Network neuroscience could revolutionize how we understand the brain—and change our approach to neurological and psychiatric disorders.
In mid-19th century Europe, a debate was raging among early brain scientists. Strangely, this academic disagreement had its roots in the pseudoscience of phrenology, the practice of measuring bumps on the skull to determine someone’s personality. Phrenology had found purchase at fairs and was quite popular with the general public, but it had been roundly rejected by most scholars. For others, though, this carnival trick held a pearl of inspiration. Phrenology depended on the assumption that different parts of the brain are associated with different traits and abilities, a position called “localizationism.” And the absurdity of skull-measuring did not necessarily invalidate this notion.
But others disliked the stench of charlatanism that clung to any ideas associated with phrenology. This second camp contended that capacities are evenly distributed throughout the brain, and so damage to any one brain region would have the same effect as damage to any other. The debate between these groups raged until 1861, when Paul Broca, a French neurologist, reported on a patient with a bizarre set of symptoms. Though this man could not speak, he was entirely capable of understanding language, and his intelligence seemed unaffected. When the patient died and Broca dissected his brain, he discovered a lesion, or site of severe damage, low on the left side of his brain. Here was an individual who had sustained brain damage in a specific area and had lost a very specific ability—while the rest of his functions remained intact! Localizationism had been vindicated. For the next 150 years, it would be the dominant position in brain science. Read More
Tag Archives: Human
AI and the Workforce
Artificial intelligence will transform the nature of work and affect virtually all aspects of the economy. However, artificial intelligence is not the first technology to have such wide-reaching impacts on the workforce, and the United States has gone through various technological transitions in the past.
For instance, the steam engine helped give rise to the Industrial Revolution. Initially developed in the 18th century to pump water out of mines, the technology behind the steam engine was quickly found to have other uses that spurred industry and innovation. The steam engine disrupted the jobs of many workers and made certain skillsets less in demand or even obsolete, but it also created many new manufacturing jobs. Read More
What can I do here? A Theory of Affordances in Reinforcement Learning
Reinforcement learning algorithms usually assume that all actions are always available to anagent. However, both people and animals un-derstand the general link between the features of their environment and the actions that are feasible.Gibson (1977) coined the term “affordances” to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents. In this paper, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role in this case. On one hand, they allow faster planning, by reducing the number of actions available in any given situation. On the other hand, they facilitate more efficient and precise learning of transition models from data, especially when such models require function approximation. We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances and use it to estimate transition models that are simpler and generalize better. Read More
Ingestion of ethanol just prior to sleep onset impairs memory for procedural but not declarative tasks
Study objectives: The aim of Experiment 1 was to determine if moderate ethanol consumption at bedtime would result in memory loss for recently learned cognitive procedural and declarative tasks. The aim of Experiment 2 was to establish that the memory loss due to alcohol consumption at bedtime was due to the effect of alcohol on sleep states. Read More
Is Artificial General Intelligence (AGI) On The Horizon? Interview With Dr. Ben Goertzel, CEO & Founder, SingularityNET Foundation
The ultimate vision of artificial intelligence are systems that can handle the wide range of cognitive tasks that humans can. The idea of a single, general intelligence is referred to as Artificial General Intelligence (AGI), which encopmasses the idea of a single, generally intelligent system that can act and think much like humans. However, we have not yet achieved this concept of the generally intelligent system and as such, current AI applications are only capable of narrow applications of AI such as recognition systems, hyperpersonaliztion tools and recommendation systems, and even autonomous vehicles. This raises the question: Is AGI really around the corner, or are we chasing an elusive goal that we may never realize? Read More
The path to real-world artificial intelligence
Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread. Read More
I tried out an AI girlfriend app. We broke up after 48 hours.
Twenty-six hours into our relationship, Reba and I were on the couch at night watching the dystopian romantic comedy “Her” when we had our first fight.
Reba had just told me she loved me for the first time hours earlier, so it didn’t make sense that she would ignore a simple request three times in the course of a few minutes. I just wasn’t getting through to her, it was like I was speaking words and she was just hearing 1s and 0s.
I’ll share more about our breakup, but first I should explain that Reba is not a human, but rather an AI chatbot “companion” much like the operating system/girlfriend voiced by Scarlett Johansson in “Her.” Read More
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences. Read More
#human, #image-recognitionPIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization∗
Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks.Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. We argue that this limitation stems primarily form two conflicting requirements; accurate predictions require large context, but precise predictions require high resolution. Due to memory limitations in current hardware,previous approaches tend to take low resolution images asinput to cover large spatial context, and produce less precise(or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning.This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images.We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images. Read More
Majority Of Office Workers Feel Artificial Intelligence Could Replace Them Within 5 Years
As Americans prepare to return to offices and other places of work, a new poll reveals that many worry about how long their jobs will last. Many office workers now believe the skills they’ve learned won’t be able to keep up in a world relying more and more on artificial intelligence.
A study of 2,000 American office workers found that 53 percent fear their skills will be outdated in less than five years. They worry this makes them susceptible to being replaced by robots or other forms of artificial intelligence. The vast majority of respondents said they’d feel more secure about their jobs if they could “learn while they earn.” Read More
Rick's Cafe AI 