In data science, the formulation of the problem is a critical step that significantly influences the success of any project. Properly defining the problem not only sets the direction for the entire analytical process but also shapes the choice of methodologies, data collection strategies, and ultimately, the interpretation of results. For data scientists, a well-formulated problem helps in honing in on the right questions to ask, allowing them to design experiments and models that are aligned with business objectives. It ensures that the analytical effort is relevant and impactful, leading to actionable insights rather than merely technical achievements.

Deploying AI systems is just the beginning. To create business impact and realize value, these systems must be sustained to remain reliable, adaptive, and compliant over time. AI sustainment is a strategic approach to extend the lifecycle of AI models, ensuring they are performant, scalable, and aligned with business needs. Algorithma emphasizes a proactive methodology that continuously improves models, manages data effectively, and follows responsible AI guidelines—maximizing value and maintaining a competitive edge.

AI is fundamentally changing how organizations operate, lead, and adapt. Beyond being a catalyst for increased productivity, it represents a novel form of capital that, when effectively harnessed, can reshape the operational and competitive landscape. Successful AI adoption requires more than technical expertise—it calls for rethinking team dynamics, leadership, and how organizations manage ongoing change.

The 2024 Nobel Prize in Physics highlights groundbreaking work done by John J. Hopfield and Geoffrey E. Hinton on neural networks, where they developed models like the Hopfield Network and the Boltzmann Machine, inspired by the behavior of physical systems. Their pioneering work in the 1980s laid the foundation for the machine learning revolution that took off around 2010. This award celebrates their contributions to the foundational technologies driving modern machine learning and artificial intelligence. The exponential growth in available data and computing power enabled the development of today’s artificial neural networks, often deep, multi-layered structures trained using deep learning methods. In this article we will dive into their discoveries and explain how these breakthroughs have become central in AI applications. 

Businesses are increasingly adopting AI to gain an edge, but success requires more than just the right technology. To fully leverage AI, a structured approach is key. Algorithma's AI Maturity Framework helps organizations assess where they stand and plan their path forward.

To achieve sustainable cost savings, businesses must first gain a deep understanding of their cost landscape—the key cost buckets and areas of expenditure that impact overall financial performance. By mapping these costs, companies can identify where inefficiencies lie, making it easier to target specific areas for savings while improving operational performance. This approach ensures that cost-cutting efforts are strategic, sustainable, and aligned with long-term business goals. AI and advanced analytics can play a critical role in each area of the cost landscape, enabling smarter decision-making, automation, and optimization throughout the organization.

In this update, Jonathan Anderson (our CTO) explains the new DSPY framework, designed to simplify and strengthen control over large language models (LLMs). LLMs, while transformative, can be unpredictable, often behaving like “black boxes.” DSPY addresses this by offering a structured approach to interaction, reducing the need for prompt tuning and making model behavior consistent and predictable.

In this video Simon, one of our Data Scientists, takes a first look at the o1 preview model. OpenAI says the model is part of a series of reasoning models for solving hard problems. With claims that performance is on par with PhD students within fields like physics and chemistry within some benchmarks. To test it, Simon gives it a math problem, and quickly analyzes its solution. Based on this, Simon discusses potential strengths and weaknesses of the model and gives an overall impression with thoughts about the future potential of this type of model.

Machine learning (ML) is currently transforming various fields, such as healthcare, finance, and creative industries. However, as data and problems become more complex, classical computing struggles to scale ML algorithms efficiently. Key challenges include the time and computational resources needed to train models on large datasets, optimize deep learning architectures, and perform tasks like data classification and clustering. These limitations drive interest in exploring quantum computing. 

Artificial intelligence is an operational necessity in many industries, in particular financial services, driving everything from credit scoring to fraud detection. But with great power comes great responsibility: AI systems, if not managed properly, can reinforce biases and inequalities, leading to unfair lending, discriminatory insurance pricing, or biased fraud alerts. In finance, bias in AI can mean denying loans to certain groups, charging higher premiums unfairly, or disproportionately flagging transactions as suspicious—all of which have significant real-world impacts on people's lives. As AI becomes more central to finance, effective data governance is key to ensuring these systems are fair, transparent, and accountable.

Graph neural networks (GNNs) offer transformative potential for businesses by uncovering hidden patterns and relationships within complex data. From detecting fraud to optimizing supply chains and accelerating drug discovery, GNNs enable smarter decision-making and drive operational efficiency. Unlike traditional machine learning models that analyze data points in isolation, GNNs excel at identifying connections and patterns within the data. For business leaders, this technology presents an opportunity to unlock new avenues for growth and innovation, maximizing the potential of their data.

Companies are increasingly relying on machine learning models to make critical decisions. ML models come with a fundamental assumption: they expect the future to look like the past. In reality, the world is constantly changing, and so is the data it generates. This change, known as data drift, can silently undermine the performance of your models, leading to poor decisions, increased costs, and missed opportunities.

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.

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.

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.

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.

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.

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.

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.

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.