The Sync from Synthminds.ai

NVIDIA’s Blackwell Chip, Intel gets $8.5 Billion grant, Microsoft makes a major hire, Grok becomes Open Source and GitHub releases a new AI feature

Synthminds.AI Weekly

This week there were some truly pivotal moments in AI:

NVIDIA’s Blackwell Chip announced, Intel gets $8.5 Billion grant, Microsoft makes a major hire, Grok becomes Open Source and GitHub releases a new AI feature. This week was one of the biggest we’ve seen for key stories across important companies that are going to impact our fast-moving future!

Read on, and please also visit: https://www.synthminds.ai/

Synthminds in Action:
This Week's Highlights…

This video keeps racking up views as more people tuned in to learn what Goda’s thoughts are on Jensen Huang’s statements at a recent conference. Even if this was before the recent Blackwell announcement it’s worth watching now.

Synaptic Labs: Perplexity.AI

Is this the new search engine that has people saying “goodbye” to Google? Joseph reviews this AI tool, and how it can fit into Obsidian and workflows.

Synthminds - Voices in AI: Greg Brockman

Known for his hands-on approach, exceptional coding skills and touted by OpenAI employees as the driving force behind the company. Learn more about Greg Brockman’s unique skills and journey.

AI in Focus: The Stories from the Week we believe are most important for you to know

The Press Release from NVIDIA has a list of AI leaders praising the leap this upgraded computer chip will bring - Blackwell GPU architecture features six transformative technologies! Take a look for the impressive list of who’s in the Global Network of Blackwell Partners.

The NVIDIA Blackwell chip is a MASSIVE story of the week

Here’s two articles to see how some people are reacting to it -
AnandTech.com: Going Bigger with Smaller Data
PCGamer.com: What we Expect and What we Want to See

We’re not saying this has anything to do with the upcoming elections, but Arizona is a swing state as this NBC News article points out. What’s more important is the investments signals the importance of compute power and the US’s ambition to continue to lead the world in this important resource

Former co-founder of Deepmind and after recently raising $1.3 billion for Inflection-AI, Mustafa Suleyman is headed to head up Microsoft’s Consumer Artificial Intelligence business. Is MSN starting to have the best AI talent in the business? Have a read and share your opinion in our Discord community

"When a vulnerability is discovered in a supported language, fix suggestions will include a natural language explanation of the suggested fix, together with a preview of the code suggestion that the developer can accept, edit, or dismiss," GitHub's Pierre Tempel and Eric Tooley said on GiHub blog

We don’t regularly put a Research paper in the News section but this opinion series report comes from UNESCO and IRCAI (international research centre on artificial intelligence). It has some key insights to where AI and Education could be going - across cultures and domains.

Elon Musk and X (formerly Twitter) release Grok as Open Source. The model behind X’s chatbot “wielding a staggering 314 billion parameters” is now publicly available. What can we all gain by this Tech powerhouse making their AI model not closed like the others we have from OpenAI, Google, or Apple? Where can people help take AI when the model is in our hands?

AI Word of the Week: Forward Pass

Simplifying Complex terms so everyone - beginner to advance - can better understand AI, ML concepts and terminology

🏀 With March Madness taking place in the USA we thought to honor this wonderful collegiate basketball tournament with an AI term that fits 😉

Forward Pass - This refers to the process of feeding input data through a neural network and obtaining an output prediction or result. A neural network is a series of interconnected nodes or neurons, organized into layers. Each layer performs specific operations on the input it receives from the previous layer, and then passes the results forward to the next layer. This process continues until the final layer, which generates the ultimate output or prediction.

Getting more Advanced: During the forward pass, the input data flows through these layers, getting transformed at each step by the network's parameters (weights and biases). The final output is the network's prediction or inference based on the input. This is what is called “Propagating” and the input signals forward layer-by-layer through the network’s parameterized functions to produces the output. Gradient descent updates parameters to minimize loss between output and training target.

Context/Relevance: The forward pass is a fundamental concept in various neural network architectures, particularly those used for classification tasks. In image recognition networks like convolutional neural networks (CNNs), the forward pass propagates the input image through a series of convolutional and pooling layers to extract visual features, followed by fully connected layers that map these features to class probabilities.

Please get in touch to see more of the AI Glossary that we’re compiling. There’s 117 terms and growing. Share what you want to see added to the list

🚀 #52weeksofAI - with Wes Shields 💡

Generating the Same Character/Subject Across Multiple Images is Now Here! This week, Wes tried out the new CREF tag that Midjourney released. This new capability helps for when you need to have a consistent character across multiple images. This has been a serious & severe limitation across AI-image generation, and this is a key step making it easier to get consistency.

Join Wes in this weekly exploration as he tests new tools & prompts. Get motivated to experiment with these cutting-edge models yourself.

🎓 Research and Discoveries:
Actionable Insights You Can Use

The top article this week from the latest research and practical applications of AI shared within our community. From theoretical breakthroughs to hands-on applications, this is the one thing to be sure to read about the evolving landscape of AI technology.

Purpose: This intriguing study explores using eye tracking as a novel way to assess user trust. It explores whether where we look and how we blink reveals if we truly trust an AI system.

Key Findings, Insights/Implications: By analyzing gaze patterns such as where people look, how they blink, and their pupil dilation when interacting with an AI system, researchers may uncover hints about the user's underlying trust levels. Gaze tracking could provide a subtle yet insightful window into these cognitive processes.

Framing the Discussion: As AI assistants become more prominent, ensuring users can trust them is crucial. Current methods like surveys have limitations, while approaches like brain scans feel too invasive. Eye tracking could strike the right balance – an unobtrusive yet revealing glimpse into the mind. Gaze tracking represents a new frontier - discreet yet immensely revealing about internal cognitive processes.

Putting it into Daily Workflows: Wherever you interact with AI systems, understanding the subtle eye cues of trust (or lack thereof) could illuminate vital user insights. It's an engaging new frontier for human-AI relationships.

Marketing could gauge consumer trust in Ai product recommendations through eye patterns during shopping trials. Leadership might evaluate team comfort with new AI tools by monitoring eye behaviors during demos and pre-sales. Coding could design UI/UX to better guide user attention and build confidence in the outputs from AI applications. Healthcare would be able to detect patient discomfort and/or assess patient trust during AI-assisted diagnoses.

🤝 Together, We Innovate

At Synthminds, we believe in the power of collaboration to unlock the full potential of AI. Your insights, experiences, and curiosity are the fuel that drives our community forward.

We very much wish for this newsletter to be what you want to read weekly. Please share your feedback and ideas with us. Thanks for reading and join us next week!

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