Summary: We’re rapidly approaching the point where AI will be so pervasive that it’s inevitable that someone will be injured or killed. If you thought this was covered by simple product defect warranties it’s not at all that clear. Here’s what we need to start thinking about.
Summary: Sales is supposed to be an area that is more immune to replacement by AI than many others because of the high level of impromptu and improvisational human contact required. That remains true. But AI is showing that it can be a valuable augment to B2B sales and some early adopters are scoring big gains.
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: Looking for your next job in an early stage company but want to make sure your startup has staying power. Follow the expert rankings by CB Insights that also show us the changing trends in how AI startups should be focusing their offerings.
Added by William Vorhies on November 5, 2018 at 4:18pm — 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: We are entering a new phase in the practice of data science, the ‘Code-Free’ era. Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear. Here’s what you need to know.
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
Summary: Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company. Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.
Summary: Getting an AI startup to scale for an IPO is currently elusive. Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy.
Added by William Vorhies on July 17, 2018 at 7:00am — No Comments
Summary: Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models. Here are a few things you need to know.
Added by William Vorhies on June 13, 2017 at 8:00am — No Comments