2018 in review and bold predictions for 2019
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Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community.

This week we look back at 2018 - from the GDPR and the Cambridge Analytica scandal, to advances in natural language processing and new open source tools. Then we offer our predications for what we expect in the year ahead, touching on just about everything in the world of AI.

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Featuring:

• Chris Benson – Twitter), GitHub), LinkedIn), Website)

• Daniel Whitenack – Twitter), GitHub), Website)

Show Notes:

2018 in Review

• Focus on more challenging ML problems

• Semi-supervised learning

• Domain adaptation

• Generative models

• Reinforcement learning

• NLP

• ELMO

• BERT

• Fear about AI

• GDPR, trust and privacy

• Cambridge analytica

• Facial recognition

• Tons of open sourced tooling, models

Predictions for 2019

• Focus on trust and transparency

• Bias

• Regulation

• GDPR and transparency, interpretability

• What will other countries do regarding regulation?

• AI for good

• Better voice and conversational results

• AI assistants

• Voice interfaces

• NLP advances

• More focus on product development, less on research

• Deep learning will explode in production product / service development

• Computer vision, NLP, speech recognition will be table stakes

• Increased accessibility of DL to software engineers / developers

• More testing/tooling

• Better training for data scientists

• Better integrations and infrastructure

• AutoML

• Organizational / Cultural Shifts

• New roles for data-based leadership - CDO, CAIO, etc.,

• Strategy - AI becoming first-class concern

• Competitive Analysis - AI and data assessments mandatory

• Fragmentation into distinct subfields - AI, analytics, data science, prognostics

• A changing relationship between humans and automation

• AI + robotics - first steps

• Pervasive AI + IoT - first steps

• The importance of creative expertise for humans

• How to school your child today to prep for tomorrow

• Narrowly-scoped, highly-specific job functions at most risk

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