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Bank of America’s big bet on AI started small

CIO

We trained the model to do just that, he says about Erica, which is built on open-source models. Hari Gopalkrishnan, head of consumer, business, and wealth management technology at BofA, says the key to Ericas success and longevity has been its small size. We are not writing essays with Erica. We are not trying to write software.

Banking 169
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How we pivoted our deep tech startup to become a SaaS company

TechCrunch

The process wasn’t precisely linear, but (looking back) we did four core things to conclude SaaS was our model: Assessed what was truly disruptive, scalable and profitable about our technology. ECM PCB Stator Technology was founded on the innovation of MIT-trained electrical and software engineer Dr. Steven Shaw, our chief scientist.

Company 247
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Gather AI secures new cash to scan inventory in warehouses using drones

TechCrunch

The trio had the idea to use drones to gather data — specifically data in warehouses, such as the number of items on a shelf and the locations of particular pallets. Arora co-founded Gather AI in 2019 with Daniel Maturana and Geetesh Dubey, graduate students at Carnegie Mellon’s Robotics Institute.

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Cellino is using AI and machine learning to scale production of stem cell therapies

TechCrunch

But today, such cell therapies are out of reach for most people because the process of cell production hasn’t been automated or made scalable and efficient. — could eventually democratize access to cell therapies. It aims to bring down costs associated with the manufacturing of human cells, while also increasing yields.

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Ulta Beauty embraces low-code to deliver better CX

CIO

Flexibility, scalability, searchability Low-code also makes experimenting less risky. About six years ago, Ulta Beauty formed a dedicated innovation team to identify technologies that resonate to improve the customer experience. In particular, Ulta utilizes an enterprise low-code AI platform from Iterate.ai, called Interplay.

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Low code/no code tools reap IT benefits—with caveats

CIO

Fidelity National Information Services And among low-code tools, for instance, FIS chose WaveMaker because its components seemed more scalable than its competitors, and its per-developer licensing model was less expensive than the per runtime model of other tools. So there’s a lot in the plus column, but there are reasons to be cautious, too.

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Interpreting predictive models with Skater: Unboxing model opacity

O'Reilly Media - Data

There is also a trade off in balancing a model’s interpretability and its performance. A deep dive into model interpretation as a theoretical concept and a high-level overview of Skater. There is often a need to verify the reasoning of such ML systems to hold algorithms accountable for the decisions predicted.