On 25th of January there will be 5 parallel workshops to choose from in which to participate. The number of participants is limited.
All workshops, except the AI Chatbot Workshop by Frederik Schröder, will be held in English. For the “Invitation only”-Workshop you can register and find out if you can attend until January 15th.
Andrew Ng compares it to electricity while Kevin Kelly believes, that the business of the next 10 thousand startups can be described simply – “take x, and add AI”.
Being great in AI is not without difficulties, as companies need to master multiple technologies, including semiconductors, cloud, data processing software, application design and cybersecurity. AI is power, and the most valuable companies in the world declared their strategy to be an AI centric one. Alphabet, Apple, Microsoft, Facebook and Amazon design their own chipsets, storage, data processing engines and application stacks to control every aspect of AI delivery.
While we speak about AI we should not forget that the word “intelligence” can cause confusion, and even mislead the audience. In current AI technology stacks everything happens by design. Companies and individual researchers are making choices which will dictate how AI services are used and perceived, and what impact they have at a micro and macro level. As AI uses brute force to scale everything, it implies several risks. A dataset can represent only certain aspects of a world view, though engineers working with it might think it can generalize. Teams of only white males design computer vision systems, without thinking to include minorities or female input. Human bias get scaled, leading to errors in the best case, and inequality in the worst.
Like any other technology, AI can be used by criminals – to attack IT systems and to cause severe distress with consumers and businesses. Poor design choices around security increases the risks of adversarial attacks and causes reputation risk.
In some cases, Machine Learning can be used for discrimination purposes, be it in pricing, or target advertising, university admissions, or defining a prison term.
In some cases, current regulatory frameworks do not allow for applications which cannot be completely understood or reverse-engineered by humans. A so-called ‘black box’ problem arises. While thinking about regulatory solutions, we should bear in mind that AI is a progressing field. As an example, Nvidia provides technology to color places in datasets, which are most important for the machine interpretation.
Artificial Intelligence touches every aspect of our life. We need a new governance model to shape the future we want, not the future we might get by lack of action, education and mindfulness. In China, Machine Learning made it into the compulsory school curriculum. Finland provides free courses for AI programming in English to every individual who wants to learn. Melinda Gates and Fei Fei Lee organize a foundation to attract high school girls into deep learning and AI. They understand this move will increase much needed diversity in the field. In the meantime, Germany – along with other industrial countries – does not have a national AI plan. AI is not just for big Internet brands or top universities. Questions around its impact on employment, life-long education, quality of living, inequality and discrimination should be on the radar of municipalities, schools, and businesses.
Frederik Schröder, Knowhere GmbH
2. get insights into current chatbot use cases (e.g. insurance, B2B, media, industry, e-commerce)
3. create a concrete chatbot demo yourself (goal: individual company mockup).
4. be able to use chatbots as an effective instrument for automated customer communication.
During the workday he works on building machine learning based insight tools for YouTube. He works with advertisers and agencies on trend analysis, best practice development, and how AI is changing creativity.
At night he is a lecturer of MS Applied Analytics at Columbia University in New York.
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