About this episode
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. Join the discussion Changelog++ members support our work, get closer to the metal, and make the ads disappear. Join today! Sponsors: Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com . Rollbar – We catch our errors before our users do because of Rollbar. Resolve errors in minutes, and deploy your code with confidence. Learn more at rollbar.com/changelog . Linode – Our cloud server of choice. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code changelog2018. Start your server - head to linode.com/changelog Algolia – Our search partner. Algolia’s full suite search APIs enable teams to develop unique search and discovery experiences across all platforms and devices. We’re using Algolia to power our site search here at Changelog.com. Get started for free and learn more at algolia.com . Featuring: Chris Benson – Website , GitHub , LinkedIn , X Daniel Whitenack – Website , GitHub , X 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 Something missing or broken? PRs welcome!