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Our final estimate of the achievable inter data center bandwidth by 2030 is 4 to 20 Pbps, which would allow for training runs of 3e29 to 2e31 FLOP. In light of this, bandwidth is unlikely to be a major constraint for a distributed training run compared to achieving the necessary power supply in the first place.

Expanding bandwidth capacity for distributed training networks presents a relatively straightforward engineering challenge, achievable through the deployment of additional fiber pairs between data centers. In the context of AI training runs potentially costing hundreds of billions of dollars, the financial investment required for such bandwidth expansion appears comparatively modest.44

We conclude that training runs in 2030 supported by a local power supply could likely involve 1 to 5 GW and reach 1e28 to 3e29 FLOP by 2030. Meanwhile, geographically distributed training runs could amass a supply of 2 to 45 GW and achieve 4 to 20 Pbps connections between data center pairs, allowing for training runs of 2e28 to 2e30 FLOP.45 All in all, it seems likely that training runs between 2e28 to 2e30 FLOP will be possible by 2030.46 The assumptions behind these estimates can be found in Figure 3 below.

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In recent years, these technological limitations have become far more pressing. Deep neural networks have radically expanded the limits of artificial intelligence—but they have also created a monstrous demand for computational resources, and these resources present an enormous financial and environmental burden. Training GPT-3, a text predictor so accurate that it easily tricks people into thinking its words were written by a human, costs $4.6 million and emits a sobering volume of carbon dioxide—as much as 1,300 cars, according to Boahen.

With the free time afforded by the pandemic, Boahen, who is faculty affiliate at the Wu Tsai Neurosciences Institute at Stanford and the Stanford Institute for Human-Centered AI (HAI), applied himself single mindedly to this problem. “Every 10 years, I realize some blind spot that I have or some dogma that I’ve accepted,” he says. “I call it ‘raising my consciousness.’”

This time around, raising his consciousness meant looking toward dendrites, the spindly protrusions that neurons use to detect signals, for a completely novel way of thinking about computer chips. And, as he writes in Nature, he thinks he’s figured out how to make chips so efficient that the enormous GPT-3 language prediction neural network could one day be run on a cell phone. Just as Feynman posited the “quantum supremacy” of quantum computers over traditional computers, Boahen wants to work toward a “neural supremacy.”

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Mathematician Bernhard Riemann was born #OTD in 1826.

Bernhard Riemann was another mathematical giant hailing from northern Germany. Poor, shy, sickly and devoutly religious, the young Riemann constantly amazed his teachers and exhibited exceptional mathematical skills (such as fantastic mental calculation abilities) from an early age, but suffered from timidity and a fear of speaking in public. He was, however, given free rein of the school library by an astute teacher, where he devoured mathematical texts by Legendre and others, and gradually groomed himself into an excellent mathematician. He also continued to study the Bible intensively, and at one point even tried to prove mathematically the correctness of the Book of Genesis.

Although he started studying philology and theology in order to become a priest and help with his family’s finances, Riemann’s father eventually managed to gather enough money to send him to study mathematics at the renowned University of Göttingen in 1846, where he first met, and attended the lectures of, Carl Friedrich Gauss. Indeed, he was one of the very few who benefited from the support and patronage of Gauss, and he gradually worked his way up the University’s hierarchy to become a professor and, eventually, head of the mathematics department at Göttingen.

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SparkLabs — an early-stage venture capital firm that has made a name for itself for backing OpenAI as well as a host of other AI startups such as Vectara, Allganize, Kneron, Anthropic, xAI, Glade (YC S23) and Lucidya AI — is gearing up to double down on more startups in the space. The VC firm announced Tuesday that it has closed a new $50 million fund, AIM AI Fund, which will back AI startups out of its own AIM-X accelerator in Saudi Arabia as well as other AI startups across the globe.

SparkLabs’ new fund and its wider investment aims underscore the bigger trends that have swirled around artificial intelligence for the last few years. The explosion of interest in generative AI in particular has led to a surge of startups in the space, as well as a rush of investors looking for the next Open AI — or at the very least, a startup that a bigger company might snap up as it looks to sharpen its own AI edge.

It also points to how the AI opportunity continues to widen beyond Silicon Valley. AIM-X is an AI-focused startup accelerator that SparkLabs launched earlier this year in the kingdom as part of its AI Mission, a national initiative to bolster AI technology over the next five years.

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Finally, the goal of any healthcare organization is to provide the best possible care to patients. Predictive AI can contribute significantly to this goal by enabling more accurate diagnoses, tailored treatment plans and earlier interventions.

From the patient’s perspective, this translates to better health outcomes, reduced hospital stays and increased satisfaction with their care. For healthcare organizations, improved patient experiences lead to higher patient retention rates, positive word-of-mouth referrals and better performance on patient satisfaction metrics, which are increasingly tied to reimbursement rates in many healthcare systems.

As we’ve explored, the benefits of predictive AI extend far beyond improved diagnostics and treatment plans. It’s a catalyst for operational excellence, financial optimization, availability of investments and long-term growth. From resource management to building an authoritative brand, predictive AI touches every aspect of the healthcare business environment.

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“For equities, all attention is now on Nvidia’s earnings release tonight, which has helped to drive significant moves recent quarters,” Deutsche Bank strategists said in a note Wednesday morning. “Bear in mind that Nvidia’s share price is already up +159% on a YTD basis, making it the top performer in the entire S&P 500, and it has risen by more than +1000% since its low in October 2022.”

Traders are also waiting on comments from Atlanta Fed President Raphael Bostic, who’s scheduled to speak after the closing bell. His remarks could provide more guidance on the path of Fed rate cuts this year, with investors pricing in as many as 150 basis points worth of cuts by year-end, according to the CME FedWatch tool.

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Innovative Diagnostic Solutions To Enhance Patient Experiences And Health Provider Decisions — Dr. Deborah Sesok-Pizzini, MD, MBA — Chief Medical Officer & Senior Vice President, Labcorp Diagnostics; Global Head of Quality And Discipline Director, Immunohematology.

Dr. Deborah Sesok-Pizzini, MD, MBA, is Chief Medical Officer And Senior Vice President, Labcorp Diagnostics, and Global Head of Quality And Discipline Director, Immunohematology, Labcorp (https://www.labcorp.com/deborah-sesok…), where she is involved in furthering the company’s initiatives to enhance the patient experience, enable health provider decisions and develop innovative testing solutions.

Dr. Sesok-Pizzini joined Labcorp with over two decades of experience in healthcare, holding multiple appointments with The Children’s Hospital of Philadelphia, including Patient Safety Officer, Chief of the Division of Transfusion Medicine and Vice-Chief of Pathology and Laboratory Medicine. She was also a professor of clinical pathology and laboratory medicine at the Perelman School of Medicine at the University of Pennsylvania in Philadelphia, PA.

Dr. Sesok-Pizzini earned her medical degree from the Penn State College of Medicine and did her residency and fellowship at The University of Pennsylvania’s Perelman School of Medicine. She also graduated from Villanova University, with a Master of Business Administration degree with a concentration in finance.

Dr. Sesok-Pizzini holds certifications in clinical pathology and blood bank and transfusion medicine. She is a member of the College of American Pathology, the American Society of Clinical Pathology, and the Association for the Advancement of Blood and Biotherapies. She is a board member of the Intersociety Council for Pathology Information and serves as an adjunct professor with the University of Pennsylvania’s Perelman School of Medicine.

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Mikey Siegel, with a background in robotics from the MIT Media Lab, shares insights from his decade-long exploration into technology’s role in human well-being and consciousness. He discusses the profound potential of Artificial General Intelligence (AGI) shaped with compassionate and wise values. Siegel emphasizes the importance of the human developmental process in creating benevolent AI and the integration of contemplative practices in tech development. He envisions a future where AGI supports human development globally with love and care, akin to a parent nurturing a child, ultimately fostering a connected and compassionate society.

00:00 Introduction to Mikey Siegel and His Work.
01:09 The Profound Impact of AGI on Humanity.
02:42 The Role of AI in Shaping Reality.
04:06 The Vision of a Compassionate Super Intelligence.
07:26 Creating AI from a Culture of Compassion.
07:51 Integrating Human Development in AI Creation.
09:28 Ownership and Developmental Stages of AI
12:13 Demystifying the Mystical Through Science.
14:53 Preparing for the Future of AI

SingularityNET was founded by Dr. Ben Goertzel with the mission of creating a decentralized, democratic, inclusive, and beneficial Artificial General Intelligence (AGI). An AGI is not dependent on any central entity, is open to anyone, and is not restricted to the narrow goals of a single corporation or even a single country.

The SingularityNET team includes seasoned engineers, scientists, researchers, entrepreneurs, and marketers. Our core platform and AI teams are further complemented by specialized teams devoted to application areas such as finance, robotics, biomedical AI, media, arts, and entertainment.

Website: https://singularitynet.io.
X: https://twitter.com/SingularityNET
Instagram: / singularitynet.io.
Discord: / discord.
Forum: https://community.singularitynet.io.
Telegram: https://t.me/singularitynet.
WhatsApp: https://whatsapp.com/channel/0029VaM8…
Warpcast: https://warpcast.com/singularitynet.
Mindplex Social: https://social.mindplex.ai/@Singulari…
Github: https://github.com/singnet.
Linkedin: / singularitynet.

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Quantum simulation enables scientists to simulate and study complex systems that are challenging or even impossible using classical computers across various fields, including financial modeling, cybersecurity, pharmaceutical discoveries, AI and machine learning. For instance, exploring molecular vibronic spectra is critical in understanding the molecular properties in molecular design and analysis.

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