Tag Archives: Brain

This Is Your Brain on Nationalism

The Biology of Us and Them
By Robert Sapolsky – To understand the dynamics of human group identity, including the resurgence of nationalism—that potentially most destructive form of in-group bias—requires grasping the biological and cognitive underpinnings that shape them.

Such an analysis offers little grounds for optimism.

Our brains distinguish between in-group members and outsiders in a fraction of a second, and they encourage us to be kind to the former but hostile to the latter. These biases are automatic and unconscious and emerge at astonishingly young ages. They are, of course, arbitrary and often fluid.

Today’s “them” can become tomorrow’s “us.” But this is only poor consolation. Humans can rein in their instincts and build societies that divert group competition to arenas less destructive than warfare, yet the psychological bases for tribalism persist, even when people understand that their loyalty to their nation, skin color, god, or sports team is as random as the toss of a coin.

At the level of the human mind, little prevents new teammates from once again becoming tomorrow’s enemies.

The human mind’s propensity for us-versus-them thinking runs deep. Numerous careful studies have shown that the brain makes such distinctions automatically and with mind-boggling speed. Stick a volunteer in a brain scanner and quickly flash pictures of faces. Among typical white subjects in the scanner, the sight of a black man’s face activates the amygdala, a brain region central to emotions of fear and aggression, in under one-tenth of a second.

In most cases, the prefrontal cortex, a region crucial for impulse control and emotional regulation, springs into action a second or two later and silences the amygdala: “Don’t think that way, that’s not who I am.” Still, the initial reaction is usually one of fear, even among those who know better.

For all this pessimism, there is a crucial difference between humans and those warring chimps. The human tendency toward in-group bias runs deep, but it is relatively value-neutral. Although human biology makes the rapid, implicit formation of us-them dichotomies virtually inevitable, who counts as an outsider is not fixed. In fact, it can change in an instant. more>

The Future of Machine Learning: Neuromorphic Processors

By Narayan Srinivasa – Machine learning has emerged as the dominant tool for the implementation of complex cognitive tasks resulting in machines that have demonstrated, in some cases, super-human performance. However, these machines require training with a large amount of labeled data and this energy-hungry training process has often been prohibitive in the absence of costly super-computers.

The ways in which animals and humans learn is far more efficient, driven by the evolution of a different processor in the form of a brain that simultaneously optimizes energy of computation with efficient information processing capabilities. The next generation of computers, called neuromorphic processors, will strive to strike this delicate balance between efficiency of computation with the energy needed for this computation.

The foundation for the design of neuromorphic processors is rooted in our understanding of how biological computation is very different from the digital computers of today (Figure).

The brain is composed of noisy analog computing elements including neurons and synapses. Neurons operate as relaxation oscillators. Synapses are implicated in memory formation in the brain and can only resolve between three-to-four bits of information at each synapse. It is well known that the brain operates using a plethora of brain rhythms but without any global clock (i.e., clock free) where the dynamics of these elements operate in an asynchronous fashion. more>

Updates from Georgia Tech

Smart Cities
By T.J. Becker – Cities have been around for thousands of years, so urbanization is hardly a new phenomenon — but it’s happening now at an unprecedented pace.

In 1950 about 30 percent of the world’s population lived in cities, a number that shot up to nearly 55 percent by 2016 and is expected to hit 60 percent by 2030, according to United Nations statistics. This dramatic growth brings challenges on a variety of fronts, transforming “smart cities” from a catchy phrase into a critical endeavor.

“Smart cities is a highly complex area, encompassing everything from resiliency and environmental sustainability to wellness and quality of life,” said Elizabeth Mynatt, executive director of Georgia Tech’s Institute for People and Technology (IPaT) and distinguished professor in the College of Computing, who is co-chairing the new council. “Although Georgia Tech has been working in this area for some time, we’re organizing research so we can be more holistic and have combined impact.”

“Instead of discrete projects, we’re moving into a programmatic approach,” agreed Jennifer Clark, associate professor of public policy and director of Georgia Tech’s Center for Urban Innovation. “Smart cities research touches on everything from computing and engineering to the social sciences. It’s a different way of thinking about technology — not just in the private sector but also the public sector — so we make cities more efficient and economically competitive places.” more> https://goo.gl/DtKr9K

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Updates from GE

Looking For The Unknown: Artificial Intelligence Is Seeking Cancer Patterns That Have Eluded Humans
By Maggie Sieger – The use of AI in healthcare, which was one of the topics discussed at GE’s recent Minds + Machines conference in Berlin, is a fast-growing field. Scientists are using so-called “deep learning networks,” which weave together hundreds, if not thousands, of data points and process this data with multiple algorithms simultaneously, mimicking the human brain.

When crossing the street, pedestrians take into account dozens of factors, including the number and speed of approaching cars, the condition of the pavement, fellow travelers and even the shoes they are wearing or what they are carrying. Deep learning has the potential to do the same thing – but with even more data points and at speeds unmatched by humans.

They are feeding millions of data points into the cloud, including decades of colorectal data collected by national registries, thousands of MRIs and CT scans, gene panels and biomarkers. The software then looks for patterns, connections and correlations with a speed and detail unmatched by humans.

As AI becomes a more common tool in healthcare, medical schools will have to change how they train physicians to make sure they have the new capabilities, skill sets and methodologies to use AI effectively, more> https://goo.gl/2kME5a

Why Your Brain Hates Other People

By Robert Sapolsky – Humans universally make Us/Them dichotomies along lines of race, ethnicity, gender, language group, religion, age, socioeconomic status, and so on. And it’s not a pretty picture.

We do so with remarkable speed and neurobiological efficiency; have complex taxonomies and classifications of ways in which we denigrate Thems; do so with a versatility that ranges from the minutest of microaggression to bloodbaths of savagery; and regularly decide what is inferior about Them based on pure emotion, followed by primitive rationalizations that we mistake for rationality.

Pretty depressing.

The brain’s fault lines dividing Us from Them are also shown with the hormone oxytocin. It’s famed for its pro-social effects—oxytocin prompts people to be more trusting, cooperative, and generous. But, crucially, this is how oxytocin influences behavior toward members of your own group. When it comes to outgroup members, it does the opposite… more> https://goo.gl/jv9WTY

Now it’s time to prepare for the Machinocene

BOOK REVIEW

Expressivism, Pragmatism and Representationalism, Author: Huw Price.

By Huw Price – One way or another, then, we are going to be sharing the planet with a lot of non-biological intelligence. Whatever it brings, we humans face this future together. We have an obvious common interest in getting it right. And we need to nail it the first time round. Barring some calamity that ends our technological civilization without entirely finishing us off, we’re not going to be coming this way again.

If we are to develop machines that think, ensuring that they are safe and beneficial is one of the great intellectual and practical challenges of this century. And we must face it together – the issue is far too large and crucial to be tackled by any individual institution, corporation or nation. Our grandchildren, or their grandchildren, are likely to be living in a different era, perhaps more Machinocene than Anthropocene.

Our task is to make the best of this epochal transition, for them and the generations to follow. We need the best of human intelligence to make the best of artificial intelligence. more> https://goo.gl/dHx4jd

The body is the missing link for truly intelligent machines

BOOK REVIEW

Basin and Range, Author: John McPhee.
Descartes’ Error, Author: Antonio Damasio.

By Ben Medlock – Things took a wrong turn at the beginning of modern AI, back in the 1950s. Computer scientists decided to try to imitate conscious reasoning by building logical systems based on symbols. The method involves associating real-world entities with digital codes to create virtual models of the environment, which could then be projected back onto the world itself.

In later decades, as computing power grew, researchers switched to using statistics to extract patterns from massive quantities of data. These methods are often referred to as ‘machine learning’. Rather than trying to encode high-level knowledge and logical reasoning, machine learning employs a bottom-up approach in which algorithms discern relationships by repeating tasks, such as classifying the visual objects in images or transcribing recorded speech into text.

But algorithms are a long way from being able to think like us. The biggest distinction lies in our evolved biology, and how that biology processes information. Humans are made up of trillions of eukaryotic cells, which first appeared in the fossil record around 2.5 billion years ago. A human cell is a remarkable piece of networked machinery that has about the same number of components as a modern jumbo jet – all of which arose out of a longstanding, embedded encounter with the natural world.

We only have the world as it is revealed to us, which is rooted in our evolved, embodied needs as an organism. Nature ‘has built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it’,

In other words, we think with our whole body, not just with the brain. more> https://goo.gl/oBgkRF

Updates from GE

Could You Soon Fly An Airplane With Your Mind?
By Geoffrey Ling – Imagine we are at the very early stages of the original cellphone. In the 1980s, they were large bricks and all they could do is make phone calls. That’s sort of where we are with brain science.

We can measure reliably certain signals associated with individual functions. We can see how to move an arm, or what is happening during a specific emotional state. We can measure those things fairly well.

The technology is still bulky and expensive. It’s still not amenable for everyday use: normal people doing real things in real time.

Much like the cellphone though, the progress is going to be staggering. We will learn to measure the signals better, and find more functionality associated with those signals. The innovation is going to explode.

This technology could take human relationships to a whole new level. We could cross boundaries of language, understanding. Misunderstandings could be a thing of the past. more> https://goo.gl/R6yvXh

We Might Live in a Virtual Universe — But It Doesn’t Really Matter


By Maxim Roubintchik – The first thing to realize is this: Our perception of reality is already separate from reality itself.

To paraphrase Morpheus from the movie The Matrix, reality is simply an electrical impulse being interpreted by your brain. We experience the world indirectly and imperfectly. If we could see the world as it is, there would be no optical illusions, no color blindness and no mind tricks.

Further, we only experience a simplified version of all this mediated sensory information. The reason? Seeing the world as it is requires too much processing power — so our brain breaks it into heuristics (or simplified but still useful representations). Our mind is constantly looking for patterns in our world and will match them with our perception.

From this we can conclude the following:

Our perception of reality is already different from reality itself. What we call reality is our brains’ attempt to process the incoming flood of sensory data.

If our perception of reality is dependent on a simplified flow of information, it doesn’t matter what the source of this information is — whether it’s the physical world or a computer simulation feeding us the same information. more> http://goo.gl/fIwQEb

Endless fun

BOOK REVIEW

Consciousness and the Social Brain, Author: Michael Graziano.

By Michael Graziano – Imagine a future in which your mind never dies. When your body begins to fail, a machine scans your brain in enough detail to capture its unique wiring. A computer system uses that data to simulate your brain.

It won’t need to replicate every last detail. Like the phonograph, it will strip away the irrelevant physical structures, leaving only the essence of the patterns. And then there is a second you, with your memories, your emotions, your way of thinking and making decisions, translated onto computer hardware as easily as we copy a text file these days.

That second version of you could live in a simulated world and hardly know the difference. You could walk around a simulated city street, feel a cool breeze, eat at a café, talk to other simulated people, play games, watch movies, enjoy yourself. Pain and disease would be programmed out of existence.

Your connectome, simulated in a computer, would recreate your conscious mind. Of course, nobody knows if the connectome really does contain all the essential information about the mind. Some of it might be encoded in other ways. Hormones can diffuse through the brain. Signals can combine and interact through other means besides synaptic connections. more> https://goo.gl/7xUMFf