Anré Williams’ Post

If you’re considering taking the leap into AI this Leap Year, 🚀 look no further than Lakeside Software for the best data. We have long been the leader in data – our SysTrack platform collects and analyzes the most endpoint and telemetry data and structures it better than anyone. https://bit.ly/3UVnWaP #AI #LeapForward #LeapDay #HappyLeapYear

How the Financial-Services Industry Can Benefit from Generative AI

Using AI with Data Tasks

Facts & Figures on AI and Complex Change: May 26 2024 - 22%: Percentage increase in app revenue for OpenAI on the day of the GPT-4o launch—GPT-4o is free, except on mobile. (AppFigures) - $50,000,000: Annual licencing payment expected from OpenAI to News Corp for the next five years for content from the Wall Street Journal, Barron's, Marketwatch, the New York Post, etc. (Wall Street Journal) - $10,000,000: Annual licencing payment expected from OpenAI to Axel Springer for the next three years. (Wall Street Journal) - $5,000,000-$10,000,000: Annual licensing payment expected from OpenAI to the FT for an unknown number of years. (Wall Street Journal) - 262%: Percentage increase in NVIDIA's quarterly revenue, as compared to the prior year quarter. (NVIDIA) - 40%: Percentage of NVIDIA's data center revenue attributable to inference—aka prompt & response. (SeekingAlpha) - 54%: Percentage of male applications with an 'AI in business' course credit who received an interview invitation from a job application in the UK. (Anglia Ruskin University) - 28%: Percentage of male applications without an 'AI in business' course credit who received an interview invitation from a job application in the UK. (Anglia Ruskin University) - 54%: Percentage of male applications with an 'AI in business' course credit who received an interview invitation from a job application in the UK. (Anglia Ruskin University) - 32%: Percentage of male applications without an 'AI in business' course credit who received an interview invitation from a job application in the UK. (Anglia Ruskin University) - 24%: Wage premium for jobs that require AI specialist skills in some markets. (PWC) - 3.5x: Multiple by which jobs that require specialist AI skills have grown faster than all jobs since 2012. (PWC) - 4.8x: Multiple by which labor productivity has grown in AI-exposed sectors versus other sectors. (PWC) - 27%: Percentage by which jobs are growing more slowly in AI-exposed occupations. (PWC) - 200%: Percentage reduction in false positives during the detection of fraudulant transactions against potentially compromised cards by Mastercard. (Mastercard) - 300%: Percentage increase of the speed of identifying merchants at-risk from—or compromised by—fraudsters by Mastercard. (Mastercard) - $14,000,000,000: Worldwide revenue from generative AI in 2020. (Bloomberg via World Economic Forum) - $1,304,000,000,000: Estimated worldwide revenue from generative AI in 2032. (Bloomberg via World Economic Forum) - 25%: Percentage of webpages from 2013 to 2023 are no longer acessible. (Pew Research) - 38%: Percentage of webpages from 2013 that are no longer accessible. (Pew Research) #ai #artificialintelligence #generativeai #airesearch #complexity #chatgpt #complexchange #changemanagement #futureofwork #artificiality #mindforourminds #intimacyeconomy #agenticweb

About 18 months ago I switched from making TV ads for fried chicken to developing strategy and messaging for enterprise AI software. I'm a curious person by nature with an insatiable appetite to learn new things. Over the past month I've learned more from Ninad Kulkarni about generative AI and large language models than I ever thought possible. I'm grateful to have so many very smart and very patient colleagues in data science and engineering. Last week Peak unveiled its approach to generative AI with Co:Driver, an autonomous assistant that looks for patterns, trends, opportunities and anomalies. It's a totally new way of thinking about a technology that has captured the imagination of millions; rather than generating net new content, we're using gen AI to synthesize information and provide a new format-agnostic layer between users and their AI. Check out this blog from Ninad with some fascinating technical details of our approach. https://lnkd.in/gVB9CQg8

HOW THE FINANCIAL-SERVICES INDUSTRY CAN BENEFIT FROM GENERATIVE AI

Explore Uber's journey from predictive to generative AI on our engblog. Learn how we're harnessing the latest advancements in AI to enhance user experiences and operational efficiencies. Dive into our innovative approaches and real-world applications today! See https://lnkd.in/g_eJ2TsG

This piece from Benedict Evans on the lack of real use cases for Ai/Chat GPT hits the nail on the head for me. This is a must read. "Hence, the cognitive dissonance of generative AI is that OpenAI or Anthropic say that we are very close to general-purpose autonomous agents that could handle many different complex multi-stage tasks, while at the same time there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL." AI is not mature enough that it can not 'yet' realise everything people want from it. As Benedict says "these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000. " That said, everything that can be automated, will be automated. It's a question of how long that will take and and whether the new HAL9000 equivalent is a distant dream? As Bill Gates said, we tend to over estimate the impact of technology in the short term and under-estimate the scale of its impact in the long term. AI will be as fundamental a shift as the industrial revolution and the World Wide Web but just because everything moves so fast doesn't mean that all use cases will be satisfied in a 12 month window... https://lnkd.in/eyVw-K9D

Let’s say your use case is to detect the sentiment of a customer service call in real-time and alert a supervisor to step in if there is heated discussion from the customer:  RoBERTa (a transformers-based model) tells you the sentiment in about 250 [milli]seconds. In comparison, the latency of Llama, i.e., an LLM, is more than 14 seconds (3 seconds with “power user” calibration).