Eighteen years later, pregnant with my first child, I started eating fish. Oily fish in particular contains plenty of long-chain omega-3 fatty acids, essential for neural development.
Alice Roberts
A single neuron in the brain is an incredibly complex machine that even today we don't understand. A single 'neuron' in a neural network is an incredibly simple mathematical function that captures a minuscule fraction of the complexity of a biological neuron.
Andrew Ng
I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.
I just thought making machines intelligent was the coolest thing you could do. I had a summer internship in AI in high school, writing neural networks at National University of Singapore - early versions of deep learning algorithms. I thought it was amazing you could write software that would learn by itself and make predictions.
In my books the technology that I choose to talk about has to serve the themes. What that means is that I end up having to cut out a lot of cool technology that would be really fun to describe and play with, but which would just confuse everybody. So in 'Amped,' I focus on neural implants.
Daniel H. Wilson
The conscious mind can only pay attention to about four things at once. If you've got these nagging voices in your head telling you to remember to pick up the laundry and call so-and-so, they're competing in your brain for neural resources with the stuff you're actually trying to do, like getting your work done.
Daniel Levitin
What does it mean, exactly, for a given system to be a 'neural correlate of consciousness'?
David Chalmers
Actually, I think my view is compatible with much of the work going on now in neuroscience and psychology, where people are studying the relationship of consciousness to neural and cognitive processes without really trying to reduce it to those processes.
The key to transforming mental models is to interrupt the automatic responses that are driven by the old model and respond differently based on the new model. Each time you are able to do this, you are actually loosening the old circuit and creating new neural connections in your brain, often referred to as self-directed neuroplasticity.
Elizabeth Thornton
Our brains have the ability to reorganize themselves by forming new neural connections throughout our lives. This ability is called neuroplasticity.
A brain scan may reveal the neural signs of anxiety, but a Kokoschka painting, or a Schiele self-portrait, reveals what an anxiety state really feels like. Both perspectives are necessary if we are to fully grasp the nature of the mind, yet they are rarely brought together.
Eric Kandel
One can, in principle, outline sort of a set of neural circuits that are critically involved and even identify disorders that affect different components of that neural circuit and see what happens if you knock out, for example, inability to recognize faces, how it affects your response to portraiture.
Just like the brain consists of billions of highly connected neurons, a basic operating unit in a neural network is a neuron-like node. It takes input from other nodes and sends output to others.
Fei-Fei Li
Our brains have been designed to blur the line between self and other. It is an ancient neural circuitry that marks every mammal, from mouse to elephant.
Frans de Waal
The pooling operation used in convolutional neural networks is a big mistake, and the fact that it works so well is a disaster.
Geoffrey Hinton
I had a stormy graduate career, where every week we would have a shouting match. I kept doing deals where I would say, 'Okay, let me do neural nets for another six months, and I will prove to you they work.' At the end of the six months, I would say, 'Yeah, but I am almost there. Give me another six months.'
All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.
The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?
My main interest is in trying to find radically different kinds of neural nets.
Everybody right now, they look at the current technology, and they think, 'OK, that's what artificial neural nets are.' And they don't realize how arbitrary it is. We just made it up! And there's no reason why we shouldn't make up something else.
Once your computer is pretending to be a neural net, you get it to be able to do a particular task by just showing it a whole lot of examples.
Now that neural nets work, industry and government have started calling neural nets AI. And the people in AI who spent all their life mocking neural nets and saying they'd never do anything are now happy to call them AI and try and get some of the money.
I get very excited when we discover a way of making neural networks better - and when that's closely related to how the brain works.
The paradigm for intelligence was logical reasoning, and the idea of what an internal representation would look like was it would be some kind of symbolic structure. That has completely changed with these big neural nets.
We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn, and so, instead of programming, you are just going to get them to learn everything.
I see a Reiki healer from time to time. She sits on my bed, and I lie in her lap. She puts her hands on me for about 45 minutes, and she reads my energy. Whenever I'm having a hard time, I call her. I also go to weekly therapy, and that has been invaluable. Also, getting on medication for my 'neural atypicalities,' I guess we might call them.
One reason I'm such a wayward prognosticator of rightwing trends is that I'm incapable of blacking out enough neural sectors to see the world through reptilian-brained eyes, a prerequisite for any true channeling of the mean resentments and implanted fears that drive hardcore conservatives.
Deep neural networks are responsible for some of the greatest advances in modern computer science.
Health care - the ability of neural networks to ingest lots of data and make predictions is very well suited to this area, and potentially will have a huge societal impact.
In the past, Google has used teams of humans to 'read' its street address images - in essence, to render images into actionable data. But using neural network technology, the company has trained computers to extract that data automatically - and with a level of accuracy that meets or beats human operators.
Cognitive neuroscience is entering an exciting era in which new technologies and ideas are making it possible to study the neural basis of cognition, perception, memory and emotion at the level of networks of interacting neurons, the level at which we believe many of the important operations of the brain take place.
If you want to make information stick, it's best to learn it, go away from it for a while, come back to it later, leave it behind again, and once again return to it - to engage with it deeply across time. Our memories naturally degrade, but each time you return to a memory, you reactivate its neural network and help to lock it in.
When I'm writing, my neural pathways get blocked. I can't read. I can barely hold a conversation without forgetting words and names. I wish I could wear the same clothes and eat the same food each day.
You are a victim of your own neural architecture which doesn't permit you to imagine anything outside of three dimensions. Even two dimensions. People know they can't visualise four or five dimensions, but they think they can close their eyes and see two dimensions. But they can't.
Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation.
Each of you possesses the most powerful, dangerous and subversive trait that natural selection has ever devised. It's a piece of neural audio technology for rewiring other people's minds. I'm talking about your language.
If you just have a single problem to solve, then fine, go ahead and use a neural network. But if you want to do science and understand how to choose architectures, or how to go to a new problem, you have to understand what different architectures can and cannot do.
My central thesis is that combining increased temporal and spatial resolution in MRI techniques with increasingly powerful data correlation techniques will allow the derivation of interpreted meanings from neural signals. I observed, further, that the techniques that exist already allow some correlations.
Fly flight is just a great phenomenon to study. It has everything - from the most sophisticated sensory biology; really, really interesting physics; really interesting muscle physiology; really interesting neural computations.
It is literally the case that learning languages makes you smarter. The neural networks in the brain strengthen as a result of language learning.
We should be exploring consciousness at the neural level and higher, where the arrow of causal analysis points up toward such principles as emergence and self-organization.
In my view, while the single neuron is the basic anatomical and information processing-signaling unit of the brain, it is not capable of generating behaviors and, ultimately, thinking. Instead, the true functional unit of the central nervous system is a population of neurons, or neural ensembles or cell assemblies.
Some scholars argue that although the brain might contain neural subsystems, or modules, specialized for tasks like recognizing faces and understanding language, it also contains a part that constitutes a person, a self: the chief executive of all the subsystems.
The genetic you and the neural you aren't alternatives to the conscious you. They are its foundations.
Neural implants could accomplish things no external interface could: Virtual and augmented reality with all five senses; augmentation of human memory, attention, and learning speed; even multi-sense telepathy - sharing what we see, hear, touch, and even perhaps what we think and feel with others.
I'm a geek through and through. My last job at Microsoft was leading much of the search engine relevance work on Bing. There we got to play with huge amounts of data, with neural networks and other AI techniques, with massive server farms.
The identification of a population of olfactory sensory neurons innervating a single glomerulus that mediates robust avoidance to a naturally occurring odorant provides insight in the neural circuitry that underlies this innate behavior.
Lies can be verbal or nonverbal, kindhearted or self-serving, devious or bald-faced; they can be lies of omission or lies of commission; they can be lies that undermine national security or lies that make a child feel better. And each type might involve a unique neural pathway.
It turns out you can train a neural network on a big body of text. It can be Wikipedia; it can be all the works of Charles Dickens; it could be all of the Internet. They can use grammar and put words together in interesting and convincing ways - and, I think, unexpected and beautiful ways.
The neural code usually refers to how your current thoughts and feelings and perceptions are encoded in the signals that neurons are passing around - and it's not the same. The code is not the same for every person.
