Lifeboat Foundation

Salesforce recently announced that it has introduced more than 50 AI-powered tools among its workforce and reported that these tools have collectively saved all of its employees in excess of 50,000 hours—or 24 years’ worth—of working time in just three months.

As a company, Salesforce serves as an especially compelling case study for the impact of AI on work—not only because the company tests tools on their own workforce, but because so many others rely on Salesforce’s products to do their jobs each day. Simply put: Salesforce is in the business of work.

Salesforce has more than 70,000 employees worldwide—a 30% increase since 2020. And the software giant builds the products that are used by employees at some 150,000 workplaces, from small businesses to Fortune 500 companies; from sales and customer service teams to marketing and tech teams.

What if you could code just by talking out loud? GitHub CEO Thomas Dohmke shows how, thanks to AI, the barrier to entry to coding is rapidly disappearing — and creating software is becoming as simple (and joyful) as building LEGO. In a mind-blowing live demo, he introduces Copilot Workspace: an AI assistant that helps you create code when you speak to it, in any language.

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When it comes to quantum computing, that chilling effect on research and development would enormously jeopardize U.S. national security. Our projects received ample funding from defense and intelligence agencies for good reason. Quantum computing may soon become the https://www.cyberdefensemagazine.com/quantum-security-is-nat...at%20allow, codebreaking%20attacks%20against%20traditional%20encryption" rel="noopener" class="">gold standard technology for codebreaking and defending large computer networks against cyberattacks.

Adopting the proposed march-in framework would also have major implications for our future economic stability. While still a nascent technology today, quantum computing’s ability to rapidly process huge volumes of data is set to revolutionize business in the coming decades. It may be the only way to capture the complexity needed for future AI and machine learning in, say, self-driving vehicles. It may enable companies to hone their supply chains and other logistical operations, such as manufacturing, with unprecedented precision. It may also transform finance by allowing portfolio managers to create new, superior investment algorithms and strategies.

Given the technology’s immense potential, it’s no mystery why China committed what is believed to be more than https://www.mckinsey.com/featured-insights/sustainable-inclu…n-quantum” rel=“noopener” class=””>$15 billion in 2022 to develop its quantum computing capacity–more than double the budget for quantum computing of EU countries and eight times what the U.S. government plans to spend.

In a fireside chat with Snowflake’s CEO, Huang described how the two companies will help enterprises process their data with accelerated computing to generate business insights.

Applied in this way, it’s not just generative AI—this is transformational AI. It goes beyond accelerating productivity; it accelerates innovation by sparking new business strategies and revamping existing operations, paving the way for a new era of autonomous enterprise.

Keep in mind that not all Large Language Models (LLMs) can be tailored for genuine business innovation. Most models are generalists that are trained on public information found on the internet and are not experts on your particular brand of doing business. However, techniques like Retrieval Augmented Generation (RAG) allow for the augmentation of general LLMs with industry-specific and company-specific data, enabling them to adapt to anyone’s requirements without extensive and expensive training.

We are still in the nascent stages of advanced AI adoption. Most companies are grappling with the basics—such as implementation, security and governance. However, forward-thinking organizations are already looking ahead. By reimagining the application of generative AI, they are laying the groundwork for businesses to reinvent themselves, ushering in an era where innovation knows no bounds.

I found this on NewsBreak: Space Engine Systems Successful in UK MoD Hypersonic Technology Challenge #Engineering

EDMONTON, Alberta—(BUSINESS WIRE)—May 29, 2024—

Space Engine Systems (SES), through its UK operations based out of Spaceport Cornwall (SES Ltd), has applied its aerospace technology expertise to a £1 Billion GBP ($1.27 Billion USD) challenge issued by the UK MoD linked to Hypersonic Technologies and was very recently notified that it had secured a place in the Hypersonic Technology and Capability Development Framework (HTCDF). DE&S to award contracts on £1 billion framework to develop UK’s first hypersonic missile — Defence Equipment & Support (mod.uk). This framework will enable the rapid development of advanced hypersonic missile capabilities, and related technology, over the next 7 years.

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Techno-optimist Vinod Khosla believes in the world-changing power of “foolish ideas.” He offers 12 bold predictions for the future of technology — from preventative medicine to car-free cities to planes that get us from New York to London in 90 minutes — and shows why a world of abundance awaits. If you love watching TED Talks like this one, become a TED Member to support our mission of spreading ideas: https://ted.com/membership Follow TED! X: / tedtalks Instagram: / ted Facebook: / ted LinkedIn: / ted-conferences TikTok: / tedtoks The TED Talks channel features talks, performances and original series from the world’s leading thinkers and doers. Subscribe to our channel for videos on Technology, Entertainment and Design — plus science, business, global issues, the arts and more. Visit https://TED.com to get our entire library of TED Talks, transcripts, translations, personalized talk recommendations and more. Watch more: https://go.ted.com/vinodkhosla • 12 Predictions for the Future of Tech… TED’s videos may be used for non-commercial purposes under a Creative Commons License, Attribution–Non Commercial–No Derivatives (or the CC BY – NC – ND 4.0 International) and in accordance with our TED Talks Usage Policy: https://www.ted.com/about/our-organiz… For more information on using TED for commercial purposes (e.g. employee learning, in a film or online course), please submit a Media Request at https://media-requests.ted.com #TED #TEDTalks

ALERT: RansomHub, a rebranded Knight ransomware, targets healthcare and major entities.

Using legitimate remote desktop tools and recruiting from shutdown groups, it shows evolving cybercriminal tactics.

Currently, computing technologies are rapidly evolving and reshaping how we imagine the future. Quantum computing is taking its first toddling steps toward delivering practical results that promise unprecedented abilities. Meanwhile, artificial intelligence remains in public conversation as it’s used for everything from writing business emails to generating bespoke images or songs from text prompts to producing deep fakes.

Some physicists are exploring the opportunities that arise when the power of machine learning — a widely used approach in AI research—is brought to bear on quantum physics. Machine learning may accelerate quantum research and provide insights into quantum technologies, and quantum phenomena present formidable challenges that researchers can use to test the bounds of machine learning.

When studying quantum physics or its applications (including the development of quantum computers), researchers often rely on a detailed description of many interacting quantum particles. But the very features that make quantum computing potentially powerful also make quantum systems difficult to describe using current computers. In some instances, machine learning has produced descriptions that capture the most significant features of quantum systems while ignoring less relevant details—efficiently providing useful approximations.