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.
Update on Scalable Capital: Here is the report after one year with SC!
My enthusiasm for Number26 is still there, even though a lot has changed since my first article:
Users can set up an overdraft facility
In the meantime, there are also EC cards
There is the possibility to deposit money with partners such as REWE and have it paid out
It is unpleasant that you can’t really pay everywhere with the cards. In London, an ATM refused to work with the credit card, and in America, several card readers showed an error message. So you should always have another credit card from another company with you, especially abroad. In Germany, the machines of the Nord-Ostseebahn did not want to accept the EC card, which, according to support, is due to the fact that the Number26 EC cards lack the Girocard/EC chip. However, this hardly detracts from my enthusiasm, because especially in America it was wonderful to be able to see in real time how much was going out of the account. Even after three days, my Miles and More credit card still doesn’t have all the bookings on the online account.
Another fintech I’m trying out right now is Scalable Capital. I cherish a (un?)healthy mistrust of the investment products recommended by banks and financial advisors, hidden costs are apparently a trivial offense, and I don’t even want to look into some of my contracts anymore because I simply had no idea when I was young. Cost transparency would therefore be a huge advantage when it comes to a new offer on the financial market. The fees at Scalable Capital are 0.75% of the invested assets plus an average of 0.25% costs for the ETFs, which are already included in the ETF prices of the providers. Wait a minute, fees for ETFs? Yes, ETFs also cost money, but usually less than an actively managed fund. But you would have paid these fees anyway, only that you might not have been aware of it. For 0.75%, everything outside the ETFs is paid, the custody account (which is often enough already available for free, just not at Scalable Capital’s cooperation partner, Baader Bank), the transactions, the brain that makes the decisions, and the salaries of the employees. Since Scalable Capital only takes on new customers from €10,000 in assets anyway, that’s at least €75 per year.
Let’s take a closer look at the “brain” that makes investment decisions, because that’s the really exciting part of this fintech. You can usually get something recommended by a financial advisor, and except for the few independent advisors who have hardly established themselves in Germany anyway, it is not unlikely that a commission will flow for the recommended product. Apart from the resulting costs, which the customer bears, the question arises as to whether the recommended product is actually the best product or whether the expected commission may not have played a role in the recommendation. And even if the financial advisor were not influenced by this at all, how can it be ensured that the best product is actually recommended? How does the financial advisor know? And how long will it remain the best product? How often does the consultant recommend that you restructure because the market has changed? If you don’t have a really high sum in your account, so that you can enjoy real investment advice, then the options for the average consumer are suboptimal at best.
So why not leave the investment decisions to an algorithm, a RoboAdvisor, which is not influenced by how much commission it receives? (Of course, this would also be technically possible… like a private auction in real-time bidding) And can also process much more information than a human is capable of? That is also scalable and does not prioritize according to the amount of assets to be managed? Actively managed funds, for example, don’t necessarily do better than passive funds, and as for the general thinking, well. Algorithms could be the better investment managers, and that’s the idea of Scalable Capital (besides the type of strategy, see below). The company will stand and fall with the performance of its algorithms. The basic principle here is that the algorithms have not been optimized for returns, but for the avoidance of losses, based on the fact that most models underestimate the risk of loss and overestimate the return potential (“asymmetry of positive and negative price developments”). The whitepaper on the website explains the principle very well. Not having to worry about it yourself anymore and being able to benefit from asset management that is otherwise only reserved for the really wealthy would be a second huge advantage. Plus, since Scalable Capital is allowed to manage assets itself, the transactions are also carried out for you. Others have already written a lot about how exactly it all works.
But to the first experiences. The account opening did not work as quickly as Number26. Although you can also register via video chat, it takes a good week until the account is actually opened and the initial investment amount is debited from the reference account and credited to the Baader call money account. Baader then also sent me a password for the website, which I couldn’t even read because it was printed so thinly that my access was blocked after three failed attempts. The telephone hotline was nice, but did not want to fix the problem immediately, and the callback did not come either. All in all, however, that worked out at some point. And then it takes a few days until money is debited from this account and the first papers are purchased. At least for me, not everything was invested immediately, but for two consecutive days, with still some money left over in the call money account. And at first, the value of my portfolio went down minimally, but as it is with such investments: first go to sleep. The DAX also went down during this period.
The app as well as the website are aesthetically and functionally designed, no frills, fast loading. The app only has informational value, On the website and now also in the app, you can initiate deposits and withdrawals and change the monthly savings rate. The portfolio shows exactly which products have been invested in, and there is absolute transparency here as well. For some reason I can’t take screenshots from the app, apparently that’s prevented by the app.
The portfolio is compared to an average portfolio, although it is not really clear how this average portfolio is structured. The help only says that it is a representative portfolio of the last 15 years, but where was this portfolio taken from? And what relevance does this information have? I can’t change my portfolio anyway.
Overall, Scalable Capital makes a good impression on me. It will take some time before the quality of the algorithms can be evaluated, from time to time I will give an update here.
Comments (since February 2020 the comment function has been removed from my blog):
Frieder says
February 2017 at 11:23 … I have made exactly 275 euros in profit from July 2016 to today with an investment amount of 20,000 euros in the medium risk strategy. There is no need to use an index comparison: A pathetic performance of the algorithms!!
It’s funny that Scalable is praised everywhere. I don’t need a service provider with a good press department.
Conclusion: I will take my money away there again in the short term!!
No recommendation!
Tom says
February 2017 at 10:41 Frieder, read the post again: invest and go to sleep For me it’s just 3.75%…
