Beep Inc., provider of autonomous shared mobility solutions, has officially announced the launch of Beep AutonomOS, which is a platform designed to let public transit operators and mobility-as-a-service companies integrate autonomous mobility services rapidly and seamlessly into their solutions. You see, software-driven mobility gets dubbed as the next big thing in the logistics industry because of the way it leverages autonomous mobility networks to combine real-time service optimization with greater efficiency and performance. But what makes AutonomOS an ideal candidate to fulfill this growing market? Well, the answer resides in its ability to provide safe, scalable, cost-effective multi-passenger autonomous mobility services. Making the whole solution more important is a fact that it provides comprehensive services capability for the deployment and management of autonomous passenger services either as a standalone solution or with integration into multimodal operations. Talk about the whole value proposition on a more granular level, the solution in question comes decked up with a unified view of service performance, fleet health. and on-road operations. Then, we have dedicated government tools to ensure mission compliance and passenger safety. Once the safety and legality concerns are duly addressed, AutonomOS brings to the fore service optimization features that integrate service performance, smart city infrastructure, and ridership data to drive greater service efficiency, optimize passenger experience, and maximize ridership across the system. Moving on, the product also has on offer service definition and planning functions to support a variety of service modes from fixed route to demand-responsive variants, while simultaneously boasting a machine learning-powered in-cabin monitoring functionality. The latter element is there to facilitate an immediate response in the event of a passenger safety or roadway issue. Another thing which enhances the prospects of this solution talks to its support for leading ADS providers, a sense of support that is further backed up by data protocol and a toolkit to enable rapid integration with further platforms. In case the whole offering still doesn’t sound attractive enough to you, then it must be mentioned how AutonomOS is even made to be outright compatible in the context of wider data standards including GTFS (General Transit Feed Specification), and GTFS-RT (General Transit Feed Specification Realtime).

“Autonomous vehicles are capable of safely navigating our streets from waypoint to waypoint, but lack the concepts of mission, service and passenger,” said Joe Moye, CEO of Beep. “Beep AutonomOS fills a void in the autonomy landscape by introducing management and orchestration logic enabling the integration of autonomous vehicles into public mobility networks. More importantly, AutonomOS adds an additional layer of functionality to address passenger safety and comfort concerns in advance of fully unattended autonomous deployments.”

Founded in 2019, Beep’s rise to prominence stems from an ability to plan, deploy, and manage autonomous shuttles in dynamic mobility networks. By doing so, the company connects people and places with solutions that reduce congestion, eliminate carbon emissions, improve road safety and enable mobility for all.

The post A Shot for Autonomous Vehicles to Become an Integral Piece of the Public Mobility Network appeared first on Enterprise Viewpoint.

The researching teams at Massachusetts Institute of Technology and MIT-IBM Watson Lab have successfully developed a technique which can empower deep-learning models to adapt to new sensor data, and more importantly, do so directly on an edge device. Before we unpack the whole development, we must try and gain an idea about the problem statement here. Basically, deep-learning models that enable artificial intelligence chatbots, in order to deliver the customization expected from them, need constant fine-tuning with fresh data. Now, given how smartphones and other edge devices lack the memory and computational power required for such a fine-tuning process, the current framework tries to navigate through that by uploading user data on cloud servers where the model is updated. So, what’s the problem here? Well, the problem talks to the data transmission process exhausting huge amounts of energy. Not just energy, there is also security risks involved, as you are sending  sensitive user data to a cloud server which always carry a risk of getting compromised. Having covered the problem, we should now get into how exactly the new technique takes on it. Named PockEngine, the new solution comes decked up with the means to determine what parts of a huge machine-learning model need alterations to improve accuracy. Complimenting the same is a fact that it only stores and computes with those specific pieces, thus leaving the rest undisturbed and safe. This marks a major shift, because up until now, whenever we would run an AI model, it instigated inference, a process where data input is passed from layer to layer till the time a prediction is generated. Hold on, the main issue presents itself after the said process is done. You see, during training and fine-tuning, the model undergoes a phase known as backpropagation. Backpropagation, in case weren’t aware, involves comparing the output to the correct answer. Next up, it runs the model in reverse, and each layer is updated as the model’s output gets closer to the correct answer. With each layer required to be duly updated, the entire model and intermediate results have to be unquestionably stored, making the fine-tuning mechanism pretty high maintenance. There is, fortunately enough, a loophole which suggests that not all layers in the neural network are important for improving accuracy, and even for layers that are important, the entire layer may not need to be updated. Hence, the surplus components don’t need to be stored. Furthermore, you also don’t have to revisit the very first layer to improve accuracy because the process can be stopped somewhere in the middle. Understanding these loopholes, PockEngine first fine-tunes each layer, one at a time, on a certain task, and then measures the accuracy improvement after each individual layer. Such a methodology can go a long way when it comes to identifying the contribution of each layer, as well as trade-offs between accuracy and fine-tuning cost, while automatically determining the percentage of each layer that needs to be fine-tuned.

“On-device fine-tuning can enable better privacy, lower costs, customization ability, and also lifelong learning, but it is not easy. Everything has to happen with a limited number of resources. We want to be able to run not only inference but also training on an edge device. With PockEngine, now we can,” said Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, a distinguished scientist at NVIDIA, and senior author of an open-access paper describing PockEngine.

Another manner in which the solution sets itself apart is concerned with the timing aspect. To put it simply, the traditional backpropagation graph is generated during runtime, meaning it demands a massive load of computation. On the other hand, PockEngine does the same during compile time, just as the model is being prepared for deployment. It essentially deletes bits of code to remove unnecessary layers or pieces of layers and create a pared-down graph of the model to be used during runtime. Then, the solution performs other optimizations on this graph to further improve efficiency. Turning this feature all the more important is a fact that the entire process needs to be conducted only once.

The researchers have already performed some initial tests on their latest brainchild. The stated tests saw them applying PockEngine to deep-learning models on different edge devices, including Apple M1 Chips, and the digital signal processors common in many smartphones and Raspberry Pi computers. Going by the available details, the solution performed on-device training up to 15 times faster, and that it did without witnessing any drop in accuracy. Apart from that, it also made a big cut back on the amount of memory required for fine-tuning. Once this bit was done, they then moved on to applying the solution across large language model Llama-V2. Here, the observations revealed that PockEngine was able to reduce the each iteration’s timeframe from seven seconds to less than one second.

“This work addresses growing efficiency challenges posed by the adoption of large AI models such as LLMs across diverse applications in many different industries. It not only holds promise for edge applications that incorporate larger models, but also for lowering the cost of maintaining and updating large AI models in the cloud,” said Ehry MacRostie, a senior manager in Amazon’s Artificial General Intelligence division.

The post Blazing Past a Major AI Bottleneck appeared first on Enterprise Viewpoint.

The researching team at University of Maryland has successfully developed a cooling glass technology, which is designed to turn down the heat indoors without electricity. According to certain reports, the stated technology comes decked up with means to cut back on temperature by 3.5°C at noon, while simultaneously boasting an ability to reduce a mid-rise apartment building’s yearly carbon emissions by 10%. But how does the whole thing actually work? Well, the answer is neatly tucked in the glass’ coating that works in two ways. For starters, it reflects up to 99% of solar radiation to stop buildings from absorbing heat. Here, the glass emits heat in the form of longwave infrared radiation into the icy universe where the temperature is generally around -270°C, or just a few degrees above absolute zero. Another aspect of it talks to how the technology leverages “radiative cooling” to help the relevant space in acting as a heat sink for the buildings. By saying so, we mean that the mechanism uses new cooling glass design, alongside so-called atmospheric transparency window, a part of the electromagnetic spectrum that passes through the atmosphere without boosting its temperature, to effectively dump large amounts of heat into the infinite cold sky beyond. To give you some context, this is pretty much how the earth organically cools itself.

“It’s a game-changing technology that simplifies how we keep buildings cool and energy-efficient,” said Xinpeng Zhao, assistant research scientist and the first author of this study. “This could change the way we live and help us take better care of our home and our planet.”

Make no mistake; the University of Maryland’s latest brainchild isn’t the first ever cooling glass in history. However, in contrast to previous attempts at developing the technology, the latest iteration is understood to be much more environmentally stable. Such stability ensures it can stand firm against deterrents like water, ultraviolet radiation, dirt, and even flames. In case that wasn’t enough, you can also apply the cooling glass to a wide assortment of surfaces like tile, brick, and metal, thus offering a value proposition which is much more scalable and adoptable.

Talk about what orchestrated the breakthrough on a granular level, the researchers realized this feat through the integration of finely ground glass particles and their subsequent application as a binder. Now, it might not seem like that big of a detail, but this simple-looking decision ousted any role for more-pervasive but hardly-durable polymers. Furthermore, the team programmed the particle size to maximize emission of infrared heat and reflect sunlight all at the same time.

“The development of the cooling glass aligns with global efforts to cut energy consumption and fight climate change” said Liangbing Hu, a professor at the University of Maryland. He notably pointed to recent reports that this year’s Fourth of July fell on what may have been the hottest day globally in 125,000 years.

“This ‘cooling glass’ is more than a new material—it’s a key part of the solution to climate change,” said Hu. “By cutting down on air conditioning use, we’re taking big steps toward using less energy and reducing our carbon footprint. It shows how new technology can help us build a cooler, greener world.”

For the immediate future, the researching team plans on conducting further tests to better understand the technology. Next up, they are looking to introduce more practical applications of the brand-new cooling glass. Apart from that, the researchers also have one eye on commercializing the technology soon, an intention visible in the team’s decision to launch a startup company CeraCool, which will be responsible for scaling the concept.

The post Taking Inspiration from the Nature to Attack the Global Warming Trend appeared first on Enterprise Viewpoint.

The researching team at U.S. Department of Energy’s (DOE) Argonne National Laboratory has reportedly discovered an intriguing cooperative behavior that occurs among complex mixtures of components across electrolytes in batteries. To give you some idea, electrolytes are basically materials that move charge-carrying particles known as ions between a battery’s two electrodes, thus converting stored chemical energy into electricity. Anyway, they got to know how combining two different types of anions (negatively charged ions) with cations (positively charged ions) can significantly improve the overall battery’s performance. This has birthed a belief that careful selection of ion mixtures can enable battery developers to precisely tailor their devices to produce desired performance characteristics. To understand the importance carried by such a development, we must acknowledge that lithium-ion batteries used today actually have a limited ability to provide performance attributes needed in critical applications like passenger electric vehicles and storing renewable energy on the grid. Given these limitations, researchers across the globe have started to deem multivalent batteries as a potentially better alternative. Multivalent batteries, in essence, use cations such as zinc, magnesium, and calcium that have a charge of +2 as opposed to +1 for lithium ions. Boasting a greater charge stock, multivalent batteries are able to store and release more energy. Apart from it, the stated technology use abundant elements supplied through stable, domestic supply chains, something which looks a lot better when you consider lithium is less abundant and has an expensive, volatile international supply chain. Such a setup not only makes multivalent batteries a more viable alternative for electric vehicles, but they also have a use case around grid storage.

Having covered their advantages, we must also mention that most multivalent batteries taken under investigation by researchers have failed to perform well. This is because ions and electrodes tend to be unstable and degrade, turning it difficult for electrolytes to efficiently transport cations. The said problem eventually diminishes the battery’s ability to generate and store electricity. So, how does the new discovery helps the case of multivalent batteries? As zinc metal forms is among their main foundations, the researching team made an effort to characterize the interactions that occur— and the structures that form— when zinc cations are combined with two different types of anions in the electrolyte. This effort included designing a laboratory-scale battery system comprised of an electrolyte and zinc anode. The electrolyte initially contained zinc cations and an anion called TFSI. Here, they observed very weak attraction to the cations. Next up, they instilled chloride anions to the electrolyte. Going by the available details, chloride showed a much stronger attraction to zinc cations. However, that’s not where things ended. Researchers built upon their initial findings with three complementary techniques. In the first one, they used X-ray absorption spectroscopy, which was conducted at Argonne’s Advanced Photon Source, a DOE Office of Science user facility. This one involved probing the electrolyte using synchrotron X-ray beams for the purpose of measuring the absorption of the X-rays. Then, there was Raman spectroscopy technique. Conducted at Argonne’s Electrochemical Discovery Laboratory, the said technique sprung to action by illuminating the electrolyte with laser light before evaluating the scattered light. Lastly, they put into practice Density functional theory at Argonne’s Laboratory Computing Resource Center where the team stimulated and calculated the structures formed by the interactions among the ions in the electrolyte.

“These techniques characterize different aspects of the ion interactions and structures,” said Mali Balasubramanian, a physicist on the research team and one of the study’s authors. “X-ray absorption spectroscopy probes how atoms are arranged in materials at very small scales. Raman spectroscopy characterizes the vibrations of the ions, atoms and molecules. We can use the data on atom arrangements and vibrations to determine whether ions are separated or move together in pairs or clusters. Density functional theory can corroborate these characterizations through powerful computation.”

Owing to their extensive investigation, the researchers were able to figure out that the presence of chloride induced TFSI anions to pair with zinc cations. This marked a significant point, as the pairing of anions with a cation can affect the rate at which the cation can be deposited as metal on the anode during charging. Not just that, it can also have a similar impact when the cation is being stripped back into an electrolyte during discharge. For the sake of re-confirming their findings, the researchers repeated these experiments with two other ion mixtures. In the first ion mixture, they swapped chloride for bromide ions, whereas the other one saw them picking iodide ions over chloride. The results included bromide and iodide’s success in inducing TFSI anions to pair with zinc cations.

“What was particularly exciting about this result is that we didn’t expect to see what we saw. The idea that we can use one anion to draw a second anion closer to a cation was very surprising,” said Justin Connell, a materials scientist on the research team and one of the study’s authors.

Although the study seems pretty significant as a whole, one area where the team placed a special emphasis talked to the cooperation which occurred among different types of ions in an electrolyte. In simple terms, it meant the presence of the weakly attracting anions reduced the amount of energy needed to pull zinc metal out of solution. On the other hand, the presence of strongly attracting anions reduced the amount of energy needed to put the zinc back in solution. Such a coordination mandated less energy to facilitate a constant flow of electrons.

“Our observations highlight the value of exploring the use of different anion mixtures in batteries to fine-tune and customize their interactions with cations,” said Connell. “With more precise control of these interactions, battery developers can enhance cation transport, increase electrode stability and activity, and enable faster, more efficient electricity generation and storage. Ultimately, we want to learn how to select the optimal combinations of ions to maximize battery performance.”

For the future, the plan is to investigate the potential of other multivalent cations like magnesium and calcium interacting with various anion mixtures. Beyond that, the researchers will also dabble with machine learning to rapidly calculate the interactions, structures and electrochemical activity that occur in and around many different ion combinations. The latter approach, if found feasible, should accelerate the selection of most promising combinations.

The post A Discovery with Potential to Re-energize the Entire Battery Landscape appeared first on Enterprise Viewpoint.

In a world awash with data, businesses and organizations encounter a series of challenges when it comes to harnessing the potential within their data streams. The data analytics and business intelligence (BI) industry is marked by complexities, from managing intricate datasets to providing accessible solutions for users with varying technical skills. In this landscape, Tableau emerges as a beacon of innovation, dedicated to transforming raw data into actionable insights.

One of the foremost challenges in the realm of data analytics is the increasingly complex and voluminous nature of data. Organizations struggle to derive meaningful insights from the wealth of data at their disposal. Data often resides in silos across an organization, isolated from one another. This fragmentation makes it arduous to consolidate and analyze data cohesively for holistic decision-making. Not all business users possess advanced technical expertise. This presents a challenge as complex data analysis tools can be intimidating and limit the ability of non-technical users to harness data for decision-making. As organizations grow, they require scalable solutions that allow for collaboration and sharing of insights. Inadequate tools can hinder this process, impeding the organization’s ability to make data-driven decisions. Tableau effectively addresses these challenges, its unique offerings, compelling marketing value proposition, the expertise that drives its success, an illustrative case study, and the promising future of the company.

Tableau simplifies data analysis through its user-friendly interface. Users with varying levels of technical expertise can create interactive data visualizations with ease. This accessibility makes complex data comprehensible. Also, Tableau bridges the gap between data silos by seamlessly connecting to various data sources. This allows organizations to centralize their data for more efficient analysis and decision-making.

The drag-and-drop functionality of Tableau expedites data analysis, providing quicker insights. Users can explore and visualize data without extensive training, making the process more efficient. Moreover, Tableau offers both on-premises and cloud-based solutions, ensuring that organizations can scale according to their needs. Additionally, features like Tableau Server and Tableau Online facilitate collaboration and secure sharing. Tableau fosters a culture of data sharing and collaboration. The user community and Tableau Public provide platforms for individuals to share their data insights, learn from peers, and engage in a collective journey of data exploration.

The Robust Data Tools

Tableau’s flagship product is a robust data analysis tool. Users can connect to a wide range of data sources, create interactive visualizations, and seamlessly share their insights with colleagues. The server enables secure sharing and collaboration within organizations. It acts as a centralized platform for data sharing and collaborative decision-making. This cloud-based solution allows users to publish and share data visualizations without the need for on-premises infrastructure. It provides flexibility and accessibility to a broader user base.

In an era of mobile workforces, Tableau Mobile ensures that data insights are readily accessible on various mobile devices, providing the freedom to make data-driven decisions anytime, anywhere. Tableau offers a free version, Tableau Public, which empowers users to publish their data visualizations on the web. This feature encourages data sharing and community engagement, broadening the reach of data insights.

Tableau’s marketing value proposition is rooted in its ability to transform data into actionable insights through. Tableau’s primary strength lies in its visual and interactive approach to data analysis. It empowers users to convert complex datasets into easy-to-understand visualizations, promoting quicker decision-making and revealing insights that might be concealed within raw data. Tableau’s user-friendly interface democratizes data analysis. It caters to both technically skilled users and those with minimal technical expertise, fostering a culture of data-driven decision-making across the organization.

Data Integration and Consolidation: By connecting to a myriad of data sources, Tableau resolves the issue of data silos. It allows organizations to centralize and consolidate data for more effective analysis and decision-making.

Efficiency and Speed: The drag-and-drop functionality accelerates the data analysis process, reducing the time required to obtain valuable insights. It empowers users to explore data and create visualizations rapidly.

Tableau’s offerings cater to organizations of varying sizes, allowing them to scale their data analytics capabilities. Features such as Tableau Server and Tableau Online facilitate collaboration and secure sharing, enhancing the collective intelligence of the organization. The Tableau community and Tableau Public promote a culture of data sharing and collaboration. They provide platforms for users to share their data insights, gain inspiration from peers, and engage in continuous learning.

The Expertise Behind Tableau

Tableau’s success is underpinned by its dedicated team of experts, including its founders Christian Chabot, Chris Stolte, and Pat Hanrahan. These visionaries brought to life the concept of a user-friendly data analytics tool, challenging the traditional norms of data analysis. With expertise in data visualization, software development, and user-centric design, the founders set a strong foundation for Tableau’s future. Tableau’s commitment to expertise extends to its employees, partners, and the global Tableau community. The company fosters an environment of learning and skill development, encouraging individuals to explore the depths of data analytics and visualization.

Realizing the Impact of Tableau

The impact of Tableau can be vividly demonstrated through a real-world case study. Consider a scenario in the retail sector, where a national chain of stores faced challenges in optimizing its product inventory. The company grappled with complex data related to sales, customer preferences, and inventory levels.  Inefficient inventory management, resulting in overstock and understock issues.

Create interactive dashboards that provided real-time insights into inventory levels, sales, and customer data. Identify sales trends and customer preferences, allowing for more accurate inventory management. Automate data analysis and reporting, significantly reducing the time required for these tasks. The results were remarkable. The retailer achieved a 15% reduction in excess inventory and a 10% increase in sales. The streamlined data analysis process also freed up valuable employee time, allowing them to focus on strategic tasks. This case study exemplifies Tableau’s ability to drive tangible business outcomes through effective data analysis and visualization.

The Future of Tableau

Tableau’s journey is far from over. The company’s future is marked by continued innovation, as it adapts to evolving data analysis needs and technologies. Enhanced AI and machine learning integration to provide predictive analytics. Expanding data connectivity to accommodate diverse data sources.

As the data analytics and BI landscape continues to evolve, Tableau remains committed to its vision of empowering organizations to make data-driven decisions. The company’s future holds the promise of even more advanced and accessible data analysis tools, ensuring that the power of data remains within reach of all those who seek to harness it. Tableau’s journey has been marked by a steadfast commitment to making data analytics accessible, intuitive, and impactful. The challenges of the data analytics and BI industry are met with innovative solutions, user-friendly offerings, and a strong sense of community. Tableau’s expertise, illustrated through a compelling case study, is propelling it toward a future where data insights are readily available to all who seek them.

Company:
Tableau

Management:
Ryan Aytay, President & CEO

Quote:

“The user community and Tableau Public provide platforms for individuals to share their data insights, learn from peers, and engage in a collective journey of data exploration”

The post Tableau: Pioneering Data Visualization and Business Intelligence appeared first on Enterprise Viewpoint.

A Chief Marketing Officer (CMO) plays a crucial role in enhancing a business operation, especially by developing and implementing marketing strategies that generate leads, acquire new customers, and increase sales. By effectively promoting products or services, a CMO can contribute to revenue growth. CMOs focus on building and strengthening the company’s brand. Enhanced brand recognition can lead to increased customer trust and loyalty, which can translate into higher sales and market share. They also play critical roles in implementing strategies to engage customers through various channels, including social media, content marketing, and email campaigns. Engaged customers are more likely to make repeat purchases and become brand advocates.

One such pioneer in the industry is Madhukar Kumar, CMO, SingleStore, a database technology company. In the role of CMO, Madhukar Kumar is responsible for overseeing and directing the marketing efforts of SingleStore. This typically includes developing and implementing marketing strategies, managing the marketing team, branding, advertising, public relations, and other aspects of promoting the company’s products or services. He plays a key role in delivering the right product to the customers and managing the relationships to drive the company to success. “Lead growth is about taking your product and making it freely available to users for them to give it a try. Then, adding a gradation of experience. In fact, if the product is strong enough, they would come along in the ride,” adds Kumar.

Driven by his passion, Kumar is a seasoned and skilled marketing expert. He’s built a strong foundation in identifying customer behavior and building the most adaptive marketing strategies. He strongly believes in the value of data in marketing and the concept of SingleStore’s services. In a nutshell, Kumar points out that, “if a customer could get free access to something that they could try on, they are basically convinced that this is something that they need and be valuable to. So that’s the core principle of marketing that I would prefer.”

Kumar is also a big fan of the marketing tactics of brands like Apple and Nike. Inspired by these giants, Kumar has also implemented innovative marketing techniques called made-on-single stroke campaigns, where it’s all about helping developers to build on top of a single store. As Kumar explains, “A shoe is a shoe unless somebody steps into it. In the same way, the idea is the make the product usable for the customers. Instead of talking about the technology itself, we started to help developers build on top of a single store and we started to highlight what others had built on top of a single store. With these types of marketing techniques, we’ve seen an exponential growth in how many people are coming in every day, and trying out a product.”

Today, SingleStore is one of the fastest-growing businesses and is close to an IPO. Behind the success of this growth strategy is Kumar and his exceptional team, driven by cutting-edge strategies to interact with customers. Kumar in his perspective is a thinker and an experimenter. “One thing I learned about marketing is, that when everybody starts doing the same marketing tactics, it’s no longer effective. In what you do things for a specific company should have a different taste and flavor. Because your product and audience are not always the same. We have a hypothesis, and then based on that, we do some short marketing experiments. If it works, we double down and if it doesn’t, then we move on to next.”

This kind of approach is what makes Kumar stand out from the crowd. “Since last few months, we have grown just on the PLG side. This may sound delusional, but by the end of next year, I would like it to be 20x growth. In my opinion, within the next few months, if we can all go towards achieving the goal of becoming an IPO, that is just a milestone, but it sets us up to become a multibillion-dollar revenue company,” Kumar concludes.

Company:
Singlestore

Management:
Raj Verma, CEO

Quote:

“One thing I learned about marketing is, when everybody starts doing the same marketing tactics, it’s no longer effective. The things you do for a specific company should have a different taste and flavor”

The post SingleStore: Your Product and Audience are Not Always the Same, So is your Marketing appeared first on Enterprise Viewpoint.

In the vast expanse of space, where mysteries abound and opportunities emerge, Slingshot Aerospace has positioned itself as a trailblazer in harnessing the power of space intelligence. Founded in 2017 by David Godwin and Melanie Stricklan, Slingshot Aerospace has rapidly ascended the ranks, becoming a pivotal player in the burgeoning field of space situational awareness and satellite data analytics. This profile delves into the origins of Slingshot Aerospace, explores its innovative solutions, dissects the challenges it addresses, examines its unique offerings, and outlines the company’s trajectory toward a future where the boundaries of space intelligence are continually pushed.

The genesis of Slingshot Aerospace traces back to a shared vision between its founders, David Godwin and Melanie Stricklan. With backgrounds deeply rooted in aerospace and defense, they recognized the critical need for advanced technologies that could decipher the complexities of space. Slingshot Aerospace emerged as a response to the escalating challenges in space situational awareness, satellite monitoring, and data analytics. Space, once a vast and seemingly untouched frontier, is now brimming with satellites, space debris, and a myriad of objects traversing its expanse. Slingshot Aerospace leverages cutting-edge analytics to process and interpret vast datasets from satellites. This enables clients to derive actionable intelligence and make informed decisions. The company tailors its solutions to meet the unique needs of clients, providing a range of services from satellite tracking to in-depth data analysis.

Slingshot Aerospace excels in fusing data from multiple sources, including satellites, ground-based sensors, and publicly available information. This holistic approach enhances the accuracy and reliability of the intelligence derived. The incorporation of machine learning algorithms enables Slingshot Aerospace to analyze vast datasets rapidly, identifying patterns and anomalies that may escape traditional analysis methods. The company’s collaborative platform facilitates information sharing and coordination among stakeholders in the aerospace industry, fostering a united approach to space situational awareness.

Slingshot Aerospace provides continuous monitoring services, ensuring that clients have real-time updates on the status and movements of satellites and space objects. Beyond commercial applications, Slingshot Aerospace plays a crucial role in addressing environmental and national security concerns. By actively contributing to space debris mitigation and collision avoidance efforts, Slingshot Aerospace promotes the long-term sustainability of space activities. The company’s solutions enhance national security by providing actionable intelligence to defense agencies, ensuring the protection of critical space assets.

At the helm of Slingshot Aerospace are founders David Godwin and Melanie Stricklan, both distinguished figures in the aerospace and defense sectors. David Godwin, with extensive experience in space operations and technology, brings a strategic vision for the company’s growth. Melanie Stricklan, recognized for her expertise in satellite communications, leads the technical innovation and development efforts. Together, their leadership forms the bedrock of Slingshot Aerospace’s success.

The company aims to continually advance its space situational awareness capabilities, incorporating new technologies and methodologies to provide even more accurate and real-time information. Slingshot Aerospace envisions fostering increased collaboration on a global scale, working with international space agencies, governments, and commercial entities to collectively address the challenges of space activities.

In the realm of space intelligence, Slingshot Aerospace emerges not only as a solution provider but as a visionary force shaping the future of space activities. By navigating the complexities of space debris, satellite monitoring, and data analytics, the company not only addresses current challenges but also anticipates and prepares for the evolving landscape of space exploration and utilization. With an unwavering commitment to innovation, collaboration, and global impact, Slingshot Aerospace stands poised at the forefront of the space intelligence revolution, where the possibilities are as vast as the cosmos itself.

Company:
Slingshot Aerospace

Management:
David Godwin, President & Co-founder

Quote:

“Slingshot Aerospace envisions fostering increased collaboration on a global scale, working with international space agencies, governments, and commercial entities to collectively address the challenges of space activities.”

The post Slingshot Aerospace: Navigating the Frontiers of Space Intelligence appeared first on Enterprise Viewpoint.

As the world continues to embrace the potential of artificial intelligence (AI), industries are being transformed by its applications. One such industry is the construction sector, where AI-powered solutions like AirWorks are changing the game. AirWorks is revolutionizing the mapping process for construction projects by using AI to create automated maps from aerial data. AirWorks is revolutionizing the way construction projects are mapped and planned. With its AI-powered platform, the company is solving a major data bottleneck in the pre-construction process and enabling project managers to deliver more projects faster. The construction industry is a massive, untapped $12T market, and AirWorks is leading the charge in automated mapping solutions.

The platform allows users to upload 2D or 3D remote sensing data into AirWorks Automate, which extracts features from 14+ layers and delivers a survey-grade planimetric/topographic base map. This process is designed to enhance and augment the creation of survey-grade base maps, and the platform is built on more than 30 thousand hours of data preparation. The platform autonomously identifies and classifies features, and generates planimetric and topographic base maps quickly.

AirWorks Automate can predict features with 90+ percent pixel accuracy, and the company’s team of CAD and civil engineers reviews all projects once AI processing is complete. This ensures that the final deliverables are 100% reliable and accurate every time, with a level of precision that is a factor in the accuracy and quality of the input dataset. AirWorks’ platform has been deployed with some of the largest civil engineering companies in the industry, establishing the company as a leader in automated mapping. The solution has also been spun out of MIT, and it plans for its technology to be the base layer of geospatial analytics for any industry.

One of the key benefits of AirWorks’ platform is that it puts reliable data in the hands of project managers, allowing them to make the best decisions, reduce risks, and be more efficient. The accuracy and quality of the data provided by AirWorks make it easier for teams to keep clients happy and grow their business as market leaders. Moreover, governments are running into the problem of data management as mapping becomes more important than ever before. AirWorks is the only solution on the market that can identify and generate vectors from geospatial data in record time, allowing governments to plug valuable information into their GIS databases for quick decision-making.

AirWorks is headquartered in Boston and employs a team of AI experts, software developers, sales experts, marketers, geographers, and civil engineers. The company is redefining the future of mapping for the built world, making it easier and more efficient for construction projects to be planned and executed. “At AirWorks, we’re not just focused on providing accurate maps and data. We’re also focused on helping our clients grow their businesses and become market leaders in their industries” says David Morczinek, CEO.

In conclusion, AirWorks’ AI-powered platform is a game-changer for the construction industry. With its ability to quickly and accurately extract features from remote sensing data, AirWorks is empowering project managers to make better decisions, reduce risks, and be more efficient. The platform’s reliability and accuracy are unmatched in the market, and the company’s plans to expand its technology to other industries make it a formidable player in the geospatial analytics space.

Company:
Bedrock Analytics

Management:
Will Salcido, CEO

Quote:

“Whether it’s understanding consumer behavior, identifying market trends, or improving supply chain efficiency, Bedrock Analytics offers a comprehensive suite of tools to drive informed decision-making”

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Autodesk has officially announced the launch of Autodesk AI, which is designed to unlock creativity, solve problems, and eliminate non-productive work across the industries that design and make the world around us. Available across the wider Autodesk portfolio, the stated solution comes well-equipped with an ability to deliver at your disposal intelligent assistance and generative capabilities that allow customers to imagine and explore freely, while simultaneously producing precise, accurate, and innovative results. Talk about how this will happen on a more granular level, the answer is rooted in a collection of dedicated sub-solutions present within the product. For instance, when we bring up architecture, engineering, and construction industry, Autodesk AI will begin its value proposition from Autodesk Forma. This one can effectively provide rapid wind, noise, and operational energy analysis so to help you conduct smart early-stage planning and take design decisions that improve outcomes rather meaningfully. Next up, we have the prospect of InfoGraphic, a Machine Learning Deluge Tool bearing the responsibility to offer feedback on the best placement for retention ponds and swales. Such functionality should help users in preventing or reducing the impact of water disasters. Moving on, then there is AutoCAD, which leverages artificial intelligence to help drafters iterate faster through handwritten notes and digital markups. The idea around AutoCAD is to determine the intent of the user to recommend context-aware actions for easily incorporating changes. The last bit of prominent details across this space comes from Construction IQ, a tool meant to again use AI to predict, prevent, and manage construction risks that might impact quality, safety, cost, or schedule.

The discipline we will now get into is of product design and manufacturing, where Autodesk will use its Blank AI acquisition to enable conceptual design exploration for the automotive industry. By doing so, it will birth accelerated outcomes, alongside 3D models that can be rapidly created, explored, and edited in real time using semantic controls and natural language, and guess what, you won’t need any advanced technical skills whatsoever around here. Another way through which Autodesk AI will enhance design and manufacturing space is through Autodesk Fusion, which allows customers to automatically generate product designs that are optimized for manufacturing method, performance, cost, and more. Furthermore, Fusion workflows are being specifically conceived to ensure automated creation of templatized Computer-Aided Manufacturing toolpaths that can be adjusted by the user as needed. Complimenting the same are automated drawings that will provide interactive experiences in sheet creation, view placement, and annotation workflows.

“As the trusted technology partner for Design and Make industries, Autodesk sees AI as a way for our customers to tackle the challenges they face and turn them into opportunities,” said Andrew Anagnost, President and Chief Executive Officer at Autodesk. “AI is the future of design and make, and Autodesk is pioneering this transition. We sit at the junction of many of the most creative and impactful industries in the world. We’ll continue to invest in AI because of its transformational potential to drive better outcomes for our customers’ businesses and the world.”

Rounding up the highlights is what Autodesk AI has on the offer for media and entertainment industry. For starters, the product banks upon generative scheduling capabilities in Autodesk Flow to automate scheduling for media and entertainment productions, doing so on an actionable note by managing the constantly shifting variables between teams and budgets. Notably, this generative scheduling approach produces results in minutes for a process which has traditionally taken days at a time. Given the dramatic difference, teams can predict, plan, and right-size resources to ensure creative bandwidth wherever needed. Turning our attention to Autodesk Flame, this one knows a thing or two about automating manual tasks such as keying, sky replacement, beauty work, and camera tracking for artists. These people can also expect to interact with the company’s 3D animation software called Maya and access its scene data using natural language text prompts. To make the offering all the more significant, Autodesk has also collaborated with Wonder Dynamics, a collaboration where AI will power a Maya plug-in to automatically animate, light, and compose computer-generated characters for live-action scenes.

The entire development provides an interesting follow-up to one recent State of Design and Make special report on AI, which claimed that out of all the companies surveyed, 77% revealed that they are planning to increase or strongly increase investment in AI over the course of next three years. As for all the leaders who were surveyed, 66% agree that in two to three years AI will be essential. But what makes Autodesk an ideal candidate to make the most of this raging trend? Well, apart from all the AI-driven solutions, the company’s credentials are also markedly rooted in the fact that it has, till date, published more than 60 peer-reviewed research papers advancing the state of the art in AI and generative AI.

“With a commitment to security and ethical AI practices, we’re focused on delivering responsible AI solutions that address our customers’ needs,” said Raji Arasu, Chief Technology Officer at Autodesk. “Autodesk AI will continue to surface across the Autodesk platform–both in our existing products and our industry clouds–to enable better ways of designing and making.”

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The researching team at US Department of Energy’s Center for Functional Nanomaterials (CFN) has successfully developed a new light-sensitive, organic inorganic hybrid material that enables high-performance patternability by EUV lithography. To understand the significance of such a development, we must start by acknowledging how, with semiconductor feature sizes now approaching only a few nanometers; it has become enormously challenging to sustain this persistent device miniaturization. The stated challenge has got our semiconductor industry to adopt a relatively more powerful fabrication method i.e. extreme ultraviolet (EUV) lithography. In case you are looking for some context, EUV lithography employs light that is only 13.5 nanometers in wavelength to form tiny circuit patterns in a photoresist, the light-sensitive material integral to the lithography process. This photoresist is essentially the template for forming the nanoscale circuit patterns in the silicon semiconductor.  However, as we continue our progression towards more advanced but complicated systems, scientists across the globe are now pitted against a challenge of identifying the most effective resist materials. Enter CFN’s latest brainchild. Made from hybrid materials, these new photoresists are composed of both organic materials (those that primarily contain carbon and oxygen atoms) and inorganic materials (those usually based on metallic elements). Furthermore, both parts of the hybrid host their own unique chemical, mechanical, optical, and electrical properties due to their unique chemistry and structures. Hence, upon combining such individually-substantial components, new hybrid organic-inorganic materials are born, materials that boast their own interesting properties. You, the result happens to be a material which is more sensitive to EUV light, meaning it doesn’t need to be exposed to as much EUV light during patterning. Such facility should make a sizeable cut back on process time. Not just that, the new hybrid material also has an improved mechanical and chemical resistance, thus offering a far better value proposition as templates for high-resolution etching.

“To synthesize our new hybrid resist materials, organic polymer materials are infused with inorganic metal oxides by a specialized technique known as vapor-phase infiltration. This method is one of the key areas of materials synthesis expertise at CFN. Compared to conventional chemical synthesis, we can readily generate various compositions of hybrid materials and control their material properties by infusing gaseous inorganic precursors into a solid organic matrix,” said Chang-Yong Nam, a materials scientist at CFN who led the project.

An intriguing detail related to the said development is a change in precursor used for the metal. Rather than banking upon aluminum like they did during previous efforts, the team leveraged indium as an inorganic component. In practice, they made the new resist using a poly (methyl methacrylate) (PMMA) organic thin film as the organic component and infiltrated it with inorganic indium oxide. By doing so, they were able to achieve improved uniformity in subsequent patterning.

That being said, EUV patterning remains a largely inaccessible commodity for now, and that is because of the costs involved.

“It’s currently really hard to do EUV patterning,” said Nam. “The actual patterning machine that industry is using is very, very expensive—the current version is more than $200 million per unit. There are only three to four companies in the world that can use it for actual chip manufacturing. There are a lot of researchers who want to study and develop new photoresist materials but can’t perform EUV patterning to evaluate them. This is one of the key challenges we hope to address.”

In terms of the researching team’s immediate plans with the technology, though, it has already started the work on other hybrid material compositions. The intention is to also make some headway around the processes involved in fabricating them, thus better positioning the industry to pattern smaller, more efficient, and sustainable semiconductor devices.

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