MOSTLY AI, a leading provider of synthetic data, announced it raised $25 million in Series A funding from top investors in Europe and the US. The funds will help the company accelerate its commercialization of synthetic data for artificial intelligence (AI) and machine learning (ML) applications.
This round of funding will enable MOSTLY AI to ramp up its European and US operations and expand its impact in the AI and ML industry.
In this article, we’ll discuss the advantages of using synthetic data and how it will likely shape the future of AI and ML.
MOSTLY AI raises $25 million to further commercialize synthetic data in Europe and the US
MOSTLY AI is a synthetic data production and Machine Learning Auto-Labelling provider founded in 2018. The company has raised $25 million to further commercialise its technology in Europe and the US for creating virtual means for training, validating, and deploying artificial intelligence (AI) applications. Valor Equity Partners led the round with participation from existing investors: Balderton Capital, Earlybird Venture Capital, Piton Capital (UK), Aleron Ventures (France), Innogy Ventures(Germany), Thomvest Ventures (US) and Kardan Ventures (Israel).
MOSTLY AI has developed an end-to-end platform comprising a cloud hosted Engine that produces rich synthetic data in various formats such as images, semantic graphs & 3D environments. This data is then used to generate high quality ‘Ground Truth Labels’ leveraging Artificial Intelligence via various cognitive automation techniques such as unlabeled Supervised Learning , Unsupervised learning & natural language processing (NLP).
The engine enables developers to easily generate hundreds of thousands of unique data sets that accurately reproduce the original input parameters. Data Sources include:
- Open source images and any other source data like 3D CAD files or GIS mapping files and target markets being any industry where visual recognition & object detection is relevant like autonomous driving.
- Medical imaging.
- Transportation & logistics.
- Industrial automation amongst many others.
Overview of synthetic data
Synthetic data is computer-generated data that has been designed to mimic real-world aspects of a domain. It trains artificial intelligence (AI) algorithms to accelerate machine learning development and improve accuracy. Synthetically generated datasets provide many benefits over traditional data, including scalability, privacy, and control of specific scenarios for testing AI applications.
MOSTLY AI raises $25 million in venture capital funding to further commercialise its synthetic data platform for customers in Europe and the US. The platform accelerates the development of AI applications by providing access to a large range of diverse synthetic datasets that are both accurate and representative of the target domain. As a result, companies can use MOSTLY AI’s platform to quickly build their training datasets and test complex machine learning systems without having access to real-world data or needing a specialist technical team.
With more organisations concerned about protecting personal information while collecting training dataset accurately, MOSTLY AI’s capability makes it an attractive solution for developing high-quality AI applications in behemoth industries like healthcare, autonomous driving, insurance providers, manufacturing industry, finance etc. This further bolsters adoption of Cloud technology. The company plans to use the new funding to build out its suite of products, scale their engineering team, expand into new industries and geographies with synthetic data generation services and provide more flexibility and control within its customer experience design operations.
Benefits of Synthetic Data
Synthetic data, which is generated from a computer from algorithms, is gaining traction as a way to improve the development of artificial intelligence applications.
Using synthetic data, rather than real-world data, AI companies can generate infinite possibilities of data scenarios that can help machine learning algorithms better understand and predict outcomes.
In this article, we’ll discuss the benefits of using synthetic data for AI applications and how it can help speed up development.
Many companies are now turning to the use of synthetic data in their artificial intelligence (AI) applications. Synthetic data is computer-generated data which can be used to train machine learning algorithms. This data type has several advantages over traditional or real-world data, including improved accuracy and increased privacy.
Creating AI algorithms requires vast data to teach machines how to recognize patterns and make decisions. In many cases, this can be difficult or impossible to source from real-world environments, as it needs to be standardised and suitably diverse. This is where synthetic data can come into play. By creating computer-generated datasets with the precise characteristics needed for AI training, this method offers a time-effective way of replicating the data type that would otherwise have been too hard and expensive to access.
Due to its precise nature, synthetic datasets often result in higher levels of accuracy than traditional datasets because they can help avoid missing values or outliers that may be unavoidable in real life sources of information. In addition, this improved level of accuracy will significantly increase the performance consistency and reliability for AI applications such as robotics or self-driving cars where precision is vital for safety reasons and business success.
This is where synthetic data can come into play. By creating computer-generated datasets with the precise characteristics needed for AI training, this method offers a time-effective way of replicating the data type that would otherwise have been too hard and expensive to access.
Another advantage is that partly or entirely synthetic datasets offer greater privacy safeguards than traditional datasets, particularly when it comes to handling sensitive information or application requirements related to GDPR compliance or protecting intellectual property (IP).
One of the most pressing challenges when creating AI applications is lack of data. Obtaining real data to train a model can involve large costs and require many resources. However, using synthetic data allows companies to develop models quickly and cost-effectively, reducing upfront investment costs.
Generating synthetic data requires significantly less effort than acquiring real-world datasets and can provide a fast path to building a model with superior accuracy compared to existing models trained on real data. Moreover, modelling with synthetic data eliminates potential pitfalls associated with bias or uneven distribution that may be present in those datasets. In addition, synthetic datasets are created from scratch, so issues like privacy are not applicable and can simply be avoided. This could lead to faster development cycles for AI applications since developers and engineers don’t need to wait for new datasets or have access to old ones.
Additionally, synthetic data can increase scalability and reduce infrastructure overhead, especially in distributed systems such as clouds or applications running on mobile devices since it does not require accessing real-world information from multiple sources at once which could prove unnecessary costly operationally speaking. This can then achieve more efficient processing power optimization which boosts speed and accuracy depending on the task at hand while remaining secure simultaneously due to its inherent privacy features.
One of the primary benefits of synthetic data is the time savings it provides. By quickly generating data, developers can spend less time collecting and organising it. This can significantly reduce the amount of work required to build AI models. Also, since synthetic data never changes, developers don’t have to continually monitor for new data sources or changes in existing ones.
Additionally, working with synthetic data is a much faster process than collecting and preparing real-world data. It eliminates tasks such as manual input and manual labelling, which can take up many productive hours per project. As a result, companies using synthetic data can deliver their AI solutions quickly and cost-effectively while still maintaining accuracy and quality standards.
Finally, using synthetic data with AI allows organisations to explore different algorithms at a rapid pace since the best algorithm for the task can be determined almost instantly by running through a large number of potential solutions with this simulated data rather than waiting for large distinct datasets for each solution exploration step. This helps to speed up experiments which give greater insight into ways that AI technologies can be used within various industries and contexts faster than alternatives that come from gathering real-world datasets every time an experiment is conducted.
Mostly AI’s Synthetic Data
Mostly AI, a startup specialised in developing and commercialising synthetic data, recently raised $25 million to further promote the use of its synthetic data in Europe and the US.
Synthetic data can help speed up the development and deployment of AI applications that can potentially disrupt any industry.
In this article, we will look at what exactly synthetic data is and the key benefits and drawbacks of using it.
Overview of Mostly AI’s synthetic data
Mostly AI is a Paris-based artificial intelligence (AI) startup committed to further commercialising synthetic data in Europe and the United States. Synthetic data is computer-generated data that often appears to be real. It is designed to mimic real-world behaviours and generate accurate insights. Recently, Mostly AI has secured $25 million funding to expand its synthetic data production capabilities with its new Series A funding led by Insight Partners.
Using its state-of-the-art AI technology, Mostly AI uses raw data like images and videos to create realistic yet machine generated datasets scaled up for training algorithms with millions of anonymized records that can help speed up the development process of AI applications. For example, through their crowdsourced platform “RealityBank©”, Mostly AI offers access to over 4 billion virtual personas from 70+ countries creating entirely new data landscapes by providing realistic but unbiased datasets on age range, profession and gender representation for modelling the world’s population virtually in a safe environment for a fraction of the cost and time it takes to produce better models for enterprises.
In addition, as an effort towards staying compliant with GDPR, all data generated from within their platform or any traditional source are fully anonymized leveraging voice recognition algorithms among other features before entering a training pipeline that can help optimise facial recognition performance outcomes or develop better forecasting models depending on the unique needs of different enterprises. As a provider of large collections of synthetic datasets encompassing the entire CI/CD pipeline, Mostly AI offers several automation packages complete with configurable options where customers can customise their requirements while drastically reducing cost and resources spent on development projects related to Machine Learning (ML).
How it works
Mostly AI is an artificial intelligence (AI) startup dedicated to accelerating the development of AI applications using synthetic data. The company has developed a suite of technology and services, which harnesses cutting-edge Artificial Intelligence methods such as Generative Adversarial Networks (GANs) and Deep Learning to produce large scale, high-quality synthetic datasets for use in training and deploying Machine Learning models. By doing so, the company believes it can help its customers dramatically reduce the time and cost of gathering large amounts of real-world data.
By leveraging Mostly AI’s synthetic datasets, companies can quickly build AI applications that normally require a sizable investment in time and labour. Through its proprietary software, Mostly AI enables users to generate accurate simulations of realistic data in various domains such as public transportation, healthcare, telecommunications and financial services. The company has also invested heavily in building powerful neural networks that leverage deep learning techniques to further improve accuracy and fidelity of generated synthetic datasets.
As part of its mission to promote adoption of its technology across multiple industries and markets worldwide, Mostly AI recently announced it has secured $25 million in Series A funding from strategic investors who have the same vision for using synthetic data to advance AI development. With this capital injection, the startup says it will be focusing on commercialising its solutions in Europe and North America over the coming months – focused on expanding existing customer relationships with existing partners and onboarding new customers from these markets.
Mostly AI’s Expansion
Recently, Mostly AI, a synthetic data platform, has secured $25 million in a Series A funding round to further expand its operations in Europe and the US.
This funding will help power the company’s mission to commercialise its synthetic data platform, which it believes could be used to speed up the development of AI applications.
Let’s explore the implications of this expansion.
Overview of Mostly AI’s $25 million funding
Mostly AI, a startup that creates data-driven solutions for large organisations, recently announced it raised $25 million in a funding round. This funding round was led by Prime Ventures and supported by Notion and existing investors. Mostly AI is based in Zurich, Switzerland and plans to expand their growth with this new capital to both Europe and the United States markets.
The company develops synthetic data solutions, which are automated tools designed to generate training data for developing Artificial Intelligence (AI) applications. Their solutions allow businesses to dramatically reduce the manual effort required to create training datasets for creating machine learning models.
Their synthetic training datasets created through this technology are said to improve overall modelling accuracy while being cheaper than traditional methods or manual efforts used in developing AI applications. The data generated can also be used quickly as it meets industry needs while remaining privacy compliant – as it is generated from scratch with heavily anonymized processes inspired by theoretical research on differential privacy.
Through this fresh injection of capital, Mostly AI plans on ramping up its commercialization efforts across Europe and the US but will continue to focus on improvements such as technological innovation and democratising quality training datasets so that they become more affordable and accessible to developers.
Following its recent $25 million funding round, Mostly AI is accelerating its plans to commercialise synthetic data across Europe and the US. Synthetic data is an AI-generated representation of real-world data that can be used to power machine learning applications quickly and cost-effectively.
To support the continued development of its synthetic data platform, Mostly AI plans to invest in various areas, from establishing a network of partner service providers to strengthening their security processes and introducing new technologies. Furthermore, they plan to expand their presence into new markets, focusing on the European Union and North America.
The European Investment Fund (EIF) has backed this strategy with a €7 million leap fund offered through InnovFin SME Venture Capital. This funding supports businesses seeking to grow in strategic areas such as machine learning and data analytics. The EIF financing also enhances access to risk capital for smaller firms making fast progress in the global market while providing stability in uncertain times.
The expansion will help empower more companies across Europe and the US by accelerating their ability to leverage AI capabilities for their businesses. Ultimately, this should create more opportunities for startups that have identified potential use cases for synthetic data-driven decision making but may otherwise find it difficult accessing funds or skilled professionals at scale.
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