SAN FRANCISCO and GENEVA, Sept. 26, 2020 /PRNewswire/ — Vectorspace AI and CERN, the European Organization for Nuclear Research and the largest particle physics laboratory in the world, are creating datasets used to detect hidden relationships between particles which have broad implications across multiple industries. These datasets can provide a significant increase in precision, accuracy, signal or alpha and for any company in any industry.
For commercial use, datasets are $0.99c per minute/update and $0.99c per data source, row, column and context with additional configurations and options available on a case by case SaaS/DaaS based monthly subscription. Over 100 billion unique and powerful datasets are available based on customized data sources, rows, columns or language models.
While data can be viewed as unrefined crude oil, Vectorspace AI produces datasets which are the refined ‘gasoline’ powering all Artificial Intelligence (AI) and Machine Learning (ML) systems. Latest research suggests “The Next Big Breakthrough in AI Will Be Around Language” – HBR.
Datasets are algorithmically generated based on formal Natural Language Processing/Understanding (NLP/NLU) models including OpenAI’s GPT-3, Google’s BERT along with word2vec and other models which were built on top of vector space applications at Lawrence Berkeley National Laboratory and the US Dept. of Energy (DOE). Over 100 billion different datasets are available based on customized data sources, rows, columns or language models.
Datasets are real-time and designed to augment or append to existing proprietary datasets such as gene expression datasets in life sciences or time-series datasets in the financial markets. Example customer and industry use cases include:
Particle Physics: Rows are particles. Columns are properties. Used to predict hidden relationships between particles.
Life Sciences: Rows are infectious diseases. Columns are approved drug compounds. Used to predict which approved drug compounds might be repurposed to fight an infectious disease such as COVID19. Applications include processing 1500 peer reviewed scientific papers every 24hrs for real-time dataset production.
Financial Markets: Rows are equities. Columns are themes or global events. Used to predict hidden relationships between equities and global events. Applications include thematic investing and smart basket generation and visualization.
Data provenance, governance and security are addressed via the Dataset Pipeline Processing (DPP) hash blockchain and VXV utility token integration. Datasets are accessed via the VXV wallet-enable API where VXV is acquired and used as a utility token credit which trades on a cryptocurrency exchange.
About Vectorspace AI:
Vectorspace AI science and technology originated in Life Sciences and currently focuses on context-controlled NLP/NLU (Natural Language Processing/Understanding) and feature engineering for hidden relationship detection in data for the purpose of powering advanced approaches in Artificial Intelligence (AI) and Machine Learning (ML). Our platform powers research groups, data vendors, funds and institutions by generating on-demand NLP/NLU correlation matrix datasets. We are particularly interested in how we can engineer machines to trade information with one another or exchange and transact data in a way that minimizes a selected loss function. Our objective is to enable any group analyzing data to save time by testing a hypothesis or running experiments with higher throughput. This can increase the speed of innovation, novel scientific breakthroughs and discoveries. Vectorspace AI offers NLP/NLU services and alternative datasets consisting of correlation matrices, context-controlled sentiment scoring and other automatically engineered feature attributes. These services are available utilizing the VXV token and VXV wallet-enabled API. Vectorspace AI is a spin-off from Lawrence Berkeley National Laboratory (LBNL) and the U.S. Dept. of Energy (DOE). The team holds patents in the area of hidden relationship discovery.
SOURCE Vectorspace AI
Published at Sat, 26 Sep 2020 18:11:15 +0000
Autonomous Solutions, Inc. (ASI) recently announced that it has been awarded a Phase Two grant from the US Army Combat Capabilities Development Command Ground Vehicles Systems Center (formerly TARDEC). Based on the progress achieved during Phase One, ASI was chosen to continue development of a Deep Learning (DL) architecture that will support sensor fusion in environments with limited, or no, GPS.
Specifically, ASI is making rapid advancements in triangulating data inputs from traditional cameras, LiDAR, and radar to feed machine learning that will provide clearer visibility, predictability, and safety in environments where GPS integrity is restricted or where GPS cannot be utilised at all.
“The objective is to create clearer real-time understanding of an autonomous vehicle’s surroundings, especially when navigating through compromised weather, environments, or conditions,” said Jeff Ferrin, Chief Technology Officer at ASI. “As self-driving vehicles advance, especially for industrial use, the need to utilise machine learning, deep learning, and other artificial intelligence algorithms to improve performance in challenging environments only increases. Therefore, the success of this project is critically important – not only for the direct application within the US military, but for applications across ASI’s multiple lines of business.”
In the case of a deep learning architecture that fuses information from LiDAR, radar and cameras, the innovation could not come soon enough for some industries – especially mining.
“As global mining operations re-evaluate orebody economics and redesign mines as a result of automation, mining operations will become increasingly complex and dependent on technology. By association, the need for advanced visibility and situational awareness increases exponentially,” explains Chris Soccio, General Manager of the Ferrexpo Yeristovo operations. “In locations where GPS or communications networks are compromised or unreliable, the ability to leverage machine learning fed by three diverse input methods becomes not only immediately desirable, but essential to ensure system redundancy for safe and efficient mining.”
ASI expects to complete the Phase Two assignment by September 2022.
Published at Sat, 26 Sep 2020 16:52:30 +0000