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Pisquare, a brand of Arima Analytics, is a Data Analytics solutions firm addressing the space of decision system optimization. Pisquare partners with organizations to transform their decision-making ecosystem with the right mix of strategic planning supported by analytical insight. The company helps organizations engineer and optimize decision-making systems for actionable insights. Using a combination of analytics and visualization, PiSquare is creating a springboard for businesses to reach the next level of performance. The company has been partnering with clients across the globe to optimize their customer engagement, supply chain, marketing & sales, delivery and talent for more aligned and mature performance. PiSquare’s solutions are broadly structured into the areas of Customer Intelligence, Operations (IT & ITES) and Talent Management. Some of its key solutions include: • AI-driven Knowledge Miner and Recommender • Text Extractor for Text from images • Work Load Balancer • Customer Satisfaction Manager • Employee Success Predictors • Customer 360 • Retail Analytics with Cross Sell Analytics, Segmentation • Promotion Intelligence and Offer management • Preventive Fraud Analytics for InsuranceAccomplishing Mission to Serve Distinctly
Pisquare has been in operation since April 2023. Jonu Rana, Chinmay Pradhan, and Rojalin Biswal are the three founders who started Pisquare with the intent of infusing AI and ML into the workflows of organizations. While Jonu and Rojalin had been part of large corporates, Chinmay had been the founder of another Machine Learning company. In many organizations, the integration of AI into enterprise work has been quite low. Mostly, even with large data science teams, the volume of opportunities is so large that either these organizations are overwhelmed or just look at the internal potentials and end up working in silos. As a result, it becomes really difficult to unlock the true potential and scale of the opportunity. So, the founders saw an opportunity to use AI to Augment the inherent intelligence in the organization and democratize the usage of analytical insights. They believed that a smarter workforce and a smarter set of processes will be possible with the infusion of AI into the current workflows of enterprises. The idea was to unlock the value of traditional processes with higher efficiency and effectiveness and take it into an exponential path of value realization. And this is possible when smarter insights are available as part of the natural workflow and systems.Leaders of Consequential Success
The team has been working very closely with organizations and governments to bring analytics into the workflow.Beneficiaries of PiSquare
With a team of 35 engineers across India, PiSquare is working with companies like Dell, Capita PLC, Mahindra Finance, Tata Capital, Sunlife, PowerSchool, Governments, Banks and insurance organizations. The company’s solutions in the areas of HR, Customers and Operations has given organizations a cost efficiency of around 22%-26%, error reduction of 18% and increase in cross sell of 6%. Currently, Pisquare has technology partnerships with Amazon Web Services, SAS and IBM.Approaches Driving Innovation
PiSquare works with large enterprises to infuse ML and AI into their work processes. Our key deliverable is a web-based application which comprises of data stores, interactive UI’s and ML& AI Algorithm engines. These are solutions which clients use to answer specific questions, simulate what-if scenarios and get alerts on key thresholds.Analytics is Transforming Industries
The founders believe that big data analytics is revolutionizing almost every field today. Organizations both big and small, are opening up to the importance of data and the amount of impact it can bring in to their decision-making. Right from optimizing their supply-chain systems and workflow management to enhancing customer experience and maintaining attrition, they are resorting to data for every decision. Integration of digital data and its implementation through analytics has been fetching humongous rewards to businesses around the world and is bound to bloom.Valuable Testimonials Challenges Strengthening Core
Jonu feels one of the key challenges the company has faced is forming the core team from various skills and proficiencies to come together and create a solution. Web-based ML and AI solutions require a team of UX, UI, Data Engineering, Application developers as well as data scientists to work collaboratively. “This challenge has been addressed by PiSquare’s core team members and we are now scaling pretty quickly,” he said. “A related challenge was of projects demanding a bigger investment and clients being tight pursed”. PiSquare, through its Data platform could engineer the solutions matching with client budgets by end-to-end execution projects from Data Management to Visual management.Future-Proof Roadmap
Jonu asserts data analytics, has gotten everyone to stand up and take notice. “We, at Pisquare, foresee a world that is simply data-driven, consumers and experts making it smart, intuitive, responsive and responsible. As AI & ML integrate with other technology to give unparalleled insights, the industry will continue to evolve in the future.”
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Artificial intelligence (AI) and its subsets are benefitting huge amounts of fields, however you’d be unable to discover one that is exploiting from them than the manufacturing area. Significant organizations around the globe are intensely putting resources into machine learning (ML) arrangements over their manufacturing procedures and seeing impressive outcomes.
From cutting down work expenses and decreasing personal time to expanding workforce profitability and in general production speed, AI – with the assistance of the Industrial Internet of Things – is introducing the period of savvy manufacturing. The numbers represent themselves; late gauges foresee that the brilliant manufacturing business sector will develop at a yearly pace of 12.5% between this year and next.1. General process improvement
One of the principal things that ring a bell when considering ML-based solutions is the way they can serve every day forms over the whole manufacturing cycle. By utilizing this innovation, makers can recognize a wide range of issues on their standard techniques for production, from bottlenecks to unbeneficial production lines.
By joining machine learning apparatuses with the Industrial Internet of Things, organizations are investigating their coordinations, stock, resources and inventory network the executives. This brings high-esteem bits of knowledge that reveal potential open doors in the manufacturing procedure as well as in the bundling and circulation also.
A great example of this can be found in the German aggregate Siemens, which has been utilizing neural systems to screen its steel plants looking for potential issues that may be influencing its proficiency. Through a mix of sensors introduced in its gear and with the assistance of its own savvy cloud (called Mindsphere), Siemens is fit for observing, recording, and examining each progression engaged with the manufacturing procedure. This dynamic is the thing that a few people call Industry 4.0, a trademark of the more astute manufacturing period.
Related: – The Rise of Machine Learning2. Product development 3. Quality control
At the point when put to great use, machine learning can improve the last item quality up to 35%, particularly in discrete manufacturing businesses. There are two manners by which ML can do this. Above all else, discovering abnormalities in items and their bundling. Through a profound assessment of the fabricated items, organizations can prevent damaged items from regularly arriving at the market. Actually, there are ponders that discussion about an up to 90% improvement in imperfection location when contrasted and human reviews.
And afterward there’s the conceivable improvement of the nature of the manufacturing procedure. Through IoT gadgets and ML applications, businesses can break down the accessibility and execution of all the hardware utilized in the manufacturing procedure. This takes into account prescient upkeep, which evaluates the best time to take care of explicit gear to broaden its life and maintain a strategic distance from exorbitant personal times.
General Electric is perhaps the greatest financial specialist in the quality control office, particularly in everything identified with prescient support. It has just made and conveyed its ML-based instruments in over a hundred thousand resources all through its specialty units and clients, including the aviation, control age and transportation ventures. Its frameworks work to identify early notice indications of inconsistencies in its manufacturing lines and furnish prognostics with long haul estimations of conduct and life.
Related: – 5 Data Trends in 2023 every Entrepreneurs need to Know4. Security
Since these machine learning solutions depend on applications, working frameworks, systems, cloud and on-premise stages, the security of the versatile applications, gadgets, and information being utilized is an unquestionable requirement for current makers. Luckily, machine learning has an answer in the Zero Trust Security (ZTS) structure. With this innovation, client access to important computerized access and data is intensely managed and restricted.
Related: – Rise of Robots and use in Supply Chain Industry5. Robots
At last, probably the most notable partners for producers are getting more brilliant with machine learning: robots. The utilization of artificial intelligence in robots enables them to take on routine errands that are mind boggling or perilous for people. These new robots outperform the sequential construction systems that they used to be consigned to, as their ML abilities enable them to handle more confounded procedures than previously.
That is absolutely what KUKA, a Chinese-claimed German manufacturing organization, is going for with its modern robots. Its will likely make robots that can work nearby people and go about as their teammates. What’s more, in that sense, the organization is bringing its robot – LBR iiwa – into the overlay. This insightful robot is outfitted with elite sensors that enable it to perform confounded errands while working next to people and figure out how to improve their profitability.
KUKA itself utilizes its robots in its industrial facilities, however there are other significant makers that do as such too. BMW, the well known auto brand, is perhaps the greatest client, and one of the businesses that is as of now finding that robots can lessen human-related mistakes, help efficiency and include an incentive all through the whole manufacturing chain.
Related: – Upcoming Trends of IoT in 2023 and Strategy for FutureSome closing thoughts
Are you looking for a comprehensive cloud data platform that can help you find, engage, visualize, and share data with ease? Look no further than Rose AI! Founded in 2024, Rose AI is a private company with three executive team members. The platform uses natural language processing and open-source LLMs to parse and integrate data from external and internal sources, providing infrastructure tools to clean, analyze, and visualize data in one centralized solution. Let’s dive deeper into what Rose AI can do for you.
Important: Rose AI is a cloud platform that simplifies finding and sharing data using natural language processing and AI. It integrates with top providers, is user-friendly, and has social media presence. It’s also funded by investors.
See More: What Is BabyAGI? How Does It Work?
Rose AI is a cloud data platform designed to help users find, engage, visualize, and share data. It offers an integrated, mutually reinforcing data workspace, analytics engine, and marketplace platform that allows transforming, sharing, and monetizing vetted and quality-controlled data. The platform also enables users to permission data for internal teams or third parties.
With Rose AI, you can easily find the data you need and make data-driven decisions with confidence. The platform leverages generative AI to help you find, visualize, and share data, providing smarter machines for beautiful data. Let’s take a closer look at how Rose AI works and what it can do for you.
Rose AI uses natural language processing to parse and understand user queries. This allows users to interact with the platform using natural language, making it more user-friendly and efficient. Natural language processing is a key field in artificial intelligence and enables seamless interaction with computers.
By leveraging natural language processing, Rose AI is able to provide a more intuitive and user-friendly experience for its users. You can simply ask the platform a question, and it will provide you with the data you need. This eliminates the need to sift through large amounts of data manually, saving you time and effort.
There are several benefits to using natural language processing in Rose AI, including:
Understanding and Parsing User Queries: Natural language processing allows Rose AI to understand and parse user queries, making the platform more user-friendly and efficient. This means that you can quickly find the data you need without having to spend hours manually sifting through data.
Extraction of Data and Information from Text-Based Documents: Natural language processing enables the extraction of data and information from text-based documents, which can improve complex analytics tasks like sentiment analysis. This means that you can get more insights from your data, making it easier to make data-driven decisions.
Seamless Interaction with Computers and Robots: The use of natural language processing in artificial intelligence and robotics enables seamless interaction with computers and robots. This means that Rose AI can provide smarter machines for beautiful data, making it easier for you to find, visualize, and share data.
By leveraging natural language processing, Rose AI is able to provide a more intuitive and user-friendly experience for its users, making it easier to find, visualize, and share data.
Rose AI’s main function is to provide a cloud data platform that helps users find, engage, visualize, and share data. It enables integration of external and internal data, with the ability to permission data for internal teams or third parties. The platform also provides infrastructure tools to clean, analyze, and visualize data.
With Rose AI, you can quickly find the data you need and make data-driven decisions with confidence. The platform is designed to be user-friendly and efficient, making it easy for you to interact with it using natural language.
According to PitchBook, Rose AI has received $5.5 million in seed funding as of September 2023. The search results do not provide information on which companies have invested in Rose AI.
This article was published as a part of the Data Science BlogathonIntroduction
In September 2023, Gartner released a separate report on artificial intelligence technologies, ranging from conceptual concepts like neural networks to hardware implementations of Machine Learning algorithms in the form of industrial robots and unmanned vehicles.
Notably, in this report, autonomous vehicles (drones, unmanned cars, and other vehicles) are now at the bottom of frustration, while Automatic Machine Learning (AutoML), Explainable AI (XAI), chatbots, and other conversational user interfaces are at the peak of inflated expectations. And in general, speech recognition systems and video card-based process acceleration (GPU) tools have reached a productivity plateau.
From the point of view of industrial applications, the most promising are technologies for creating robotic software for the automation of production processes and calendar planning. These trends correlate with the most in-demand trends in the Internet of Things, which people have been discussing since 2023.ML Trend #1 – AutoML
AutoML is Automated machine learning or AutoML. To tell the truth, the AutoML paradigm is about having one big button that lets you “build a good model”.
The popularity of such tools grows every day, but it is too early to talk about AutoML as a stand-alone approach, especially in the context of large corporations.ML Trend #2 – XAI
We are talking about Explainable Artificial Intelligence (Explainable AI or XAI).
The fact is that it is extremely important for the business user to understand the logic behind the decision-making, which is more typical for areas of activity that were historically dominated by easily interpretable models such as logistic regression or decision trees (calculation of credit risks, targeted marketing, insurance).
Recently, methods like LIME and XSHAPE have been closing the gap between interpretation and accuracy and, judging by the activity in the academic environment, they are expected to spread further.ML Trend #3 – RL
RL or Reinforcement Learning is learning with reinforcement and is essentially a development of the concept of continuous A/B testing with the only difference that instead of two segments we have thousands of them, and the process functions continuously.
Once it was used only for games, but in recent years it has been used to solve great business problems. Today experts continue to improve the methods of using RL for business. There are already lots of successful application cases on the market:
– choosing the most appropriate campaign in marketing optimization;
– personalization of pages and mailings in digital marketing;
– work with bad debts in credit risks, etc.ML Trend #4 – graph analytics
Graph analytics refers to a set of methods that focus on analyzing the structure of relationships between entities, rather than the properties of those entities. For example, connections between people in social networks, connections of bank account through transfers passing through these accounts, different ownership structures, etc.
The methods of graph analytics are used to analyze the structure of relations and to identify non-obvious relations. As for the ML problems, graph analytics gives the opportunity to build stronger predictors – variables that describe the neighborhood of the entity of interest. For example, we can answer the question of how the credit rating of the firm is affected by the rating of its counterparties.
Using methods of graph analytics, you can be limited not only to direct links but also to neighborhoods by links of different lengths.
Today graphs are successfully used to analyze entities that have a “natural” network structure, such as social networks. It is predicted that graphs for entities with non-obvious network structures will become more and more frequent. Such graphs are good for building sequences of customer events or analyzing cause-effect relationships for marketing communications management tasks.ML Trend #5 – ModelOps
MLOps (Machine Learning Operations) is a kind of DevOps for machine learning that helps standardize the process of developing machine learning models and reduces the time to roll them out into production.
MLOps helps to break down the barrier between Data scientists and Data engineers. What often happens is that the Data Scientist experiments develop a new model, gives it to the Data Engineer, and goes off again to set up new experiments and try new models. And the Data Engineer tries to deploy that model in production, but since he was not involved in its development, it may take about several months to do so. It can take up to six months from the time he starts developing the model to its deployment in production. All that time the model is not working and useful, the data becomes obsolete, and the reproducibility of experiments becomes an issue.
With MLOps, the new model is quickly put into production and begins to benefit the business. MLOps also solves model tracking, versioning, and model monitoring tasks in production.
If you use the MLOps approach and special tools, like Kubeflow, in conjunction with proper planning, such as at chúng tôi you can significantly speed up the process of rolling out experimental models into production, which means that they will solve business problems faster.
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Like many industries, financial firms are adapting to workers using their mobile phones for workflows in their day-to-day. Often times, work occurs in a mix of locations, including a home office, client site, and corporate office. But wherever work takes place, financial firms still face stringent compliance and regulatory requirements because of the sensitive information they handle on behalf of clients. More so, their employees and clients expect a mobile, “always on” approach to the technology provided by their jobs and financial services providers.
Samsung’s ecosystem of mobile devices and applications effectively supports financial firms in various ways. Here are five ways they can help your firm in today’s hybrid work environment.1. DeX
Samsung DeX is a PC-like experience powered entirely by a Samsung Galaxy mobile phone, such as the latest Galaxy S23 series or tablet. With Samsung DeX, workers can connect their phone or tablet to a bigger screen, keyboard, and mouse, allowing them to interact with multiple apps simultaneously. DeX helps minimize the need for staff to tote a laptop around to client and vendor meetings, in the branch or even in the office along with a phone or a tablet. DeX can also help cut costs for a financial firm, for example, by eliminating the need to purchase separate monitors, keyboards, and a thin client PC for each person working in a retail bank branch.
Here are just two examples of DeX in action:
A wealth manager can visit a client bringing just their Galaxy phone or tablet, then connect to on-site equipment to make a presentation in a conference room.
An in-branch bank associate can quickly connect their Galaxy mobile device to a monitor to pull up the account information of a client visiting the branch.2. S Pen 3. Samsung Notes
Samsung Notes is an innovative, intuitive platform for capturing ideas and information, preloaded on all Galaxy devices. Though the app can benefit a variety of financial workers, it can be beneficial for insurance claims adjusters, who are always in the field, conducting interviews, taking pictures, and analyzing information. For instance, insurance adjusters often work out of their car and may not want to type out notes at the end of the day. Samsung Notes lets you add a live voice recording to notes or instantly convert handwriting to text. Need to mark up a document? Switch between different kinds of pens, highlighters, and erasers by tapping the icons in the app’s toolbar.What’s next for the future of finance?
Samsung surveyed 1,000 finance professionals about the future of mobile tech. Here’s what they said. Download Now4. Samsung Knox
Samsung Knox protects the financial services workforce, whether they’re in the office, remote, or on the go. Knox enables financial services companies to secure devices throughout the enterprise mobility management (EMM) journey, streamline deployment to set up thousands of devices with ease, and simplify device management with Samsung Knox and Android Enterprise.5. Galaxy connected ecosystem
Thanks to the Galaxy connected ecosystem, staff can enjoy continuity across devices. Take a client call on a Galaxy S23 mobile phone on the way to the office, listening through Galaxy Buds2 Pro, then seamlessly transition to speaking on a Galaxy Book, for example. Have bank workers know which client just walked through their branch door through a notification on their Galaxy Watch5. Or connect a Galaxy Z Fold4 to a monitor, mouse, and keyboard to enjoy a desktop computer experience. Overall, the Galaxy connected ecosystem helps to increase productivity including individual and team productivity in the financial services workforce.
For a full overview of all Samsung technology solutions for the Finance industry, please visit this page. And sign up for a Samsung Business Account to get exclusive offers, including volume pricing discounts, on our newest devices like Galaxy S23 series, Galaxy Z Fold4 and Galaxy Z Flip4.
As a MacBook Pro user, having access to external SSD storage is important given the price of build-to-order SSD upgrades. For example, a 4TB SSD upgrade alone on the 2023 MacBook Pro can set you back $3400, more than the price of the laptop itself.
With this in mind, such upgrades can’t be reasonably justified for many users, which means relying on external storage where necessary. Thankfully, there is no shortage of external storage solutions for the MacBook Pro, with many of them featuring bus-powered USB-C connectivity for plug and play functionality.
SanDisk’s Extreme Portable SSD, available in various storage capacities, is one such product. Watch our video hands-on for the details.Specifications
Up to 550 MB/s read speeds
Up to 2TB of storage capacity
Shock and vibration resistant
IP55 rated against dust and water
USB 3.1 gen 2 support
Comes with USB-C to USB-C cable, and USB-A adapter
3-year limited warranty
The first thing that stands out about the SanDisk Extreme Portable SSD is its size. At roughly 3.5-inches tall, less than 2-inches wide, and .25-inches thick, it’s very compact and can easily fit in a pants pocket. The drive is made up of plastic and soft touch rubber on the rear, along with a full rubber bumper surrounding the exterior of the enclosure.Video review
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The drive doesn’t feel particularly robust, especially when compared to other drives, like the Samsung T5, with metal housings on the outside. Yet, SanDisk says it designed its SSD with portability and somewhat precarious environments and situations in mind. For example, it’s IP55 rated, meaning it’s protected from limited dust ingress, and from low pressure water jets from any direction. SanDisk also notes that the Extreme Portable SSD is vibration and shock-resistant, and is able to withstand up to a 2-meter drop. All of these claims are backed up by a 3-year limited warranty, which should offer users some peace of mind.
Inside the box, you’ll find the drive, a short 7-inch USB-C to USB-C cable, and a USB-A adapter for connecting to legacy ports. The SanDisk Extreme Portable SSD comes with a single USB-C port on the bottom for connecting to the host computer, which means it is fully bus-powered and ready to go upon connection.
I tested the 1 TB version, but you’ll also find 250 GB, 500 GB, and 2 TB storage options. Prices range from roughly less than $100 to a little over $500 for the top end storage size.Speed Tests
The drive is pre-formated using ExFat, which allows for both Mac and Windows compatibility out of the box. If you’re a Mac user, you can venture into Disk Utility and format the drive however you’d like.
I ran a couple of speed tests using my two favorite Mac drive benchmarking tools: Blackmagic Disk Speed Test and QuickBench. Here are the results:
As you can see, read speeds in the Blackmagic test averaged around 521 MB/s, while speeds with the QuickBench sequential test trended closer to the 550 MB/s rating on the box.
This drive is plenty fast enough for 4K ProRes 422 HQ workflows at 60 frames per second, which makes it a solid MacBook Pro / Final Cut Pro X companion drive. I tested both ExFat and Mac OS Extended (Journaled) formats using these two speed test utilities, and found the results to be identical.Conclusion
My biggest reservation with this drive is with its longterm durability. The plastic front exterior doesn’t exactly exude confidence as far as build materials are concerned. Yet, SanDisk has obviously designed this drive to be portable, traveled-with, and in some cases used in less-than-ideal environments. The drive even includes a hole in the upper right-hand corner for attaching a loop or even something like a carabiner, further emphasizing its on-the-go ability. So, maybe I shouldn’t be so worried about the drive’s long-term durability? I’ll keep testing and report back.
My favorite things about this drive include its extremely light weight, that it’s bus-powered, and it lends you plenty of room for handling storage-heavy tasks like video editing. If you need more MacBook Pro storage, then a compact drive like the SanDisk Extreme Portable SSD may be just what the doctor ordered. You can also find the drive available from B&H.
Do you like the idea of using a portable SSD with your MacBook Pro, or would you rather drop lots of money for more of Apple’s ridiculously-fast internal SSD storage?
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