On 27 and 28 June, Amsterdam hosted the AI Expo Europe 2018 conference (https://www.ai-expo.net/europe/). The conference included three simultaneous events: AI Expo, Blockchain Expo and IoT TechExpo, which could be regarded as today’s leading, most disruptive technologies and the ones that could have the greatest transformational impact on our society. Figures from the conference show evidence of the interest aroused by these technologies: 18 lectures, 8,000+ participants, 300+ lecturers and300+ exhibitors.
This post offers a summary of the attendance at a great many of the lectures and a compilation of the principal ideas and highlights of the conference, especially with regard to AI.
We should stress that although it was a technology conference on AI, many of the lectures spoke of non-technological issues, such as ethics, transparency, sustainability, environment, security. Without a doubt, AI transcends technology.
This transcendence is a clear indicator of the huge impact that AI will have on society and, despite being a currently fashionable issue and hype of future trends, it is obvious that this is not a minor issue or a passing fad.
As the UN representative, Irakli Beridze, mentioned in his lecture (which I’ll comment on later in this post), the first discussions at the UNGA (United Nations General Assembly) regarding AI were in 2015. In 2017 the UNGA concluded: “Artificial Intelligence is a game changer that can boost development and transform lives in spectacular fashion, but it may also have a dramatic impact on labour markets and, indeed, on global security and the very fabric of societies.”
The question also arose regarding… Why do we want AI? And some of the answers it got were pretty interesting too: … to live longer and happier.
What do we understand by AI?
One of the conclusions drawn from the conference is that AI is not just one technology: it’s a series of technologies that we could list in rising order of complexity:
RPA (Robotic Process Automation): software that mimics human behaviour.
Expert Systems: computer system that emulates the ability to take the decisions of a human expert.
Virtual agents or chatbots: software agent that can carry out tasks or services for a person.
Autonomous Systems: control methods with cognition-enabled robots.
SensitiveAI: self-aware machines that have human-level intelligence – they are considered by many to be the height of AI creation.
Evidently, we still haven’t reached the last state of Sensitive AI (self-aware machines) but some lecturers did point to synthesis of emotion as the new frontier in AI, introducing the concept of AEI (Artificial Emotional Intelligence).
With regard to the subject of virtual agents or chatbots two types were identified:
Task-oriented agents (declarative):
Robust and interactive FAQ system.
Questions initiated by the user with pre-set answers and automated menus.
Use NLP (Natural Language Processing) but little Machine Learning.
Highly specialized and structured interactions.
Data-driven agents (conversational):
Similar conversation to humans, contextualized and personalized based on the user profile and the history of prior conversations and behaviours.
Use NLP and Machine Learning.
Integrated with big data sources.
Chatbot success stories were highlighted, such as the T-Mobile initiative in Austria: a chatbot with 75,000 users and 150,000 questions answered a month: https://www.t-mobile.at/tinka/
In this respect, references and guidelines were given for the roll-out of chatbots, as well as general information about AI, Machine Learning and Deep Learning, which I’m posting here as a reference:
Regarding the current state of adoption of AI, according to the CXP Group consultancy (http://cxpgroup.com/) in a survey conducted with 240 European companies of more than 1,000 employees in January 2018, the answers to the question: “Do you have an overall strategy for AI?”
26% under discussion
34% planned within the next 2 years
29% being set up
11% already in place
According to the ASGARD consultancy (http://asgard.vc/global-ai/) :
Europe: 7,500 companies claiming to build AI
USA is the clear world market leader for Artificial Intelligence
China is number two in the world for AI but leader in 2030
The role of Europe in AI was discussed, and Europe’s current fragmentation in this area was highlighted, with some lines of action being proposed:
We need more Deep Tech
We need more AI Research
We need more visionary capital for AI
We need a European AI Strategy
Lecture by the United Nations representative
Of note was the lecture given by the United Nations representative, Irakli Beridze, from the Centre for Artificial Intelligence and Robotics, UNICRI: “Maximizing the Chances of Global Prosperity – Artificial Intelligence to Transform our World”
He started from some very positive premises: “we are richer, we are smarter and we are connected”, and he made some interesting statements:
“AI is potentially the most powerful technology we have ever created”
Unprecedented opportunities to accelerate solutions for “unsolvable” problems
He spoke of the UN’s 17 sustainable development goals with 169 targets and 230 indicators for transforming the world by 2030:
Good health and well-being
Clean water and sanitation
Affordable and clean energy
Decent work and economic growth
Industry innovation and infrastructure
Sustainable cities and communities
Responsible consumption and production
Life below water
Life on land
Peace, justice and strong institutions
Partnerships for the goals
He stated that the USA currently leads the way in AI with over 10 billion euros in investments in Venture Capital, and that China is predicted to become leader by 2030 thanks to the impetus of programmes such as the “Next Generation Artificial Intelligence Development Plan – World Leader by 2030”.
You can see the details of the plan via this link:
Genís Berbel is Director of Technology at the Universitat Oberta de Catalunya's eLearn Center. He has solid experience acting as liaison between business and technology, providing IT solutions based on understanding the strategy of companies and operational processes.
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