Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become. Of course there are also some interesting winner and loser stories.
The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020. The really big news is how many excellent choices are now available. In a remarkable move, the whole field of…Continue
Summary: Centaur AI is the best marriage of the machine’s ability to remember, analyze, and detect issues along with the human’s intuition to evaluate or take action on those results. Instead of focusing on AI replacing humans, we should focus on AI in its role of augmenting humans.
Added by William Vorhies on February 11, 2020 at 1:19pm — No Comments
Summary: Can all AI strategies be defined by a few common needs or are the different AI strategy models sufficiently unique that they need to be considered as separate approaches.
Added by William Vorhies on February 3, 2020 at 11:15am — No Comments
Summary: Workforce forecasting and scheduling applications are rapidly upgrading their use of AI. Techniques of time series forecasting ranging from the simple Holt Winters to the complex, DNNs and Multiple Temporal Aggregation are available on some but not all platforms. Increasingly, AI differentiates the usefulness of these apps.
Added by William Vorhies on January 28, 2020 at 2:15pm — No Comments
Summary: Just how much should you trust your AI systems? Best practice points to constant review, strong governance, and the willingness to override results that seem illogical.
Added by William Vorhies on January 20, 2020 at 8:26am — No Comments
Summary: Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role. Here’s a little more detail.
Summary: The results are in. There is only one demonstrably successful strategy for creating big wins for AI-first companies. We’ll briefly summarize the other contenders that have fallen by the wayside and then lift the curtain on the winner.
Added by William Vorhies on January 7, 2020 at 8:30am — No Comments
Summary: Reinforcement Learning (RL) is going to be critical to achieving our AI/ML technology goals but it has several barriers to overcome. While reliability and a reduction in training data may be achievable within a year, the nature of RL as a ‘black box’ solution will bring scrutiny for its lack of transparency.
Added by William Vorhies on December 30, 2019 at 11:18am — No Comments
Summary: For all the hype around winning game play and self-driving cars, traditional Reinforcement Learning (RL) has yet to deliver as a reliable tool for ML applications. Here we explore the main drawbacks as well as an innovative approach to RL that dramatically reduces the training compute requirement and time to train.
Added by William Vorhies on December 23, 2019 at 7:30am — No Comments
Summary: There have been several stories over the last several months around the theme that AI is about to hit a wall. That the rapid improvements we’ve experienced and the benefits we’ve accrued can’t continue at the current pace. It’s worth taking a look at these arguments to see if we should be adjusting our plans and expectations.
Summary: A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.
Our profession of…Continue
Added by William Vorhies on December 4, 2019 at 3:12pm — No Comments
Summary: Too many solutions. We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.
Added by William Vorhies on November 25, 2019 at 9:44am — No Comments
Summary: AML has been around since at least 2016 but only in the last year have Gartner and Forrester begun to offer their opinions. Here’s where we stand.
Added by William Vorhies on November 18, 2019 at 12:00pm — No Comments
Summary: Booz Allen just launched a one-stop shop for all manner of pretested DNN models. They’re even guaranteeing price. This makes buying just like picking accounting, CRM, or HRIS software. Equally as important, it’s a genius example of platform strategy to lock in customers and lock out competitors.
Summary: Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML. The tech is already here from recommendation engines. The need to be more efficient and to become AI-augmented in our decision making is now. Getting the contextual awareness is the hard part.
Added by William Vorhies on October 28, 2019 at 9:43am — No Comments
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Summary: Data Scientists from Booking.com share many lessons learned in the process of constantly improving their sophisticated ML models. Not the least of which is that improving your models doesn’t always lead to improving business outcomes.
Summary: Here’ a proposal for real ‘zero touch’, ‘set-em-and-forget-em’ machine learning from the researchers at Amazon. If you have an environment as fast changing as e-retail and a huge number of models matching buyers and products you could achieve real cost savings and revenue increases by making the refresh cycle faster and more accurate with automation. This capability likely will be coming soon to your favorite AML platform.
Added by William Vorhies on October 7, 2019 at 7:28am — No Comments
Summary: Autonomous vehicles (AUVs) and many other systems that need to accurately perceive the world around them will be much better off when image classification moves from 2D to 3D. Here we examine the two leading approaches to 3D classification, Point Clouds and Voxel Grids.
Added by William Vorhies on September 23, 2019 at 2:24pm — No Comments
Summary: 99% of our application of NLP has to do with chatbots or translation. This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels. The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.