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Fortify raises a $20M Series B for its composite manufacturing 3D printer

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There’s been quite a bit of movement in the additive manufacturing space in recent months. If I had to pinpoint a reason, I would say that — much like robotics (another space I follow fairly closely) — the category has gotten a boost in interest from the pandemic. Medical applications are understandably of interest lately, as is alternative manufacturing.

Desktop Metal, Markforged and new-comer Mantel have all made pretty big announcements in recent weeks, and now Fortify is making the round with a significant raise. The Boston-based startup announced a $20 million Series B equity round, led by Cota Capital with additional participation from Accel Partners, Neotribe Ventures and Prelude Ventures.

Fortify is attempting to stake out a claim in material deposits. Using digital light processing (DLP) tech, the company can mix and print in a variety of different materials, with a wide range of properties. The list includes some useful traits, including electromagnetic and thermal.

Like Mantel, the company looks to be targeting manufacturing tools, including injection molding.

“Fortify has been focused on proving the viability of our product and market opportunity over the past 18+ months, and exceeded our goals set at the beginning of 2020,” CEO Josh Martin said in a release. “This next round will expand our go-to-market footprint in key verticals such as injection mold tooling while enabling us to capture market share in end-use electronic devices.”

Recent months have also found the company enlisting other 3D printing vets. Paul Dresens (ex Desktop Metal) signed on as VP of Engineering, while former GrabCad (a Stratasys acquisition) market exec Rob Stevens has signed on as an advisor.

 

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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Facebook faces ‘mass action’ lawsuit in Europe over 2019 breach

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Facebook is to be sued in Europe over the major leak of user data that dates back to 2019 but which only came to light recently after information on 533M+ accounts was found posted for free download on a hacker forum.

Today Digital Rights Ireland (DRI) announced it’s commencing a “mass action” to sue Facebook, citing the right to monetary compensation for breaches of personal data that’s set out in the European Union’s General Data Protection Regulation (GDPR).

Article 82 of the GDPR provides for a ‘right to compensation and liability’ for those affected by violations of the law. Since the regulation came into force, in May 2018, related civil litigation has been on the rise in the region.

The Ireland-based digital rights group is urging Facebook users who live in the European Union or European Economic Area to check whether their data was breach — via the haveibeenpwned website (which lets you check by email address or mobile number) — and sign up to join the case if so.

Information leaked via the breach includes Facebook IDs, location, mobile phone numbers, email address, relationship status and employer.

Facebook has been contacted for comment on the litigation.

The tech giant’s European headquarters is located in Ireland — and earlier this week the national data watchdog opened an investigation, under EU and Irish data protection laws.

A mechanism in the GDPR for simplifying investigation of cross-border cases means Ireland’s Data Protection Commission (DPC) is Facebook’s lead data regulator in the EU. However it has been criticized over its handling of and approach to GDPR complaints and investigations — including the length of time it’s taking to issue decisions on major cross-border cases. And this is particularly true for Facebook.

With the three-year anniversary of the GDPR fast approaching, the DPC has multiple open investigations into various aspects of Facebook’s business but has yet to issue a single decision against the company.

(The closest it’s come is a preliminary suspension order issued last year, in relation to Facebook’s EU to US data transfers. However that complaint long predates GDPR; and Facebook immediately filed to block the order via the courts. A resolution is expected later this year after the litigant filed his own judicial review of the DPC’s processes).

Since May 2018 the EU’s data protection regime has — at least on paper — baked in fines of up to 4% of a company’s global annual turnover for the most serious violations.

Again, though, the sole GDPR fine issued to date by the DPC against a tech giant (Twitter) is very far off that theoretical maximum. Last December the regulator announced a €450k (~$547k) sanction against Twitter — which works out to around just 0.1% of the company’s full-year revenue.

That penalty was also for a data breach — but one which, unlike the Facebook leak, had been publicly disclosed when Twitter found it in 2019. So Facebook’s failure to disclose the vulnerability it discovered and claims it fixed by September 2019, which led to the leak of 533M accounts now, suggests it should face a higher sanction from the DPC than Twitter received.

However even if Facebook ends up with a more substantial GDPR penalty for this breach the watchdog’s caseload backlog and plodding procedural pace makes it hard to envisage a swift resolution to an investigation that’s only a few days old.

Judging by past performance it’ll be years before the DPC decides on this 2019 Facebook leak — which likely explains why the DRI sees value in instigating class-action style litigation in parallel to the regulatory investigation.

“Compensation is not the only thing that makes this mass action worth joining. It is important to send a message to large data controllers that they must comply with the law and that there is a cost to them if they do not,” DRI writes on its website.

It also submitted a complaint about the Facebook breach to the DPC earlier this month, writing then that it was “also consulting with its legal advisors on other options including a mass action for damages in the Irish Courts”.

It’s clear that the GDPR enforcement gap is creating a growing opportunity for litigation funders to step in in Europe and take a punt on suing for data-related compensation damages — with a number of other mass actions announced last year.

In the case of DRI its focus is evidently on seeking to ensure that digital rights are upheld. But it told RTE that it believes compensation claims which force tech giants to pay money to users whose privacy rights have been violated is the best way to make them legally compliant.

Facebook, meanwhile, has sought to play down the breach it failed to disclose in 2019 — claiming it’s ‘old data’ — a deflection that ignores the fact that people’s dates of birth don’t change (nor do most people routinely change their mobile number or email address).

Plenty of the ‘old’ data exposed in this latest massive Facebook leak will be very handy for spammers and fraudsters to target Facebook users — and also now for litigators to target Facebook for data-related damages.

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Geoffrey Hinton has a hunch about what’s next for AI

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Back in November, the computer scientist and cognitive psychologist Geoffrey Hinton had a hunch. After a half-century’s worth of attempts—some wildly successful—he’d arrived at another promising insight into how the brain works and how to replicate its circuitry in a computer.

“It’s my current best bet about how things fit together,” Hinton says from his home office in Toronto, where he’s been sequestered during the pandemic. If his bet pays off, it might spark the next generation of artificial neural networks—mathematical computing systems, loosely inspired by the brain’s neurons and synapses, that are at the core of today’s artificial intelligence. His “honest motivation,” as he puts it, is curiosity. But the practical motivation—and, ideally, the consequence—is more reliable and more trustworthy AI.

A Google engineering fellow and cofounder of the Vector Institute for Artificial Intelligence, Hinton wrote up his hunch in fits and starts, and at the end of February announced via Twitter that he’d posted a 44-page paper on the arXiv preprint server. He began with a disclaimer: “This paper does not describe a working system,” he wrote. Rather, it presents an “imaginary system.” He named it, “GLOM.” The term derives from “agglomerate” and the expression “glom together.”

Hinton thinks of GLOM as a way to model human perception in a machine—it offers a new way to process and represent visual information in a neural network. On a technical level, the guts of it involve a glomming together of similar vectors. Vectors are fundamental to neural networks—a vector is an array of numbers that encodes information. The simplest example is the xyz coordinates of a point—three numbers that indicate where the point is in three-dimensional space. A six-dimensional vector contains three more pieces of information—maybe the red-green-blue values for the point’s color. In a neural net, vectors in hundreds or thousands of dimensions represent entire images or words. And dealing in yet higher dimensions, Hinton believes that what goes on in our brains involves “big vectors of neural activity.”

By way of analogy, Hinton likens his glomming together of similar vectors to the dynamic of an echo chamber—the amplification of similar beliefs. “An echo chamber is a complete disaster for politics and society, but for neural nets it’s a great thing,” Hinton says. The notion of echo chambers mapped onto neural networks he calls “islands of identical vectors,” or more colloquially, “islands of agreement”—when vectors agree about the nature of their information, they point in the same direction.

“If neural nets were more like people, at least they can go wrong the same ways as people do, and so we’ll get some insight into what might confuse them.”

Geoffrey Hinton

In spirit, GLOM also gets at the elusive goal of modelling intuition—Hinton thinks of intuition as crucial to perception. He defines intuition as our ability to effortlessly make analogies. From childhood through the course of our lives, we make sense of the world by using analogical reasoning, mapping similarities from one object or idea or concept to another—or, as Hinton puts it, one big vector to another. “Similarities of big vectors explain how neural networks do intuitive analogical reasoning,” he says. More broadly, intuition captures that ineffable way a human brain generates insight. Hinton himself works very intuitively—scientifically, he is guided by intuition and the tool of analogy making. And his theory of how the brain works is all about intuition. “I’m very consistent,” he says.

Hinton hopes GLOM might be one of several breakthroughs that he reckons are needed before AI is capable of truly nimble problem solving—the kind of human-like thinking that would allow a system to make sense of things never before encountered; to draw upon similarities from past experiences, play around with ideas, generalize, extrapolate, understand. “If neural nets were more like people,” he says, “at least they can go wrong the same ways as people do, and so we’ll get some insight into what might confuse them.”

For the time being, however, GLOM itself is only an intuition—it’s “vaporware,” says Hinton. And he acknowledges that as an acronym nicely matches, “Geoff’s Last Original Model.” It is, at the very least, his latest.

Outside the box

Hinton’s devotion to artificial neural networks (a mid-2oth century invention) dates to the early 1970s. By 1986 he’d made considerable progress: whereas initially nets comprised only a couple of neuron layers, input and output, Hinton and collaborators came up with a technique for a deeper, multilayered network. But it took 26 years before computing power and data capacity caught up and capitalized on the deep architecture.

In 2012, Hinton gained fame and wealth from a deep learning breakthrough. With two students, he implemented a multilayered neural network that was trained to recognize objects in massive image data sets. The neural net learned to iteratively improve at classifying and identifying various objects—for instance, a mite, a mushroom, a motor scooter, a Madagascar cat. And it performed with unexpectedly spectacular accuracy.

Deep learning set off the latest AI revolution, transforming computer vision and the field as a whole. Hinton believes deep learning should be almost all that’s needed to fully replicate human intelligence.

But despite rapid progress, there are still major challenges. Expose a neural net to an unfamiliar data set or a foreign environment, and it reveals itself to be brittle and inflexible. Self-driving cars and essay-writing language generators impress, but things can go awry. AI visual systems can be easily confused: a coffee mug recognized from the side would be an unknown from above if the system had not been trained on that view; and with the manipulation of a few pixels, a panda can be mistaken for an ostrich, or even a school bus.

GLOM addresses two of the most difficult problems for visual perception systems: understanding a whole scene in terms of objects and their natural parts; and recognizing objects when seen from a new viewpoint.(GLOM’s focus is on vision, but Hinton expects the idea could be applied to language as well.)

An object such as Hinton’s face, for instance, is made up of his lively if dog-tired eyes (too many people asking questions; too little sleep), his mouth and ears, and a prominent nose, all topped by a not-too-untidy tousle of mostly gray. And given his nose, he is easily recognized even on first sight in profile view.

Both of these factors—the part-whole relationship and the viewpoint—are, from Hinton’s perspective, crucial to how humans do vision. “If GLOM ever works,” he says, “it’s going to do perception in a way that’s much more human-like than current neural nets.”

Grouping parts into wholes, however, can be a hard problem for computers, since parts are sometimes ambiguous. A circle could be an eye, or a doughnut, or a wheel. As Hinton explains it, the first generation of AI vision systems tried to recognize objects by relying mostly on the geometry of the part-whole-relationship—the spatial orientation among the parts and between the parts and the whole. The second generation instead relied mostly on deep learning—letting the neural net train on large amounts of data. With GLOM, Hinton combines the best aspects of both approaches.

“There’s a certain intellectual humility that I like about it,” says Gary Marcus, founder and CEO of Robust.AI and a well-known critic of the heavy reliance on deep learning. Marcus admires Hinton’s willingness to challenge something that brought him fame, to admit it’s not quite working. “It’s brave,” he says. “And it’s a great corrective to say, ‘I’m trying to think outside the box.’”

The GLOM architecture

In crafting GLOM, Hinton tried to model some of the mental shortcuts—intuitive strategies, or heuristics—that people use in making sense of the world. “GLOM, and indeed much of Geoff’s work, is about looking at heuristics that people seem to have, building neural nets that could themselves have those heuristics, and then showing that the nets do better at vision as a result,” says Nick Frosst, a computer scientist at a language startup in Toronto who worked with Hinton at Google Brain.

With visual perception, one strategy is to parse parts of an object—such as different facial features—and thereby understand the whole. If you see a certain nose, you might recognize it as part of Hinton’s face; it’s a part-whole hierarchy. To build a better vision system, Hinton says, “I have a strong intuition that we need to use part-whole hierarchies.” Human brains understand this part-whole composition by creating what’s called a “parse tree”—a branching diagram demonstrating the hierarchical relationship between the whole, its parts and subparts. The face itself is at the top of the tree, and the component eyes, nose, ears, and mouth form the branches below.

One of Hinton’s main goals with GLOM is to replicate the parse tree in a neural net—this is would distinguish it from neural nets that came before. For technical reasons, it’s hard to do. “It’s difficult because each individual image would be parsed by a person into a unique parse tree, so we would want a neural net to do the same,” says Frosst. “It’s hard to get something with a static architecture—a neural net—to take on a new structure—a parse tree—for each new image it sees.” Hinton has made various attempts. GLOM is a major revision of his previous attempt in 2017, combined with other related advances in the field.

“I’m part of a nose!”

GLOM vector

Hinton face grid

MS TECH | EVIATAR BACH VIA WIKIMEDIA

A generalized way of thinking about the GLOM architecture is as follows: The image of interest (say, a photograph of Hinton’s face) is divided into a grid. Each region of the grid is a “location” on the image—one location might contain the iris of an eye, while another might contain the tip of his nose. For each location in the net there are about five layers, or levels. And level by level, the system makes a prediction, with a vector representing the content or information. At a level near the bottom, the vector representing the tip-of-the-nose location might predict: “I’m part of a nose!” And at the next level up, in building a more coherent representation of what it’s seeing, the vector might predict: “I’m part of a face at side-angle view!”

But then the question is, do neighboring vectors at the same level agree? When in agreement, vectors point in the same direction, toward the same conclusion: “Yes, we both belong to the same nose.” Or further up the parse tree. “Yes, we both belong to the same face.”

Seeking consensus about the nature of an object—about what precisely the object is, ultimately—GLOM’s vectors iteratively, location-by-location and layer-upon-layer, average with neighbouring vectors beside, as well as predicted vectors from levels above and below.

However, the net doesn’t “willy-nilly average” with just anything nearby, says Hinton. It averages selectively, with neighboring predictions that display similarities. “This is kind of well-known in America, this is called an echo chamber,” he says. “What you do is you only accept opinions from people who already agree with you; and then what happens is that you get an echo chamber where a whole bunch of people have exactly the same opinion. GLOM actually uses that in a constructive way.” The analogous phenomenon in Hinton’s system is those “islands of agreement.”

“Geoff is a highly unusual thinker…”

Sue Becker

“Imagine a bunch of people in a room, shouting slight variations of the same idea,” says Frosst—or imagine those people as vectors pointing in slight variations of the same direction. “They would, after a while, converge on the one idea, and they would all feel it stronger, because they had it confirmed by the other people around them.” That’s how GLOM’s vectors reinforce and amplify their collective predictions about an image.

GLOM uses these islands of agreeing vectors to accomplish the trick of representing a parse tree in a neural net. Whereas some recent neural nets use agreement among vectors for activation, GLOM uses agreement for representation—building up representations of things within the net. For instance, when several vectors agree that they all represent part of the nose, their small cluster of agreement collectively represents the nose in the net’s parse tree for the face. Another smallish cluster of agreeing vectors might represent the mouth in the parse tree; and the big cluster at the top of the tree would represent the emergent conclusion that the image as a whole is Hinton’s face. “The way the parse tree is represented here,” Hinton explains, “is that at the object level you have a big island; the parts of the object are smaller islands; the subparts are even smaller islands, and so on.”

Figure 2 from Hinton’s GLOM paper. The islands of identical vectors (arrows of the same color) at the various levels represent a parse tree.
GEOFFREY HINTON

According to Hinton’s long-time friend and collaborator Yoshua Bengio, a computer scientist at the University of Montreal, if GLOM manages to solve the engineering challenge of representing a parse tree in a neural net, it would be a feat—it would be important for making neural nets work properly. “Geoff has produced amazingly powerful intuitions many times in his career, many of which have proven right,” Bengio says. “Hence, I pay attention to them, especially when he feels as strongly about them as he does about GLOM.”

The strength of Hinton’s conviction is rooted not only in the echo chamber analogy, but also in mathematical and biological analogies that inspired and justified some of the design decisions in GLOM’s novel engineering.

“Geoff is a highly unusual thinker in that he is able to draw upon complex mathematical concepts and integrate them with biological constraints to develop theories,” says Sue Becker, a former student of Hinton’s, now a computational cognitive neuroscientist at McMaster University. “Researchers who are more narrowly focused on either the mathematical theory or the neurobiology are much less likely to solve the infinitely compelling puzzle of how both machines and humans might learn and think.”

Turning philosophy into engineering

So far, Hinton’s new idea has been well received, especially in some of the world’s greatest echo chambers. “On Twitter, I got a lot of likes,” he says. And a YouTube tutorial laid claim to the term “MeGLOMania.”

Hinton is the first to admit that at present GLOM is little more than philosophical musing (he spent a year as a philosophy undergrad before switching to experimental psychology). “If an idea sounds good in philosophy, it is good,” he says. “How would you ever have a philosophical idea that just sounds like rubbish, but actually turns out to be true? That wouldn’t pass as a philosophical idea.” Science, by comparison, is “full of things that sound like complete rubbish” but turn out to work remarkably well—for example, neural nets, he says.

GLOM is designed to sound philosophically plausible. But will it work?

Chris Williams, a professor of machine learning in the School of Informatics at the University of Edinburgh, expects that GLOM might well spawn great innovations. However, he says, “the thing that distinguishes AI from philosophy is that we can use computers to test such theories.” It’s possible that a flaw in the idea might be exposed—perhaps also repaired—by such experiments, he says. “At the moment I don’t think we have enough evidence to assess the real significance of the idea, although I believe it has a lot of promise.”

The GLOM test model inputs are ten ellipses that form a sheep or a face.
LAURA CULP

Some of Hinton’s colleagues at Google Research in Toronto are in the very early stages of investigating GLOM experimentally. Laura Culp, a software engineer who implements novel neural net architectures, is using a computer simulation to test whether GLOM can produce Hinton’s islands of agreement in understanding parts and wholes of an object, even when the input parts are ambiguous. In the experiments, the parts are 10 ellipses, ovals of varying sizes, that can be arranged to form either a face or a sheep.

With random inputs of one ellipse or another, the model should be able to make predictions, Culp says, and “deal with the uncertainty of whether or not the ellipse is part of a face or a sheep, and whether it is the leg of a sheep, or the head of a sheep.” Confronted with any perturbations, the model should be able to correct itself as well. A next step is establishing a baseline, indicating whether a standard deep-learning neural net would get befuddled by such a task. As yet, GLOM is highly supervised—Culp creates and labels the data, prompting and pressuring the model to find correct predictions and succeed over time. (The unsupervised version is named GLUM—“It’s a joke,” Hinton says.)

At this preliminary state, it’s too soon to draw any big conclusions. Culp is waiting for more numbers. Hinton is already impressed nonetheless. “A simple version of GLOM can look at 10 ellipses and see a face and a sheep based on the spatial relationships between the ellipses,” he says. “This is tricky, because an individual ellipse conveys nothing about which type of object it belongs to or which part of that object it is.”

And overall, Hinton is happy with the feedback. “I just wanted to put it out there for the community, so anybody who likes can try it out,” he says. “Or try some sub-combination of these ideas. And then that will turn philosophy into science.”

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Pakistan temporarily blocks social media

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Pakistan has temporarily blocked several social media services in the South Asian nation, according to users and a government-issued notice reviewed by TechCrunch.

In an order titled “Complete Blocking of Social Media Platforms,” the Pakistani government ordered Pakistan Telecommunication Authority to block social media platforms including Twitter, Facebook, WhatsApp, YouTube, and Telegram from 11am to 3pm local time (06.00am to 10.00am GMT) Friday.

The move comes as Pakistan looks to crackdown against a violent terrorist group and prevent troublemakers from disrupting Friday prayers congregations following days of violent protests.

Earlier this week Pakistan banned the Islamist group Tehrik-i-Labaik Pakistan after arresting its leader, which prompted protests, according to local media reports.

An entrepreneur based in Pakistan told TechCrunch that even though the order is supposed to expire at 3pm local time, similar past moves by the government suggests that the disruption will likely last for longer.

Though Pakistan, like its neighbor India, has temporarily cut phone calls access in the nation in the past, this is the first time Islamabad has issued a blanket ban on social media in the country.

Pakistan has explored ways to assume more control over content on digital services operating in the country in recent years. Some activists said the country was taking extreme measures without much explanations.

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