This talk is already a few months old, but I had forgotten to share it here. It covers my peer-reviewed and accepted paper “Bridging the Analytics Gap: Optimizing Content Performance using Actionable Knowledge Discovery” for HT ’24. The paper is available in the Proceedings of the 35th ACM Conference on Hypertext and Social Media.
Artificial Intelligence (AI), Large Language Models (LLMs), Data Science, Machine Learning, Data Mining, and Statistics: What’s the difference?
The terms Artificial Intelligence (AI), Machine Learning, Data Science, Data Mining, Statistics, and Large Language Models (LLMs) are often used interchangeably or misunderstood. Clearly differentiating between these concepts helps you navigate discussions and make informed decisions in data-driven contexts.
Artificial Intelligence (AI)
AI encompasses techniques and algorithms that enable computers to perform tasks traditionally requiring human intelligence, such as reasoning, decision-making, and pattern recognition.
Machine Learning (ML)
ML is a subset of AI where systems learn from data to improve decision-making or predictions without explicit programming. Applications include recommendation engines, fraud detection, and image recognition.
Data Science
Data Science is an interdisciplinary field combining scientific methods, processes, and systems to extract actionable insights from data. It integrates domain expertise, statistical techniques, and data analysis skills to make informed business decisions.
Data Mining
Data Mining involves exploring large datasets to discover meaningful patterns, correlations, or trends. Common applications include customer segmentation, market basket analysis, and anomaly detection.
Statistics
Statistics forms the mathematical basis for Data Science and Machine Learning. It includes methods for collecting, analyzing, interpreting, and presenting data, ensuring rigorous analysis and reliable results.
Large Language Models (LLMs)
Large Language Models are a specialized, advanced type of Machine Learning model that process and generate natural language text. They excel at tasks such as content summarization, text generation, language translation, and interactive dialogue (e.g., ChatGPT).
The Connection Between These Terms:
- Artificial Intelligence is the overarching goal of creating systems that simulate human intelligence.
- Machine Learning is a key approach to achieving AI through data-driven learning.
- Data Science covers the broader methodology of turning data into actionable insights.
- Data Mining focuses specifically on finding meaningful patterns in large datasets.
- Statistics underpins these fields, providing the mathematical rigor needed for trustworthy analysis.
- Large Language Models are an advanced application of Machine Learning, focusing on language understanding and generation.
Why clarity matters
While “Data Science” has dominated conversations in recent years, many discussions have now shifted towards AI and especially Large Language Models. However, even with the buzz around AI, it’s important to remember that successful projects often rely heavily on foundational Data Science and robust statistical methods. Clearly distinguishing these concepts allows you to harness the full potential of data-driven solutions and avoid common misconceptions.
OpenAIs Advanced Data Analysis (war: Code Interpreter)
Update: OpenAI hat den Code Interpreter in “Advanced Data Analysis” umbenannt.
The End of Mass Employment
In the ZEIT of December 4, 2014, there is again a writing about alternative forms of economy due to the loss of jobs due to technology, this time in an interview by Uwe Jean Heuser with Jeremy Rafkin. It quotes a speech by Larry Summers from 2001, who said that the economy will see a new revolution like that of e-electrification, because marginal costs for video, audio and text information will drop to almost 0. Profits could then only be made through monopolies, but it was not yet known which system would replace market capitalism. This, according to Rafkin, is actually paradoxical, because the market economy would then have created the most efficient markets of all, but then there would be no more profits, so that an economy of sharing could emerge.
Furthermore, according to Rafkin, the Internet of Things is a tripartite division of the Internet into a communication network, an energy network and a transport network. By the transport network, he means, for example, car sharing. Sensors would create complete transparency. At the same time, long-established companies such as RWE & Co are suffering the same fate as the music industry. Rafkin also sees the danger that jobs could be lost and there could be a break in society. “The third revolution in the 21st century will put an end to mass wage and salary work. But that takes half a century. […] We can still offer mass employment for two generations because we first have to create the infrastructure for the super Internet of Things. [… Once this platform is up and running, it will be powered by analytics and algorithms and managed by a small group of supervisory boards.” Rafkin assumes that the rest of the people will then do more social work and so-called social capital will be created. For example, Thatcher & Co should be grateful for the fact that the social sectors had to learn to finance themselves. Where this leads, in my opinion, is written in many other articles in the ZEIT: It is cared for according to the cash situation in hospitals and homes, unnecessary operations, etc.
Keynes allegedly wrote as early as 1930 that technology will replace jobs faster than new ones can be created. Rather, one should embrace this opportunity in order to “free humanity from the soulless duties of the market”. We have already read elsewhere that this does not work as hoped for with the shared economy at the beginning.
We are before the peak of the hype cycles
What the video does not mention: There will also be new jobs. Because it’s easy to save money with new technologies. The art is to build something new with new technologies that creates additional business.
From man-machine becomes man against machine
Roman Pletter writes in the 29/2014 issue of ZEIT about the potential loss of highly qualified jobs due to ever-improving algorithms. In the so-called second machine revolution, machines can learn on their own (I already did something like this at Ask.com in 2006, on a very small scale…), but now it’s enough for more than winning in chess.
Which doctor can have read all the studies on a topic? Does a lawyer really know all the verdicts? Can a banker really take all the factors into account for a business? The computers could. We are already seeing harbingers of this development in online advertising: Instead of an advertising banner being placed on a website in a global galactic manner, an algorithm decides which user sees which banner in a fraction of a second using statistical methods. Based on data, algorithms can also learn which personality profiles are particularly suitable for certain tasks, so that personnel selection could be taken over by machines in the future.
The consequence of all these developments? What happens if the so-called middle class loses its jobs? The author of the ZEIT quotes an MIT economist: “Brynjolfsson pleads for states to rethink the old idea of granting their citizens a basic income in order to allow them to participate in the productivity gains.” I’m not sure if this has really been thought through to the end. Looking at the news, I have great doubts that anyone in the world is actually willing to agree on a new economic system. And what about all the countries on earth that are still far from advancing such a level of automation that their populations can no longer work? Or that are already dependent on the work of other countries anyway?
At the same time, you have to keep one thing in mind: We haven’t even reached the peak of the hype cycle yet, perhaps because the empty promises of the New Economy were not so long ago and people are no longer so gullible. Yes, the development will be exponential. But it won’t be as easy as you think.