...make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.

Many will remember 2017 as the year of the big hack: two major cybersecurity events made headlines and put millions of people and their data at risk. The first was the WannaCry ransomware attack in May. Among other things, it froze operations at multiple hospitals in the UK’s National Health Service and caused hundreds of millions of dollars in damages. The second, in September, was the Equifax credit bureau breach in which more than 140 million individual records were compromised.

A team of Harvard University and MIT researchers report their new neural networking method for monitoring earthquakes is more accurate and orders of magnitude faster than traditional approaches.

Laboratory studies of value-based decision-making often involve choosing among a few discrete actions. Yet in natural environments, we encounter a multitude of options whose values may be unknown or poorly estimated. Given that our cognitive capacity is bounded, in complex environments, it becomes hard to solve the challenge of whether to exploit an action with known value or search for even better alternatives. In reinforcement learning, the intractable exploration/exploitation tradeoff is typically handled by controlling the temperature parameter of the softmax stochastic exploration policy or by encouraging the selection of uncertain options. We describe how selectively maintaining high-value actions in a manner that reduces their information content helps to resolve the exploration/exploitation dilemma during a reinforcement-based timing task. By definition of the softmax policy, the information content (i.e., Shannon's entropy) of the value representation controls the shift from exploration to exploitation. When subjective values for different response times are similar, the entropy is high, inducing exploration. Under selective maintenance, entropy declines as the agent preferentially maps the most valuable parts of the environment and forgets the rest, facilitating exploitation. We demonstrate in silico that this memory-constrained algorithm performs as well as cognitively demanding uncertainty-driven exploration, even though the latter yields a more accurate representation of the contingency. We found that human behavior was best characterized by a selective maintenance model. Information dynamics consistent with selective maintenance were most pronounced in better-performing subjects, in those with higher non-verbal intelligence, and in learnable vs. unlearnable contingencies. Entropy of value traces shaped human exploration behavior (response time swings), whereas uncertainty-driven exploration was not supported by Bayesian model comparison. In summary, when the action space is large, strategic maintenance of value information reduces cognitive load and facilitates the resolution of the exploration/exploitation dilemma.

You don’t have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world.

The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we’re far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins.

ONE OF THE most ubiquitous features of the internet is the ability to link to content elsewhere. Everything is connected via billions of links and embeds to blogs, articles, and social media. But a federal judge’s ruling threatens that ecosystem. Katherine Forrest, a Southern District of New York judge, ruled Thursday that embedding a tweet containing an image in a webpage could be considered copyright infringement. The decision can be appealed, but if it stands and is adopted by other courts, it could change the way online publishing functions.

Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning.

We present a novel end-to-end trainable OCR system combining a CNN for feature extraction with 1-D LSTMs for sequence modeling. We present results on English and Arabic handwriting data, and on English machine print data, showing state-of-the-art performance. We believe that our method is simpler than existing 2D LSTM models, and will make it easier to use techniques borrowed from CNN research in computer vision to improve OCR performance.

The Team Data Science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. TDSP helps improve team collaboration and learning. It contains a distillation of the best practices and structures from Microsoft and others in the industry that facilitate the successful implementation of data science initiatives. The goal is to help companies fully realize the benefits of their analytics program.

A group of quantitative hedge fund traders long dreamed of bringing their artificial intelligence strategies to all. And now they have with the first exchange-traded fund to combine the worlds of AI and blockchain.

Computers that operate more like the human brain than computers—a field sometimes referred to as neuromorphic computing—have promised a new era of powerful computing.

While this all seems promising, one of the big shortcomings in neuromorphic computing has been that it doesn’t mimic the brain in a very important way. In the brain, for every neuron there are a thousand synapses—the electrical signal sent between the neurons of the brain. This poses a problem because a transistor only has a single terminal, hardly an accommodating architecture for multiplying signals.

Now researchers at Northwestern University, led by Mark Hersam, have developed a new device that combines memristors—two-terminal non-volatile memory devices based on resistance switching—with transistors to create what Hersam and his colleagues have dubbed a “memtransistor” that performs both memory storage and information processing.

This most recent research builds on work that Hersam and his team conducted back in 2015 in which the researchers developed a three-terminal, gate-tunable memristor that operated like a kind of synapse.

AI is only loosely modeled on the brain. So what if you wanted to do it right? You’d need to do what has been impossible until now: map what actually happens in neurons and nerve fibers.

Studying 70,000 mouse neurons could help Andreas Tolias build smarter AI.

The term “smart city” is interesting yet not important, because nobody defines it. “Smart” is a snazzy political label used by a modern alliance of leftist urbanites and tech industrialists. To deem yourself “smart” is to make the nimbyites and market-force people look stupid.

Smart-city devotees all over this world will agree that London is particularly smart. Why? London is a huge, ungainly beast whose cartwheeling urban life is in cranky, irrational disarray. London is a god-awful urban mess, but London does have some of the best international smart-city conferences.

We’ve seen plenty of examples of neural networks listening to speech, reading characters, or identifying images. KickView had a different idea. They wanted to learn to recognize radio signals. Not just any radio signals, but Orthogonal Frequency Division Multiplexing (OFDM) waveforms.

OFDM is a modulation method used by WiFi, cable systems, and many other systems. In particular, they look at an 802.11g signal with a bandwidth of 20 MHz. The question is given a receiver for 802.11g, how can you reliably detect that an 802.11ac signal — up to 160 MHz — is using your channel? To demonstrate the technique they decided to detect 20 MHz signals using a 5 MHz bandwidth.

YOU DON'T READ privacy policies. And of course, that's because they're not actually written for you, or any of the other billions of people who click to agree to their inscrutable legalese. Instead, like bad poetry and teenagers' diaries, those millions upon millions of words are produced for the benefit of their authors, not readers—the lawyers who wrote those get-out clauses to protect their Silicon Valley employers.

But one group of academics has proposed a way to make those virtually illegible privacy policies into the actual tool of consumer protection they pretend to be: an artificial intelligence that's fluent in fine print. Today, researchers at Switzerland's Federal Institute of Technology at Lausanne (EPFL), the University of Wisconsin and the University of Michigan announced the release of Polisis—short for "privacy policy analysis"—a new website and browser extension that uses their machine-learning-trained app to automatically read and make sense of any online service's privacy policy, so you don't have to.

Memory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation of brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation and often target the hippocampus and medial temporal lobes. Here we use a closed-loop system to monitor and decode neural activity from direct brain recordings in humans. We apply targeted stimulation to lateral temporal cortex and report that this stimulation rescues periods of poor memory encoding. This system also improves later recall, revealing that the lateral temporal cortex is a reliable target for memory enhancement. Taken together, our results suggest that such systems may provide a therapeutic approach for treating memory dysfunction.

The Alphabet company is the latest to offer an "operating system" for the age of urban mobility.