[1] Eleven years after IBM Watson wowed the tech world by beating two of Jeopardy's biggest champions, the days of tackling "moonshot"
AI goals outside the research lab are over, IBM chairman and CEO Arvind Krishna told reporters yesterday.
"I don't want us to work only on 'moonshots,'" he said. "That should be [the] research you work with in the lab." Instead, he said IBM's focus will be firmly on AI projects that provide
near-term value for clients, adding that he believes generalized artificial intelligence is "still a long time away."
However, that isn't stopping the company from plowing ahead with
specific AI projects that, while clearly not "moonshots," are examples of IBM's focus on enterprise applied AI.
>> Read more. [2] ProteanTecs, an Israel-based company providing
deep data solutions for electronics health and performance monitoring, claims it has built advanced cloud and edge enterprise solutions that monitor the health and performance of chips powering electronics from design to field.
In a press release, ProteanTecs CEO and cofounder, Shai Cohen, said advancements in technologies and how companies use them mean that
monitoring and analysis are imperative to achieve scale and predictability. Cohen also said ProteanTecs is helping companies transform production and service by providing critical business insights.
ProteanTecs claims its proprietary
chip telemetry data and ML-based technology enables its analytics platform to provide teams with actionable insights, allowing them to immediately identify potential problems. This results in increased reliability and performance.
>> Read more.
[3] San Francisco-based Databricks, a company that combines the capabilities of a
data warehouse and data lake in a single "lakehouse" architecture, today deepened its integration with Amazon Web Services (AWS) with a new
pay-as-you-go offering on the AWS Marketplace.
The solution, as the company explains, gives enterprises a quick way to discover, launch and build a Databricks lakehouse from their AWS Marketplace account. It is designed to ensure seamless integration of existing AWS configuration and security with Databricks' data lakehouse paradigm.
While AWS has long been associated with Databricks — in fact, AWS was the
company's first cloud partner — the move marks a major development because deploying Databricks on AWS traditionally required a lot of hands-on setup and configuration — which took up much of an enterprise's time and resources.
With the new pay-as-you-go experience, organizations can deploy and administer the Databricks lakehouse with just a few clicks in the AWS console, using their existing credentials and account settings.
>> Read more. 
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