![]() Synthetic Benchmarks for Scientific Research in Explainable Machine Learning releases XAI-Bench, a suite of synthetic data sets that can be configured and re-engineered to simulate real-world date. Research Ībacus.AI published research papers at the Conference on Neural Information Processing Systems. This data can then be used to train the models similar to the processes mentioned. This allowed the platform to take in data in a streaming fashion providing companies the ability stream real-time events like clickstream data (a user’s online activity), online purchases, social media interactions, media views from websites and “ internet of things” sensors. However, in August 2021, this service was expanded to any TensorFlow or PyTorch model. This service was originally confined to the automatic models. They further improved this technique using data augmentation (DAGAN), creating synthetic data sets when not enough data is available for training. This AI service uses generative adversarial network (GAN), a technique that generates new similar data given a training set. While this might lead to neural networks not being trained with data that is relevant to a customer, Abacus.AI says it creates synthetic data that augments the original dataset, and then trains a deep learning model on the combined dataset. Ībacus.AI uses an autonomous AI generation service, announced in January 2020. For example, for incident detection, Abacus.AI uses a technique called variational encoders. Different use-cases such as demand forecasting, churn reduction, and name entity recognition will use different NAS techniques to provide the best model. The data is given to the platform then evaluated to determine which “tool” (NAS) is best suited for that use-case. Once the data is transformed, Abacus.AI utilizes neural architecture search (NAS) techniques to create a custom neural network based on the dataset provided and the use-case. Ībacus.AI is able to connect to various data sources including S3, Google Cloud and Azure which makes it easy to set up data transformations for machine learning. The company has invented several neural architecture search methods that can create custom neural networks from data sets based on a specific use-case. In addition to the core platform, Abacus.AI provides use-case specific workflows including personalization, forecasting, and anomaly detection. Technology Ībacus.AI can be used to set up data pipelines, specify custom machine learning specific transformations, train models and deploy and monitor them, and build deep learning systems. ![]() In 2021, it raised $50 million led by Tiger Global Management. In 2020, it raised $13 million led by Index Ventures (changing its name to Abacus.AI in January), and $22 million led by Coatue. The company raised $5.3 million in seed funding round led by Eric Schmidt in 2019. Abacus.AI markets using the terms artificial intelligence and machine learning. ![]() Initially known as RealityEngines.AI, the company was founded by Bindu Reddy, Arvind Sundararajan, and Siddartha Naidu in 2019. American AI and machine learning company Abacus.AI IndustryĪrtificial intelligence, machine learningīindu Reddy, Arvind Sundararajan, and Siddartha NaiduĪbacus.AI is an Artificial Intelligence and Machine Learning platform headquartered in the San Francisco Bay Area.
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