MOSTLY AI, a Munich-based startup, has announced a $25 million Series A funding round led by Earlybird Venture Capital.
The company focuses on bringing its synthetic data technology to enterprise customers, helping them to create high-quality datasets for training their artificial intelligence and machine learning models.
This article will dive deeper into the technology and how they plan to use the funding.
Overview of MOSTLY AI
MOSTLY AI is an AI data generation platform that enables developers and organisations to generate realistic, reliable, and confidential datasets for AI model training. As an end-to-end platform, MOSTLY AI provides services related to data modelling and simulation to acquire the most accurate datasets available for machine learning projects.
The company’s technology can generate proprietary datasets for various applications such as autonomous driving, medical imaging analysis, predictive maintenance and natural language processing. Instead of gathering large amounts of personal data which carries the risk of a data breach or violates any privacy legislation – their technology can produce non-sensitive but rich “synthetic” data derived from diverse sources! This dataset is helpful for companies that must adhere to privacy laws or provide confidential information.
Utilising powerful machine learning algorithms such as Graph Neural Networks (GNNs), Accelerated 3D Image Rendering (AR3D) and Generative Adversarial Networks (GANs), MOSTLY AI’s platform leverages basic properties of a digital system to create simulated environment – essentially a “private” playground where no one else has access. Through this process the company can synthesise large scale training datasets without having access to real-world resources. Ultimately, this system produces high fidelity labels that enable machines to learn more efficiently.
Highlighting the company’s technology
At a time when more organisations are relying on artificial intelligence (AI) to get work done, the need for realistic data to train the models that power AI systems is increasing. But collecting such data can be a challenge. Fortunately, there is a solution in the form of artificial dataset generation technology.
The company’s AI-driven artificial dataset generator eliminates the need to manually collect and label training data. Instead, it uses GANs or Generative Adversarial Networks to ingest real datasets of any scale and complexity and simulate additional data sets from there. This process generates an array of high-fidelity, diverse datasets that can be used to train any AI model quickly and easily.
The benefits of this technology include more accurate models as it better captures real features like variability for real-time testing scenarios; improved training run times; significantly reduced costs associated with manual labour; higher return on investment due to improved accuracy; increased flexibility for testing various configurations; greater accuracy in predicting the effect of taking action in production environment; improved readability and organisation of data sets through module autoencoding feature; elimination of need for human intervention while generating data sets, reducing tedious labour hours There are other benefits as well such as scalability, faster experimentation cycles, improved performance optimization capabilities and so on.
The Need for Synthetic Data
With the rise of Artificial Intelligence and Machine Learning technologies, it is becoming increasingly important for enterprises to have access to high-quality and diverse datasets.
Synthetic data is essential to this, as it can provide precisely the kind of accurate and realistic data required to train AI models. Recently, MOSTLY AI has raised $25M to bring synthetic data to every enterprise, highlighting the importance of such data.
Let’s look at the need for synthetic data in more detail.
Challenges faced by enterprises
Generating synthetic data for training AI models is an emerging technology that has become increasingly important for enterprise applications. However, data can quickly become outdated as customers, products and processes constantly evolve, leaving organisations needing to train their models with synthetic data.
Despite this trend, many enterprises face significant challenges when using synthetic data, especially regarding availability, complexity and cost. Availability can be a tricky problem due to the large amounts of data needed for model training and the difficulty of finding ready-made datasets available for research and deployment. Complexity is another challenge as various tools are required to generate highly realistic datasets. Finally, cost is also a factor because obtaining ready-made or generating custom datasets can be expensive.
The need to overcome these challenges has led many organisations to utilise the latest technologies such as AI software platforms and open source libraries designed to facilitate the generation of high-quality synthetic data sets that are both cost effective and efficient. By leveraging these technologies, companies can create realistic scenarios which mimic real-world conditions through machine learning algorithms that understand input parameters such as spatial structure, temporal dynamics and volume fluctuation while ensuring compliance with various standards such as GDPR or HIPPA.
With access to high-quality data from trustworthy sources being more critical than ever for AI model training purposes, companies must invest in technologies like synthetic data generation to future proof their businesses against potential risks associated with changing customer behaviour, emerging regulatory considerations or rising regulatory competition in the digital age.
Benefits of using synthetic data
Synthetic data generation and applications have become an increasingly important part of artificial intelligence (AI) technology. Machine learning models require large training datasets to develop accurate and effective models for their use case. Traditional data generation methods can be costly and time consuming, making it hard for businesses to acquire the clean, balanced data they need in reasonable timeframes. Synthetic data has several advantages over real world data that can help businesses understand the value and benefit of leveraging synthetic data to train machine learning models and create realistic simulations quickly.
Synthetic data comes with several distinct advantages compared to real world datasets, including:
- Eliminating the need for large amounts of data from limited sources which can include laborious processes such as manual labelling;
- Generating tailored production input/output features at scale with controlled output values;
- Simulating scenarios which are hard or impossible to recreate in a true environment;
- Being able to accurately train AI models on complex relationships between datasets that wouldn’t be easily found using real world datasets;
- Having access to vast amounts of generalizable synthetic datasets that represent different environments or locations without needing access to highly sampled or collected combinations;
- Testing AI models with diverse, unpredictable populations before launching into production so that AI systems perform well in any context without bias.
Synthetic data has many benefits over traditional methods of generating training input/output pairs, making it an attractive option for businesses that rely on machine learning technology to take their products/services forward. With this technology companies can generate high quality training samples at scale, gain insight into how their model will behave in unknown situations, optimise costs, reduce risks when deploying AI systems and increase overall user trust by avoiding bias from source material used for training machine learning models.
MOSTLY AI Raises $25M to Bring Synthetic Data to Every Enterprise
MOSTLY AI, a Berlin-based startup, recently raised $25 million to bring synthetic data to every enterprise. Their technology is designed to generate realistic data for training AI models.
This data can help enterprises build more powerful and smarter AI models and accelerate their AI development cycles.
Let’s dive deeper into MOSTLY AI’s technology.
Generating realistic data
At MOSTLY AI, we use advanced middle-ware and proprietary algorithms to create synthetic data that can be used as a surrogate for real data. The synthetically generated data is methodically designed to replicate real world characteristics of transactional and behavioural datasets down to the granular details.
The process starts with ingesting existing real world datasets and extracting key parameters that can be used to create realistic new datasets using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP). We then use a combination of unsupervised learning techniques and domain knowledge to generate statistically balanced datasets with ‘ground truth’ labels by applying certain constraints. This enables us to accurately simulate every aspect of the original dataset including its statistical properties such as population distribution, record appearance, individual component distribution, and correlations among variables. We can also generate synthetic images according to a dataset’s visual distribution.
The use of synthetic data enables our customers to have training data with crowd-sourced digital twins, ensuring privacy compliance regardless of the intended use case. In addition, combining our technology with cloud computing allows for iterative improvements made through AI models that could not have been achieved with traditional methods or metrics because the generated dataset is difficult or impossible for humans to perceive on its own.
Automated data generation
MOSTLY AI is a company that specialises in automated data generation for artificial intelligence (AI) models. This type of technology involves generating realistic virtual data to train algorithms. For example, MOSTLY AI can generate accurate images, audio, text and structured data such as trajectories using sophisticated algorithms. The generated data can be used as synthetic datasets for training, testing and validation.
MOSTLY AI offers developers access to powerful tools to generate large quantities of data quickly and with high accuracy. They also offer end-to-end solutions that cover the creation, annotation and curation of synthetically generated data. In addition, the automated synthesised datasets often contain finer details and more diverse examples than publicly available datasets, providing an effective training resource for AI and machine learning models.
The company’s technology provides great flexibility as it allows developers to create customised synthetic datasets, meeting specific requirements in terms of size and characteristics. For example, they can readily simulate any given environment or object motion with pixel-level granularity, creating highly realistic scenes that leverage all available information from existing environments such as GPS coordinates or 3D scans. Furthermore, users have full control on parameters such as the distribution range or the number of annotations needed for a given task to be thoroughly trained on the generated dataset.
With MOSTLY AI’s automated platform users can save time by generating accurate results at scale with minimal effort while avoiding costly labelling services and tedious manual processes usually entailed by traditional image annotation techniques when large amounts of customised dataset need to be collected quickly.
Benefits of Using MOSTLY AI
MOSTLY AI is a company that provides synthetic data for AI model training. Their technology can generate realistic data for training AI models, which can benefit enterprises looking for high-quality data for their AI applications.
With the recent raise of $25M, MOSTLY AI is set to bring synthetic data to every enterprise. Let’s look further into the advantages of using MOSTLY AI.
Reducing cost and time for data collection
MOSTLY AI’s technology uses real-world data to generate realistic datasets for training Artificial Intelligence & Machine Learning models. This reduces costs and time associated with manually collecting data by leveraging existing data sources such as images and videos. As a result, non-technical staff no longer need to manually record or collect large data sets that AI models would have needed to achieve a desired accuracy in training.
By significantly reducing cost and time associated with manual annotation and curation, MOSTLY AI’s technology allows businesses to realise the full potential of their Artificial Intelligence initiatives faster than ever before. Moreover, the accuracy and value of the generated datasets are validated by comparison with existing real-world datasets and complemented with fine-tuned controls over the data landscape offered by MOSTLY AI platform.
With this highly sophisticated simulated environment, businesses can experiment on different techniques without leaving the comfort of their work area, providing them more insights about how effective their Artificial Intelligence model will be before making any expensive investments.
Increasing accuracy of AI models
MOSTLY AI’s technology can dramatically improve the accuracy of AI models. By generating realistic data for training and testing, the company’s synthetic datasets provide AI models with more accurate representations of the real world. This enhanced accuracy means that these models are better equipped to understand different inputs, deal with noisy data more effectively, and make fewer mistakes when deployed in real-world applications.
The data used in AI development often reflects real-world information’s structure and characteristics. However, collecting enough accurate data samples to achieve reliable ML and CV results is often difficult. MOSTLY AI solves this by offering users access to huge databases of synthetic images, text, and audio and video samples generated using deep learning algorithms. The resulting Artificial Intelligence Datasets (AIDs) accurately reflect true user behaviour and environment preferences, giving teams much more control over their models from day one.
Furthermore, the dataset generated by MOSTLY AI includes labelled images that allow for more efficient training and enables developers to quickly adjust hyperparameters for their desired results. It also offers a unique defence against possible security threats including identity theft or fraud recognition when combined with other methods such as biometrics authentication or facial recognition systems. Finally, its generalised datasets support testing with relative ease while maintaining scale.
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