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Category: Exec Tech
AI Trends

Don’t even think about bringing AI/ML in-house, seriously!

Bringing proprietary AI in-house is not a workable plan that has occasional side effects. It is a bad move with a straight line to failure – and that’s not hyperbole. Gartner reports that 85% of enterprise AI initiatives fail directly, meaning they never advance beyond the prototype (proof-of-concept) stage. Producing a fieldable product is an outright impossibility for most enterprise teams. Those that beat the odds by getting past the prototype stage still only stand a desperate hope for success. Success with proprietary AI requires surviving a gauntlet of execution challenges covering training at scale, generalization, performance, field qualification testing, software engineering, systems integration, human-centered design, and other workstreams. Mix in a highly technical domain that is packed with esoteric engineering challenges and the typical enterprise stands no chance. I’m sorry, but that’s the reality.

April 18, 2023
Build Fast

Stop trying to be Agile

Abandon all efforts to get better at Scrum, SAFe, or whatever Agile snake oil you’ve been pitched. None of those make a difference. Whether your organization is good at Scrum, or terrible at it, the outcome is the same. As for SAFe, the closer you get – the worse things are. For ordinary software development

March 10, 2023
Build Fast

Navigating Project Management, Product Management, and Production Management

For various reasons, software development has long been overseen by Project Management. Since the 1970’s, every major attempt to reform software development with an iterative-incremental lifecycle, from Royce’s Waterfall to Boehm’s Spiral to Rational’s Unified Process to modern Agile, has run into the same brick wall: Project Management.

February 9, 2023
Exec Tech

Debunking the myths: Why over-emphasizing data labeling is a misstep in AI success

Debunking the myths: Why over-emphasizing data labeling is a misstep in AI success Last week, the article “Pentagon’s AI chief says data labeling is key to win race with China” caught my eye. The title tells you almost everything you need to know. The Pentagon believes that success in AI depends on collecting and labeling

February 6, 2023
Exec Tech

SphereOI at AUSA with CyberWorld

SphereOI attended AUSA’s annual meeting and spoke in partnership with CyberWorld about new developments in cybersecurity and AI.

October 17, 2022
Exec Tech

Model drift can cost you. Just ask Zillow.

Model drift can cost you. Just ask Zillow. AI decision making may be the future for more effective business decisions. But when AI changes how it makes decisions, without anyone knowing – it can get you into a world of trouble. That is what likely happened with Zillow Offers where an AI algorithm overestimated the

September 13, 2022
Exec Tech

Test your AI for the real world

When field qualifying any AI system, one the most important questions is whether the model will perform well, or at least degrade gracefully (and safely), when exposed to real-world situations that differ from training. The reason you used AI in the first place, as opposed to some form of finite state automata, is because you need an economically tractable solution that generalizes to a wide spectrum of real-world situations.

September 2, 2022
House
Exec Tech

How Zillow could have avoided its $500M AI mistake

Trustworthy systems of any type, AI systems included, require us to understand the data and domain deeply enough to ensure that the data we have can support the decisions we need.

September 1, 2022
Manufacturing equipment in factory
Artificial Intelligence

Will I benefit from an independent model assessment?

3 minThe team has built an AI model and performance appears adequate. But there is a lot riding on the deployment. Do I need independent model test and evaluation? When is the cost justified? Many AI Engineering teams, especially newer ones, often fail to realize the many ways in which bias and performance limits can

August 29, 2022

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