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I rarely use the words transformative or breakthrough for neuroscience findings. The brain is complex, noisy, chaotic, and often unpredictable. One intriguing result under one condition may soon fail for a majority of others. What’s more, paradigm-shifting research trends often require revolutionary tools. When we’re lucky, those come once a decade.

But I can unabashedly say that the 2010s saw a boom in neuroscience breakthroughs that transformed the field and will resonate long into the upcoming decade.

In 2010, the idea that we’d be able to read minds, help paralyzed people walk again, incept memories, or have multi-layered brain atlases was near incomprehensible. Few predicted that deep learning, an AI model loosely inspired by neural processing in the brain, would gain prominence and feed back into decoding the brain. Around 2011, I asked a now-prominent AI researcher if we could automatically detect dying neurons in a microscope image using deep neural nets; we couldn’t get it to work. Today, AI is readily helping read, write, and map the brain.

We could essentially use deep learning to get to the theory of everything if we digitize all processes.


By Rajat Jain, SAP

No single technology can ever replace humans and their unique value. Yet, the addition of hyperautomation is opening a world of new possibilities for the strategic nature of the employee experience – turning highly manual, labor-intensive tasks into nearly no-touch, rules-based processes.

Unquestionably, hyperautomation brings businesses closer to their vision of an intelligent enterprise that is customer-centric and operationally efficient. But first, innovation teams will need to rethink how they will deliver this technology capability. According to McKinsey, 70% of such complex and large digital initiatives do not achieve their stated goals. So it’s no wonder that only 6% of executives are satisfied with the performance of these efforts.

The COVID-19 pandemic has put an incredible strain on global supply chains, from medical supplies to household goods, as spikes in demand stress-test logistics infrastructures. There is an opportunity for unmanned delivery vehicles to assist in addressing this demand and help to reduce the risk of spreading infection.

Here’s a look at some of the challenges and opportunities for automated vehicles (AVs) in last-mile deliveries and local logistics.

Starship Technologies has launched a robot food delivery service in Tempe, Ariz., as part of the autonomous delivery startup’s expansion plans following a $40 million funding round announced last August.

Starship Technologies, which was launched in 2014 by Skype co-founders Ahti Heinla and Janus Friis, has been ramping up commercial services in the past year, including a plan to expand to 100 universities by late summer 2021.

Now, with the COVID-19 pandemic forcing traditional restaurants to close and placing more pressure on gig economy workers, Starship Technologies has an opportunity to accelerate that growth.

Chip maker Intel has been chosen to lead a new initiative led by the U.S. military’s research wing, DARPA, aimed at improving cyber-defenses against deception attacks on machine learning models.

Machine learning is a kind of artificial intelligence that allows systems to improve over time with new data and experiences. One of its most common use cases today is object recognition, such as taking a photo and describing what’s in it. That can help those with impaired vision to know what’s in a photo if they can’t see it, for example, but it also can be used by other computers, such as autonomous vehicles, to identify what’s on the road.

But deception attacks, although rare, can meddle with machine learning algorithms. Subtle changes to real-world objects can, in the case of a self-driving vehicle, have disastrous consequences.

#Technology in #medicine: What will the #future #healthcare be like? https://www.neurozo-innovation.com/post/future-health Technologies have made many great impacts on our medical system in recent years. The article will first give a thorough summarization of them, and then the expectations and potential problems regarding future healthcare will be discussed. #AI #5G #VR #AR #MR #3DPrinting #BrainComputerInterface #telemedicine #nanotechnology #drones #SelfDriving #blockchain #robotics #innovation #trend


Technology has many beneficial effects on modern people’s lives, and one of them is to prolong our lifespan through advancing the medical field. In the past few years, new techniques such as artificial intelligence, robots, wearable tech, and so on have been used to improve the quality of our healthcare system, and some even newer innovations such as flying vehicles and brain computer interface are also considered valuable to the field. In this article, we will first give a thorough discussion about how these new technologies will shape our future healthcare, and then some upcoming problems that we may soon face will be addressed.

Focus is on Physical Sciences Research and Management of Complex Systems

WASHINGTON, D.C. — Today, the U.S. Department of Energy (DOE) announced a plan to provide up to $30 million for advanced research in machine learning (ML) and artificial intelligence (AI) for both scientific investigation and the management of complex systems.

The initiative encompasses two separate topic areas. One topic is focused on the development of ML and AI for predictive modeling and simulation focused on research across the physical sciences. ML and AI are thought to offer promising new alternatives to traditional programming methods for computer modeling and simulation.