Complete library
The complete collection of all our insights, offering a comprehensive range of articles spanning foundational concepts to advanced strategies, providing guidance and inspiration for every stage of the algorithmic business journey.
AI as a tool to offset electrical power scarcity
Sweden's major population centers, including Gothenburg, Stockholm, and Malmö, are faces with a looming threat of power shortages due to capacity constraints in the national grid. Property owners, transportation sectors, and heavy industries will face challenges to drive their business. AI is part of the toolbox to solve this - but getting started is key.
Six critical strategies to navigate AI unpredictability
Artificial intelligence, while offering significant opportunities, is inherently unpredictable. Algorithma's previous articles have explored the complexities of AI, particularly the challenges posed by the risk of AI producing outcomes that are difficult to predict or explain. This unpredictability is not just a technical issue but a strategic concern for businesses that rely on AI for critical operations. Without robust risk management, businesses face potential disruptions and challenges that could undermine the long-term success of their AI programs and have severe adverse consequences for brand reputation, regulatory compliance, or operational robustness.
Building the algorithmic business: Machine learning and optimization in decision support systems
The ability to leverage the combined strengths of machine learning and optimization to enhance decision-making processes can significantly transform business operations. By integrating these technologies, businesses can achieve increased efficiency, reduce operational costs, and improve overall outcomes. This transformative potential is realized through practical applications in decision-making, whether by supporting human decisions or performing them autonomously.
Advancing ESG reporting with AI solutions
Effective ESG reporting is crucial for transparency and for meeting regulatory requirements, such as the new Corporate Sustainability Reporting Directive (CSRD) in the EU, and attracting investors. In this context, artificial intelligence can be a powerful tool to transform and enhance this reporting, providing accurate, comprehensive, and real-time insights. By automating complex processes and delivering deeper insights, AI can support organizations in improving their ESG performance and transparency, paving the way for more sustainable and responsible business practices.
Using AI to analyze brain research data
Mats Andersson, a PhD student at Sahlgrenska Academy's neuroscience department, is researching how synapses in the brain work. This research is important for understanding conditions where synaptic turnover is affected, such as autism, schizophrenia, and depression, as well as neurodegenerative diseases like Alzheimer's and Parkinson's. Using cutting-edge tools and collaborating with other scientists, this research aims to make a real difference in understanding and eventually treating or managing these conditions.
CTO update: How to get impact from generative AI
Unlike traditional computers that provide deterministic outputs, Large Language Models (LLMs) introduce a new paradigm with their probabilistic nature. This shift allows for variability and adaptability, closely mimicking human-like behavior and expanding the scope of what technology can achieve. This means we need to take a new approach to computers, and a structured approach to architecture and implementations.
“Responsible AI by Design”: Practical sustainability considerations in adopting Gen AI
AI offers significant opportunities for innovation and efficiency. However, alongside these advancements it is important to ensure AI is developed and deployed responsibly. We have all heard about “by design”-approaches, and now is the time for "Responsible AI by design". This approach mitigates risks, reduces long-term AI model maintenance costs, and builds trust with stakeholders. It is also key to reducing the environmental impact of AI.
AI in predictive manufacturing
Collaborative thought leadership between Opticos and Algorithma: Manufacturing companies face increasing pressure to optimize operations, reduce costs, and enhance competitiveness. To meet these challenges, manufacturing and supply chain companies are turning to predictive manufacturing. This approach leverages advanced analytics and AI algorithms to anticipate disruptions, optimize production processes, and enhance overall efficiency.
Why information retrieval systems are foundational for trustworthy and factual application of generative AI
More and more companies are relying on the analytical and generative capabilities of LLMs and other generative models in their day to day activities. Simultaneously there are growing concerns about how factual errors and underlying biases produced by these models may have negative consequences.
Did we accidentally make computers more human?
Traditionally, computers have been deterministic machines - systems that produce the same output given the same input. However, the emergence of Large Language Models (LLMs) challenges this, introducing a new paradigm where computers exhibit behavior that seems almost human-like in its variability and adaptability. In a world where humans are still trusting computers to be deterministic, and where businesses are rushing to implement generative AI wherever they can, it is more important than ever to be targeted, thoughtful, well-scoped and armed with clear metrics to track impact and success.
Power up your AI with serverless: Scalability, security, speed, and cost efficiency
Most of us have experienced serverless architecture as a way to build and run applications and services without having to manage infrastructure. One of the key advantages of serverless technology is its ability to handle dynamic workloads. AI applications often require processing large volumes of data, and serverless platforms can automatically scale to meet these demands.
Building the algorithmic business
AI is, since the 2022 landmark launch of ChatGPT, often associated with generative models like chatbots and image generators. But its potential extends far beyond these applications. One of the most impactful uses of AI is in predictive analytics, a powerful tool for forecasting business trends and shaping strategic decisions - enabling businesses to become algorithmic at the core.
AI as a tool to advance the circular economy
With a growing world population and escalating consumption levels, production and consumption patterns need to shift towards sustainability. To combat climate change effectively, business models must separate resource use from economic gain. This could be the circular business model, which revolves around reusing and recycling resources within an infinite system. Through this approach, known as decoupling, the link between resource consumption and environmental degradation can be broken. Transitioning to a circular economy is essential to meet climate targets and pave the way for a more sustainable future.
CTO Update: Training LLMs on ROCm platform
At Algorithma, we're constantly pushing the boundaries of Large Language Models (LLMs). In this CTO update, Jonathan explores the exciting potential of AMD's ROCm software platform and the next-gen MI300x accelerators for powering these models.
Managing and maintaining AI models in the long run
Building powerful AI models is just the first step. To unlock full potential and ensure responsible use, ongoing management and maintenance of these models are crucial. This involves a multifaceted approach that combines well-known best practices for application management and maintenance, with elements of data governance, data engineering, and Machine learning operations.
Creating certainty in uncertainty: Ensuring robust and reliable AI models through uncertainty quantification
AI is often seen as black-box complexity, but what if the answer to your problem lies not in sophisticated algorithms, but in simpler approaches? At Algorithma, we champion the power of naive models. Often overlooked due to their basic nature, they offer a surprising set of advantages that can be incredibly valuable for businesses of all sizes.
Why naive models are still relevant in the age of complex AI
AI is often seen as black-box complexity, but what if the answer to your problem lies not in sophisticated algorithms, but in simpler approaches? At Algorithma, we champion the power of naive models. Often overlooked due to their basic nature, they offer a surprising set of advantages that can be incredibly valuable for businesses of all sizes.
Laying the foundation: Data infrastructure is instrumental for successful AI projects
Data infrastructure is the backbone for enabling successful artificial intelligence projects. It consists of the ecosystem of technologies and processes that govern how businesses and organizations collect, store, manage, and analyze the operational data that fuels its AI initiatives. Without a robust data infrastructure, driving successful AI initiatives becomes almost impossible – your journey will likely grind to a halt after a few implementations.
Unlocking the potential of LiDAR: Leveraging AI to bring 3D vision to life
Imagine a world where security cameras not only show what's happening, but also precisely measure distances and object sizes. This futuristic vision is becoming a reality with LiDAR (Light Detection and Ranging) technology. With the ability to measure objects in 3D space, LiDAR holds immense value, especially in security applications where personnel could instantly determine the size and distance of a potential intruder. This area is rapidly developing but still advancements have to be made before these 3D environments become so lifelike that they can be integrated into consumer products. Could AI perhaps be the solution?
Building an on-premise AI infrastructure: key considerations
We invite you to explore the strategic possibilities of on-premise AI infrastructure in our new white paper. This white paper dives deeper into the advantages, practical considerations, and how to build a future-ready on-premise AI infrastructure solution for your organization.