Wednesday, 29 June 2022

VB Daily | June 29 - How AI and ML will unify and strengthen financial services šŸ’µ

Daily Roundup
Presented by   
The Lead šŸ—ž️
[1] Wells Fargo CIO: AI and machine learning will move financial services industry forward  
[2] Siemens and Nvidia partner to enable digital twin for the industrial metaverse 
[3] Gong's AI enables B2B sales teams to spot bad deals early with revenue intelligence
The Follow šŸ“°
[1] In the financial industry, more so than others, institutions are notoriously siloed. Largely because the industry is so competitive and highly regulated, there hasn't been much incentive for institutions to share data, collaborate or cooperate in an ecosystem. 
Customer data is deterministic (that is, relying on first-person sources), so with customers "living across multiple parties," financial institutions aren't able to form a precise picture of their needs, said Chintan Mehta, CIO and head of digital technology and innovation at Wells Fargo.
"Fragmented data is actually detrimental," he said. "How do we solve that as an industry as a whole?"
While advocating for ways to help solve this customer data challenge, Mehta and his team also consistently incorporate artificial intelligence (AI) and machine learning (ML) initiatives to accelerate operations, streamline services, and enhance customer experiences at Wells Fargo. >> Read more.
[2] Industrial technology giant, Siemens, has long been modeling different elements of the real world in software, and it's now looking to advance its approach to enabling an industrial metaverse. To support its efforts, today, Siemens detailed an extended partnership with Nvidia to enable artificial intelligence (AI) digital twin capabilities.
The partnership will see Siemens industrial design and development technology integrated with the Nvidia Omniverse platform, which enables users to create photorealistic virtual simulations.
Tony Hemmelgarn, president and CEO of Siemens, says bringing Siemens technology together with Nvidia Omniverse allows industrial organizations to make decisions faster. >> Read more.
[3] Gong, a seven-year-old startup that makes revenue intelligence software for business-to-business (B2B) sales teams, today launched a new functionality inside its frontline Reality Platform called Economic Pulse. 
This new feature records, transcribes and analyzes all sales calls; it then identifies, tracks and alerts sales and revenue leaders when economic trends are mentioned in customer conversations.
The ability to read customer data and accurately identify important sales trends gives the company a much clearer look at the reality of how it is performing in the market, head of product marketing, Sheena Badani, told VentureBeat. >> Read more.
Watch the Supermicro Computex CEO Keynote — Charles Liang and the Building Blocks of IT Growth
The Buzz šŸ
Ada Lovelace Institute
šŸ“¢As biometrics move into more aspects of our lives, Ada has conducted a three-year programme of research with experts, regulators and the public.

Our new report identifies the need for comprehensive legislation and enforcement on the use of biometrics.

https://t.co/EQDgX5zKcK
Edward White
#China is fast cementing its position as the Saudi Arabia of clean tech, near complete global dependence on Beijing for the main materials key to wind turbines, solar and electric vehicles. via @FinancialTimes w @cheng_leng_ https://t.co/aXOjz45vX8 https://t.co/xdcYVkXCu7
By The Numbers šŸ”¢
MLops can help simplify AI consumption so that applications can make use of machine learning models for inference in a scalable, maintainable manner. This capability is, after all, the primary value that AI initiatives are supposed to deliver.
It aims to address key challenges around taking AI applications into production. These include repeatability, availability, maintainability, quality, scalability and consistency.
The following six proven MLops techniques can measurably improve the efficacy of AI initiatives, in terms of time to market, outcomes and long-term sustainability:
  1. ML pipelines
  2. Inference services
  3. Continuous deployment
  4. Blue-green deployments
  5. Automatic drift detection
  6. Feature stores
>> Read more from our DataDecisionMakers community.
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