Category Archives: Deep Mind
Good Morning, Illini Nation: Deep dive on the AP Top 25 – Champaign/Urbana News-Gazette
Welcome to Good Morning, Illini Nation, your daily dose of college basketball news from Illini beat writer and AP Top 25 voter Scott Richey. Hell offer up insights every morning on Brad Underwoods team:
Illinois was again on the outside looking in when the latest Associated Press Top 25 poll was released late Monday morning. The Illini were among the 17 other teams receiving votes and nominally "ranked" 29th in the nation. Let's dive a little deeper on this week's voting:
Illinois drew votes on 16 ballots this week, which was double what it got last week. The Kansas City Star's Jesse Newell, who is on record for basing his vote mostly off advanced metrics, which are called the "computer numbers" in college hoops circles, moved the Illini up from 18th to 13th this week. That comes after a similar move in said computer numbers following the blowout win against St. Francis (Pa.).
Both voters in the state of Illinois (myself and colleague Steve Greenberg from the Sun-Times) voted Illinois at No. 25 this week. I can't speak for Steve, but my vote for the Illini was based on a combination of other teams losing and needing a 25th team. Illinois, with four top 100 KenPom wins, had as good a resume as any.
I'll never catch Newell for "most extreme" ballot, but I did move into second place in that regard. The "extreme" nature of my ballot (again, that's ranking a team five or more spots from where it actually ends up on the poll) is basically centered around having Xavier higher and Houston lowerthan most voters. I had seven "extreme" picks out of 25 this week.
No one voted San Francisco or Minnesota higher than I did, and no one voted Houston lower. Only one voter, the New Haven Register's Dave Borges, ranked Xavier higher than I did. How two voters aren't even including the Musketeers boggles my mind. Xavier has both the computer numbers (23rd in KenPom and 12th in Torvik with preseason bias weeded out) and, you know, actual good wins (Ohio State, Virginia Tech, Oklahoma State, Cincinnati and Marquette are all top 100 quality).
It wasn't a unanimous week for Baylor at No. 1 after getting all the votes a week ago. Former News-Gazette beat writer Paul Klee changed his vote to Arizona this week. Baylor did struggle a bit before rallying to beat Oregon to end last week, but similarly unbeaten Arizona didn't exactly add a big one to its resume with wins against Northern Colorado and Cal Baptist. Having seen the Wildcats in person, of course, I can confirm they're legit.
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Good Morning, Illini Nation: Deep dive on the AP Top 25 - Champaign/Urbana News-Gazette
Deep Learning Market Growth, Size, Competitive Situation 2021, Trend Analysis, Product Scope, Industry, Factors, Share Estimation, Demand and Supply…
Deep Learning market report contains detailed information on factors influencing demand, growth, opportunities, challenges, and restraints. It provides detailed information about the structure and prospects for global and regional industries. In addition, the report includes data on research & development, new product launches, product responses from the global and local markets by leading players. The structured analysis offers a graphical representation and a diagrammatic breakdown of the Deep Learning market by region.
The deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017-2023.
Global Deep Learning Market Global Drivers Restraints Opportunities Trends and Forecasts up to 2023
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Market OverviewDeep learning can be considered as a subset of machine learning and consists of algorithms that allow a software to self-train to execute tasks such as image and speech recognition by exposing multilayered neural networks to bulk data. It can have a profound impact on various industries such as finance automotive aerospace telecommunication and information technology oil and gas industrial defense media and advertising medical and others. The increasing research and development activities in this domain is expanding the end use areas for the technology.
The factors that contribute to the high market share are parallelization high computing power swift improvements in information storage capacity in automotive and healthcare industries. A few major applications for deep learning systems are in autonomous cars data analytics cyber security and fraud detection. It has become imperative for both small and big organizations to analyze and extract meaningful information from visual content. Advanced technologies such as graphic processing units are highly accepted in scientific disciplines such as deep learning and data sciences.
Valuable insights are extracted from bulk data by using deep learning neural networks to improve customer experience and generate innovative products. The development in artificial intelligence capabilities in natural language processing computer vision areas and image and speech recognition are driving the growth for deep learning.The use cases for deep learning is diverse ranging from detecting gene abnormalities and predicting weather patterns to identifying fraudulent insurance claims stock market analysis robotics drones finance agriculture. Deep learning systems have wide applications in the banking and financial sector.
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It helps bank employees expand their capabilities so that they can focus more on customer interactions rather than regular banking transactions. The deep learning software can offer solutions based on a clients background and history and thus can provide evidence and context-based reasoning for every problem. Industries worldwide are generating enormous data which require high processing power and this data is being generated at an unprecedented rate and volume. This has created an enormous opportunity for deep learning powered applications. A plethora of start-ups are coming up with vertical specific solutions and global corporations are supporting these start-ups to innovate faster.
Market AnalysisAccording to Reportocean Research the Global Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017-2023. The market is driven by factors such as faster processor performance large training data size and sophisticated neural nets. The future potential of the market is promising owing to opportunities such as development in big data technologies expanding end-user base and extensive R&D. The market growth is curbed by restraining factors such as implementation challenges rigid business models dearth of skilled data scientists affordability of organizations and data security concerns and inaccessibility.
Segmentation by SolutionsThe market has been segmented and analyzed by the following components: Software and Hardware.
Segmentation by End-UsersThe market has been segmented and analyzed by the following end-users: Medical Automotive Retail Finance IT & Telecommunications Industrial Aerospace and Defence Media and Advertising Oil Gas and Energy and Others.
Segmentation by RegionsThe market has been segmented and analyzed by the following regions: North America EMEA Latin America APAC and Latin America.Segmentation by ApplicationsThe market has been segmented and analyzed by the following applications: Image Recognition Voice Recognition Video Surveillance and Diagnostics Data mining and Others.
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Region/Country Cover in the Report
North America EMEA Latin America and APAC
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Key Players Covered in the Report
Microsoft CorporationIBM CorporationAmazon Web ServicesNvidia CorporationDeepmind Technologies Ltd
This report covers aspects of the regional analysis market.The report includes data about North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa.This report analyzes current and future market trends by region, providing information on product usage and consumption.Reports on the market include the growth rate of every region, based on their countries over the forecast period.
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The report analyzes companies across the globe in detail.The report provides an overview of major vendors in the market, including key players.Reports include information about each manufacturer, such as profiles, revenue, product pricing, and other pertinent information about the manufactured products.This report includes a comparison of market competitors and a discussion of the standpoints of the major players.Market reports provide information regarding recent developments, mergers, and acquisitions involving key players.
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When thinking of baby Jesus, remember the role Joseph played – Gaston Gazette
Michael K. McMahan| The Gaston Gazette
This is how the birth of Jesus the Messiah came about: His mother Mary was pledged to be married to Joseph, but before they came together, she was found to be pregnant through the Holy Spirit. Because Joseph her husband was faithful to the law, and did not want to expose her to public disgrace, he had in mind to divorce her quietly. But after he had considered this, an angel of the Lord appeared to him in a dream and said, Joseph, son of David, do not be afraid to take Mary home as your wife, because what is conceived in her is from the Holy Spirit. She will give birth to a son, and you are to give him the name Jesus, because he will save his people from their sins. (Matthew 1: 18 21)
You are a young man a few years out of college. As you grew up around the construction industry, you prefer working with your hands to sitting at a desk. You have built a small business contracting carpentry work to large corporate home builders. You are up early. You work hard. It is all good, but one thing is missing.
And there she is. Eight years younger, but very mature, an administrative assistant with one of the big companies who contracts for your services, smart and pretty, and kind. You are in love with her when you first see her.
In a very short time, you discretely slip a ring on the third finger of her left hand. Surprised, tears spring to her eyes. You say, Will you marry me? She nods through those tears. You hug one another and both say, I love you, and you laugh.
Two weeks later there is a knock at your door. You hear her voice. You straighten your shirt and wish you had brushed your teeth after breakfast. You open the door and she is sobbing.
Whats wrong, you say as you help her into your small apartment. You set two straight chairs facing one another and sit in front of her, holding both her hands.
She sobs, but soon wipes her eyes and says, Not long before we met, she sobs, takes a deep breath, and says, I made a mistake. Im pregnant.
Emotions pelt you from every direction surprise, confusion, anger, disappointment, and, as you see her trembling in front of you, compassion and love. You stand and pull her to her feet. You hug her as she places her face on your shoulder and sobs quietly.
You know four things. She is the person you want to be with for the rest of your life. She is hurting. You love her. It will be her baby and you will love the child as you love her.
In a different culture two thousand years ago a successful young man who was faithful to the law and to Yahweh, and was known to be a good and kind person was chosen by a family who loved their daughter to be her husband. He was a carpenter, or more likely a home builder like my friends Doug McSpadden and Bob Rouse.
Soon he learned the young woman to whom he was to be married was pregnant. Because he had compassion for her and respect for her family, he decided he would quietly withdraw from the marital arrangement. But something happened.
Matthew expresses it as a visit from an angel in a dream. It was a dramatic event for Joseph, an epiphany. It told him Mary was a devout young woman. She was not only worthy of his respect and honor, the child within her would be special.
So, it is one of the great characters in the Bible accepted the responsibility of protecting and loving Mary, the mother of Jesus, and caring for Marys son as his earthly father.
We know so little about him. It is likely he died at a relatively young age. Otherwise, Jesus might have begun his ministry sooner than at the age of 30. The later date may indicate that Jesus needed to provide for his mother and family because of Josephs death. We do not know. But we know Joseph was a faithful husband and father. We know he was a righteous and deeply good man, a man I believe was chosen by God for a role that few men on earth at that time could have fulfilled.
At this time of year, especially, we should be thankful for the life of Joseph, the husband of Mary, the earthly father of Jesus, the protector of both. In our thanksgiving we should be grateful for all fathers, regardless of their circumstances, who respect and honor the mothers of their children and provide for their families as Joseph did for his.
As you think of the baby, Jesus, and his mother, Mary, remember the deeply good man who loved and cared for them both and pray a special prayer that fathers everywhere will follow his example.
Michael K. McMahan is a resident of Gastonia and regular contributor to The Gaston Gazette.
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When thinking of baby Jesus, remember the role Joseph played - Gaston Gazette
In search of adventure, I headed to Switzerland to hike a glacier and paraglide never mind my lifelong fear of heights – Toronto Star
As our van ascends higher and higher up the narrow mountain road of the Niederhorn, a peak overlooking picturesque Interlaken, Switzerland, I spot them: colourful crescents riding the valleys air currents, gliding against the spectacular backdrop of the Bernese Alps, including its most famous threesome, the snow-covered Eiger, Mnch and Jungfrau.
My heart moves a few inches higher into my throat. Soon I will be among them, paragliding nearly 1,500 metres above the town, chalets and lakes flying like a bird, albeit one strapped into a tandem harness with my local pilot, Sebastien Bourquin.
While Bourquin prepares our equipment for the short run down the slope to inflate the wing, I try to remember how I got here. As a 50-something woman with vertigo and a lifelong fear of heights, paragliding seems, well, a bit wild, if not downright ludicrous. Perhaps my recent, enthusiastic consumption of melted raclette cheese and fortifying kirsch had gone to my head.
But the truth is, Im here in Switzerland intent on taking myself out of my comfort zone. Pre-pandemic, I was an average adventurer, happy to hike on marked trails and ski inbounds. But after so many months of limited thrills, Im eager to live each day to its fullest again. I want to feel the joy, pain and transformative power of pushing my mind and body beyond old limits.
Switzerland strikes me as the perfect place to test my boundaries. Despite its relatively small size, the country brims with outdoor excitement, including hiking on alpine glaciers, catching first tracks at the many ski resorts, and paragliding above charming mountain villages. There is no shortage of soft and hard adventure for travellers of all styles and ages.
We can thank 19th-century British mountaineers (and their local guides) for revealing the beauty of Switzerland to the world. The legacy of their successful summits of iconic peaks like the Jungfrau inspiring a rush of Belle poque visitors, including Queen Victoria opened the country to travellers keen to discover the grandeur of the landscape.
Fortunately, visitors dont have to submit to the rigours of extreme mountain climbing to enjoy the wide variety of outdoor experiences, which are easily accessible in each of Switzerlands 26 cantons.
The country is home to more than 65,000 kilometres of marked hiking trails, and on past trips Ive done my share of the scenic mountain walks, which I found neither too difficult or intimidating. Im ready for a more ambitious challenge, and this time my plan entails embarking on a practically vertical ascent, walking on an alpine glacier and taking to the skies in a paraglider.
Things start poorly. A swollen knee prevents me from tackling a via ferrata named Diavolo, outside Andermatt. Built by Swiss Army soldiers at the crossroads of four mountain passes, this devilish iron path ascends nearly 500 metres up the granite face of the Schllenen, overlooking its famous gorge and Devils Bridge. The Diavolo is categorized as moderately difficult, yet somehow also ideal for beginners, but my knee is in no shape to mount the 265 metal stakes on this foggy fall day.
Fortunately, all I need are a few days of rest, in preparation for my glacier hike above the idyllic alpine village of Saas-Fee. Located in the Valais canton and surrounded by snow-covered, 4,000-metre peaks, this pedestrian-only community is a mecca for outdoor adventure in all seasons, including walking on the Fee Glacier.
As with many glaciers in the Alps, the ice sheet is retreating. I can hear water running below me as our small group of roped hikers navigates the blindingly white surface. I do well on the wide, flat portions of our walk, but the narrow ledges that drop into crevasses on either side have me gripping my poles with sweaty palms.
I carefully plant each crampon-booted foot one after the other until our mountain guide stops. A two-foot-wide crevasse looms ahead, and Im suddenly rooted in place, unsure of what to do next. Its obvious we have to cross the yawning gap, but fear has me almost hyperventilating.
Put your pole on the other side of the crevasse, then take a big step across, instructs Michael Schwarzl, an Austrian whos been guiding in Saas-Fee for nearly 25 years. And breathe normally, he adds sensibly, a reminder I need.
I repeat an inner monologue to muster up my nerve I can do this, Im not going to fall and do as he says. As I peer into the indeterminate depths of the crevasse, I see the bright turquoise ice change to a stark black abyss. My metal crampons dig into the ancient glacier to secure a foothold. With a sigh of relief, I happily accept Schwarzls hand as he pulls me to safety across the way.
An hour later, at a mountaintop restaurant, we toast our triumph over cliff edges and deep, dark places. My crevasse-crossing experience isnt, however, the boldest part of my Swiss itinerary. The most intimidating of my planned adventures awaits: paragliding in the skies above Interlaken.
Interlaken bills itself as Europes number one destination for adventure sports, and in every season, the sky is filled with single and tandem paragliders winging their way toward a soft landing on the grassy Hhematte Park in the middle of town.
Thats our flight plan, too. As Bourquin tightens my straps and checks the harness, I tamp down my apprehension and smile nervously. Ready? he asks in French. Oui, I respond, still trying to convince myself. We start running downhill, and as the wing catches the wind, in an instant, were aloft.
Settling into our seated position in the harness, all I need to do is sit back and enjoy the flight which, to my surprise, I greatly do. The sensation of soaring above the deep blue lakes and church steeples of Interlaken is gentler than I imagined yet breathtaking.
As the lifting currents pull us upward in large circles above the town, Im giddy with the wonder of flight. After 10 minutes, my reverie is broken when Bourquin asks if I want to land or continue.
Any hesitation has vanished in thin air. Lets keep going, I shout happily. I want more of this freedom, this release from fear that all my epic adventures in Switzerland have granted me.
Writer Claudia Laroye travelled as a guest of Switzerland Tourism, which did not review or approve this article. The federal government recommends Canadians avoid non-essential travel. This article is meant to inspire plans for future travel.
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In search of adventure, I headed to Switzerland to hike a glacier and paraglide never mind my lifelong fear of heights - Toronto Star
DeepMind Cracks ‘Knot’ Conjecture That Bedeviled …
The artificial intelligence (AI) program DeepMind has gotten closer to proving a math conjecture that's bedeviled mathematicians for decades and revealed another new conjecture that may unravel how mathematicians understand knots. Live Science reports: The two pure math conjectures are the first-ever important advances in pure mathematics (or math not directly linked to any non-math application) generated by artificial intelligence, the researchers reported Dec. 1 in the journal Nature. [...] The first challenge was setting DeepMind onto a useful path. [...] They focused on two fields: knot theory, which is the mathematical study of knots; and representation theory, which is a field that focuses on abstract algebraic structures, such as rings and lattices, and relates those abstract structures to linear algebraic equations, or the familiar equations with Xs, Ys, pluses and minuses that might be found in a high-school math class.
In understanding knots, mathematicians rely on something called invariants, which are algebraic, geometric or numerical quantities that are the same. In this case, they looked at invariants that were the same in equivalent knots; equivalence can be defined in several ways, but knots can be considered equivalent if you can distort one into another without breaking the knot. Geometric invariants are essentially measurements of a knot's overall shape, whereas algebraic invariants describe how the knots twist in and around each other. "Up until now, there was no proven connection between those two things," [said Alex Davies, a machine-learning specialist at DeepMind and one of the authors of the new paper], referring to geometric and algebraic invariants. But mathematicians thought there might be some kind of relationship between the two, so the researchers decided to use DeepMind to find it. With the help of the AI program, they were able to identify a new geometric measurement, which they dubbed the "natural slope" of a knot. This measurement was mathematically related to a known algebraic invariant called the signature, which describes certain surfaces on knots.
In the second case, DeepMind took a conjecture generated by mathematicians in the late 1970s and helped reveal why that conjecture works. For 40 years, mathematicians have conjectured that it's possible to look at a specific kind of very complex, multidimensional graph and figure out a particular kind of equation to represent it. But they haven't quite worked out how to do it. Now, DeepMind has come closer by linking specific features of the graphs to predictions about these equations, which are called Kazhdan-Lusztig (KL) polynomials, named after the mathematicians who first proposed them. "What we were able to do is train some machine-learning models that were able to predict what the polynomial was, very accurately, from the graph," Davies said. The team also analyzed what features of the graph DeepMind was using to make those predictions, which got them closer to a general rule about how the two map to each other. This means DeepMind has made significant progress on solving this conjecture, known as the combinatorial invariance conjecture.
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DeepMind Cracks 'Knot' Conjecture That Bedeviled ...
DeepMinds AI lights path to faster drug development
Googles parent company, Alphabet, has announced the launch of Isomorphic Labs, an AI-driven drug discovery company, built on research from its DeepMind subsidiary.
For over a decade DeepMind has been in the vanguard of advancing the state-of-the-art in AI, Demis Hassabis, founder and CEO of both Isomorphic Labs and DeepMind, wrote in a blog post.
We are at an exciting moment in history now where these techniques and methods are becoming powerful and sophisticated enough to be applied to real-world problems, he added, including scientific discovery itself.
The challenge: Drug discovery starts with scientists identifying potential treatment targets typically proteins or genes linked to a disease. Theyll then look for a molecule or compound that affects or hits that target.
Drug discovery takes, on average, more than 10 years and $2.8 billion per drug.
A recent example of this was the identification of the spike protein on the coronavirus as a target for COVID-19. This led to the discovery of vaccines and monoclonal antibodies that can stick to the spike and neutralize it.
The problem with this approach to drug discovery is that it usually takes on average more than 10 years and $2.8 billion per drug. Most diseases arent like COVID-19, with one big, clear target with well-known weapons, like antibodies, to hit it. There may be thousands of potential candidates and identifying promising ones is largely a process of trial and error.
AI-driven drug discovery: AI has emerged as a way to speed up drug discovery. Trained systems can look at a treatment target and then identify promising drug candidates from a library of options far more quickly than scientists in a lab.
This isnt just a theory Alphabet is banking on, either weve already seen examples of it in action.
An AI developed by U.K. company Exscientia, for example, discovered an anticancer molecule thats now heading into clinical trials and it did so in just eight months. Without the AI, that discovery would have likely taken 4 to 5 years.
Isomorphics mission could not be a more important one: find cures for some of humanitys most devastating diseases.
The cold water: AI might be able to help us quickly identify candidates that look promising on a molecular level, but thats just one aspect of drug discovery those candidates will still need to prove themselves in lab tests, animal trials, and human trials.
The laborious, resource-draining work of doing the biochemistry and biological evaluation of, for example, drug functions [will remain], Helen Walden, a professor of structural biology at the University of Glasgow, told the Verge with regards to AIs use in drug discovery.
Looking ahead: Isomorphic Labs will reportedly collaborate with DeepMind researchers, when appropriate, but will also operate independently. It is now looking to staff up with biologists, chemists, AI experts, and more to help it meet its lofty goal.
Isomorphics mission could not be a more important one: to use AI to accelerate drug discovery, and ultimately, find cures for some of humanitys most devastating diseases, Hassabis wrote.
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DeepMinds AI lights path to faster drug development
Meadows said the ‘only thing on my mind’ was RBG’s vacant seat when she died: book – Business Insider
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Former White House Chief of Staff Mark Meadows says he immediately began thinking about the newly vacant seat on the Supreme Court after the death of liberal Supreme Court Justice Ruth Bader Ginsburg on September 2020.
According to Meadows' new book, "The Chief's Chief," Meadows learned of Ginsburg's death while his boss, former President Donald Trump, spoke at a rally in Minnesota.
As was reported at the time, Meadows said he wanted to avoid letting Trump know about the liberal justice's death until after he was done speaking, because he worried about optics of the crowd cheering on her death.
"Because of the energy of the crowd, and the general tendency of people in large groups to act unpredictably, I decided to direct [Dan Scavino] and [Johnny McEntee] to withhold the news about Ginsburg from the president until he had finished speaking," Meadows wrote. "To be blunt, I believed that if he announced the news from the podium, the crowd would likely erupt in cheers over the new vacancy on the court."
But Meadows himself was already fixated on what would come next.
"The only thing on my mind was that newly vacant seat on the Supreme Court, and how it was my job my only job, at least for the next few weeks to make sure President Trump was allowed to fill it, as was his duty according to the United States Constitution," he wrote.
Meadows wrote that he respected the liberal justice for her own convictions and for her famed friendship with the late conservative Justice Antonin Scalia.
"I had developed a deep admiration for her strength and resolve," Meadows wrote. "I also knew that she was the last of a rare breed in Washington: someone who could be kind, even friendly, to people who didn't share her political beliefs."
He praised Ginsburg for her "iron will" in declining to retire despite pressure from former President Barack Obama in 2013.
"Now that I've been in the Oval Office and seen the way that room, not to mention the office of the presidency itself, seems to intimidate people into acquiescence, I know that it must have taken an iron will to refuse such a request," he wrote.
"On that night in September, however, I wasn't thinking about any of that," Meadows wrote.
Meadows also wrote that he and Dan Scavino, Trump's Deputy Chief of Staff for Communications, tried to get the news of Ginsburg's death to Trump before reporters did at the rally. "I knew that this was a sensitive topic and that even a few wrong words could significantly decrease our chances of filling the vacancy left by Justice Ginsburg," he wrote.
Ultimately, though, Trump did learn about the news from reporters, prompting an unusually gracious response from the former president.
"She led an amazing life. What else can you say? Whether you agree or not, she led an amazing life," he said.
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Meadows said the 'only thing on my mind' was RBG's vacant seat when she died: book - Business Insider
The Best Science and Tech Breakthroughs of 2021 – Nerdist
Scientists and engineers explored new frontiers in every technological category in 2021. Advances in everything from spaceflight to microrobotics to artificial intelligence abounded, offering a glimpse of a world in which humanity is a multiplanet species. As well as one physiologically connected to intelligent machines. Below are the best science and tech breakthroughs of 2021, in our humble opinion, which may change when we get our Neuralink brain implants.
Although SpaceX had several spectacular failures trying to fly and land its prototype Starship rocket, that just made the first successful attempt (below) all the sweeter. According to SpaceX, the company plans to use Starships to send people to the Moon and Mars. The complete Starship system, once it comes online, will be an astounding 394 feet tall.
While seeing rovers roll around on Mars can feel commonplace, mobility breakthroughs on the Red Planet are beginning to happen. Below is video of the first-ever (mini) helicopter flight on Mars, which occurred on April 19. The flight, while short, was exceptional thanks not only to the helicopters long journey to Mars in the Perseverance rover, but also the planets super-thin atmosphere.
In July of this year, Googles DeepMind subsidiary announced it had solved a grand challenge in biology known as the protein folding problem. Using its cutting-edge AI, AlphaFold, DeepMind released the structures of 350,000 proteins. And noted that the tech will eventually be able to help identify and cure diseases.
Engineers the world over have been working on ways to shrink robots. Emblematic of the efforts from this year are microflier robots that can float on the wind. While the microfliers themselves will reportedly record things like changes in climate and the spread of disease, we cant help but experience foreboding Black Mirror vibes.
As their name implies, brain organoids, or cerebral organoids, are very much like tiny human brains; a fact that makes scientists giving them eye balls in August of this year all the wilder. The eyed organoids, while somewhat disturbing, will hopefully help to cure congenital retinal disorders and even personalize drug testing. And help to raise some important issues for bioethics as well, we imagine.
Smart clothes that can sense and record all of your movements, as well as give you posture suggestions, are now here thanks to MIT. While not wholly new, MITs smart clothes are unique because they consist of simple, knitted conductive yarn, and are amenable to mass production. As well as collecting large amounts of data from their users for robot training.
Finally on the list is Neuralinks breakthrough demonstration of a monkey telepathically playing Pong. Or, in this context, MindPong. Neuralink was able to pull off the feat by plunging 1,024 ultra-thin electrodes into a Macaques brain. (Banana smoothies were essential as well.) The company says that, in the near-term, the tech could help paralyzed people surf the net and express themselves artistically. Merging with superintelligent AI is also apparently not off the table for this rapidly moving decade.
Feature image: Neuralink/Cell Stem Cell/NASA
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The Best Science and Tech Breakthroughs of 2021 - Nerdist
Funding news: Cyrus lands $18M and buys startup developing COVID-19 therapeutic; Spare Labs snags $18M for mobility software – GeekWire
The news: Seattle-based protein engineering company Cyrus Biotechnology has raised $18 million and acquired Orthogonal Biologics, a spinout from the University of Illinois at Urbana Champaign, which is developing a COVID-19 therapeutic.
Combining forces: Cyrus has built up a software-and-screening platform to re-design natural proteins, leveraging tech spun out of the University of Washingtons Institute for Protein Design. IPDs tools to predict protein folding have been a boon to Cyrus, which was co-founded in 2015 by former IPD postdoc Lucas Nivon. The company recently inked a deal with immune biotech Selecta Biosciences worth up to $1.5. billion and has worked with more than 90 other industry partners, including pharma giant Janssen.
The new acquisition brings on board Orthogonals platform for deep mutational scanning, a method that can assess up to one million mutant versions of a protein in a single experiment. Orthogonal also adds two new protein-based therapeutics to Cyrus pipeline, including a potential COVID-19 drug.
Counteracting COVID-19: Orthogonals COVID-19 agents are built to resemble ACE2, the human protein that the COVID-19 virus uses to enter human cells. The agents are designed to act as decoys, binding to the virus and disarming it.
Why it matters: Drug companies are fast leveraging IPDs recently-released RoseTTAfold and another powerful tool to predict protein folding developed by DeepMind, AlphaFold. Plugging in different tech and drug pipelines, such as those developed by Orthogonal, promises to accelerate the development of new therapeutics. Cyrus recently brought on RoseTTAfold, building on its use of an earlier IPD tool, called Rosetta.
Cyrus has proven the power of its Rosetta-based platform as a software and services company. We are very excited to now apply those software and laboratory tools directly for Cyruss partners and in house drug discovery, said Geeta Vemuri, founder and managing partner at Agent Capital, in a statement.
The field is growing rapidly. Alphabet, for instance, in November launchedIsomorphic Labs to build off of DeepMinds protein folding research.
The backers: Investors in the new deal include OrbiMed Advisors, Trinity Ventures, Agent Capital, Yard Ventures, Washington Research Foundation (WRF), iSelect Fund, W Fund, family offices, and individual investors. Selecta Bioscience is a strategic investor in the Series B round, which brings total funding to date to $28.9 million, including $8 million in venture funding raised in 2017. Terms of the acquisition were not disclosed.
Whats next: The cash will be used to move Cyrus labs from a temporary space atAlexandria LaunchLabstoa buildingnear the Seattle waterfront that houses Universal Cells and other biotechs. Cyrus will also partner with contract research organizations for preclinical testing of the the COVID-19 agent and other therapies.
The small Orthogonal team has moved to Seattle, including COOKui K. Chanand CEOErik Procko, a University of Illinois professor of biophysics and quantitative biology, now on leave. Both are former senior fellows in the lab of David Baker, IPD head. Cyrus will continue relationships with key University of Illinois researchers, including professorsJalees RehmanandAsrar B. Malik, who are performing studies in animals. Cyrus is hiring protein biochemists and senior leadership in drug discovery, aiming to grow from 25 to 30 employees in the next six months.
By merging our company with Cyrus we can create a unified biologics discovery platform, said Procko.
More deals:
Koch Investment Group invests $100 million in Vancouver, B.C.-based Standard Lithium.Standard Lithium is testing the commercial viability of extracting lithium, a key component of electric batteries, at a 150,000-acre location in Arkansas. The company has commissioned a demonstration plant to extract the metal. It also has 45,000 acres of mineral leases in the Mojave Desert in San Bernardino County, Calif.
Barn2Door raises $6 million to advance software that connects farmers to customers. Seattle-based Barn2Door serves thousands of farmers across the U.S., helping them sell food directly to consumers with e-commerce software that manages sales, inventory, logistics, and more. The new funding brings total funding to $17.6 million to date, building on a $6 million round in August, 2020. The latest funding was led by Quiet Capital, with participation from existing major investors Bullpen Capital, lead Edge Capital, RAINE Ventures, Sugar Mountain Capital, as well as new investors Serra Ventures and Navigate Ventures.
Vancouver, B.C.-based Spare Labs raises $18M for mobility software. Spare Labs provides software for public transit, ride-sharing and other shared transportation. It will use the funding to enable better cooperation between different transportation providers. The Series A round was led by Inovia Capital with participation from Kensington Capital, Link VC, Ramen VC, Ridge Ventures, TransLink Capital and Japan Airlines (as JAL Innovation Fund) and Nicola Wealth, amongst others.
Editors note: This story has been updated to include Cyrus future plans.
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Funding news: Cyrus lands $18M and buys startup developing COVID-19 therapeutic; Spare Labs snags $18M for mobility software - GeekWire
UC Berkeley’s Sergey Levine Says Combining Self-Supervised and Offline RL Could Enable Algorithms That Understand the World Through Actions – Synced
The idiom actions speak louder than words first appeared in print almost 300 years ago. A new study echoes this view, arguing that combining self-supervised and offline reinforcement learning (RL) could lead to a new class of algorithms that understand the world through actions and enable scalable representation learning,
Machine learning (ML) systems have achieved outstanding performance in domains ranging from computer vision to speech recognition and natural language processing, yet still struggle to match the flexibility and generality of human reasoning. This has led ML researchers to search for the missing ingredient that might boost these systems ability to understand, reason and generalize.
In the paper Understanding the World Through Action, UC Berkeley assistant professor in the department of electrical engineering and computer sciences Sergey Levine suggests that a general, principled, and powerful framework for utilizing unlabelled data could be derived from RL to enable ML systems leveraging large datasets to better understand the real world.
Several hypotheses have been advanced to address this missing ingredient question in ML systems, such as causal reasoning, inductive bias, and better algorithms for self-supervised or unsupervised learning. Levine says that while the problem is challenging and involves a great deal of guesswork, recent progress in AI can provide some guiding principles: 1) The unreasonable effectiveness of large, generic models supplied with large amounts of training data; 2) How manual labelling and supervision do not scale nearly as well as unsupervised or self-supervised learning.
Levine believes the next bottleneck facing ML researchers involves deciding how to train large models without manual labelling or manual design of self-supervised objectives so as to acquire models that distill a deep and meaningful understanding of the world and are able to perform downstream tasks with robust generalization and even a degree of common sense.
To achieve this goal, autonomous agents will require an understanding of their environments that is causal and generalizable. Such agents would advance beyond the current RL paradigm, where 1) RL algorithms require a task goal (i.e., a reward function) to be specified by experts; and 2) RL algorithms are not inherently data-driven, but rather learn from online experience, an approach that limits both generalization ability and the ability to learn about how the real world works.
Levine envisions algorithms that, rather than aiming at a single user-specified task, seek to accomplish whatever outcomes they infer are possible in the real world. He proposes developing offline RL algorithms that can effectively utilize previously collected datasets to enable a system that can use its training time to learn and perform user-specified tasks while also using its collected experience as offline training data to learn to achieve a wider scope of outcomes.
Levine believes offline RL has the potential to significantly increase the applicability of self-supervised RL methods, and can be utilized in combination with goal-conditioned policies to learn entirely from previously collected data.
Overall, the paper explores how self-supervised RL combined with offline RL could realize scalable representation learning. Self-supervised training can enable models to understand how the world works, and fulfilling self-supervised RL objectives can allow models to gain a causal understanding of the environment. Such techniques must be applicable at scale to real-world datasets, a challenge met by offline RL, which enables the use of large, diverse previously collected datasets.
The paper Understanding the World Through Action is on arXiv.
Author: Hecate He |Editor: Michael Sarazen
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UC Berkeley's Sergey Levine Says Combining Self-Supervised and Offline RL Could Enable Algorithms That Understand the World Through Actions - Synced