New brain map could improve AI algorithms for machine vision


IMAGE: By analyzing digital images of marmoset brains injected with neuronal tracers (indicated by the arrows), Cold Spring Harbor Laboratory researchers discovered that the primate's visual system worked differently than previously... view more Despite years of research, the brain still contains broad areas of unchartered territory. A team of scientists, led by neuroscientists from Cold Spring Harbor Laboratory and University of Sydney, recently found new evidence revising the traditional view of the primate brain's visual system organization using data from marmosets. This remapping of the brain could serve as a future reference for understanding how the highly complex visual system works, and potentially influence the design of artificial neural networks for machine vision. In the quest of the whole-brain connectivity in marmosets, the team found that parts of the primate visual system may work differently than previously thought. Mapping out how distinct types of cells connect can help researchers understand how groups of cells play in concert to relay and process sensory information from the outside environment to the brain.

Healthcare Needs AI, AI Needs Causality


AI should be built on rigorous knowledge... Note: This is a follow-up to an earlier article on causal machine learning, "AI Needs More Why". There's much to be excited about with artificial intelligence (AI) in healthcare: Google AI is improving the workflow of clinicians with predictive models for diabetic retinopathy [2], many new approaches are achieving expert-level performance in tasks such as classification of skin cancer [3], and others surpassing the capabilities of doctors -- notably the recent report of DeepMind's AI for predicting acute kidney disease, capable of detecting potentially fatal kidney injuries 48 hours before symptoms are recognized by doctors [4]. Yet medical practitioners and researchers at the intersection of machine learning (ML) and medicine are quick to point out these successes are not representative of the more nuanced, non-trivial challenges presented by medical research and clinical applications. These ML success stories (notably all deep learning) are disease prediction problems, learning patterns that map well-defined inputs to well-labeled outputs [5]. Domains where instinctive pattern recognition works powerfully are what psychologist Robin Hogarth termed "kind learning environments" [6].

Will we ever control the world with our minds?


Science-fiction can sometimes be a good guide to the future. In the film Upgrade (2018) Grey Trace, the main character, is shot in the neck. His wife is shot dead. Trace wakes up to discover that not only has he lost his wife, but he now faces a future as a wheelchair-bound quadriplegic. He is implanted with a computer chip called Stem designed by famous tech innovator Eron Keen – any similarity with Elon Musk must be coincidental – which will let him walk again.

Artificial Intelligence (AI) Stats News: AI Augmentation To Create $2.9 Trillion Of Business Value


The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington

An Electronic Chip That Makes 'Memories' Is A Step Towards Creating Bionic Brains - Liwaiwai


What better way to build smarter computer chips than to mimic nature's most perfect computer – the human brain? Being able to store, delete and process information is crucial for computing, and the brain does this extremely efficiently. Our new electronic chip uses light to create and modify memories, moving us closer towards artificial intelligence (AI) that can replicate the human brain's sophistication. To develop this, we drew inspiration from a new technique called optogenetics, to develop a device that replicates the way the brain stores (and loses) information. Optogenetics involves using light to control cells in living tissue, typically nerve cells (neurons).

AR AI model can objectively identify, locate pain in real time


Machine learning, combined with neuroimaging data, has the potential to objectively determine whether a patient is suffering pain and where it's located. Accurate pain assessment is critical to provide proper diagnosis and treatment, medical experts agree. However, it's difficult to quantify pain, and most assessments are subjective. Subjective assessments are inconsistent and can't be used when a patient can't communicate, such as during surgery. They're also of limited value in understanding the neurophysiological processes underlying different types of pain.

Robots at conference in China can fly, swim and even do brain surgery

Daily Mail - Science & tech

Cutting-edge robots are on display at the 2019 World Robot Conference in Beijing, running from August 20 to 25, are expected to attract nearly 200 guests from 22 countries. The conference features a series of exhibition areas for new robotic technologies and products - including medical, multi-legged, and smart logistics - as well as four contests with an anticipated 4,500 professional participants. Over 700 robots specialising with more than 21 industrial applications will be exhibited between now and the close of the conference. Among those exhibiting will be HRG Robotics, whose, president Wang Meng, said: 'We will be showcasing a string of successful companies which have got off the ground through the help of HRG, alongside our representative products at WRC 2019, as we aim to form new partnerships with companies around the world.' Also on display will be SmartBird, created by German firm Festo, whose design was inspired by the herring gull and whose flight mimics that of the bird.

Artificial Intelligence (AI) to Diagnose and Treat Autism


Manatee, a Denver startup specializing in AI apps for people with autism, is working with a company called Robauto to developing a robot called BiBli that can talk children through challenging interactions without judgment--at the child's own pace. Manatee co-founder and CEO Damayanti Dipayana recognizes both the benefits and limitations of a technology like BiBli: "I don't think AI can provide all kinds of therapy, but it's a scalable way to provide care for kids who wouldn't get care," she tells Verywell. Many kids with autism or anxiety disorder find it easier to talk with the screen or the robot. In the long run, the information collected by a robot or app can be analyzed and shared with a therapist to provide a therapist with insight into what issues are challenging."

Three Invaluable Ways AI and Neuroscience Are Driving Each Other Forward


DeepMind's Demis Hassabis once pointed to the human brain as a paramount inspiration for building AI with human-like intelligence. The meteoric success of deep learning showcases how insights from neuroscience--memory, learning, decision-making, vision--can be distilled into algorithms that bestow silicon minds with a shadow of our cognitive prowess. This month, the prestigious journal Nature published an entire series highlighting the symbiotic growth between neuroscience and AI. It's been a long time coming. At their core, both disciplines are solving the same central problem--intelligence--but coming from different angles, and at different levels of abstraction.