The latest ZDNet survey on AI actionability and accountability finds that IT teams are taking a direct lead, with most companies building in-house systems. However, oversight of AI-generated decisions is lagging.
Latest from Joe McKendrick
'Machine learning projects are much more complicated and bigger than machine learning model algorithms.'
Successful AI implementation takes more than training data and elastic compute power.
Stanford's latest release of its ongoing 'One-Hundred-Year Study on Artificial Intelligence' urges a greater blending of human and machine skills.
'Bias in AI is not solely a technical problem; it is interweaved across departments.'
Business is bullish on AI, but it takes a well-developed understanding to deliver visible business benefits.
The use of conversational AI tech has been increasingly rapidly, in part thanks to covid.
A call for greater enterprise architecture in building AI ecosystems, leveraging both cloud and on-premises systems.
Is there a way for IT leaders to be proactive about AI and machine learning without ruffling and rattling an organization of people who want the miracles of AI and ML delivered tomorrow morning? The answer is yes.
IT pros, please take 10-15 minutes to add your voice to this important project.