Imagine you’re cruising in your new Tesla, autopilot engaged. Suddenly you feel yourself veer into the other lane, and you grab the wheel just in time to avoid an oncoming car. When you pull over, pulse still racing, and look over the scene, it all seems normal. But upon closer inspection, you notice a series of translucent stickers leading away from the dotted lane divider. And to your Tesla, these stickers represent a non-existent bend in the road that could have killed you.
… As more machines become artificially intelligent, computer scientists are learning that A.I. can be manipulated into perceiving the world in wrong, sometimes dangerous ways. And because these techniques “trick” the system instead of “hacking” it, federal laws and security standards may not protect us from these malicious new behaviors — and the serious consequences they can have. Read More
Monthly Archives: August 2019
Rhythm and Synchrony in a Cortical Network Model
We studied mechanisms for cortical gamma-band activity in the cerebral cortex and identified neurobiological factors that affect such activity. This was done by analyzing the behavior of a previously developed, data-driven, large-scale network model that simulated many visual functions of monkey V1 cortex (Chariker et al., 2016). Gamma activity was an emergent property of the model. The model’s gamma activity, like that of the real cortex, was (1) episodic, (2) variable in frequency and phase, and (3) graded in power with stimulus variables like orientation. The spike firing of the model’s neuronal population was only partially synchronous during multiple firing events (MFEs) that occurred at gamma rates. Detailed analysis of the model’s MFEs showed that gamma-band activity was multidimensional in its sources. Most spikes were evoked by excitatory inputs. A large fraction of these inputs came from recurrent excitation within the local circuit, but feedforward and feedback excitation also contributed, either through direct pulsing or by raising the overall baseline. Inhibition was responsible for ending MFEs, but disinhibition led directly to only a small minority of the synchronized spikes. As a potential explanation for the wide range of gamma characteristics observed in different parts of cortex, we found that the relative rise times of AMPA and GABA synaptic conductances have a strong effect on the degree of synchrony in gamma. Read More
Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex
A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model. Read More
A Mathematical Model Unlocks the Secrets of Vision
This is the great mystery of human vision: Vivid pictures of the world appear before our mind’s eye, yet the brain’s visual system receives very little information from the world itself. Much of what we “see” we conjure in our heads.
“A lot of the things you think you see you’re actually making up,” said Lai-Sang Young, a mathematician at New York University. “You don’t actually see them.” Read More
This Tesla Mod Turns a Model S Into a Mobile ‘Surveillance Station’
Automatic license plate reader cameras are controversial enough when law enforcement deploys them, given that they can create a panopticon of transit throughout a city. Now one hacker has found a way to put a sample of that power—for safety, he says, and for surveillance—into the hands of anyone with a Tesla and a few hundred dollars to spare. Read More
Meet Tesla's self-driving car computer and its two AI brains
Designing your own chips is hard. But Tesla, one of the most aggressive developers of autonomous vehicle technology, thinks it’s worth it. The company shared details Tuesday about how it fine-tuned the design of its AI chips so two of them are smart enough to power its cars’ upcoming “full self-driving” abilities.
Tesla Chief Executive Elon Musk and his colleagues revealed the company’s third-generation computing hardware in April. But at the Hot Chips conference Tuesday, chip designers showed how heavy optimizations in Tesla’s custom AI chips dramatically boosted performance — a factor of 21 compared to the earlier Nvidia chips. As a bonus, they’re only 80% the cost, too. Read More
Andrew Ng’s AI companies expand to Medellin, Colombia
After his tenure as chief scientist at Baidu, Andrew Ng, the founder of the Google Brain project and former CEO of Coursera, set up a number of different projects that all focus on making AI more approachable. These include the education startup Deeplearning.ai, the AI Fund startup studio for building AI companies and Landing.ai, which helps enterprises (and especially manufacturing companies) use AI. Today, Ng announced he has opened a second office for these projects in Medellin, Colombia.
At first, Medellin may seem like an odd choice. But today’s Medellin is very different from the one you may have seen on Narcos (and a lot safer). It’s home to a number of universities and, over the course of the last few years, it’s a hub for Colombia’s startup scene thanks to incubators like Ruta N and others. Read More
Why Digital Transformation Won’t Succeed Without Cultural Change
A lot of businesses frequently use the term Digital Transformation, but what does it actually mean?
We have become so accustomed to digital technology, we are no longer able to imagine life without it. And yet, when it comes to the digital transformation of the work environment, we still have a long way to go. The greatest obstacle for digital transformation is simply, people. … A number of key factors in implementing digital solutions are related to people. And while is relatively easy to change and adapt to new technology, people are not. Read More
New brain map could improve AI algorithms for machine vision
Despite years of research, the brain still contains broad areas of uncharted 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. Read More
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