On a chilly evening last fall, I stared into nothingness out of the floor-to-ceiling windows in my office on the outskirts of Harvard's campus. As a purplish-red sun set, I sat brooding over my dataset on rat brains. I thought of the cold windowless rooms in downtown Boston, home to Harvard's high-performance computing center, where computer servers were holding on to a precious 48 terabytes of my data. I have recorded the 13 trillion numbers in this dataset as part of my Ph.D. experiments, asking how the visual parts of the rat brain respond to movement. Printed on paper, the dataset would fill 116 billion pages, double-spaced. When I recently finished writing the story of my data, the magnum opus fit on fewer than two dozen printed pages. Performing the experiments turned out to be the easy part. I had spent the last year agonizing over the data, observing and asking questions. The answers left out large chunks that did not pertain to the questions, like a map leaves out irrelevant details of a territory.
Example of a longitudinal comparison between radiologist-segmented MS lesions and ANN-segmented MS lesions, showing a good overlap between the two volumes. At the MRI follow-up of August 2016, the patient showed a large new lesion with a faint central contrast enhancement (both indicated by the arrows in FLAIR and T1 after contrast, respectively), which the ANN correctly detected. The lesion then regressed under therapy and was unremarkable at the next follow-up. While underestimating the volumetric size of the lesions, the ANN consistently reproduced the volumetric trend of the follow-up for this patient.
As most scientists will tell you, we are still decades away from building artificial general intelligence, machines that can solve problems as efficiently as humans. On the path to creating general AI, the human brain, arguably the most complex creation of nature, is the best guide we have. Advances in neuroscience, the study of nervous systems, provide interesting insights into how the brain works, a key component for developing better AI systems. Reciprocally, the development of better AI systems can help drive neuroscience forward and further unlock the secrets of the brain. For instance, convolutional neural networks (CNN), one of the key contributors to recent advances in artificial intelligence, are largely inspired by neuroscience research on the visual cortex.
The 38 years old Finnish science fiction author, along with data scientist friend Samuel Halliday, got his hands on a simple wearable brain scanner and started wondering how he could use the technology to tell more engaging stories. So in 2012, they came up with a story that could be read wearing the wireless headset, and branch and change depending on whether the reader showed more affinity for life or death imagery. Think of it as a modern version of the text-only interactive games of the late 70's, or a Choose Your Own Adventure eBook, but where your brain's electrical activity determines the choices. The project has been open-sourced to encourage innovation, meaning with a $400 piece of hardware, some machine learning and writing skills, everyone can venture into the depths of the design space created by emerging brain-computer interface technologies. While there is a lot of fuss these days around whether we can make artificial intelligence (or AI) truly intelligent, giving'brains' to machines might not always be enough.
Tech these days is often accused of encouraging forms of addiction, but emerging "cyborg" technology may offer an answer for treating the opioid epidemic. Embedding microchips in the brains of addicts could help to, essentially, rewire them. He's among millions of people in America affected by what has become a national plague that kills hundreds each day. He hopes, though, that the computer chip in his brain can break him from addiction's hold. His dependence took hold after he dislocated his shoulder when he was 15.
"Taken together, [studies show] internet addiction is associated with structural and functional changes in brain regions involving emotional processing, executive attention, decision making, and cognitive control." But what about kids who aren't "addicted" per se? Addiction aside, a much broader concern that begs awareness is the risk that screen time is creating subtle damage even in children with "regular" exposure, considering that the average child clocks in more than seven hours a day (Rideout 2010). As a practitioner, I observe that many of the children I see suffer from sensory overload, lack of restorative sleep, and a hyperaroused nervous system, regardless of diagnosis--what I call electronic screen syndrome. These children are impulsive, moody, and can't pay attention--much like the description in the quote above describing damage seen in scans.
A Phoenix laboratory is trying to stop Alzheimer's disease by using artificial intelligence. Arizona has the fastest growing rate of Alzheimer's disease in the country. According to a 2018 report released by the Alzheimer's Association, in the next few years, the number of people living with the disease in Arizona is expected to increase by 43 percent. Sonora Quest Laboratories created the RestoreU Method, a program for people experiencing memory loss, cognitive impairment or dementia. The lab partnered with uMETHOD Health in North Carolina to identify and address underlying causes of cognitive decline tailored to each individual patient.
DeepMind has been trying to bridge the gap between AI and biology for quite some time now. All their endeavours revolve around solving the problem of intelligence in machines. The straightforward trivial tasks for humans can be very, very sophisticated and almost for devices. While human brains are hardcoded with millions of years of learning, the machines have many limitations when it comes to data. They can be fed with data that has been documented or prepared by humans, the magnitude of which is historically insignificant when compared to humans.
Artificial Intelligence (AI) is more linked to dopamine-reinforced learning than you may think. That's a mouthful, so for now just think of Pavlov's dog study. DeepMind AI published a blog post on their discovery that the human brain and AI learning methods are closely linked when it comes to learning through reward. Their findings were also published in the journal Nature on Wednesday. It's been a well-known fact for a while now that we humans, and many animals, learn through reward.
It is not often that one witnesses a transformational advance in medicine. But the application of artificial intelligence (AI) to improve the early detection of disease is exactly that. I was a co-author of the paper recently published in Nature showing that an AI system developed by Google was better at spotting breast tumours than doctors. Now, researchers in the US have reported that AI-supported laser scanners are faster than doctors at detecting brain tumours. These are very exciting developments that will, ultimately, have a big impact on the accuracy, logistics and speed of diagnosis.