IBM’s Rob Thomas details key AI trends in shift to hybrid cloud

Rob Thomas

The last year has seen a major spike in the adoption of AI models in production environments, in part driven by the need to drive digital business transformation initiatives. While it’s still early days as far as AI is concerned, it’s also clear AI in the enterprise is entering a new phase. Rob Thomas, senior vice president for software, cloud, and data platform at IBM, explains to VentureBeat how this next era of AI will evolve as hybrid cloud computing becomes the new norm in the enterprise.

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State of the Edge report projects edge computing will reach $800B by 2028

State of the Edge report

A battle for control over edge computing environments is expected to drive a total of $800 billion in spending through 2028, according to a report published today by the LF Edge arm of the Linux Foundation. The State of the Edge report is based on analysis of the potential growth of edge infrastructure from the bottom up across multiple sectors modeled by Tolaga Research. The forecast evaluates 43 use cases spanning 11 vertical industries.

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KPMG: AI adoption is accelerating in the pandemic

KPMG rate of adoption

A survey published by KPMG today suggests that a large number of organizations have increased their investments in AI during the pandemic to the point that executives are now concerned about moving too fast. In fact, most of the survey respondents cited a definite need for increased AI regulation.
The survey covered 950 business decision-makers and/or IT decision-makers with at least a moderate amount of AI knowledge at companies with more than $1 billion in revenue.

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IBM’s Arin Bhowmick explains why AI trust is hard to achieve in the enterprise

IBM AI Trust

While appreciation of the potential impact AI can have on business processes has been building for some time, progress has not nearly been as quick as many initial forecasts led many organizations to expect.
Arin Bhowmick, chief design officer for IBM, explained to VentureBeat what needs to be done to achieve the level of AI explainability that will be required to take AI to the next level in the enterprise.

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Vivun raises $35 million to advance presales engineering platform

Vivun platform

Vivun provides a software-as-a-service (SaaS) platform dubbed Hero that automates the management of presales processes. Today the company revealed it has garnered $35 million in additional funding via a series B round led by Menlo Ventures. While customer relationship management (CRM) software is widely employed to manage sales processes, applications optimized for presales teams — made up of engineers who often have more insights into which deals are likely to close than other members of the sales team — are not widely deployed, Vivun cofounder and CEO Matt Darrow said.

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C3.ai exec says lack of automation is holding back AI progress

Model driven architecture

After more than a decade of providing a platform-as-a-service (PaaS) environment for building and deploying AI applications, C3.ai launched an initial public offering (IPO) in December 2020. Earlier this month, in partnership with Microsoft, Shell, and the Baker Hughes unit of General Electric, the company launched the Open AI Energy Initiative to enable organizations in the energy sector to more easily share and reuse AI models.

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Community Health Network prescribes AI to combat COVID-19

AI is about to play a much larger role in identifying individuals who are at risk for contracting COVID-19 in Indiana. Community Health Network (CHN), an accountable care organization (ACO), revealed today that it is beginning to employ an AI platform from Jvion to analyze members’ electronic health care records that are stored in a platform from Epic Systems.

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The Fusion Project aims to optimize data collection from vehicles

The Fusion Project, which promises to provide a more efficient way to collect the data required to train AI models for autonomous vehicles, today launched with Airbiquity, Cloudera, NXP Semiconductors, Teraki, and Wind River onboard. The goal is to compress the data collected from autonomous vehicles to the point that it becomes possible to update the AI models employed in an autonomous vehicle faster.

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Weights and Biases raises $45 million to advance MLOps governance

Weights and Biases, provider of a platform for enabling collaboration and governance across teams building machine learning models, today revealed it has raised a $45 million series B round led by Insight Partners. The company provides a software-as-a-service (SaaS) platform designed to make it easier for AI teams to first reproduce results and then ultimately explain how an AI model actually works, Weights and Biases CEO Lukas Biewald said.

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SAP’s acquisition of Signavio goes to the core of its digital business strategy

SAP’s acquisition of Signavio, a provider of tools for analyzing existing business processes, will fill a critical gap in the company’s digital business transformation strategy. The deal was announced yesterday, along with a managed Rise with SAP service through which the company will manage digital business transformation initiatives on behalf of customers.

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University of Pisa leans into the I/O challenge AI applications create

At a time when workloads that employ machine and deep learning algorithms are being built and deployed more frequently, organizations need to optimize I/O throughput in a way that enables those workloads to cost-effectively share the expensive GPU resources used to train AI models. Case in point: the University of Pisa, which has been steadily expanding the number of GPUs it makes accessible to AI researchers in a green datacenter optimized for high-performance computing (HPC) applications.

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Feature store repositories emerge as an MLOps linchpin for advancing AI

A battle for control over machine learning operations (MLOps) is beginning in earnest as organizations embrace feature store repositories to build AI models more efficiently. A feature store is at its core a data warehouse through which developers of AI models can share and reuse the artifacts that make up an AI model as well as an entire AI model that might need to be modified or further extended. In concept, feature store repositories play a similar role as a Git repository does in enabling developers to build applications more efficiently by sharing and reusing code.

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