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Welcome! You are invited to join a webinar: Winning partnerships — Key to 5G Monetization: Evolving Ecosystems to the Metaverse. After registering, you will receive a confirmation email about joining the webinar

Successfully navigating the 5G transformation requires automation every step of the way—from network planning and preparation through implementation and monetization. 5G has made the consumers more empowered and demanding and this creates a need for CSPs to monetize beyond data bundles and introduce indirect monetization mechanisms. What CSPs must now do is look at investing in platforms that enable them to monetize innovative 5G business models.

CSPs have a huge opportunity to create new complex products and solutions for the B2B2C market assembled with the help of multiple partners. But this isn’t just a one-way opportunity. The biggest benefit of this model is the CSPs’ ability to participate in value chains and ecosystems that are orchestrated jointly with partners. CSPs will increasingly use partners to extend owned capabilities across product cocreation, marketing, sales, delivery, and customer support.

Moreover, CSPs need to evolve towards becoming service enablers and partner with businesses, developers, and other players across different domains and industries in order to create unique 5G service offerings to differentiate themselves in the market. To find success in the 5G era, they will need to maintain an ecosystem of partners that allow them to innovate and expand its reach across industry verticals. This will result in automated processes, the ability to launch any partner model, reduced time to market and reduced operational costs.

Matter–antimatter gigaelectron volt gamma ray laser rocket propulsion

face_with_colon_three circa 2012.

It is shown that the idea of a photon rocket through the complete annihilation of matter with antimatter, first proposed by Sänger, is not a utopian scheme as it is widely believed. Its feasibility appears to be possible by the radiative collapse of a relativistic high current pinch discharge in a hydrogen–antihydrogen ambiplasma down to a radius determined by Heisenberg’s uncertainty principle. Through this collapse to ultrahigh densities the proton–antiproton pairs in the center of the pinch can become the upper gigaelectron volt laser level for the transition into a coherent gamma ray beam by proton–antiproton annihilation, with the magnetic field of the collapsed pinch discharge absorbing the recoil momentum of the beam and transmitting it by the Moessbauer effect to the spacecraft. The gamma ray laser beam is launched as a photon avalanche from one end of the pinch discharge channel. Because of the enormous technical problems to produce and store large amounts of anti-matter, such a propulsion concept may find its first realization in small unmanned space probes to explore nearby solar systems. The laboratory demonstration of a gigaelectron volt gamma ray laser by comparison requiring small amounts of anti-matter may be much closer.

Amid ‘biotech winter,’ Insilico turns up the heat with Sanofi deal worth $1.2B in biobucks

Insilico Medicine is radiating heat amid the biotech winter, kindling its fires with a Sanofi collaboration that could be worth up to $1.2 billion in biobucks—the AI drug discovery company’s larges | Insilico Medicine is radiating heat amid the biotech winter, kindling its fires with a Sanofi collab that could be worth up to $1.2 billion in biobucks—the AI drug discovery company’s largest deal to date.

Automated Economies & Unemployment

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Many fear that future automation may turn out to be the bane of civilization rather than its liberator. How do we ensure we take the path to a prosperous world and not one of ruin?

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Credits:
What Happens If We Can’t Leave Earth?
Science & Futurism with Isaac Arthur.
Episode 368, November 10, 2022
Produced & Narrated by Isaac Arthur.

Written By:
Isaac Arthur.

Editors:

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Webinar — How to Build your Career in Machine Learning

Have you struggled to take your career in data or software engineering to the next level?

After working with hundreds of alumni, FourthBrain’s curriculum and career services staff has developed a framework with key strategies that you can implement today to help you find your focus, showcase your unique skills, and take your ML career to the next level.

FourthBrain graduates who are job seeking increase their salary by an average of $27k and 87% land new jobs within 6 months of graduation.

Watch our session with Dr. Greg Loughnane (Director of Curriculum) and James Van (Director of Career Services).

If you have any questions you can reach us at: [email protected].

Good luck on your journey!

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BodyTrak wrist camera constructs 3D models of the body in real time

Wearable technology is capable of tracking various measures of human health and is getting better all the time. New research shows how this could come to mean real-time feedback on posture and body mechanics. A research team at Cornell University has demonstrated this functionality in a novel camera system for the wrist, which it hopes to work into smartwatches of the future.

The system is dubbed BodyTrak and comes from the same lab behind a face-tracking wearable we looked at earlier in the year that is able to recreate facial expressions on a digital avatar through sonar. This time around, the group made use of a tiny dime-sized RGB camera and a customized AI to construct models of the entire body.

The camera is worn on the wrist and relays basic images of body parts in motion to a deep neural network, which had been trained to turn these snippets into virtual recreations of the body. This works in real time and fills in the blanks left by the camera’s images to construct 3D models of the body in 14 different poses.

AI Researchers At Mayo Clinic Introduce A Machine Learning-Based Method For Leveraging Diffusion Models To Construct A Multitask Brain Tumor Inpainting Algorithm

The number of AI and, in particular, machine learning (ML) publications related to medical imaging has increased dramatically in recent years. A current PubMed search using the Mesh keywords “artificial intelligence” and “radiology” yielded 5,369 papers in 2021, more than five times the results found in 2011. ML models are constantly being developed to improve healthcare efficiency and outcomes, from classification to semantic segmentation, object detection, and image generation. Numerous published reports in diagnostic radiology, for example, indicate that ML models have the capability to perform as good as or even better than medical experts in specific tasks, such as anomaly detection and pathology screening.

It is thus undeniable that, when used correctly, AI can assist radiologists and drastically reduce their labor. Despite the growing interest in developing ML models for medical imaging, significant challenges can limit such models’ practical applications or even predispose them to substantial bias. Data scarcity and data imbalance are two of these challenges. On the one hand, medical imaging datasets are frequently much more minor than natural photograph datasets such as ImageNet, and pooling institutional datasets or making them public may be impossible due to patient privacy concerns. On the other hand, even the medical imaging datasets that data scientists have access to could be more balanced.

In other words, the volume of medical imaging data for patients with specific pathologies is significantly lower than for patients with common pathologies or healthy people. Using insufficiently large or imbalanced datasets to train or evaluate a machine learning model may result in systemic biases in model performance. Synthetic image generation is one of the primary strategies to combat data scarcity and data imbalance, in addition to the public release of deidentified medical imaging datasets and the endorsement of strategies such as federated learning, enabling machine learning (ML) model development on multi-institutional datasets without data sharing.

My Robot Wife

My AI Girlfriend won’t talk to me unless I renew my annual Netflix subscription.

— You in five years

Everyone has written about the dangers of AI and the uncertain future of humanity, and many of these worries focus on large scale issues like disinformation, democracy, wartime decision making by computers, etc. However, it is the small and personal changes to human life that tend to create the biggest effects down the line. If we assume that a sizeable portion of the population will have, at some point, some form of AI assistant, friend, companion, etc. and that these AI assistants are designed by for-profit companies to perfectly press our psychological buttons, then we are in serious danger of handing ourselves over to the whims of those companies, or governments.

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