KM (Knowledge Management) has been a recurring topic in the past year. It keeps coming up again and again in the most diverse of situations, several times I have been asked ‘is KM back?’ and the answer has to be “It looks like it probably is…..’. KM was a hot topic in the late ’90s …
This week I spoke at the inaugural MarTech Conference in Delhi, and both the excellent presentations and the many off the cuff conversations led me to be a bit concerned about where this market is heading. MarTech is a term that has been used for a few years now, and simply connotes the merging of …
In our upcoming book ‘The AI Playbook’ we discuss in some detail the issue of bias in AI. For those that don’t know, AI bias is the phenomena of an AI system giving prejudiced results due to misassumptions in the process. It’s easy to label biases as mistakes, but frequently they are not, they are answers that we do not agree with.
It seems like every week; a technology vendor tells me how their AI product will free workers from mundane jobs and enable them to do more exciting work. And, every week I respond the same way (though sometimes more diplomatically) ‘that is not true.’ As AI works its way through blue-collar jobs, lower-paid white-collar jobs and now into higher-paid professions, that sales pitch that falls flat. In theory, AI automation could free workers from the mundane and create new and more exciting jobs. But in reality, that will seldom happen, workers are made redundant.
This past spring, we have been publishing a series of articles on CMSWire on the topic of artificial intelligence. The goal was to demystify the subject and give tips on how and where to get started. These follow on from our in-depth online training course in partnership with AIIM that was released earlier this year.
Recently we were contacted by Veritone to request an analyst briefing. At first I wasn’t sure that this was in the scope of our focus at Deep Analysis, but it turned out I was wrong. In short, Vertione is a publicly listed (NASDAQ – VERI) artificial intelligence vendor based in Southern California with roots in media and entertainment. Indeed it is still well known in that world, enabling media firms, studios, and sports organizations to analyze and monetize their digital assets.
Finding value within mountains of unstructured data is both the challenge and the opportunity. Organizations have amassed millions, and in some instances billions, of files over the years. They pay heavily to store them as they believe that hidden in that mass of files are some of value. For sure there is value hidden within the mass, but there is also a high likelihood that there are files there that could cause organizational damage.
The first thing to note is Salesforce is no longer trying to compete with IBM Watson. Rather than leading with AI as a broad solution, Salesforce has baked Einstein into its core offerings very tactically. Instead of touting complexity and power, Salesforce is delivering its AI, with easy to use interfaces and rather than requiring a specialist data scientist to set it up, Einstein can be configured by Salesforce administrators. In the same vein, the outputs of the 35 different AI modules that Einstein runs today are equally simple to digest and leverage. At Deep Analysis we like the approach of small tactical modules baked into service offerings that are easy to use.