AI system Archives | Oxide AI https://oxide.ai/tag/ai-system/ Quantified AI Decisions™ Tue, 10 Jun 2025 12:15:36 +0000 en-US hourly 1 https://wordpress.org/?v=6.7.1 https://oxide.ai/wp-content/uploads/2022/04/o_icon.svg AI system Archives | Oxide AI https://oxide.ai/tag/ai-system/ 32 32 Finding Trustworthy Signals in a Sea of Financial Data https://oxide.ai/2025-06-10-finding-trustworthy-signals-in-a-sea-of-financial-data/ Tue, 10 Jun 2025 12:07:33 +0000 https://oxide.ai/?p=10823 Scaling domain-tuned AI without the cost of proprietary models.

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Finding Trustworthy Signals in a Sea of Financial Data

Scaling domain-tuned AI without the cost of proprietary models

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Proud moment for Oxide AI!


We’re super excited to be featured in Meta LLaMA’s latest case studies, which highlights our mission to bring transparency, openness and precision to AI in finance.

 

At Oxide AI, we believe that true innovation happens out in the open. That’s why we’ve used LLaMA —  no black boxes, no closed-source shortcuts. It’s the only way to build trust and promote human-AI collaboration where it makes most sense.

 

What this means in practice:

 

  • Open & Transparent: We build using openly available large language models.
  • Exact & Fine-Tuned: Precision isn’t optional. Our SLMs/LLMs are fine-tuned to specific domains. Combined with our computational AI, they deliver accurate, trustworthy insights.
  • Robust Signal Discovery: In complex financial datasets, our AI surfaces meaningful signals helping you make smarter decisions.

 

Oxogen isn’t just another AI tool. It’s the result of extensive work to pioneer a culture of open innovation where financial accuracy meets verifiable results.

 

A big thank you to the teams at Meta & IBM for spotlighting our collaboration!

 

Check out the case study and don’t forget to grab the full PDF version!

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Put AI to Work with Oxide AI https://oxide.ai/2025-05-26-join-us-at-ibms-put-ai-to-work-event-in-stockholm-this-week/ Mon, 26 May 2025 20:18:59 +0000 https://oxide.ai/?p=10730 Don't miss Oxide AI and other innovators at IBM's event in Stockholm this week!

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Put AI to Work with Oxide AI

Don’t miss Oxide AI and other innovators at IBM’s event in Stockholm this week!

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This week you want to make sure you attend IBM‘s excellent Put AI to Work Event in Stockholm. Enterprises are leaning in on AI but how do you get to sustainable value creation, strong trust and higher competitiveness?

Oxide AI will be there so come and say hi 👋 . Also, don’t miss Anders Tylman-Mikiewicz talk on how Hybrid-AI systems already drive tangible results and value in financial research and analysis for global organisations in finance – in ways that help them further enhance their competitive edge.

 

Let’s put AI to work for real!


Program and registration 

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Supercomputing for AI Development, the European Context https://oxide.ai/2024-12-11-ai-use-case-for-supercomputing-in-europe/ Wed, 11 Dec 2024 12:55:27 +0000 https://oxide.ai/?p=8893 Listen to podcast where we discuss AI use cases for HPC.

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Supercomputing for AI Development, the European Context

Listen to podcast about AI and HPC

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In this episode of “Supercomputing in Europe” podcast Lars Hård and Apostolos Vasileiadis discuss the AI promise for multiple sectors and the use of supercomputing for AI development. 

 

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Systemic AI as the Foundation for an Integrative Approach to Longevity https://oxide.ai/2024-10-17-systemic-ai-as-the-foundation-for-an-integrative-approach-to-longevity/ Thu, 17 Oct 2024 11:45:18 +0000 https://oxide.ai/?p=7898 Join us at AI & Longevity Summit 2024!

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Systemic AI as the Foundation for an Integrative Approach to Longevity

Oxide team

Join us at AI & Longevity Summit 2024!

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Are you in a beautiful city of Stockholm next week? Oxide AI will be there at the AI x Longevity Summit 2024.

🗓 Date: October 23, 2024
⌚ Time: 09:30 – 16:30
📍 Location: Epicenter Stockholm

Lars Hard‘s Keynote is at 10:50,
“Systemic AI as the Foundation for an Integrative Approach to Longevity.”

👉 Reserve your seat here 

Event is hosted by AI Cockpit, Swedish Longevity Cluster & Epicenter Stockholm. Ingrid af Sandeberg

Let’s explore how AI can unlock a longer and healthier future!

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Speaking Highlights: September 2024 https://oxide.ai/2024-09-26-we-were-speaking-september-2024/ Thu, 26 Sep 2024 12:26:24 +0000 https://oxide.ai/?p=7873 Our CEO Lars Hard at Nordic events, autumn 2024.

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Speaking Highlights: September 2024

Oxide team

Our CEO Lars Hard at Nordic events, autumn 2024

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September was an incredibly eventful month for Oxide AI!

 

We traveled across several Nordic cities, meeting talented individuals who continue to inspire and challenge us.

 

Throughout the month, our CEO Lars Hard contributed to the innovation space by delivering keynote speeches, participating in panel discussions, and he also managed to do some insightful podcasts and interviews (stay tuned for these!).

 

A huge thank you to our amazing partners and friends at RISE Research Institutes of Sweden, IBM Innovation Studio, and Innovation Skåne for their support and collaboration.

 

It’s been a fantastic kickoff to the season, and there’s much more to come!

special thanks: Ingrid af Sandeberg, Marta Schulze, Urban Roth, Christer Månsson, Sonja Schwarzenberger

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Lars Hard’s keynote at Nordic Industry Days https://oxide.ai/2024-08-26-lars-hards-keynote-at-nordic-industry-days/ Mon, 26 Aug 2024 12:26:44 +0000 https://oxide.ai/?p=7810 Meet us at "Supercomputing, the Gateway to AI"!

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Lars Hard’s keynote at Nordic Industry Days

Oxide team

Meet us at “Supercomputing, the Gateway to AI”!

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We are happy to share that in just one week, Oxide AI will be participating in the Nordic Industry Days: “Supercomputing, the Gateway to AI”! 🚀

This event will bring together Nordic industry leaders to share their expertise on leveraging high-performance computing for AI applications. It’s a fantastic opportunity to learn from the best and explore how supercomputing is shaping the future of AI.

The in-person event is fully booked, but don’t worry—you can still join us online! Click the link to register and be a part of this super exciting discussion.

Lars Hard will be there both days for:

 

  • 13:35-14:30, September 2-  Keynote session
    Globally Competitive AI: Small Teams with Large Computers

 

  • 10:15-11:45, September 3 – Panel discussion
    Emerging AI Use Cases on HPC

Event by Danish Industry, DeiC, ENCCS – EuroCC National Competence Centre Sweden, EuroCC_Finland, Nasjonalt kompetansesenter for HPC – EuroCC@Norway, EuroCC Denmark

More To Explore

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Blog: Information Pollution in Financial AI  https://oxide.ai/2024-07-22-information-pollution-in-financial-ai/ Mon, 22 Jul 2024 18:14:57 +0000 https://oxide.ai/?p=7691 Financial markets increasingly rely on AI and algorithms, but the quality of these systems is threatened by information pollution that distorts market perceptions and decision-making. Read how to ensure high-quality data for the accuracy and reliability of financial AI systems.

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Blog: Information Pollution in Financial AI 

Learn how to Minimize Data pollution in ai systems

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Today’s financial markets are driven by lightning-fast algorithms and AI, but a growing problem often goes unnoticed: information pollution. Financial AI, widely used by banks, hedge funds, and modern fintech companies, relies on vast datasets for decision-making. But not all data is equal. The increasing flood of unstructured, noisy, and often irrelevant information can confuse these algorithmic judgements, leading to distorted market perceptions and potentially dangerous financial decisions. The accuracy of financial AI systems is fundamentally tied to the quality of their input data, making the issue of information pollution particularly critical. 

 

Data Pollution Maze
AI models navigate through data maze, encountering various data types and data pollution traps.

Financial AI systems gather data from numerous sources, including news articles, social media, patent applications and economic reports. Poorly curated datasets introduce errors, biases, manipulations, and anomalies that inevitably infiltrate AI models. Additionally, content produced by LLMs introduces further complexity. Models trained on real-world data can embed these flaws within their architecture, creating a feedback loop that amplifies errors. Over time, these issues compound and could result in incorrect conclusions and decisions. This issue is particularly pronounced in systems where each step in a chain of AI models adds its own errors, resulting in amplified inaccuracies as the processing continues. 

 

This highlights the need for rigorous validation and refinement of data and models, emphasizing the importance of source data quality for the integrity of financial AI. 

 

Oxide AI’s Recommendation to Minimize Data Pollution 

 

To effectively utilize massive data streams across financial markets requires advanced systems for data acquisition, transformation, validation, and processing. With extensive experience in large-scale AI, the Oxide AI team knows the importance of getting the data right from the start. High-quality training data, even with a mediocre algorithm, consistently outperforms stronger algorithms trained on poor-quality data. 

Here are our key areas to focus on for acquiring and managing data in Financial AI: 

 

1. Acquisition Principles 
 

  • Proximity to Data Generation: Acquire data as close to its source as possible to minimize errors introduced through translation or refinement, ensuring greater fidelity and reliability. 
  • Log-based Data Capture: Implement real-time recording of data events, encoding them with essential metadata like timestamps and source references. This practice preserves data integrity and provides critical contextual information for tracing and validating origin and timeline, supporting accurate analysis and informed decision-making. 

 

2. Deteriorating Data 

 

  • Capture Temporally Sensitive Data: Focus on acquiring data that rapidly loses relevance over time, such as dynamic content requiring specific timestamps or short-lived internet updates. 
     

3. Historical Data 

 

  • Capture Source Data with Metadata: Acquire data alongside essential metadata (e.g., origin, time, collection method) to enhance future utility. Metadata provides crucial context that enriches data analysis and application. 
  • Importance of Managed Data: Properly captured and managed source data, enriched with metadata, forms a foundation for creating reliable, transparent, and high-performing AI solutions. 
     

4. Redundancy 

 

  • Multi-perspective Source Data Collection: Gather data from various sources to improve evidence and factual accuracy, essential even for hard financial data prone to issues like currency discrepancies or rounding errors. 
  • Ensemble Modeling: Use multiple models to provide diverse data perspectives to determine facts and events. Leveraging each model’s strengths for enhanced reliability and robustness. 

 

5. Validation 

 

  • Dual Validation Approach: Trained AI models can in many cases act as validation proxies to ensure output quality and accuracy.  
  • Combining AI model outputs with heuristic models provides a balanced validation mechanism. This approach leverages learned patterns and expert knowledge to detect discrepancies and ensure data integrity, improving overall reliability. 
  • To some extent, resources for human validation are a basic requirement. The sampling scheme needs to be determined through careful data analysis. 

 

6. Source Data Scoring 

 

  • Critical Data Evaluation: Source data scoring evaluates the quality and reliability of data from diverse sources. This process considers factors like accuracy, completeness, and more. It establishes trust in data-driven initiatives by ensuring stakeholders rely on the integrity of data used in analytics and AI applications. 

More To Explore

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Blog: Practical Approaches to Tackle Common Real-World AI Pitfalls https://oxide.ai/2024-05-23-practical-approaches-to-tackle-common-real-world-ai-pitfalls/ Thu, 23 May 2024 12:35:37 +0000 https://oxide.ai/?p=7583 Learn how to navigate hurdles in AI systems and models.

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Blog: Practical Approaches to Tackle Common Real-World AI Pitfalls

Learn how to Navigate hurdles in AI SYSTEMS & MODELS

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Applying artificial intelligence in the real-world settings extends far beyond the controlled environment of academic research, Kaggle competitions, or personal experiments with prototype models in popular Python AI/ML frameworks. When AI steps out of the lab and into our daily lives, the stakes rise significantly due to the real-world consequences its applications can entail. 


While AI technology has become readily accessible and most organizations possess some form of data, crafting a quick solution can often seem deceptively simple. Yet, advancing these solutions by integrating an AI model or system into a live operational environment typically involves a labyrinth of complexities. 


The team at Oxide AI has compiled some common challenges encountered in real-world AI deployments, along with insights on how to navigate these hurdles. It is important to note that this overview is not exhaustive; every industry presents its unique set of challenges.  


In this blog post, we focus entirely on the real-world applications of AI models, saving the technical operations for another post. While there are many other perspectives to consider, such as responsible, ethical, and environmental aspects, our insights provide a solid foundation for understanding the essential considerations in nearly every AI implementation. 


1) Model Overfitting 


A machine learning model that excels at interpolation might be overfitting, performing very well on training data but poorly on unseen data. This suggests that the model has learned the specific details and noise of the training data, rather than capturing the underlying general patterns necessary for effective extrapolation. 


What to do: 


  • Validate and analyze the model carefully if it produces “too good to be true” results. 
  • Test it with more varied data or add noise to your input data and measure the response. 
  • Use more training data, if possible, to help the model learn more generalized patterns. 
  • Perform early stopping during training to prevent the model from learning the noise in the training data. 
  • More advanced: hyperparameter tuning, random dropout, regularization, cross-validation on different subsets of the data. 

2) Unpredictability in Extrapolation 


Interpolation enables neural networks to predict within the range of the training data, and generative AI (like LLMs) models can indeed be quite powerful in predicting outcomes based on learned data distributions.  


However, when neural networks extrapolate, they attempt to predict based on patterns beyond previous observations. This can lead to unpredictable or completely incorrect outputs, as the network essentially “guesses” relationships in regions outside its training data. In critical sectors, such as healthcare or autonomous driving, erroneous extrapolations can result in severe consequences.  


The challenges of relying on deep learning in finance automation also stem from this issue. With inputs constantly evolving beyond training data, future predictions based solely on historical data for training become less viable.  



What to do: 


  • If a solution handles highly varied input data, consider replacing neural networks with more efficient model-based predictions, facilitating easier inspection and understanding of the model. 
  • Use ensemble methods to combine predictions from multiple models to improve robustness and reliability. 
  • Leverage a proxy model (often referred to as an “oracle”) to validate predictions at scale. 
  • Introduce heuristic rules to capture the “soundness” of the results produced. 
  • Implement continuous monitoring to identify and address drift in model performance over time. 
  • Introduce a triage system to route uncertain outputs to human review, based on some type of confidence score reflecting how certain the output is. 
  • Always log inputs and results produced to enable a thorough review of edge cases.


3) Inherent Biases 


Both interpolation and extrapolation are susceptible to biases inherent in the training data. When training data is biased, predictions—whether within the data range (interpolation) or extending beyond it (extrapolation)—are likely to reflect and potentially amplify those biases. 


What to do: 


  • Ensure the training dataset is representative of the real-world scenarios to mitigate biases. 
  • Conduct regular audits of model predictions to assess and address biases effectively. 
  • Utilize fairness metrics to evaluate and enhance the model’s performance across different groups. 
  • Complement real data with synthetic data to ensure representation of underrepresented groups and scenarios. 
  • Institute a formalized bias mitigation workflow that includes steps for data collection, bias detection, bias addressing, and validation. 
  • Establish transparency in the modeling process by documenting data sources, model choices, and mitigation strategies, making it easier to identify and correct biases. 
  • Use explainable AI (XAI) where possible to provide full transparency, especially for numerically oriented domains.   
     

4) Underestimating the Complexity of Real-World Problems 


Many real-world AI problems involve high levels of complexity that go beyond what can be handled through simple interpolation or extrapolation. These scenarios demand an understanding of intricate causal relationships, contextual nuances, and multi-dimensional data, aspects that often lie beyond the capabilities of current AI models. 


What to do: 


  • Understand the data thoroughly to prevent models from capturing spurious correlations. 
  • Collect comprehensive surrounding data to uncover valuable properties or interactions. 
  • Perform scenario analysis and stress testing to see how the model behaves under various conditions and to identify potential weaknesses. Baseline testing with pure randomness is always a good practice. 
  • Model and simulate systemic and macro-level interactions to manage large error margins stemming from external changes. 
  • Regularly update the model using new data and retrain it to reflect recent trends and changes in the problem space. For domains where information is under a lot of change, leverage continuous model adaptation based on optimization rather than cyclical retraining.  
  • Use advanced techniques like transfer learning to build robust models that operate well in varying real-world environments. 


error amplification


5) Error Propagation  


Real-world AI systems often use multiple AI models in sequence. For instance, one AI model might extract facts from documents, while another utilizes this data for estimations. This approach can create a chain of errors, with inaccuracies accumulating at each stage. Similarly, error accumulation can also occur in poorly designed LLM-based solutions that repeatedly use output from past trials as input for new ones. When errors occur early in the model chain or in the training data, they have potential to propagate and magnify, leading to significant inaccuracies or even system malfunctions. This can compromise system robustness, as final output validation may fail to capture these cumulative errors effectively. 


What to do: 


  • Be mindful of error accumulation when utilizing the output of one model as input for another. 
  • Validate and measure errors after each model stage (intermediate checkpoints) to understand the extent of error accumulation. Use uncertainty quantification techniques to gauge and communicate the level of confidence in model outputs at each stage. 
  • Establish and maintain golden test data sets for each model, where both input and outcomes are known and validated. 
  • Incorporate robust fallback strategies or rule-based systems to handle scenarios where model confidence is low or where there is a high potential for error propagation. 
  • In complex AI systems with many dependencies, prioritize solid validation of outputs in real-world applications. Institute periodic reviews and updates of the entire model chain. 

More To Explore

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Lars Hard speaking at AI Sweden Rendezvous https://oxide.ai/2024-04-08-speaking-at-ai-sweden-rendezvous/ Mon, 08 Apr 2024 13:49:57 +0000 https://oxide.ai/?p=7401 Oxide participated in AI Sweden Rendevous, where Lars shared experiences on managing AI projects.

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Lars Hard speaking at AI Sweden Rendezvous

How to drive AI projects

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Last Friday Oxide AI had a fantastic time at the local AI event organized by AI Sweden (South node)! It was great to see some practical cases of AI application in action. Huge thanks to AI Sweden for giving Lars Hard the opportunity to share some of our international industrial experiences with local entrepreneurs and public sector!
Special shoutout to all speakers for delivering insightful talks, and a big thank you to all the participants for engaging in great after-talks.

We are already looking forward to more events like this, which play a vital role in strengthening our AI community and contributing to a better AI adoption in Sweden.

Join Sweden’s AI community and check the upcoming events here.

AI Sweden: Anneli Xie, Tommy Boije, Carl Malm & Emma
Speakers: Nikolas Ramstedt & Ehsan Z., Melina Katkić, Adrian Johansson & Björn, Anders Lööf, Aslak Felin, Lars Hard
Companies & public sector: Helsingborgs stad, Lunds kommun, NordAxon, Neodev AB, RISE Research Institutes of Sweden, Oxide AI

Ai Rendezvous

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Lars Hard will be speaking at IBM Build to Win https://oxide.ai/2024-03-14-oxide-ais-ceo-lars-hard-will-be-speaking-at-ibm-build-to-win/ Thu, 14 Mar 2024 11:28:32 +0000 https://oxide.ai/?p=7347 Oxide AI's CEO & Chief AI Officer Lars Hard will be speaking at IBM Build to Win in Stockholm

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Lars Hard will be speaking at IBM Build to Win

Learn how to leverage watsonx.ai to kickstart with AI in your organization

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Next week on March 21st, our CEO & Chief AI Officer Lars Hard will be sharing Oxide’s experience of using watsonx.ai

 

Join us for informative session with valuable insights into AI technologies applied in the real-world fintech. Register for this IBM event and get fantastic opportunity to kickstart with AI in your organization!

 

Agenda and Registration Link to the Event https://www.ibm.com/events/reg/flow/ibm/iepzawmb/landing/page/landing

Lars Hard at IBM Build to Win

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