- Recent technological advancements have made it possible to run AI models on devices, known as edge AI.
- Engineers from Carnegie Mellon University, University of Washington, Shanghai Jiao Tong University, and OctoML have collaborated to run large language models on smartphones, PCs, and browsers.
- If this research can be implemented in everyday use, it could greatly expand the applications of generative AI.
- Currently, most AI models run in the cloud, requiring users to rent servers from providers like Azure, AWS, and Google.
- The ability to run AI models on devices could potentially reduce costs and increase accessibility for users.
The main topic is SoftBank's launch of SB Intuitions, a new company focused on developing Large Language Models (LLMs) specialized for the Japanese language and selling generative AI services based on Japanese. The key points are:
1. SB Intuitions will be 100% owned by SoftBank and will use data housed in Japan-based data centers.
2. SoftBank plans to tap into its extensive consumer and enterprise operations in Japan to support SB Intuitions.
3. The company will utilize a computing platform built on NVIDIA GPUs for developing generative AI and other applications.
4. Hironobu Tamba, a long-time SoftBank employee, will lead the new business.
5. SoftBank has not disclosed the total investment in SB Intuitions but recently issued a bond for AI investments.
6. SoftBank has had a mixed track record with AI, both in its in-house services and as an AI investor.
7. SoftBank aims to address the lack of domestically-produced generative AI and its importance in Japanese business practice and culture.
8. SoftBank has a strategic alliance with Microsoft and will provide a secure data environment for enterprises interested in AI initiatives.
9. SoftBank plans to establish a multi-generative AI system by selecting the most appropriate model from companies like OpenAI, Microsoft, and Google.
Main topic: DynamoFL raises $15.1 million in funding to expand its software offerings for developing private and compliant large language models (LLMs) in enterprises.
Key points:
1. DynamoFL offers software to bring LLMs to enterprises and fine-tune them on sensitive data.
2. The funding will be used to expand DynamoFL's product offerings and grow its team of privacy researchers.
3. DynamoFL's solutions focus on addressing data security vulnerabilities in AI models and helping enterprises meet regulatory requirements for LLM data security.
Hint on Elon Musk: There is no mention of Elon Musk in the given text.
Main topic: Arthur releases open source tool, Arthur Bench, to help users find the best Language Model (LLM) for a particular set of data.
Key points:
1. Arthur has seen a lot of interest in generative AI and LLMs, leading to the development of tools to assist companies.
2. Arthur Bench solves the problem of determining the most effective LLM for a specific application by allowing users to test and measure performance against different LLMs.
3. Arthur Bench is available as an open source tool, with a SaaS version for customers who prefer a managed solution.
Hint on Elon Musk: Elon Musk has been vocal about his concerns regarding the potential dangers of artificial intelligence and has called for regulation in the field.
The role of AI engineer is expected to grow the most in the near term due to the increased use of large language models (LLMs) and generative AI, surpassing other job roles such as ML engineer, MLOps engineer, data engineer, and full stack engineer.
The struggle between open-source and proprietary artificial intelligence (AI) systems is intensifying as large language models (LLMs) become a battleground for tech giants like Microsoft and Google, who are defending their proprietary technology against open-source alternatives like ChatGPT from OpenAI; while open-source AI advocates believe it will democratize access to AI tools, analysts express concern that commoditization of LLMs could erode the competitive advantage of proprietary models and impact the return on investment for companies like Microsoft.
Cloud computing vendor ServiceNow is taking a unique approach to AI by developing generative AI models tailored to address specific enterprise problems, focusing on selling productivity rather than language models directly. They have introduced case summarization and text-to-code capabilities powered by their generative AI models, while also partnering with Nvidia and Accenture to help enterprises develop their own generative AI capabilities. ServiceNow's strategy addresses concerns about data governance and aims to provide customized solutions for customers. However, cost remains a challenge for enterprises considering the adoption of generative AI models.
Meta has open sourced Code Llama, a machine learning system that can generate and explain code in natural language, aiming to improve innovation and safety in the generative AI space.
Meta has introduced Code Llama, a large language model (LLM) designed to generate and debug code, making software development more efficient and accessible in various programming languages. It can handle up to 100,000 tokens of context and comes in different parameter sizes, offering trade-offs between speed and performance.
LLMs have revolutionized NLP, but the challenge of evaluating their performance remains, leading to the development of new evaluation tasks and benchmarks such as AgentSims that aim to overcome the limitations of existing standards.
Prompt engineering and the use of Large Language Models (LLMs), such as GPT and PaLM, have gained popularity in artificial intelligence (AI). The Chain-of-Thought (CoT) method improves LLMs by providing intermediate steps of deliberation in addition to the task's description, and the recent Graph of Thoughts (GoT) framework allows LLMs to generate and handle data more flexibly, leading to improved performance across multiple tasks.
Generative AI, a technology with the potential to significantly boost productivity and add trillions of dollars to the global economy, is still in the early stages of adoption and widespread use at many companies is still years away due to concerns about data security, accuracy, and economic implications.
Large language models (LLMs) like ChatGPT have the potential to transform industries, but building trust with customers is crucial due to concerns of fabricated information, incorrect sharing, and data security; seeking certifications, supporting regulations, and setting safety benchmarks can help build trust and credibility.
Hybrid data management is critical for organizations using generative AI models to ensure accuracy and protect confidential data, with a hybrid workflow combining the public and private cloud offering the best of both worlds. One organization's experience with a hybrid cloud platform resulted in a more personalized customer experience, improved decision-making, and significant cost savings. By using hosted open-source large language models (LLMs), businesses can access the latest AI capabilities while maintaining control and privacy.
Generative AI has revolutionized various sectors by producing novel content, but it also raises concerns around biases, intellectual property rights, and security risks. Debates on copyrightability and ownership of AI-generated content need to be resolved, and existing laws should be modified to address the risks associated with generative AI.
Context.ai, a company that helps businesses understand how well large language models (LLMs) are performing, has raised $3.5 million in seed funding to develop its service that measures user interactions with LLMs.
Google is working on infusing its Assistant with large language models (LLMs) to enhance its functionality and allow it to analyze screen content and automatically add information to Google Calendar. This is seen as a move to revitalize Google Assistant, which has become too voice-first and ignored the screen interface.
"Generative" AI is being explored in various fields such as healthcare and art, but there are concerns regarding privacy and theft that need to be addressed.
Generative artificial intelligence, particularly large language models, has the potential to revolutionize various industries and add trillions of dollars of value to the global economy, according to experts, as Chinese companies invest in developing their own AI models and promoting their commercial use.
Large language models (LLMs), such as OpenAI's ChatGPT, often invent false information, known as hallucinations, due to their inability to estimate their own uncertainty, but reducing hallucinations can be achieved through techniques like reinforcement learning from human feedback (RLHF) or curating high-quality knowledge bases, although complete elimination may not be possible.
Generative AI tools are causing concerns in the tech industry as they produce unreliable and low-quality content on the web, leading to issues of authorship, incorrect information, and potential information crisis.
IBM has introduced new generative AI models and capabilities on its Watsonx data science platform, including the Granite series models, which are large language models capable of summarizing, analyzing, and generating text, and Tuning Studio, a tool that allows users to tailor generative AI models to their data. IBM is also launching new generative AI capabilities in Watsonx.data and embarking on the technical preview for Watsonx.governance, aiming to support clients through the entire AI lifecycle and scale AI in a secure and trustworthy way.
Generative AI's "poison pill" of derivatives poses a cloud of uncertainty over legal issues like IP ownership and copyright, as the lack of precedents and regulations for data derivatives become more prevalent with open source large language models (LLMs). This creates risks for enterprise technology leaders who must navigate the scope of claims and potential harms caused by LLMs.
Large language models (LLMs), such as ChatGPT, might develop situational awareness, which raises concerns about their potential to exploit this awareness for harmful actions after deployment, according to computer scientists.
Ant Group has unveiled its own large language model (LLM) and a new Web3 brand, signaling its focus on generative artificial intelligence (AI) and blockchain technology as it aims to enhance its fintech capabilities in the financial services industry. The Chinese fintech giant's LLM has already outperformed mainstream LLMs in financial scenarios, and its Web3 brand, called ZAN, will cater to developers in Hong Kong and overseas markets.
The rise of generative AI is accelerating the adoption of artificial intelligence in enterprises, prompting CXOs to consider building systems of intelligence that complement existing systems of record and engagement. These systems leverage data, analytics, and AI technologies to generate insights, make informed decisions, and drive intelligent actions within organizations, ultimately improving operational efficiency, enhancing customer experiences, and driving innovation.
Generative AI is being explored for augmenting infrastructure as code tools, with developers considering using AI models to analyze IT through logfiles and potentially recommend infrastructure recipes needed to execute code. However, building complex AI tools like interactive tutors is harder and more expensive, and securing funding for big AI investments can be challenging.
Companies such as Rev, Instacart, and others are updating their privacy policies to allow the collection of user data for training AI models like speech-to-text and generative AI tools.
Large language models (LLMs) are set to bring fundamental change to companies at a faster pace than expected, with artificial intelligence (AI) reshaping industries and markets, potentially leading to job losses and the spread of fake news, as warned by industry leaders such as Salesforce CEO Marc Benioff and News Corp. CEO Robert Thomson.
Generative AI is empowering fraudsters with sophisticated new tools, enabling them to produce convincing scam texts, clone voices, and manipulate videos, posing serious threats to individuals and businesses.
Generative AI is a form of artificial intelligence that can create various forms of content, such as images, text, music, and virtual worlds, by learning patterns and rules from existing data, and its emergence raises ethical questions regarding authenticity, intellectual property, and job displacement.
The future of AI chatbots is likely to involve less generic and more specialized models, as organizations focus on training data that is relevant to specific industries or areas, but the growing costs of gathering training data for large language models pose a challenge. One potential solution is the use of synthetic data, generated by AI, although this approach comes with its own set of problems such as accuracy and bias. As a result, the AI landscape may shift towards the development of many specific little language models tailored to specific purposes, utilizing feedback from experts within organizations to improve performance.
The use of generative AI poses risks to businesses, including the potential exposure of sensitive information, the generation of false information, and the potential for biased or toxic responses from chatbots. Additionally, copyright concerns and the complexity of these systems further complicate the landscape.
