AI/DS Column

Tiny city samples won’t close the 3.7–3.9 million-home gap Use real-time public and private data under shared standards and privacy rules Governments set rails, platforms supply feeds, and weekly human review turns signals into units

AI chatfishing hides bots in dating, removing consent and raising risks Declared AI companions can help, but still need strict guardrails Require clear disclosure, platform accountability, and education to close the consent gap When

General AI predicts probabilities, not context-specific safety Domain-specific AI fits the task and lowers risk in classrooms and markets Use ISO 42001, NIST RMF, and the EU AI Act, and test on domain benchmarks Reported AI incid

Bootcamps produce tool users, not frontier researchers ASEAN needs a scientist-first AI talent strategy Fund PhDs, labs, and compute to invent, not import The statistic that should jolt us awake is simple: almost 40% of jobs

The core risk is AI safety rule prioritization, not model math Evidence shows agents bypass weak guardrails under pressure Schools need layered, auditable refusals and staged tool access One number should change th

Antitrust breakups miss the real battleground: AI assistants, not blue links Prioritize interoperability and open defaults to keep markets contestable Track assistant-led discovery, not just search share, to safeguard users and educators

Trusted news wins when fakes surge Make “proof” visible—provenance, corrections, and methods—not just better detectors Adopt open standards and clear labels so platforms, schools, and publishers turn credibility into a product feature

Europe’s schools rely on foreign AI infrastructure, creating vulnerability A neutral European stack with local compute and governance can secure continuity This ensures resilient, interoperable education under global tensions

AI scans simplify elections but risk bias Clear rules and provenance reduce errors With oversight, even losers can trust them The largest election year ever recorded coincides with the most persuasive media techn

The real risk isn’t the LLM’s words but the agent’s actions with your credentials Malicious images, pages, or files can hijack agents and trigger privileged workflows Treat agents as superusers: least privilege, gated tools, full logs, and human checks

U.S.

GIAI's primary research objective with the coming cycle's of MSc AI/Data Science is to build a graph-based Shapley Value for HR contribution analysis.

AI talks turned the table and become more pessimisticIt is just another correction of exorbitant optimism and realisation of AI's current capabilitiesAI can only help us to replace jobs in low noise data

Top brains in AI/Data Science are driven to challenging jobs like modelingSeldom a 2nd-tier company, with countless malpractices, can meet the expectations

People following AI hype are mostly completely misinformedAI/Data Science is still limited to statistical methodsHype can only attract ignorance As a professor of AI/Data Science, I from time to time receive emails from a bunch of hyped followers claiming what they call 'recent AI' can solve things that I have been pessimistic. They usually think 'recent AI' is close to 'Artificial General Intelligence', which means the program learns by itself and it is beyond human intelligence level.

Transition from column to matrix, matrix to tensor as a baseline of data feeding changed the scope of data science,

Web novel to Webtoon conversion is not only based on 'profitability'If the novel author is endowed with money or bargaining power, 'Webtoonization' may be nothing more than a marketting tool for the web novel.

Asian companies convert degrees into years of work experienceWithout adding extra values to AI degree, it doesn't help much in salary

The relationship between a commercial district and the concentration of consumers in a specific generation mostly is not by causal effectSimultaneity oftern requires instrumental variables

One-variable analysis can lead to big errors, so you must always understand complex relationships between various variables. Data science is a model research project that finds complex relationships between various variables. Obsessing with one variable is a past way of thinking, and you need to improve your way of thinking in line with the era of big data. When providing data science speeches, when employees come in with wrong conclusions, or when I give external lectures, the point I always emphasize is not to do 'one-variable regression.'