Some of our favourite quotes from a year of podcasts
Here are some of our favourite quotes from a full year of podcast episodes…
Georg Hildebrand on his work with High Performance Computing with Zalando
I was always fascinated with algorithms and software and how they inter play together, so when I joined Zalando about seven years ago we were not in the cloud yet but we had this idea that in this growing and emerging business we needed more High-Performance Computing…
Zalando already had data centres at that point and we had really good network and infrastructure engineers, we put all these heads together and thought about what to build. At first we thought about building a Hadoop cluster but then we thought, thats not what data scientists need here, then we thought about building a traditional workload schedule with high performance software.
This was actually really really interesting interesting and exciting to build this for Zalando and nowadays Zalando has a rather large use case for that.
Dino Scheidt on encouraging collaboration between business and technology
“…As an example, in the US their was a private equity company in the health industry and when you think about that industry one of the first thing that comes to mind is cancer. So when you look up cancer and AI, you see that computer vision and AI is doing great things, it performs better on mean average than any radiologist or doctor…
So this private equity company thought, lets aggregate all of this technology, build a nice software suite and connect all the radiologist use out there. They were expecting a money making machine, with plenty of hospitals signing up and shortly after they cancelled because of very low ROI.
What happened is that doctors didn’t use the technology, because maybe they did not want to use it or it didn’t work as well as proposed. Then they changed their governance approach, buying 30 miss performing hospitals, radiologist clinics etc. created the whole value propositon with AI at the core…
Those hospitals then with the same technology are performing better then hospitals in other regions and they are able to treat more patients…
The value was created, not because of AI but how you actually introduce this change into the organisation…. How can we actually create the foundation and innovation layer that makes us believe that this technology is the logical next step.”
Ron Bekkerman on his 20 year journey to joining Cherre –
“…When I finished my undergrad 20 years ago I was looking for something interesting to start working on, when while reading some scientific papers I stumbled on something called ‘web mining’. Back then the internet was so new and so I got a little excited about it and thought okay I am going to do my Masters on web mining.
It turned out that web mining was very closely related to a completely new topic called machine learning. After I did my Masters, the market was not great at that moment, I struggled to find a job. I decided to go back to school and do my PhD…
After a few years working in HP labs, I joined a small start up called LinkedIn as a Research Scientist. This changed my world, I found a connection between the data and the business, how the data can affect the business. I figured out that it is the most interesting topic that a person with my skills can work on…
In 2009 we were a team of four or five research scientists, my boss came over for a meeting and said ‘okay we are not going to call you research scientists anymore, we are going to call you Data Scientists. The bosses name is DJ Patil, who later became the Chief Data Scientist of the United States of America.
That was the first time that the title Data Scientist was used in the industry, so I think I am like one of the first four or five data scientists in the world.”
Kendell Timmers on how the New York Times are maximising their revenue through Advertising
“…So, there is a couple of ways that we are doing that. Designing new ways for advertisers to target, creating insight tools to guide advertisers on how to create the best possible campaign and devising test plans to optimize the sight experience itself for advertising.
You know, as everyone is aware the digital advertising ecosystem is going through some pretty big changes and everybody is in the process of re-thinking how or even if advertising can actually fund internet content and at that moment every cent that you can get from advertising matters.
So to give an example of some of the things that my team works on, there is a targeting tool for advertisers that is officially called perspective targeting.
The idea was that the data science team used machine learning to predict what a typical emotional response will be to a given article and an advertiser can choose to have their ads appear on articles that fit most neatly with the emotional content of their ad.
So the targeting is purely contextual, which means we are only scoring based on the article, there is no differentiating by the user, making it more privacy friendly, which given the changes with GDPR and other privacy regulations coming up this will be much more robust.
It has also been extremely successful with a 40% improvement in click through rates so far.”
Fabian J.G. Westerheide on how Germany and Europe can become global AI Leaders-
“…We did a case study last year we analyzed 11,000 AI companies, we actually kicked out 6,000 of them because it turned out they didn’t do AI they just claimed to. So how does the AI world look? I would I say there is three blocks; China, Europe and United States, clearly the United States is the market leader, with 42% of AI companies based in the US…
Number two China, which is surprising not today but it was surprising back then because China didn’t exist on the digital hub for decades before so they emerged very fast.
Number three is a surprise because of the size is Israel, then there is London, UK, France, Germany and Canada.
Well lets start at number one… United States, why are they strong? Well they are strong because they financed the whole digital ecosystem, DARPA, NSA, CAA they are huge drivers, CAA made Facebook big, DARPA made Google big. All of them were subsidised, financed by the American government always with a military purpose.
This is not an alternative for Europe, we don’t want this but these are the facts. From this huge ecosystem grew Google, Amazon, Facebook, Microsoft. You have world leading companies who have now turned to AI…
With China, there is a very different approach but a very dominant player. What Russia used to be in the 1960’s and 70’s it is China today. What they did is they had a Sputnik moment, that when AlphaGo beat a world champion Go player…
They realised then that they had to do something and now they have invested over 130 billion. Sei Ping said we want to be the global market leader in AI by 2013 and they executed perfectly on several layers…
If you want to see the future today you have to go to Shanghai not Silicon Valley. If you cross the street in Shanghai and the light is red, the camera will recognise you and take the money from your bank account right away.
It’s terrible and beautiful at the same time, its beautiful technology but terrible with what surveillance is doing… So you already have an algorithm steering societal behavior, which is what George Orwell wrote in 1984 but they are doing it in real life…
Also what’s interesting about China is that they don’t have much research. They don’t have Oxford MIT or Stanford but still they have AI because AI today is free, the knowledge is free, open source is free, you don’t need ground breaking research, you need to apply it. That is the strength of China, they have a really deep integration, they have a lot of data they share a lot of data, they are heavily executing on AI and that is why they have a good start…”
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