Mediaboss Marketing

Sep 12: Digitization has changed many sectors across the globe and that also include the logistic sector. With digitization, machine learning and artificial intelligence have become the norm. Logistic sectors have been implementing machine learning and artificial intelligence to innovate the sector and improve it further. The usage of artificial intelligence and machine learning has improved the productivity of the logistic sector. According to a report by Katrine Spina and Anastasiya Zharovskikh, the productivity of the logistic sector will increase by 40% by 2035 with the help of artificial intelligence and machine learning.

With the help of big data, logistic companies have been helpful in making clear predictions that were useful to improve their performance. Visibility and prediction have become possible due to the implementation of artificial intelligence and machine learning in the logistic sector. Here is how machine learning and artificial intelligence has been helpful in the logistic sector.

1. Robotics can be used to help the workforce

Including robotics in the logistic sector has been helpful in logistic companies likeDelhivery primarily with autonomous navigation. It has also further reduced the burden from the workforce and has been helpful in providing cost-effective solutions. Automated robots in the logistic sectors have been helpful in material selection and handling, long-haul distribution along last-mile delivery.

2. Warehouse management and optimization of supply chain planning

Warehouse management in the logistic sector can only be optimized when it is accurately predicted when things need to be moved and what equipment is needed to handle it. This can improve the overall productivity of the warehouse. Accuracy of such predictions is possible with the help of big data. Also, with the help of contextual intelligence, effective planning can be made in logistic companies like Ekart. AI-based solutions are helpful in forecasting demand and machine learning can also be applied in order to improve the efficiency of the supply chain too.

3. Autonomous vehicles

Autonomous vehicles have become popular all across the world and it would not have been possible if artificial intelligence did not exist. Artificial intelligence allows autonomous vehicles to perceive and then further, predict the changes in the environment with the help of sensing technologies. With autonomous vehicles, last-mile delivery can be fastened. Many logistic companies have been experimenting with autonomous vehicles as a part of their development strategy and Google and Tesla have been working hard towards this sector.

4. Improved customer experience

Gone are the days when the general queries of the customers used to be handled by real people. Thankfully, customer experiences are now handled with the help of chatbots and this has made things so much easier in ensuring a satisfactory customer experience. Many companies have accepted that the customer experience played a vital role in the growth of the company. The use of artificial intelligence in customer experience has been helpful in improving customer loyalty and retention with personalization.

5. Efficient planning and resource management

For the growth of any business and not just the logistic sector, efficient planning and resource management are important. Artificial intelligence plays a key role in efficient planning and resource management by helping companies to reduce the cost and optimize the movement of commodities, which also improves the supply chain of the logistic sector in real-time.

6. Time Route Optimization

Artificial intelligence also makes it possible for real-time route optimization which increases the efficiency of the delivery and thereby, helps in reducing the waste of resources. Many logistics companies have already been using an autonomous delivery system which has made it possible to deliver items at a much quicker pace and that too without the requirement of human labor. Artificial intelligence has always been helpful in freight management by helping in efficient logistic management by lowering the shipping costs and improving the delivery process.

In addition to the factors mentioned above, machine learning and artificial intelligence also help in demand prediction, sales and marketing optimization, product inspection and back-office automation. Competitive advantage will be in the hands of logistic sectors that use artificial intelligence and machine learning for the growth of the company. The current demands of the customers include real-time visibility, super-fast deliveries and it is possible to meet such expectations of the customers only by accepting technology in the logistics sector.

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How Machine Learning And AI Is Transforming The Logistic Sector? - Daijiworld.com

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