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Banks bet on AI to deliver digital efficiencies

CIO

In the face of shrinking budgets and rising customer expectations, banks are increasingly relying on AI, according to a recent study by consulting firm Publicis Sapiens. Around 42% percent of banks rely on personalized customer journeys to improve the customer experience.

Banking 212
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Taktile makes it easier to leverage machine learning in the financial industry

TechCrunch

Meet Taktile , a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. They could use that data to train new models and roll out machine learning applications.

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

TechCrunch

technology, machine learning, hardware, software — and yes, lasers! Founded by a team whose backgrounds include physics, stem cell biology, and machine learning, Cellino operates in the regenerative medicine industry. — could eventually democratize access to cell therapies.

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How AI orchestration has become more important than the models themselves

CIO

As many companies that have already adopted off-the-shelf GenAI models have found, getting these generic LLMs to work for highly specialized workflows requires a great deal of customization and integration of company-specific data. Large language models (LLMs) just keep getting better. From Llama3.1 to Gemini to Claude3.5

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Hugging Face collaborates with Microsoft for new AI-powered Azure service

TechCrunch

Fresh off a $100 million funding round , Hugging Face, which provides hosted AI services and a community-driven portal for AI tools and data sets, today announced a new product in collaboration with Microsoft. ” “The mission of Hugging Face is to democratize good machine learning,” Delangue said in a press release.

Azure 246
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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

-based companies, 44% said that they’ve not hired enough, were too siloed off to be effective and haven’t been given clear roles. “The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low access across an organization.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly Media - Ideas

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. What cultural and organizational changes will be needed to accommodate the rise of machine and learning and AI?