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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.

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Lifebeam VI: Experiences after updates

This week there was both an OTA update for the LifeBEAM Vi (02.00.00.00) and a new app version (1.1.0 (14)). With the update of the app, the bug that prevented my experiences with the Effort Guide, the AI, was fixed. Immediately after the update, I went running in the evening and was initially rewarded with a bitter disappointment. The Vi told me during the run that she would be able to tell me more soon if she knew me better.

So still no artificial intelligence. After more than 7 hours of running, this should be the case, especially since the support had promised me that the previous data would be included despite bugs. After the run, however, the friendly voice said that she now knew enough about me. So the next morning purely out of curiosity I put on my running shoes again (not a sufficient recovery period, I know), and now Vi told me when I would get into my fat burning zone and leave it again. Although she doesn’t say that clearly, only that I “hurry”.

The myth of the fat burning zone

Fat burning zone. There she is again. Anyone who takes a closer look at it, for example through this wonderful article by Dr. Moosburger, knows that it is usually misunderstood. Above all, the stickers on cardio equipment in fitness centers are usually not very helpful. Often, the optimal fat burning pulse is understood to mean that you should not get over it under any circumstances, because then no more fat is burned. And that’s how some fitness trainers like to tell it. That’s wrong. For this popular but wrong interpretation of the fat burning zone, however, my pulse was much too high for Vi to see me in this zone. So what does LifeBEAM mean by the fat burning zone? An initial inquiry in the comments yielded only a standard answer, so I cheekily continued to drill. Here is the second answer:

Hi Tom- apologies for not being more specific. Your basic assumption about that zone is generally correct (i.e. more fat is burned than glucose but the more intense the workout, the more calories are burned, and even when the ratio is lower, more energy is taken from fat), moreover, Vi will also take into consideration your personalized calibrated biometric thresholds (heart rate, cadence, pace consistency) and BMI to make sure you are indeed in the right zone. Keep it up!!

P.S

There’s one last component you are probably aware of which is a term called after-burn zone where you are burning fat significantly also after the run itself, all directed by the amount and endurance of effort taken during your last run. Based on our studies we still don’t feel fully comfortable to add this metric to our off training screens due to its scientific tolerance, but we promise to keep you guys in the loop once we do so.

The BMI is just such a myth, apart from the fact that the answer is again very wishy-washy, but at least I get an idea of what the LifeBEAMers want to do.

Improvements and problems of the LifeBEAM Vi

The behavior has become much better when switching on the LifeBEAM Vi. It now reacts within a few seconds, so you no longer have to worry that the button might be defective. A solution has also been found for the bass problem, a bass boost can now be defined.

Not solved is the problem that Vi still doesn’t seem to understand that you slow down when you run up a mountain. She says that they have slowed down and that they should please try to run more evenly. But sometimes she also praises you for the steady speed, even though you have slowed down. In the illustration on the left you can see the dilemma: My pulse gets higher, at the same time I slow down, even if there are small plateaus in between where I try to speed up again. My pulse is often at 170 and more, I don’t know if I really want to reach my theoretical maximum pulse of ~180 at my age

I still haven’t understood the music selection either.

Cadence and stride frequency

The LifeBEAM Vi tries to convince me to increase my cadence on every run. That looks a bit stupid at the speed I’m supposed to keep. But the beat played to it is actually enormously helpful. Sometimes I manage to get over 160 steps on average. Here, too, science is not yet in complete agreement on what the optimal step frequency is. Achim Achilles’ calculator provides fewer steps for me. But it feels like the shorter steps actually help to not have quite as tired legs the next day.

Buy: Yes or No?

I’m not sure if I wanted to recommend the LifeBEAM Vi to a non-early adopter for purchase yet. On the one hand, I firmly believe in the benefits of artificial intelligence in sports. Instead of reading any rule values from tables, a machine can certainly make better individual recommendations for an athlete, provided there is enough meaningful data. On the other hand, I would speak of a chaotic system in the case of a body, in the sense of a lack of important data points, so that the wrong conclusions are drawn from the existing data.

I never managed to blog about my Omegawave ECG (maybe I’ll make up for it), but shouldn’t the pulse values during training also be related to how recovered the body is from the last workout? Quite apart from that, the scientists do not really agree on which values are good or bad when and where. So I’m just not sure if the data available to the LifeBEAM Vi is actually sufficient. And whether we know enough about how to handle this data to actually be a help in training.

The question of how LifeBEAM will proceed with data collection in the future is also interesting. Where is the data actually evaluated? In the app? In the device itself (unlikely, because my bug was only fixed after the app update)? Or somewhere in the cloud? The data is apparently uploaded there, and if no half-life is calculated here, then a lot of data will accumulate per user in the next few years. It is not unlikely that LifeBEAM will therefore introduce an additional monthly fee at some point, similar to Omegawave. You buy the device, but in order to be able to create an evaluation, you need the service. Maybe an early adopter will be exempt from these fees, after all, we are suffering from all childhood diseases right now. But that’s not likely.

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Lifebeam Vi Update Experiences and Problems

After running more than 50 kilometers and almost 6 hours with the Vi, there is still no coaching effect. On the contrary, the “intelligentsia” talks to me less and less, and recently it hasn’t even advised me to take smaller steps. She only tells me that last time I slowed down at the end and that this time I should please keep the pace. What she measures, but apparently doesn’t evaluate: I always walk up a mountain at the end (well, not a real mountain, the Elbberg holds, but at least more than 25 meters difference in altitude), and she should understand that. Supposedly, one of the next software versions will take this into account.

After I had “fought” my way through the support forum, it was clear to me that it was not up to me. An email to the support, who then answers immediately despite Saturday evening. I should please uninstall and reinstall the app. OK. And what is the point of that? “Apologies for any confusion. I just took a closer look into your log files and it looks like you are running into a calibration issue.” And now I’m waiting for the bugfix I hope that the data collected so far is not useless and I don’t have to start all over again.

Other points of criticism:

  • The power button is not very responsive, sometimes you have to press for a very long time until the device is turned on.
  • I don’t understand the logic with the Spotify lists, I definitely won’t get into running mode that way. But first of all, solving the missing intelligence is more important to me.
  • In the forums, users complain that Lifebeam had running on the treadmill in the Kickstarter video, but this functionality is now not available. And without further ado, the video was also removed. Supposedly, however, this functionality is still to come.
  • The battery lasted 3 runs for me, so probably less than 3 hours.

I’ve definitely gotten fitter now, at least I can do the Elbberg better now. But I could have done that with Runkeeper.

What’s cool is that you get the raw data. Apparently every second is logged, and this is what the data looks like:

[code]

53.5439109802246

9.93943977355957

3.01765032412035

2380.0

Value>157

158

[/code]

In principle, you could also do something with this data yourself…

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Running with Artificial Intelligence: LifeBEAM Vi

In the middle of last year, I supported LifeBEAM’s Kickstarter campaign, and this week the LifeBEAM Vi arrived: “The first true artificial intelligence personal trainer”, supposedly the first real personal trainer based on artificial intelligence. LifeBEAM has so far mainly produced helmets for fighter pilots, which can measure vital signs with special sensors; so the company can already be expected to have some experience with sensors. And measuring the pulse via the ear definitely works better than with a watch on the wrist, the Fitbit Blaze has often disappointed me here. AI is “the next big thing”, so why not have an assistant like in Her to improve my fitness?

We are still a long way from “Her”, but a “Her” for only one area (domain-specific), in this case sports or even more limited running, is realistic. It is also her-like that LifeBEAM has kept the interface with voice very minimalist, so that your imagination can play with what Vi looks like for YOU.

Great packaging, not so great manual

The fun cost $219 including shipping, plus another 50€ customs, which I think is an impudence, but apparently also not discussable. I was also just too excited. I don’t have an unboxing video, there are enough of them on the net. The packaging is great, it all feels very valuable, only the documentation has been saved. Although there is a small manual, it does not say, for example, which voice commands exist, and other questions can only be found out by rummaging through the forum posts. The support side, on the other hand, is rather poor.

Setting up the LifeBEAM Vi

It’s great that the LifeBEAM Vi doesn’t come with an empty battery, so you can get started right away. It’s a pity, however, that you can’t see anywhere how full the battery is. The supposedly possible charging within 45 minutes does not work either.

The Vi cannot speak German and does not understand German, and the corresponding app only exists in English. But at least you can switch the units to the metric system, so you don’t have to convert miles while running.

The suboptimal thing about the setup is that you have the headphones in to hear “them”, but then you have to pay attention to a blue light, which you can only see if you don’t have the headphones inside. By the way, the sound is great. At least when you’re not running.

The first run

First of all, you don’t feel this stirrup at all. It’s super light anyway. Due to the fact that you get different pins for different ear sizes and also a small hook that is supposed to hold the headphone in the ear, the in-ear headphones hold very well for me, which is rarely the case. You just have to be very careful that the small green light, the heart rate monitor, is not visible, because then it does not measure the pulse.

For the first run, I just wanted to run 5 kilometers, started the app, the connection was found immediately, and off I went. The LifeBEAM Vi talks quite a lot at first, while a Spotify playlist is playing, although I didn’t understand which one it was. Spotify’s running feature didn’t work. The music gets a little quieter when Vi speaks, but it was still too loud at times, so I didn’t always understand Vi.

What I think she said is that she has to spend 2 hours of training with one until she has enough data together to be able to make suggestions. In the forum, some users complain that nothing happened after 2 hours. In this case, however, it was probably because the coaching apparently only works on the iPhone at the moment. By the way, the sound didn’t seem quite as great when running, but this may also be due to the fact that I had replaced the earbuds again shortly before.

First Coaching

What the LifeBEAM Vi says is definitely more personal than what Runkeeper tells me. The voice is more natural, and it’s less predictable. It was also nice that she told me after 2 kilometers that my steps were too big and “put on” a cool beat with which I could try a different stride length. She praised me after 2/3 of the run that I could keep the speed well. That seemed much more individual than Runkeeper.

What I didn’t understand was how to get her to tell me my heart rate. At first I tried it in the classic way with “Vi, what is my Heart Rate?”, but she hadn’t responded. Then I pressed the right headphone, because I thought I dimly remembered that it worked. In fact, all you have to do is say “Heart Rate”, and if there isn’t quite as much wind blowing against the Elbe, then she understands it. It’s just funny that 20 seconds later she explains to me how I can ask her for the heart rate. She didn’t say anything about the fact that my pulse was relatively high. She also didn’t understand the question of “distance”; Sometimes I would have liked to know how much I still have ahead of me.

You’ll never run alone

Shortly before the end of the run, she told me that I had made it right away. It seemed like an eternity to me afterwards, but that may also have been due to the Elbberg, which gets in my way every time (hence the high pulse and low speed in the screenshot).

When it was over, it was nice to have someone to give you feedback, even if it was only a summary. Overall, it was a good experience to have someone to talk to you, because sometimes I’m bored while running. Some people think about problems while running, but I try to clear my head. The LifeBEAM Vi helped a lot with this.

Next steps

So I still have an hour and a half missing until the LifeBEAM can coach Vi; I will report on that then. Until then, I can also report which other voice commands of the Vi work and how fast the battery actually lasts and charges. Overall, the impression is positive for the time being, even if the high expectations raised by the initial videos were not completely fulfilled.

Update: The video I had embedded here was set to private by LifeBeam. The background is probably that a man was shown on a treadmill in the video. But Vi can’t handle that at all

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First experiences with Scalable Capital & Update N26

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

  1. 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

  1. February 2017 at 10:41 Frieder, read the post again: invest and go to sleep For me it’s just 3.75%…
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We love the robots

In the period from December 17, 2014, Felix Lill reports on the Japanese researcher Hiroshi Ishiguro, who not only created a robot copy of himself but also sends it on trips to give lectures in his place. He calls these copies Gemonoids, and he also believes it’s possible that one day we can love robots. If children already develop feelings for Tamagotchis, why shouldn’t people also develop feelings for robots?

This idea is not new, for example Fritz Lang’s Metropolis is based on the fact that a machine human is built in the shape of Maria and the men fall for her. David Levy wrote a thesis about it in 2008, which he processed into a popular science book a year later.

For me, the question arises as to what robots can do better in relationships than humans? Thus, each robot could better respond to a person’s psychological deficits and, so to speak, “behaviorally therapeutize” them.

To that fits a video by Björk:

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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.