ML Job Interview

There are many variants of this joke floating around It is so cool. Here is my favorite which I found on Twitter:

Interviewer: What’s your biggest strength?

Me: Expert in Machine learning

Interviewer: What’s 9 + 10?

Me: 5.

Interviewer: Nope. 19.

Me: It’s 14.

Interviewer: Wrong. 19.

Me: It’s 19.

Interviewer: What’s 2 + 2?

Me: 19.

Interviewer: You’ll overfit right in!

Between Hype And Extraordinary Benefits

Last week I was at the ML Conference in Berlin. The conference was very instructive for me. The lectures were about the technology behind machine learning, data science, security and algorithms. But then it was also discussed how to set up an ML project, which mistakes can be made, and why container technologies like Docker or Kubernetes make ML projects much easier. Most exciting were the lectures about concrete applications of machine learning and artificial intelligence. And of course the discussions with other participants did not come too short.

My Conclusion

Most companies are currently experimenting with machine learning. There are only a few ideas on how these new technologies can bring real business benefits today. The hype in the media – both positive and negative – was far away. At the same time, everyone I spoke to agreed that in a few years, any company will have to use machine learning to stay in the market. It will be the lowest common denominator.

It will be used both in the public cloud and in the company’s own data center. The biggest problem in your own data center seems to be the slow processes. “It takes far too long for me to get a system set up. I’m much faster in the public cloud,” several people told me.

From VMware’s (my employer’s) point of view, I noticed that some participants knew us, but none associated VMware with machine learning and artificial intelligence. VMware offers interesting options when it comes to the flexibility and security of ML applications. Well, I’ll deal with that another time.

Here are two more highlights from my point of view:

How we avoid maritime accidents

The Keynote Data to the Rescue! Preventing Accidents at Sea by Dr. Yonit Hoffman of the Israeli startup Windward inspired me. Did you know that 10% of all ships have an accident once a year? With big data analysis and machine learning, Windward has developed models to predict and prevent accidents. 150 million data points on ship positions, ship types, weather information, water depths, proximity to ports, and many more have been brought together and translated into deep knowledge and understanding of what happens at sea. The lecture was really captivating.

The results have led to a very fruitful and interesting cooperation with insurance companies. Imagine which worlds meet. Maritime insurance companies have been working in a similar way for 350 years – often with paper. Now something new is emerging. And it is clearly driven by business. This is how I imagine the future of machine learning.

Rainwater drainage pipes for China

The most exciting conversation was with an employee of a company that manufactures sanitary systems for large buildings and rainwater drainage pipes. A special challenge are large flat roofs, e.g. for industrial plants or shops. The drainage pipes must have exactly the right diameter. If they are too small, too little water flows off. If they are too large, there is no suction effect. With the optimum diameter of the pipes, a suction is created in the pipe, which optimally drains off the rainwater.

I have learned that many years of professional experience are necessary to design these optimal pipes. This is a problem when you want to enter new markets. Now the company has managed to determine optimum rainwater pipe diameters much more quickly using machine learning. Now, for the first time, it is possible to enter new markets such as China without having employees there who have decades of experience.

If machine learning and artificial intelligence already deliver value in this extraordinary way today, then we will certainly see even more examples in the future. The limits are our creativity and imagination. Knowledge of technology is not enough. This is only a necessary, but not a sufficient condition for the successful use of machine learning. Identifying business ideas is the real challenge.

DeepL – AI Translates Best

I write my articles first in German and used Google Translate to create the English version. That was about 80% good, a bit polished, then the second language was online.

A colleague told me yesterday that DeepL Translator could be a much better translation engine for me. I tried it, and I’m thrilled. In a direct comparison with Google Translate, DeepL is much better at translating my German articles into English.

What makes DeepL so special? I think it’s the consistent use of neural networks, machine learning and artificial intelligence. This is described on the DeepL Press website:

“2016 – The team begins to construct a neural network capable of translating any kind of text. It later becomes known as DeepL Translator. This system builds on the team’s years of experience with machine learning and natural language processing, and has been trained with over a billion high quality translations collected by Linguee’s web crawler.”

Now another 3 years have passed and DeepL is getting better and better. That’s what I love about good technology. Keep it up, DeepL Team!

Deep Learning Helps Protect Honeybees

After some critical thoughts, I write about an article that shows me how machine learning technology is actually doing something very good for the world: Deep Learning helps protect honeybees. It’s about a topic that has been on everyone’s lips for a long time in IT: Automation!

Here the counting and evaluation of the honeycombs of the honey bees is automated and thus beekeepers get much easier information about the state of health of their hives. I think it’s so cool and I’m happy that researchers are working on it.

More and more often I read and hear that collecting data and setting up the data model are the biggest hurdles when using machine learning. It’s the same here.

The algorithms and tools are described, but not a word about infrastructure. That would interest me: Where do the tools run and what can be done better? I suppose that this case is well taken care of in a public cloud.

Maybe I will meet the author of this blog post at the Machine Learning Conference in Berlin.

1984, Alfred Nobel, And Artificial Intelligence

I had written that I want to make a positive contribution to this world and that I would take care of the spread of machine learning and artificial intelligence. Does that even fit together? What is the positive contribution of artificial intelligence today?

Amazon Alexa, Google Home, Apple Siri, self-propelled cars, intelligent websites are the harbingers of a wave of artificial intelligence that is just beginning. These harbingers are often criticized in the media. Is the permissive handling of our data justifiable? Without our data, without a lot of data, machine learning and artificial intelligence will not work. Are we prepared to pay this price?

If you want to deal with artificial intelligence, then I strongly recommend that you read George Orwell’s novel “1984”. Orwell wrote it in 1947/1948, it was published in 1949, and it is more topical than ever. “Big Brother is watching you!” In this novel we accompany Winston, who works at the Ministry of Truth and paraphrases newspaper articles, so that predicting the government is true in the archives, even if they do not arrive. And Winston, like all citizens, is watched day and night on telescreens in their homes and at work. The telescreens are reminiscent of devices like Amazon Alexa, which also listen in – and soon also observe, like the telescreens in the novel “1984”. What seemed like crazy science fiction for many years is now everyday life. Read the novel and get your own picture of the world we could create with artificial intelligence.

New technologies are neutral at first. Like a kitchen knife. It can be used for cooking, or it can hurt people. Good and evil are both possible. Alfred Nobel invented dynamite to do something good. He wanted to simplify mining. He did that, too. But then dynamite was also used to wage war. That made Nobel so difficult that he used his fortune to make the Nobel Prizes possible.

Every new technology is in this dichotomy. Since the Stone Age. The spear provided food when an animal was killed with it. And it was also used for fights between humans. The same sides exist with the steam engine, electricity, engines, airplanes, nuclear fission, whatever.

Machine learning and artificial intelligence are also able to make people’s lives much easier, autonomous vehicles will make roads safer and we will no longer waste time in traffic jams, but use them. The downside could be comprehensive monitoring and transparency of my data. Who knows.

I look forward to working with new technologies. I believe that in the end it will make the world a better place. “Technology as a force for good,”

said Pat Gelsinger, VMware CEO. That’s true. But we also have to reflect and consider ethical aspects. The ethics of new technologies is a field of its own, an important field. We have to be careful not to neglect this despite all the progress we have made.

That’s a core topic for my work at VMware.

My Path To Artificial Intelligence And Machine Learning

One of my personal goals is to make a positive contribution to this world. For me, this includes voluntary work, supporting family and friends, but also my job as an employee at VMware.

People spend money on things that improve their lives. Companies offer things. They can be products or services.

In this context, I look at artificial intelligence and machine learning (AI/ML). Can products and services be improved by AI/ML? Can AI/ML make a positive contribution to people’s lives? For many this is an open question. There are advocates and skeptics. I assume that the answer is “yes”.

Then the question follows: How can I help with my work at VMware?

VMware is a company that offers state-of-the-art IT infrastructures. IT infrastructures are the backbone of companies. This is where companies manage their products, customer data, processes and communications. And more and more companies are using IT infrastructures to improve their products or even offer completely new products and services. And these products or services should ideally improve people’s lives.

If a company wants to use AI/ML, it must first consider what it will be used for. What is the reason for using AI/ML? Then follows the consideration of which technology, which algorithms or which combination of technologies and algorithms are best suited for this requirement.

Then follows the determination of the AI/ML infrastructure. And at this point VMware can provide a very good foundation. Here I can make my positive contribution to the world by helping to connect the dots. The potential of VMware, the people, the companies, the technology, the ethics, and other aspects that are important when using AI/ML. It’s a complex field with many pendencies. AI/ML will conquer more and more areas. VMware’s contribution to the world’s IT infrastructure can be significant. I don’t know exactly what it will look like. It’s a journey with an uncertain outcome. But it is an exciting journey.

I will document my journey with the following contributions. I will raise questions, and maybe answer some questions. It is my personal view of the world of new technologies.