Summary: Business doesn’t want AI. Business wants results. While we were focused inward on our Advanced Analytic Platforms, smart competitors were rolling up AI/ML with other capabilities into “Intelligent Automation” platforms. This large scale integration of capabilities of which AI/ML is only a part looks a lot like the development of ERPs in the late 90s.
Added by William Vorhies on June 24, 2019 at 8:23am — No Comments
Summary: Forrester has just released its “New Wave™: Automation-Focused Machine Learning Solutions, Q2 2019” report on leading stand-alone automated machine learning platforms. This is our first good side-by-side comparison. You might also want to consider some who were not included.
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: The ability to train large scale CNNs directly on your cell phone without sending the data round trip to the cloud is the key to next gen AI applications like real time computer vision and safe self-driving cars. Problem is our current GPU AI chips won’t get us there. But neuromorphic chips look like they will.
Added by William Vorhies on June 4, 2019 at 9:00am — No Comments
Summary: Real Time Computer Vision (RTCV) that requires processing video DNNs at the edge is likely to be the next killer app that powers a renewed love affair with our mobile devices. The problem is that current GPUs won’t cut it and we have to wait once again for the hardware to catch up.
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: GANs (Generative Adversarial Nets) originally thought to be a tool for inexpensively creating DNN training data have instead become the tools for creating deep fake images. However the deep fake technology is now finding uses in the commercial world and promises valuable results.
Added by William Vorhies on May 6, 2019 at 7:57am — 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: Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models. This is what prescriptive analytics was meant to be.
Just when I thought we’d figured it all out, something comes along to make…Continue
Summary: McKinsey says platform companies will represent 30% of global business revenue by next year (2020). In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow. Here are the rest.
McKinsey says platform companies will represent 30% of global…Continue
Added by William Vorhies on April 15, 2019 at 7:43am — No Comments
Summary: McKinsey says platform companies will represent 30% of global business revenue by next year (2020). Here are some lessons and examples to help mature companies evaluate where they can create AI/ML-enabled platforms to remain competitive. This is a long topic so this will be Part 1 of 2.
Added by William Vorhies on April 8, 2019 at 9:29am — No Comments
Summary: A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive success. Adopting this new approach requires a deep change in mindset and is quite different from just adopting AI/ML to optimize your current operations.
Added by William Vorhies on April 1, 2019 at 9:29am — No Comments
Summary: Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.
Added by William Vorhies on March 25, 2019 at 8:30am — 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: Recurrent Neural Nets (RNNs) are at the core of the most common AI applications in use today but we are rapidly recognizing broad time series problem types where they don’t fit well. Several alternatives are already in use and one that’s just been introduced, ODE net is a radical departure from our way of thinking about the solution.
Added by William Vorhies on March 11, 2019 at 7:43am — No Comments
Summary: In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable. C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list. Here’s what’s changed.
For data scientists these are miraculous times. We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so. Still the rate…Continue
Added by William Vorhies on March 4, 2019 at 9:41am — No Comments
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 9:22am — No Comments
Summary: True prescriptive analytics requires the use of real optimization techniques that very few applications actually use. Here’s a refresher on optimization with examples of where and how they’re best used.
Summary: A major pain point is standing in the way of many companies’ ability to maximize the value of their ML/AI initiatives. The competing goals of data flexibility versus single version of the truth can only be solved with an effective data governance strategy.
Added by William Vorhies on February 11, 2019 at 10:15am — No Comments