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.
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‘If I have seen further, it is by standing on the shoulders of giants’ Sir Isaak Newton, 1619 Technical disciplines have always progressed by researchers building on past work, but the deep learning research community does this in spades with transfer learning. Transfer learning builds new deep learning systems on top of previously developed ones. … Continue reading “Machines Learning From Machines”
Training a Machine Learning system requires a journey through the cost terrain, where each location in the terrain represents particular values for all ML system parameters, and the height of the terrain is the cost, a mathematical value that reflects how well the ML system is performing for that parameter set (smaller cost means better … Continue reading “Navigating the Cost Terrain with Minibatches”
Machine learning applications generally rely on supervised learning, learning from training samples that have been labeled by a human ‘teacher’. Unsupervised learning learns what it can from unlabeled training samples. What can be learned this way are basic structural characteristics of the training data, and this information can be a useful aid to supervised learning. In my latest … Continue reading “Learning Without a Teacher”