Thomson Reuters’ “Future of Professionals C-Suite Report,” reveals that while the majority of the C-suite is already or planning to use Gen AI at work, an overwhelming 87% of execs are concerned with how it will be deployed.
Deci Unveils DeciLM-7B: A Leap Forward in Language Model Performance and Inference Cost Efficiency
December 14, 2023 by
Deci, the deep learning company harnessing AI to build AI, unveiled the latest addition to its suite of innovative generative AI models, DeciLM-7B, a 7 billion parameter large language model. Building upon the success of its predecessor DeciLM 6B, DeciLM 7B is setting new benchmarks in the large language model (LLM) space, outperforming prominent open-source models such as Llama2 7B and Mistral 7B in both accuracy and efficiency.
Delphina Raises $7.5 Million to Build Copilot for Data Science
December 13, 2023 by
Delphina, an LLM-powered copilot for data science, announced the closing of a $7.5 million seed round co-led by Costanoa Ventures and Radical Ventures with participation from 20+ prominent angel investors, including Stanford professor Fei-Fei Li. The funding will be used to expand its team and accelerate product development.
What Can the Sports Betting Industry Teach About Building Predictive Models?
December 13, 2023 by
In this contributed article, Dr. Darryl Woodford PhD, CTO at Cipher Sports, shares his insights about the key features of a good sports betting prediction model. We’ve already seen how fast and accurate these models can be. In the wagering industry, the next frontier is to apply these recent advancements to in-play and micro-betting markets, requiring another step forward in both data processing and data acquisition.
Get Lit: 5 Steps to Building your Organization’s Data Literacy as you Prep for AI
December 12, 2023 by
In this contributed article, Christine Andrukonis, Workplace Transformation Expert and founder of Notion Consulting, believes that the ultimate goal of data literacy is to provide a framework for data-driven decision-making. Nothing is stopping you from developing a learning program that’s also fun, engaging, and beneficial to employees in all parts of their lives.
Heard on the Street – 12/12/2023
December 12, 2023 by
Welcome to insideAI News’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.
Crafting Precision Content Using Large Language Models
December 11, 2023 by
[SPONSORED POST] Our friends over at DAC recently explored the potential of LLMs to interpret or generate text considering numerous parameters with extraordinary precision. They have demonstrated their ability to evaluate unstructured text blocks and grade them along various scales to great success. Additionally, they have been able to generate specific, tailored content using a complex series of instructions that can be tuned with precision to the same set of scales.
The Power of Startups in Turning Big Data into Big Impact
December 9, 2023 by
In this contributed article, Ben Younkman, Regional Director at Village Capital, explores how forward-thinking startups are spearheading data-driven innovation to create digital solutions that increase access to critical services — including the financial, educational and healthcare sectors — resulting in a more equitable and inclusive world.
Hallucination Index Identifies Best LLMs for Most Popular AI Use Cases
December 8, 2023 by
Galileo, a leading machine learning (ML) company for unstructured data, released a Hallucination Index developed by its research arm, Galileo Labs, to help users of today’s leading LLMs determine which model is least likely to hallucinate for their intended application.
Tips for Responsible Use of Generative AI in Enterprise
December 7, 2023 by
In this contributed article, Thor Philogéne, CEO and Founder of Stravito, discusses how the effective integration of generative AI in enterprise requires the identification of clear goals and pain points; reliable data and a human-centric design.
