Thread regarding SAS Institute layoffs

"Tesla (ev)
AWS
Facebook

The list is very long..."

The list is very false.

Tesla and Facebook each needed less than a decade to IPO. Facebook was the dominant social media platform well before its IPO. AWS, a subsidiary of Amazon, never IPO'd, but most of Amazon's retail sites were using it before a decade had passed.

These facts are easy to check. I wish Viya would succeed, but lies won't help it.

"I'm sure they were also experts at the hot buzzword that proceeded Big Data".

Before the term "Big Data" gained traction in the early 2000s, the buzzword of choice was Business Intelligence (BI).

Google question/answer:
What is big data in AI?

In AI, big data refers to the large, complex, and diverse datasets that act as the "fuel" for AI systems, enabling them to learn, identify patterns, and make sophisticated decisions. Big data possesses characteristics like high Volume, rapid Velocity, and varied Variety, which traditional methods struggle to process but AI-powered analytics can effectively analyze to extract valuable insights and drive innovation.

The Relationship Between Big Data and AI
AI relies on big data: AI systems require vast amounts of data to train their algorithms, enabling them to develop human-like reasoning, learn from historical information, and improve their decision-making processes.
Big data relies on AI: Big data analytics tools use AI to process, interpret, and analyze these massive, complex datasets, uncovering trends and correlations that would be impossible to find manually.

Key Characteristics of Big Data (The "Vs")
Volume: The sheer quantity of data being generated at unprecedented rates from sources like the internet and IoT devices.
Velocity: The speed at which data is generated and needs to be processed, such as in real-time financial systems.
Variety: The different types of data, including structured (like spreadsheets), unstructured (like text or video), and semi-structured data.
Veracity: The trustworthiness and accuracy of the data, emphasizing the need for cleaning and validation.
Value: The ultimate goal of big data analysis, which is to extract meaningful and actionable insights.

How AI Uses Big Data
Training: AI models are trained on big data to recognize patterns, anomalies, and correlations within the datasets.
Analysis: AI-powered analytics can process complex, mixed data types (text, images, video, etc.) to derive meaningful insights quickly.
Decision-Making: AI uses the insights derived from big data to support data-driven decisions, provide personalized recommendations, and improve efficiency in various applications.

@ar

“Big Data” retired to the Florida Keys.

From what I've heard, it would be more accurate to say "was retired", not "retired".

it was used to train foundation models so now all we hear about is "gen AI"

@ca+1k4w572z0

proceeded
preceded

I dunno but all the Big Data talking heads are now AI talking heads. I'm sure they were also experts at the hot buzzword that proceeded Big Data

“ Really? Name a product of similar scope to Viya that suddenly took off after a decade+ of dormancy.”

Tesla (ev)
AWS
Facebook

The list is very long. Obviously there are differences between all of the, and I’m not saying Viya is AWS. But I answered the snarky question you asked

Does SAS still have a VP of Big Data (the title originally given to P.K.)?

"There is still hope for Viya."

Really? Name a product of similar scope to Viya that suddenly took off after a decade+ of dormancy.

“Big Data” retired to the Florida Keys.

Big data is the norm, its no longer a wow effect or bragging right.

It hasn’t gone away. There is still hope for Viya.