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Apples: Mathematical analysis reveals how the apple gets its shape

Daily Mail - Science & tech

The apple gets its'dimple-like cusp' and shape as a result of different rates of growth between the bulk and the stalk, according to a new mathematical study of the fruit. Apples are relatively spherical apart from the dimple at the top, according to a team from Harvard University in Cambridge Massachusetts, who set out to see if they could understand, mathematically, why the fruit has this unusual form factor. They turned to a gel that can adapt its shape over time, to replicate the way an apple grows, and compared it to the growth of real apples from an orchard. Combining this with mathematical models revealed that the underlying fruit anatomy, the way it grows at different rates, and mechanical instability, play joint roles in the rise of the dimple, bottom ridges and general shape of the fruit. Apples first evolved in central Asia from the wild ancestor Malus sieversii - which is still growing today.


Scientists give up on artificial intelligence, begin work on artificial stupidity - The Beaverton

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Cambridge, Massachusetts ― A team of engineers at the Massachusetts Institute of Technology unveiled the world's first artificial stupidity prototype yesterday. They have dubbed their project the Artificial Stupidity System, or ASS for short. "We originally had a robot with nearly perfect AI, but we had to drastically modify the system," explained head researcher Susan Wilcox. "The old model could respond to anything a human said, learn to perform tasks, simulate a wide range of emotions ― all of that was easy. But it just couldn't pass the Turing test."


What Evolution Can Teach Us About Innovation

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Many people believe that the process for achieving breakthrough innovations is chaotic, random, and unmanageable. Breakthroughs can be systematically generated using a process modeled on the principles that drive evolution in nature: variance generation, which creates a variety of life-forms; and selection pressure to select those that can best survive in a given environment. Flagship Pioneering, the venture-creation firm behind Moderna Therapeutics, uses such an approach, which it calls emergent discovery. It involves prospecting for ideas in novel spaces; developing speculative conjectures; and relentlessly questioning hypotheses. On November 30, 2020, Moderna Therapeutics announced that Phase III clinical trials for its messenger RNA vaccine demonstrated 95% protective efficacy against the SARS-CoV-2 virus that had killed almost 1.5 million people worldwide in the previous 10 months. A relative upstart in the Covid-19 vaccine race and a company that few people had heard of before the pandemic, Moderna looked to be an overnight success. But as its CEO, Stéphane Bancel, has noted, that success was 10 years in the making. Far from a one-and-done stroke of luck, the vaccine was the product of a repeatable process that has been used countless times by the company from which Moderna emerged: Flagship Pioneering, a venture-creation firm based in Cambridge, Massachusetts, whose mission is to conceive, make, and commercialize breakthrough innovations in previously unexplored domains of the life sciences. The misconception about the Moderna case, as with many other breakthrough innovations, is understandable. Breakthrough innovations are typically seen as the result of chaotic, random, and unmanageable efforts--the product of pure serendipity or the inspiration of a rare visionary. That view, we believe, is deeply flawed. From our different vantage points (Afeyan has spent the past three decades starting ventures based on breakthrough science and technology, and Pisano has studied innovation processes during the same period), we have come to realize that breakthroughs tend to emerge from a relatively well-defined process modeled on the basic principles that drive evolution in nature: variance generation, which creates a variety of life-forms, and selection pressure to select those that can best survive and reproduce in a given environment. The approach, called emergent discovery, is a structured and disciplined process of intellectual leaps, iterative search and experimentation, and selection.


Machine Learning Finds Powerful Peptides That Could Improve Drug Delivery

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Duchenne muscular dystrophy (DMD), a rare genetic disease usually diagnosed in young boys, gradually weakens muscles across the body until the heart or lungs fail. Symptoms often show up by age 5; as the disease progresses, patients lose the ability to walk around age 12. Today, the average life expectancy for DMD patients hovers around 26. It was big news, then, when Cambridge, Massachusetts-based Sarepta Therapeutics announced in 2019 a breakthrough drug that directly targets the mutated gene responsible for DMD. The therapy uses antisense phosphorodiamidate morpholino oligomers (PMO), a large synthetic molecule that permeates the cell nucleus in order to modify the dystrophin gene, allowing for production of a key protein that is normally missing in DMD patients. It's not very good at entering cells," says Carly Schissel, a PhD candidate in MIT's Department of Chemistry. To boost delivery to the nucleus, researchers can affix cell-penetrating peptides (CPPs) to the drug, thereby helping it cross the cell and nuclear membranes to reach its target. Which peptide sequence is best for the job, however, has remained a looming question. MIT researchers have now developed a systematic approach to solving this problem by combining experimental chemistry with artificial intelligence to discover nontoxic, highly-active peptides that can be attached to PMO to aid delivery. By developing these novel sequences, they hope to rapidly accelerate the development of gene therapies for DMD and other diseases. Results of their study have now been published in the journal Nature Chemistry in a paper led by Schissel and Somesh Mohapatra, a PhD student in the MIT Department of Materials Science and Engineering, who are the lead authors. Rafael Gomez-Bombarelli, assistant professor of materials science and engineering, and Bradley Pentelute, professor of chemistry, are the paper's senior authors. Other authors include Justin Wolfe, Colin Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna Wood, Annika Malmberg, and Andrei Loas. "Proposing new peptides with a computer is not very hard.


Machine learning discovers new sequences to boost drug delivery

#artificialintelligence

Duchenne muscular dystrophy (DMD), a rare genetic disease usually diagnosed in young boys, gradually weakens muscles across the body until the heart or lungs fail. Symptoms often show up by age 5; as the disease progresses, patients lose the ability to walk around age 12. Today, the average life expectancy for DMD patients hovers around 26. It was big news, then, when Cambridge, Massachusetts-based Sarepta Therapeutics announced in 2016 a breakthrough drug that directly targets the mutated gene responsible for DMD. The therapy uses antisense phosphorodiamidate morpholino oligomers (PMO), a large synthetic molecule that permeates the cell nucleus in order to modify the dystrophin gene, allowing for production of a key protein that is normally missing in DMD patients. It's not very good at entering cells," says Carly Schissel, a PhD candidate in MIT's Department of Chemistry. To boost delivery to the nucleus, researchers can affix cell-penetrating peptides (CPPs) to the drug, thereby helping it cross the cell and nuclear membranes to reach its target. Which peptide sequence is best for the job, however, has remained a looming question. MIT researchers have now developed a systematic approach to solving this problem by combining experimental chemistry with artificial intelligence to discover nontoxic, highly-active peptides that can be attached to PMO to aid delivery. By developing these novel sequences, they hope to rapidly accelerate the development of gene therapies for DMD and other diseases. Results of their study have now been published in the journal Nature Chemistry in a paper led by Schissel and Somesh Mohapatra, a PhD student in the MIT Department of Materials Science and Engineering, who are the lead authors. Rafael Gomez-Bombarelli, assistant professor of materials science and engineering, and Bradley Pentelute, professor of chemistry, are the paper's senior authors. Other authors include Justin Wolfe, Colin Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna Wood, Annika Malmberg, and Andrei Loas. "Proposing new peptides with a computer is not very hard.


Machine learning discovers new sequences to boost drug delivery

#artificialintelligence

Duchenne muscular dystrophy (DMD), a rare genetic disease usually diagnosed in young boys, gradually weakens muscles across the body until the heart or lungs fail. Symptoms often show up by age 5; as the disease progresses, patients lose the ability to walk around age 12. Today, the average life expectancy for DMD patients hovers around 26. It was big news, then, when Cambridge, Massachusetts-based Sarepta Therapeutics announced in 2019 a breakthrough drug that directly targets the mutated gene responsible for DMD. The therapy uses antisense phosphorodiamidate morpholino oligomers (PMO), a large synthetic molecule that permeates the cell nucleus in order to modify the dystrophin gene, allowing for production of a key protein that is normally missing in DMD patients. It's not very good at entering cells," says Carly Schissel, a Ph.D. candidate in MIT's Department of Chemistry. To boost delivery to the nucleus, researchers can affix cell-penetrating peptides (CPPs) to the drug, thereby helping it cross the cell and nuclear membranes to reach its target. Which peptide sequence is best for the job, however, has remained a looming question. MIT researchers have now developed a systematic approach to solving this problem by combining experimental chemistry with artificial intelligence to discover nontoxic, highly-active peptides that can be attached to PMO to aid delivery. By developing these novel sequences, they hope to rapidly accelerate the development of gene therapies for DMD and other diseases. Results of their study have now been published in the journal Nature Chemistry in a paper led by Schissel and Somesh Mohapatra, a Ph.D. student in the MIT Department of Materials Science and Engineering, who are the lead authors. Rafael Gomez-Bombarelli, assistant professor of materials science and engineering, and Bradley Pentelute, professor of chemistry, are the paper's senior authors. Other authors include Justin Wolfe, Colin Fadzen, Kamela Bellovoda, Chia-Ling Wu, Jenna Wood, Annika Malmberg, and Andrei Loas. "Proposing new peptides with a computer is not very hard.


How low-code development could boost AI adoption

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Every company may want to put artificial intelligence to work, but most companies aren't blessed with the ability to hire battalions of data scientists–nor is that necessarily the right approach. As Gartner analyst Svetlana Sicular once argued, often the best possible data scientist is the person you already employ who knows your data and simply needs help figuring out how to unlock it. For many business line owners, it's this kind of approach that may make the most sense, as they seek to be smarter with the data they already have. One company working to enable this vision is Cambridge, Massachusetts-based machine learning startup Akkio, which pairs AI with low code in an attempt to democratize AI. I caught up with company co-founder and COO Jon Reilly to learn more.


Artificial intelligence approaches human intellectuality.

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Even if there has been a lot of development in artificial intelligence, the human brain is the most complex and dynamic knowledge processing to date. There is a significant lag time-period when new and accurate knowledge becomes accessible and when used artificial intelligence systems are updated. Still, it is not needed for newly generated and newly built artificial intelligence systems to be retrained. Now, the Cambridge, Massachusetts-based company Nara Logics, which a 2010 MIT graduate created, is working to advance artificial intelligence by focusing on the functionality of the brain. New developments in neuroscience are used in artificial intelligence to imitate the circuit work and simulate it correctly.


The AI Research Paper Was Real. The 'Coauthor' Wasn't

WIRED

David Cox, the head of a prestigious artificial intelligence lab in Cambridge, Massachusetts, was scanning an online computer science bibliography in December when he noticed something odd--his name listed as an author alongside three researchers in China whom he didn't know on two papers he didn't recognize. At first, he didn't think much of it. The name Cox isn't uncommon, so he figured there must be another David Cox doing AI research. "Then I opened up the PDF and saw my own picture looking back at me," Cox says. It isn't clear how prevalent this kind of academic fraud may be, or why someone would list as a coauthor someone not involved in the research.


Six researchers who are shaping the future of artificial intelligence

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As artificial intelligence (AI) becomes ubiquitous in fields such as medicine, education and security, there are significant ethical and technical challenges to overcome. While the credits to Star Wars drew to a close in a 1970s cinema, 10-year-old Cynthia Breazeal remained fixated on C-3PO, the anxious robot. "Typically, when you saw robots in science fiction, they were mindless, but in Star Wars they had rich personalities and could form friendships," says Breazeal, associate director of the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, Massachusetts. "I assumed these robots would never exist in my lifetime." A pioneer of social robotics and human–robot interaction, Breazeal has made a career of conceptualizing and building robots with personality.