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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.

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

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.

How can rapidly emerging #AI develop into a trustworthy, equitable force? Proactive policies and smart governance, says Salesforce.


These initial steps ignited AI policy conversations amid the acceleration of innovation and technological change. Just as personal computing democratized internet access and coding accessibility, fueling more technology creation, AI is the latest catalyst poised to unlock future innovations at an unprecedented pace. But with such powerful capabilities comes large responsibility: We must prioritize policies that allow us to harness its power while protecting against harm. To do so effectively, we must acknowledge and address the differences between enterprise and consumer AI.

Enterprise versus consumer AI

Salesforce has been actively researching and developing AI since 2014, introduced our first AI functionalities into our products in 2016, and established our office of ethical and human use of technology in 2018. Trust is our top value. That’s why our AI offerings are founded on trust, security and ethics. Like many technologies, there’s more than one use for AI. Many people are already familiar with large language models (LLMs) via consumer-facing apps like ChatGPT. Salesforce is leading the development of AI tools for businesses, and our approach differentiates between consumer-grade LLMs and what we classify as enterprise AI.

Our guest in this episode is Dr. Mark Kotter. Mark is a neurosurgeon, stem cell biologist, and founder or co-founder of three biotech start-up companies that have collectively raised hundreds of millions of pounds: bit.bio, clock.bio, and Meatable.

In addition, Mark still conducts neurosurgeries on patients weekly at the University of Cambridge.

We talk to Mark about all his companies, but we start by discussing Meatable, one of the leading companies in the cultured meat sector. This is an area of technology which should have a far greater impact than most people are aware of, and it’s an area we haven’t covered before in the podcast.

Selected follow-ups:

• Dr Mark Kotter at the University of Cambridge (https://www.stemcells.cam.ac.uk/peopl…)
• Meatable (https://meatable.com/)
• bit.bio (https://www.bit.bio/)
• clock.bio (https://clock.bio/)
• After 25 years of hype, embryonic stem cells are still waiting for their moment (https://www.technologyreview.com/2023…) — Article in MIT Technology Review.
• The Nobel Prize in Physiology or Medicine 2012 (https://www.nobelprize.org/prizes/med…)
• Moo’s Law: An Investor’s Guide to the New Agrarian Revolution (https://www.harriman-house.com/mooslaw) — book by Jim Mellon.
• What is the climate impact of eating meat and dairy? (https://interactive.carbonbrief.org/w…)
• Guidance for businesses on cell-cultivated products and the authorisation process (https://www.food.gov.uk/business-guid…)
• Wild mammals make up only a few percent of the world’s mammals (https://ourworldindata.org/wild-mamma…) — Our World In Data.
• BlueRock Therapeutics (https://www.bluerocktx.com/)
• Therapies under development at bit.bio (https://www.bit.bio/therapeutics2023)
• Stem Cell Gene Therapy Shows Promise in ALS Trial (https://www.technologynetworks.com/ne…) — from Cedars-Sinai Medical Center.

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration.