Lakeside Software | August 02, 2022
Lakeside Software, an enterprise-class digital experience management software provider, has been named a Leader in The Forrester Wave™: End-User Experience Management, Q3 2022 report. Among the vendors analyzed, Lakeside was cited for enabling deep historical analysis of telemetry data for RCA.
The Q3 2022 report evaluated the most significant end-user experience management (EUEM) providers and scored them using 30 criteria. Lakeside scored the highest possible marks in the strategy criteria of partner integration, market approach, and supporting products and services. Lakeside's telemetry monitoring was also scored highest, a benefit of SysTrack's Intelligent Edge which collects 10,000 metrics out of the box. The report notes that the exceptional UI of Lakeside's Prevent solution makes it easy for service desk administrators to quickly identify common high-severity events and remediate them at scale.
The evaluation findings were supported by strong references from the customer community. Customers cited Lakeside's "exceptional support, granular data analysis, and improved usability." The report also highlighted differentiated support services in the form of DEX packs, which help customers quickly implement new services such as proactive management.
"In an increasingly digital working environment for many of today's global enterprises, we are proud to serve our customers with IT solutions that will drive employee engagement and productivity, while reducing downtime caused by tech disruptions. "We believe earning a leader position in the end-user experience management category demonstrates our continued commitment to offer best in class solutions that meet the needs of today's modern enterprises."
David Keil, CEO, Lakeside Software
Lakeside has experienced tremendous market share and talent acquisition growth. The company recently announced the appointment of three new executives who will lead national and international markets.
About Lakeside Software
Lakeside Software is a leader in cloud-based digital experience management. Lakeside's Digital Experience Cloud, powered by SysTrack, gathers and analyzes data on everything that may impact end-user experience and business productivity and provides the unmatched visibility IT teams need to design and support rapidly changing digital workplaces. Customers use Lakeside's technology to perform end-user experience management, digital workplace planning, IT asset optimization, remote work management, and proactive service desk operations.
AI TECH,SOFTWARE,FUTURE TECH
Babel Street | September 02, 2022
Babel Street, the world's leading AI-enabled data-to-knowledge company, today announced the immediate availability of three new Insight APIs that allow customers and partners to incorporate data onto any chosen platform. While the Babel Street Platform has been implemented in a variety of environments, these APIs dramatically increase flexibility for customers and partners. With the new Insight APIs, Babel Street now offers customers more control and configuration capabilities than any other Open-Source Intelligence (OSINT) platform.
Highlights of the Babel Street Insight APIs include the global and multilingual capabilities, which make it possible to process any publicly available input in hundreds of languages, as well as access to the AI and ML-enabled Babel Data Library, the world's largest and most diverse collection of enriched data. The library empowers customers with data that has undergone a range of improvements like normalization, location extraction, sentiment analysis, violent intent detection, entity identification, and curation by topic or location. As a result, insights relevant to customer challenges can now go anywhere to rapidly inform multi-disciplinary teams and agents, including customer data lakes and internal applications.
Each day over 2.5 quintillion bytes of data are created online, adding to the massive trove of data already available. Intelligence professionals rely on Babel Street's technology to access valuable data, position it for fast, accurate analysis, and ultimately generate critical insights in the proper context. The Insight APIs put customers in control with configuration and channel functionality that accelerates time-to-insights.
"Our customers don't just need publicly available data. They need that data fast, accurate, and to arrive in context. Above all else, they need data that is accessible in the right place, consistent with intelligence operations, standardized for consistency across hundreds of sources, and available for analysis in real-time. "Our Insight APIs bring agility and flexibility to the intelligence process, even as the firehose of data grows by the day."
Pat Butler, SVP Strategy at Babel Street
The OSINT market is expected to grow to over $20 billion by 2027, with national security teams and large enterprises leading adoption. Analysts have also predicted that by 2025 over 463 exabytes of data will be generated each day globally. Given the rapid growth and disparate nature of data, it's critical that providers make their technologies increasingly flexible and customizable. Babel Street's Insight APIs ensure a perfect match with national security and enterprise requirements while expanding the possibilities for end-users.
"This new offering represents the latest step in our push to make open-source intelligence solutions easier to use and consume for both public and private sector customers," said Michael Southworth, CEO of Babel Street. "These APIs create incredibly flexible opportunities to ingest data into an existing platform while increasing the depth and speed of relevant insights. We're proud to be the only OSINT provider who can reliably put intelligence anywhere and do it at scale."
The Babel Street Insight APIs include:
The Channels API provides access to Babel Street's curated collection of documents indexed by topic and location to provide real-time global situational awareness, focusing on breaking news, disease outbreaks, mass casualty attacks, national disasters, terror events, transportation, and other significant events.
The Document API leverages Babel Street's extensive enriched data library, which processes hundreds of millions of documents per day across tens of millions of publicly available sites to make full-form feeds available for indexing and search in near real-time.
The Identity API provides access to person-based public records data and related documents. Searches are performed using identifiers, such as name, address, phone, email, social handle, and more.
About Babel Street
Babel Street is the world's leading AI-enabled data-to-knowledge company. The company's technology allows customers to rapidly discover and decipher the insights they need to empower their missions, regardless of origin, language, or platform. Babel Street's patented analytics software transforms the most relevant insights for our customers through AI-enabled, cross-lingual, conceptual, and persistent search of information from around the world. State-of-the-art linguistics technology deciphers actionable insights from public or private data sources unbound by origin or language. With Babel Street, governments and organizations empower their teams with critical and timely insights on a single pane of glass for immediate analysis, action, and mission success. Babel Street software serves as a force multiplier for customers to uncover threats and opportunities – known and unknown, foreign or domestic, physical or cyber – and make the world a safer, more prosperous place. Babel Street is privately held and is headquartered in the Washington, D.C. area, with offices in London, Canberra, and Ottawa.
AI TECH,GENERAL AI
Hyperspec AI | September 15, 2022
Hyperspec AI, an artificial intelligence startup, released a new tool for developers working on ADAS enabled and autonomous vehicles (AV). The company has developed a unified platform called RoadMentor that allows users to create, train, and deploy machine-learning (ML) models for real-time mapping. Hyperspec integrates the map into the ML training loop so that real-time mapping models can be developed, giving ADAS enabled and autonomous vehicles the ability to perform outside of the HD map geofence. This expands navigable roads from less than 5% today to over 95%, for any vehicle, so the autonomous systems can learn from the ubiquitous exposure.
Today, the ML development process is fragmented with no integrated process for data collection, data management, model training, verification & validation, deployment, and fleet learning. Each step is another data transfer, leading to inefficiency and a lack of true visibility. RoadMentor enables the industry to scale through deep learning by consolidating the loop training process into one optimized infrastructure designed specifically for autonomous driving.
"We wanted to create a product that focuses on our customers' pain points. RoadMentor streamlines data flow, drastically reducing processing time, standardizes data throughout the cycle, and moves data access and control in-house rather than with a third-party.
Sravan Puttagunta, CEO and co-founder of Hyperspec
Autonomous driving data is largely skewed towards highway and arterial road domains. The release of RoadMentor increases test coverage from less than 5% to over 95% across all roads, enabling edge case library build out across the long tail of scenarios. Now ADAS functionality and autonomous driving usage and coverage can further develop allowing us to reach levels 3, 4, and 5 autonomy.
"We're very excited about the progress we have made in recent months," says Puttagunta, "right now it's all about collecting usable test miles and collating good data to improve the system. Solving the long-tail problem is something we have been thinking about for a long time."
RoadMentor is offered as a freemium SaaS product, so users can process a certain amount of data at no cost. We invite developers to sign up for exclusive beta access to RoadMentor through our developer program, limited seats are available. Attendees of the International Auto Show Tech Days will be able to learn first-hand how RoadMentor improves the release cadence of autonomous driving technology development.
Deci | September 26, 2022
Deci, the deep learning company harnessing AI to build AI, today announced a new set of industry-leading semantic segmentation models, dubbed DeciSeg. Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated semantic segmentation models that significantly outperform the most powerful models publicly available, such as the MobileViT released by Apple, and the DeepLab family released by Google. Deci’s models deliver more than 2x lower latency, as well as 3-7% higher accuracy.
Semantic segmentation is one of the most widely used computer vision tasks across many business verticals, including automotive, smart cities, healthcare, and consumer applications, and is often required for many edge AI applications. However, significant barriers exist to running semantic segmentation models directly on edge devices, such as high latency and the inability to deploy those models due to their size.
With DeciSeg models, semantic segmentation tasks that previously could not be carried out at the edge because they were too resource intensive are now possible. This allows companies to develop new use cases and applications on edge devices, reduce inference costs (since AI practitioners will no longer need to run these tasks in expensive cloud environments), open new markets, and shorten development times.
“DeciSegs are an example of the power of Deci’s AutoNAC engine capabilities to generate custom hardware-aware deep learning models with unparalleled performance on any hardware. AI teams can easily use DeciSegs models or leverage Deci’s AutoNAC engine to build and deploy custom models that run real-time computer vision tasks on their edge devices.” said Yonatan Geifman, PhD, co-founder and CEO of Deci.
Deci’s platform has a proven-track record in enabling AI at the edge and empowering AI teams to build and deploy production grade deep learning models. Earlier this year, Deci announced the discovery of DeciNets for CPUs, which reduced the gap between a model’s inference performance on a GPU versus a CPU by half, without sacrificing the model’s accuracy, enabling AI to run on lower cost, resource constrained hardware.
“In the world of automated deep neural network design and construction, Deci’s AutoNAC technology is a game changer. It uses deep learning to search vast spaces of neural networks for the model most appropriate for a particular task and particular AI chip. In this case, AutoNAC was applied to the Pascal VOC Semantic Segmentation task on NVIDIA’s Jetson Xavier NX™ chip and we are very pleased with the results.” said Ran El-Yaniv, co-founder and Chief Scientist of Deci and Professor of Computer Science at the Technion – Israel Institute of Technology.
Deci’s platform is serving customers across industries in various production environments including edge, mobile, data centers and cloud. To learn more about how leading AI teams leverage Deci’s platform to build production grade models and accelerate inference performance, visit here.
Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company's deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products. The platform is powered by Deci's proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models' architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded by Yonatan Geifman, Jonathan Elial, and Professor Ran El-Yaniv, Deci's team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.