Summary: Despite our concerns about China taking the lead in AI, our own government efforts mostly through DARPA continue powerful leadership and funding to maintain our lead. Here’s their plan to maintain that lead over the next decade.
Think all those great ideas that have powered AI/ML for the…Continue
Added by William Vorhies on June 10, 2019 at 8:28am — No Comments
Summary: If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.
Summary: Especially in consumer goods and retail the value of AI/ML is only part of the story. AI/ML will increasingly need to integrate with helper technologies to deliver maximum value. Up your game in IoT, 5G, and robotics to ensure you’re giving your operating team all the best options for their investment.Continue
Added by William Vorhies on May 13, 2019 at 8:06am — No Comments
Summary: Communicating with your Board of Directors about AI/ML is different from conversations with top operating executive. It’s increasingly likely your Board will want to know more and planning that communication in advance will make your presentation more successful.
Added by William Vorhies on April 29, 2019 at 9:35am — No Comments
Summary: Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place. Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.
Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford’s Human-Centered AI Institute. This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques.
Added by William Vorhies on February 25, 2019 at 9:00am — No Comments
Summary: The world of healthcare may look like the most fertile field for AI/ML apps but in practice it’s fraught with barriers. These range from cultural differences, to the failure of developers to really understand the environment they are trying to enhance, to regulatory and logical Catch 22s that work against adoption. Part 3 of 3.
Summary: Despite hundreds of projects and thousands of data scientists devoted to bringing AI/ML to healthcare, adoption remains low and slow. A good portion of this problem is our own fault for failing to see the processes being disrupted through the eyes of the physician users. Here we lay out the healthcare opportunity landscape but for data scientists following classical disruption strategies, it may be more of a minefield. Part 2 of…Continue
Added by William Vorhies on January 14, 2019 at 8:00am — No Comments
Summary: If you want to understand the promise of AI/ML in healthcare you need to see it through the eyes of physicians, the ultimate users. Turns out these folks aren’t the rapid adopters you’d think they’d be and the problem is largely with the way data scientists have tried to implement. Part 1 of 3.
Summary: Here are our 5 predictions for data science, machine learning, and AI for 2019. We also take a look back at last year’s predictions to see how we did.
Added by William Vorhies on December 17, 2018 at 8:50am — No Comments
Summary: There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways. If you’re going to explain or recommend AI to a non-data scientist, it’s important to understand the difference.
For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming. “Big Data” – totally misleading…Continue
Added by William Vorhies on November 27, 2018 at 8:23am — No Comments
Summary: Even if you’re not big enough to have a full blown data science group that shouldn’t hold you back from benefiting from AI. The market has evolved so that there are now industry and process specific vertical applications available from 3rd party AI vendors that you can implement. There are just a few things to look out for.
Added by William Vorhies on October 23, 2018 at 7:30am — No Comments
Summary: How about we develop a ML platform that any domain expert can use to build a deep learning model without help from specialist data scientists, in a fraction of the time and cost. The good news is the folks at the Stanford DAWN project are hard at work on just such a platform and the initial results are extraordinary.
Added by William Vorhies on September 4, 2018 at 8:02am — No Comments