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.
Added by William Vorhies on December 4, 2018 at 9:59am — 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: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us.
Added by William Vorhies on November 18, 2018 at 11:14am — 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: Digital Decisioning Platforms is a new segment identified by Forrester that marries Business Process Automation, Business Rules Management, and Advanced Analytics. For platform developers it’s a new way to slice the market. For users it eases integration of predictive models into the production environment.
Added by William Vorhies on October 30, 2018 at 8:30am — 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: Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations. We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.Continue
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: There are several approaches to reducing the cost of training data for AI, one of which is to get it for free. Here are some excellent sources.
Added by William Vorhies on October 2, 2018 at 7:23am — No Comments
Summary: If you’re still writing code to clean and prep your data you're missing big opportunities for efficiency and consistency with modern data prep platforms.
Summary: Purpose Built Analytic Modules (PBAMs) such as those for Fraud Detection represent a fourth way to practice data science, a new model for the good use of Citizen Data Scientists, and a new market for AI-first companies.
Added by William Vorhies on September 18, 2018 at 9:07am — No Comments
Summary: The role of Analytics Translator was recently identified by McKinsey as the most important new role in analytics, and a key factor in the failure of analytic programs when the role is absent.
The role of Analytics Translator was recently…Continue
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: Remember when we used to say data is the new oil. Not anymore. Now Training Data is the new oil. Training data is proving to be the single greatest impediment to the wide adoption and creation of deep learning models. We’ll discuss current best practice but more importantly new breakthroughs into fully automated image labeling that are proving to be superior even to hand labeling.
Added by William Vorhies on August 28, 2018 at 7:27am — No Comments
Summary: IBM’s Watson QAM (Question Answering Machine), famous for its 2011 Jeopardy win was supposed to bring huge payoffs in healthcare. Instead both IBM and its Watson Healthcare customers are rapidly paring back these projects that have largely failed to pay off. Watson was the first big out-of-the-box commercial application in ML/AI. Has it become obsolete?
Added by William Vorhies on August 21, 2018 at 10:51am — No Comments
Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this. However, the latest trends in CNNs may make this obsolete.
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.
Added by William Vorhies on August 7, 2018 at 7:25am — No Comments
Summary: Now that we’ve detailed the four main AI-first strategies: Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it’s time to pick. Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup.
Added by William Vorhies on July 31, 2018 at 8:20am — No Comments
Summary: The fourth and final AI strategy we’ll review is Systems of Intelligence (SOI). This is getting nearly as much attention as the Vertical strategy we previously reviewed. It’s appealing because it seems to offer the financial advantages of a Horizontal strategy but its ability to create a defensible moat requires some fine tuning.
Added by William Vorhies on July 24, 2018 at 9:00am — No Comments