Best Synthetic Data Generation Processes

Aditya Chakurkar | January 15, 2022 | 531 views

Best_Synthetic_Data
Synthetic data is a replacement for the real thing when researchers and scientists can't get their hands on it because of confidentiality or privacy concerns. In simple words, synthetic data is a replacement for real data.

With the growing demand for artificial intelligence, there's no way we can produce enough real-world examples on our own. As a result, the need to create synthetic data has never been more pressing than it is now.


What is Synthetic Data?

Synthetic data is artificial data that is created through algorithms. It simulates the statistical properties of real people but without revealing any identifying information. Businesses across various industries need synthetic data for three major reasons:

  • Training machine learning algorithms
  • Product testing
  • Privacy

Artificial intelligence is becoming more and more advanced, with even the most complicated tasks being handled by these computer systems. For us humans who work in a less sophisticated field than AI development to make sense out of what they're doing sometimes, we need help from "synthetic" data which reflects real-world events mathematically or statistically but does not come directly from any person.

Synthetic data is a bit like diet soda. To be effective, it has to resemble the “real thing” in certain ways. Diet soda should look, taste, and fizz like regular soda. Similarly, a synthetic dataset must have the same mathematical and statistical properties as the real-world dataset it's standing in for. It looks like it, and has formatting like it.”

Kalyan Veeramachaneni, principal investigator of the Data to AI (DAI) Lab and a principal research scientist in MIT’s Laboratory for Information and Decision Systems


What are the Best Synthetic Data Generation Techniques?

Different methods of data synthesis can be used to generate synthetic data. Businesses should choose a method according to the requirements and level desired for specific purposes in generating this type of information, such as decision tree learning versus deep-learning techniques. Let’s learn more about some of the best synthetic data generation techniques.


Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)

Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) are deep generative models. VAE is a method of encoding data so that it can be transmitted more efficiently. The encoder creates an output that represents the original input, and then there's training on how well those two match up to each other through optimization methods.

On the other hand, the GAN model is an artificial intelligence technique that takes data from real-world events and generates synthetic ones. This process happens in two parts: generators create this new information, using something like machine learning or statistical analysis, and discriminants compare these synthetically created datasets with their original versions based on certain conditions set before training begins.


Monte Carlo Method

It has been said that if you have the data, then gambling on its distribution can be an effective means of generating synthetic samples. This is because businesses can determine what their best-fit model should look like by considering both observed information as well as potential outcomes based on different models or theories about how this particular set might behave; once they know these parameters it becomes possible for them to use Monte Carlo synthetic data generation method.


According to Dataset Distribution

For cases where real data does not exist but an analyst has a comprehensive understanding of how dataset distribution would look like, they can generate a random sample from any given distribution such as Exponential, Lognormal, Uniform, Chi-Square, Normal or t-distributions.


Scikit-Learn

Scikit-Learn is a method of synthetic data generation using Python, one of the most popular languages in the field of data science. Scikit-Learn is one of Python's three libraries that can be used to generate synthetic data. This powerful Python tool can be used for machine learning tasks like regression, classification and clustering. It also allows you to generate synthetic data if there's no real-world example of your researched topic.


Best Tools for Synthetic Data Generation

Generating synthetic data includes two steps: Data preparation and data synthesis. Data preparation is necessary before data synthesis. While several vendors can perform these two steps, we have compiled the three, best synthetic data generation tools for all:


Hazy

Hazy offers unique models capable of generating high-quality synthetic data with a differential privacy mechanism. Data can be tabular, sequential (containing time-dependent events), or dispersed across several tables in relational databases.


Edgecase.AI

Edgecase.AI takes the need for large-scale data annotation and generation of training images/videos to a whole new level with its proprietary platform. It helps solve fundamental problems in multiple industries like security, industry 4.0, healthcare, agriculture and retail.


MOSTLY AI

MOSTLY AI offers the leading synthetic data platform, which enables enterprises to unlock, share and fix their data. Thanks to its advanced artificial intelligence system, MOSTLY AI promises unmatched synthetic data accuracy while maintaining granular-level information.


Frequently Asked Questions


Which business functions can consider using synthetic data for improved overall efficiency?

The benefits of synthetic data are many. Data is an incredibly important component in all areas and can help us continue innovating new products, services or solutions when the necessary information isn't readily available. The following business functions can make the most of synthetic data for the best outcomes:

  • Human resources
  • DevOps
  • Agile Development
  • Machine Learning
  • Marketing


What are the key benefits of synthetic data?

Synthetic data has many benefits over real data. For example,

  • It focuses on multivariate relationships
  • It is immune to several, common, statistical problems
  • It overcomes restrictions that may arise while using real data


What are the main limitations of synthetic data?

Even though synthetic data has many advantages, there are some challenges around it as well. For example,

  • It requires output control
  • It is time-consuming
  • It is yet to gain significant user acceptance
  • Data quality depends heavily on data source

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Miro Announces AI-Powered Solution to Supercharge Innovation for Global Enterprises

Prnewswire | May 29, 2023

Miro®, the visual workspace for innovation, today announced Miro AI - a set of Artificial Intelligence (AI)-powered features that optimize the most common and complex workflows on Miro. The features are available to all enterprise customers and users. Miro AI harnesses the power of large language models and generative AI - including leveraging Miro's own in-house models - to streamline a variety of processes and workflows, helping teams bring products and services to market more quickly. Miro is a critical tool in hybrid work environments where distributed, cross-functional teams must collaborate on complex workflows, including customer research analysis, strategy workshops, technical diagramming, and customer-centric product design. Miro AI is optimized for these workflows and functions across the platform to ensure every team member has access to the same information and capabilities designed for innovation. As an enterprise-grade solution, Miro AI is designed with security and privacy in mind. Miro has partnered with Azure OpenAI Service by Microsoft, a secure and enterprise-ready AI partner, enabling it to build enterprise-ready features at scale. Utilizing Miro AI does not change the robust admin controls on the platform, company admins can still track, manage, and update permissions with ease. Input data is only kept for the period that it's being processed in order to generate the output. No input data is stored after that, and information placed on Miro is not used to train AI models. "Miro AI plays a key part in helping teams accelerate every stage of the product life cycle. It's especially valuable for supporting enterprise product teams responsible for research and development to accelerate the creation of new products and services", says Varun Parmar, head of product at Miro. "Miro AI can perform tasks like creating technical diagrams, interpreting code, and clustering and summarizing board content. The goal is maximizing output and eliminating tedious overhead." "AI should enrich and support the work you do, and I'm already seeing multiple ways in which Miro AI will benefit my team," says Vlore Krug, Head of Organizational Development, mobile.de, Germany's biggest online vehicle marketplace. "Our agile coaches often facilitate brainstorms with up to 15 participants. At the end of an activity we spend a huge amount of time 'clustering' comments and ideas posted in sticky notes. With Miro AI, these notes can be organized and summarized automatically in seconds. This means less time spent on time-intensive, manual tasks where we risk losing concentration and momentum, and more time collaborating with customers or colleagues." Miro will initially bring 12 AI features to market. These include the ability to generate, summarize, and cluster ideas based on sentiment and keyword, bring ideas to life by automatically generating images based on text, and instantly visualize complexity using sequence diagrams. "Miro AI will be invaluable in supporting our client-facing activities," says Martin Gleitsmann, Business Strategist at not yet normal, a business development agency based in Hamburg, Germany. "We want every participant - from Designer to CMO - to feel included and empowered in our creative and innovation processes. AI-based technology will help to eliminate the knowledge gaps for participants during sprints providing them with a helpful tool of expression. In addition, with Miro AI supporting our operations, we can really deep-dive with our clients on Miro to make observations that bring us a deeper, more complex understanding of trends - eliminating the human research-bias and doing this in seconds, rather than hours or days." Miro AI exemplifies Miro's ability to bring big, innovative ideas to life through constant iteration and experimentation and is purposefully built with its users in mind, addressing the most common and most complex challenges that slow innovation. New features and improvements will be introduced on a rolling basis. Miro invites its Community to share the features they would like to see next by visiting the Miro AI Wish List. About Miro Miro is a visual workspace for innovation that enables distributed teams of any size to build the next big thing. The platform's infinite canvas enables teams to lead engaging workshops and meetings, design products, brainstorm ideas, and more. Miro, co-headquartered in San Francisco and Amsterdam, serves more than 50M users worldwide, including 99% of the Fortune 100. Miro was founded in 2011 and currently has more than 1,500 employees in 12 hubs around the world. To learn more, please visit https://miro.com.

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AI TECH, AI APPLICATIONS, SOFTWARE

Rocketium Launches New Agile CreativeOps Platform With Novel AI Features

Accesswire | May 30, 2023

Rocketium, a platform that specializes in improving in-app and website conversions through video and display banner automation, has unveiled its latest breakthrough: an innovative and agile CreativeOps platform. This cutting-edge platform marks a significant milestone in the realm of creative operations, as it seamlessly integrates with robust generative AI features, the latest in automation capabilities, and advanced creative analytics. As a company that specializes in the field of creative technology solutions, Rocketium has combined these components into a solution to empower global teams when it comes to enhancing their design processes and making astute decisions that yield substantial business impact. At the core of Rocketium's new CreativeOps platform lies its generative AI technology, which revolutionizes the way creative content is generated and produced. Leveraging the power of artificial intelligence, the platform generates high-quality visuals, videos, and other creative assets with remarkable efficiency. This AI-driven capability enables teams to streamline their creative workflows, eliminating repetitive tasks and freeing up valuable time for more strategic endeavors. Rocketium's new platform offers automation features that provide users with a range of automated tools and processes. These features automate various aspects of the design process, such as asset organization, version control, and collaboration, reducing the likelihood of errors and improving overall productivity. The automation capabilities allow teams to work swiftly, resulting in faster turnaround times and increased agility in responding to market demands. Creative analytics is another pivotal component integrated into Rocketium's platform, enabling teams to make data-driven design decisions. The use of advanced analytics and reporting functionalities allows the platform to provide valuable insights into the performance and impact of creative assets. Teams can measure key metrics, such as engagement, conversions, and brand reach, gaining a comprehensive understanding of the effectiveness of their designs. Armed with these insights, teams can optimize their creative strategies, refine their approach, and ultimately achieve more substantial business outcomes. The comprehensive nature of Rocketium's CreativeOps platform ensures that global teams can leverage its features and benefits across multiple dimensions. Whether collaborating remotely or across different time zones, the platform facilitates seamless communication and coordination, fostering effective teamwork. The platform's intuitive user interface and user-friendly tools ensure ease of use, making it accessible to professionals across various skill levels. Sandeep Shekhawat, who works with Rocketium as a tech advisor, says that the CreativeOps platform will help the company as it continues to grow and scale. The company's tech has already earned the trust of over 100 brands including Fortune 500 and Forbes Global 2000 companies.. Professionals from a wide range of industries, including marketers, designers, e-commerce managers, and product managers, rely on Rocketium's platform to take their ideas to market more quickly. The company works with large enterprises, eCommerce marketplaces, and retail omnichannel companies with teams spread across countries. "The team at Rocketium is dedicated to assisting brands and businesses in scaling and enhancing their visual content and driving improved conversion rates," says Shekhawat. "The launch of this agile CreativeOps platform means that the landscape of design decision-making undergoes a remarkable transformation. Through the integration of generative AI features, automation capabilities, and creative analytics, the platform equips global teams with the tools they need to make informed and impactful design choices." Shekhawat adds that Rocketium's growth strategy involves expanding its operations in the US to target enterprise customers with complex content creation workflows and pipelines. "They are specifically focused on creating advertising campaign content and digital display content in retail for campaigns and price displays," Shekhawat reveals. Shekhawat, an expert in the areas of electronic price displays, digital transformation, and IoT-based Smart Retail, has worked with some of the biggest brands in the world, developing platforms that help these massive corporations deliver content in a reliable, efficient manner, with the ability to update content over the air in real-time, while using low power. "Enhancing in-store experience with video displays using automated and personalized content creation will be a game changer for big name retailers," Shekhawat affirms. "Creating compelling content to showcase product features, provide helpful information, or entertain customers while they shop will help retailers have the edge in the low margin retail business." With Rocketium's agile CreativeOps platform, users can harness the power of this innovative solution, and businesses can unlock new levels of creativity, efficiency, and effectiveness. About Rocketium Rocketium, established in 2015, is an agile CreativeOps platform that helps enterprises and Fortune 500 companies take their communications to market in cost- and time-efficient ways. The company utilizes creative automation, seamless collaboration, automated compliance, and creative analytics with the goal of helping enterprises maximize their existing teams, processes, and tools.

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