Today thanks to Artificial Intelligence – machines engineered to have human-like intelligence – Siri answers our questions, Safety Sense 2.0 drives our Toyotas, cultures are bridged by Google Translate, and AlphaGo is the world champion of Go, a 2500-year-old game with more move choices than the number of atoms in the observable universe.
“It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture.”Andrew Ng: Founder Google Brain and deeplearning.ai; Adjunct Professor, Stanford University
Based in the Portland, Oregon area, Thinking Teams consults with decision makers and organizations in the US and internationally. We help our clients navigate opportunities and risks presented by the development and use of advanced AI technologies.
- What is AI? What can it do? What can’t it do?
- How will AI change the world of work?
- How can AI and people best complement each other?
- What are the opportunities, challenges, and risks introduced by AI?
With broad and deep experience in AI technologies and an extensive background in organizational leadership and consulting, we work with our clients to co-create organizations founded on clear vision, open and respectful communication, effective collaboration, wholeheartedness, and where human and machine intelligence work in harmony.
Latest from the Blog
Professor Russell’s book starts out with an entertaining journey through the history of AI and automation, as well as cautionary thinking about them. This discussion is well informed – he is a renown AI academic and co-author of a comprehensive and widely used AI textbook. Having provided historical background, the remainder of the book argues … Continue reading “Review: Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell”
Common sense makes humans very efficient learners, so machine learning researchers have been working on ways to imbue machines with at least some ‘common sense’. In a previous blog we discussed using pictures to train natural language processing systems, in a sense giving the systems partial ‘knowledge’ of what words represent in the physical world. ML systems … Continue reading “Learning Common Sense from Video”
Natural language processing (NLP) machines have made great progress by learning to recognize complex statistical patterns in sentences and paragraphs. Work with modern deep learning models such as the transformer has shown that sufficiently large networks (hundreds of millions parameters) can do a good job processing language (e.g., translation), without having any information about what … Continue reading “Learning Words with Pictures”