MIT Creates Light-Sensitive ‘Reprogrammable’ Ink



You probably had to agonize over colors the last time you bought a car, a pair of shoes, or anything else where color matters. What if you didn’t have to pick a single color, though? Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new color-changing ink that you can “reprogram” with light to produce different colors and patterns. Best of all, you can change the colors as many times as you want. 

MIT calls this “PhotoChromeleon” ink, and it’s a mix of multiple polychromatic dyes that can be spray-painted on almost any object. The team started by mixing standard automotive lacquer with cyan, yellow, and magenta polychromatic dyes. Each individual dye changes color when exposed to UV light. 

When you mix all three dyes together, you have a paint that can produce a wide range of colors. By understanding how each dye reacts to light, the researchers are able to precisely control each color channel. It’s similar to the way a modern inkjet printer uses individual cartridges of cyan, magenta, and yellow ink to create almost any shade you want. 

The team tested the programmable ink on various objects like a show, a phone case, and a (quite fittingly) a toy chameleon. The video below demonstrates how the clear ink becomes DLP projector shines high-intensity light onto the object’s surface as it spins on a turntable. However, it does take a few hours to create a pattern on small objects in the lab. The result is a colorized layer that looks like it was painted on. It was in a sense, but you can change the paint job whenever you want. 

This system is an evolution of ColorMod, a previous MIT experiment that used a 3D printer to create objects that could change color. In that case, the printer had to create each “pixel,” so the resulting patterns weren’t very clear. PhotoChromeleon produces crisp images, and you can quickly “reset” the ink with an ultraviolet light. 

MIT believes PhotoChromeleon technology could eventually make manufacturing more efficient and reduce waste. Imagine that you didn’t need to get two different pairs of shoes in different colors. You could just reset the color and change it to something else whenever you want. Bored with your phone case? Don’t buy a new one, just change the pattern with light. MIT is already working with Ford to advance the PhotoChromeleon project, which also supported the development of ColorMod.

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Joi Ito resigns as MIT Media Lab head in wake of Jeffrey Epstein reporting – gpgmail


Joichi Ito, the embattled director of the M.I.T. Media Lab, has stepped down according to a statement by MIT’s president, L. Rafael Reif. The news was first reported by The New York Times, which had received a copy of an email sent by Ito to university provost Martin A. Schmidt.

“After giving the matter a great deal of thought over the past several days and weeks,” the now-former director writes, “I think that it is best that I resign as director of the media lab and as a professor and employee of the Institute, effective immediately.”

In addition to resigning as director, Reif’s statement also confirmed that Ito resigned as a professor of the university.

The ‘matter’ to which the letter refers to is Ito’s reported connections to Jeffrey Epstein. The financier died in prison by hanging on August 10, following an arrest a month prior on federal charges of sex tracking minors.

Ito was among several high profile and powerful people whose alleged ties to the disgraced billionaire came into sharp focus following his arrest. In the immediate aftermath of that arrest, it came to light that the MIT Media Lab and Ito personally received funds from Epstein, to which Ito apologized in an August 15th letter.

The allegations against Ito intensified overnight following a report by Ronan Farrow in The New Yorker that Ito’s engagement of Epstein were far deeper than had been previously been acknowledged. According to emails and documents discovered by Farrow, Ito and MIT Media Lab’s head of development, Peter Cohen, worked in tandem to conceal Epstein’s contributions from MIT’s central fundraising office, such as by marking donations anonymous and keeping his name out of disclosure statements.

Ito has long stood firm that the facts of his relationship with Epstein have been misreported. In an email to the Times, Ito said that the New Yorker piece was “full of factual errors.”

In response to Farrow’s piece, M.I.T, president L. Rafael Reif in today’s statement said:

Because the accusations in the story are extremely serious, they demand an immediate, thorough and independent investigation. This morning, I asked MIT’s General Counsel to engage a prominent law firm to design and conduct this process. I expect the firm to conduct this review as swiftly as possible, and to report back to me and to the Executive Committee of the MIT Corporation, MIT’s governing board.

The MIT Media Lab is a storied research center with a long legacy of contributions to science, technology, and innovation. There are no indications yet on who might replace Ito.

In addition to the MIT Media Lab, Ito sits as a board director for The New York Times Company, where he sits on the company’s audit committee.

One can’t help but point out a perennial tweet that Ito wrote more than a decade ago about fundraising:


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Teaching ethics in computer science the right way with Georgia Tech’s Charles Isbell – gpgmail


The new fall semester is upon us, and at elite private colleges and universities, it’s hard to find a trendier major than Computer Science. It’s also becoming more common for such institutions to prioritize integrating ethics into their CS studies, so students don’t just learn about how to build software, but whether or not they should build it in the first place. Of course, this begs questions about how much the ethics lessons such prestigious schools are teaching are actually making a positive impression on students.

But at a time when demand for qualified computer scientists is skyrocketing around the world and far exceeds supply, another kind of question might be even more important: Can computer science be transformed from a field largely led by elites into a profession that empowers vastly more working people, and one that trains them in a way that promotes ethics and an awareness of their impact on the world around them?

Enter Charles Isbell of Georgia Tech, a humble and unassuming star of inclusive and ethical computer science. Isbell, a longtime CS professor at Georgia Tech, enters this fall as the new Dean and John P. Imlay Chair of Georgia Tech’s rapidly expanding College of Computing.

Isbell’s role is especially given Georgia Tech’s approximately 9,000 online graduate students in Computer Science. This astronomical number of students in the CS field is the result of a philosophical decision made at the university to create an online CS master’s degree treated as completely equal to on-campus training.

Another counterintuitive philosophical decision made at Georgia Tech — for which Isbell proudly evangelized while speaking at conferences like the MIT Technology Review’s EmTech Next, where I met him in June — is to admit every student who has the potential to earn a degree, rather than making any attempt at “exclusivity” by rejecting worthy candidates. In the coming years all of this may lead, Isbell projected at EmTech Next, to a situation in which up to one in eight of all people in the US who hold a graduate degree in CS will have earned it at Georgia Tech.

isbell 1

Isbell speaks to Gideon Lichfield, Editor-in-chief of the MIT Technology Review, at its EmTech Next conference in June. Image via MIT Technology Review.

“What they’ve done is pretty remarkable,” said Casey Fiesler, a 3x recent graduate of Georgia Tech and a founding faculty member and CS professor at the University of Colorado’s College of Media, Communication, and Information.

And it’s promising that Fiesler, who has become known in the tech ethics field for her comparative study of curricula and teaching approaches, told me, “ethics can be integrated into online [CS] courses just as easily as it can be into face to face courses.”

Still, it is as daunting as it is impressive to think about how one public school like Georgia Tech might be able to successfully and ethically educate such an enormous percentage of the students in arguably the most influential academic field in the world today. So I was glad to be able to speak to Isbell, an expert on statistical machine learning and artificial intelligence, for this gpgmail series on the ethics of technology.

Our conversation below covers the difference between equality and equity; cultural issues around women in American CS, and what it would look like for ethics to be so integrated into the discussion of computing that students and practitioners wouldn’t even think of it as ethics.

Greg Epstein: Around 1/8 of Computer Science graduate degrees will be delivered by your school in the coming years; you’re thinking inclusively about providing a relatively huge number of opportunities for people who would not otherwise get the opportunity to become computer scientists. How have you achieved that?

Charles Isbell: There’s an old joke about organizations: don’t tell me what your values are, show me your budget and then I’ll tell you what your values are. Because you spend money on the things that you care about.


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Elliptic banks $23M to shrink crypto risk, eyeing growth in Asia – gpgmail


Crypto means risk. To UK company Elliptic it also means business. The startup has just closed a $23M Series B to step up growth for a crypto risk-management play that involves selling tech and services to help others navigate the choppy darks of cryptocurrencies.

The round was led by financial services and asset management firm SBI Group, a Tokyo-based erstwhile subsidiary of SoftBank . Also joining as a new investor this round is London-based AlbionVC. Existing investors including SignalFire, Octopus Ventures and Santander Innoventures also participated. SBI Group’s Tomoyuki Nii and Ed Lascelles of AlbionVC are also joining Elliptic’s board.

Flush with a sizeable injection of Series B capital, Elliptic is especially targeting business growth at Asia — with a plan to open new offices in Japan and Singapore. It says client revenues in the region have risen 11x over the past two years.

We last spoke to Elliptic back in 2016 when it had just raised a $5M Series A.

The 2013-founded startup began by testing the crypto waters with a storage product before zeroing in on financial compliance as a pain-point worth its time. It went on to develop machine learning tech that screens transactions to identify suspicious patterns and, via them, dubious transactors.

Now it offers an integrated suite of products and services for financial institutions and crypto businesses to screen volumes of crypto-flows that sum to billions of dollars in transactions per day — analyzing them for links to illicit activity such as money laundering, terrorist financing, sanctions evasion, and other financial crimes.

It’s focused on selling anti-money laundering compliance, crypto forensics and cryptocurrency investigation services to the private sector — though has also sold tools direct to law enforcement agencies in the past.

Billions of dollars in financial services terms is of course just a tiny drop in a massive ocean of money movements. And growth in the crypto risk-management space has clearly required more than a little patience, from a startup perspective.

Three years ago Elliptic’s first blockchain analytics product had 10-20 Bitcoin companies as customers. That’s now up to 100+ crypto businesses and financial institutions using its products to shrink their risk of financial crime when dealing with crypto-assets. But the more three than year gap between Elliptic’s Series A and B is notable.

“To date, we’ve focused on product development and assembling the right team as the market has matured. This new funding will help us expand in the right way, namely by making the push into Asia without diluting our focus on the US and EMEA,” says co-founder and CEO James Smith when asked about the gap between financing rounds.

He declines to comment on how far off Elliptic is from achieving breakeven or profitability yet.

“We provide best-in-class transaction monitoring products for crypto-assets, which are trusted by crypto exchanges and financial institutions worldwide,” he adds of its product suite. “Our products are used as key components of larger compliance processes that are designed to minimise money laundering risks.”

With the addition of SBI Group to its investor roster Elliptic gains a strategic partner in Asia to help push what it dubs “bank-grade risk data” at a new wave of established financial institutions it believes are eyeing crypto with growing appetite for risk as larger players wade in.

Larger players like Facebook . Elliptic’s PR name-drops the likes of Facebook’s Libra cryptocurrency, Line Corporation’s LINK and central bank digital currencies, as markers of a rise in mainstream attention on crypto assets. And it says Series B funds will be used to accelerate product development to support “an emerging class of asset-backed crypto-assets”.

Regulatory attention on crypto — which has been rising globally for years but looks set to zip up several gears now that Facebook has ripped the curtain off of an ambitious global digital currency plan which also has buy-in from a number of other household tech and fintech names — is another claimed feed in for Elliptic’s business. More crypto implies growing risk.

It also points to the intergovernmental Financial Action Task Force’s global regulatory framework for crypto-assets as an example of some of the wider risk-based requirements and now wrapped around those dealing in crypto.

The focus on Asia for business expansion is a measure of relative maturity of interest in opportunities around crypto-assets and localized attention to regulation, according to Smith.

“Revenue growth is certainly very strong in this region. We have been working with customers in Asia for a number of years and have seen first-hand how vibrant their crypto-asset ecosystems are. Countries such as Singapore and Japan have developed clear crypto-asset regulatory frameworks, and businesses based in these countries are serious about meeting their compliance obligations,” he says.

“We have also found that traditional financial institutions in Asia are particularly keen to engage with crypto-assets, and we will be working with them as they take their first steps into this new asset class.”

“We believe that crypto-assets will play an increasingly important role in our everyday lives and are shaping the future of banking. Our investment in Elliptic is a further commitment to this belief and to SBI Holding’s appetite to help build the digital asset-related ecosystem,” adds Yoshitaka Kitao, CEO of the SBI Group, in a supporting statement.

“Elliptic’s pioneering approach is enabling the transparency, integrity, and trust necessary for this vision to become reality. We are seeing a growing demand for their services across our portfolio of crypto-assets related companies and view Elliptic as best-placed to meet this considerable opportunity.”

While Elliptic’s business is focused on reducing the risk for other businesses of inadvertently transacting with criminals using crypto to launder money or otherwise shift assets under the legal radar, the proportion of transactions that such illicit activity represents in the Bitcoin space represents a tiny fraction of overall transactions.

“According to our analysis, approximately $1BN in Bitcoin has been spent on the dark web, so far in 2019, on items ranging from narcotics to stolen credit cards. This represents a very small share of all Bitcoin activity — less than 0.5% of Bitcoin payments over this period,” says Smith.

Not that that diminishes the regulatory risk. Nor, therefore, the business opportunity for Elliptic to sell support services to help others avoid touching the hot stuff.

“Crypto money launderers are continually developing new techniques to cover their tracks — from the use of mixers to transacting in privacy coins such as monero,” Smith adds. “We are also constantly innovating to keep pace with this and help our clients to detect money laundering. For example our work with researchers from MIT and IBM demonstrated the application of deep learning techniques to the identification of illicit crypto-asset transactions.”


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MIT’s Autonomous ‘Roboats’ Learn to Shapeshift


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Self-driving cars may be the future for most cities, but Amsterdam is about one-quarter water thanks to its extensive canal network. So, maybe autonomous boats are worth exploring? MIT has been working with the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) to create just that. When we last checked in on the so-called “Roboat” vessels, they had just learned to reliably link up while floating on water. Now, MIT reports Roboats can shapeshift into different conformations in just a few minutes. 

Each individual Roboat is a fully fledged vessel, but the idea is not to have you hop on a single boat and use it as a taxi. From the start, MIT and AMS Institute have worked toward a multi-use role for the robotic vessels. Rather than designing different boats for different tasks, the Roboats can link up to become whatever people need. They could form bridges, stages, cargo transports, and floating busses. 

Progress has been swift for the Roboat project. In 2016, MIT tested a prototype boat that could move along pre-programmed routes. In 2018, it developed a method to 3D print the boats and tested advanced location tracking algorithms. Earlier this year, MIT and AMS Institute demoed the latching system that lets the robots link together on the water. 

The latest advance adds a layer of complexity to the Roboat docking system, marking a major step toward the goals of the project. MIT says it has developed new algorithms that allow the boats to smoothly reshape themselves in a few minutes. So, controllers can ask for a confirmation of Roboats like lines, squares, and L-shapes. The boats talk amongst themselves and determine the best way to morph into the desired shape. 

Testing the automated latching.

The shapes demonstrated in a pool at MIT are admittedly simple, consisting of just a handful of boats. However, the programming that went into it is still incredibly complex. Engineers had to make sure each boat was aware of its location relative to others, as well as how the group could move while shapeshifting to avoid collisions. To make that happen, the team created a division of labor. Both classes of Roboat have four propellers, wireless communication gear, and multiple docking hardpoints. The coordinators also have GPS and inertial measurement units (IMUs) that allow them to form the “core” of a structure. One or more worker Roboats connect to the coordinator and use actuators to help steer it. 

The current Roboats are one-quarter scale versions of the planned units. They’re about one meter long and half a meter wide. The team believes the trajectory-planning algorithms developed for the smaller boats will scale up to the full-sized ones when they exist in a few years.

Now read:




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MIT’s new thread-like robots could travel through blood vessels in the brain for more effective surgery – gpgmail


MIT has developed robotic thread that could make even the least invasive current brain surgery techniques even less so, and potentially make it easier and more accessible to treat brain blood vessel issues like blockages and lesions that can cause aneurysms and strokes.

The new development from MIT researchers combines robotics with current endovascular (i.e. within blood vessel) surgery techniques, reducing the risks associated with guiding incredibly thin wires through complicated brain blood vessel pathways. Today, this type of procedure, which is much less invasive than past methods of brain surgery, nonetheless requires an incredibly skilled surgeon to guide the wire manually. It’s a very difficult surgery for surgeons, and it also means that they’re exposed to radiation from the X-rays required to provide a view of the path they’re weaving through the patient’s brain.

These “robot-threads” developed by MIT expand on research done on so-called “hydrogels,” which are materials made mostly of water that work well within the human body. At the thread’s core is a material called “nitinol” that can bend, and is springy, meaning it has a natural tendency to spring back to its original shape when bent.

The material is coated in an ink-like substance, which is then bonded with a hydrogel, thus regulating it in a magnetically manipulable material that can still survive within the human body. Using a large magnet, the researchers could then steer the thread through a demonstration obstacle course they built to show off how it could work in a surgical situation.

MIT’s researchers also note that you can modify the core construction of the robot threads with other materials to serve different functions, and showed this by replacing the nitinol at its center with a fiber-optic filament, which in practice could be used to transmit laser light to blast away a blockage in a brain blood vessel.

The tech could be put to use to make it so surgeons can operate the threads from a safe distance — or even remotely. This would not only be safer for the doctors, but could also open up more access to this highly specialized kind of surgery for patients, too.


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MIT develops a sensor that can work underwater without a battery and send back data – gpgmail


MIT researchers have created a new underwater sensor and communication system that doesn’t require batteries, and barely uses any power at all. This could help set up an underwater ‘internet of things’ according to MIT, which would allow for real-time sea temperature and marine life monitoring, without requiring regular equipment and power swaps to make it work. Without that requirement, it would even be possible to set up networks of underwater sensors in the seas of distant planets.

The system devised by MIT researchers use a transmitter that sends out sound waves underwater, which then hit sensors with embedded receivers, transmitting a tiny amount of energy in the process. The sensor then either uses that energy to answer back – or doesn’t, which corresponds to either a 1 or a 0, meaning it can effectively communicate in binary. The only energy required for this to work is the power stored in the sound wave sent by the transmitter.

The inspiration for devising this system came from a somewhat unlikely source: Fadel Adib, an assistant professor in the MIT Media Lab and one of the researchers who worked on the project was watching nature doc “Blue Planet” and thought about how much of the Earths oceans are left unstudied, and also about how the solution for that can’t be battery-powered sensors since that could result in a lot of excess pollution.

Essentially, the system works but allowing piezoelectric resonators, which have been used in things like microphones for well over 100 years, to either deform in response to a sound wave, or retain their shape and reflect, based on information contained in any kind of sensor you might want to pair with the piezoelectric material. That sends back the binary signal, which can then be collected and interpreted.

Next up for the research team is to show that this can work at longer distances, and in concert with other sensors for simultaneous transmission. Eventually, it might even be able to transmit sound and even low-res images, which would be a huge development in terms of establishing remote monitoring stations – especially as we pursue more science and research on worlds not our own.


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Biotech researchers venture into the wild to start their own business – gpgmail


Much of Silicon Valley mythology is centered on the founder-as-hero narrative. But historically, scientific founders leading the charge for bio companies have been far less common.

Developing new drugs is slow, risky and expensive. Big clinical failures are all too common. As such, bio requires incredibly specialized knowledge and experience. But at the same time, the potential for value creation is enormous today more than ever with breakthrough new medicines like engineered cell, gene and digital therapies.

What these breakthroughs are bringing along with them are entirely new models — of founders, of company creation, of the businesses themselves — that will require scientists, entrepreneurs and investors to reimagine and reinvent how they create bio companies.

In the past, biotech VC firms handled this combination of specialized knowledge + binary risk + outsized opportunity with a unique “company creation” model. In this model, there are scientific founders, yes; but the VC firm essentially founded and built the company itself — all the way from matching a scientific advance with an unmet medical need, to licensing IP, to having partners take on key roles such as CEO in the early stages, to then recruiting a seasoned management team to execute on the vision.

Image: PASIEKA/SCIENCE PHOTO LIBRARY/Getty Images

You could call this the startup equivalent of being born and bred in captivity — where great care and feeding early in life helps ensure that the company is able to thrive. Here the scientific founders tend to play more of an advisory role (usually keeping day jobs in academia to create new knowledge and frontiers), while experienced “drug hunters” operate the machinery of bringing new discoveries to the patient’s bedside. This model’s core purpose is to bring the right expertise to the table to de-risk these incredibly challenging enterprises — nobody is born knowing how to make a medicine.

But the ecosystem this model evolved from is evolving itself. Emerging fields like computational biology and biological engineering have created a new breed of founder, native to biology, engineering and computer science, that are already, by definition, the leading experts in their fledgling fields. Their advances are helping change the industry, shifting drug discovery away from a highly bespoke process — where little knowledge carries over from the success or failure of one drug to the next — to a more iterative, building-block approach like engineering.

Take gene therapy: once we learn how to deliver a gene to a specific cell in a given disease, it is significantly more likely we will be able to deliver a different gene to a different cell for another disease. Which means there’s an opportunity not only for novel therapies but also the potential for new business models. Imagine a company that provides gene delivery capability to an entire industry — GaaS: gene-delivery as a service!

Once a founder has an idea, the costs of testing it out have changed too. The days of having to set up an entire lab before you could run your first experiments are gone. In the same way that AWS made starting a tech company vastly faster and easier, innovations like shared lab spaces and wetlab accelerators have dramatically reduced the cost and speed required to get a bio startup off the ground. Today it costs thousands, not millions, for a “killer experiment” that will give a founding team (and investors) early conviction.

What all this amounts to is scientific founders now have the option of launching bio companies without relying on VCs to create them on their behalf. And many are. The new generation of bio companies being launched by these founders are more akin to being born in the wild. It isn’t easy; in fact, it’s a jungle out there, so you need to make mistakes, learn quickly, hone your instincts, and be well-equipped for survival. On the other hand, given the transformative potential of engineering-based bio platforms, the cubs that do survive can grow into lions.

Image via Getty Images / KTSDESIGN/SCIENCE PHOTO LIBRARY

So, which is better for a bio startup today: to be born in the wild — with all the risk and reward that entails — or to be raised in captivity

The “bred in captivity” model promises sureness, safety, security. A VC-created bio company has cache and credibility right off the bat. Launch capital is essentially guaranteed. It attracts all-star scientists, executives and advisors — drawn by the balance of an innovative, agile environment and a well-funded, well-connected support network. I was fortunate enough to be an early executive in one of these companies, giving me the opportunity to work alongside industry luminaries and benefit from their well-versed knowledge of how to build a world-class bio company with all its complex component parts: basic, translational, clinical research, from scratch. But this all comes at a price.

Because it’s a heavy lift for the VCs, scientific founders are usually left with a relatively small slug of equity — even founding CEOs can end up with ~5% ownership. While these companies often launch with headline-grabbing funding rounds of $50m or above, the capital is tranched — meaning money is doled out as planned milestones are achieved. But the problem is, things rarely go according to plan. Tranched capital can be a safety net, but you can get tangled in that net if you miss a milestone.

Being born in the wild, on the other hand, trades safety for freedom. No one is building the company on your behalf; you’re in charge, and you bear the risk. As a recent graduate, I co-founded a company with Harvard geneticist George Church. The company was bootstrapped — a funding strategy that was more famine than feast — but we were at liberty to try new things and run (un)controlled experiments like sequencing heavy metal wildman Ozzy Osbourne.

It was the early, Wild West days of the genomics revolution and many of the earliest biotech companies mirrored that experience — they weren’t incepted by VCs; they were created by scrappy entrepreneurs and scientists-turned-CEO. Take Joshua Boger, organic chemist and founder of Vertex Pharmaceuticals: starting in 1989 his efforts to will into existence a new way to develop drugs, thrillingly captured in Barry Werth’s The Billion-Dollar Molecule and its sequel The Antidote in all its warts and nail-biting glory, ultimately transformed how we treat HIV, hepatitis C and cystic fibrosis.

Today we’re in a back-to-the-future moment and the industry is being increasingly pushed forward by this new breed of scientist-entrepreneur. Students-turned-founder like Diego Rey of in vitro diagnostics company GeneWEAVE and Ramji Srinivasan of clinical laboratory Counsyl helped transform how we diagnose disease and each led their companies to successful acquisitions by larger rivals.

Popular accelerators like Y Combinator and IndieBio are filled with bio companies driven by this founder phenotype. Ginkgo Bioworks, the first bio company in Y Combinator and today a unicorn, was founded by Jason Kelly and three of his MIT biological engineering classmates, along with former MIT professor and synthetic biology legend Tom Knight. The company is not only innovating new ways to program biology in order to disrupt a broad range of industries, but it’s also pioneering an innovative conglomerate business model it has dubbed the “Berkshire for biotech.”

Like the Ginkgo founders, Alec Nielsen and Raja Srinivas launched their startup Asimov, an ambitious effort to program cells using genetic circuits, shortly after receiving their PhDs in biological engineering from MIT. And, like Boger, renowned machine learning Stanford professor Daphne Koller is working to once again transform drug discovery as the founder and CEO of Instiro.

Just like making a medicine, no one is born knowing how to build a company. But in this new world, these technical founders with deep domain expertise may even be more capable of traversing the idea maze than seasoned operators. Engineering-based platforms have the potential to create entirely new applications with unprecedented productivity, creating opportunities for new breakthroughs, novel business models, and new ways to build bio companies. The well-worn playbooks may be out of date.

Founders that choose to create their own companies still need investors to scrub in and contribute to the arduous labor of company-building — but via support, guidance, and with access to networks instead. And like this new generation of founders, bio investors today need to rethink (and re-value) the promise of the new, and still appreciate the hard-earned wisdom of the old. In other words, bio investors also need to be multidisciplinary. And they need to be comfortable with a different kind of risk: backing an unproven founder in a new, emerging space. As a founder, if you’re willing to take your chances in the wild, you should have an investor that understands you, believes in you, can support you and, importantly, is willing to dream big with you.


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Why AI needs more social workers, with Columbia University’s Desmond Patton – gpgmail


Sometimes it does seem the entire tech industry could use someone to talk to, like a good therapist or social worker. That might sound like an insult, but I mean it mostly earnestly: I am a chaplain who has spent 15 years talking with students, faculty, and other leaders at Harvard (and more recently MIT as well), mostly nonreligious and skeptical people like me, about their struggles to figure out what it means to build a meaningful career and a satisfying life, in a world full of insecurity, instability, and divisiveness of every kind.

In related news, I recently took a year-long paid sabbatical from my work at Harvard and MIT, to spend 2019-20 investigating the ethics of technology and business (including by writing this column at gpgmail). I doubt it will shock you to hear I’ve encountered a lot of amoral behavior in tech, thus far.

A less expected and perhaps more profound finding, however, has been what the introspective founder Prayag Narula of LeadGenius tweeted at me recently: that behind the hubris and Machiavellianism one can find in tech companies is a constant struggle with anxiety and an abiding feeling of inadequacy among tech leaders.

In tech, just like at places like Harvard and MIT, people are stressed. They’re hurting, whether or not they even realize it.

So when Harvard’s Berkman Klein Center for Internet and Society recently posted an article whose headline began, “Why AI Needs Social Workers…”… it caught my eye.

The article, it turns out, was written by Columbia University Professor Desmond Patton. Patton is a Public Interest Technologist and pioneer in the use of social media and artificial intelligence in the study of gun violence. The founding Director of Columbia’s SAFElab and Associate Professor of Social Work, Sociology and Data Science at Columbia University.

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Desmond Patton. Image via Desmond Patton / Stern Strategy Group

A trained social worker and decorated social work scholar, Patton has also become a big name in AI circles in recent years. If Big Tech ever decided to hire a Chief Social Work Officer, he’d be a sought-after candidate.

It further turns out that Patton’s expertise — in online violence & its relationship to violent acts in the real world — has been all too “hot” a topic this past week, with mass murderers in both El Paso, Texas and Dayton, Ohio having been deeply immersed in online worlds of hatred which seemingly helped lead to their violent acts.

Fortunately, we have Patton to help us understand all of these issues. Here is my conversation with him: on violence and trauma in tech on and offline, and how social workers could help; on deadly hip-hop beefs and “Internet Banging” (a term Patton coined); hiring formerly gang-involved youth as “domain experts” to improve AI; how to think about the likely growing phenomenon of white supremacists live-streaming barbaric acts; and on the economics of inclusion across tech.

Greg Epstein: How did you end up working in both social work and tech?

Desmond Patton: At the heart of my work is an interest in root causes of community-based violence, so I’ve always identified as a social worker that does violence-based research. [At the University of Chicago] my dissertation focused on how young African American men navigated violence in their community on the west side of the city while remaining active in their school environment.

[From that work] I learned more about the role of social media in their lives. This was around 2011, 2012, and one of the things that kept coming through in interviews with these young men was how social media was an important tool for navigating both safe and unsafe locations, but also an environment that allowed them to project a multitude of selves. To be a school self, to be a community self, to be who they really wanted to be, to try out new identities.


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MIT researchers are working on AI-based knitting design software that will let anyone, even novices, make their own clothes – gpgmail


The growing popularity of 3D printing machines and companies like Thingiverse and Shapeways have given previously unimaginable powers to makers, enabling them to create everything from cosplay accessories to replacement parts. But even though 3D printing has created a new world of customized objects, most of us are still buying clothes off the rack. Now researchers at MIT are working on software that will allow anyone to customize or design their own knitwear, even if they have never picked up a ball of yarn.

A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), led by computer scientist Alexandre Kaspar, released two new papers describing the software today. One is about a system called InverseKnit that automatically creates patterns from photos of knitted items. The other one introduces new design software, called CADKnit, that allows people with no knitting or design experience to quickly customize templates, adjusting the size, final shape and decorative details (like the gloves shown below).

The final patterns can be used with a knitting machine, which have been available to home knitters for years, but still require a fair amount of technical knowledge in order to design patterns for.

Gloves made using CADknit

Both CADKnit and InverseKnit want to make designing and making machine-knitted garments as accessible as 3D printing is now. Once the software is commercialized, Kaspar envisions “knitting as a service” for consumers who want to order customized garments. It can also enable clothing designers to spend less time learning how to write knitwear patterns for machines and reduce waste in the prototyping and manufacturing process. Another target audience for the software are hand-knitters who want to try a new way of working with yarn.

“If you think about it like 3D printing, a lot of people have been using 3D printers or hacking 3D printers, so they are great potential users for our system, because they can do that with knitting,” says Kaspar.

One potential partner for CADKnit and InverseKnit is Kniterate, a company that makes a digital knitting machine for hobbyists, makerspaces and small businesses. Kaspar says he has been talking to Kniterate’s team about making knitwear customization more accessible.

CADKnit combines 2D images with CAD and photo-editing software to create customizable templates. It was tested with knitting newbies, who despite having little machine knitting experience were still able to create relatively complex garments, like gloves, and effects including lace motifs and color patterns.

To develop InverseKnit, researchers first created a dataset of knitting patterns with matching images that were used to train a deep neural network to generate machine knitting patterns. The team says that during InverseKnit’s testing, the system produced accurate instructions 94% of the time. There is still work to do before InverseKnit can be commercialized. For example, the machine was tested using one specific type of acrylic yarn, so it needs to be trained to work with other fibers.

“3D printing took a while before people were comfortable enough to think they could do something with it,” says Kaspar. “It will be the same thing with what we do.”


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