AI Modeling Data Quality Explainable AI 
Unite.AI
SEPTEMBER 23, 2024
McKinsey Global Institute estimates that generative AI could add $60 billion to $110 billion annually to the sector. From technical limitations to data quality and ethical concerns, it’s clear that the journey ahead is still full of obstacles. But while there’s a lot of enthusiasm, significant challenges remain.
Towards AI
OCTOBER 3, 2024
.– Model Robustness: Ensuring that models can handle unforeseen inputs without failure is a significant hurdle for deploying AI in critical applications. Research focuses on creating algorithms that allow models to learn from data on local devices without transferring sensitive information to central servers.
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IBM Journey to AI blog
AUGUST 12, 2024
Regulatory insights: Current AI regulations in financial services Existing AI regulations in financial services are primarily focused on ensuring transparency, accountability, and data privacy. Regulators require financial institutions to implement robust governance frameworks that ensure the ethical use of AI.
Defined.ai blog
AUGUST 10, 2023
It can quickly process large amounts of data, precisely identifying patterns and insights humans might overlook. Businesses can transform raw numbers into actionable insights by applying AI. For instance, an AI model can predict future sales based on past data, helping businesses plan better.
Unite.AI
JUNE 24, 2024
And with synthetic data then you can avoid privacy issues, and fill in the gaps in training data that’s small or incomplete. This can be helpful for training a more domain-specific generative AI model, and can even be more effective than training a “larger” model, with a greater level of control.
IBM Journey to AI blog
OCTOBER 12, 2023
The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly. Here’s what’s involved in making that happen.
Towards AI
APRIL 2, 2024
However, the AI community has also been making a lot of progress in developing capable, smaller, and cheaper models. This can come from algorithmic improvements and more focus on pretraining data quality, such as the new open-source DBRX model from Databricks. As per the official blog, Grok-1.5
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