It's well-known that AI models are incredibly resource-intensive. Whether they're simple or complex, these systems fundamentally follow the "input-data, output-data" principle.
In simple terms, the quality of the input data significantly influences the outcome or results produced, hence this principle is commonly known as "garbage in, garbage out" in everyday speech.
This indicates that the advancement of AI relies on maintaining a continuous supply of access to comprehensive and varied datasets to guarantee these systems generate high-quality outcomes. Lacking this, such technologies may find it difficult to provide meaningful benefits and genuinely foster the competitive edge they advertise.
Regarding Generative Artificial Intelligence, Large Language Models are educated using extensive publicly available information such as online resources. Currently, the intricacies of copyright legislation are at the center of many discussions concerning the regulation of AI moving ahead.
Nevertheless, this diverts attention from the far more critical issue of data processing. Large enterprises accumulate vast datasets as part of their routine activities and customer engagements within their supply chains. These collections frequently exceed multiple petabytes—one quadrillion bytes, equivalent to 1,000 terabytes—yet they represent areas capable of producing some of the most innovative outcomes. Consider applications like instant public opinion analysis in advertising technology or forecasting tools that proactively modify the supply chain.
As this is proprietary data, copyright isn’t an issue. In reality, the biggest challenge here is finding ways to process, sort, and analyse these data sets without causing costs and timeframes to spiral out of control.
As we progress with AI, addressing the related infrastructure challenges will be crucial for broad acceptance, aiding in deriving significant worth from AI-generated insights.
In terms of pioneering advancements in data management, SQream is at the forefront of GPU-accelerated big data processing. Recently, the firm has named tech industry veteran Liam Galin as their new CEO with the aim to develop comprehensive solutions for managing vast amounts of data within enterprises.
Liam Galin and SQream 2.0
As CEO, Liam Galin will drive SQream 2.0’s expansion and create a powerful AI Factory enabler that brings together complimentary data acceleration technologies and emerging GPU innovations to slash the amount of time needed to perform complex, real-time queries that unlock the most valuable insights from company data.
The idea of an 'AI Factory' has been increasingly acknowledged within technological circles after GTC, the firm's yearly developers' conference. During these events, the semiconductor production leader repeatedly highlighted this notion, emphasizing the necessity to derive value from data at a faster pace so as to transform artificial intelligence into a direct catalyst for competitive edge.
This is among the issues that SQream has aimed to address since its establishment in 2010. By assisting businesses in transitioning from conventional computing setups based on CPUs to utilizing GPUs, enterprises can handle intricate inquiries and data processing much more swiftly.
Moreover, SQream has collaborated extensively with Nvidia and their advanced GPU technologies to streamline the integration process for organizations. With this, Galin announces the advent of SQream 2.0, positioning the company at the forefront of the upcoming AI factory revolution.
This perspective stems from his leadership roles spanning over twenty years at the helm of top-tier multinational technology firms. During this time, he guided teams through every phase—from fledgling start-ups to expansive globally traded corporations employing more than 400 individuals spread across all four continents. Throughout these endeavors, he successfully secured significant capital investments and achieved cumulative international revenues surpassing $300 million.
This market entry approach builds on the efforts of Ami Gal The co-founder of SQream, who was at the helm as CEO for 14 years, stated, "Liam's extensive knowledge in expanding technology firms coupled with his dedication and background in managing international teams qualifies him perfectly to guide SQream as our new CEO."
Large-scale implementation of AI for quicker return on investment
As an AI facilitator, SQream is transforming the way companies handle, examine, and derive valuable predictions from huge datasets. Using its patented GPU-based SQL engine, SQream assists organizations in uncovering prescient insights with unmatched scalability and effectiveness. The firm is now poised to lead the charge toward widespread AI empowerment.
"This marks a crucial moment driven by three major market changes: GPUs becoming central, AI experiencing explosive growth, and big data scaling to unparalleled levels, presenting an extraordinary chance for SQream to take the lead," Galin stated.
As a result, the firm’s updated approach to entering the market aims to ensure that these robust data empowerment tools become accessible to additional sectors, thereby aiding large-scale data and analytical projects.
“As an AI Factory, we are uniquely positioned to meet the surging demand for AI-ready infrastructure by empowering enterprises to extract meaningful foresight from their massive data,” Galin said.
This issue extends beyond merely speeding up individual projects. Given the rapid growth of interest in artificial intelligence, we're quickly depleting computational resources and putting extraordinary pressure on data centers.
Sophisticated AI applications like Agentic AI consider numerous potential answers prior to choosing the optimal one. This process of repeated reasoning consumes significant computational resources. 100 times faster Than traditional inference. In the meantime, global demand for data center capacity might increase annually by 19% to 22% till 2030, as stated according to McKinsey ,
SQream’s sophisticated SQL-driven engine provides timely business insights swiftly and at roughly one-tenth the expense. Beyond saving businesses valuable time and resources, this also enables the technology industry as a whole to leverage computing power more effectively, preparing for future advancements in cutting-edge tools and applications.
Unlocking AI-driven foresights
For big corporate entities, their data serves as a treasure trove of valuable insights and possibilities. Acting as the AI hub for widespread AI implementation, SQream is prepared to assist with this.