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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 2023, 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.
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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.
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While choosing a social media that’s right for your business can be overwhelming, you can easily pick the network that’s right for you if you know your goals, audience, and capabilities.Define Your Goals
Before deciding what social network to use, you have to decide on what you want to achieve first. You have to define your social media marketing goals, and make sure that it is specific, actionable, and reasonable. From here, you can choose which social network can deliver most of your needs, as well as help you meet your goals.Know Your Traffic Handling Capabilities
It’s also important that you can determine how you can handle the traffic that social media may deliver to your business—most especially on your website. That’s because a website that can’t handle too much traffic can be shut down. In addition, more traffic could tantamount to more incoming messages that need your response. So, choose the social media website that can bring the traffic that you can handle.Determine Your Audience’s Online Habits
Your audience’s online habit is also important in choosing which social media platform to use. That’s because it will determine where most of your traffic will come from. Since a user’s online habit will depend on his or her interests, it will also influence the type of content that they’ll read or sharing. Thus, pick the social network that majority of your audience use, and where you can deliver your content to garner the most traffic.Find Your Resources
Finding your resources is actually the difficult part of social media marketing. Since the platform you’re trying to use requires interaction, you need to create and find the right resources that you can share online. That way, they will be compelled to read it, which could boost your website’s traffic.Establish Time of Interaction
As mentioned earlier, social media can bring much traffic to your business. With a lot of people possibly reaching out to you for inquiries other reason, it can be daunting for you to respond to each of them. That’s why you need to establish a time wherein you won’t do anything but interact with your fans or followers, as well as draft press releases or official statement regarding a frequently discussed topic.
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.”
The Restricted Boltzmann Machine, developed by Geoffrey Hinton in 1985, is indeed a network of symmetrically interconnected systems that functions like neurons and makes stochastic judgments. After the Netflix Competition, where RBM walt is a type of unsupervised utilized as an information retrieval strategy to forecast ratings and reviews for movies and outperform most of its competition, this deep learning model gained a lot of notoriety. It is helpful for collaborative filtering, feature learning, dimensionality reduction, regression, classification, and feature learning.
Let’s understand what are restricted Boltzmann Machines in depth.Restricted Boltzmann Machine
Restricted Boltzmann Machines have stochastic two-layered neural networks that can automatically identify underlying patterns in data by reconstructing input. They are a subset of energy-based models. They have two layers, one of which is hidden. The hidden layer is made up of nodes that create the visible layer and collect characteristic information from the data. They do not have any output nodes, which might appear weird, and they don’t have the typical binary output that allows for the learning of patterns. They vary because learning cannot take place without that capacity. We don’t concern about hidden nodes; we only care about input nodes.
RBM is utilized in numerous real-time commercial use cases, including.
Since it can be difficult to understand handwritten writing or a random pattern, RBM is used for feature extraction in pattern recognition applications.
Recommendation Engines: RBM is frequently used in information retrieval approaches to forecast the recommendations that should be made to a client in order for that client to enjoy utilizing a specific application or platform. For example, both books and movies come highly recommended.
Radar Target Recognition: In this situation, RBM is used to locate intra pulses in radar systems that have high noise levels and exceptionally low SNR.Restricted Boltzmann Machine Features
Some key characteristics of the Boltzmann machine are
They employ symmetric and recurring structures.
RBMs aims to connect low-energy states with the highest probability ones as well as vice versa as part of their learning process.
The layers are not connected to one another.
It uses input data lacking labeled responses to generate inferences; this makes it an algorithm in unsupervised learning.
In this section, we will contrast a Boltzmann machine with a constrained Boltzmann machine. Each algorithm has two levels: an apparent level and a secret one. The Boltzmann Machine connects each individual neuron for each layer as well as every neuron inside the visible layer to every neuron in the hidden layer layer. However, RBM differs from previous examples of the Boltzmann machine in that the neurons in the layer are not connected. i.e. There really is no intra-layer communication, making each other independent and easier to implement as provisional freedom means that researchers do need to determine only negligible probability, which is easier to compute.Operation Of RBM
How does Restricted Boltzmann Machine, an unsupervised learning method, learn without needing any output data, as was previously mentioned? A hidden layer neuron adds a bias value to input data received from a visible layer neuron, multiplies the result by some weights, and then output is produced. Then, the hidden layer neuron’s output value becomes a new input, which is multiplied by the same weights, and the visible layer’s bias is added to create the new input. Reconstruction nor backward pass are the two names for this procedure. The original input and the newly generated input will then be compared to see if they match or not.Training In RBM
Gibbs Sampling & Contrastive Divergence are used to train RBM.
to make an approximation of the gradient, which is a graphical slope depicting the link between such a network’s weights as well as its error, Contrastive A crude Maximum-Likelihood learning strategy is divergence. It is used when we cannot directly evaluate a functional as well as set of probabilities and need to approximate the learning slope of the algorithm and determine what direction to go in.RBM Applications Conclusion
AI is finally living up to the hype that has surrounded it for decades. While AI is not (yet) the saviour of humanity, it has progressed from concept to reality, and practical applications are improving our environment.
However, much like Clark Kent, many of AI’s astounding exploits are veiled, and its impacts can only be seen when you look past the ordinary mask. Consider BNP Paribas Cardif, a large insurance corporation with operations in more than 30 countries. Every year, the organisation handles around 20 million client calls. They can evaluate the content of calls using speech-to-text technology and natural language processing to satisfy specific business purposes such as controlling sales quality, understanding what customers are saying and what they need, getting a sentiment barometer, and more.”
Consider AES, a leading producer of renewable energy in the United States and around the world. Renewable energy necessitates far more instruments for management and monitoring than traditional energy. AES’ next-level operational effectiveness is driven by data science and AI, which provide data-driven insights that supplement the actions and decisions of performance engineers. This guarantees that uptime requirements are met and that clients receive renewable energy as promptly, efficiently, and cost-effectively as feasible. AES, like Superman, is doing its part to save the planet.
These are only a few of the many AI applications that are already in use. They stand out because, until now, the potential of AI has been constrained by three major constraints:Compute Power
Traditionally, organizations lacked the computing power required to fuel AI models and keep them operational. Companies have been left wondering if they should rely only on cloud environments for the resources they require, or if they should split their computing investments between cloud and on-premise resources.Centralized Data
Data has traditionally been collected, processed, and stored in a centralised location, sometimes referred to as a data warehouse, in order to create a single source of truth for businesses to work from.
Maintaining a single data store simplifies regulation, monitoring, and iteration. Companies now have the option of investing in on-premises or cloud computation capability, and there has been a recent push to provide flexibility in data warehousing by decentralizing data.
Data localization regulations can make aggregating data from a spread organization unfeasible. And a fast-growing array of edge use cases for data models is undermining the concept of unique data warehouses.Training Data
A lack of good data has been a major impediment to the spread of AI. While we are theoretically surrounded by data, gathering and keeping it may be time-consuming, laborious, and costly. There is also the matter of bias. When designing and deploying AI models, they must be balanced and free of bias to ensure that they generate valuable insights while causing no harm. However, data, like the real world, has bias. And if you want to scale your usage of models, you’ll need a lot of data.
To address these issues, businesses are turning to synthetic data. In fact, synthetic data is skyrocketing. According to Gartner, by 2024, 60% of data for AI applications would be synthetic. The nature of the data (actual or synthetic) is unimportant to data scientists. What matters is the data’s quality. Synthetic data eliminates the possibility of prejudice. It’s also simple to scale and less expensive to obtain. Businesses can also receive pre-tagged data with synthetic data, which drastically reduces the amount of time and resources required to build and generate the feedstock to develop your models.
The need to interpret the vast data is growing unprecedently in the world. With digitization taking over industries, more and more organizations are generating digital data like never before. The growing data is not only a huge asset but also presenting immense opportunities for the industries. To derive interpretations and insights from the data means going a rigorous process of collecting, transforming, loading, and finallyBidding Goodbye to Traditional Processes
When it comes to managing data, most businesses were using the same traditional on-site infrastructure a few years back. While this worked a few years ago due to a variety of reasons, the winds of change have taken over. Enterprises looking for smarter solutions, because their data was increasing and so were the data management costs. This led to huge turbulence in the traditional data management system, which was mainly on-site. Since the on-site data warehouse was not only difficult to manage but also had more than a few issues, enterprises found their solution in the cloud. Ad as we know today, a cloud data warehouse is excessively popular among enterprises and helping them make sense of all the data. They help businesses streamline their operations and gain visibility to all departments running within. Moreover, cloud data warehouses help enterprises serve their customers and create further opportunities in the market. As businesses come up with new plans and products, data warehouses begin to play even a more important role in the process. They are becoming the new norm. Gone are the days when an enterprise had to purchase hardware, create server rooms along with hire, train, and maintain a dedicated team of staff to run it. Today, the tables have turned and everything is being managed on the cloud. But, to precisely understand why cloud data warehouses outperform traditional systems we need to dive down into their differences.Cloud Data Warehouses Becoming the New Norm
Today’s businesses are moving faster than ever. In other words, they are racing out too far more customers and accomplishing a lot more things. Data has become a part of their core processes. For example, banks are processing the credit and debit cards of customers at every second. Similarly, insurance companies are maintaining their customer profiles and updating them frequently with policy-related information and changes. On the other hand, we have brick and mortar stores, process in-store purchases while the online stores process the purchases made digitally. The idea behind this is that all these stores process information that is transactional in nature. They have to be written and updated frequently. Right now businesses have an online transaction processing database to take care of these. This is just one side of the coin. The other side means managing revenue, business operations, customer engagements, and many other things, that are potentially based on the transactional data. Moreover, this data is only growing and businesses need a solution for their optimization. The problem is, however, that online transaction processing systems are designed for managing and processing one small transaction at a time. When it comes to tons of data they fail to deliver the required results. This is where the solution of data warehouses emerges. They already can perform processing on large amounts of data. As a link to the traditional transactional database, they will hold a copy of it and store it safely in the cloud. Moreover, the best part of using a cloud data warehouse is that they only charge you for the services you use. For example, based on your company data, you will require a certain amount of space in the cloud. Similarly, for the number of computations, you have to perform you will need a separate computational space. In theAuthor Bio
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