Reflecting on company leadership and their understanding of AI

C-Suite board room

65% of executives cannot explain how their AI models make decisions. This isn’t a number that we should just brush aside. It’s a number that should, at a minimum, be disappointing. It doesn’t make sense that a CEO wouldn’t be able to explain how one of their tools works. Certainly the rapid adoption of AI tools, can make it difficult to keep up, but excuses shouldn’t be made for technology that can have life altering consequences.

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Google’s Gradient Ventures leads $8.2M Series A for Vault Platform’s misconduct reporting SaaS

Vault platform - Gradient Ventures

Fixing workplace misconduct reporting is a mission that’s snagged London-based Vault Platform backing from Google’s AI focused fund, Gradient Ventures, which is the lead investor in an $8.2 million Series A that’s being announced today.

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Dashboards for interpreting & comparing Machine Learning Models

Comparing different models can be one way of selecting the best model, but it is time taking process where we will create different machine learning models and then compare their performance. It is also not feasible because most of the models are black-box and we don’t know what is going on inside the model and how it will behave. In short, we don’t know how to interpret the model because of the model complexity and model being black-box.

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Google and Harvard unveil the largest High-Resolution Map of the Brain yet

Google and Harvard High-res map of the brain

Last Tuesday, teams from Google and Harvard published an intricate map of every cell and connection in a cubic millimeter of the human brain. The mapped region encompasses the various layers and cell types of the cerebral cortex, a region of brain tissue associated with higher-level cognition, such as thinking, planning, and language. According to Google, it’s the largest brain map at this level of detail to date, and it’s freely available to scientists (and the rest of us) online. (Really. Go here. Take a stroll.)

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[Video Highlights] Supercharging Data Scientists’ productivity at Netflix

Apply ML data conference

Netflix’s unique culture affords its data scientists an extraordinary amount of freedom. They are expected to build, deploy, and operate large machine learning workflows autonomously with only limited experience in systems or data engineering. Metaflow, our ML framework (now open-source at metaflow.org), provides them with delightful abstractions to manage their project’s lifecycle end-to-end, leveraging the strengths of the cloud: elastic compute and high-throughput storage.

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