AI & Data Analytics: Sports with AI ⚽️ AI Digital Twin 👬 Meta's Content Policy 🗒️

EDITOR’S NOTE

Dear Reader, according to a survey report, as of 2024, 73.7% of companies have Chief Data or Analytics Officers, up from 12% in 2012, and 92.1% report returns on data and AI investments, a leap from 48.1% in 2017. In today’s newsletter, we will explore the transformative power of AI analytics in modern business, enhancing decision-making.

In Today’s Newsletter:

• AI Takes the Field: Transforming Sports, from Play to Applause

• AI's Virtual Doppelgangers for a Smarter Future

• Machine Learning vs. GenAI: Yin & Yang

• Other AI developments in Data Analytics

• Latest AI news from around the world

TOP NEWS
AI in Sports: From Performance to Fanfare

AI is revolutionizing the sports industry to new heights, leveraging data for player performance predictions, injury prevention, and equipment enhancement. It’s transforming how sports are played, watched & officiated.

Why is it relevant?
AI is reshaping sports, from analyzing performance with Statcast to upgrading gear like Google's Jacquard, revolutionizing how athletes train, personalizing how fans connect with games with Wimbledon's IBM collaboration and LaLiga's Beyond Stats, and making judging fairer, all while reducing human errors. Read More

TOP NEWS
Twinning with AI with Virtual Doppelgangers

A digital twin is a virtual model of a physical asset, mirroring its real-time status and behavior to predict potential failures through AI sensor data analysis. Think Iron Man, Avatar, and The Sims!

Why is it relevant?
AI digital twins unlock new potentials for pre-implementation testing, optimization, and personalization: In manufacturing, Ford utilizes digital twins to optimize production lines and energy efficiency. In healthcare, AI tools like Foresight create patient digital twins to forecast health outcomes and treatment responses. Even companies like topmate.io are exploring AI twins for creators to engage audiences at scale.

NANOBITS LEARNS
Machine Learning vs. Generative AI

Machine Learning and Generative AI are like the two sides of the same AI coin. ML makes smart predictions and helps us automate tasks, while Generative AI brings creative sparks, making cool things that can blow our minds.

Key Differences:

• ML learns from labeled data, Generative AI creates new content from scratch.

• ML models learn patterns, Generative models generate novel data.

• ML applications: classification, regression; Generative AI: text/image generation.

• ML requires high training data, Generative AI can learn from smaller datasets.

We’ll leave you with this exercise to ponder over later: Will you use Machine Learning or Generative AI to do the following tasks?

• Forecasting sales volume based on historical sales data

• Creating new advertising slogans based on product descriptions

MORE NEWS ON DATA ANALYTICS

Berkeley researchers developed a GPT-4-based AI forecasting system that outperforms humans in certain scenarios by aggregating, analyzing news articles to predict future events, potentially assisting policymakers, public health officials, and addressing AI risks. Read More

Salesforce AI, Moira, revolutionizes time series forecasting, offering universal, zero-shot prediction capabilities across domains & frequencies, outperforming traditional models, reducing computational costs, enabling more accurate predictions. Read More

LATEST NEWS IN AI

Starting May, Meta will label AI-generated content on all its platforms, and, from July, it won't remove such content if it meets community guidelines, adapting AI content’s rising influence. Read More

Microsoft warns China may use AI content to influence elections in the US, India, South Korea, following tactics observed during Taiwan's election, emphasizing the need for vigilance against AI-driven election interference. Read More

Tech giants are racing to purchase training data for AI: companies like Photobucket negotiating to license billions of images/videos to train models like ChatGPT, signaling a shift from free internet scraping to paid content acquisition. Read More