A team of Polish astronomers has created the most accurate three-dimensional map of the Milky Way to date, revealing surprising distortions and irregularities along the galactic disk.
Building an accurate map of the Milky Way is not easy.
Our location deep inside the gigantic structure means we can’t observe our galaxy externally, forcing us to envision its form from within. Dense expanses of stars, gas, and dust complicate our view even further. Despite these limitations, we know that the Milky Way is a spiral galaxy measuring around 120,000 light-years across, and that we’re located around 27,000 light-years from the galactic core.
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team of scientists from the Astronomical Observatory at the University of Warsaw has compiled the most accurate 3D map of the Milky Way to date. Astronomer Dorota Skowron led the study, which was published today in Science.
Animation showing the twisted shape of our galaxy.
GIF: J. Skowron/OGLE/Astronomical Observatory, University of Warsaw/Gizmodo
Among several other new findings, the updated 3D map shows the S-shaped structure of our galaxy’s distorted stellar disk. The Milky Way is not flat like a pancake, and is instead “warped and twisted,” in the words of co-author Przemek Mroz, who described his team’s work in a related video. That our galaxy is warped was already known, but the new research further characterizes the surprising extent of these distortions. As the new research shows, this warp starts at ranges greater than 25,000 light-years from the galactic core, and it gets more severe with distance.
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The new research also showed that the thickness of the Milky Way is variable throughout. Our galaxy gets thicker with distance from the core. At our location, for example, the galactic disk is about 500 light-years thick, but at the outer edges it’s as much as 3,000 light-years thick.
Milky Way Cepheids on the Milky Way map.
Image: J. Skowron/Serge Brunier
To create the 3D map, Skowron and her colleagues charted the location of Cepheid variable stars. These young, pulsating supergiants are ideal for this research because their brightness changes in a very regular pattern. Ultimately, the location of Cepheid stars within the Milky Way can be more accurately pinned down than other kinds of stars, which is precisely what was needed for this mapping project.
A sample of over 2,400 Cepheids was used to create the new map, the majority of which were identified with the Optical Gravitational Lensing Experiment (OGLE) survey, which monitors the brightness of nearly 2 billion stars. In total, the researchers observed the galactic disk for six years, taking 206,726 images of the sky.
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If this work sounds familiar, it’s because research published earlier this year in Nature Astronomy employed a similar technique, in which scientists from the Chinese Academy of Sciences reached similar conclusions, using a different group of Cepheids for their map. One of the scientists behind the previous research, Xiaodian Chen from the National Astronomical Observatories at the Chinese Academy of Sciences, took issue with the fact that the authors of the new paper did not cite his team’s work. Nonetheless, he still liked the new science.
Amazon-owned home security company Ring is pursuing contracts with police departments that would grant it direct access to real-time emergency dispatch data, Gizmodo has learned.
The California-based company is seeking police departments’ permission to tap into the computer-aided dispatch (CAD) feeds used to automate and improve decisions made by emergency dispatch personnel and cut down on police response times. Ring has requested access to the data streams so it can curate “crime news” posts for its “neighborhood watch” app, Neighbors.
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An internal police email dated April 2019, obtained by Gizmodo last week via a records request, stated that more than 225 police departments have entered into partnerships with Ring. (The company has declined to confirm that, or provide the actual number.) Doing so grants the departments access to a Neighbors “law enforcement portal” through which police can request access to videos captured by Ring doorbell cameras.
Ring says it does not provide the personal information of its customers to the authorities without consent. To wit, the company has positioned itself as an intermediary through which police request access to citizen-captured surveillance footage. When police make a request, they don’t know who receives it, Ring says, until a user chooses to share their video. Users are also prompted with the option to review their footage before turning it over.
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Through its police partnerships, Ring has requested access to CAD, which includes information provided voluntarily by 911 callers, among other types of data automatically collected. CAD data is typically compromised of details such as names, phone numbers, addresses, medical conditions and potentially other types of personally identifiable information, including, in some instances, GPS coordinates.
In an email Thursday, Ring confirmed it does receive location information, including precise addresses from CAD data, which it does not publish to its app. It denied receiving other forms of personal information.
Ring CAD materials provided to police.
According to some internal documents, police CAD data is received by Ring’s “Neighbors News team” and is then reformatted before being posted on Neighbors in the form of an “alert” to users in the vicinity of the alleged incident.
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Earlier this year, when the Seattle Police Department sought access to CAD software, it triggered a requirement for a privacy impact report under a city ordinance concerning the acquisition of any “surveillance technologies.”
According to the definition adopted by the city, a technology has surveillance capability if it can be used “to collect, capture, transmit, or record data that could be used to surveil, regardless of whether the data is obscured, de-identified, or anonymized before or after collection and regardless of whether technology might be used to obscure or prevent the capturing of certain views or types of information.”
Some CAD systems, such as those marketed by Central Square Technologies (formerly known as TriTech), are used to locate cellular callers by sending text messages that force the return of a phone-location service tracking report. CAD systems also pull in data automatically from phone companies, including ALI information—Automatic Location Identification—which is displayed to dispatch personnel whenever a 911 call is placed. CAD uses these details, along with manually entered information provided by callers, to make fast, initial decisions about which police units and first responders should respond to which calls.
According to Ring’s materials, the direct address, or latitude and longitude, of 911 callers is among the information the Neighbors app requires police to provide, along with the time of the incident, and the category and description of the alleged crime.
Ring said that while it uses CAD data to generate its “News Alerts,” sensitive details, such as the direct address of an incident or the number of police units responding, are never included.
Oddly enough no mention is made of voice recordings. Considering Amazon is building a huge database of voices and people through Alexa, cross referencing the two should be trivial and allow Amazon to surveil the population more closely
An artificial intelligence system should be recognised as the inventor of two ideas in patents filed on its behalf, a team of academics says.
The AI has designed interlocking food containers that are easy for robots to grasp and a warning light that flashes in a rhythm that is hard to ignore.
Patents offices insist innovations are attributed to humans – to avoid legal complications that would arise if corporate inventorship were recognised.
And it could see patent offices refusing to assign any intellectual property rights for AI-generated creations.
As a result, two professors from the University of Surrey have teamed up with the Missouri-based inventor of Dabus AI to file patents in the system’s name with the relevant authorities in the UK, Europe and US.
Unlike some machine-learning systems, Dabus has not been trained to solve particular problems.
Instead, it seeks to devise and develop new ideas – “what is traditionally considered the mental part of the inventive act”, according to creator Stephen Thaler
The first patent describes a food container that uses fractal designs to create pits and bulges in its sides. One benefit is that several containers can be fitted together more tightly to help them be transported safely. Another is that it should be easier for robotic arms to pick them up and grip them.
Image copyrightRyan AbbottImage caption This diagram shows how a container’s shape could be based on fractals
Law professor Ryan Abbott told BBC News: “These days, you commonly have AIs writing books and taking pictures – but if you don’t have a traditional author, you cannot get copyright protection in the US.
“So with patents, a patent office might say, ‘If you don’t have someone who traditionally meets human-inventorship criteria, there is nothing you can get a patent on.’
“In which case, if AI is going to be how we’re inventing things in the future, the whole intellectual property system will fail to work.”
Instead, he suggested, an AI should be recognised as being the inventor and whoever the AI belonged to should be the patent’s owner, unless they sold it on.
However, Prof Abbott acknowledged lawmakers might need to get involved to settle the matter and that it could take until the mid-2020s to resolve the issue.
A spokeswoman for the European Patent Office indicated that it would be a complex matter.
“It is a global consensus that an inventor can only be a person who makes a contribution to the invention’s conception in the form of devising an idea or a plan in the mind,” she explained.
“The current state of technological development suggests that, for the foreseeable future, AI is… a tool used by a human inventor.
“Any change… [would] have implications reaching far beyond patent law, ie to authors’ rights under copyright laws, civil liability and data protection.
“The EPO is, of course, aware of discussions in interested circles and the wider public about whether AI could qualify as inventor.”
The UK’s Patents Act 1977 currently requires an inventor to be a person, but the Intellectual Property Office is aware of the issue.
“The government believes that AI technology could increase the UK’s GDP by 10% in the next decade, and the IPO is focused on responding to the challenges that come with this growth,” said a spokeswoman.
America’s trade watchdog has officially told millions in the US not to apply for the $125 it promised each of them as part of the deal it struck with Equifax – and instead take up an offer of free credit monitoring.
In a memo on Wednesday, FTC assistant director Robert Schoshinski said the regulator has been overwhelmed by people filing claims against Equifax after the biz was cyber-looted by hackers in 2017.
He then warned that, because the settlement with the mega-hacked outfit had been capped, it is very unlikely people will end up receiving that promised $125 each. In fact, the deal may be worth no more than 21 cents. We note that the FTC website folks can file claims through, ftc.gov/equifax, no longer mentions a $125 option, whereas the settlement website it redirects to still offers the cash lump sum.
“There is a downside to this unexpected number of claims,” noted Schoshinski.
“The pot of money that pays for that part of the settlement is $31 million. A large number of claims for cash instead of credit monitoring means only one thing: each person who takes the money option will wind up only getting a small amount of money. Nowhere near the $125 they could have gotten if there hadn’t been such an enormous number of claims filed.”
Since unfurling the spacecraft’s silver solar sail last week, mission managers have been optimizing the way the spacecraft orients itself during solar sailing. After a few tweaks, LightSail 2 began raising its orbit around the Earth. In the past 4 days, the spacecraft has raised its orbital high point, or apogee, by about 2 kilometers. The mission team has confirmed the apogee increase can only be attributed to solar sailing, meaning LightSail 2 has successfully completed its primary goal of demonstrating flight by light for CubeSats.
“We’re thrilled to announce mission success for LightSail 2,” said LightSail program manager and Planetary Society chief scientist Bruce Betts. “Our criteria was to demonstrate controlled solar sailing in a CubeSat by changing the spacecraft’s orbit using only the light pressure of the Sun, something that’s never been done before. I’m enormously proud of this team. It’s been a long road and we did it.”
LightSail is a citizen-funded project from The Planetary Society to send a small spacecraft, propelled solely by sunlight, to Earth orbit.
The milestone makes LightSail 2 the first spacecraft to use solar sailing for propulsion in Earth orbit, the first small spacecraft to demonstrate solar sailing, and just the second-ever solar sail spacecraft to successfully fly, following Japan’s IKAROS, which launched in 2010. LightSail 2 is also the first crowdfunded spacecraft to successfully demonstrate a new form of propulsion.
The Humanitarian Data Exchange (HDX) is an open platform for sharing data across crises and organisations. Launched in July 2014, the goal of HDX is to make humanitarian data easy to find and use for analysis. Our growing collection of datasets has been accessed by users in over 200 countries and territories. Watch this video to learn more.
HDX is managed by OCHA’s Centre for Humanitarian Data, which is located in The Hague. OCHA is part of the United Nations Secretariat and is responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The HDX team includes OCHA staff and a number of consultants who are based in North America, Europe and Africa.
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We define humanitarian data as:
data about the context in which a humanitarian crisis is occurring (e.g., baseline/development data, damage assessments, geospatial data)
data about the people affected by the crisis and their needs
data about the response by organisations and people seeking to help those who need assistance.
HDX uses an open-source software called CKAN for our technical back-end. You can find all of our code on GitHub.
When it comes to knowing where humans around the world actually live, resources come in varying degrees of accuracy and sophistication.
Heavily urbanized and mature economies generally produce a wealth of up-to-date information on population density and granular demographic data. In rural Africa or fast-growing regions in the developing world, tracking methods cannot always keep up, or in some cases may be non-existent.
This is where new maps, produced by researchers at Facebook, come in. Building upon CIESIN’s Gridded Population of the World project, Facebook is using machine learning models on high-resolution satellite imagery to paint a definitive picture of human settlement around the world. Let’s zoom in.
Connecting the Dots
Will all other details stripped away, human settlement can form some interesting patterns. One of the most compelling examples is Egypt, where 95% of the population lives along the Nile River. Below, we can clearly see where people live, and where they don’t.
While it is possible to use a tool like Google Earth to view nearly any location on the globe, the problem is analyzing the imagery at scale. This is where machine learning comes into play.
Finding the People in the Petabytes
High-resolution imagery of the entire globe takes up about 1.5 petabytes of storage, making the task of classifying the data extremely daunting. It’s only very recently that technology was up to the task of correctly identifying buildings within all those images.
To get the results we see today, researchers used process of elimination to discard locations that couldn’t contain a building, then ranked them based on the likelihood they could contain a building.
Facebook identified structures at scale using a process called weakly supervised learning. After training the model using large batches of photos, then checking over the results, Facebook was able to reach a 99.6% labeling accuracy for positive examples.
Why it Matters
An accurate picture of where people live can be a matter of life and death.
For humanitarian agencies working in Africa, effectively distributing aid or vaccinating populations is still a challenge due to the lack of reliable maps and population density information. Researchers hope that these detailed maps will be used to save lives and improve living conditions in developing regions.
For example, Malawi is one of the world’s least urbanized countries, so finding its 19 million citizens is no easy task for people doing humanitarian work there. These maps clearly show where people live and allow organizations to create accurate population density estimates for specific areas.
Visit the project page for a full explanation and to access the full database of country maps.
Authorities in the United Kingdom have made unauthorized copies of data stored inside a EU database for tracking undocumented migrants, missing people, stolen cars, or suspected criminals.
Named the Schengen Information System (SIS), this is a EU-run database that stores information such as names, personal details, photographs, fingerprints, and arrest warrants for 500,000 non-EU citizens denied entry into Europe, over 100,000 missing people, and over 36,000 criminal suspects.
The database was created for the sole purpose of helping EU countries manage access to the passport-free Schengen travel zone.
The UK was granted access to this database in 2015, even if it’s not an official member of the Schengen zone.
According to the report, UK officials made copies of this database and stored it at airports and ports in unsafe conditions. Furthermore, by making copies, the UK was always working with outdated versions of the database.
This meant UK officials wouldn’t know in time if a person was removed from SIS, resulting in unnecessary detainments, or if a person was added to the database, allowing criminals to move through the UK and into the Schengen travel zone.
Furthermore, they also mismanaged and misused this data by providing unsanctioned access to this highly-sensitive and secret information to third-party contractors, including US companies (IBM, ATOS, CGI, and others).
The report expressed concerns that by doing so, the UK indirecly allowed contractors to copy this data as well, or allow US officials to request the database from a contractor under the US Patriot Act.
Organisations that deploy Facebook’s ubiquitous “Like” button on their websites risk falling foul of the General Data Protection Regulation following a landmark ruling by the European Court of Justice.
The EU’s highest court has decided that website owners can be held liable for data collection when using the so-called “social sharing” widgets.
The ruling (PDF) states that employing such widgets would make the organisation a joint data controller, along with Facebook – and judging by its recent record, you don’t want to be anywhere near Zuckerberg’s antisocial network when privacy regulators come a-calling.
‘Purposes of data processing’
According to the court, website owners “must provide, at the time of their collection, certain information to those visitors such as, for example, its identity and the purposes of the [data] processing”.
By extension, the ECJ’s decision also applies to services like Twitter and LinkedIn.
Facebook’s “Like” is far from an innocent expression of affection for a brand or a message: its primary purpose is to track individuals across websites, and permit data collection even when they are not explicitly using any of Facebook’s products.
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On Monday, the ECJ ruled that Fashion ID could be considered a joint data controller “in respect of the collection and transmission to Facebook of the personal data of visitors to its website”.
The court added that it was not, in principle, “a controller in respect of the subsequent processing of those data carried out by Facebook alone”.
‘Consent’
“Thus, with regard to the case in which the data subject has given his or her consent, the Court holds that the operator of a website such as Fashion ID must obtain that prior consent (solely) in respect of operations for which it is the (joint) controller, namely the collection and transmission of the data,” the ECJ said.
The concept of “data controller” – the organisation responsible for deciding how the information collected online will be used – is a central tenet of both DPR and GDPR. The controller has more responsibilities than the data processor, who cannot change the purpose or use of the particular dataset. It is the controller, not the processor, who would be held accountable for any GDPR sins.
How is this possible? In the simplest of terms, the scientists measured the electrooculographic signals generated when eyes make specific movements (up, down, left, right, blink, double blink) and created a soft biomimetic lens that responds directly to those electric impulses. The lens created was able to change its focal length depending on the signals generated.
Therefore the lens could literally zoom in the blink of an eye.
Incredibly, the lens works regardless of whether the user can see or not. It’s not about the sight, it’s about the electricity produced by specific movements.
Modern aircraft systems are becoming increasingly reliant on networked communications systems to display information to the pilot as well as control various systems aboard aircraft. Small aircraft typically maintain the direct mechanical linkage between the flight controls and the flight surface. However, electronic controls for flaps, trim, engine controls, and autopilot systems are becoming more common. This is similar to how most modern automobiles no longer have a physical connection between the throttle and the actuator that causes the engine to accelerate.
Before digital systems became common within aircraft instrumentation, the gauges and flight instruments would rely on mechanical and simple electrical controls that were directly connected to the source of the data they were displaying to the pilot. For example, the altitude and airspeed indicators would be connected to devices that measure the speed of airflow through a tube as well as the pressure outside the aircraft. In addition, the attitude and directional indicators would be powered by a vacuum source that drove a mechanical gyroscope. The flight surfaces would be directly connected to the pilot’s control stick or yoke—on larger aircraft, this connection would be via a hydraulic interface. Some flight surfaces, such as flaps and trim tabs, would have simple electrical connections that would directly turn motors on and off.
Modern aircraft use a network of electronics to translate signals from the various sensors and place this data onto a network to be interpreted by the appropriate instruments and displayed to the pilot. Together, the physical network, called a “vehicle bus,” and a common communications method called Controller Area Network (CAN) create the “CAN bus,” which serves as the central nervous system of a vehicle using this method. In avionics, these systems provide the foundation of control systems and sensor systems and collect data such as altitude, airspeed, and engine parameters such as fuel level and oil pressure, then display them to the pilot.
After performing a thorough investigation on two commercially available avionics systems, Rapid7 demonstrated that it was possible for a malicious individual to send false data to these systems, given some level of physical access to a small aircraft’s wiring. Such an attacker could attach a device—or co-opt an existing attached device—to an avionics CAN bus in order to inject false measurements and communicate them to the pilot. These false measurements may include the following:
Incorrect engine telemetry readings
Incorrect compass and attitude data
Incorrect altitude, airspeed, and angle of attack (AoA) data
In some cases, unauthenticated commands could also be injected into the CAN bus to enable or disable autopilot or inject false measurements to manipulate the autopilot’s responses. A pilot relying on these instrument readings would not be able to tell the difference between false data and legitimate readings, so this could result in an emergency landing or a catastrophic loss of control of an affected aircraft.
While the impact of such an attack could be dire, we want to emphasize that this attack requires physical access, something that is highly regulated and controlled in the aviation sector. While we believe that relying wholly on physical access controls is unwise, such controls do make it much more difficult for an attacker to access the CAN bus and take control of the avionics systems.
A hacker raided Capital One’s cloud storage buckets and stole personal information on 106 million credit card applicants in America and Canada.
The swiped data includes 140,000 US social security numbers and 80,000 bank account numbers, we’re told, as well as one million Canadian social insurance numbers, plus names, addresses, phone numbers, dates of birth, and reported incomes.
The pilfered data was submitted to Capital One by credit card hopefuls between 2005 and early 2019. The info was siphoned between March this year and July 17, and Capital One learned of the intrusion on July 19.
Seattle software engineer Paige A. Thompson, aka “erratic,” aka 0xA3A97B6C on Twitter, was suspected of nicking the data, and was collared by the FBI at her home on Monday this week. The 33-year-old has already appeared in court, charged with violating the US Computer Fraud and Abuse Act. She will remain in custody until her next hearing on August 1.
According to the Feds in their court paperwork [PDF], Thompson broke into Capital One’s cloud-hosted storage, believed to be Amazon Web Services’ S3 buckets, and downloaded their contents.
The financial giant said the intruder exploited a “configuration vulnerability,” while the Feds said a “firewall misconfiguration permitted commands to reach and be executed” by Capital One’s cloud-based storage servers. US prosecutors said the thief slipped past a “misconfigured web application firewall.”
Basically they don’t like data being shared with third parties doing predictive profiling with the data and they don’t like all the telemetry being sent everywhere, nor do they like MS being able to view and running through content such as text, pictures and videos.
The way the robots work is simple. Guided by cameras and computer systems trained to recognize specific objects, the robots’ arms glide over moving conveyor belts until they reach their target. Oversized tongs or fingers with sensors that are attached to the arms snag cans, glass, plastic containers, and other recyclable items out of the rubbish and place them into nearby bins.
The robots — most of which have come online only within the past year — are assisting human workers and can work up to twice as fast. With continued improvements in the bots’ ability to spot and extract specific objects, they could become a formidable new force in the $6.6 billion U.S. industry.
Researchers like Lily Chin, a PhD. student at the Distributed Robotics Lab at MIT, are working to develop sensors for these robots that can improve their tactile capabilities and improve their sense of touch so they can determine plastic, paper and metal through their fingers. “Right now, robots are mostly reliant on computer vision, but they can get confused and make mistakes,” says Chin. “So now we want to integrate these new tactile capabilities.”
Denver-based AMP Robotics, is one of the companies on the leading edge of innovation in the field. It has developed software — a AMP Neuron platform that uses computer vision and machine learning — so robots can recognize different colors, textures, shapes, sizes and patterns to identify material characteristics so they can sort waste.
The robots are being installed at the Single Stream Recyclers plant in Sarasota, Florida and they will be able to pick 70 to 80 items a minute, twice as fast as humanly possible and with greater accuracy.
Bulk Handling Systems Max-AI AQC-C robot
Bulk Handling Systems
“Using this technology you can increase the quality of the material and in some cases double or triple its resale value,” says AMP Robotics CEO Mantaya Horowitz. “Quality standards are getting stricter that’s why companies and researchers are working on high tech solutions.”
The encryption debate is typically framed around the concept of an impenetrable link connecting two services whose communications the government wishes to monitor. The reality, of course, is that the security of that encryption link is entirely separate from the security of the devices it connects. The ability of encryption to shield a user’s communications rests upon the assumption that the sender and recipient’s devices are themselves secure, with the encrypted channel the only weak point.
After all, if either user’s device is compromised, unbreakable encryption is of little relevance.
This is why surveillance operations typically focus on compromising end devices, bypassing the encryption debate entirely. If a user’s cleartext keystrokes and screen captures can be streamed off their device in real-time, it matters little that they are eventually encrypted for transmission elsewhere.
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Facebook announced earlier this year preliminary results from its efforts to move a global mass surveillance infrastructure directly onto users’ devices where it can bypass the protections of end-to-end encryption.
In Facebook’s vision, the actual end-to-end encryption client itself such as WhatsApp will include embedded content moderation and blacklist filtering algorithms. These algorithms will be continually updated from a central cloud service, but will run locally on the user’s device, scanning each cleartext message before it is sent and each encrypted message after it is decrypted.
The company even noted that when it detects violations it will need to quietly stream a copy of the formerly encrypted content back to its central servers to analyze further, even if the user objects, acting as true wiretapping service.
Facebook’s model entirely bypasses the encryption debate by globalizing the current practice of compromising devices by building those encryption bypasses directly into the communications clients themselves and deploying what amounts to machine-based wiretaps to billions of users at once.
Asked the current status of this work and when it might be deployed in the production version of WhatsApp, a company spokesperson declined to comment.
Of course, Facebook’s efforts apply only to its own encryption clients, leaving criminals and terrorists to turn to other clients like Signal or their own bespoke clients they control the source code of.
The problem is that if Facebook’s model succeeds, it will only be a matter of time before device manufacturers and mobile operating system developers embed similar tools directly into devices themselves, making them impossible to escape. Embedding content scanning tools directly into phones would make it possible to scan all apps, including ones like Signal, effectively ending the era of encrypted communications.
Governments would soon use lawful court orders to require companies to build in custom filters of content they are concerned about and automatically notify them of violations, including sending a copy of the offending content.
Rather than grappling with how to defeat encryption, governments will simply be able to harness social media companies to perform their mass surveillance for them, sending them real-time alerts and copies of the decrypted content.
Update 4/8/19 Bruce Schneier is convinced that this story has been concocted from a single source and Facebook is not in fact planning to do this currently. I’m inclined to agree.
Deep TabNine is what’s known as a coding autocompleter. Programmers can install it as an add-on in their editor of choice, and when they start writing, it’ll suggest how to continue each line, offering small chunks at a time. Think of it as Gmail’s Smart Compose feature but for code.
Jacob Jackson, the computer science undergrad at the University of Waterloo who created Deep TabNine, says this sort of software isn’t new, but machine learning has hugely improved what it can offer. “It’s solved a problem for me,” he tells The Verge.
Jackson started work on the original version of the software, TabNine, in February last year before launching it that November. But earlier this month, he released an updated version that uses a deep learning text-generation algorithm called GPT-2, which was designed by the research lab OpenAI, to improve its abilities. The update has seriously impressed coders, who have called it “amazing,” “insane,” and “absolutely mind-blowing” on Twitter.
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Deep TabNine is trained on 2 million files from coding repository GitHub. It finds patterns in this data and uses them to suggest what’s likely to appear next in any given line of code, whether that’s a variable name or a function.
Using deep learning to create autocompletion software offers several advantages, says Jackson. It makes it easy to add support for new languages, for a start. You only need to drop more training data into Deep TabNine’s hopper, and it’ll dig out patterns, he says. This means that Deep TabNine supports some 22 different coding languages while most alternatives just work with one.
(The full list of languages Deep TabNine supports are as follows: Python, JavaScript, Java, C++, C, PHP, Go, C#, Ruby, Objective-C, Rust, Swift, TypeScript, Haskell, OCaml, Scala, Kotlin, Perl, SQL, HTML, CSS, and Bash.)
Most importantly, thanks to the analytical abilities of deep learning, the suggestions Deep TabNine makes are of a high overall quality. And because the software doesn’t look at users’ own code to make suggestions, it can start helping with projects right from the word go, rather than waiting to get some cues from the code the user writes.
The software isn’t perfect, of course. It makes mistakes in its suggestions and isn’t useful for all types of coding. Users on various programming hang-outs like Hacker News and the r/programming subreddit have debated its merits and offered some mixedreviews (though they mostly skew positive). As you’d expect from a coding tool built for coders, people have a lot to say about how exactly it works with their existing editors and workflow.
One complaint that Jackson agrees is legitimate is that Deep TabNine is more suited to certain types of coding. It works best when autocompleting relatively rote code, the sort of programming that’s been done thousands of times with small variations. It’s less able to write exploratory code, where the user is solving a novel problem. That makes sense considering that the software’s smarts come from patterns found in archival data.
Deep TabNine being used to write some C++.
So how useful is it really for your average coder? That’ll depend on a whole lot of factors, like what programming language they use and what they’re trying to achieve. But Jackson says it’s more like a faster input method than a human coding partner (a common practice known as pair programming).
For example, aspirin was discovered in 1897, and an explanation of how it works followed in 1995. That, in turn, has spurred some research leads on making better pain relievers through something other than trial and error.
This kind of discovery — answers first, explanations later — I call “intellectual debt.” We gain insight into what works without knowing why it works. We can put that insight to use immediately, and then tell ourselves we’ll figure out the details later. Sometimes we pay off the debt quickly; sometimes, as with aspirin, it takes a century; and sometimes we never pay it off at all.
Be they of money or ideas, loans can offer great leverage. We can get the benefits of money — including use as investment to produce more wealth — before we’ve actually earned it, and we can deploy new ideas before having to plumb them to bedrock truth.
Indebtedness also carries risks. For intellectual debt, these risks can be quite profound, both because we are borrowing as a society, rather than individually, and because new technologies of artificial intelligence — specifically, machine learning — are bringing the old model of drug discovery to a seemingly unlimited number of new areas of inquiry. Humanity’s intellectual credit line is undergoing an extraordinary, unasked-for bump up in its limit.
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Technical debt arises when systems are tweaked hastily, catering to an immediate need to save money or implement a new feature, while increasing long-term complexity. Anyone who has added a device every so often to a home entertainment system can attest to the way in which a series of seemingly sensible short-term improvements can produce an impenetrable rat’s nest of cables. When something stops working, this technical debt often needs to be paid down as an aggravating lump sum — likely by tearing the components out and rewiring them in a more coherent manner.
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Machine learning has made remarkable strides thanks to theoretical breakthroughs, zippy new hardware, and unprecedented data availability. The distinct promise of machine learning lies in suggesting answers to fuzzy, open-ended questions by identifying patterns and making predictions.
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Researchers have pointed out thorny problems of technical debt afflicting AI systems that make it seem comparatively easy to find a retiree to decipher a bank system’s COBOL. They describe how machine learning models become embedded in larger ones and then be forgotten, even as their original training data goes stale and their accuracy declines.
But machine learning doesn’t merely implicate technical debt. There are some promising approaches to building machine learning systems that in fact can offer some explanations — sometimes at the cost of accuracy — but they are the rare exceptions. Otherwise, machine learning is fundamentally patterned like drug discovery, and it thus incurs intellectual debt. It stands to produce answers that work, without offering any underlying theory. While machine learning systems can surpass humans at pattern recognition and predictions, they generally cannot explain their answers in human-comprehensible terms. They are statistical correlation engines — they traffic in byzantine patterns with predictive utility, not neat articulations of relationships between cause and effect. Marrying power and inscrutability, they embody Arthur C. Clarke’s observation that any sufficiently advanced technology is indistinguishable from magic.
But here there is no David Copperfield or Ricky Jay who knows the secret behind the trick. No one does. Machine learning at its best gives us answers as succinct and impenetrable as those of a Magic 8-Ball — except they appear to be consistently right. When we accept those answers without independently trying to ascertain the theories that might animate them, we accrue intellectual debt.
Electricity output was curtailed at six reactors by 0840 GMT on Thursday, while two other reactors were offline, data showed. High water temperatures and sluggish flows limit the ability to use river water to cool reactors.
In Germany, PreussenElektra, the nuclear unit of utility E.ON, said it would take its Grohnde reactor offline on Friday due to high temperatures in the Weser river.
The second heatwave in successive months to hit western Europe is expected to peak on Thursday with record temperatures seen in several towns in France.
Utility EDF, which operates France’s 58 nuclear reactors, said that generation at its Bugey, St-Alban and Tricastin nuclear power plants may be curbed until after July 26 because of the low flow rate and high temperatures of the Rhone.
Its two reactors at the 2,600 megawatt (MW) Golfech nuclear power plant in the south of France were offline due to high temperatures on the Garonne river.
EDF’s use of water from rivers as a coolant is regulated by law to protect plant and animal life and it is obliged to cut output in hot weather when water temperatures rise, or when river levels and flow rates are low.
Atomic power from France’s 58 reactors accounts for over 75 percent of its electricity needs. Available nuclear power supply was down 1.4 percentage points at 65.3% of total capacity compared with Wednesday.
A spokeswoman for grid operator RTE said that although electricity demand was expected to rise due to increased consumption for cooling, France had enough generation capacity to cover demand. Peak power demand could be above 59.7 GW reached the previous day.
First Amazon, then Google, and now Apple have all confirmed that their devices are not only listening to you, but complete strangers may be reviewing the recordings. Thanks to Siri, Apple contractors routinely catch intimate snippets of users’ private lives like drug deals, doctor’s visits, and sexual escapades as part of their quality control duties, the Guardian reported Friday.
As part of its effort to improve the voice assistant, “[a] small portion of Siri requests are analysed to improve Siri and dictation,” Apple told the Guardian. That involves sending these recordings sans Apple IDs to its international team of contractors to rate these interactions based on Siri’s response, amid other factors. The company further explained that these graded recordings make up less than 1 percent of daily Siri activations and that most only last a few seconds.
That isn’t the case, according to an anonymous Apple contractor the Guardian spoke with. The contractor explained that because these quality control procedures don’t weed out cases where a user has unintentionally triggered Siri, contractors end up overhearing conversations users may not ever have wanted to be recorded in the first place. Not only that, details that could potentially identify a user purportedly accompany the recording so contractors can check whether a request was handled successfully.
“There have been countless instances of recordings featuring private discussions between doctors and patients, business deals, seemingly criminal dealings, sexual encounters and so on. These recordings are accompanied by user data showing location, contact details, and app data,” the whistleblower told the Guardian.
And it’s frighteningly easy to activate Siri by accident. Most anything that sounds remotely like “Hey Siri” is likely to do the trick, as UK’s Secretary of Defense Gavin Williamson found out last year when the assistant piped up as he spoke to Parliament about Syria. The sound of a zipper may even be enough to activate it, according to the contractor. They said that of Apple’s devices, the Apple Watch and HomePod smart speaker most frequently pick up accidental Siri triggers, and recordings can last as long as 30 seconds.
While Apple told the Guardian the information collected from Siri isn’t connected to other data Apple may have on a user, the contractor told a different story:
“There’s not much vetting of who works there, and the amount of data that we’re free to look through seems quite broad. It wouldn’t be difficult to identify the person that you’re listening to, especially with accidental triggers—addresses, names and so on.”
Staff were told to report these accidental activations as technical problems, the worker told the paper, but there wasn’t guidance on what to do if these recordings captured confidential information.
All this makes Siri’s cutesy responses to users questions seem far less innocent, particularly its answer when you ask if it’s always listening: “I only listen when you’re talking to me.”
Fellow tech giants Amazon and Google have faced similar privacy scandals recently over recordings from their devices. But while these companies also have employees who monitor each’s respective voice assistant, users can revoke permissions for some uses of these recordings. Apple provides no such option in its products.
The majority of YouTube videos about the climate crisis oppose the scientific consensus and “hijack” technical terms to make them appear credible, a new study has found. Researchers have warned that users searching the video site to learn about climate science may be exposed to content that goes against mainstream scientific belief.
Dr Joachim Allgaier of RWTH Aachen University in Germany analysed 200 YouTube videos to see if they adhered to or challenged the scientific consensus. To do so, he chose 10 search terms:
The videos were then assessed to judge how closely they adhered to the scientific consensus, as represented by the findings of reports by UN Intergovernmental Panel on Climate Change (IPCC) from 2013 onwards.
These concluded that humans have been the “dominant cause” of global warming since the 1950s. However, Allgaier found that the message of 120 of the top 200 search results went against this view.
To avoid personalised results, Allgaier used the anonymisation tool Tor, which hides a computer’s IP address and means YouTube treats each search as coming from a different user.
The results for the search terms climate, climate change, climate science and global warming mostly reflected the scientific consensus view. Allgaier said this was because many contained excerpts from TV news programmes or documentaries.
The same could not be said for the results of searches related to chemtrails, climate engineering, climate hacking, climate manipulation, climate modification and geoengineering. Very few of these videos explained the scientific rationale behind their ideas, Allgaier said.
Using a technique to 3D-print liquids, the scientists created millimeter-size droplets from water, oil and iron-oxides. The liquid droplets keep their shape because some of the iron-oxide particles bind with surfactants — substances that reduce the surface tension of a liquid. The surfactants create a film around the liquid water, with some iron-oxide particles creating part of the filmy barrier, and the rest of the particles enclosed inside, Russell said.
The team then placed the millimeter-size droplets near a magnetic coil to magnetize them. But when they took the magnetic coil away, the droplets demonstrated an unseen behavior in liquids — they remained magnetized. (Magnetic liquids called ferrofluids do exist, but these liquids are only magnetized when in the presence of a magnetic field.)
When those droplets approached a magnetic field, the tiny iron-oxide particles all aligned in the same direction. And once they removed the magnetic field, the iron-oxide particles bound to the surfactant in the film were so jam-packed that they couldn’t move and so remained aligned. But those free-floating inside the droplet also remained aligned.
The scientists don’t fully understand how these particles hold onto the field, Russell said. Once they figure that out, there are many potential applications. For example, Russell imagines printing a cylinder with a non-magnetic middle and two magnetic caps. “The two ends would come together like a horseshoe magnet,” and be used as a mini “grabber,” he said.
In an even more bizarre application, imagine a mini liquid person — a smaller-scale version of the liquid T-1000 from the second “Terminator” movie — Russell said. Now imagine that parts of this mini liquid man are magnetized and parts aren’t. An external magnetic field could then force the little person to move its limbs like a marionette.
“For me, it sort of represents a sort of new state of magnetic materials,” Russell said. The findings were published on July 19 in the journal Science.
Stock trading service Robinhood has admitted today to storing some customers’ passwords in cleartext, according to emails the company has been sending to impacted customers, and seen by ZDNet.
“On Monday night, we discovered that some user credentials were stored in a readable format within our internal system,” the company said.
“We resolved the issue, and after thorough review, found no evidence that this information was accessed by anyone outside our response team.”
Robinhood is now resetting passwords out of an abundance of caution, despite not finding any evidence of abuse.
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Storing passwords in cleartext is a huge security blunder; however, Robinhood is in “good company.” This year alone, Facebook, Instagram, and Google have all admitted to storing users passwords in cleartext.
The scientific consensus that humans are causing global warming is likely to have passed 99%, according to the lead author of the most authoritative study on the subject, and could rise further after separate research that clears up some of the remaining doubts.
Three studies published in Nature and Nature Geoscience use extensive historical data to show there has never been a period in the last 2,000 years when temperature changes have been as fast and extensive as in recent decades.
It had previously been thought that similarly dramatic peaks and troughs might have occurred in the past, including in periods dubbed the Little Ice Age and the Medieval Climate Anomaly. But the three studies use reconstructions based on 700 proxy records of temperature change, such as trees, ice and sediment, from all continents that indicate none of these shifts took place in more than half the globe at any one time.
The Little Ice Age, for example, reached its extreme point in the 15th century in the Pacific Ocean, the 17th century in Europe and the 19th century elsewhere, says one of the studies. This localisation is markedly different from the trend since the late 20th century when records are being broken year after year over almost the entire globe, including this summer’s European heatwave.
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“There is no doubt left – as has been shown extensively in many other studies addressing many different aspects of the climate system using different methods and data sets,” said Stefan Brönnimann, from the University of Bern and the Pages 2K consortium of climate scientists.
Commenting on the study, other scientists said it was an important breakthrough in the “fingerprinting” task of proving how human responsibility has changed the climate in ways not seen in the past.
“This paper should finally stop climate change deniers claiming that the recent observed coherent global warming is part of a natural climate cycle. This paper shows the truly stark difference between regional and localised changes in climate of the past and the truly global effect of anthropogenic greenhouse emissions,” said Mark Maslin, professor of climatology at University College London.
Previous studies have shown near unanimity among climate scientists that human factors – car exhausts, factory chimneys, forest clearance and other sources of greenhouse gases – are responsible for the exceptional level of global warming.
A 2013 study in Environmental Research Letters found 97% of climate scientists agreed with this link in 12,000 academic papers that contained the words “global warming” or “global climate change” from 1991 to 2011. Last week, that paper hit 1m downloads, making it the most accessed paper ever among the 80+ journals published by the Institute of Physics, according to the authors.
