Topol is a dreamer. “One can imagine that AI will rescue medicine from all that ails it, including diagnostic inaccuracy,” he writes. (There are roughly 12 million misdiagnoses of serious illness in the United States every year, and medical error kills a quarter-million Americans annually.) But even Topol admits that this hope is far from being actualized. Indeed.
Cardiologist Eric Topol explores the benefits of artificial intelligence in medicine.
A recent news topic that raised some eyebrows was the revelation that the human brain can literally reprogram itself. The brain is such a complex piece of hardware that there’s practically no way we’ll ever understand it without using a greater intelligence. This is one of the reasons why the emergence of artificial intelligence is so disruptive. Startups like Kernel are actually using AI to “read/write” long term memories directly from the brain. Amazing technological advances like these should adorn the front page of every news paper out there. Instead, we opt for mind-numbing “facts” like this:
As a nod to World Mental Health Day today, what we’d like to do is put together an exhaustive list of every single neurotech related company out there. To get this started, let’s turn towards an article published on the Crunchbase blog which lists 23 different neurotech companies (it’s actually 22 because one was an impostor). To that list, we then added 7 names from our own research. What we end up with is a list of 29 different neurotech companies (the companies we’ve already covered are linked to the relevant articles on this site below):
A solution that enables industrial robots to scan and manipulate metallic objects that are too “shiny” for machine vision to process has been developed by Southwest Research Institute (SwRI) and ROS-Industrial, the industry consortium initiated by SwRI in 2012 to support cost-shared applied R&D for advanced factory automation. The project integrates intelligent part reconstruction using the second generation of the Robot Operating System (ROS2) framework to improve 3D image perception when robots autonomously sand and finish parts.
We’ve seen autonomous aircraft doing everything from spraying crops to surveying wildlife, and now the Swiss air rescue organization Rega has announced a drone that’s capable of searching for and finding missing people all on its own.
“The singularity for this level of the simulation is coming soon,” Musk replied to a tweet by the official Twitter account of the television show Rick and Morty in 2017. “I wonder what the levels above us look like.”
READ MORE: Elon Musk Reveals the One Question He Would Ask a Human-Level A.I. [Inverse]
More on the simulation hypothesis: MIT Prof: It’s More Likely We’re Living in a Simulation Than Not.
Lidar, the radar-style detection system which works by bouncing laser light, is most commonly associated with self-driving cars. However, it may have another useful, albeit morbid, application: Helping find bodies which have been buried in unmarked graves.
Sound like something out of an episode of CSI? In fact, it’s a new piece of research coming out of Tennessee’s Oak Ridge National Laboratory, where scientists have been investigating how lidar could be used a forensics tool to find missing murder victims — potentially even from an aircraft.
“Missing persons investigations pose a significant societal challenge, as well as a time-sensitive technological challenge,” Dr. Katie Corcoran, one of the researchers on the project, told Digital Trends. “Of the millions of missing persons worldwide who are unaccounted for, some are thought to be deceased and buried in unmarked graves. A gravesite can go unnoticed because of natural processes, where the site becomes covered with grass or leaves, for instance. Or the site could have been deliberately masked by a perpetrator trying to hide the body. In either case, the longer the gravesite goes unnoticed, the more difficult it is to locate.”
Humanity could be on the verge of an unprecedented merging of human biology with advanced technology, fusing our thoughts and knowledge directly with the cloud in real-time – and this incredible turning point may be just decades away, scientists say.
In a new research paper exploring what they call the ‘human brain/cloud interface’, scientists explain the technological underpinnings of what such a future system might be, and also address the barriers we’ll need to address before this sci-fi dream becomes reality.
At its core, the brain/cloud interface (B/CI) is likely to be made possible by imminent advances in the field of nanorobotics, proposes the team led by senior author and nanotechnology researcher Robert Freitas Jr from the Institute for Molecular Manufacturing in California.
News-Medical speaks to David Dambman from Biosero about the emerging importance of automation in scientific research and how a centralized scheduling software is an essential first step for any laboratory looking to automate their workflow.
Why has automation become so critical to advancing scientific research?
There are many reasons why automation is useful in scientific research. First and foremost, automation is about being able to walk away from your experiments and spend time analyzing your results, rather than carrying out mundane tasks such as transferring liquids from one plate to another.
Training bigger neural networks can be challenging when faced with accelerator memory limits. The size of the datasets being used by machine learning models is very large nowadays. For example, a standard image classification datasets like hashtagged Instagram contains millions of images. With the increasing quality of the images, the memory required will also increase. Today, the memory available on NVIDIA GPUs is only 32 GB.
Therefore, there needs to be a tradeoff between memory allocated for the features in a model and how the network gets activated. It is only understandable why the accelerator memory limit needs to be breached.
