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Workday, which specializes in cloud-based accounting and human resources software, announced new machine learning and automation features in several of its products.

Workday Adaptive Planning will now sport a Machine Learning Forecaster that allows automated generation of forecasts that can incorporate historical or third-party data like weather reports and labor statistics. Workday said the software enhancements have led to a more than 60% speed improvement for data import and export.

Workday Strategic Sourcing, meanwhile, now has a contract automation feature that extracts key metadata and clauses from third-party paper and legacy contracts to aid in identifying and searching for key contract terms, as well as uncovering risks and managing contract obligations.

Workday Expenses will have a new Expense Protect feature that will automatically detect potential duplicate expenses, which will reduce the need for manual review.

The company also announced new solutions for environmental, social and governance-related reporting. Workday Strategic Sourcing now has supplier diversity discovery boards that can provide data about supplier diversity ratios. Further, a new solution called Workday Supplier Sustainability gives users information about their suppliers' science-based targets, actual and derived CO2 emissions, and their ESG ratings from third-party analysts.

The company also announced an Industry Accelerators program to help organizations transition operations to Workday. The Industry Accelerators combine industry practices, solutions and connectors for banking, health care, insurance and technology companies. They will also help automate and streamline operations for customers.

"While it's a complex environment for finance professionals, it's also an opportunity for them to partner more closely with the business to mitigate risk and surface valuable insights for their organization," said Terrance Wampler, group general manager of the office of the CFO at Workday, in a statement. "At Workday, our innovations are aimed at helping advance the finance function by streamlining business processes in the cloud and accelerating data analysis so teams can respond faster and take action."

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Workday announces new machine learning and automation capacities on product line - Accounting Today

The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MITs plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues, says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.

These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT, says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. The college plays a pivotal role in MITs overarching effort to hire climate-focused faculty introducing the critical role of computing to address the health of the planet through innovative research and curriculum.

The four new faculty members are:

SaraBeerywill join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023.Beeryreceived her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide andworks towardincreasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

PriyaDontiwill join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24.Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Ins Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is alsoco-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

SherrieWangwill join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wangis currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell.

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Computing for the health of the planet - MIT News

PITTSBURGH, Sept. 13, 2022 /PRNewswire/ -- MSP360, a provider of simple and reliable backup and IT management solutions for managed services providers (MSPs) and IT departments worldwide, is now fully integrated with Deep Instinct, a prevention-first approach to stopping ransomware and other malware using the world's first deep learning cybersecurity framework. With a click of a button, MSP360 customers can access the Deep Instinct platform through either MSP360 Managed Backupor MSP360 RMM.

"Our goal has always been to provide best-in-class solutions for our customers," said MSP360 CEO Brian Helwig. "We've continued to do that successfully by listening to them and adjusting our efforts accordingly. Rightfully so, our customers have continued to express their concerns of being able to fully protect their customers from the ever-growing cyber threat landscape. Our partnership with Deep Instinct addresses many of their fears by not only preventing but also predicting many of the threats they're facing today."

While MSP360 already provides several types of solutions to assist MSPs with combating cybercriminals, including backup, remote monitoring and management (RMM), and remote connect, a layered approach to cybersecurity is needed to fully protect MSPs and their customers from today's evolving cybersecurity threats, many of which include ransomware as a service (RaaS), compromised or weak credentials, brute force, phishing, distributed denial of service (DDoS), malicious insiders, misconfiguration, and more.

MSP360's integration with Deep Instinct enables MSP360 customers to prevent unknown attacks with greater accuracy than many endpoint detection and response (EDR), extended detection and response (XDR), and antivirus (AV) solutions in the market today by using deep learning, the most advanced form of artificial intelligence (AI). With deep learning, the computer learns just like the human brain does. By ingesting data and working autonomously, Deep Instinct's deep learning framework teaches itself to predict, detect, and prevent threats, unlike many basic machine learning (ML)-based tools.

"We are thrilled to partner with the world's leading backup and RMM solution," said Joe Santamorena, AVP of Global MSSP Programs for Deep Instinct. "MSPs are the number one targeted vertical industry for ransomware and combining Deep Instinct with MSP360's robust backup architecture will deliver the highest efficacy for preventing a ransomware attack."

About MSP360

Established in 2011 by a group of IT professionals, MSP360 provides simple and reliable cutting-edge backup and IT management solutions for MSPs and IT departments worldwide. The MSP360 platform combines the number one easy-to-use backup solution to deliver best-in-class data protection, secure remote access software to provide support to customers or team members, and painless RMM to handle all aspects of IT infrastructure.

About Deep Instinct

Deep Instinct takes a prevention-first approach to stopping ransomware and other malware using the world's first and only purpose-built, deep learning cybersecurity framework. We predict and prevent known, unknown, and zero-day threats in <20 milliseconds, 750X faster than the fastest ransomware can encrypt. Deep Instinct has >99% zero-day accuracy and promises a <0.1% false positive rate. The Deep Instinct Prevention Platform is an essential addition to every security stackproviding complete, multi-layered protection against threats across hybrid environments. For more, visit http://www.deepinstinct.com.

Media Contact:Christopher Joseph (CJ) ArlottaCJ Media Solutions, LLC for MSP360C: 631-572-3019[emailprotected]

SOURCE MSP360

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MSP360 Partners with Deep Instinct to Fully Integrate the World's First Deep Learning Cybersecurity Framework Solution - PR Newswire

LOS ALTOS, Calif.--(BUSINESS WIRE)--Kinara, the developers of AI processors for edge computing applications, today announced its collaboration with NXP Semiconductors, the world leader in secure connectivity solutions for embedded applications. Through this collaboration, customers of NXP Semiconductors AI-enabled product portfolio will have the option to further scale their AI acceleration needs by utilizing the Kinara Ara-1 Edge AI processor for high performance inferencing with deep learning models. Working together, the two companies have tightly integrated the computer vision capabilities of the NXP i.MX applications processors with the performance- and power-optimized inferencing of the Kinara Ara-1 AI processor to deliver computer vision analytics for a range of applications that include smart retail, smart city, and industrial.

Kinaras patented Edge AI processor, named Ara-1, delivers a ground-breaking combination of performance, power, and price for integrated cameras and edge servers. Kinara AI complements its processing technology with a comprehensive and robust set of development tools that allow its customers to easily convert their neural network models into highly optimized computation flows ready to be deployed on the Ara-1 chip.

"Intelligent vision processing is an exploding market that is a natural fit for machine learning. But vision systems are getting increasingly complex, with more and larger sensors, and model sizes are growing. To keep pace with these trends requires dedicated AI accelerators that can handle the processing load efficiently both in power and silicon area, said Kevin Krewell, principal analyst at TIRIAS Research. The best modular approach to vision systems is a combination of an established embedded processor and a power-efficient AI accelerator, like the combination of NXPs i.MX family of embedded applications processors and the Kinara AI accelerator."

NXPs AI processing solutions encompass its microcontrollers (MCUs), i.MX RT series of crossover MCUs and i.MX applications processor families, which represent a variety of multicore solutions for multimedia and display applications. NXPs portfolio covers a very large portion of AI processing needs natively, and for any use case that requires even higher performance AI due to increases in frame rates, image resolution, and number of sensors, the demand can be accommodated by integrating NXP processors with Kinaras Ara-1 to deliver a scalable, system-level solution where customers can scale up and partition the AI workload between the NXP device and the Ara-1, while maintaining a common application software running on the NXP processors.

Our processing solutions and AI software stacks enable a very wide range of AI performance requirements this is a necessity given our extremely broad customer base, said Joe Yu, Vice President and General Manager, IoT Edge Processing, NXP Semiconductors. By working with Kinara to help satisfy our customers requirements at the highest end of edge AI processing, we will bring high performance AI to smart retail, smart city, and industrial markets.

We see two general trends with our Edge AI customers. One trend is a shift towards a Kinara solution that significantly reduces the cost and energy of their current platforms that use a traditional GPU for AI acceleration. The other trend calls for replacing Edge AI accelerators from well-known brands with Kinaras Ara-1 allowing the customer to achieve at least a 4x performance improvement at the same or better price, said Ravi Annavajjhala, CEO, Kinara. Our collaboration with NXP will allow us to offer very compelling system-level solutions that include commercial-grade Linux and driver support that complements the end-to-end inference pipeline.

Access a new White Paper outlining how the Kinara and NXP collaboration can help boost the AI performance of embedded platforms here.

About Kinara

Kinara is deeply committed to designing and building the worlds most power- and price-efficient edge AI inference platform supported by comprehensive AI software development tools. Designed to enable smart applications across retail, medical, industry 4.0, automotive, smart cities, and much more; Kinaras AI processors, modules and software can be found at the heart of the AI industrys most exciting and influential innovations. Led by Silicon Valley veterans and a world class development team in India, Kinara envisions a world of exceptional customer experiences, better manufacturing efficiency and greater safety for all. Kinara is a member of the NXP Partner Program. Learn more at http://www.kinara.ai

All registered trademarks and other trademarks belong to their respective owners.

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Kinara and NXP Collaborate to Provide Customers with Scalable AI Solutions Optimized for Deep Learning at the Edge - Business Wire

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Todays demand for real-time data analytics at the edge marks the dawn of a new era in machine learning (ML): edge intelligence. That need for time-sensitive data is, in turn, fueling a massive AI chip market, as companies look to provide ML models at the edge that have less latency and more power efficiency.

Conventional edge ML platforms consume a lot of power, limiting the operational efficiency of smart devices, which live on the edge. Thosedevices are also hardware-centric, limiting their computational capability and making them incapable of handling varying AI workloads. They leverage power-inefficient GPU- or CPU-based architectures and are also not optimized for embedded edge applications that have latency requirements.

Even though industry behemoths like Nvidia and Qualcomm offer a wide range of solutions, they mostly use a combination of GPU- or data center-based architectures and scale them to the embedded edge as opposed to creating a purpose-built solution from scratch. Also, most of these solutions are set up for larger customers, making them extremely expensive for smaller companies.

In essence, the $1 trillion global embedded-edge market is reliant on legacy technology that limits the pace of innovation.

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ML company Sima AI seeks to address these shortcomings with its machine learning-system-on-chip (MLSoC) platform that enables ML deployment and scaling at the edge. The California-based company, founded in 2018, announced today that it has begun shipping the MLSoC platform for customers, with an initial focus of helping solve computer vision challenges in smart vision, robotics, Industry 4.0, drones, autonomous vehicles, healthcare and the government sector.

The platform uses a software-hardware codesign approach that emphasizes software capabilities to create edge-ML solutions that consume minimal power and can handle varying ML workloads.

Built on 16nm technology, the MLSoCs processing system consists of computer vision processors for image pre- and post-processing, coupled with dedicated ML acceleration and high-performance application processors. Surrounding the real-time intelligent video processing are memory interfaces, communication interfaces, and system management all connected via a network-on-chip (NoC). The MLSoC features low operating power and high ML processing capacity, making it ideal as a standalone edge-based system controller, or to add an ML-offload accelerator for processors, ASICs and other devices.

The software-first approach includes carefully-defined intermediate representations (including the TVM Relay IR), along with novel compiler-optimization techniques. This software architecture enables Sima AI to support a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX, etc.) and compile over 120+ networks.

Many ML startups are focused on building only pure ML accelerators and not an SoC that has a computer-vision processor, applications processors, CODECs, and external memory interfaces that enable the MLSoC to be used as a stand-alone solution not needing to connect to a host processor. Other solutions usually lack network flexibility, performance per watt, and push-button efficiency all of which are required to make ML effortless for the embedded edge.

Sima AIs MLSoC platform differs from other existing solutions as it solves all these areas at the same time with its software-first approach.

The MLSoC platform is flexible enough to address any computer vision application, using any framework, model, network, and sensor with any resolution. Our ML compiler leverages the open-source Tensor Virtual Machine (TVM) framework as the front-end, and thus supports the industrys widest range of ML models and ML frameworks for computer vision, Krishna Rangasayee, CEO and founder of Sima AI, told VentureBeat in an email interview.

From a performance point of view, Sima AIs MLSoC platform claims to deliver 10x better performance in key figures of merit such as FPS/W and latency than alternatives.

The companys hardware architecture optimizes data movement and maximizes hardware performance by precisely scheduling all computation and data movement ahead of time, including internal and external memory to minimize wait times.

Sima AI offers APIs to generate highly optimized MLSoC code blocks that are automatically scheduled on the heterogeneous compute subsystems. The company has created a suite of specialized and generalized optimization and scheduling algorithms for the back-end compiler that automatically convert the ML network into highly optimized assembly codes that run on the machine learning-accelerator (MLA) block.

For Rangasayee, the next phase of Sima AIs growth is focused on revenue and scaling their engineering and business teams globally. As things stand, Sima AI has raised $150 million in funding from top-tier VCs such as Fidelity and Dell Technologies Capital. With the goal of transforming the embedded-edge market, the company has also announced partnerships with key industry players like TSMC, Synopsys, Arm, Allegro, GUC and Arteris.

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Machine learning at the edge: The AI chip company challenging Nvidia and Qualcomm - VentureBeat