Tassilo Klein and Moin Nabi (SAP AI Research)

Deep learning has heralded a new era in artificial intelligence, establishing itself in integral parts of today’s world within a short time. Despite its immense power — often achieving super-human performance at specific tasks — modern AI suffers from numerous shortcomings and is still far away from what is known as general artificial intelligence. These shortcomings become particularly prominent in AI’s limited capability in understanding human language. Everyone who has interacted in one way or another with a chatbot or text generation engine (e.g. OpenAI’s GPT-3) might have noticed that the longer the interaction goes on with the machine, the staler it gets. When generating long passages of text, for instance, a lack of consistency and human-feel can be observed. Essentially, this highlights that the model behind does not really understand what it says and does. Rather it is more or less walking along paths of statistical patterns of word usage and argument structure, which it acquired during training from perusing through huge text corpora. …

The 2018 conference on Computer Vision and Pattern Recognition (CVPR) took place between June 18–22 in Salt Lake City, Utah. As the premier and highly competitive conference in the realm of computer vision, CVPR provides a platform for a diverse group of academics, researchers, technologists, industrial giants and high-tech start-ups to showcase the field’s latest innovations.

CVPR this year has shown significant growth; making it the largest CVPR conference with more than 6,000 attendees. Known for its diligent and high-quality review process, CVPR received 3309 conference paper submissions this year, out of which only 979 papers were accepted. …

Verena Eitle and Tassilo Klein (ML Research Berlin)

The emergence of machine learning has led to a new level of automation across the majority of business lines such as finance, supply chain and sales. The latter, especially, has attracted great attention in academia and business, where the technology is used to enhance sales processes or assists professionals in making data-driven decisions.

Image for post
Image for post

Most companies have a formal sales process in place including clearly specified milestones that are commonly understood by its salesforce. In particular, the effectiveness of managing the sales pipeline has a major impact on revenue growth as shown in a study of Harvard Business Review.

But what does the elusive sales pipeline really mean? In short, it is a visual representation of a company’s sales prospects structured into different phases along the sales process, from initial contact to closing a sales deal. If customers express initial interest in buying a product, they are defined as leads, for whom the company has only little information available. After retrieving more insights and targeting them with certain marketing initiatives like campaigns, it is to decide whether these leads can be converted into opportunities. Once classified as such, the company’s salesforce uses sales activities such as demos to turn opportunities into actual customers. …

Robin Geyer, Tassilo Klein and Moin Nabi (ML Research Berlin)

Generally, standard machine learning approaches create the need to store training data in one central spot. However, with the recent upswing of privacy protection in machine learning, a new field of research, known as federated learning, has sparked global interest. In this blog post we present our first results regarding privacy-preserving collaborative machine learning, following up on our previous blog post introducing three different approaches to tackle the privacy problematic in this area.

Image for post
Image for post

However, before diving deeper into our proposed approach, let’s recapture the concept’s main points. The idea of federated learning is to train machine learning models without explicitly sharing data or concealing training participation. This scenario is relevant across-industry as well as at a personal level and becomes especially important in scenarios where malicious clients might want to infer another client’s participation. …

What Has Happened at SAP ML Research in 2017

The last year has seen a great deal of new trends and developments in the machine learning (ML) field. This rate of progression will definitely increase throughout the new year with more and more players joining the AI game of as the topic is gaining sweeping prominence. Moreover, it can be seen as the logical next step following the emergence and integration of ML services into the business sphere and into our daily lives.

A more in-depth look back at the year 2017 reveals a number of breakthroughs and findings. Among the most notable ones was the Google DeepMind AI program AlphaGo Zero, which outperformed its predecessor AlphaGo by 100 to 0 games. Prior to this event, AlphaGo, a machine trained with moves of historical games as well as self-play, had been praised for the defeat of human world champion Lee Sedol in the ancient Chinese game Go. The triumph of AlphaGo Zero in turn was achieved by simply learning the game of Go from scratch without any further instructions. Within only 40 days, AlphaGo Zero achieved to create winning strategies that go beyond human capabilities, just by playing Go against itself. …

The Conference on Neural Information Processing Systems (NIPS) took place between December 4th and 9th in Long Beach, CA, USA.

As one of the top machine learning and computational neuroscience conferences, this year’€™s NIPS was a complete success experiencing an even larger rush of attendees compared to previous years. Apart from the rising number of participants, the conference has also seen a strong increase in submitted papers. Of the total 3240 papers submitted, 679 papers were accepted resulting in a 21% acceptance rate compared to last year’s 24%. …

Frederik Pahde, Tassilo Klein and Moin Nabi (ML Research Berlin)

In recent years, deep learning techniques have achieved remarkable results in computer vision, constantly pushing the boundaries of what is possible. These advances can be explained by improvements to algorithms and model architecture along with increasing computational power and growing availability of big data. However, the big data assumption for training, which is key for deep learning applications, is not always realistic. Particularly, in enterprise or healthcare scenarios, labelling samples is often very expensive or even impossible. …


Tassilo Klein

Senior Researcher at SAP ML Research, Berlin

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store