the introduction of CRISPR marks a significant stride in the ongoing efforts to eliminate biases in large language models. The research findings, presented in the paper “CRISPR: Eliminating Bias Neurons from an Instruction-following Language Model,” showcase the method’s effectiveness and practicality.
In the expanding natural language processing domain, text embedding models have become fundamental. These models convert textual information into a numerical format, enabling machines to understand, interpret, and manipulate human language. This technological advancement supports various applications, from search engines to chatbots, enhancing efficiency and effectiveness. The challenge in this field involves enhancing the retrieval accuracy of embedding models without excessively increasing computational costs. Current models need help to balance performance with resource demands, often requiring significant computational power for minimal gains in accuracy.
CRISPR-based genome editing technologies have revolutionized gene study and medical treatment by enabling precise DNA alterations. AI integration has enhanced these technologies’ precision, efficiency, and affordability, particularly for diseases like Sickle Cell Anemia and Thalassemia. AI models such as DeepCRISPR, CRISTA, and DeepHF optimize guide RNA (gRNA) design for CRISPR-Cas systems by considering factors like genomic context and off-target effects.
Multinational AI Consortium (MAGIC) Proposes Exclusive Global Control to Ensure Safe and Equitable Advanced AI Development
The Llama Impact Grants program offers $500,000 in grants to projects using Llama 2, an open-source large language model, to address significant challenges in education, the environment, and social innovation.
Sam Altman dismissed from OpenAI, Inc., the 501(c)(3) nonprofit overseeing the AI startup responsible for ChatGPT, DALL-E 3, GPT-4, and other advanced generative AI systems. He will exit both the company’s board of directors and his role as CEO.
Computer vision is a rapidly growing field of artificial intelligence that enables computers to understand and interpret images and videos. It has a wide range of applications, including self-driving cars, medical imaging, and robotics.
Transformers are at the forefront of modern artificial intelligence, powering systems that understand and generate human language. They form the backbone of several influential AI models, such as Gemini, Claude, Llama, GPT-4, and Codex, which have been instrumental in various technological advances. However, as these models grow in size & complexity, they often exhibit unexpected behaviors, some of which may be problematic. This challenge necessitates a robust framework for understanding and mitigating potential issues as they arise.
Artificial Intelligence (AI) is currently experiencing a significant surge in popularity. Following the viral success of OpenAI’s conversational agent, ChatGPT, the tech industry has been abuzz with excitement about Large Language Models (LLMs), the technology that powers ChatGPT. Tech giants like Google, Meta, and Microsoft, along with well-funded startups such as Anthropic and Cohere, have all launched their own LLM products. Companies across various sectors are rushing to integrate LLMs into their services, with OpenAI counting customers like fintech companies using them for customer service chatbots, edtech platforms like Duolingo and Khan Academy for educational content generation, and even video game companies like Inworld for providing dynamic dialogue for non-playable characters (NPCs). With widespread adoption and a slew of partnerships, OpenAI is on track to achieve annual revenues exceeding one billion dollars.
IBM has made a great advancement in the field of software development by releasing a set of open-source Granite code models designed to make coding easier for people everywhere. This action stems from the realization that, although software plays a critical role in contemporary society, the process of coding is still difficult and time-consuming. Even seasoned engineers frequently struggle to keep learning new things, adjust to new languages, and solve challenging problems.
