Lifeboat Foundation

The is ideal for deep learning application developers and data scientists seeking a faster and efficient deep learning training workflow for various industry verticals such as Intelligent Video Analytics (IVA) and Medical Imaging. Transfer Learning Toolkit abstracts and accelerates deep learning training by allowing developers to fine-tune NVIDIA provided pre-trained models that are domain specific instead of going through the time-consuming process of building Deep Neural Networks (DNNs) from scratch. The pre-trained models accelerate the developer’s deep learning training process and eliminate higher costs associated with large scale data collection, labeling, and training models from scratch.

The term “transfer learning” implies that you can extract learned features from an existing neural network and transfer these learned features by transferring weights from an existing neural network. The Transfer Learning Toolkit is a Python based toolkit that enables developers to take advantage of NVIDIA’s pre-trained models and offers technical capabilities for developers to add their own data to make the neural networks smarter by retraining and allowing them to adapt to the new network changes. The capabilities to simply add, prune and retrain networks improves the efficiency and accuracy for deep learning training workflow.

Last year, my brother, then an employee at Silicon Valley-based tech company Nvidia, declared that all the AI and deep learning that is happening in healthcare is being powered by Nvidia’s graphics processing units (GPUs)…however that’s just the tip of the iceberg.

Nvidia holds a dominant position in terms of making the chips that power artificial intelligence projects, but can the Silicon Valley tech company with roots in the world of gaming and graphics succeed in healthcare?

By Arundhati Parmar

Continue reading “Meet the ‘preeminent AI company on earth,’ but can it succeed in healthcare?” »

Korean researchers have been experimenting further in the bioprinting of tracheal implants, publishing recent results in ‘Trachea with Autologous Epithelial Cells and Chondrocytes.’ The team of scientists details their use of polycaprolactone and hydrogel mixed with nasal epithelial and auricular cartilage cells.

After bioprinting an artificial trachea with these materials and tissue, they transplanted them into 15 rabbits, six of which were a control group. The goal was to find a way to overcome tracheal problems due to tumors, the most common of which are adenoid cystic carcinomas and squamous cell carcinomas. Previously there have been substantial challenges in creating viable tracheas that are anatomically correct and can produce a ciliated epithelium. Issues have arisen with infection, implants that become dislodged, have migrated, or experienced obstruction.

Apple Inc. is said to be testing a sleep monitor for a future version of its smartwatch, a feature that would bolster the company in the health- and fitness-tracking market.

Over the last three years, Google and Verily—Alphabet’s life sciences and healthcare arm—have developed a machine learning algorithm to make it easier to screen for disease, as well as expand access to screening for DR and DME. As part of this effort, we’ve conducted a global clinical research program with a focus on India. Today, we’re sharing that the first real world clinical use of the algorithm is underway at the Aravind Eye Hospital in Madurai, India.

Google and Verily share updates to their initiative to diagnose diabetic eye disease leveraging machine learning.

Neuroscientists have used an artificial intelligence to read brain signals resulting in intelligible, recognizable, audible speech for the first time ever.

Meanwhile, LIVE in Hollywood, FL today.

Brian Manning Delaney will join us with a presentation on “Navigating the Labyrinth of Dietary Restriction Methods.“
Followed with an Age Reversal Update by William Faloon.

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“A software tool called CaImAn automates the arduous process of tracking the location and activity of neurons. It accomplishes this task using a combination of standard computational methods and machine-learning techniques. In a new paper, the software’s creators demonstrate that CaImAn achieves near-human accuracy in detecting the locations of active neurons based on calcium imaging data.”

Journal Article: https://www.simonsfoundation.org/…/caiman-calcium-imaging-…/

D-Wave, the well-funded quantum computing company, today announced its next-gen quantum computing platform with 5,000 qubits, up from 2,000 in the company’s current system. The new platform will come to market in mid-2020.

The company’s new so-called Pegasus topology connects every qubit to 15 other qubits, up from six in its current topology. With this, developers can use the machine to solve larger problems with fewer physical qubits — or larger problems in general.

It’s worth noting that D-Wave’s qubits are different from those of the company’s competitors like Rigetti, IBM and Google, with shorter coherence times and a system that mostly focuses on solving optimization problems. To do that, D-Wave produces lots of qubits, but in a relatively high-noise environment. That means you can’t compare D-Wave’s qubit count to that of its competitors (with D-Wave claiming the superiority of its machine for certain problems), which are building universal quantum computers.

Continue reading “D-Wave announces its next-gen quantum computing platform” »