Obesity is increasing worldwide, especially among children and teenagers. More than 150 million children in the world are obese in 2019. These children have increased risk of heart disease, cancers and Type 2 diabetes.

Teenagers with obesity are likely to remain obese as adults. If these trends continue, 70 per cent of adults aged 40 years could be either overweight or obese by 2040.

I am a neuroscientist and my research investigates how diet changes the brain. I want to understand how unhealthy diets impact the developing brain, and also why young people today are so prone to developing obesity.

Adolescents are the greatest consumers of calorie-rich junk foods. During puberty, many children have an insatiable appetite as rapid growth requires lots of energy. Heightened metabolism and growth spurts can protect against obesity, to an extent. But excessively eating high-calorie junk foods and increasingly sedentary lifestyles can outweigh any metabolic protection.

The teenage years are a key window of brain development. Adolescence coincides with a new-found social autonomy and the independence to make personal food choices.

Read more: Your brain on sugar: What the science actually says

During adolescence, connections between different brain regions and individual neurons are also being refined and strengthened. The adolescent brain is malleable because of increased levels of neuroplasticity.

This means the brain is highly receptive to being shaped and rewired by the environment including diet. In turn, these changes can become hardwired when development is complete. So the adolescent brain is vulnerable to diet-induced changes, but these changes may endure through life.

Neuroscientists use functional brain imaging to examine how the brain responds to specific events. Brain scans show that the prefrontal cortex a key brain area for behavioural control and decision-making doesnt fully mature until the early 20s.

The prefrontal cortex controls and overrides urges triggered by events in the environment. Resisting eating a whole bag of candy or buying cheap junk foods can be particularly difficult for teenagers.

In contrast to the immature prefrontal cortex, the brains reward system the mesocorticolimbic dopamine system is fully developed at a much earlier age.

Teenagers are particularly drawn to rewards, including sweet and calorie-dense foods. This is due to increased numbers of dopamine receptors in the adolescent brain, so the feeling of reward can be exaggerated. Frequent stimulation of the reward system results in enduring brain adaptations.

During adolescence, these changes may cause long-lasting shifts to the balance of brain chemicals.

Taken together, the teenage brain has a voracious drive for reward, diminished behavioural control and a susceptibility to be shaped by experience.

This manifests as a reduced ability to resist rewarding behaviours. So its not surprising that teenagers prefer to eat foods that are easy to obtain and immediately gratifying, even in the face of health advice to the contrary. But what are the enduring brain consequences?

Functional imaging studies show brain activity during tasks or viewing images of foods. Brain circuits that process food rewards are more active in adolescents with obesity compared to those considered normal weight.

Interestingly, lower activity is seen in regions of the prefrontal cortex. This shows that obesity can both heighten activation of the reward system and reduce brain activity in centres that can override the desire to eat.

Importantly, successful weight loss in adolescents restores levels of activity in the prefrontal cortex. This provides critical knowledge that the prefrontal cortex is a key area of the brain for controlling food intake, and that diet interventions increase activity in brain regions that exert self control.

Transcranial magnetic stimulation (TMS), a way scientists can modify brain activity in the prefrontal cortex, can change inhibitory control of eating behaviour. Repeated TMS treatment could be a new therapy to restore cognitive control over eating, helping with long-term weight loss.

Excessively eating junk foods during adolescence could alter brain development, leading to lasting poor diet habits. But, like a muscle, the brain can be exercised to improve willpower.

Increased brain plasticity during adolescence means the young mind may be more receptive to lifestyle changes. Physical exercise boosts brain plasticity, helping to set in place new healthy habits. Identifying how the brain is changed by obesity provides opportunities to identify and intervene.

Functional brain imaging adds a new layer of information where clinicians can identify at-risk individuals and track brain changes during nutritional and lifestyle interventions.

Even more, TMS could be a new treatment approach to improve re-calibration of the young brain to prevent enduring changes into adulthood.

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How junk food shapes the developing teenage brain - The Conversation CA

AlphaGo is a computer program that plays the board game Go.[1] It was developed by DeepMind Technologies [2] which was later acquired by Google. AlphaGo had three far more powerful successors, called AlphaGo Master, AlphaGo Zero[3] and AlphaZero.

In October 2015, the original AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 1919 board.[4][5] In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicap.[6] Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association.[7] The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled AlphaGo,[8] directed by Greg Kohs. It was chosen by Science as one of the Breakthrough of the Year runners-up on 22 December 2016.[9]

At the 2017 Future of Go Summit, its successor AlphaGo Master beat Ke Jie, the world No.1 ranked player at the time, in a three-game match (the even more powerful AlphaGo Zero already existed but was not yet announced). After this, AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[10]

AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously "learned" by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play.[11] A neural network is trained to predict AlphaGo's own move selections and also the winner's games. This neural net improves the strength of tree search, resulting in higher quality of move selection and stronger self-play in the next iteration.

After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas.[12] Starting from a 'blank page', with only a short training period, AlphaGo Zero achieved a 100-0 victory against the champion-defeating AlphaGo, while its successor, the self-taught AlphaZero, is currently perceived as the world's top player in Go as well as possibly in chess.

Go is considered much more difficult for computers to win than other games such as chess, because its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alphabeta pruning, tree traversal and heuristic search.[4][13]

Almost two decades after IBM's computer Deep Blue beat world chess champion Garry Kasparov in the 1997 match, the strongest Go programs using artificial intelligence techniques only reached about amateur 5-dan level,[11] and still could not beat a professional Go player without a handicap.[4][5][14] In 2012, the software program Zen, running on a four PC cluster, beat Masaki Takemiya (9p) twice at five- and four-stone handicaps.[15] In 2013, Crazy Stone beat Yoshio Ishida (9p) at a four-stone handicap.[16]

According to DeepMind's David Silver, the AlphaGo research project was formed around 2014 to test how well a neural network using deep learning can compete at Go.[17] AlphaGo represents a significant improvement over previous Go programs. In 500 games against other available Go programs, including Crazy Stone and Zen,[18] AlphaGo running on a single computer won all but one.[19] In a similar matchup, AlphaGo running on multiple computers won all 500 games played against other Go programs, and 77% of games played against AlphaGo running on a single computer. The distributed version in October 2015 was using 1,202 CPUs and 176 GPUs.[11]

In October 2015, the distributed version of AlphaGo defeated the European Go champion Fan Hui,[20] a 2-dan (out of 9 dan possible) professional, five to zero.[5][21] This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap.[22] The announcement of the news was delayed until 27 January 2016 to coincide with the publication of a paper in the journal Nature[11] describing the algorithms used.[5]

AlphaGo played South Korean professional Go player Lee Sedol, ranked 9-dan, one of the best players at Go,[14][needs update] with five games taking place at the Four Seasons Hotel in Seoul, South Korea on 9, 10, 12, 13, and 15 March 2016,[23][24] which were video-streamed live.[25] Out of five games, AlphaGo won four games and Lee won the fourth game which made him recorded as the only human player who beat AlphaGo in all of its 74 official games.[26] AlphaGo ran on Google's cloud computing with its servers located in the United States.[27] The match used Chinese rules with a 7.5-point komi, and each side had two hours of thinking time plus three 60-second byoyomi periods.[28] The version of AlphaGo playing against Lee used a similar amount of computing power as was used in the Fan Hui match.[29]The Economist reported that it used 1,920 CPUs and 280 GPUs.[30] At the time of play, Lee Sedol had the second-highest number of Go international championship victories in the world after South Korean player Lee Changho who kept the world championship title for 16 years.[31] Since there is no single official method of ranking in international Go, the rankings may vary among the sources. While he was ranked top sometimes, some sources ranked Lee Sedol as the fourth-best player in the world at the time.[32][33] AlphaGo was not specifically trained to face Lee nor was designed to compete with any specific human players.

The first three games were won by AlphaGo following resignations by Lee.[34][35] However, Lee beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then continued to achieve a fourth win, winning the fifth game by resignation.[36]

The prize was US$1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, including UNICEF.[37] Lee Sedol received $150,000 for participating in all five games and an additional $20,000 for his win.[28]

In June 2016, at a presentation held at a university in the Netherlands, Aja Huang, one of the Deep Mind team, revealed that they had patched the logical weakness that occurred during the 4th game of the match between AlphaGo and Lee, and that after move 78 (which was dubbed the "divine move" by many professionals), it would play as intended and maintain Black's advantage. Before move 78, AlphaGo was leading throughout the game, but Lee's move caused the program's computing powers to be diverted and confused.[38] Huang explained that AlphaGo's policy network of finding the most accurate move order and continuation did not precisely guide AlphaGo to make the correct continuation after move 78, since its value network did not determine Lee's 78th move as being the most likely, and therefore when the move was made AlphaGo could not make the right adjustment to the logical continuation.[39]

On 29 December 2016, a new account on the Tygem server named "Magister" (shown as 'Magist' at the server's Chinese version) from South Korea began to play games with professional players. It changed its account name to "Master" on 30 December, then moved to the FoxGo server on 1 January 2017. On 4 January, DeepMind confirmed that the "Magister" and the "Master" were both played by an updated version of AlphaGo, called AlphaGo Master.[40][41] As of 5 January 2017, AlphaGo Master's online record was 60 wins and 0 losses,[42] including three victories over Go's top-ranked player, Ke Jie,[43] who had been quietly briefed in advance that Master was a version of AlphaGo.[42] After losing to Master, Gu Li offered a bounty of 100,000 yuan (US$14,400) to the first human player who could defeat Master.[41] Master played at the pace of 10 games per day. Many quickly suspected it to be an AI player due to little or no resting between games. Its adversaries included many world champions such as Ke Jie, Park Jeong-hwan, Yuta Iyama, Tuo Jiaxi, Mi Yuting, Shi Yue, Chen Yaoye, Li Qincheng, Gu Li, Chang Hao, Tang Weixing, Fan Tingyu, Zhou Ruiyang, Jiang Weijie, Chou Chun-hsun, Kim Ji-seok, Kang Dong-yun, Park Yeong-hun, and Won Seong-jin; national champions or world championship runners-up such as Lian Xiao, Tan Xiao, Meng Tailing, Dang Yifei, Huang Yunsong, Yang Dingxin, Gu Zihao, Shin Jinseo, Cho Han-seung, and An Sungjoon. All 60 games except one were fast-paced games with three 20 or 30 seconds byo-yomi. Master offered to extend the byo-yomi to one minute when playing with Nie Weiping in consideration of his age. After winning its 59th game Master revealed itself in the chatroom to be controlled by Dr. Aja Huang of the DeepMind team,[44] then changed its nationality to the United Kingdom. After these games were completed, the co-founder of Google DeepMind, Demis Hassabis, said in a tweet, "we're looking forward to playing some official, full-length games later [2017] in collaboration with Go organizations and experts".[40][41]

Go experts were impressed by the program's performance and its nonhuman play style; Ke Jie stated that "After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong... I would go as far as to say not a single human has touched the edge of the truth of Go."[42]

In the Future of Go Summit held in Wuzhen in May 2017, AlphaGo Master played three games with Ke Jie, the world No.1 ranked player, as well as two games with several top Chinese professionals, one pair Go game and one against a collaborating team of five human players.[45]

Google DeepMind offered 1.5 million dollar winner prizes for the three-game match between Ke Jie and Master while the losing side took 300,000 dollars.[46][47][48] Master won all three games against Ke Jie,[49][50] after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association.[10]

After winning its three-game match against Ke Jie, the top-rated world Go player, AlphaGo retired. DeepMind also disbanded the team that worked on the game to focus on AI research in other areas.[12] After the Summit, Deepmind published 50 full length AlphaGo vs AlphaGo matches, as a gift to the Go community.[51]

AlphaGo's team published an article in the journal Nature on 19 October 2017, introducing AlphaGo Zero, a version without human data and stronger than any previous human-champion-defeating version.[52] By playing games against itself, AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.[53]

In a paper released on arXiv on 5 December 2017, DeepMind claimed that it generalized AlphaGo Zero's approach into a single AlphaZero algorithm, which achieved within 24 hours a superhuman level of play in the games of chess, shogi, and Go by defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case.[54]

On 11 December 2017, DeepMind released AlphaGo teaching tool on its website[55] to analyze winning rates of different Go openings as calculated by AlphaGo Master.[56] The teaching tool collects 6,000 Go openings from 230,000 human games each analyzed with 10,000,000 simulations by AlphaGo Master. Many of the openings include human move suggestions.[56]

An early version of AlphaGo was tested on hardware with various numbers of CPUs and GPUs, running in asynchronous or distributed mode. Two seconds of thinking time was given to each move. The resulting Elo ratings are listed below.[11] In the matches with more time per move higher ratings are achieved.

In May 2016, Google unveiled its own proprietary hardware "tensor processing units", which it stated had already been deployed in multiple internal projects at Google, including the AlphaGo match against Lee Sedol.[57][58]

In the Future of Go Summit in May 2017, DeepMind disclosed that the version of AlphaGo used in this Summit was AlphaGo Master,[59][60] and revealed that it had measured the strength of different versions of the software. AlphaGo Lee, the version used against Lee, could give AlphaGo Fan, the version used in AlphaGo vs. Fan Hui, three stones, and AlphaGo Master was even three stones stronger.[61]

As of 2016, AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. It uses Monte Carlo tree search, guided by a "value network" and a "policy network," both implemented using deep neural network technology.[4][11] A limited amount of game-specific feature detection pre-processing (for example, to highlight whether a move matches a nakade pattern) is applied to the input before it is sent to the neural networks.[11]

The system's neural networks were initially bootstrapped from human gameplay expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves.[20] Once it had reached a certain degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using reinforcement learning to improve its play.[4] To avoid "disrespectfully" wasting its opponent's time, the program is specifically programmed to resign if its assessment of win probability falls beneath a certain threshold; for the match against Lee, the resignation threshold was set to 20%.[63]

Toby Manning, the match referee for AlphaGo vs. Fan Hui, has described the program's style as "conservative".[64] AlphaGo's playing style strongly favours greater probability of winning by fewer points over lesser probability of winning by more points.[17] Its strategy of maximising its probability of winning is distinct from what human players tend to do which is to maximise territorial gains, and explains some of its odd-looking moves.[65] It makes a lot of opening moves that have never or seldom been made by humans, while avoiding many second-line opening moves that human players like to make. It likes to use shoulder hits, especially if the opponent is over concentrated.[citation needed]

AlphaGo's March 2016 victory was a major milestone in artificial intelligence research.[66] Go had previously been regarded as a hard problem in machine learning that was expected to be out of reach for the technology of the time.[66][67][68] Most experts thought a Go program as powerful as AlphaGo was at least five years away;[69] some experts thought that it would take at least another decade before computers would beat Go champions.[11][70][71] Most observers at the beginning of the 2016 matches expected Lee to beat AlphaGo.[66]

With games such as checkers (that has been "solved" by the Chinook draughts player team), chess, and now Go won by computers, victories at popular board games can no longer serve as major milestones for artificial intelligence in the way that they used to. Deep Blue's Murray Campbell called AlphaGo's victory "the end of an era... board games are more or less done and it's time to move on."[66]

When compared with Deep Blue or with Watson, AlphaGo's underlying algorithms are potentially more general-purpose, and may be evidence that the scientific community is making progress towards artificial general intelligence.[17][72] Some commentators believe AlphaGo's victory makes for a good opportunity for society to start discussing preparations for the possible future impact of machines with general purpose intelligence. (As noted by entrepreneur Guy Suter, AlphaGo itself only knows how to play Go, and doesn't possess general-purpose intelligence: "[It] couldn't just wake up one morning and decide it wants to learn how to use firearms"[66]) In March 2016, AI researcher Stuart Russell stated that "AI methods are progressing much faster than expected, (which) makes the question of the long-term outcome more urgent," adding that "in order to ensure that increasingly powerful AI systems remain completely under human control... there is a lot of work to do."[73] Some scholars, such as Stephen Hawking, warned (in May 2015 before the matches) that some future self-improving AI could gain actual general intelligence, leading to an unexpected AI takeover; other scholars disagree: AI expert Jean-Gabriel Ganascia believes that "Things like 'common sense'... may never be reproducible",[74] and says "I don't see why we would speak about fears. On the contrary, this raises hopes in many domains such as health and space exploration."[73] Computer scientist Richard Sutton said "I don't think people should be scared... but I do think people should be paying attention."[75]

In China, AlphaGo was a "Sputnik moment" which helped convince the Chinese government to prioritize and dramatically increase funding for artificial intelligence.[76]

In 2017, the DeepMind AlphaGo team received the inaugural IJCAI Marvin Minsky medal for Outstanding Achievements in AI. AlphaGo is a wonderful achievement, and a perfect example of what the Minsky Medal was initiated to recognise, said Professor Michael Wooldridge, Chair of the IJCAI Awards Committee. What particularly impressed IJCAI was that AlphaGo achieves what it does through a brilliant combination of classic AI techniques as well as the state-of-the-art machine learning techniques that DeepMind is so closely associated with. Its a breathtaking demonstration of contemporary AI, and we are delighted to be able to recognise it with this award.[77]

Go is a popular game in China, Japan and Korea, and the 2016 matches were watched by perhaps a hundred million people worldwide.[66][78] Many top Go players characterized AlphaGo's unorthodox plays as seemingly-questionable moves that initially befuddled onlookers, but made sense in hindsight:[70] "All but the very best Go players craft their style by imitating top players. AlphaGo seems to have totally original moves it creates itself."[66] AlphaGo appeared to have unexpectedly become much stronger, even when compared with its October 2015 match[79] where a computer had beaten a Go professional for the first time ever without the advantage of a handicap.[80] The day after Lee's first defeat, Jeong Ahram, the lead Go correspondent for one of South Korea's biggest daily newspapers, said "Last night was very gloomy... Many people drank alcohol."[81] The Korea Baduk Association, the organization that oversees Go professionals in South Korea, awarded AlphaGo an honorary 9-dan title for exhibiting creative skills and pushing forward the game's progress.[82]

China's Ke Jie, an 18-year-old generally recognized as the world's best Go player at the time,[32][83] initially claimed that he would be able to beat AlphaGo, but declined to play against it for fear that it would "copy my style".[83] As the matches progressed, Ke Jie went back and forth, stating that "it is highly likely that I (could) lose" after analysing the first three matches,[84] but regaining confidence after AlphaGo displayed flaws in the fourth match.[85]

Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary general of the International Go Federation, both reason that in the future, Go players will get help from computers to learn what they have done wrong in games and improve their skills.[80]

After game two, Lee said he felt "speechless": "From the very beginning of the match, I could never manage an upper hand for one single move. It was AlphaGo's total victory."[86] Lee apologized for his losses, stating after game three that "I misjudged the capabilities of AlphaGo and felt powerless."[66] He emphasized that the defeat was "Lee Se-dol's defeat" and "not a defeat of mankind".[26][74] Lee said his eventual loss to a machine was "inevitable" but stated that "robots will never understand the beauty of the game the same way that we humans do."[74] Lee called his game four victory a "priceless win that I (would) not exchange for anything."[26]

Facebook has also been working on its own Go-playing system darkforest, also based on combining machine learning and Monte Carlo tree search.[64][87] Although a strong player against other computer Go programs, as of early 2016, it had not yet defeated a professional human player.[88] Darkforest has lost to CrazyStone and Zen and is estimated to be of similar strength to CrazyStone and Zen.[89]

DeepZenGo, a system developed with support from video-sharing website Dwango and the University of Tokyo, lost 21 in November 2016 to Go master Cho Chikun, who holds the record for the largest number of Go title wins in Japan.[90][91]

A 2018 paper in Nature cited AlphaGo's approach as the basis for a new means of computing potential pharmaceutical drug molecules.[92]

AlphaGo Master (white) v. Tang Weixing (31 December 2016), AlphaGo won by resignation. White 36 was widely praised.

The AlphaGo documentary film[93][94] raised hopes that Lee Sedol and Fan Hui would have benefitted from their experience of playing AlphaGo, but as of May 2018 their ratings were little changed; Lee Sedol was ranked 11th in the world, and Fan Hui 545th.[95] However the overall Go community may have been moved forward in how it plays the game.[citation needed]

Original post:

AlphaGo - Wikipedia

The personnel changes at Alphabet continue, this time with Mustafa Suleyman one of the three co-founders of the companys influential AI lab DeepMind moving to Google.

Suleyman announced the news on Twitter, saying that after a wonderful decade at DeepMind, he would be joining Google to work with the companys head of AI Jeff Dean and its chief legal officer Kent Walker. The exact details of Suleymans new role are unclear but a representative for the company told The Verge it would involve work on AI policy.

The move is notable, though, as it was reported earlier this year that Suleyman had been placed on leave from DeepMind. (DeepMind disputed these reports, saying it was a mutual decision intended to give Suleyman time out ... after 10 hectic years.) Some speculated that Suleymans move was the fallout of reported tensions between DeepMind and Google, as the former struggled to commercialize its technology.

DeepMind breaks ground in AI research, but spends a lot of money doing it

Although DeepMind has achieved a number of research milestones in the AI world, most notably the success of its AlphaGo program in 2016, the lab has also recorded significant financial losses. In 2018, it doubled its revenues to 102.8 million ($135 million), but its expenditures also rose to 470.2 million ($618 million) and it recorded a total debt of more than 1 billion ($1.3 billion).

Suleyman, who founded DeepMind in 2010 along with Demis Hassabis (now CEO) and Shane Legg (now chief scientist), had spearheaded the companys health team, which offered the lab one avenue to monetize its research. DeepMinds engineers designed a number of health algorithms that broke new ground, and its team built an assistant app for nurses and doctors that promised to save time and money. But the venture was also criticized strongly for its mishandling of UK medical data, and in 2018 was absorbed into Google Health.

In addition to this, Suleyman also led the DeepMind for Google team, which aimed to put the companys research to practical uses in Google products, delivering tangible commercial benefits like improved battery life on Android devices and a more natural voice for Google Assistant.

Its difficult to parse the meaning behind Suleymans move to Google without more details on his new role, but its clear that DeepMind is still working out how to position itself for the future as highlighted by the publication of a blog post by Hassabis timed with the announcement of Suleymans departure.

In the post, Hassabis charts the journey of DeepMind from unlikely start-up to major scientific organization. And although he highlights collaborations the lab has made with other parts of Alphabet, he ultimately focuses on the fundamental breakthroughs and grand challenges that DeepMind hopes to tackle most notably, using artificial intelligence to augment scientific research. It seems clear that long-term research, not short-term profits, are still the priority for DeepMinds scientists.

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DeepMind co-founder moves to Google as the AI lab positions itself for the future - The Verge

In March 2010, anthropologists discovered a tiny, lone finger bone in the Denisova cave in Siberia. They determined it belonged to previously undiscovered species of human ancestor. Replica of a Denisovan finger bone fragment, originally found in Denisova Cave in 2008, at the Museum of Natural Sciences in Brussels, Belgium. Thilo Parg/Wikimedia Commons

Genetic analysis revealed that Denisovans (named after the cave in which they were found) were an enigmatic offshoot of Neanderthals.

Thus far, fossilized Denisovan remains have only been found in Siberia and Tibet. The species disappeared about 50,000 years ago but passed some of their genetic makeup to Homo sapiens.Denisovan DNA can be found in the genes of modern humans across Asia and some Pacific islands;up to 5% of modern Papua New Guinea residents' DNA shows remnants of interbreedingwith Denisovans.

People in Tibet today also possess some Denisovan traits and these traits appear to help Sherpas weather high altitudes.

Scientists discovered that both Neanderthals and Denisovans interbred with modern humans extensively.

Curiosity is the largest and most capable rover ever sent to Mars. It joined fellow rover Opportunity in searching the red planet for signs of water and clues about whether Mars was capable of supporting microbial lifeforms.

The Kepler mission was charged with finding and identifying Earth-like planets in our galaxy that existed within a star's "Goldilocks," or habitable, zone. Kepler 22-b is 600 light-years away.

Planets in habitable zones are capable of hosting liquid water, one of the requisites for being considered Earth-like.

NASA launched Voyager 1 in 1977. After flying by Jupiter and Saturn, Voyager 1 crossed into interstellar space. It continues to collect data to this day.

In 2019, Voyager 1's successor, Voyager 2, also entered interstellar space. Both probes have been flying longer than any other spacecraft in history.

Voyager 2 has beamed back unprecedented data about previously unknown boundary layers at the far edge of our solar system an area known as the heliopause. The discovery of these boundary layers suggests there are stages in the transition from our solar bubble to interstellar space that scientists did not previously know about.

SpaceX's groundbreaking spaceship was called Dragon.

Previously, only four governments the United States, Russia, Japan, and the European Space Agency had achieved this challenging technical feat.

Seven years later, SpaceX launched Dragon's successor, Crew Dragon, into orbit for the first time. Crew Dragon is designed to ferry astronauts to the ISS; its 2019 trip marked the first time that a commercial spaceship designed for humans had ever left Earth.

The Higgs Boson is nicknamed the "God particle" because it gives mass to all other fundamental particles in the universe that have mass, like electrons and protons.

Scientists knew a particle akin to the Higgs Boson had to exist otherwise nothing in the universe would have mass, and we wouldn't exist but had failed to find evidence of such a particle until 2012.

Crispr-Cas9 technology enables researchers to edit parts of the genome by removing, adding, or altering sections of DNA. Since 2012, scientists have edited mosquito, mushroom, and lizard DNA, among others. In 2018, a Chinese scientist announced he had edited the genetic information of two human embryos.

This discovery made Europa only the second known oceanic world in our solar system aside from Earth; NASA observed jets of water vapor spewing from Saturn's moon Enceladus in 2005.

The presence of liquid water and ice make these two moons ideal places to search for life in our corner of the galaxy.

Since 2013, water has also been discovered on the dwarf-planet Pluto, a moon of Neptune called Triton, and multiple other moons of Jupiter and Saturn.

In September 2012, NASA announced its Curiosity rover had identified gravel made by an ancient river in Mars' Gale Crater.

Then in March 2013, scientists found chemical ingredients for life sulfur, nitrogen, hydrogen, oxygen, phosphorus, and carbon in powder that Curiosity had drilled from rock near the ancient streambed.

"A fundamental question for this mission is whether Mars could have supported a habitable environment," Michael Meyer, who worked as the lead scientist for NASA's Mars Exploration Program at the time, said in a press release about the finding. "From what we know now, the answer is yes."

In the following years, evidence has mounted that the planet was once home to a vast ocean.

After three years of studying Mars, Italian scientists determined in July 2018 that it's possible the red planet has a 20-kilometer-wide lake of liquid water at its polar ice cap today.

"If these researchers are right, this is the first time we've found evidence of a large water body on Mars," Cassie Stuurman, a geophysicist at the University of Texas,told the Associated Press.

Other parts of Mars are too cold for water to stay liquid unless it's deep underground.

In a March 2019 study, researchers suggested that seasonal flow patterns in Mars's crater walls could come from pressurized groundwater 750 meters below the surface, which travels upward through cracks in the ground.

Researchers found the particles using the IceCube Neutrino Observatory, an array of sensors embedded in Antarctic ice. Neutrinos are nearly mass-less and unstoppable; they move at the speed of light and get discharged in the aftermath of exploding stars.

Scientists can use neutrinos to understand events happening in distant galaxies. In 2018, they found more of the particles in Antarctica, then traced them back to the source: a rapidly spinning black hole, millions of times the mass of the sun, that's gobbling up gas and dust.

The burger which took two years and $325,000 to make consists of 20,000 thin strips of cow muscle tissue that were grown in a Netherlands laboratory.

Since 2013, the lab-grown meat industry has grown in popularity and dropped in price. In 2015, one of the researchers responsible for the first lab-grown burger, said the per-pound cost had dropped to $37.

It took Rosetta 10 years to reach and orbit the comet, then launch a lander down to the surface.

Rosetta's lander, Philae, took the first-ever surface images of a comet.

Two spelunkers had accidentally stumbled across the Homo naledi fossils two years earlier, in a hidden cave 100 feet below the surface.

All told, the chamber contained 1,550 bones belonging to at least 15 individuals who all lived between 330,000 and 250,000 years ago.

The epigenome is made up of chemicals and proteins that can attach to DNA and modify its function turning our genes on and off.

An individual's lifestyle and environment factors like whether they smoke or what their diet looks like can prompt sometimes deadly changes in their epigenome that can cause cancer.

Mapping the epigenome may help scientists understand how tumors develop and cancer spreads.

NASA's Cassini spacecraft found that Enceladus emits plumes of water into space following the probe's arrival in 2004. But in 2015, scientists confirmed that the source of these plumes was a giant saltwater oceanhidden beneath the moon's icy crust.

That wasn't the first time AI beat humans in a complex game.

In 2011, IBM's supercomputer, Watson, defeated two "Jeopardy!" champions including Ken Jennings in a three-day contest.

A year after AlphaGo's success, anAI named Libaratus beat four of the world's top professional players in 120,000 hands of no-limit, two-player poker. Then, in 2019, another DeepMind AI program named AlphaStar bested 99.8% of human players in the popular video game "Starcraft II."

The catastrophic collision created ripples in space-time, also known asgravitational waves. Einstein predicted the existence of these gravitational waves in 1915, but he thought they'd be too weak to ever pick up on Earth. New detection tools have proved otherwise.

This collision was the first event scientists observed using gravitational-wave detectors. Then in 2017, they observed two neutron stars merging. In August 2019, astrophysicists detected the billion-year-old aftermath of a collision between a black hole and a neutron star(the super-dense remnant of a dead star).

The lost land ofZealandiasits on the ocean floor between New Zealand and New Caledonia.

It wasn't always sunken researchers have found fossils that suggested novel kinds of plants and organisms once lived there. Some argue that Zealandia should be countedalongsideour (more visible) seven continents.

In 2019, scientists found that another ancient continent had slid under what is now southern Europe about 120 million years ago. The researchers named this continent Greater Adria. Its uppermost regions formed mountain ranges across Europe, like the Alps.

All living creatures' DNA is made up of two types of amino acid pairs: A-T (adenine thymine) and G-C (guanine cytosine). This four-letter alphabet forms the basis for all genetic information in the natural world.

But scientists invented two new letters, an unnatural pair of X-Y bases, that they seamlessly integrated into the genetic alphabet of E. coli bacteria.

Floyd Romesburg, who led the research, previously told Business Insider that his invention could improve the way we treat diseases. For example, it could change the way proteins degrade inside the body, helping drugs stay in your system longer. Romesburg said his team will be investigating how the finding might help cancer treatments and drugs for autoimmune diseases.

In September 2017, Audi announced it had produced the world's first "Level 3" autonomous car meaning its self-driving mode requires no human feet, hands, or eyes. The A8 sedan can wholly, safely control itself in self-driving mode, only needing a human to take over in the event of bad weather or disappearing lane lines.

Tesla Autopilot drivers, for comparison, have to be ready to take over at any moment, so they're counseled to keep their eyes on the road at all times.

Just two months later, Waymo the autonomous vehicle division of Alphabet, Google's parent company revealed that it was testing self-driving minivans in the streets of Arizona without any humans at all behind the wheel. In 2018, Waymo launched the first fully autonomous taxi service in the US.

The two massive, exploded stars hit each other at one-third the speed of light and created gravitational waves. Scientific instruments on Earth picked up the waves from that crash, an event astronomers say only happens once every 100,000 years.

The crash happened 130 million light years away from Earth, researchers discovered. It caused the formation of $100,000,000,000,000,000,000,000,000,000 worth of gold and produced huge stores of silver and platinum, too.

Scientists only had a few weeks to study the interstellar interloper before it got too far, and too dim, to see with Earth-based telescopes.

Guesses as to what the object is run the gamut from comet to asteroid to alien spaceship. One Harvard University astronomer, Avi Loeb, has speculated that 'Oumuamua was an extraterrestrial scout, but nearly all other experts who have studied 'Oumuamua say that hypothesis is extraordinarily unlikely.

Cassini had been exploring Saturn and its moons for 13 years before the probe plunged to its death on September 15. Scientists planned the crash to ensure that Cassini wouldn't one day run out of fuel and hit one of Saturn's potentially habitable moons (thereby contaminating it with Earthly bacteria).

During its final dive, Cassini beamed back amazing photos of Saturn as we'd never seen the planet before. That last portion of the mission began with a flyby of the planet's moon, Titan. Then Cassini jetted through a 1,200-mile opening between Saturn and its rings of ice an unprecedented feat.

The spacecraft then angled down into the planet's clouds and burned up.

The cure for a form of hereditary blindness called leber congenital amaurosis is the first gene therapy approved by the FDA for an inherited disease.

The treatment, called Luxturna, is a one-time virus dose that gets injected into a patient's retina. The corrected gene in the virus taps out the flawed, blindness-inducing gene in the eye, and produces a key vision-producing protein that patients with the disease normally can't make.

People start noticing a difference in their sight within a month. In clinical trials of the treatment, 13 out of 20 patients saw positive results. The treatment costs $425,000 per eye, or $850,000 total, however.

Jiankui claimed to have edited genes in a pair of twins born in China in November. By using the DNA-editing technique called CRISPR, he said, the babies were born immune to HIV.

This type of genetic manipulation is banned in most parts of the world, since any genetic mutations that the babies may have would get passed on to their offspring, with potentially disastrous consequences.

In 2019, the MIT Technology Review released excerpts from Jiankui's research. The unpublished manuscripts revealed that in the process of trying to manipulate the babies' HIV resistance which some experts say was unsuccessful Jiankui may have introduced unintended mutations.

NASA's InSight lander spent more than six months careening through space before it landed safely on Martian soil.

The robot is charged with exploring Mars' deep interior and helping scientists understand why Mars wound up a cold desert planet while Earth did not.

InSight has given scientists the unprecedented ability to detect and monitor Mars quakes seismic events deep inside the planet.

Fossil fuels like coal contain carbon dioxide, methane, and other compounds that trap heat from the sun. When we extract and burn these fuels for energy, that releases those gases into the atmosphere, where they accumulate and heat up the Earth over time.

That's what made 2016 the hottest year on record. So far, 2019 is the second-hottest year since records began 140 years ago, with July being the hottest month ever recorded.

A landmark report by the Intergovernmental Panel in Climate Change (IPCC) warned that slashing greenhouse-gas emissions in the next decade is crucial in order to avoid the worst consequences of severe climate change.

An April 2019 studyrevealed that the Greenland ice sheet is sloughing off an average of 286 billion tons of ice per year. Two decades ago, the annual average was just 50 billion.

In 2012, Greenland lostmore than 400 billion tons of ice.

Antarctica, meanwhile, lost an average of 252 billion tons of ice per year in the last decade. In the 1980s, by comparison, Antarctica lost 40 billion tons of ice annually.

What's more, parts of Thwaites Glacier in western Antarctica are retreating by up to 2,625 feet per year, contributing to4% of sea-level rise worldwide.A study published in July suggested that Thwaites' melting is likely approachingan irreversible point after which the entire glacier could collapse into the ocean. If that happens, global sea levels could rise by more than 1.5 feet.

The object, called MU69, is nicknamed Arrokoth, which means "sky" in the Powhatan/Algonquian language (it was previously nicknamed Ultima Thule). It's themost distant objecthumanity has ever visited.

The New Horizons probe took hundreds of photographs as it flew by the space rockat 32,200 miles per hour.

Images revealed that Arrokoth isflat like a pancake, rather than spherical in shape. The unprecedented data will likely reveal new clues about the solar system's evolution and how planets like Earth formed, though scientists are still receiving and processing the information from the distant probe.

The Japan Aerospace Exploration Agency (JAXA) launched itsHayabusa-2probe in December 2014. Hayabusa-2 arrived at Ryugu in June 2018, but didn'tland on the asteroid's surface until this year.

In order to collect samples from deep within the space rock, Hayabusa-2blasted a hole in the asteroid before landing. The mission plan calls for the probe to bring those samples back to Earth. By studying Ryugu's innermost rocks and debris which have been sheltered from the wear and tear of space scientists hope to learn how asteroids like this may have seeded Earth with key ingredients for life billions of years ago.

The unprecedented photo shows the supermassive black hole at the center of the Messier 87 galaxy, which is about54 million light-years away from Earth. The black hole's mass is equivalent to 6.5 billion suns.

Though theimage is somewhat fuzzy, it showed that, as predicted, black holeslook like dark spheres surrounded by a glowing ring of light.

Scientists struggled for decades to capture a black hole on camera, since black holes distort space-time, ensuring that nothing can break free of their gravitational pull even light. That's why the image shows a unique shadow in the form of a perfect circle at the center.

In September, scientists announced they'ddetected water vapor on a potentially habitable planet for the first time.The planet, K2-18b, is asuper-Earththat orbits a red dwarf star 110 light-years away.

NASA's planet-hunting Kepler space telescope discovered K2-18b in 2015, three years before the telescope was shut down. During its nine-year mission, Kepler discovered more than 2,500 exoplanets.

But K2-18b is the only known planet outside our solar systemwith water, an atmosphere, and a temperature range that could support liquid water on its surface. That makes it our "best candidate for habitability," one researcher said.

In the pilot program, children up to 2 years old in Malawi, Ghana, and Kenya can receive the vaccine. The new vaccine prevented 4 in 10 malaria cases in clinical trials, including 3 in 10 life-threatening cases.

Malaria kills about 435,000 people each year, most of them children.

"We need new solutions to get the malaria response back on track, and this vaccine gives us a promising tool to get there," Tedros Adhanom Ghebreyesus, director-general of the World Health Organization, said in a release. "The malaria vaccine has the potential to save tens of thousands of children's lives."

The vaccine comes in addition to two experimental treatments proven to dramatically boost Ebola survival rates.

The two new treatments, called REGN-EB3 and mAb-114, are cocktails of antibodies that get injected into people's bloodstreams. These therapies saved about 90% of new infected patients in the Congo after theWHO declared the Ebola outbreak in Africa to be a global health emergency.

Morgan McFall-Johnsen contributed to this story.

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Biggest scientific discoveries of the 2010s decade: photos - Business Insider

Facebook AI Research (FAIR) says its created the latest AI to achieve state-of-the-art performance when playing the card game Hanabi. The AI system achieves a score of 24.61 out of 25, while the previous best got 23.92 out of 25. In February, researchers from Google, DeepMind, Carnegie Mellon University, and Oxford University proposed a Hanabi benchmark and the creation of more AI that can play the game in order to achieve a new frontier for AI research.

Unlike other game challenges that pit AI versus humans, like chess or Go, Hanabi is a cooperative game where participants play together to work towards a common goal.

One of the really exciting things about this is that the improvement were observing is really orthogonal to the improvements that are being observed with deep reinforcement learning: You can add this on top of any strategy, and it will make it much stronger, Facebook AI researcher Noam Brown told VentureBeat in a phone interview. Were seeing that the results are far beyond what we or other researchers expected. In fact, the benefits that we get from search are stronger than the benefits that have been gained through all of the deep reinforcement learning algorithms that have been used in the past.

Facebooks Hanabi AI draws some of its search smarts from Pluribus, a poker-playing AI Facebook introduced earlier this year that bested some human champions.

Facebooks AI team achieved the feat by applying search techniques in conjunction with deep reinforcement learning. The search algorithm converts a problem into a single agent setting by making all but one agent carry out an agreed-upon policy, a reinforcement learning algorithm referred to as the blueprint. The blueprint allows the search agent to treat the known policy of other agents as part of the environment and maintain beliefs about the hidden information based on others actions, according to a paper on the subject titled Improving Policies via Search in Cooperative Partially Observable Games.

Ultimately, Facebook researchers believe AI akin to its Hanabi bot could help robotics systems, self-driving vehicles, or conversational AI agents better respond to human activity by solving theory of mind challenges, Brown said. Theory of mind is the idea of putting yourself in another persons shoes to infer their next action. An example of this in the real world is if youre driving and the car in front of you rolls to a stop, you may infer or deduce that a person is about to enter a crosswalk even if you cant see that person.

This is something that comes very naturally to humans, this idea of being able to put yourself in the shoes of another person and understand why theyre taking the actions theyre taking, what theyre thinking, and even if they dont know certain things. But its something that AI has historically really struggled with, he said. Theres been this long debate about whether primates have theory of mind and at what age do humans babies develop theory of mind, and I think its really fascinating to finally be seeing this sort of behavior in AI. And I think that thats going to be really important if we want to deploy AI in the real world to interact with humans because humans expect this behavior.

In other gameplay and AI news, last week Go master Lee Sedol said he plans to retire from playing the game. Sedol beat DeepMinds AlphaGo once in a best out of five series of games in 2016, but plans to retire due to the rise of superhuman AI that cannot be defeated, according to the BBC.

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Facebooks Hanabi-playing AI achieves state-of-the-art results - VentureBeat

David Bacon, senior software engineer in Googles quantum lab: Quantum computers do computations in parallel universes. This by itself isnt useful. U only get to exist in 1 universe at a time! The trick: quantum computers dont just split universes, they also merge universes. And this merge can add and subtract those other split universes.

David Reilly, principal researcher and director of the Microsoft quantum computing lab in Sydney, Australia: A quantum machine is a kind of analog calculator that computes by encoding information in the ephemeral waves that comprise light and matter at the nanoscale. Quantum entanglement likely the most counterintuitive thing around holds it all together, detecting and fixing errors.

Daniel Lidar, professor of electrical and computer engineering, chemistry, and physics and astronomy at the University of Southern California, with his daughter Nina, in haiku:

Quantum computers solve some problems much faster but are prone to noise

Superpositions: to explore multiple paths to the right answer

Interference helps: cancels paths to wrong answers and boosts the right ones

Entanglement makes classical computers sweat, QCs win the race

Scott Aaronson, professor of computer science at the University of Texas at Austin: A quantum computer exploits interference among positive and negative square roots of probabilities to solve certain problems much faster than we think possible classically, in a way that wouldnt be nearly so interesting were it possible to explain in the space of a tweet.

Alan Baratz, executive vice president of research and development at D-Wave Systems: If were honest, everything we currently know about quantum mechanics cant fully describe how a quantum computer works. Whats more important, and even more interesting, is what a quantum computer can do: A.I., new molecules, new materials, modeling climate change

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This Week in Tech: What on Earth Is a Quantum Computer? - The New York Times

Details Published: Wednesday, 11 December 2019 07:59

More than half (54 percent) of cyber security professionals have expressed concerns that quantum computing will outpace the development of security technologies, according to new research from the Neustar International Security Council (NISC). Keeping a watchful eye on developments, 74 percent of organizations said that they are paying close attention to the technologys evolution, with 21 percent already experimenting with their own quantum computing strategies.

A further 35 percent of experts claimed to be in the process of developing a quantum strategy, while just 16 percent said they were not yet thinking about it. This shift in focus comes as the vast majority (73 percent) of cyber security professionals expect advances in quantum computing to overcome legacy technologies, such as encryption, within the next five years. Almost all respondents (93 percent) believe the next-generation computers will overwhelm existing security technology, with just 7 percent under the impression that true quantum supremacy will never happen.

Despite expressing concerns that other technologies will be overshadowed, an overwhelming number (87 percent) of CISOs, CSOs, CTOs and security directors are excited about the potential positive impact of quantum computing. The remaining 13 percent were more cautious and under the impression that the technology would create more harm than good.

At the moment, we rely on encryption, which is possible to crack in theory, but impossible to crack in practice, precisely because it would take so long to do so, over timescales of trillions or even quadrillions of years, said Rodney Joffe, Chairman of NISC and Security CTO at Neustar. Without the protective shield of encryption, a quantum computer in the hands of a malicious actor could launch a cyber attack unlike anything weve ever seen.

For both todays major attacks, and also the small-scale, targeted threats that we are seeing more frequently, it is vital that IT professionals begin responding to quantum immediately. The security community has already launched a research effort into quantum-proof cryptography, but information professionals at every organization holding sensitive data should have quantum on their radar. Quantum computing's ability to solve our great scientific and technological challenges will also be its ability to disrupt everything we know about computer security. Ultimately, IT experts of every stripe will need to work to rebuild the algorithms, strategies, and systems that form our approach to cyber security, added Joffe.

http://www.nisc.neustar

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Security leaders fear that quantum computing developments will outpace security technologies - Continuity Central

Back to Menu By Luke Dormehl December 10, 2019 3:00AM PST Close IBM Research

In 1950, a man named John Bennett, an Australian employee of the now-defunct British technology firm Ferranti, created what may be historys first gaming computer. It could play a game called Nim, a long-forgotten parlor game in which players take turns removing matches from several piles. The player who loses is the one who removes the last match. For his computerized version, Bennett created a vast machine 12 feet wide, 5 feet tall, and 9 feet deep. The majority of this space was taken up by light-up vacuum tubes which depicted the virtual matches.

Bennetts aim wasnt to create a game-playing machine for the sake of it; the reason that somebody might build a games PC today. As writer Tristan Donovan observed in Replay, his superlative 2010 history of video games: Despite suggesting Ferranti create a game-playing computer, Bennetts aim was not to entertain but to show off the ability of computers to do [math].

Jump forward almost 70 years and a physicist and computer scientist named Dr. James Robin Wootton is using games to demonstrate the capabilities of another new, and equally large, experimental computer. The computer in this question is a quantum computer, a dream of scientists since the 1980s, now finally becoming a scientific reality.

Quantum computers encode information as delicate correlations with an incredibly rich structure. This allows for potentially mind-boggling densities of information to be stored and manipulated. Unlike a classical computer, which encodes as a series of ones and zeroes, the bits (called qubits) in a quantum computer can be either a one, a zero, or both at the same time. These qubits are composed of subatomic particles, which conform to the rules of quantum rather than classical mechanics. They play by their own rules a little bit like Tom Cruises character Maverick from Top Gun if he spent less time buzzing the tower and more time demonstrating properties like superpositions and entanglement.

I met Wootton at IBMs research lab in Zurich on a rainy day in late November. Moments prior, I had squeezed into a small room with a gaggle of other excited onlookers, where we stood behind a rope and stared at one of IBMs quantum computers like people waiting to be allowed into an exclusive nightclub. I was reminded of the way that people, in John Bennetts day, talked about the technological priesthood surrounding computers: then enormous mainframes sequestered away in labyrinthine chambers, tended to by highly qualified people in white lab coats. Lacking the necessary seminary training, we quantum computer visitors could only bask in its ambience from a distance, listening in reverent silence to the weird vee-oing vee-oing vee-oing sound of its cooling system.

Wottons interest in quantum gaming came about from exactly this scenario. In 2016, he attended a quantum computing event at the same Swiss ski resort where, in 1925, Erwin Schrdinger had worked out his famous Schrdinger wave equation while on vacation with a girlfriend. If there is a ground zero for quantum computing, this was it. Wotton was part of a consortium, sponsored by the Swiss government, to do (and help spread the word about) quantum computing.

At that time quantum computing seemed like it was something that was very far away, he told Digital Trends. Companies and universities were working on it, but it was a topic of research, rather than something that anyone on the street was likely to get their hands on. We were talking about how to address this.

Wootton has been a gamer since the early 1990s. I won a Game Boy in a competition in a wrestling magazine, he said. It was a Slush Puppy competition where you had to come up with a new flavor. My Slush Puppy flavor was called something like Rollin Redcurrant. Im not sure if you had to use the adjective. Maybe thats what set me apart.

While perhaps not a straight path, Wootton knew how an interest in gaming could lead people to an interest in other aspects of technology. He suggested that making games using quantum computing might be a good way of raising public awareness of the technology.He applied for support and, for the next year, was given to my amazement the chance to go and build an educational computer game about quantum computing. At the time, a few people warned me that this was not going to be good for my career, he said. [They told me] I should be writing papers and getting grants; not making games.

But the idea was too tantalizing to pass up.

That same year, IBM launched its Quantum Experience, an online platform granting the general public (at least those with a background in linear algebra) access to IBMs prototype quantum processors via the cloud. Combined with Project Q, a quantum SDK capable of running jobs on IBMs devices, this took care of both the hardware and software component of Woottons project. What he needed now was a game. Woottons first attempt at creating a quantum game for the public was a version of the game Rock-Paper-Scissors, named Cat-Box-Scissors after the famous Schrdingers cat thought experiment. Wootton later dismissed it as [not] very good Little more than a random number generator with a story.

But others followed. There was Battleships, his crack at the first multiplayer game made with a quantum computer. There was Quantum Solitaire. There was a text-based dungeon crawler, modeled on 1973s Hunt the Wumpus, called Hunt the Quantpus. Then the messily titled, but significant, Battleships with partial NOT gates, which Wootton considers the first true quantum computer game, rather than just an experiment. And so on. As games, these dont exactly make Red Dead Redemption 2 look like yesterdays news. Theyre more like Atari 2600 or Commodore 64 games in their aesthetics and gameplay. Still, thats exactly what youd expect from the embryonic phases of a new computing architecture.

If youd like to try out a quantum game for yourself, youre best off starting with Hello Quantum, available for both iOS and Android. It reimagines the principles of quantum computing as a puzzle game in which players must flip qubits. It wont make you a quantum expert overnight, but it will help demystify the process a bit. (With every level, players can hit a learn more button for a digestible tutorial on quantum basics.)

Quantum gaming isnt just about educational outreach, though. Just as John Bennett imagined Nim as a game that would exist to show off a computers abilities, only to unwittingly kickstart a $130 billion a year industry, so quantum games are moving beyond just teaching players lessons about quantum computing.Increasingly, Wootton is excited about what he sees as real world uses for quantum computing. One of the most promising of these is taking advantage of quantum computings random number generating to create random terrain within computer games. In Zurich, he showed me a three-dimensional virtual landscape reminiscent of Minecraft. However, while much of the world of Minecraft is user generated, in this case the blocky, low-resolution world was generated using a quantum computer.

Quantum mechanics is known for its randomness, so the easiest possibility is just to use quantum computing as a [random number generator], Wootton said. I have a game in which I use only one qubit: the smallest quantum computer you can get. All you can do is apply operations that change the probabilities of getting a zero or one as output. I use that to determine the height of the terrain at any point in the game map.

Plenty of games made with classical computers have already included procedurally generated elements over the years. But as the requirements for these elements ranging from randomly generated enemies to entire maps increase in complexity, quantum could help.

Gaming is an industry that is very dependent on how fast things run

Gaming is an industry that is very dependent on how fast things run, he continued. If theres a factor of 10 difference in how long it takes something to run that determines whether you can actually use it in a game. He sees today as a great jumping-on point for people in the gaming industry to get involved to help shape the future development of quantum computing. Its going to be driven by what people want, he explained. If people find an interesting use-case and everyone wants to use quantum computing for a game where you have to submit a job once per frame, that will help dictate the way that the technology is made.

Hes now reached the point where he thinks the race may truly be on to develop the first commercial game using a quantum computer. Weve been working on these proof-of-principle projects, but now I want to work with actual game studios on actual problems that they have, he continued. That means finding out what they want and how they want the technology to be [directed].

One thing thats for certain is that Wootton is no longer alone in developing his quantum games. In the last couple of years, a number ofquantum game jams have popped up around the world. What most people have done is to start small, Wootton said. They often take an existing game and use one or two qubits to help allow you to implement a quantum twist on the game mechanics. Following this mantra, enthusiasts have used quantum computing to make remixed versions of existing games, including Dr. Qubit (a quantum version of Dr. Mario), Quantum Cat-sweeper (a quantum version of Minesweeper), and Quantum Pong (a quantum version of, err, Pong).

The world of quantum gaming has moved beyond its 1950 equivalent of Nim. Now we just have to wait and see what happens next. The decades which followed Nim gave us MITs legendary Spacewar in the 1960s, the arcade boom of the 1970s and 80s, the console wars of Sega vs. Nintendo, the arrival of the Sony PlayStation in the 1990s, and so on. In the process, classical computers became part of our lives in a way they never were before. As Whole Earth Catalog founder Stewart Brand predicted as far back as 1972 Rolling Stone in his classic essay on Spacewar: Ready or not, computers are coming to the people.

At present, quantum gamings future is at a crossroads. Is it an obscure niche occupied by just a few gaming physics enthusiasts or a powerful tool that will shape tomorrows industry? Is it something that will teach us all to appreciate the finer points of quantum physics or a tool many of us wont even realize is being used, that will nevertheless give us some dope ass games to play?

Like Schrdingers cat, right now its both at once. What a superposition to be in.

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Inside the weird, wild, and wondrous world of quantum video games - Digital Trends

Executive Summary

Over the next few decades, agility will not come from speed; it will come from the ability to explore multiple domains at once and combine them into something that produces value. This means computer scientists working with cancer scientists, for example, to identify specific genetic markers that could lead to a cure. This change will be profound and we will need to rethink old notions about how we compete, collaborate, and bring new products to market. Here are three key shifts.

For the past few decades, agility in the technology sector has largely meant moving faster and faster down a predetermined path; innovation has largely been driven by our ability to cram more transistors onto a silicon wafer. With every new generation of chips came new possibilities and new applications. The firms that developed those applications the fastest won.

Over the coming decades, however, agility will take on a new meaning: the ability to explore multiple domains at once and combine them into something that produces value. Well need computer scientists working with cancer scientists, for example, to identify specific genetic markers that could lead to a cure. To do this, well need to learn how to go slower to have a greater impact.

This change will be profound. We will need to rethink old notions about how we compete, collaborate, and bring new products to market. More specifically, we will have to manage three profound shifts that will force us to widen and deepen connections between talent, technology, and information rather than just moving fast and breaking things.

Shift 1: From A Digital To A Post-Digital Age. Its hard to imagine that 30 years ago, most American households didnt have a computer, much less a mobile phone. Yet today, a typical teenager armed with a smartphone has access to more information than a highly trained specialist working at a major institution a generation ago.

Whats driven all this advancement has been Moores Law, our ability to double the power of our computing technology about every 18 months. Yet now Moores Law is approaching theoretical limits and will most likely come to an end in the next decade. New computing architectures, such as quantum and neuromorphic technologies, have great potential to further advancement, but will be far more complex than digital chips. Make no mistake, the transition will not be seamless.

At the same time, were seeing the rise of nascent technologies, such as synthetic biology, advanced materials science and artificial intelligence. Again, these new technologies represent a significant increase in complexity. Were rapidly moving from an environment where we understand the technologies we use and their implications extremely well to an era in which we do not. If we continue to move fast and break things, we are likely to break something important.

Shift 2: From Rapid Iteration to Exploration. Over the past 30 years, weve had the luxury of working with technologies we understand extremely well. Every generation of microchips opened vast new possibilities, but worked exactly the same way as the last generation, creating minimal switching costs. The main challenge was to design applications.

So it shouldnt be surprising that rapid iteration emerged as a key strategy. When you understand the fundamental technology that underlies a product or service, you can move quickly, trying out nearly endless permutations until you arrive at an optimized solution. Thats often far more effective than a more planned, deliberate approach.

Over the next decade or two, however, the challenge will be to advance technology that we dont understand well at all. Quantum and neuromorphic computing are still in their nascent stages. Exponential improvements in genomics and materials science are redefining the boundaries of those fields. There are also ethical issues involved with artificial intelligence and genomics that will require us to tread carefully.

So in the future, we will need to put greater emphasis on exploration. We will need to spend time understanding these new technologies and how they relate to our businesses. Most of all, its imperative to start exploring early. By the time many of these technologies hit their stride, it may be too late to catch up.

Shift 3: From Hypercompetition to Mass Collaboration.The competitive environment weve become used to has been relatively simple. For each particular industry, there have been distinct ecosystems based on established fields of expertise. Competing firms raced to transform fairly undifferentiated digital inputs (chips, code, components, etc.) into highly differentiated products and services. You needed to move fast to get an edge.

This new era, on the other hand, will be one of mass collaboration in which government partners with academia and industry to explore new technologies in the pre-competitive phase. For example, the Joint Center for Energy Storage Research combines the work of five national labs, a few dozen academic institutions, and hundreds of companies to develop advanced batteries.

Or consider the Manufacturing Institutes, which focus on everything from advanced fabrics and biopharmaceuticals to robotics and composite materials. These active hubs allow companies to collaborate with government labs and top academics to develop the next generation of technologies. They also operate dozens of testing facilities to help bring new products to market faster.

Ive visited some of these facilities and have had the opportunity to talk with executives from participating companies. What has struck me is how how excited they are for the possibilities of this new era. Agility for them doesnt mean learning to run faster down a chosen course, but to widen and deepen connections throughout a technological ecosystem.

Not so long ago, this kind of mass collaboration, often involving direct competitors would have seemed strange, if not hopelessly naive. Yet today, high performing firms from corporate VCs to corporate accelerators are increasingly aware that they need to connect or get shut out. One example is especially instructive. When IBM decided to develop the PC in 1980, they sent a team to Boca Raton to work in secret and launched the product a year later. To develop quantum computing, however, theyve created a Q Network, which includes several of the National Labs, research universities, potential end users like major banks and manufacturers as well as startups.

Whats becoming increasingly clear is that the breakthrough applications of the future will not be based on a single technology like a digital microchip. These new technologies are far too complex for anyone to develop on their own. Thats why we can expect the basis of competition to shift away from design sprints, iterating, and pivoting to building meaningful relationships in order to solve grand challenges. Power in this new era will not sit at the top of industrial hierarchies, but will emanate from the center of networks and ecosystems.

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Why Move Fast and Break Things Doesn't Work Anymore - Harvard Business Review

Our bodies are made up of seven chakras and the most important one among them is the sixth one, the third eye. Also known as the 'Ajna chakra', our third eye is considered to be extremely powerful because it is a potent source of intuitive wisdom which can guide you towards creative pursuits, do away with negativity, provide knowledgeable insight and perhaps lead you to the highest form of intelligence helping open your eyes towards what needs attention. When acting in full force, the third eye can help you see clearly, clear mental blockages and also improve mental flexibilities. In fact, the third eye has been hailed as the most important form of sense in many cultures and activating it is considered to be the most crucial. What if we told you, there is a certain form of meditation which can open up your powerful sixth sense and get you in touch with your inner self? While the third eye carries the benefit of connecting us to our gut feelings and work one step ahead of our basic five senses, it mostly stays dormant or closed. That's where the benefits of meditation come into play. According to healers, meditation is the simplest and the best way to awaken, vitalize and activate your third eye.

Meditation helps clear out the negative toxins from the body, channelize your energies and help you concentrate better. Forms of meditation can also help you be self-aware, active your Ajna chakra, shift your state of consciousness to higher states with every session, thereby taking away anxiety and worries from the root and transcend into the innermost thought processes and make it work at the fullest capacity. Doing so can also put back focus on your mind, improve concentration and boost clarity.

The best way of opening up your third eye

Excerpt from:

All you need to know about third eye meditation - Times of India