Sustainability starts in the design process, and AI can help

By incloudhosting.co.uk

In association withAutodesk Artificial intelligence helps build physical infrastructure like modular housing, skyscrapers, and factory floors. “…many problems that we wrestle with in all forms of engineering and design are very, very complex problems…those problems are beginning to reach the limits of human capacity,” says Mike Haley, the vice president of research at Autodesk. But there’s hope with AI capabilities, Haley continues “This is a place where AI and humans come together very nicely because AI can actually take certain very complex problems in the world and recast them.” And where “AI and humans come together” is at the start of the process with generative design, which incorporates AI into the design process to explore solutions and ideas that a human alone might not have even considered. “You really want to be able to look at the entire lifecycle of producing something and ask yourself, ‘How can I produce this by using the least amount of energy throughout?’” This kind of thinking will reduce the impact of, not just construction, but any sort of product creation on the planet. The symbiotic human-computer relationship behind generative design is necessary to solve those “very complex problems”—including sustainability. “We are not going to have a sustainable society until we learn to build products—from mobile phones to buildings to large pieces of infrastructure—that survive the long-term,” Haley notes. The key, he says, is to start in the earliest stages of the design process. “Decisions that affect sustainability happen in the conceptual phase, when you’re imagining what you’re going to create.” He continues, “If you can begin to put features into software, into decision-making systems, early on, they can guide designers toward more sustainable solutions by affecting them at this early stage.” Using generative design will result in malleable solutions that anticipate future needs or requirements to avoid having to build new solutions, products, or infrastructure. “What if a building that was built for one purpose, when it needed to be turned into a different kind of building, wasn’t destroyed, but it was just tweaked slightly?” That’s the real opportunity here—creating a relationship between humans and computers will be foundational to the future of design. “The consequence of bringing the digital and physical together,” Haley says, “is that it creates a feedback loop between what gets created in the world and what is about to be created next time.” Show notes and references “What is Generative Design, and How Can It Be Used in Manufacturing?” by Dan Miles, Redshift by Autodesk, November 19, 2021 “4 Ways AI in Architecture and Construction Can Empower Building Projects” by Zach Mortice, Redshift by Autodesk, April 22, 2021 Full transcript Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. Our topic today is about how to design better with artificial intelligence, everything from modular housing to skyscrapers to manufactured products and factory floors can be designed with and benefit from AI and machine learning technologies. As artificial intelligence helps humans with design options, how can it help us build smarter? Two words for you: sustainable design.My guest is Mike Haley, the vice president of research at Autodesk. Mike leads a team of researchers, engineers, and other specialists who are exploring the future of design and making.This episode of Business Lab is produced in association with Autodesk.Welcome, Mike. Mike Haley: Hi Laurel. Thanks for having me. Laurel: So for those who don’t know, Autodesk technology supports architecture, engineering, construction, product design, manufacturing, as well as media and entertainment industries. And we’ll be talking about that kind of design and artificial intelligence today. But one specific aspect of it is generative design. What is generative design? And how does it lend itself to an AI-human collaboration? Mike: So Laurel, to answer that, first you have to ask yourself: What is design? When designers      are approaching a problem, they’re generally looking at the problem through a number of constraints, so if you’re building a building, there’s a certain amount of land you have, for example. And you’re also trying to improve or optimize something. So perhaps you’re trying to build the building with a very low cost, or have low environmental impact, or support as many people as possible. So you’ve got this simultaneous problem of dealing with your constraints, and then trying to maximize these various design factors. That is really the essence of any design problem. The history of design is that it is entirely a human problem. Humans may use tools. Those tools may be pens and pencils, they may be calculators, and they may be computers to solve that. But really, the essence of solving that problem lies purely within the human mind. Generative design is the first time we’re producing technology that is using the computational capacity of the computer to assist us in that process, to help us go beyond perhaps where our usual considerations go. As you and I’m sure most of the audience know, people talk a lot about bias in AI algorithms, but bias generally comes from the data those algorithms see, and the bias in that data generally comes from humans, so we are actually very, very biased. This shows up in design as well. The advantage of using computational assistance is you can introduce very advanced forms of AI that      are not actually based on data. They’re based on algorithmic or physical understandings of the world, so that when you’re trying to understand that building, or design an airplane, or design a bicycle, or what it might be, it can actually use things like the laws of physics, for example, to understand the full spread of possible solutions to address that design problem I just talked about. So in some ways, you can think of generative design as a computer technology that allows designers to expand their minds and to explore spaces and possibilities of solutions that they perhaps wouldn’t go otherwise. And it might even be outside of their traditional comfort zone, so biases might prevent them from going there. One thing you find with generative design is when we watch people use this technology, they tend to use it in an iterative fashion. They will supply the problem to the computer, let the computer propose some solutions, and then they will look at those solutions and then begin to adjust their criteria and run it again. This is almost this symbiotic kind of relationship that forms between the human and the computer. And I really enjoy that because the human mind is not very good at computing. The popular idea is you can hold seven facts in your head at once, which is a lot smaller than the computer, right?     But human minds are excellent at responding and evaluating situations and bringing in a very broad set of considerations. That in fact is the essence of creativity. So if you bring that all together and look at that entire process, that is really what generative design is all about. Laurel: So really what you’re talking about is the relationship between a human and a computer. And the output of this relationship is something that’s better than either one could do by themselves. Mike: Yes, that’s right. Exactly. I mean, humans have a set of limitations, and we have a set of skills that we bring together really when we’re being creative. The same is true of a computer. The computer has certain things like computation, for example, and understanding the laws of physics and things like that. But it’s far better than we are. But it’s also highly limited in being able to evaluate the efficacy of a solution. So generative is really about bringing those two things together. Laurel: So there’s been a lot of discussion about how AI and automation replacing workers is a fear. What is the AI human collaboration that you’re envisioning for the future of work? How can this partnership continue? Mike: There’s an incredibly interesting relationship between AI and actually not just solving problems in the world together with humans, but also improving the human condition. So when we talk about the tension between AI and human work, I really like to look at it through that lens, so that when we think of AI learning the world, learning how to do things, that can lead to something like automation. Those learnings—those digital learnings—can drive things like a robot, or a machine in a factory, or a machine in a construction site, or even just a computer algorithm that can decide on something for you. That can be powerful if managed appropriately. Of course, you’ve always got the risks of bias and unfairness and those kinds of things that you have to be aware of. But there’s another effect of AI learning: it is now able to also better understand what a human being is doing. So imagine an AI that watches you type in a word processor, for example. And it watches you type for many, many years. It learns things about your writing style. Now one of the obvious automation things it can do is begin to make suggestions for your writing, which is fine. We’re beginning to see that today already. But something it could also do is actually begin to evaluate your writing and actually understand, maybe in a very nuanced way, how you compare to other writers. So perhaps you’re writing a kind of fiction, and it’s saying, “Well, generally in this realm of fiction, people that write like you are targeting these sorts of audiences. And maybe you want to consider this kind of tone, or nature of your writing.” In doing that, the AI is actually providing more tuned ways of teaching you as a human being through interpretation of your actions and working again in a really iterative way with a person to guide them to improve their own capability. So this is not about automating the problem. It’s actually in some ironic way, automating the process of training a person and improving their skills. So we really like to put that lens on AI and look at that way in that, yes, we are automating a lot of tasks, but we can also use that same technology to help humans develop skills and improve their own capacity. The other thing I will mention in this space is that many problems that we wrestle with in all forms of engineering and design are very, very complex, and we’re talking about some of them right now. Those problems are beginning to reach the limits of human capacity. We have to start simplifying them in some ways. This is a place where AI and humans come together very nicely because AI can actually take certain very complex problems in the world and recast them. They can be recast or reinterpreted into language or sub problems that human beings can actually understand, that we can wrestle with and provide answers. And then the AI can take those answers back and provide a better solution to whatever problem we happen to be wrestling with at that time. Laurel: So speaking of some of those really difficult problems, climate change, sustainability, that’s certainly one of those. And you actually wrote, and here’s a quote from your piece, quote, “Products need to improve in quality because an outmoded throw-away society is not acceptable in the long-term.” So you’re saying here that AI can help with those types of big societal problems too. Mike: Yeah, exactly. This is exactly the kind of difficult problem that I was just talking about.For example, how many people get a new smartphone, and within a year or two, you’re tossing it to get your new one? And this is becoming part of just the way we live. And we are not going to have a sustainable society until we actually learn to build products, and products can be anything from a mobile phone to a building, or large pieces of infrastructure, that survive long-term. Now what happens in the long-term? Generally, requirements change. The power of things change. People’s reaction to that, again, like I just said, is to throw them away and create something new. But what if those things were amenable to change in some ways? What if they could be partially recreated halfway through their lifespan? What if a building that was built for one purpose, when it needed to be turned into a different kind of building, wasn’t destroyed, but it was just tweaked slightly? Because when the designer first designed that building, there was a way to contemplate what all future users of that building could be. What are the patterns of those? And how could that building be designed in such a way to support those future uses? So, solving that kind of design problem, solving a problem where you’re not just solving your current problem, but you’re trying to solve all the future problems in some ways is a very, very difficult problem. And it was the kind of problem I was talking about earlier on. We really need a computer to help you think through that. In design terms, this is what we call a systems problem because there’s multiple systems you need to think of, a system of time, a system of society, of economy, of all sorts of things around it you need to think through. And the only way to think through that is with an AI system or a computational system being your assistant through that process. Laurel: I have to say that’s a bit mind bending, to think about all the possible iterations of a building, or an aircraft carrier, or even a cell phone. But that sort of focus on sustainability certainly changes how products and skyscrapers and factory floors are designed. What else is possible with AI and machine learning with sustainability? Mike: We tend to think normally along three axes. So one of the key issues right now we’re all aware of is climate change, which is rooted in carbon. And many, many practices in the world involve the production of enormous amounts of carbon or what we call retained carbon. So if we’re producing concrete, you’re producing extra carbon in the atmosphere. So we could begin to design buildings, or products, or whatever it might be, that either use less carbon in the production of the materials, or in the creation of the structures themselves, or in the best case, even use things that have negative carbon. For example, using a large amount of timber in a building can actually reduce overall carbon usage because at the lifetime that tree was growing, it consumed carbon. It embodied the carbon inside the atmosphere into itself. And now you’ve used it. You’ve trapped it essentially inside the wood, and you’ve placed that into the building. You didn’t create new carbon as a result of producing the wood. Embodied energy is something else we think of too.  In creating anything in the world, there is energy that is going to go into that. That energy might be driving a factory, but that energy could be shipping products or raw materials across the world.  You really want to be able to look at the entire lifecycle of producing something and ask yourself, “How can I produce this by using the least amount of energy throughout?” And you will have a lower impact on the planet. The final example is waste. This is a very significant area for AI to have an effect because waste in some ways is about a design that is not optimal. When you’re producing waste from something, it means there are pieces you don’t need. There’s material you don’t need. There’s something coming out of this which is obviously being discarded.  It is often possible to use AI to evaluate those designs in such a way to minimize those waste-ages, and then also produce automations, like for example, a robot saw that can cut wood for a building, or timber framing in a building, that knows the amount of wood you have. It knows where each piece is going to go. And it’s kind of cutting the wood so that it’s sure that it’s going to produce as little off cuts that are going to be thrown away as possible. Something like that can actually have a significant effect at the end of the day. Laurel: You mentioned earlier AI could help, for example, something writing, and how folks write and their styles, etc. But also, understanding systems and how systems work is also really important. So how could AI and ML be applied to education? And how does that affect students and teaching in general? Mike: One of the areas that I’m very passionate about where generative design and learning come together is around a term that we’ve been playing around with for a while in all of this research, which is this idea of generative learning, which is learning for you.  a little bit along the lines of some of the stuff we talked about before, where you’re almost looking at the human as part of a loop together with the computer. The computer understands what you’re trying to do. It’s learning more about how you compare to others, perhaps where you could improve in your own proficiencies. And then it’s guiding you in those directions. Perhaps it’s giving you challenges that specifically push you on those. Perhaps it’s giving you directions. Perhaps it’s connecting you with others that can actually help improve you.  Like I said, we think of that as sort of a generative learning. What you’re trying to optimize here is not a design, like what we talked about before, but we’re trying to optimize your learning. We’re trying to optimize your skillset. Also, I think underlying a lot of this as well is a shift in a paradigm.  Up until fairly recently, computers were really just seen as a big calculator. Right? Certainly in design, even in our software here at Autodesk. I mean, the software was typically used to explore a design or to document a design. The software wasn’t used to actually calculate every aspect of the design. It was used really in some ways as a very complex kind of drafting board, in some sense. This is changing now with technologies like generative design, where you really are, like I talked about earlier, working in the loop with the computer. So the computer is suggesting things to you. It’s pushing you as a designer. And you as a designer are also somewhat of a curator now. You’re reacting to things that the computer is suggesting or providing to you. So embracing this paradigm early on in education, with the students coming into design and engineering today, is really, really important. I think that they have an opportunity to take the fields of design and engineering to entirely different levels as the result of being able to use these new capabilities effectively. Laurel: Another place that this has to be also applied is the workplace. So employees and companies have to understand that the technology will also change the way that they work. So what tools are available to navigate our evolving workplace? Mike: Automation can have a lot of unintended side effects in a workplace. So one of the first things any company has to do is really wrestle with that. You have to be very, very real about what’s the effect on your workforce. If automation is going to be making decisions, what’s the risk that those decisions might be unfair or biased in some ways?  One of the things that you have to understand is that this is not just a plug it in, switch it on, and everything’s going to work. You have to even involve your workforce right from the beginning in those decisions around automation. We see this in our own industry, the companies that are the most successful in adopting automation are the ones that are listening the most closely to their workforce at the same time. It’s not that they’re not doing automation, but they’re actually rolling it out in a way that’s commensurate with the workforce, and there’s a certain amount of openness in that process. I think the other aspect that I like to look at from a changing work environment is the ability to focus our time as human beings on what really matters, and not have to deal with so much tedium in our lives. So much of our time using a computer is tedious. You’re trying to find the right application. You’re trying to get help on something. You’re trying to work around some little thing that you don’t understand in the software. Those kinds of things are beginning to fall away with AI and automation. And as they do, we’ve still got a fair way to go on that. But as we go further down the line on that, what it means is that creative people can spend more time being creative. They can focus on the essence of a problem. So if you’re an architect who is laying out desks in an office space, you’re probably not being paid to actually lay out every desk. You’re being paid to design a space. So what if you design the space and the computer actually helps with the actual physical desk layout? Because that’s a pretty simple thing to kind of automate. I think there’s a really fundamental change in where people will be spending their time and how they’ll be actually spending their time. Laurel: And that kind of comes back to a topic we just talked about, which is AI and ethics. How do companies embrace ethics with innovation in mind when they are thinking about these artificial intelligence opportunities? Mike: This is something that’s incredibly important in all of our industries. We’re seeing this rise, the awareness of this rise, obviously it’s there in the popular society right now. But we’ve been looking at this for a while, and a couple of learnings I can give you straight off the bat is any company that’s dealing with automation and AI needs to ensure that they have support for an ethical approach to this right from the very top of the company because the ethical decisions don’t just sit at the technical level, they sit at all levels of decision making. They’re going to be business decisions. They’re going to be market decisions. They’re going to be production decisions, investment decisions and technology decisions. So you have to make sure that it’s understood within any corporate or industrial environment. Next is that everybody has to be aligned internally to those organizations on: What does ethics actually mean? Ethics is a term that’s used pretty broadly. But when it actually gets down to doing something about it, and understanding if you’re being successful at it, it’s very important to be quite precise on it. This brings me to the third point, that if you are going to announce, if you’ve done that, and you now have an understanding of what it is, you now need to make sure that you’re solving a concrete problem because ethics can be a very, very fuzzy topic. You can do ethics washing very, very easily in an organization. And if you don’t quickly address that and actually define a very specific problem, it will continue to be fuzzy, and it will never have the effect that you would like to see within a company. And the last thing I will say is you have to make it cultural. If you are not ensuring that ethical behavior is actually part of the cultural values of your organization, you’re never going to truly practice it. You can put in governance structures, you can put in software systems, you can put in all sorts of things that ensure a fairly high level of ethics. But you’ll never be certain that you’re really doing it unless it’s embedded deeply within the culture of actually how people behave within your organization. Laurel: So when you take all of this together, what sorts of products or applications are you seeing in early development that we can expect or even look forward to in the next, say, three to five years? Mike: There’s a number of things. The first category I like to think of is the raise-all-the-boats category, which means that we are beginning to see tools that just generally make everybody more efficient at what they do, so it’s similar to what I was talking about earlier on about the architect laying out desks. It could be a car designer that is designing a new car. And in most of today’s cars, there’s a lot of electrical wiring. Today, the designer has to route every cable through that car and show, tell the software exactly where that cable goes. That’s not actually very germane to the core design of the car, but it’s a necessary evil to specify the car. That can be automated. We’re beginning to see these fairly simple automations beginning to become available to all designers, all engineers, that just allow them to be a little bit more efficient, allow them to be a little bit more precise without any extra effort, so I like to think of that as the raise-all-the-boats kind of feature. The next thing, which we touched on earlier in the session, was the sustainability of solutions. It turns out that most of the key decisions that affect the sustainability of a product, or a building, or really anything, happen in the earliest stages of the design. They really happen in this very sort of conceptual phase when you’re imagining what you’re going to create. So if you can begin to put features into software, into decision-making systems early on, they can guide designers towards more sustainable solutions through affecting them at this early stage. That’s the next thing I think we’re going to see. The other thing I’m seeing appears quite a lot already, and this is not just true in AI, but it’s just generally true in the digital space, is the emergence of platforms and very flexible tools that shape to the needs of the users themselves. When I was first using a lot of software, as I’m sure many of us remember, you had one product. It always did a very specific thing, and it was the same for whoever used it. That era is ending, and we’re ending up seeing tools now that are highly customizable, perhaps they’re even automatically reconfiguring themselves as they understand more about what you need from them. If they understand more about what your job truly is, they will adjust to that. So I think that’s the other thing we’re seeing. The final thing I’ll mention is that over the next three to five years, we’re going to see more about the breaking down of the barrier between digital and physical.  Artificial intelligence has the ability to interpret the world around us. It can use sensors. Perhaps it’s microphones, perhaps it’s cameras, or perhaps it’s more complicated sensors like strain sensors inside concrete, or stress sensors on a bridge, or even understanding the ways humans are behaving in a space. AI can actually use all of those sensors to start interpreting them and create an understanding, a more nuanced understanding of what’s going on in that environment. This was very difficult, even 10 years ago. It was very, very difficult to create computer algorithms that could do those sorts of things. So if you take for example something like human behavior, we can actually start creating buildings where the buildings actually understand how humans behave in that building. They can understand how they change the air conditioning during the day, and the temperature of the building. How do people feel inside the building? Where do people congregate? How does it flow? What is the timing of usage of that building? If you can begin to understand all of that and actually pull it together, it means the next building you create, or even improvements to the current building can be better because the system now understands more about: How is that building actually being used? There’s a digital understanding of this. This is not just limited to buildings, of course. This could be literally any product out there. And this is the consequence of bringing the digital and physical together, is that it creates this feedback loop between what gets created in the world and what is about to be created next time. And the digital understanding of that can constantly improve those outcomes. Laurel: That’s an amazing outlook. Mike, thank you so much for joining us today on what’s been a fantastic conversation on The Business Lab. Mike: You’re very welcome, Laurel. It was super fun. Thank you. Laurel: That was Mike Haley, vice president of research at Autodesk, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River. That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the director of insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com. This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Collective Next. Thanks for listening. Click here to learn how Autodesk partners with customers across industries to imagine bigger, collaborate smarter, move faster, and build better. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

Tonga’s volcano blast cut it off from the world. Here’s what it will take to get it reconnected.

By incloudhosting.co.uk

Hunga Tonga–Hunga Ha‘apai, an underwater volcano off the coast of Tonga, has erupted several times in the last 13 years, but the most recent, on January 15, was likely its most destructive. The blast has had global consequences: more than 6,000 miles away, waves caused by the eruption drowned two people in Peru. But the effect of the volcanic blast on Tongans living closer to ground zero isn’t yet known, though it’s feared that the ensuing tsunami may have killed many people and displaced many more from their homes. That’s because Tonga has been suddenly cut off from the internet, making it that much harder to coordinate aid or rescue missions. In a highly interconnected world, Tonga is now completely dark, and it’s almost impossible to get word out. Getting the country back online is vital—but it could take weeks. To support MIT Technology Review’s journalism, please consider becoming a subscriber. Internet traffic plunged to near-nothing around 5:30 p.m. local time on January 15, according to data from web performance firm Cloudflare. That connection hasn’t yet been restored, says Doug Madory of Kentik, an internet observatory company, who has been monitoring the country’s web traffic. The reason Tonga fell offline isn’t yet known for certain, but initial investigations have suggested that the undersea cable connecting its internet to the rest of the world has been destroyed by the blast. “Tonga primarily uses a single subsea cable to connect to the internet,” says Madory. The Tonga Cable System runs 514 miles between Tonga and Fiji, bringing internet service to the two island nations. Previously, that connection has been backed up by a satellite internet connection. “I guess they’re not able to do that this time, because of some technical failure preventing them from being able to switch over,” says Madory. He believes that the wave resulting from the volcano explosion could have taken out the satellite dishes. Jamaica-based mobile network operator Digicel, which owns a minority stake in the cable alongside the Tongan government, said in a statement: “All communication to the outside world in Tonga is affected due to damage.” Southern Cross Cable, a New Zealand–based company that runs cables interconnecting with the Tonga Cable System, believes there’s a possible break around 23 miles offshore. It’s also believed that the domestic subsea cable is broken around 30 miles from Tonga’s capital, Nukuʻalofa. Such breaks are usually found by sending light down the fiber-optic core of the cabling and calculating how long it takes for the signal to bounce back—which it does when interrupted, says Christian Kaufmann, vice president of network technology at content delivery network Akamai. If that’s confirmed, it’s just about the worst possible news for Tonga’s connectivity. “It will be days—maybe weeks—before the cable is fixed,” says Madory. The outage isn’t the first time that Tonga’s internet infrastructure has been plagued with problems. In January 2019, the country experienced a “near-total” internet blackout when an undersea cable was cut. Initial reports indicated that a magnetic storm and lightning may have damaged the connection—but a subsequent investigationfound that a Turkish-flagged ship dropping anchor had severed the line. Fixing the issue cost an estimated $200,000, and while it was being fixed, the island relied on satellite internet connections. Those same satellite connections are likely to be the only savior for Tonga’s internet in the near term—but with unknown damage to them, the country could be in for a difficult period. “They were probably thinking: ‘Well, if the cable goes down, we have the satellites for resilience,’” says Madory. “If a volcano detonates right next to you and takes out both your cable and your satellite, there’s not much you can do.” Huge amounts of ash thrown up into the air by the eruption could also be affecting satellite connectivity, says Kaufmann. Fixing the broken cable won’t be easy. Specialized shipping vessels tasked with fixing breakages—which occur every week somewhere around the world, albeit with less force than is likely to have resulted from the eruption—need to be sent to the site of the problem. One vessel that could help is the CS Resilience, currently off Papua New Guinea, nearly 3,000 miles away. It’s estimated that any vessel could take days or weeks to remedy the issue. “There’s a priority over whose cable gets fixed first,” says Madory. “Countries pay a little premium to get fixed first.” Once one of these vessels arrives on scene, which itself could take days, it drops a hook to snag the cable that runs along the sea floor. The hooked cable, which when in the deep ocean can be as thin as a common garden hose, is then winched up onto the deck of the vessel, where technicians work to fix the break. “The cabling itself is not the most sturdy thing,” says Kaufmann. It’s then lowered gently back into the water. “That process hasn’t changed much in the 150 years or so that we’ve had submarine cables,” says Madory. There are, of course, compounding factors that can complicate the process. Tonga is likely to be besieged by vessels looking to deliver aid to the country, which may mean internet cabling takes a back seat to saving lives, restoring power, and delivering vital food and water supplies. The precise location of the rupture can also make things complicated: generally, the further out the break is from shore, the deeper the cable—and the harder it is to reach and drag up from the floor. That’s before considering that the onshore power lines that help keep the connection online may well be damaged beyond easy repair. “Tonga is on an extremity of the internet,” says Madory. “Once you go out from the core of the internet, you’re just going to have fewer options.” The internet outage shows how dependent the world’s internet connectivity can be on single points of failure. “It’s one of those stories that put the lie to the idea that the internet was designed to withstand nuclear wars,” says Alan Woodward, a professor of cybersecurity at the University of Surrey in the UK. “Chewing gum holds most of it together.” Woodward suggests that rare physical events such as volcanic explosions are difficult to design for, but countries should try to maintain redundancy through multiple undersea connections, and ideally ones that follow different routes so that a localized incident won’t affect multiple lines.  Yet redundancy doesn’t come cheap—especially for a small nation of just over 100,000 people like Tonga. It’s also likely that with a massive eruption such as this one, the movement of the seabed would have caused a fissure in any secondary cable, even if it was laid on the other side of Tonga.  “There’s a broader message around the resilience of infrastructure,” says Andrew Bennett, who analyzes internet policy at the Tony Blair Institute for Global Change. “Although the UK or US isn’t going to be like Tonga, increasingly there are geopolitical tensions and debate[around] discussing things like undersea cables that are pushing us into a more fractious place. You don’t want to end up in a place where you have sovereign cables for the allies and other cables for everyone else.” Bennett suggests two options to bridge the connectivity gap. One is rapid rollout of satellite internet—and the satellite constellations are being launched into space as we speak. The other is to devote more money to the problem. “If you look at resilient internet infrastructure as a public good, countries who can afford it should pay for it and provide it to others,” he says. Closing the global digital divide by 2030 would cost just 0.2% of the gross national income of OECD countries per year, according to the institute. Given that the internet is increasingly seen as a fourth vital service, alongside heat, power, and water, such a long outage for 100,000 people is a major disaster—compounding the immediate physical effects of the eruption. And it highlights the fragility of certain parts of the internet, particularly outside the rich Western world. “The internet’s not necessarily crumbling at the core,” says Woodward. “But it’s always going to be a little frayed around the edges.”

Going bald? Lab-grown hair cells could be on the way

By incloudhosting.co.uk

Biologists at several startups are applying the latest advances in genetic engineering to the age-old problem of baldness, by creating new hair-forming cells that could restore a person’s ability to grow hair. Some researchers tell MIT Technology Review they are using the techniques to grow human hair cells in their labs and even on animals. A startup called dNovo sent us a photograph of a mouse sprouting a dense clump of human hair—the result of a transplant of what the company says are human hair stem cells. The company’s founder is Ernesto Lujan, a Stanford University-trained biologist. He says his company can produce the components of hair follicles by genetically “reprogramming” ordinary cells, like blood or fat. More work needs to be done, but Lujan is hopeful that the technology could eventually treat “the underlying cause of hair loss.” To support MIT Technology Review’s journalism, please consider becoming a subscriber. We’re born with all the hair follicles we’ll ever have—but aging, cancer, testosterone, bad genetic luck, even covid-19 can kill the stem cells inside them that make hair. Once these stem cells are gone—so is your hair. Lujan says his company can convert any cell directly into a hair stem cell by changing what patterns of genes are active in it. “In biology, we now understand cells as a ‘state’” rather than a fixed identity, says Lujan. “And we can push cells from one state to another.”  Reprogramming cells The chance of replacing hair is one corner of a wider exploration of whether reprogramming technology can defeat the symptoms of aging. In August, MIT Technology Review reported on stealthy company, Altos Labs, that plans to explore whether people can be rejuvenated using reprogramming. Another startup, Conception, is trying to extend fertility by converting blood into human eggs. A key breakthrough came in the early 2000s, when Japanese researchers hit on a simple formula to turn any type of tissue into powerful stem cells, similar to ones in an embryo. Imaginations ran wild. Scientists realized they could potentially manufacture limitless supplies of nearly any type of cell—say nerves or heart muscle. In practice, though, the formula for producing specific cell types can prove elusive, and then there’s the problem of getting lab-grown cells back into the body. So far, there have been only a few demonstrations of reprogramming as a method to treat patients. Researchers in Japan tried transplanting retina cells into blind people. Then, last November, a US company, Vertex Pharmaceuticals, said it might have cured a man’s Type 1 diabetes after an infusion of programmed beta cells, the kind that respond to insulin. The concept being pursued by startups is to collect ordinary cells from patients, say skin, then convert these into hair-forming cells. In addition to dNovo, a company called Stemson (its name is a portmanteau of stem cell and Samson) has raised $22.5 million including from the drug company AbbVie. Co-founder and CEO Geoff Hamilton says his company is transplanting reprogrammed cells onto the skin of mice and pigs to test the technology. Both Hamilton and Lujan think there is a substantial market. About half of men undergo male-pattern baldness, some starting in their 20s. When women lose hair, it’s often a more general thinning, but no less a blow to a person’s self-image. These companies are bringing high-tech biology to an industry known for illusions. There are plenty of bogus claims about both hair loss remedies and the potential of stem cells. “You’ve got to be aware of scam offerings,” Paul Knoepfler, a stem cell biologist at UC Davis, wrote in November. A close up of a skin organoid that is covered with hair follicles. JIYOON LEE AND KARL KOEHLER, HARVARD MEDICAL SCHOOL Tricky business So is stem cell technology going to cure baldness or become the next false hope? Hamilton, Stemson’s founder, was invited to give the keynote at this year’s Global Hair Loss Summit, and says he tried to emphasize that the company still has plenty of research ahead of it. “We have seen so many [people] come in and say they have a solution. That has happened a lot in hair, and so I have to address that,” says Hamilton. “We’re trying to project to the world that we are real scientists and that it’s risky to the point I can’t guarantee it’s going to work.” Right now, there are some approved drugs for hair loss, like Propecia and Rogaine, but they’re of limited use. Another procedure involves a surgeon cutting strips of skin from where a person still has hair and transplanting those follicles onto a bald spot. Lujan says in the future, hair-forming cells grown in the lab could be added to a person’s head with a similar surgery. “I think people will go pretty far to get their hair back. But at first it will be a bespoke process and very costly,” says Karl Koehler, a professor at Harvard University. Hair follicles are surprisingly complicated organs that arise through the molecular crosstalk between several cell types. And Koehler says pictures of mice growing human hair aren’t new. “Anytime you see these images,” says Koehler, “There is always a trick and some drawback to translating it to humans.” Koehler’s lab makes hair shafts in an entirely different way—by growing organoids. Organoids are small blobs of cells which self-organize in a petri dish. Koehler says he originally was studying deafness cures and wanted to grow the hair-like cells of the inner ear. But his organoids ended up becoming skin instead, complete with hair follicles. Koehler embraced the accident and now creates spherical skin organoids which grow for about 150 days and become quite large—about two millimeters across. The tube-like hair follicles are clearly visible and, he says, are the equivalent of the downy hair that covers a fetus. One surprise is that the organoids grow backwards, with the hairs pointing inwards. “You can see a beautify architecture although why they grow inside out is a big question,” says Koehler. The Harvard lab uses a supply of reprogrammed cells established from a 30-year-old Japanese man. But it’s looking at cells from other donors to see if organoids could lead to hair with distinctives colors and textures. “There is absolutely demand for it,” says Koehler. “Cosmetics companies are interested. Their eyes light up when they see the organoids.”

In a further blow to the China Initiative, prosecutors move to dismiss a high profile case

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On Friday, federal prosecutors recommended that the US Department of Justice dismiss all three charges against MIT nanotechnology professor Gang Chen, ending a two-year ordeal stemming from accusations that he hid funding from Chinese entities on grant disclosure forms.  Chen had pleaded not guilty to all charges, while his employer had indicated that the funding in question was for the university, rather than Chen personally. MIT is paying his legal fees. The university declined to comment on a pending court case. (MIT Technology Review is funded by MIT, but is editorially independent.)  Chen was one of the most prominent scientists charged under the China Initiative, a federal effort launched in 2018 to counter economic espionage and national security threats from the People’s Republic of China. Despite these aims, an investigation by MIT Technology Review found that the China Initiative has increasingly focused on “research integrity” violations, like Chen’s, rather than on trade secret theft.  The motion to dismiss has yet to be filed, and final approval for dismissal rests with the judge. If approved, Chen’s would be the eighth research integrity case to be dismissed before trial, according to MIT Technology Review’s database of cases. An additional research integrity case, that of University of Knoxville nanotechnologist Anming Hu, ended first in a mistrial and then a full acquittal. The only China Initiative research integrity case that has been successfully tried in front of a jury is that of Charles Lieber, who was found guilty of six charges of false statements and tax fraud last month. (See all research integrity cases here.) Research integrity cases center around students and academics that have been accused of failing to fully disclose relationships with Chinese entities, primarily on grant or visa forms. Public accusation Chen’s problems began in January 2020, as he was returning to the United States from a university-backed trip to China with other MIT faculty and students. Detained and questioned at Boston Logan International Airport, he was released after his phone and computer were confiscated.  A year later, Chen was arrested on suspicions of federal grant fraud and publicly accused of disloyalty to the US—a charge typically leveled in espionage cases, not grant fraud, as Chen’s defense team pointed out in its attempt to formally sanction the US Attorney’s Office for the statement. Chen was ultimately charged with three counts of wire fraud, false statements, and a failure to file a report on a foreign bank account.  But the heart of the case was whether the nanotechnologist had disclosed contracts, appointments, and awards from entities in the People’s Republic of China, including a Chinese talent program and more than $19 million in funding from the Chinese government, while receiving federal grant funding from the Department of Energy.  That question became less important when a Department of Energy official confirmed that grant requirements in 2017, when Chen submitted his application, did not stipulate that he must disclose posts in China, but that disclosure would not have affected his grants, as the Wall Street Journal first reported. The money at the centerpiece of the fraud allegations—$25 million—was intended for MIT to support a new collaborative research center at China’s Southern University of Science and Technology, rather than Chen individually. “While Professor Chen is its inaugural MIT faculty director, this is not an individual collaboration; it is a departmental one, supported by the Institute,” MIT President Raphael Reif explained in a letter to the MIT community last year. As one of the most prominent scientists charged under the initiative, Chen’s case received widespread attention. MIT faculty members wrote an open letter supporting the scholar that also reflected the broader concerns of the academic community about the criminalization of standard academic activity. “In many respects, the complaint against Gang Chen is a complaint against all of us, an affront to any citizen who values science and the scientific enterprise,” they wrote.  What next? With Chen’s charges all but certain to be dismissed, six more research integrity cases remain pending. Four are scheduled to go to trial later this spring. Meanwhile, an increasing number of disparate groups, from scientific associations, civil rights organizations, lawmakers and even former officials involved in shaping the program have been calling for either an end to the program, or at least the targeting of academics.  The Justice Department is “reviewing our approach to countering threats posed by the PRC government,” department spokesman Wyn Hornbuckle told MIT Technology Review in an email. “We anticipate completing the review and providing additional information in the coming weeks.” He referred questions about Chen’s case to the US Attorney’s Office in Boston, which has not yet responded to a request for comment. Meanwhile, on January 4 the White House Office of Science and Technology Policy published updated guidance on strengthening protections for American research and development against foreign interference, which included additional details on disclosure requirements for principal investigators. As for Chen, “He is looking forward to resolving the criminal matter as soon as possible,”his attorney, Robert Fischer, told MIT Technology Review. Additional reporting by Jess Aloe. 

WordPress Statistics 2021

By incloudhosting.co.uk

Favoured option of webmasters to create any kind of Website and when it comes to the best blog sites, WordPress is ranked on the top of the list. In this post, we have created an infographic of all the WordPress Statistics you need to know.