelligence analytic solutions. The box integrates all the above functions through the underlying Xilinx Zynq UltraScale+ MPSoC to perform AI computing on demand.
• ROS on Ultra96: The open source Robot Operating System (ROS) runs on the Avnet Ultra96 development board, which features the Xilinx Zynq UltraScale+ MPSoC. The programmable logic part of the Zynq UltraScale+ MPSoC provides deep learning acceleration capabilities, while consolidating a range of ROS functions such as control, SLAM, and navigation. The small form-factor Ultra96 single board computer running ROS makes an ideal platform for developing autonomous robots, service robots, and general-purpose ROS experimentation.

In the partner demonstration area, Avnet’s suppliers and partners will also showcase their innovations:

• ON Semiconductor: A variety of advanced imaging technologies such as high speed, short exposure, global shutter and platform solutions will be displayed to address the application of different scenarios such as factory automation and the challenges faced in industrial AI applications, to accelerate innovation.
• Samtec: Fast-growing technologies like Artificial Intelligence are driving new system architectures that demand increased bandwidths, frequencies and densities. To meet these challenges, Samtec offers innovative high-performance interconnects that exceed AI industry standards.
• STMicroelectronics: Introduce embedded AI solutions based on deep learning models running on high performance 32-bit microcontroller and also machine learning based MEMS sensor.
• Western Digital: IX SN530 NVMeTM industrial-grade SSD will be displayed, which supports a new generation of data-rich industrial design and autonomous vehicle design
• Xilinx: Will showcase its real-time multi-task autonomous driving AI perception processing solution, which uses the industry-leading lightweight optimization algorithm to achieve vehicle detection, lane line detection, lane detection in ADAS and autonomous driving scenarios through a single model. It can perform multiple tasks such as driving area detection and depth estimation. In addition, Xilinx will also demonstrate the application of Versal-based DPU in low-latency automatic driving and pose detection.
• YAGEO Group: Will showcase the flagship products from its main brands YAGEO, KEMET and PULSE, including YAGEO resistors, KEMET polymer capacitors, and PULSE network devices, to provide high reliability polymer and ceramic capacitor solutions for AI chips and DC power supplies for autopilot computers

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Led by former engineers from Tesla and Faraday Future, Hemant Sikaria and Mayank Sikaria, Sibros is an automotive technology company offering a next-generation vehicle-to-cloud automotive software platform. 

CEO and co-founder Hemant Sikaria had the light bulb moment to start Sibros when he had to make multiple trips to his car dealer due to a software recall. Upon further research, he realised the majority of OEMs did not have the necessary capabilities for advanced full vehicle updates. In fact, global OEMs have been facing significant software and data complexity challenges as vehicles were increasingly shifting towards electrification and self-driving modes. Synergising Hemant’s experience at Tesla in building systems that could solve these problems with Over-the-Air (OTA) programming, and Mayanks’s significant contributions to the software, data and battery management systems at Faraday, the duo teamed up to form Sibros.

Analytics India Magazine got in touch with the founders to understand how Sibros is leveraging machine learning and artificial intelligence to help automakers build new connectivity use cases and business models.

Leading The Way With ML

The advancement in 5G technology along with software-defined vehicles has allowed Over-the-Air software updates to extend beyond just telematics and head unit applications. As a matter of fact, the connected car revolution has made OTA software an industry standard for automakers. For the uninitiated, OTA can reduce consequential software recalls to create new revenue opportunities, and enhance the customer experience by shortening the time required to update, fix, and maintain vehicles throughout their entire lifecycle.

Sibros’ flagship product, OTA Deep Updater executes full vehicle Over-the-Air (OTA) software updates while logging vital vehicle data in real-time from every sensor and microchip. The solution supports any vehicle platform, architecture and hardware, starting from two to four wheelers, commercial trucks/busses to electric, and internal combustion to hybrid vehicles. 

Explaining the process, Hemant stated: Sibros collects data at the edge that is processed with machine learning and artificial intelligence to help automakers build new connectivity use cases, and business models, like variable insurance, driver personalisation, fixing software recalls overnight, crash detection, etc.

With its modular system that provides full vehicle OTA updates to every sensor or engine control unit (ECU) while logging real-time vehicle data, Sibros’ deep connectivity platform really stands out in the market. “As modern vehicles can produce petabytes of data monthly, Sibros selectively capture the most meaningful data based on customisable events and triggers. This data is then further compressed and uploaded to the cloud backend,” explained Hemant. “This method enables efficient cloud computing throughput and enables OEMs to leverage live vehicle data for fleet/service management, developing new connected apps, building third party marketplaces and much more.” 

The company uses machine learning, data pipelines and IoT to abstract, process and interpret vehicle data, such as fault codes, into human-readable formats on visual dashboards. “Our efforts on the AI and analytics front are all geared towards improving capabilities around safety measurement, failure detection and preventative maintenance,” said Mayank. “We offer insights into driving behaviour of each user on the platform by combining vehicle data such as speed or harsh acceleration and braking with external data sources like traffic, data and time.”

Sibros OTA Deep Updater can also automatically detect vehicle movement risks by flagging abnormalities in a vehicle’s manoeuvring patterns. Component usage like blending weather, altitude and region information, battery health, and performance also play a crucial role in providing insights to automakers for determining the optimal configuration of a battery in different markets.

Sibros’ core technology stack leverages Golang as the primary backend language to handle high currency workloads, with an outstanding balance between performance and developer velocity. However, for the frontend, the company leveraged React.js and React Native to deliver the product to multiple platforms without having to rewrite the stack from scratch. “We also heavily leverage cloud-native solutions including SQL/No-SQL and Kubernetes for container orchestration to avoid reinventing the wheel, and build our infrastructure as code using Terraform,” said Mayank.

COVID Impact

“While initially, the COVID pandemic has ground the automotive industry to a halt, slowing the progress with plants shutting down, the things have now started to pick up slowly, and we see a tremendous uptick in business traction,” said Hemant. 

As remote working and contactless interactions are becoming the new norm, Sibros was able to deliver an embedded software and cloud/analytics solution remotely. The solution is also enabling field technicians not to be physically present while performing R&D and testing the beta vehicles. “As our solution is designed to enable fully Over-the-Air remote services, this ultimately benefits end customers to limit their exposure for visiting a dealer for servicing or utilising new connected services, such as in-vehicle payments for goods/food/tolling/parking,” added Hemant.

Despite COVID, Sibros has been able to build a diverse and inclusive work culture. The employees can work directly with clients in their initial months to see their ideas become a reality in their product roadmap and offerings. “Our current and future job requisitions span the gamut — from embedded firmware engineers and full-stack engineers to UI/UX, sales and marketing. We look for people who are coachable and rising stars, instead of necessarily looking for specific skill sets,” said Hemant.

Wrapping up

Sibros currently has presence in multiple locations and is engaged with the largest Indian OEMs of two, three, and four wheeler vehicles. The founders believe that India is a high growth area for Sibros in terms of talent acquisition and building a customer base. The country holds great promise for the future of major automotive trends, particularly in areas of rapid charging infrastructure and EV production/adoption, they said.

The company has grown to over 40 employees and has raised $15 million in a Series A funding round led by Nexus Venture Partners. “Our customer base includes the vehicle manufacturers directly spanning some of the world’s largest funded EV startups to some of the biggest makers of two and four-wheeler vehicles,” added Hemant.

Speaking about the company’s five-year plans, Hemant said Sibros intends to enter other verticals where the technology can be applied with minimal changes. “We also plan to stand up a centre of excellence in Pune as well as expanding our presence in Germany, Japan and China. Our vision is that in five years, the connected vehicle ecosystem will become a mainstream reality, powered by Sibros. Much like smartphone ecosystems gave birth to disruptive services and apps such as Uber or Airbnb, so will the vehicle ecosystem. It’s moving rapidly, and we expect to gain much progress over the next five years to accomplish this vision,” he added.

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UK: Considering all the talks around the Big Data industry and the growing issues around this market, most of the companies experience inconvenience while integrating various analytical tools. Even now, the majority of big data analytics companies spend more than 80% of their workflow on finding, evaluating, purchasing and formatting the data before the analysis starts. Besides that, big data trading & analysis is a fast growing $200 billion (USD) market, whereby by 2030, more than 95% of business decisions are expected to be data-driven.

GeoDB is a decentralized peer-to-peer data sharing ecosystem that boasts over 12 million users from 150 countries. They are on the forefront of addressing some of the major issues in the big data industry. The company has successfully raised over $3 million (USD) from several closed private investment rounds as well as a crowd sale held on crowdcube, the leading crowdfunding platform in the UK.

GeoDB has established several strategic partnerships with third party applications whose users will share data while being rewarded via the GeoDB ecosystem. With over 12 million users the company is confident that they will become one of the largest company by amount of token holders already in 2020. This would make GeoDB one of the first crypto/blockchain projects to achieve mass user adoption, which has been a major challenge for most blockchain based projects.

Apart from merely boasting about having a large user base, GeoDB also launched their in-house GeoCash application about 5 months ago and during this time, over 1000 wallets were created and more than 15 million locations where uploaded to the platform. To achieve this without any marketing activity is nothing short of phenomenal. It’s a clear indication that the company is on the right path and that they have an active, passionate community.

This can be further validated by the fact that they have won several big startup competitions like the AI & Blockchain Summit in Barcelona in 2019 as well as a competition held by Alastria, the leading blockchain ecosystem in Spain.

According to the founders, the platform has already reached 70% development on test net and they expect to be fully complete by the end of 2019 with main net launch scheduled for the first half of 2020.

The post GeoDB democratizing Big Data with Blockchain-based data sharing ecosystem appeared first on Artificial Intelligence.

Analyttica Datalab is a technology-enabled analytical solutions company that drives business impact through a strong focus on its customers. The company offers an optimal man-machine blend of solutions that combine analytical expertise and technology and can repeatably be used by enterprises at significantly lower cost.

The company was founded by Rajeev Baphna, a veteran in the data and technology space, having worked with Citigroup for 16 years. Analyttica is basically around platforms, tools, technologies, and people. There are certain challenges in the tech ecosystem in relation to tools, technology and people, which led to Analyttica being founded. The first challenge is when you look at Big Data, AI and ML, there’s no holistic way to be able to define a business problem, solve it, extract the solution and run it at scale in a business. These components are addressed by 10-15 different tools and technologies. The second challenge is the ability for a business’s ROI to be visible right away. And the third challenge is talent, as the way people use these technologies and tools are still very process-oriented, and the ability to apply in a business context is still left to a human being. Analyttica was formed to be able to create solutions to these problems. They have also patented their approach in the United States.

The organisation has two primary products they take pride in. These products are built around the ‘learn, apply, and solve’ concepts. Analyttica TreasureHunt® (ATH) SimuLab is used by enterprises to create a long-term and sustainable data culture. Analyttica TreasureHunt® (ATH) Precision creates value in terms of solving the business problems highly rapidly and with 100 percent accuracy.

The ATH SimuLab product helps organisations scale and become AI-ready faster. It’s not restrictive in nature. The learners or the talent focuses on applying themselves contextually within an environment that simulates real life scenarios and do not need to be distracted by the coding aspects while doing so.

With ATH Precision, they are trying to develop a holistic analytical ecosystem for organisations, where they can connect to their data and different applications and contextualise their business problems, instead of just randomly running some AI or ML algorithms. As a team, they can collaborate and are able to solve the problems. They can institutionalise their learning and the experiences they gain along the way.

The organisation believes that you need to have a strong blend of subject matter expertise, business analytics exposure and technology, as well as a culture that fosters collaboration.

Their workplace has an agile design, so that everybody is accessible to each other all the time and they frequently huddle together to solve problems.

Developing the product was not a cakewalk for them initially on a technology perspective, but things slowly improved. They base ATH as a set of microservices and they have Docker-based deployment. They’ve also leveraged a lot of mature open source libraries and tools. The incremental and iterative approach helped the team to accelerate their learning faster.

The hierarchy at Analyttica is pretty flat and stands apart from other data and analytics companies. Most of their employees previously worked in bigger organisations with expansive processes, which also came with silos and boundaries. At Analyttica, those boundaries don’t exist, and individuals get exposure to diverse fields. They follow the vision to learn, apply and solve internally as well. Almost all meetings are an open door. Those who are hungry to learn and grow are given access to more challenges that they can help solve. Even amidst all the impactful work they do, Analyttica is able to maintain a fun-loving atmosphere, with games and potlucks, and the team is like a large family.

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The NDA government has come into its new term with a renewed gusto towards analytics in the public sector. Recognising the disruptive effect that the upcoming AI wave will have on citizen’s day-to-day activities, the government has put it on a spotlight.

One of the biggest needs for a healthy analytics ecosystem in any given environment is data. Identifying the data-hungry nature of the new data science and analytics startups in India, the government initiated the Open Government Data Platform at data.gov.in.

This move allows data scientists and machine learning engineers alike to harness one of the biggest collections of datasets available to the public.

Origin Of OGD Platform

The OGD platform is very old, but is recently coming to prominence owing to the rise of data science in India. First conceived of as a joint effort between India and the United States in 2010, the platform was launched in 2012, along with the adoption of the National Data Sharing and Accessibility Policy.

The combined adoption of both of these standards enabled India to build up the portfolio of the open government datasets. This was said to allow individuals to access data about the inner workings of the government, allowing for transparency and ease of access for citizens.

The website also aimed to reduce the amount of time and effort put in by companies to clean and harvest datasets available in the public sector. Moreover, it also reduces RTI time for the citizen, while increasing accessibility. The initiative was not heavily used in its first few years, both due to governments not updating information on the portal and updated information not being relevant to the citizens.

This has changed over the last two years, as the number of datasets uploaded on the site has increased from around 73,000 in 2017 to over 294,000 today.

The Scope Of NDSAP And OGD

In addition to the datasets, there are over 4,500 catalogs, 11,000 APIs and 1,600 visualizations. These datasets have been viewed over 22.4 million times, and have been downloaded over 7 million times.

The site has resources from 148 departments, uploaded by 221 chief data officers. This makes it one of the most comprehensive undertakings of the Indian government.

This is, mostly, owing to the enforcement of the National Data Sharing and Accessibility PLatform. According to the site,

The initiative focuses on making the data available in machine-readable format, allowing data scientists and analysts to make the most of it. Under the NDSAP, the government has sanctioned the collection of data that is created, generated, collected and archived by the use of public funds.

This means that almost all of the data collected by the data will be subject to sharing under the NSDAP. The sentiment here is also that citizens can see exactly how their money is being spent, allowing for transparency in a market filled with illicit transactions.

To prevent sensitive information from falling into the hands of the public, the datasets are segmented into negative lists and open lists. Negative lists contain personal information such as names, addresses and more.

In keeping with the times, the OGD has transitioned from using PDFs to represent datasets to using more modern formats such as CSV, XLS, ODS, XML, RDF, KML, and RSS/ATOM feeds for fast changing data.

The NDSAP had also created a position titled Chief Data Officer, who is an individual appointed to manage the data of a particular department or organization.

The Effects Of The OGD

The site is comprised of four modules made to capitalize on the effects that the dataset will have on the population. They are the data management system for managing the datasets, content management system for managing the content types of the platform, a feedback module known as the visitor relationship management, and a set of community forums.

The dataset now has data ranging across verticals, including healthcare, education, demographics, economy, industries, transport and labour. Not only datasets, visualizations, blogs and forums populate the site. These are heavily frequented by data scientists discussing the datasets on the site.

The site is a godsend for data scientists today, and offers datasets for the exploration of the Indian government through data science and analytics. The site also offers a method for companies operating in the public sector to practice on datasets collected from a large number of individuals.

The post India’ Open Government Data Platform Is Helping Data Scientists Kick-start Their ML Journey appeared first on Artificial Intelligence.

To help geoscientists take advantage of machine learning and deep learning technology, CGG GeoSoftware has developed a machine learning ecosystem. It provides open access to data within its geophysical and petrophysical applications. Python-scripted machine learning lets users get their hands dirty if they like to tinker under the hood, or users can select pre-built recipes. Many tasks can now be completed more quickly and with more detailed results; for example, well log editing, petrophysical analysis, facies classification, and reservoir property prediction. Meanwhile, deep neural networks provide benefits for tasks as varied as reservoir quality assessment and near-surface characterisation.

Even if geoscientists are not using machine learning personally, it is increasingly involved across various aspects of geoscience projects and workflows around them.

Before the industry gets to the point where it can truly benefit from big data analytics and take full advantage of machine learning, there is a need to reach a minimum common denominator in terms of the data itself. Recent efforts have seen the liberation of huge volumes of data from legacy formats, migrating to new data management platforms, including an increasing mix of cloud storage. CGG Smart Data Solutions help to ease this digital transition with end-to-end services, from expert upcycling of legacy data into the cloud to the deployment of their modern and flexible GeoTrove data management platform.

Integration and interoperability of geoscience data becomes important to really take advantage of data analytics and machine learning applications. CGG has spent the last few years gaining valuable experience while taking its geological library into the digital realm, using a proprietary taxonomy and ontology to create a unique framework for its GeoVerse data set. Meanwhile, its multi-client seismic library is now assessable through its new GeoStore portal, with controlled access to historical client entitlement data. Upload to the cloud is underway for the entire multi-client seismic library.

The cloud offers more than just data storage – cloud computing provides scalable and flexible solutions to compute-intensive reservoir characterisation workflows and very large projects. Through its technical collaboration with Microsoft, CGG’s latest GeoSoftware releases run seamlessly in the Microsoft Azure Cloud Environment, with other major cloud platforms soon to follow.

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