I was recently interviewed by TechTarget journalist Mark Labbe, regarding the need for regulating AI (Artificial Intelligence). The interview was prompted by the release of a new draft US government directive called Guidance for Regulation of Artificial Intelligence Applications.
Hiding our heads in the sand and pretending there is not a problem, is not going to cut it anymore. The concerns over bias and the use of Deep Learning are going mainstream. A recent article in the UK’s Guardian newspaper goes as far as to say that some innovation is worth stifling. Sadly, that is correct, shinier, faster, and more efficient isn’t always better.
As AI works its way into the enterprise, we have noticed one particular term gaining traction, that of ‘Deep Learning.’ Both in conversations with buyers of AI and technology vendors of AI, Deep Learning appears to have caught the imagination. That is worrisome as Deep Learning is a branch of AI that promises a lot, but you should approach it with extreme caution.
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.
To be fair, Artificial Intelligence and Machine Learning have been used for a long time in enterprise applications but their usage has really been for really complicated scenarios such as enterprise search (e.g., for for proximity, sounds etc) or sentiment analysis of social media content. But it has never been easy to use machine learning for relatively simpler use cases. Additionally, no vendor provided any SDKs or APIs using which you could use machine learning on your own for your specific use cases.