New drone video shows the recent damage wrought on California's iconic Highway 1, where part of the road collapsed after heavy rains washed it into the ocean last week. The video, released by the Monterey County Sheriff's Office, shows a large part of the highway still flooded and covered with debris from recent rainfall and mudslides. At the point of collapse, about 45 miles south of Carmel in the Big Sur area, both lanes of the road are completely gone, with a massive hole sloping toward the Pacific Ocean in its place. The sheriff's office video shows water running through the collapsed part of the road, which by Friday had fallen into the sea. California has been plagued by extensive mudslides, largely in areas burned out during the previous season's wildfires.

TalkingData is a data intelligence service provider that offers data products and services to provide businesses insights on consumer behavior, preferences, and trends. One of TalkingData's core services is leveraging machine learning and deep learning models to predict consumer behaviors (e.g., likelihood of a particular group to buy a house or a car) and use these insights for targeted advertising. For example, a car dealer will only want to show their ads to customers who the model predicts are most likely to buy a car in the next three months. Initially, TalkingData was building an XGBoost model for these types of predictions, but their data science team wanted to explore whether deep learning models could have a significant performance improvement for their use case. After experimentation, their data scientists built a model on PyTorch, an open source deep learning framework, that achieved a 13% improvement on recall rate.

Soft robots with translucent "skin" can detect human touch with internal cameras and differentiate between a prod, a stroke or a hug. The technology could lead to better non-verbal communication between humans and robots. Guy Hoffman and his colleagues at Cornell University, New York, created a prototype robot with nylon skin stretched over a 1.2-metre tall cylindrical scaffold atop a platform on wheels. Inside the cylinder sits a commercial USB camera which is used to interpret different types of touch on the nylon.

Soft robots with translucent "skin" can detect human touch with internal cameras and differentiate between a prod, a stroke or a hug. The technology could lead to better non-verbal communication between humans and robots. Guy Hoffman and his colleagues at Cornell University, New York, created a prototype robot with nylon skin stretched over a 1.2-metre tall cylindrical scaffold atop a platform on wheels. Inside the cylinder sits a commercial USB camera which is used to interpret different types of touch on the nylon.

One of the best-studied networks in neuroscience is the brain of a fruit fly, in particular, a part called the mushroom body. This analyzes sensory inputs such as odors, temperature, humidity and visual data so that the fly can learn to distinguish friendly stimuli from dangerous ones. Neuroscientists have long known how this section of the brain is wired. It consists of a set of cells called projection neurons that transmit the sensory information to a population of 2,000 neurons called Kenyon cells. The Kenyon cells are wired together to form a neural network capable of learning. This is how fruit flies learn to avoid potentially hazardous sensory inputs -- such as dangerous smells and temperatures -- while learning to approach foodstuffs, potential mates, and so on.

Civil rights activists have successfully pushed for bans on police use of facial recognition in cities like Oakland, San Francisco, and Somerville, Massachusetts. Now, a coalition led by Amnesty International is setting its sights on the nation's biggest city--New York--as part of a drive for a global moratorium on government use of the technology. Amnesty's #BantheScan campaign is backed by Legal Aid, the New York Civil Liberties Union, and AI For the People among other groups. After New York, the group plans to target New Delhi and Ulaanbaatar in Mongolia. "New York is the biggest city in the country," says Michael Kleinman, director of Amnesty International's Silicon Valley Initiative.

The US should not agree to ban the use or development of autonomous weapons powered by artificial intelligence (AI) software, a government-appointed panel has said in a draft report for Congress. The panel, led by former Google chief executive Eric Schmidt, on Tuesday concluded two days of public discussion about how the world's biggest military power should consider AI for national security and technological advancement. Its vice-chairman, Robert Work, a former deputy secretary of defense, said autonomous weapons are expected to make fewer mistakes than humans do in battle, leading to reduced casualties or skirmishes caused by target misidentification. "It is a moral imperative to at least pursue this hypothesis," he said. For about eight years, a coalition of non-governmental organisations has pushed for a treaty banning "killer robots", saying human control is necessary to judge attacks' proportionality and assign blame for war crimes.

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry that executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contribution of the paper is the BioScript type system. The type system ensures that certain types of errors, specific to biochemistry, do not occur, such as the interaction of chemicals that may be unsafe. Results are obtained using a custom-built compiler that implements the BioScript language and type system. The last two decades have witnessed the emergence of software-programmable laboratory-on-a-chip (pLoC) technology, enabled by technological advances in microfabrication and coupled with scientific understanding of microfluidics, the fundamental science of fluid behavior at the micro- to nanoliter scale. The net result of these collective advancements is that many experimental laboratory procedures have been miniaturized, accelerated, and automated, similar in principle to how the world's earliest computers automated tedious mathematical calculations that were previously performed by hand. Although the vast majority of microfluidic devices are effectively application-specific integrated circuits (ASICs), a variety of programmable LoCs have been demonstrated.16, With a handful of exceptions, research on programming languages and compiler design for programmable LoCs has lagged behind their silicon counterparts. To address this need, this paper presents a domain-specific programming language (DSL) and type system for a specific class of pLoC that manipulate discrete droplets of liquid on a two-dimensional grid. The basic principles of the language and type system readily generalize to programmable LoCs, realized across a wide variety of microfluidic technologies.

Over the past decade, calls for better measures to protect sensitive, personally identifiable information have blossomed into what politicians like to call a "hot-button issue." Certainly, privacy violations have become rampant and people have grown keenly aware of just how vulnerable they are. When it comes to potential remedies, however, proposals have varied widely, leading to bitter, politically charged arguments. To date, what has chiefly come of that have been bureaucratic policies that satisfy almost no one--and infuriate many. Now, into this muddled picture comes differential privacy. First formalized in 2006, it's an approach based on a mathematically rigorous definition of privacy that allows formalization and proof of the guarantees against re-identification offered by a system. While differential privacy has been accepted by theorists for some time, its implementation has turned out to be subtle and tricky, with practical applications only now starting to become available. To date, differential privacy has been adopted by the U.S. Census Bureau, along with a number of technology companies, but what this means and how these organizations have implemented their systems remains a mystery to many. It's also unlikely that the emergence of differential privacy signals an end to all the difficult decisions and trade-offs, but it does signify that there now are measures of privacy that can be quantified and reasoned about--and then used to apply suitable privacy protections. A milestone in the effort to make this capability generally available came in September 2019 when Google released an open source version of the differential privacy library that the company has used with many of its core products. In the exchange that follows, two of the people at Google who were central to the effort to release the library as open source--Damien Desfontaines, privacy software engineer; and Miguel Guevara, who leads Google's differential privacy product development effort--reflect on the engineering challenges that lie ahead, as well as what remains to be done to achieve their ultimate goal of providing privacy protection by default.

In his 2019 Turing Award Lecture, Geoff Hinton talks about two approaches to make computers intelligent. One he dubs--tongue firmly in cheek--"Intelligent Design" (or giving task-specific knowledge to the computers) and the other, his favored one, "Learning" where we only provide examples to the computers and let them learn. Hinton's not-so-subtle message is that the "deep learning revolution" shows the only true way is the second. Hinton is of course reinforcing the AI Zeitgeist, if only in a doctrinal form. Artificial intelligence technology has captured popular imagination of late, thanks in large part to the impressive feats in perceptual intelligence--including learning to recognize images, voice, and rudimentary language--and bringing fruits of those advances to everyone via their smartphones and personal digital accessories.