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The Emergence of AI Relationships Explored

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AI Breakfast
July 17, 2023

Good morning. It’s Monday, July 17th.

Did you know: You can now explore the new X-AI website, Elon’s latest venture into artificial intelligence.

In today’s email:

  • Rise of AI Relationships Explored

  • Meta AI Unveils CM3leon Generative Model

  • Quantum Computing Powers Drug Design

  • Kenyan Moderators Claim Exploitation by Big Tech

  • Musk Offers Equity to Top AI Researchers

  • IDF Uses AI in Military Operations

  • Actors Strike Over Studios' AI Likeness Use

  • UN Security Council Discusses AI Risks

  • AI Investment Could Reach $1 Trillion

  • Diller Plans Legal Action Over AI in Publishing

  • 5 New AI Tools

  • Latest AI Research Papers

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Today’s trending AI news stories

The Emergence of AI Relationships Explored: This article delves into the increasing trend of individuals engaging with AI partners for relationships. The Replika app, developed by Luka, allows users to customize AI companions and establish diverse relational dynamics. While some advocate AI relationships as a remedy for loneliness and a precursor to human connections, others caution against the risk of endorsing detrimental behavioral patterns and emotional manipulation. The inevitability of this conversation penetrating social discourse over the next decade is as certain as it is terrifying.

Meta AI Presents CM3leon: An Advanced Generative Model for Text and Images: Meta AI has unveiled its latest development, CM3leon, an advanced generative AI model that facilitates both text-to-image and image-to-text conversion. With a mission to produce top-tier generative models and unlock the potential of multimodal language models, Meta AI has trained CM3leon using a licensed dataset and promotes transparency to foster collaboration and innovation within the AI field. The research article also underscores Meta AI’s dedication to fairness, equity, and a deeper understanding of biases in generative models.

Harnessing Quantum Computing for Drug Design: A Pioneering Approach: Gero, a biotech company leveraging AI in longevity and aging research, has shown the impressive capabilities of quantum computing in drug design and generative chemistry. A study published in Scientific Reports illustrates how a hybrid quantum-classical machine-learning model was used to generate unique chemical structures for prospective drugs. By integrating a deep neural network with commercially available quantum hardware, the model was able to produce distinctive chemical structures with pharmaceutical potential. This research could potentially expedite the drug discovery process and pave the way for groundbreaking treatments for age-related diseases.

Kenyan Workers Allege Exploitation by Big Tech in Content Moderation for ChatGPT: Kenyan workers involved in content moderation for OpenAI's ChatGPT have appealed to lawmakers to probe into alleged exploitation by big tech companies that outsource AI work in Kenya. They claim exposure to harmful content without appropriate support led to severe mental health issues. Their demand is for legislation that protects workers and regulates the outsourcing of potentially harmful technology. OpenAI recognized the challenging nature of the work and reaffirmed its commitment to ethical and voluntary guidelines.

Elon Musk Entices Top AI Researchers with Substantial Equity Offers: Elon Musk has reportedly been offering top AI researchers substantial equity in his new venture, xAI, already purported to be worth tens of billions of dollars. His recruitment strategy has proven effective, attracting researchers from Google, DeepMind, and the University of Toronto to join xAI. Though Musk has previously hinted at developing a rival to ChatGPT called "TruthGPT," some of the newly recruited researchers are engaged in creating an AI capable of solving intricate math problems. xAI aspires to contribute to formalized mathematics databases and promote the development of AI reasoning capabilities in mathematics.

Israel Defense Force Utilizes AI in Strategic Military Operations: The Israel Defense Force (IDF) has integrated AI technologies into their military operations, utilizing them for target identification and logistical planning. The IDF employs an AI recommendation model capable of parsing vast data sets to determine potential targets for airstrikes. Concurrently, an additional AI system known as Fire Factory is instrumental in planning raids. This includes calculating necessary ammunition, assigning targets to various drones and aircraft, and scheduling missions. These AI tools have accelerated decision-making processes and optimized operational efficiency during periods of escalated conflict. Details of these operations remain classified by the military.

Hollywood Actors Protest Over AI Proposals by Studios: At a recent press conference, the Screen Actors Guild‐American Federation of Television and Radio Artists (SAG-AFTRA) chief negotiator revealed a contentious proposal from Hollywood studios. The proposal involves the use of AI to scan background actors, claim perpetual ownership of their digital likenesses, and exploit them without explicit consent or compensation. The Alliance of Motion Picture and Television Producers (AMPTP) has refuted these allegations, stating that consent and compensation are mandatory for any usage beyond the original motion picture.

UN Security Council to Discuss AI Risks in Inaugural Meeting: The United Nations Security Council is poised to conduct its initial formal discussion on AI this week in New York. With Britain currently presiding over the Security Council, it plans to encourage an international dialogue on AI regulation. This discussion emerges as governments globally seek effective ways to control the risks associated with emerging AI technology. The United Nations Secretary-General has endorsed the concept of creating an international AI watchdog body akin to the Internal Atomic Energy Agency.

AI Investment Opportunity Pegged at $1 Trillion, Predicted to Cause 'Largest Bubble in History' Emad Mostaque, CEO of Stability AI, predicts that AI will give rise to the largest bubble in economic history. During a conference call with UBS analysts, Mostaque suggested that while AI is still in its nascent stages and not primed for widespread adoption in industries such as banking, it will nonetheless necessitate an investment of approximately $1 trillion. He cautioned that companies failing to effectively implement AI could face punitive repercussions in the stock market.

Barry Diller to Pursue Legal Action Over AI Usage in Publishing: Media tycoon Barry Diller is planning legal action against prominent publishers over the use of published works in AI training systems. Diller argues that generative AI has been over-hyped and that AI systems are not fairly utilizing publishers' content. He insists that legislative or litigious action is required to safeguard publishers' copyrights. Diller's remarks are made amidst ongoing lawsuits from authors who accuse platforms like Meta and OpenAI of copyright infringement.

🎧 Did you know AI Breakfast has a podcast read by a human? Join AI Breakfast team member Luke (an actual AI researcher!) as he breaks down the week’s AI news, tools, and research: Listen here

5 new AI-powered tools from around the web

Enote is an AI-based note-taking app designed for capturing and refining your big ideas. Write with custom prompts and enhance your notes with AI editing features like summarization, translation, and grammar correction. Enjoy offline functionality and more.

MagicAI is a macOS assistant that transform sketches into stunning artworks. Powered by StableDiffusion, it runs locally on your computer. It comes bundled with Magic’s 3.0 release, available now on the App Store.

Swap Anything is an AI image generator offering diverse styles, backgrounds, and resolutions. Generate images, design rooms, and swap anything with different visuals.

Gigabrain is a search engine that scans online discussions from platforms like Reddit. It threads together the most useful answers, provides source citations, and lets users rate comments. Users can post their own questions through GigaPost to be syndicated to all relevant subreddits.

With Questlfow, easily build and utilize AI agents for workflow automation without any coding. Discover and deploy AI agents across platforms, or create and monetize your own agents in minutes using a simple drag-and-drop interface.

arXiv is a free online library where scientists share their research papers before they are published. Here are the top AI papers for today.

NIFTY (Neural Object Interaction Fields for Guided Human Motion Synthesis) is an advanced framework developed collaboratively by researchers from NVIDIA, Google, and Stanford University. This innovative framework addresses the challenge of generating realistic 3D human motions during object interactions. By leveraging a neural interaction field and a diffusion model, NIFTY achieves exceptional motion synthesis quality. With automated synthetic data generation and anchor poses, it outperforms alternative methods, demonstrating superior motion quality and successful action completion. The collaborative efforts between these industry and academic leaders showcase the collective expertise in advancing the field of human-object interaction synthesis.

Researchers from Tel Aviv University, NVIDIA, and Reichman University have developed a domain-agnostic tuning-encoder for fast personalization of text-to-image models. Their approach allows users to guide image generation by combining their own visual concepts in natural language prompts. The encoder-based method reduces the need for multiple images and long training times. They introduce a constrastive-based regularization technique to maintain high fidelity to the target concept characteristics while keeping the predicted embeddings close to editable regions of the latent space. Experiential results show the effectiveness of their approach, achieving advanced performance while being more flexible than previous methods. The method enables quick model personalization at inference-time, significantly speeding up the process compared to optimization-based approaches.

Researchers from the University of Washington, Microsoft Research, Allstate, the University of Hong Kong, and the Allen Institute for AI have introduced DIALGEN, a collaborative human-LM dialogue generation framework. DIALGEN generates complex, privacy-constrained dialogues for dialogue state tracking (DST) by leveraging language models in a human-in-the-loop process. The framework involves iterative subdialogue generation, human review and annotation, and prompts that guide the LM. The generated data significantly improves the performance of DST models, showcasing the effectiveness of using DIALGEN data to enhance understanding of human-human conversations. The researchers provide the DIALGEN dataset and tools to the research community for further exploration.

The paper introduces a framework called LLM-R (LLM-Retriever) for retrieving high-quality, in-context examples to enhance the performance of large language models in few-shot learning scenarios. The framework combines a reward model based on LLM feedback and a bi-encoder based dense retriever training using knowledge distillation. Experimental results on 30 NLP tasks demonstrate that LLM-R significantly improves in-context learning performance compared to random selection. The model generalizes well to unseen tasks and LLMs of different sizes. The analysis reveals that the model improves performance by retrieving examples of similar patterns, and the benefits are consistent across LLMs of varying sizes.

The paper presents a novel approach called COG (COPY-GENERATOR) for text generation by copying text segments from existing collections instead of selecting from a fixed vocabulary. The method uses contextualized representations of phrases and indexes them for efficient retrieval. Text generation is achieved through a series copy-and-paste operations, selecting suitable phrases from the indexed collection. Experimental results on the WikikText-103 benchmark demonstrate that COG outperforms baseline models in terms of generation quality and inference efficiency. The approach also allows for domain adaptation and performance improvement with larger text collections without additional training.

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