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Alex Zhavoronkov
Co-founder
Insilico Medicine
CSO
The Biogerontology Research Foundation, UK

Ulrich Muehlner
Founder & Managing Director
GrowthCube Partners, Switzerland

Download agenda here for complete speaker list

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Recent advances in machine learning and specifically deep learning demonstrated unprecedented results in many applications with artificially intelligent systems surpassing human and superhuman accuracy in image recognition, voice recognition and advanced strategy games including the game of Go. These advances are rapidly propagating into the many industries with transformative impact. In just under two years since achieving near-human accuracy in image recognition, deep learning systems powered by the Graphics Processing Unit (GPU) computing enabled autonomous driving and were implemented in mass produced consumer vehicles and enabled driverless trucking. Artificially intelligent systems are expected to transform industries, where large amounts of data are available for training, and the pharmaceutical industry is one of the few industries, where multi-modal data is abundant. The impact of the recent advances in AI on the pharmaceutical industry is expected to be immense at every level from lead generation to clinical trials management to marketing. Aside from the cost-cutting potential, where the artificially intelligent systems can replace entire divisions, the efficiency of pharmaceutical R&D may be significantly increased. Like Uber, Amazon and Tesla every pharmaceutical company should consider setting up artificial intelligence research and implementation departments with significant flexibility and agile “skunkworks-style” project management capabilities. However, this transformation needs to start at the helm of every organization and the clear vision for the role of artificial intelligence in the pharmaceutical industry. The main obstacle impeding the propagation of the recent advances in artificial intelligence into the pharmaceutical industry is the gap between expertise in biology, chemistry, medicine and computer science. The first drug companies to bridge this gap will be the future industry leaders. This workshop will help bridge this gap at the executive level starting from the vocabulary and the overview of the data requirements to the understanding of the near-term hardware roadmaps covering GPU and other emerging technologies.

 

Objectives of the workshop:

  • Provide an umbrella view of the current state and directions in deep learning
  • Introduce the industry executives to deep learning and the basic terminology
  • Present the recent trends in deep reinforcement learning and generative models.
  • Big picture of the business areas within the pharmaceutical companies, where deep learning technologies may have transformative effects
  • Provide use cases about adaption of AI technology in drug discovery and clinical trials management
  • Executive-level introduction to the “who is who” in deep learning and artificial intelligence
  • Provide an overview of the competitive landscape in artificial intelligence for drug discovery

 

Benefits of the workshop:

  • Learn to speak the same language with the leaders of AI-powered industries
  • Effectively integrate AI into the every part of the organization
  • Understand the hardware and data ecosystem for successful applications of AI
  • Learn about the “hot areas” in AI

This event will provide all delegates the chance to meet one another through our many networking opportunities. With pre-event, mid-morning and mid-afternoon breaks as a standard, this event will also feature a complimentary networking dinner for all participants.

This evening will allow you to meet with our expert speaker panel who represent many stakeholder groups in a less formal setting. Our networking lunches will also allow you plenty of time to meet with your peers and colleagues whilst you refuel through the event.

CEOs, CSOs, presidents, executives vice presidents, board members, strategy officers, senior innovation officers, HR executives: Scientific Officers, Chief Information Officers, Chief Technology Officers, Knowledge & Data Management (Discovery, Clinical & Real-World Data), R&D Analytics, Informatics, R&D Innovation, External Alliances & Innovation, R&D Strategy.

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