Crypto Kid: Hello. Thanks for coming on the show.
Dr. Anna Becker: Hello, hello.
Crypto Kid: Why don’t you tell my audience… introduce yourself, tell them a little bit about yourself.
Dr. Anna Becker: Hi, my name is Dr. Anna Becker. I’m CEO of EndoTech, the company, they’re doing trading and algorithmic trading in currently crypto and Forex markets. It’s nice to meet you all.
Crypto Kid: Thank you, thank you. It’s a pleasure to meet you too. One of the topics is powerful women taking up the space in a male dominant field. Why don’t you explain a little bit about that and how you’re facing the hurdles and defeating the challenges?
Dr. Anna Becker: Okay. I think, yes, there is a problem, and I’m constantly asked about whether there is a problem. For many people, it’s a question of whether the problem exists. But, there are still certain countries and still certain areas where there are very few women that are present in the field. For example, trading and algorithmic trading is one of the fields where I practically don’t meet women. There are women, and I know them one by one, but it’s definitely a male area. I think it comes from many angles. One angle is that the trading itself, while women are very successful in manual trading, the environment is very male-oriented, the exchange is where it started from and ending with all the schools and all the kind of male style environment, male emotions, and male kind of driven try to success, and it’s more like gambling.
I think in gambling it’s also lots of women, but not on the high stakes. We see also on TV that in… poker and other, there are fewer women than men. But here, I think it’s one of the fields where we don’t have enough education. There are no schools where women can apply, so it’s only entrepreneurial women that want to go and join the field, and then they’re not really accepted by the field. How do we face the hurdles are just… we don’t think about them. We just go and we try to find our way, trying to find where we can learn. I took 12 years of kind of trying to go to different fields, trying to have, as a consulting company, to learn about the field from different angles, and taking it easy till you find your own way.
I think if I were to be a male I would be more going to the standard road of looking for a mentor and looking for the standard way to find one company, then another company, and then another company to grow with. I was not able to… I applied for different jobs, but I was not successful to get the job, so I had to go with a different route of just being a consultant in a different company until I figured out what works for me in algo trading and how I can be successful.
Crypto Kid: Now, are you mentored? Do you have women that you mentor now?
Dr. Anna Becker: Definitely. Not in this field specifically, because I don’t find women interested in this field, this is a different topic. I don’t have girls coming up and saying, “Okay, tell me how to do this.” I have lots of students, but again, male students that come to me and say, “Please help us to figure out this and this topic.” For women, what I do is I have many other startups and we’re trying to… I enjoy working with women because I think their way of thinking is very different from male and they’re much more kind of hungry for different brainstorming, for different areas, for mentorship itself. All of my CEOs of the other companies are female, but again, in algo trading, no, there is not much interest from the girls.
Crypto Kid: Okay. Now, with algo trading, are you just focusing on educating young people about how to make sensible financial risks?
Dr. Anna Becker: It’s one of the aspects that I think is very important. Our focus is, first of all, to provide them with tools because you can educate them, but if you don’t give them the tools to work with, it’s just words. First of all, we are focused to create the tools for them where they can, once they understand the risks, once they understand what to do, they go and they take the tools and they use them for their investment. This is EndoTech. Me, personally as a part of EndoTech, I think when you go to high-risk investments, you need to get some education about risk, because we, as humanity, are really underdeveloped in this area. Whenever you say the word risk, it’s connected to gambling, and we’re not talking about gambling, we’re talking about taking risks. It’s very different. When you’re talking about the risk of sports or extreme sports, it’s one thing, right? But, if you’re talking about the risk of jumping from the cliff, it’s a different risk. Risks are different.
In investments, risks are unnecessary. It’s like in fitness, you want to become ready for fitness so what you do is you put stress on your body, so stress as a word usually has a bad connotation, but for fitness is a good connotation because without stress on your body, you will not get muscles, you will not get endurance. The same for wealth, you cannot get wealthy without taking financial risks, again, not gambling, not harming yourself, not harming your finances, but taking risks. All wealthy people, they’re very well educated on how to take risks. That’s why they’re becoming more and more wealthy. Young people and people with less ability to have risk capital or to have additional capital, usually just work completely without risks, they are so against any type of risk that you often hear, “Okay, I can put this investment, but it’s very important for me to not risk anything.”
This approach cannot get you to a different level of your wealth, so we educate, I educate people that you need to take risks. You absolutely have to take risks and you have to take them every year, you have to take them continuously. Then, within 10 years, you can expect to be wealthy. The same is for sport, it’s not about one day, it’s not about one decision, it’s about doing it all the time. This is the same, it’s financial fitness. The way you do it, you check whether what you… first of all, you need to have risk capital. You need to put very little from what you have already because it’s high risk. But from another point of view, it’s something that can take you 10 times, 100 times, 1000 times within 10 years, and it’s very important because what we saw that for people to go to the next level of their financial wealth, they need to have, let’s say instead of $1,000, they need to have $400,000 and then they feel different.
If you have in your pocket free $1,000, then you feel on one level. If you have available $400,000, you feel on a different level of wealth, you can buy a house, you can allow yourself different things and feel different about your time, about your possibilities and so on. I was saying, you cannot get this $400,000 in the lottery, but you can get it with the correct investments year after year within, let’s say, 10 years. This is the path. We teach about how to evaluate your investments, how to find whether this investment is a high-risk but possible investment, and whether it’s a higher opportunity investment. We teach about this. Again, EndoTech is one of the tools that suit this category of financial opportunities.
Crypto Kid: Okay. Same here, when I first got into cryptos it was such a scary thing to get into and it kind of held me back a little bit because I heard about people’s accounts getting hacked and then it is used in the… what is it? The Black Market, so it was kind of sketchy from the beginning. I really didn’t have very much capital to invest in the first place, but as I started getting more knowledgeable about it, I started doing financial research and it is right. You got to take calculated risks and be willing to put in, at times, more than you’re willing to lose to afford, but also holding back and using smart decisions and not completely burying yourself where you’re financially broken. Using other ways to make money, and at the same time, taking that little bit and making it like, how do I say…? Diversify your accounts. Does that make sense?
Dr. Anna Becker: That’s right. Yes, definitely. That’s all very much… the thing is that in order for you to succeed, you had to really study. I think most of the people, they don’t have the luxury of taking this as an additional field like you’re a person that suits you. For you, it’s one of the things that you can educate yourself and you are one of the people that actually can make their own investment decision. The majority of the people, even if they would like to take risks, for them, it’s very hard to become financially, investment-wise or training-wise, professional or semiprofessional. I think what we need to do as companies and as, again humanity, is to kind of really bring these tools for the clients so that they can be able to manage their risks. Instead of telling them, “Listen, you need to educate yourself on what to choose,” it’s our job. Our job, as financial companies, is to bring them these opportunities and they need just to decide how they separate their risks between different companies. I think that’s the goal. That’s the ultimate goal.
Crypto Kid: Yes, exactly. Then, also, it’s okay to make mistakes. That’s how you grow. You’ve got to be able to learn from your failures, and just like, “Okay, I shouldn’t have done that. What’s the next approach I should do that…? How should I do this a little bit differently so I don’t end up in the mess that I’m in now?”
Dr. Anna Becker: That’s true. I think, again, if we look at the wealthy people, again, it’s not that you’re taking the risk and it’s automatically getting the reward, it’s the opposite. You’re getting the risk and then you need to be very careful about how not to get into the losing part. If you got to the losing part, but you were smart enough to use it as learning capital, then you’re fine. If you took it as one shot that you put in, all of your available risk capital, that’s very much true.
Crypto Kid: Yes. Elon Musk is a prime example. He learned so much from his rockets failing and just going at a different approach. Breaking through the barriers in the AI industry, why don’t you explain a little bit about that? How is AI going to…?
Dr. Anna Becker: Much less barriers, at least for women, in AI. This is good for us. But, AI is also a very interesting field for us because, from one side, it’s long time already that we’re all talking about AI since ’70, and AI had winters like crypto, it had Springs, it had summers, it had whatever. We are still very much at the beginning of AI. We have lots of topics that I think are relevant that everybody’s talking about, whether AI will be smarter people, whether we should be worried about… there are lots of topics about AI that I think are not only premature, but we didn’t squeeze out of AI what we need to squeeze on the first level. I’m looking always at different levels where different theories go and topics go, and we are very much at the beginning with AI.
We are not superintelligence, we’re not there. Right now, the question that we have is to start thinking in this algorithmic fashion, to start thinking in the AI fashion in order to get what we need to get. For example, in marketing, we’re there, right? In marketing, there are lots of AI systems that know how to do classification and personalization and other elements, but in very basic fields and computer vision, of course, there is deep learning and we cracked it, but in the rest of the fields, for example, finances. Finances, the problem is that we have very few data points. We don’t have enough data points in the process, we don’t have enough data points in lots of elements, so you cannot use computer vision because you don’t have millions of millions of data to learn from.
You cannot use other successes, so you really need to go and try to strip it to your needs, and that’s what we do. For example, we understood that the main issue of using AI in finance is over-fitting because you have very few data points and if you just teach your AI how to predict it, all it will do, it will model it so carefully that it will just learn exactly what you told it. I give this example, if… I have AI systems is sitting in my office, this is my office, right? I accept people into my office like visitors and partners, and I will ask my AI to define what is a male. After, let’s say, three, or four meetings that people will come into my office, AI wrote to me, “I know what is male. Male is the person with black socks.” This is what… it’s true because all of them came with black socks, it’s fairly accepted, and I don’t wear black socks so I’m the only female in algo trading office so she’s right, but it doesn’t make any sense.
She modeled correctly with the minimal number of parameters, but next time the other male will come with the red socks because it’s also accepted, but not between business people, and it will not mark it as a male, it will mark it as female. It did not try to really figure out what’s the difference between male and female, it did not try to figure out that there is different chromosomes, different expressions, different voice, different this, so it didn’t try to do this job. In finance, it’s exactly like this. There are so few points in the price that if you just try to approach it face on and give to the system without explaining it what to look at, for example, with the male and female situation, you could say, “Okay, try first to look at the mass of the person, the head of the person and the facial hair.” You can give different characteristics for the system to look at. Then, she would say, “Okay, I figured it out. On this parameter, it should be this, and this parameter should be that.”
The same we need to give to the finance all the time. We need to give all these different parameters that we know as a people and work together with AI to help it learn, grow, and develop. Again, it’s not only finance, it’s many, many different fields that people are not there yet. They don’t understand that they need to be very deep in their research and try to figure out what is the source of the knowledge, the source of the differences in this modeling. It’s, again, even in computer vision, one thing is just to distinguish different elements, but other is to solve problem using computer vision, it’s to do research using computer vision. Again, AI is still… from my point of view, it’s very much at the beginning. We’re asking lots of advanced questions, but we didn’t break through with lots of basic questions. I’m not referring to all of the AI in all of the fields, but it’s still, again, we are very undeveloped, from my point.
Crypto Kid: Okay. I thought AI can teach itself though and kind of outsmart humans.
Dr. Anna Becker: Not yet, this is our misconception. Again, there are lots of misconceptions that we all have. In the year ’99, I came to the field of trading and I was under the impression that I can buy a bot, I can buy any system and trade it to my account because I would say, “I know the computers so advanced, why it’s not available in trading, it’s so basic?” This is the same. Everybody thinks that these examples that you see on TV, that the robot stands up and starts walking… this is very first learning because it’s a very simple task. It’s not a simple task from their details point of view, it’s lots of elements in this robot and this, but it’s a very simple task that he needs to keep his balance, so he teaches himself how to keep the balance and how to walk. Reinforcement learning, you’re right, he’s not smarter than any kid. Maybe he’s doing it little bit faster than a kid, but he’s not smarter. Smartness is a different element, so we don’t have this super intelligence yet. We don’t have it on any level yet.
Crypto Kid: How close are we to it though, like 10 years down the road, 20?
Dr. Anna Becker: It’s very hard to estimate because we’re sometimes doing the leaps, as humanity we’re doing the leaps. The way I see it right now, very few teams are working in this direction. The question is it will not be superhuman, it will be a superhuman that is specializing in very specific tasks. Then, it might be in this task it might be better than a human, of course. But, as a human being that needs to solve physical problems about space, about this, about that, to be smarter than a person, to be able to think outside the box, to be able to bring different concepts… again, I wish that within the next few years that there will be some forced attempt to do that, but I don’t see it yet. I heard about this, but again, it’s very hard for me to believe that it will be achieved that soon. Again, it will be because we all want it.
We want to have super abilities because it will give us access to magic. It will give us access to new things, new fields, and new possibilities. We are very much stuck right now with our own internal boxes, internal wishes, and the way we’re structured, so of course, it’ll be amazing and it’s magical. But, I think we’re not there yet.
Crypto Kid: Okay. How will artificial intelligence shape financial markets from crypto to stock market and hedge funds?
Dr. Anna Becker: Again, it’s used… the field is already there. Right now it’s used primarily as assistance to human thinking and it’s shaping it in a way that, yes, we use it extensively. I think it’s one of the fields that uses AI extensively. There is medical field, there is space field, and there is this field that’s using it extensively. It’s hard to say. Again, lots of successes, lots of failures. Again, it’s complicated for us because we cannot use it just… give thousands of example is computer vision and get the result, it’s more complicated.
Crypto Kid: You were involved with it in the nineties and you wrote an article, Optimizing of Peer Methods and Conditioning [inaudible 00:21:51] Op Permission Algorithms from the Vortex Feedbacks Set Problem. I saw that. It was very complex.
Dr. Anna Becker: Very good, you’re almost there.
Crypto Kid: Almost there. I’ll put the link in the article if anybody wants to read it. It was on… what is it? A science website.
Dr. Anna Becker: Actually, we had the breakthrough in graph theory for this algorithm and it was published in Artificial Intelligence, which is a big journal for… it’s like Nature for the sciences, and then it’s Artificial Intelligence for artificial intelligence. Yes, it’s used in many fields. It’s a graph theory that helps us to resolve different interference problems. It’s lots of terminologies that I can’t use right now, I don’t think your listeners need to figure it out, as you were struggling through the name. It’s an optimization algorithm that helps interference for AI and for other plannings through uncertainty to do that. Yes, I think it was published… I have a very, fairly good citation index for the scientist for these articles that were published. We worked with… in Genome Project together with genetics, and with NIH, National Institute of Health. We were working with different fields, so I was enjoying to work with academia, to work with in university.
Crypto Kid: Okay. How many institutions are involved with your project, your company?
Dr. Anna Becker: Listen, we have… it’s complicated because we are a distributed company, we have lots of employees working from different countries. Again, it’s heavy technology, it’s heavy support, it’s heavy on every level because algorithm trading for retail clients is not a simple task. You need to take the algo and be sure that it’s executed for every client in the perfect way. Technologically, it’s not a simple task, so we have, again, quite a number of developers, support, trading desk, and so on and so on. From the partner’s point of view, we have a few partners that help us to distribute our algos to the clients, to bring us clients, and these partnerships grow. Right now, we’re signing another two. I think overall we reached already 170,000 clients. We’re working with 120 million under our belt, so it’s fun, I can tell you. We are the first ones that reached these type of numbers and reached this type of access to retail clients.
Crypto Kid: Okay. Hey, I appreciate you coming on and spending the time, I know you’re working on much bigger projects. Are there any last words that you want to share with my audience before we sign off?
Dr. Anna Becker: I think it’s even that we had Corona and we had COVID and the rest of the things, the last few years were kind of magical for us. We got where we wanted to be and we are doing quite innovative and interesting things, so I would just like to wish all of your listeners to have such great years and fun with their life and think about your finances, your wellbeing, and it’s the most important.
Crypto Kid: I’ll leave all the links to the website that you’re working on. I wish you the best in your endeavors.
Dr. Anna Becker: Thank you so much. Thank you so much.
Crypto Kid: Take it easy.
Dr. Anna Becker: Thank you. Bye-bye.
Crypto Kid: Bye.
To listen to the PodCast, click here: https://podcasts.apple.com/us/podcast/endotech-with-dr-anne-becker/id1390248793?i=1000571087307