Daily Archives: July 29, 2019

Meet the AI robots being used to help solve America’s recycling crisis

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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.

CNBC: trash seperating robot
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.”

Source: Meet the robots being used to help solve America’s recycling crisis

Facebook’s answer to the encryption debate: install spyware with content filters! (updated: maybe not)

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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.

[…]

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.

Source: The Encryption Debate Is Over – Dead At The Hands Of Facebook

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.

source: More on Backdooring (or Not) WhatsApp

Deep TabNine AI-powered autocompletion software is Gmail’s Smart Compose for coders

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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.

[…]

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 mixed reviews (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).

Source: This AI-powered autocompletion software is Gmail’s Smart Compose for coders – The Verge