⚡️ Stanford's AI Framework

Good Morning. With the digital landscape evolving, Stanford University researchers are pioneering an effort to address one of the AI industry's pressing issues. Let’s dive in.

Today’s Highlights:

• ByteDance suspended from ChatGPT

• Virtuleap raises $2.5M to use VR and AI for brain health

• DeepMind AI Solves Elusive Math Conundrum

DEEP DIVE

Stanford Researchers' AI Framework Tackles Data Authenticity in LLMs

Stanford University's cutting-edge research is taking on the colossal challenge of data accuracy in Large Language Models (LLMs). These AI behemoths excel in text-based tasks, yet their tendency to 'hallucinate' or emit bogus information poses a significant hurdle.

Unified AI Attribution Approach

The Stanford team proposes a comprehensive framework to handle LLM attributions. Their innovative approach encompasses citation generation and Training Data Attribution (TDA), dealing with corroborative and contributive attributions, respectively.

In this context;

• Contributive attributions focus on pinpointing the genesis of an LLM's created content.

• Corroborative attributions ensure the output's accuracy aligns with authenticated external knowledge.

The team has meticulously defined each type of attribution and demonstrated its real-world application, showcasing scenarios where such precise attributions are essential.

For instance:

• While drafting legal documents, it's necessary to confirm the source (contributive) and conform to legal standards (corroborative).

• In medical question answering, both attributions work in tandem to ensure response accuracy and source understanding.

Real-world Impact and Future Use

This unified framework may well lead to a new era in LLM application and credibility, aiming at a universal format for evaluating attribution systems in various domains. This could spearhead a more consistent and reliable approach to LLM attributions, alleviating the crucial issue of output dependability.

Keep reading here.

PUNCHLINES

Bots & Frauds: Many employees pass off AI-generated work as their own, study finds.

Typing with a Tune: A new AI called CoAtNet deciphers passwords just by the sound of your typing.

VR Mind Gym: Virtuleap raises $2.5M to use VR and AI for brain health.

Scrolling Past the Profits: News publisher files class action antitrust suit against Google.

Grindr's AI Cupid: Grindr dating app adds an AI ‘wingman’ to help users find love.

TLDR

Intel's AI-Focused Core Ultra Chips: Intel debuts Meteor Lake Core Ultra chips with built-in NPU, boasting up to 79% less power use than AMD's previous-gen and 11% faster multitasking. The chips optimize AI tasks and are primed for features like Windows 11’s Studio Effects, delivering up to 34 TeraOPS in AI workloads.

DeepMind AI Solves Elusive Math Conundrum: DeepMind's FunSearch, pairing a pre-trained LLM with an evaluator, solves the cap set problem, a challenge that baffled mathematicians. This novel approach prevents AI from generating erroneous "hallucinations,” marking a first in which an AI-derived solution contributes new, verifiable knowledge to science.

SITA report signals AI boom in aviation: A SITA report forecasts an impressive 97% of airlines to harness Generative AI by 2033. The aviation landscape eyes huge changes with digital identities, electric air taxis, and a surge in Urban Air Mobility investments. Biometric boarding and contactless travel are on the rise, promising a modernized industry with enhanced customer experiences.

ByteDance suspended from ChatGPT: OpenAI halts ByteDance's access to ChatGPT, citing misuse of API to secretly develop a rival AI. ByteDance's Project Seed, aiming to replicate GPT's capabilities for China, allegedly violated OpenAI's terms, leading to an ongoing investigation and potential account deletion.

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🤖 GradesAI: AI-powered app predicting exam questions for students

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🎨 Snapby: Create ultra-realistic images instantly using text prompts

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