Data Science Central

Q1 2024 saw a marked 28% increase in the average number of cyber attacks per organization from the last quarter of 2023, though a 5% increase in Q1 YoY. Traditional cybersecurity systems relying on static rules and signature-based detection are struggling to keep pace with innovative attackers. This urgency has caused a critical shift: the integration of AI and machine learning solutions into cybersecurity.

The cost of Technical Debt Image Source: The Scalers (For illustrative purposes only) Rudrendu Paul, Debjani Dhar and Ted Ghose co-authored this article. In boardrooms across the globe, the conversation has shifted. It’s no longer a question of whether a company should adopt Artificial Intelligence, but rather how quickly it can be deployed to drive a competitive advantage.

If AI can pass a CFA Level III exam in minutes, and people still say AI is not intelligent, then what else would intelligence mean? It takes intelligence to pass such an exam, so why would AI pass and still be categorized by many as unintelligent? What are the types of intelligence that may indicate that AI has some and not others? If people use AI for tasks, does it benefit or taper human intelligence? Some are recommending that others boycott AI use to avoid cognitive decline.

Every data engineering team operates on a set of accepted principles, a playbook of “best practices” intentionally designed to ensure scalability, governance, and performance. But with cloud-native systems, explosive data growth, and the rise of AI, a critical question dominates the data leaders’ mindshare: What happens when the trusted playbook is the very thing holding you back?

Interview with Bhupinder Bhullar In a world increasingly dominated by AI, massive data creation, and energy-hungry compute infrastructure, the question isn’t just how to store our data, but how to store it better. On the latest episode of the AI Think Tank Podcast, I had the pleasure of reconnecting with Bhupinder Bhullar, CEO of Swiss Vault, to discuss a truly groundbreaking approach to data storage.

Your organization has likely invested millions of dollars into a modern data stack, providing your team with powerful cloud warehouses and cutting-edge tools. And yet, a persistent feeling of friction often remains, a sense that getting trusted insights is far harder than it should be. The source of this friction can sometimes be misdiagnosed as isolated bugs or bad code, but the reality is far more systemic.

A radiologist looks at hundreds of CT images to find a tiny shadow that could be cancer. At these moments, every pixel matters. AI can make that decision faster and more precisely today, but only if trained on perfectly labeled medical images. Adding labels to diagnostic images isn’t just a technical step in AI research; it’s the most important one. Without labeled datasets, no medical AI model can be trusted to give consistent outcomes in real-world clinical settings.

The dialogue surrounding AI often raises anxiety: Will I be automated out of a job? The fact is, things are far more optimistic; AI is not abolishing human potential but instead is fundamentally transforming it. For professionals in the job market seeking a career in AI, this transformation brings with it incredible opportunities to redefine what it means to work, leverage human strength with machine efficiency. PwC’s AI Jobs Barometer (2025) cites evidence of this transition being driven by AI.

Language models have existed for decades — long before today’s so-called “LLMs.” In the 1990s, IBM’s alignment models and smoothed n-gram systems trained on hundreds of millions of words set performance records. By the 2000s, the internet’s growth enabled “web as corpus” datasets, pushing statistical models to dominate natural language processing (NLP). Yet, many believe language modelling began in 2017 with Google’s Transformer architecture and BERT.

AI benchmarks have created a false impression about how to evaluate AI models: test AI for complex questions that several humans can’t answer. Even if AI does well, they conclude that AI has not matched vital human intelligence. If AI gets things right that several humans would — on average — not get right, should that not be considered a significant leap?