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Salesforce's $500M AI Fund đ˘
The venture capital arm's $500 million fund brings its total AI investment to $1 billion, reflecting the company's focus on integrating advanced AI capabilities into its enterprise solutions.
Today in AI
News roundup
Todayâs big story
AI research changing the world
The tool weâre loving
This week on the podcast
News roundup
The top stories in AI today.
FUTURE OF WORK
Jobs are changing â are you?
NEW LAUNCHES
The latest features & products in AI innovation.
New AI Tool Lets You Upload Silent Videos and Read Speakersâ Lips
Googleâs AI Tool âFood Moodâ Will Help You Create Mouth-Watering Meals
Runway Launches Enterprise API for Its New Video Generation Model
Google Merges Gemini AI with Gmail for Smarter Email Management
Amazon Android App Gets UI Upgrade and New AI Assistant âRufusâ
MODELS
Deployment, research, training, infrastructure
GOVERNMENT
Press releases, regulation, defense & politics.
EU Watchdog Probes Googleâs AI Data Use for GDPR Compliance
Americans Donât Trust Election Information from AI, Survey Finds
How AI Could Widen â or Bridge â the Global Inequality Gap
The Benefits Generative AI Can Bring to Fraud Management and AML
Stephen Fryâs Latest Take on How to Live Well In the AI Era
FINANCE & ECONOMICS
Economics, wall Street & fundraising
Whatâs happening in AI right now
Salesforce bets big on AI agents
Salesforce is making a bold move in the AI space with the launch of AgentForce, a suite of AI-powered autonomous agents, and a new $500 million AI focused venture fund. This brings the company's total AI investment to $1 billion over the past 18 months. CEO Marc Benioff, inspired by a conversation he had with Steve Jobs in 2010, has positioned AgentForce as Salesforce's sole focus, aiming to revolutionize enterprise software with intelligent systems capable of complex reasoning and decision-making. Benioff describes Agentforce as the âbiggest and most exciting piece of technologyâ Salesforce has ever worked on.
AgentForce is now integrated into every Salesforce cloud, offering out-of-the-box agents for various roles and allowing customization using low-code tools. Early adopters report significant productivity gains, with one customer noting a 35% increase in customer service efficiency. However, questions remain about how AgentForce will differentiate itself in a crowded AI market and whether the massive financial commitment will translate into proportional gains for Salesforce and its customers.
Salesforce's moves are likely to accelerate innovation in the AI startup ecosystem, potentially leading to new enterprise facing solutions. By allocating significant resources to AI development and startups, Salesforce aims to enhance its suite of customer relationship management (CRM) and cloud-based solutions with advanced AI capabilities. For businesses, AgentForce promises to transform operations across various functions, but companies will need to carefully consider how to integrate these AI agents into their existing workflows and train employees to work alongside them effectively.
As we watch Salesforce's billion-dollar AI gamble unfold, it's clear that the enterprise software landscape is on the brink of a significant shift. Business leaders should start planning for AI integration, invest in AI literacy for their workforce, and consider the ethical implications of AI deployment simultaneously. Whether AgentForce will be the catalyst for widespread change remains to be seen, but its impact will undoubtedly be felt across the industry.
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AI research changing the world
The latest breakthroughs and most pivotal papers â broken down in language anyone can understand.
Bigger may be better, reveals OpenAI's latest research
In an era where AI is rapidly evolving, OpenAI's latest research uncovers intriguing insights about the scalability of neural language models. This study, conducted by a team of experts from both OpenAI and Johns Hopkins University, delves into the relationship between model size, dataset size, and computational power in language processing AI. The findings suggest that the path to more efficient AI could lie in scaling up model sizes, a move that may shift the focus from 'big data' to 'big models'. OpenAI's research presents a compelling argument for the power of larger models. It turns out that these behemoths are not just more capable but also more data-efficient. They achieve better performance with fewer training steps, suggesting that when it comes to training AI, size does matter.
This revelation could redefine strategies for AI development, pushing the boundaries of what these models can achieve. One of the key takeaways from the study is the discovery of power-law relationships in the performance of AI models. This means that as the size of the model increases, the performance improves in a predictable manner. It's not just about adding more parameters haphazardly; it's about scaling up intelligently. The research demonstrates that when model size, dataset size, and computational power are increased in tandem, the performance of language models improves smoothly and predictably. The implications of these findings are significant for industries relying on AI for language processing tasks. By investing in larger models, companies can potentially achieve better results with less data, which could translate to cost savings and efficiency gains. Moreover, the study's insights into the minimal impact of architectural details on performance could simplify the design of future AI systems.
The AI tool weâre loving right now
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This week on the podcast
Canât get enough of our newsletter? Check out our podcast Future-Proof.
In this episode Anthony, Shane, and Elizabeth discuss the transformative impact of AI on sales processes and e-commerce. The conversation highlights the competitive edge that AI-native companies have over traditional businesses, the evolving landscape of customer interactions, and the potential for AI to streamline workflows and enhance personalization at the same time.
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