Lifeboat Foundation

Taiwan has been the world’s hardware hub for decades, so the shift toward AI makes the most of the existing inexpensive engineering talent. A refocus on AI, however, reduces reliance on hardware, which can easily be made somewhere else, such as China, at lower costs. Multinational tech companies have already shown interest in tapping Taiwan’s talent in software, including AI.

To move things along further, the government of Hsinchu County, near Taipei, will open a 126,000-square-meter (about 1.3 million square feet) AI business park near one of Taiwan’s major all-purpose high-tech zones and two top universities.

“[The park] will not just help [promote] industry-academia cooperation, but also let AI-oriented startups and companies have a demo space to verify AI product services,” says Shirley Tsai, a research manager with IDC Taiwan’s enterprise solution group. “It will be helpful as well to attract the companies who are interested in the AI field and then accelerating the AI ecosystem.”

Drug development is an extremely arduous and costly process, and failure rates in clinical trials that test new drugs for their safety and efficacy in humans remain very high. According to current estimates, only 13.8% of all tested drugs demonstrate ultimate clinical success and obtain approval by the Food and Drug Administration (FDA). There are also increasing ethical concerns relating to the use of animal studies. As a result, there has been a world-wide search to find replacements for animal models.

To help address this bottleneck in drug development, Donald Ingber, M.D., Ph.D., and his team at Harvard’s Wyss Institute for Biologically Inspired Engineering, developed the first human “Organ-on-a-Chip” (Organ Chip) model of the lung that recapitulates human organ level physiology and pathophysiology with high fidelity, which was reported in Science in 2010. Organ Chips are microfluidic culture devices composed of a clear flexible polymer the size of a computer memory stick, which contains two parallel hollow channels that are separated by a porous membrane. Organ-specific cells are cultured on one side of the membrane in one of the channels, and vascular endothelial cells recapitulating a blood vessel line the other, while each channel is independently perfused with cell type-specific medium.

CS230 | Deep Learning

https://www.newworldai.com/cs230-deep-learning-stanford-engineering/

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Researchers have developed a new Artificial Intelligence (AI)-based technique that can detect low-sugar levels from raw ECG signals via wearable sensors without any fingerprint test. Current methods to measure glucose requires needles and repeated fingerpicks over the day. Fingerpicks can often be painful, deterring patient compliance.

The new technique developed by researchers at University of Warwick works with an 82 per cent reliability, and could replace the need for invasive finger-prick testing with a needle, especially for kids who are afraid of those.

“Our innovation consisted in using AI for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping,” said Dr Leandro Pecchia from School of Engineering in a paper published in the Nature Springer journal Scientific Reports.

As the U.S. Army increasingly uses facial and object recognition to train artificial intelligent systems to identify threats, the need to protect its systems from cyberattacks becomes essential.

An Army project conducted by researchers at Duke University and led by electrical and computer engineering faculty members Dr. Helen Li and Dr. Yiran Chen, made significant progress toward mitigating these types of attacks. Two members of the Duke team, Yukun Yang and Ximing Qiao, recently took first prize in the Defense category of the CSAW ‘19 HackML competition.

“Object recognition is a key component of future intelligent systems, and the Army must safeguard these systems from cyberattacks,” said MaryAnne Fields, program manager for intelligent systems at the Army Research Office. “This work will lay the foundations for recognizing and mitigating backdoor attacks in which the data used to train the object recognition system is subtly altered to give incorrect answers. Safeguarding object recognition systems will ensure that future Soldiers will have confidence in the intelligent systems they use.”

Holly Jean Buck is a fellow at UCLA’s Institute of the Environment and Sustainability. This is an adapted excerpt from her upcoming book After Geoengineering: Climate Tragedy, Repair, and Restoration (September 2019, Verso Books).

Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science Advances Tyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.

The map indicated the possibility of training physical wave systems to learn complex features in temporal data using standard training techniques used for neural networks. As proof of principle, they demonstrated an inverse-designed, inhomogeneous medium to perform English vowel classification based on raw audio signals as their waveforms scattered and propagated through it. The scientists achieved performance comparable to a standard digital implementation of a recurrent neural network. The findings will pave the way for a new class of analog machine learning platforms for fast and efficient information processing within its native domain.

The recurrent neural network (RNN) is an important machine learning model widely used to perform tasks including natural language processing and time series prediction. The team trained wave-based physical systems to function as an RNN and passively process signals and information in their native domain without analog-to-digital conversion. The work resulted in a substantial gain in speed and reduced power consumption. In the present framework, instead of implementing circuits to deliberately route signals back to the input, the recurrence relationship occurred naturally in the time dynamics of the physics itself. The device provided the memory capacity for information processing based on the waves as they propagated through space.

Coming soon to crowd suppression near you…

30 upgraded KARGU (Autonomous Tactical Multi-Rotor Attack UAV) kamikaze drones developed by Turkish defense contractor Defense Technologies Engineering and Trade Inc. (STM) will join the Turkish Armed Forces’ inventory as of 2020 to take part in critical operations in the country’s east and along the Syrian border.

The KARGU battle drone, which was developed by the STM to support the tactical and field needs of Turkish security forces, eliminates targets more efficiently with new features such as enhanced ammo capacity and improved accuracy. The 30 drones will also have the capacity to destroy an entire brigade and warship.

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STEM Bootstrapping in Bio-Medicine! — On this recent ideaXme (https://radioideaxme.com/) episode, I was joined by 24 year old Malawian inventor, Sanga Marcarios Kanthema, founder and CEO of two companies, Dolphin Health Innovations and QubiX Robotics, who’s bringing health tech innovations to one of the world’s poorest countries — #Ideaxme #Malawi #Robotics #EKG #Stethoscope #Prosthetics #MobileHealth #SmartPhones #Telemedicine #MedicalDrones #Health #Wellness #Longevity #IraPastor #Bioquark #Regenerage

Ira Pastor, ideaXme exponential health ambassador and founder of Bioquark, interviews Sanga Kanthema, 24 year old electronics specialist and founder and CEO of two Malawi-based companies, Dolphin Health Innovations and QubiX Robotics.

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