Join Us for “Preventive Health Solutions with AI and Digital Innovations” on September 17th!

Oxide AI will participate in event hosted by Invest in Skåne. Lars Hard will be the keynote speaker at an insightful seminar exploring the future of healthcare. He will be sharing thoughts on the transformative power of AI in healthcare industry.

📅 Date: September 17th
📍 Location: Malmömässan

This is an exceptional opportunity to engage with leading experts and innovators in southern Sweden's healthcare sector. If you're curious about how AI can revolutionize preventive health, this event is for you! Whether you’re in the healthcare field or simply interested in the potential of AI, we invite you to join us for this 3-hour session packed with insights and networking opportunities.

💡 Don’t miss out! Sign up now and/or share this event with anyone who might be interested

In modern AI development, the spotlight often falls on sophisticated AI models. However, beyond these advanced tools and algorithms, there lies a solid foundation of undeniable importance – the quality of source data involved.

Even the most powerful AI model, when fed with poor-quality data, is outperformed by a weaker AI model trained on high-quality data.

This contemporary saying circulating in the tech world emphasizes the significance of data quality in AI development. While cutting-edge AI models are integral for producing impressive outcomes, they aren’t the only crucial factor. Data serves as the critical fuel that drives AI models and its quality fundamentally determines their effectiveness. Regardless of a model’s superiority, it is only as good as the data it’s fed.

Since the establishment of our company Oxide AI, we have actively prioritized source data. Our laser-focused dedication has led us to develop proprietary AI and data representation technology, designed for the precise task of harvesting vast quantities of source data in the global financial sector. Our commitment to data management excellence has enabled Oxide’s lean team to make a significant impact and take comprehensive control over the data. This allows us to leverage the potential of AI automation for scaling, rather than relying on manpower. Most rewarding of all, our solid data practices pave the way for limitless AI application possibilities; the well-managed and organized data can easily be transformed and packaged for varying use cases.

Oxide’s approach is based on a deep understanding of several essential elements of source data. We start by examining data’s origin and establishing its authenticity. Following that, we also assess its intrinsic value and evaluate the sufficiency of its representation and validation. Finally, we consider the different metrics that can effectively quantify it. We only move forward with developing and training AI models once we’ve done such a thorough analysis.

To guide you in thinking more strategically about your data in the context of AI, we offer a few key insights grounded in multiple years practical industrial experience:

Data Veracity: assessing quality, accuracy, and authority is challenging and time-consuming for large datasets. The key is tracing the data generation process, especially if it involves human input, which often introduces errors. If a validation process exists, it’s critical to deeply comprehend it.

Data Structuring: transforming unstructured data into a structured format is often highly beneficial. This is particularly true when unstructured data, such as customer interactions, detailed product descriptions, or internal documents, can enrich structured datasets. Such data is invaluable for training modern statistical machine learning models, like large language models (LLMs), and can be harnessed in chatbots or advanced search systems.

Volume and Velocity: large data volumes demand significant engineering resources, so evaluate the value of data against the incurred costs. Accumulating data without a clear purpose is inefficient. When dealing with high-velocity data, consider real-time AI-transformation models to capitalize on the data’s immediate value.

Temporal Data: timestamps on data creation, modification, and storage can unlock potential future value. It’s advisable to initially preserve data in its raw form before database insertion. This approach preserves the capacity to replay historical data for new storage methods or AI refinement without loss of information.

Uniqueness and Information Decay: unique data is inherently more valuable. Even widely accessible data can gain value when intelligently linked with multiple sources. Recognize that information value degrades over time; prioritizing the utility of data at different intervals can optimize its usefulness.

Watch our video on this topic:

https://www.youtube.com/watch?v=VZFhaCMo2Qw&t=5s

On Feb 1, we attended IBM’s Fintechx Demo that marked a significant milestone for Oxide AI. Our team has spent the past three months advancing generative AI for our B2C and B2B technologies, and thanks to our collaboration with IBM, we have made substantial progress.

We are pleased to announce that Oxide AI has received dual awards for the service oxogen.ai

“Best Potential Investment” (by Findec & Resurs)

“Best use of GenAI” (by IBM)

These awards affirm the strength of our vision and the dedication of our team in bringing innovative solutions to the financial market.

Oxide AI supports IBM’s commitment to open language models in watsonx, and we aim to further contribute to this initiative by sharing data and models with the AI community.

Looking ahead, we are eager to continue our collaboration with IBM as we bring our financial service platform Oxogen to a global market.

We extend our appreciation to the team at IBM Innovation Studio in Stockholm for orchestrating the Fintechx program and Client Engineering Org for support, which provided a critical platform for technical and business development.
We would like to thank Antouman Jallow, Emma Bachner, Stefan Palm, Stefan Terelius and Urban Roth for their exceptional contributions.

And of course thanks to the panel and program partners from
Crosskey, Findec, Handelsbanken, IBM Ventures, Resurs, Shibuya, Wellstreet and Ålandsbanken Abp.

P.S. Special recognition goes to the Oxide and IBM squad doing all the dev work.

Since November we have been partnering up with the IBM team to explore the potential in LLMs and the IBM platform watsonx. Mid December, we had a successful demo of gen AI solution for Oxogen, which has been co-created in just 4 weeks! 

Read more about Oxide AI’s experience using watsonx in this brand new case story published by IBM!

Learn more about Oxide AI's recently released AI companion - oxogen.ai.
What is Oxogen? It is your personal AI companion, doing automated deep web research of the whole markets. Oxogen’s AI models use both quantitative data and qualitative evidence to bring high-value insights without information overload.

Oxogen has been developed as opportunity discovery tool, not an investment robot. While Oxogen's AI engines work around the clock to suggest companies with potential investment value, the final decision and action is yours. Oxogen is collaboration between human and AI, where individuals are enabled to focus on what matters most and empowered to achieve higher goals. 

Read more in https://oxide.ai/2023-12-14-introducing-oxogen/

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