machine learning

IBM Hides Watson Under a Red Hat

A charitable assessment might have called it an experiment, really; an experiment to find out if the headwinds on the hardware and infrastructure services side of the house could be mitigated to some meaningful extent by perceived tailwinds on the software side of the house. A less charitable assessment might have called it a gamble... But now, with the (Sunday evening) October 28, 2018 announcement that it will be acquiring open source software leader, Red Hat for an astounding multiple of nearly 12x FY2018 revenue, or $34 billion, IBM signals that the promise of the high-profile experiment / gamble within their software group - relabeled, Cognitive Solutions in 2015, and where "Watson" became the poster child for the recent hyperbolic expectations of artificial intelligence (AI) - was not likely to arrive soon enough to stem the internal bleeding caused by competitive threats within the cloud infrastructure services market. With Red Hat, IBM now turns to defend its 3rd fiddle position in the cloud market (just ahead of Google) - which [...]

By |2020-10-05T21:23:58-04:00November 12th, 2018|Open|

Alphacution Riffs Ep 2 – Measuring the Pace of Automation

Episode 2 of our new video series, Alphacution Riffs, picks up where Episode 1 left off - and begins to describe our research mission, modeling methodology and research workflow. We also begin to lay the foundation for our "T-Greeks" benchmarking framework that focuses on measuring and comparing "return on technology" (RoT) - otherwise known as "technical leverage" - for banks and asset managers. Here, we describe how much of our core research effort is currently built on the basis of just 3 simple data points collected for a model library that currently represents 200 large banks, asset managers, hedge funds, and even certain proprietary trading groups, among others - more than 250+ FSI-related companies in all - and how each of those models covers several years, with many of our core models beginning in 2005. With these 3 data points and our 360-degree modeling strategy, we can move beyond the benefits of various market-sizing exercises to more impactful benchmarking exercises. This tutorial is important for our clients and broader network [...]

By |2020-10-05T21:19:20-04:00March 28th, 2018|Video|

The Source of #AI Hype

Apple doesn't mention it... Amazon doesn't mention it... Alphabet (aka - Google) does mention it - but doesn't link it specifically to financial performance... IBM? You betcha. More than 155 times... In case you have been living under a rock - which, now that I think of it, has some increasing allure - artificial intelligence (and its slightly less sexy twin, machine learning) has succeeded 2016's marketing darling, blockchain, to become the blinking-neon-sign-outside-your-hotel-room term for 2017. Sorry, folks. The budgets have already been allocated. Go find predictive analytics (2014) and digital transformation (2015) in the dust bin of over-exposed marketing terms if you are not yet hip to how this game is played. Now, let's take a quick step back for a second: This is NOT an anti-AI hit piece. Nor is this an IBM-gotcha piece. I am a fan of both. But, this is simply a commentary based on the convergence of connect-the-dots exercises that have come out of our modeling and research. Yes, AI has an incredibly promising - if not, slightly scary [...]

By |2020-10-05T21:15:45-04:00April 27th, 2017|Open|