Last April, in that small glass conference room, the studio's decades of experience were marshaled in service of figuring out what Frost Giant's debut game would be called. Around the room, sheets of paper held up by blue tape were marked with key pillars of a good video game title, including "Game Fit," "Cool Factor," and for Frost Giant's debut specifically, "Hopeful/Optimistic." Tim Morten, the studio's CEO and co-founder, quietly recused himself from the selection process. Broad-shouldered and bearing a thin, near-constant smile, Morten was a selling point for the studio; some Frost Giant employees defected from high-ranking positions elsewhere to work with him. When the meeting began, he agreed to take notes.

"... Heard that before IMO - no such thing CAN exist (yet).. This came up indirectly in a round table discussion at Data 2030 Summit MEA last week. Let's unpack this How most of these AI tools work They look at historical trends of data coming in different columns and profile the data - calculating the average range of values, type of data seen in these data assets. Based on this, create value thresholds, regex rules maybe determining what would be the most appropriate values for each of those columns. What's missing Profiling the data is a small subset of data quality There are usually considerable business rules which have to be validated Those need to be understood and coded / queried by someone - Also these tend to change (off course) and cannot be determined looking at the data The ONLY way these can be automatically created are by reading the ETL jobs If the jobs are pure SQL through and through from ingestion to semantic then this can'theoretically' be doable If the jobs are written in Python / Scala / Spark then it would be almost impossible at this point to infer those automatically It's good to have those thresholds coming from a profiling tool automatically, but will not be enough at all.

ETL and ELT are data integration pipelines that transfer data from multiple sources to a single centralized source and perform some transformation and processing steps to it. The difference between these two is ETL transforms the data before loading, and ELT transforms the data after loading. But before diving deeply into them, let's first understand the meaning of E, L, and T. T for Transform - Transforming the data is a process of cleaning and modifying the data in a format so that it can be used for business analysis. L for Loading - It involves loading data to a target system, which may be a data warehouse or a database. ETL is the first standardized data integration method that emerged in the 1970s due to the evolution of disk storage.

You can see the full list of travel articles from BuzzFeed's "Buzzy" AI tool right here. Right now, there are 44 posts covering destinations like Morocco, Stockholm, and Cape May, New Jersey. The articles are "written with the help of Buzzy the Robot (aka our Creative AI Assistant) but powered by human ideas," BuzzFeed says on Buzzy's profile. The top of each story I've seen includes a line noting that an article was "collaboratively written" by a human and Buzzy.

AI is great for what I do--create content for the entertainment industry--and I have no plans to use AI for world domination. Not like a character I'm basing-on for my new series titled City Of Danger. It's a work in progress set for release this fall--2022. I didn't invent the character. I didn't have to because he exists in real life, and he's a mover and shaker behind many world economic and technological advances including promoting artificial intelligence. His name is Klaus Schwab.

Intro: AI-powered chatbots are becoming a common virtual assistant tool used by businesses in a variety of Industries. Chatbot use cases can be found across all industries and business functions including customer service, sales, marketing, and even internal process automation. The most common, however, is the use of an online customer service chatbot. Artificial Intelligence has profoundly influenced and altered business-to-customer communication. From streamlining the business process to improving customer engagement and increasing productivity, the conversational chatbot applications will demonstrate how to hold AI-enabled bots useful for business.

Large language models (LLMs) trained on more text will generally be superior to an LLM with less. As a result, expect publishers with valuable text content to become a licensing battleground for LLM makers and for language acquisition costs (LAC) to become a real expense. Google is said to pay $15B per year to be the default search engine on Apple devices. These traffic acquisition costs (TAC) are in aggregate over $50B a year for Google -- but the exclusivity gained is critical for Google to cement its search lead. With the battle for large language model (LLM) supremacy underway, exclusive access to language/text will also become critical.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. The Federal Trade Commission (FTC) received a new complaint today from the Center for AI and Digital Policy (CAIDP), which calls for an investigation of OpenAI and its product GPT-4. The complaint argues that the FTC has declared that the use of AI should be "transparent, explainable, fair, and empirically sound while fostering accountability," but claims that OpenAI's GPT-4 "satisfies none of these requirements" and is "biased, deceptive, and a risk to privacy and public safety." CAIDP is a Washington, D.C.-based independent, nonprofit research organization that "assesses national AI policies and practices, trains AI policy leaders, and promotes democratic values for AI." It is headed by president and founder Marc Rotenberg and senior research director Merve Hickok.

AI hands are reaching further into the tech industry. Microsoft has added Security Copilot, a natural language chatbot that can write and analyze code, to its suite of products enabled by OpenAI's GPT-4 generative AI model. Security Copilot, which was announced on Wednesday, is now in preview for select customers. Microsoft will release more information through its email updates about when Security Copilot might become generally available. Microsoft Security Copilot is a natural language artificial intelligence data set that will appear as a prompt bar.

Two days after an open letter called for a moratorium on the development of more powerful generative AI models so regulators can catch up with the likes of ChatGPT, Italy's data protection authority has just put out a timely reminder that some countries do have laws that already apply to cutting edge AI: it has ordered OpenAI to stop processing people's data locally with immediate effect. The Italian DPA said it's concerned that the ChatGPT maker is breaching the European Union's General Data Protection Regulation (GDPR), and is opening an investigation. Specifically, the Garante said it has issued the order to block ChatGPT over concerns OpenAI has unlawfully processed people's data as well as over the lack of any system to prevent minors from accessing the tech. The San Francisco-based company has 20 days to respond to the order, backed up by the threat of some meaty penalties if it fails to comply. It's worth noting that since OpenAI does not have a legal entity established in the EU, any data protection authority is empowered to intervene, under the GDPR, if it sees risks to local users. The GDPR applies whenever EU users' personal data is processed.