Künstliche Intelligenz (KI) hat bereits zu zahlreichen, aufsehenerregenden Anwendungen geführt, beispielsweise in der Bilderkennung, der medizinischen Diagnostik, der Sprachübersetzung oder der Mobilität. Wie ein vom Bundesrat in Auftrag gegebener Bericht zeigt, ist die Schweiz für die Anwendung und die Herausforderungen von künstlicher Intelligenz grundsätzlich gut aufgestellt. In verschiedenen Bereichen besteht jedoch in unterschiedlichem Ausmasse Handlungsbedarf. Dieser ist erkannt und wird weitgehend bereits angegangen. Der Bundesrat hat den unter Federführung des Eidgenössischen Departements für Wirtschaft, Bildung und Forschung (WBF) verfassten interdepartementalen Bericht an seiner Sitzung vom 13. Dezember 2019 zur Kenntnis genommen. Auf der Grundlage des Berichts werden nun strategische Leitlinien erarbeitet.
Für die Schweiz ist es zentral, die Potenziale, die sich durch die neuen Möglichkeiten der künstlichen Intelligenz ergeben, zu nutzen. Dazu gilt es, bestmögliche Rahmenbedingungen zu gewährleisten, damit die Schweiz sich als einer der führenden innovativen Standorte für Forschung und Entwicklung im Bereich von KI weiter etablieren kann. Gleichzeitig gilt es, die mit der Anwendung von künstlicher Intelligenz verbundenen Risiken zu erkennen und rechtzeitig Massnahmen zu ergreifen.
Der vom Bundesrat in Auftrag gegebene Bericht nimmt eine breite Auslegeordnung und sorgfältige Analyse der relevanten Rahmenbedingungen im Hinblick auf eine verantwortungsvolle Nutzung von künstlicher Intelligenz vor. Weiter beleuchtet er die spezifischen Herausforderungen in verschiedenen Anwendungsfeldern über alle Politikbereiche der Bundesverwaltung hinweg und diskutiert möglichen Anpassungsbedarf auf Bundesebene.
Digital transformation is radically reshaping almost every aspect of our society. The explosion of artificial intelligence (AI) and big data analytics applications is enabled by the extreme availability of data in combination with the substantial computing power of modern highly distributed computing infrastructures connected by high-speed networks. Machine learning technologies can be trained to perform specific tasks with an efficiency and an accuracy that can supplement and, in some cases, outperform that of humans. These systems provide deep insights by learning from data and interactions with users, which is already leading to a profound transformation of numerous industries, professions and society at large. The current state of AI is, however, still far from delivering truly intelligent behaviour that is comparable to human intelligence. An AI research strategy should therefore carefully analyse AI’s history with its various waves of large promises and conceptual shortcomings.
Recent advancements in machine learning have enabled AI technologies to become extremely successful. Speech recognition, natural language interaction with machines and facial recognition based on deep learning are now commodities that have changed the way people interact. The machine learning strategy of emulating human performance by learning from human experience promises a solution to the knowledge extraction problem. However, the automated reasoning process is as opaque as human decision making. Evolution has enabled humans to collectively reason and act on our collective experience, though other humans are often black boxes. Today, we are confronted with computational artefacts that are adapted to complex human decision making and, thereby, have inherited a similar “black box” behaviour.
Given the penetration of AI across most industries, its potential impact on GDP promises to be very high. In Switzerland, AI is already reshaping industries such as banking, insurance, pharmaceuticals and manufacturing. Furthermore, Switzerland is the European country that has the highest number of AI start-ups per citizen, with more than 100 startups. Many leading countries are heavily investing in AI development strategies and the establishment of technology transfer centres in this field.
To date, Switzerland has not developed a dedicated AI strategy. AI is one of many topics covered in the strategy “Digitale Schweiz”. An interdepartmental working group on AI which should ensure knowledge exchange in the domain of AI within the federal administration and coordinate Switzerland’s positions in international bodies, is mandated to submit a report to the Federal Council by September 2019. Furthermore, an interdisciplinary study on behalf of TASWISS is evaluating the opportunities and risks of AI on the basis of various focal points: work, education, media, consumption and administration. The publication of that study is planned for the end of 2019.
Swissintell keeps growing in Zurich! With an event on 11 September, we continued our new Zurich-based series of meetings titled “The Next Big Thing”. Every three months, we meet to discuss how to identify, monitor, and respond to those trends that will shape or disrupt business in the decade ahead.
Our second meeting took place at the Ema House hotel. The meeting consisted of three parts. The first part was led by Aleksandra Bielska, our Zurich area manager. Aleksandra began by reminding the participants of the association’s goals, our vision and activities. She then explained the importance of emerging technologies to the field of competitive intelligence. In particular, she referred to the impact of these technologies on business environment, organizations, Competitive Intelligence discipline, and ourselves.
The second part was led by German Ramirez, social media and blockchain pioneer, book author and co-founder at the Relevance House. German talked about the development of the blockchain technology and the problems that currently affect the field. He stressed that many claims about blockchain should not be taken at face value and reminded us of the importance of focusing on the utility of blockchain-based solutions and not on the “buzz” that the technology generates. He looked at the relevance of blockchain-enabled products and services through the prism of three pillars: value, visibility and credibility. He also provided us with a list of mistakes to avoid and good practices to follow when adopting blockchain-based solutions. German ended his presentation stressing that a complete paradigm shift is needed to change the established standards.
Augmented Reality (AR) was the focus of the last part of the meeting. Robert Adelmann, a former researcher at ETH and MIT and a co-founder at Scandit presented a brief history of AR before he elaborated on the current state of the art. He described how AR could be used in industries as disparate as maintenance, logistics and education. He used short videos to illustrate relevant use cases. He specifically mentioned the growing popularity of AR-based mobile applications that allow customers to virtually place products in their environment and, hence, reduce the risk of purchasing something that does not fit or that the customer does not like.
Optimistic that the virtual and the real world are merging, Robert concluded his presentation with an outlook into the future evolution of AR. After the traditional question and answer session, the meeting concluded with its participants engaging in a long discussion over a glass of wine.