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Daily Archives: December 15, 2020

China Injects Record 950 billion yuan in Medium-Term Liquidity

Source : Bloomberg and Bank of China

DeepMind’s AI Makes Gigantic Leap in Solving Protein Structures

Ewen Callaway wrote . . . . . . . . .

An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology’s grandest challenges — determining a protein’s 3D shape from its amino-acid sequence.

DeepMind’s program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference — held virtually this year — that takes stock of the exercise.

“This is a big deal,” says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. “In some sense the problem is solved.”

AI protein-folding algorithms solve structures faster than ever

The ability to accurately predict protein structures from their amino-acid sequence would be a huge boon to life sciences and medicine. It would vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery.

AlphaFold came top of the table at the last CASP — in 2018, the first year that London-based DeepMind participated. But, this year, the outfit’s deep-learning network was head-and-shoulders above other teams and, say scientists, performed so mind-bogglingly well that it could herald a revolution in biology.

“It’s a game changer,” says Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Tübingen, Germany, who assessed the performance of different teams in CASP. AlphaFold has already helped him find the structure of a protein that has vexed his lab for a decade, and he expects it will alter how he works and the questions he tackles. “This will change medicine. It will change research. It will change bioengineering. It will change everything,” Lupas adds.

In some cases, AlphaFold’s structure predictions were indistinguishable from those determined using ‘gold standard’ experimental methods such as X-ray crystallography and, in recent years, cryo-electron microscopy (cryo-EM). AlphaFold might not obviate the need for these laborious and expensive methods — yet — say scientists, but the AI will make it possible to study living things in new ways.

The structure problem

Proteins are the building blocks of life, responsible for most of what happens inside cells. How a protein works and what it does is determined by its 3D shape — ‘structure is function’ is an axiom of molecular biology. Proteins tend to adopt their shape without help, guided only by the laws of physics.

For decades, laboratory experiments have been the main way to get good protein structures. The first complete structures of proteins were determined, starting in the 1950s, using a technique in which X-ray beams are fired at crystallized proteins and the diffracted light translated into a protein’s atomic coordinates. X-ray crystallography has produced the lion’s share of protein structures. But, over the past decade, cryo-EM has become the favoured tool of many structural-biology labs.

Scientists have long wondered how a protein’s constituent parts — a string of different amino acids — map out the many twists and folds of its eventual shape. Early attempts to use computers to predict protein structures in the 1980s and 1990s performed poorly, say researchers. Lofty claims for methods in published papers tended to disintegrate when other scientists applied them to other proteins.

Moult started CASP to bring more rigour to these efforts. The event challenges teams to predict the structures of proteins that have been solved using experimental methods, but for which the structures have not been made public. Moult credits the experiment — he doesn’t call it a competition — with vastly improving the field, by calling time on overhyped claims. “You’re really finding out what looks promising, what works, and what you should walk away from,” he says.

DeepMind’s 2018 performance at CASP13 startled many scientists in the field, which has long been the bastion of small academic groups. But its approach was broadly similar to those of other teams that were applying AI, says Jinbo Xu, a computational biologist at the University of Chicago, Illinois.

The first iteration of AlphaFold applied the AI method known as deep learning to structural and genetic data to predict the distance between pairs of amino acids in a protein. In a second step that does not invoke AI, AlphaFold uses this information to come up with a ‘consensus’ model of what the protein should look like, says John Jumper at DeepMind, who is leading the project.

The team tried to build on that approach but eventually hit the wall. So it changed tack, says Jumper, and developed an AI network that incorporated additional information about the physical and geometric constraints that determine how a protein folds. They also set it a more difficult, task: instead of predicting relationships between amino acids, the network predicts the final structure of a target protein sequence. “It’s a more complex system by quite a bit,” Jumper says.

Startling accuracy

CASP takes place over several months. Target proteins or portions of proteins called domains — about 100 in total — are released on a regular basis and teams have several weeks to submit their structure predictions. A team of independent scientists then assesses the predictions using metrics that gauge how similar a predicted protein is to the experimentally determined structure. The assessors don’t know who is making a prediction.

AlphaFold’s predictions arrived under the name ‘group 427’, but the startling accuracy of many of its entries made them stand out, says Lupas. “I had guessed it was AlphaFold. Most people had,” he says.

Some predictions were better than others, but nearly two-thirds were comparable in quality to experimental structures. In some cases, says Moult, it was not clear whether the discrepancy between AlphaFold’s predictions and the experimental result was a prediction error or an artefact of the experiment.

AlphaFold’s predictions were poor matches to experimental structures determined by a technique called nuclear magnetic resonance spectroscopy, but this could be down to how the raw data is converted into a model, says Moult. The network also struggles to model individual structures in protein complexes, or groups, whereby interactions with other proteins distort their shapes.

Overall, teams predicted structures more accurately this year, compared with the last CASP, but much of the progress can be attributed to AlphaFold, says Moult. On protein targets considered to be moderately difficult, the best performances of other teams typically scored 75 on a 100-point scale of prediction accuracy, whereas AlphaFold scored around 90 on the same targets, says Moult.

About half of the teams mentioned ‘deep learning’ in the abstract summarizing their approach, Moult says, suggesting that AI is making a broad impact on the field. Most of these were from academic teams, but Microsoft and the Chinese technology company Tencent also entered CASP14.

Mohammed AlQuraishi, a computational biologist at Columbia University in New York City and a CASP participant, is eager to dig into the details of AlphaFold’s performance at the contest, and learn more about how the system works when the DeepMind team presents its approach on 1 December. It’s possible — but unlikely, he says — that an easier-than-usual crop of protein targets contributed to the performance. AlQuraishi’s strong hunch is that AlphaFold will be transformational.

“I think it’s fair to say this will be very disruptive to the protein-structure-prediction field. I suspect many will leave the field as the core problem has arguably been solved,” he says. “It’s a breakthrough of the first order, certainly one of the most significant scientific results of my lifetime.”

Demis Hassabis, DeepMind’s chief executive, says that the company is learning what biologists want from AlphaFold.Credit: OLI SCARFF/AFP/Getty

Faster structures

An AlphaFold prediction helped to determine the structure of a bacterial protein that Lupas’s lab has been trying to crack for years. Lupas’s team had previously collected raw X-ray diffraction data, but transforming these Rorschach-like patterns into a structure requires some information about the shape of the protein. Tricks for getting this information, as well as other prediction tools, had failed. “The model from group 427 gave us our structure in half an hour, after we had spent a decade trying everything,” Lupas says.

Demis Hassabis, DeepMind’s co-founder and chief executive, says that the company plans to make AlphaFold useful so other scientists can employ it. (It previously published enough details about the first version of AlphaFold for other scientists to replicate the approach.) It can take AlphaFold days to come up with a predicted structure, which includes estimates on the reliability of different regions of the protein. “We’re just starting to understand what biologists would want,” adds Hassabis, who sees drug discovery and protein design as potential applications.

In early 2020, the company released predictions of the structures of a handful of SARS-CoV-2 proteins that hadn’t yet been determined experimentally. DeepMind’s predictions for a protein called Orf3a ended up being very similar to one later determined through cryo-EM, says Stephen Brohawn, a molecular neurobiologist at the University of California, Berkeley, whose team released the structure in June. “What they have been able to do is very impressive,” he adds.

Real-world impact

AlphaFold is unlikely to shutter labs, such as Brohawn’s, that use experimental methods to solve protein structures. But it could mean that lower-quality and easier-to-collect experimental data would be all that’s needed to get a good structure. Some applications, such as the evolutionary analysis of proteins, are set to flourish because the tsunami of available genomic data might now be reliably translated into structures. “This is going to empower a new generation of molecular biologists to ask more advanced questions,” says Lupas. “It’s going to require more thinking and less pipetting.”

“This is a problem that I was beginning to think would not get solved in my lifetime,” says Janet Thornton, a structural biologist at the European Molecular Biology Laboratory-European Bioinformatics Institute in Hinxton, UK, and a past CASP assessor. She hopes the approach could help to illuminate the function of the thousands of unsolved proteins in the human genome, and make sense of disease-causing gene variations that differ between people.

AlphaFold’s performance also marks a turning point for DeepMind. The company is best known for wielding AI to master games such Go, but its long-term goal is to develop programs capable of achieving broad, human-like intelligence. Tackling grand scientific challenges, such as protein-structure prediction, is one of the most important applications its AI can make, Hassabis says. “I do think it’s the most significant thing we’ve done, in terms of real-world impact.”


Source : Nature

2020 Worst U.K. Contraction Since 1709

Source : BofA Global Research

No Privacy, No Property: The World in 2030 According to the WEF

Antony P. Mueller wrote . . . . . . . . .

The World Economic Forum (WEF) was founded fifty years ago. It has gained more and more prominence over the decades and has become one of the leading platforms of futuristic thinking and planning. As a meeting place of the global elite, the WEF brings together the leaders in business and politics along with a few selected intellectuals. The main thrust of the forum is global control. Free markets and individual choice do not stand as the top values, but state interventionism and collectivism. Individual liberty and private property are to disappear from this planet by 2030 according to the projections and scenarios coming from the World Economic Forum.

Eight Predictions

Individual liberty is at risk again. What may lie ahead was projected in November 2016 when the WEF published “8 Predictions for the World in 2030.” According to the WEF’s scenario, the world will become quite a different place from now because how people work and live will undergo a profound change. The scenario for the world in 2030 is more than just a forecast. It is a plan whose implementation has accelerated drastically since with the announcement of a pandemic and the consequent lockdowns.

According to the projections of the WEF’s “Global Future Councils,” private property and privacy will be abolished during the next decade. The coming expropriation would go further than even the communist demand to abolish the property of production goods but leave space for private possessions. The WEF projection says that consumer goods, too, would be no longer private property.

If the WEF projection should come true, people would have to rent and borrow their necessities from the state, which would be the sole proprietor of all goods. The supply of goods would be rationed in line with a social credit points system. Shopping in the traditional sense would disappear along with the private purchases of goods. Every personal move would be tracked electronically, and all production would be subject to the requirements of clean energy and a sustainable environment.

In order to attain “sustainable agriculture,” the food supply will be mainly vegetarian. In the new totalitarian service economy, the government will provide basic accommodation, food, and transport, while the rest must be lent from the state. The use of natural resources will be brought down to its minimum. In cooperation with the few key countries, a global agency would set the price of CO2 emissions at an extremely high level to disincentivize its use.

In a promotional video, the World Economic Forum summarizes the eight predictions in the following statements:

  1. People will own nothing. Goods are either free of charge or must be lent from the state.
  2. The United States will no longer be the leading superpower, but a handful of countries will dominate.
  3. Organs will not be transplanted but printed.
  4. Meat consumption will be minimized.
  5. Massive displacement of people will take place with billions of refugees.
  6. To limit the emission of carbon dioxide, a global price will be set at an exorbitant level.
  7. People can prepare to go to Mars and start a journey to find alien life.
  8. Western values will be tested to the breaking point..

Beyond Privacy and Property

In a publication for the World Economic Forum, the Danish ecoactivist Ida Auken, who had served as her country’s minister of the environment from 2011 to 2014 and still is a member of the Danish Parliament (the Folketing), has elaborated a scenario of a world without privacy or property. In “Welcome to 2030,” she envisions a world where “I own nothing, have no privacy, and life has never been better.” By 2030, so says her scenario, shopping and owning have become obsolete, because everything that once was a product is now a service.

In this idyllic new world of hers, people have free access to transportation, accommodation, food, “and all the things we need in our daily lives.” As these things will become free of charge, “it ended up not making sense for us to own much.” There would be no private ownership in houses nor would anyone pay rent, “because someone else is using our free space whenever we do not need it.” A person’s living room, for example, will be used for business meetings when one is absent. Concerns like “lifestyle diseases, climate change, the refugee crisis, environmental degradation, completely congested cities, water pollution, air pollution, social unrest and unemployment” are things of the past. The author predicts that people will be happy to enjoy such a good life that is so much better “than the path we were on, where it became so clear that we could not continue with the same model of growth.”

Ecological Paradise

In her 2019 contribution to the Annual Meeting of the Global Future Councils of the World Economic Forum, Ida Auken foretells how the world may look in the future “if we win the war on climate change.” By 2030, when CO2 emissions will be greatly reduced, people will live in a world where meat on the dinner plate “will be a rare sight” while water and the air will be much cleaner than today. Because of the shift from buying goods to using services, the need to have money will vanish, because people will spend less and less on goods. Work time will shrink and leisure time will grow.

For the future, Auken envisions a city where electric cars have substituted conventional combustion vehicles. Most of the roads and parking spaces will have become green parks and walking zones for pedestrians. By 2030, agriculture will offer mainly plant-based alternatives to the food supply instead of meat and dairy products. The use of land to produce animal feed will greatly diminish and nature will be spreading across the globe again.

Fabricating Social Consent

How can people be brought to accept such a system? The bait to entice the masses is the assurances of comprehensive healthcare and a guaranteed basic income. The promoters of the Great Reset promise a world without diseases. Due to biotechnologically produced organs and individualized genetics-based medical treatments, a drastically increased life expectancy and even immortality are said to be possible. Artificial intelligence will eradicate death and eliminate disease and mortality. The race is on among biotechnological companies to find the key to eternal life.

Along with the promise of turning any ordinary person into a godlike superman, the promise of a “universal basic income” is highly attractive, particularly to those who will no longer find a job in the new digital economy. Obtaining a basic income without having to go through the treadmill and disgrace of applying for social assistance is used as a bait to get the support of the poor.

To make it economically viable, the guarantee of a basic income would require the leveling of wage differences. The technical procedures of the money transfer from the state will be used to promote the cashless society. With the digitization of all monetary transactions, each individual purchase will be registered. As a consequence, the governmental authorities would have unrestricted access to supervise in detail how individual persons spend their money. A universal basic income in a cashless society would provide the conditions to impose a social credit system and deliver the mechanism to sanction undesirable behavior and identify the superfluous and unwanted.

Who Will Be the Rulers?

The World Economic Forum is silent about the question of who will rule in this new world.

There is no reason to expect that the new power holders would be benevolent. Yet even if the top decision-makers of the new world government were not mean but just technocrats, what reason would an administrative technocracy have to go on with the undesirables? What sense does it make for a technocratic elite to turn the common man into a superman? Why share the benefits of artificial intelligence with the masses and not keep the wealth for the chosen few?

Not being swayed away by the utopian promises, a sober assessment of the plans must come to the conclusion that in this new world, there would be no place for the average person and that they would be put away along with the “unemployable,” “feeble minded,” and “ill bred.” Behind the preaching of the progressive gospel of social justice by the promoters of the Great Reset and the establishment of a new world order lurks the sinister project of eugenics, which as a technique is now called “genetic engineering” and as a movement is named “transhumanism,” a term coined by Julian Huxley, the first director of the UNESCO.

The promoters of the project keep silent about who will be the rulers in this new world. The dystopian and collectivist nature of these projections and plans is the result of the rejection of free capitalism. Establishing a better world through a dictatorship is a contradiction in terms. Not less but more economic prosperity is the answer to the current problems. Therefore, we need more free markets and less state planning. The world is getting greener and a fall in the growth rate of the world population is already underway. These trends are the natural consequence of wealth creation through free markets.

Conclusion

The World Economic Forum and its related institutions in combination with a handful of governments and a few high-tech companies want to lead the world into a new era without property or privacy. Values like individualism, liberty, and the pursuit of happiness are at stake, to be repudiated in favor of collectivism and the imposition of a “common good” that is defined by the self-proclaimed elite of technocrats. What is sold to the public as the promise of equality and ecological sustainability is in fact a brutal assault on human dignity and liberty. Instead of using the new technologies as an instrument of betterment, the Great Reset seeks to use the technological possibilities as a tool of enslavement. In this new world order, the state is the single owner of everything. It is left to our imagination to figure out who will program the algorithms that manage the distribution of the goods and services.


Source : Mises Institute


Read also at WEF:

8 predictions for the world in 2030 . . . . .