Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

AI for industries: chemical, forest, pharmaceutical, mining

528 views

Published on

A presentation on the current situation with AI from the industry-specific perspective. Industries: chemical, forest, pharmaceutical, mining.

Published in: Technology
  • Be the first to comment

AI for industries: chemical, forest, pharmaceutical, mining

  1. 1. AI for industries: - Chemical - Forest - Pharmaceutical - Mining Juho Vaiste
  2. 2. The “Magical” AI - Digitalization, automation, data analytics, simulations, mathematical analysis - Ongoing processes from 60’s - Basic and current forms of AI (machine learning, computer vision, natural language processing ) as new techniques and tools - Breakthroughs: in machine learning, computation power, the amount of data
  3. 3. Some industry-related applications - Machine learning, deep learning, neural networks - Ability to learn without being explicitly programmed - Classification, regression, clustering, anomaly detection - Computer vision - Automating what human visual system can do - Natural Language Processing - Reducing workload from researchers and employees
  4. 4. Reinforcement learning and other advanced approaches - A learning agent taking action towards maximizing the rewards - An approach which full capacity we don’t know yet - AlphaGo example: finding strategies humans haven’t ever found - Experiments in video games: AI developing abilities not designed - Highly funded research projects, but progressing - Change of mindset: planning and designing goals and rewards
  5. 5. Business perspective - Greater efficiency, reducing risk - Tailoring, specializing, rapid changes - As a tool for new R&D discoveries - Releasing time for creativity (learning and studying, wellbeing at work, shorter working hours?) Societal and ethical perspectives - The problem of responsibility (especially in medicine) - Adding efficiency → Easiest way is reducing the human labor
  6. 6. 1. AI for industries ➔ Mostly good old technological progress ➔ Take time to understand (data, digitalization, AI) and follow research. ➔ Goal-oriented approach in the future
  7. 7. Resources Nokia, Siilasmaa (so, don’t worry, you aren’t late) https://blog.networks.nokia.com/innovation/2017/11/09/study-ai-machine-learning/ MOOC-courses (Coursera, Fast.ai) https://www.coursera.org/learn/machine-learning http://www.fast.ai/ Stanford - One Hundred Year Study on Artificial Intelligence (2016 report) https://ai100.stanford.edu/2016-report
  8. 8. Collaboration in Turku region Turku AI Society - Connects researchers and students of AI impacts aisociety.fi Turku.ai Meetup - Technical perspective (approx. monthly meetup with changing topics) turku.ai Research: TUGS, universities Growing number of companies (some “AI” companies)

×