SOUTH SIOUX CITY, Neb. (KTIV) -- Typically the burning of something is meant to completely remove it.

Saturday, fire was a symbol of liberation.

"This retires flags that are no longer serviceable, it honors the flags where they have flown," said Post 307 Commander John Ludwick.

The ceremony proved important for South Sioux City personnel because of what it takes for one of their own flags to get to the point of retirement.

"These flags have served, not only here at home, but overseas, and wherever this flag goes, it gives people hope. So that's why we honor the flags here today and retire them properly," said Ludwick.

Even though the flags are physically destroyed, area veterans and service men and women continue to honor each of them.

"The flag still stands. It's still holds a special place, or it should have, in every American's heart. Because it is liberty, not just freedom, not just democracy, but liberty," said Ludwick.

Once the flags had been completely burned, the ashes were collected, buried at the Siouxland Freedom Park in South Sioux City, and commemorated with a plaque.

View original post here:
Siouxland veterans and service men and women honor retired flags - KTIV

My husband is 70 and I am 63. We both want to retire as soon as our son finishes college, if not before. He is currently a sophomore. We will have approximately $90,000 per year to live on (not including expenses for health insurance supplemental plans). Right now, were in excellent health and have been working to pay off debt and our sons education, which we pay as we go. I have even thought about taking the Social Security survivors benefit and working part time until Im 67. Is that a good option?

Also, wheres the best place to retire to live comfortably and to afford to travel?

Thank you!

L.B.

See:Im 60, my spouse is 45 can I retire if our expenses are $12,000 a month?

Dear L.B.,

Congratulations on your near retirement! It will certainly be something to celebrate, and that youve already figured out what your retirement income will be is a great start.

I want to focus my answer to your question around the Social Security component. Social Security is such a major factor in Americans retirement plans, but it can be challenging to know how exactly it works and when is the right time to claim benefits.

For example, in your question, you mentioned the survivor benefit, but thats not available to everyone. It may have been a typo, where you meant to say spousal, or it may be that you do qualify. Americans qualify for survivor benefits in a few scenarios, including if they are a widow or widower age 60 or older; a divorced spouse from a marriage that lasted 10 years and who did not remarry before age 60; or a widow or widower at any age caring for the deceaseds child under age 16. Either way, I just want to clarify that there are various forms of benefits associated with Social Security including survivor and spousal and by knowing the difference and which are applicable to your situation, you can find strategies that maximize what you receive.

Spousal benefits can be very confusing, said Kate Gregory, a financial planner and president of Gregory Advisors Inc. As a spouse, youre entitled to 50% of your husbands primary insurance benefit that hed receive at his Full Retirement Age (FRA, which in his case is 66 years old), but he has to have filed for his benefits before you can do so. Hes 70, which means he probably already has, since thats the latest a person can claim retirement benefits and well get to that in a moment.

Now heres where it gets tricky: if your own retirement benefit is higher than 50% of your husbands, youll get your own benefit not the spousal benefit. You dont get both. Youll have to file for retirement benefits and then the Social Security Administration will calculate the benefit for you, analyzing your own versus half of your husbands. Youll either get the equivalent of his half or, if yours is more, your own.

Heres an example, provided by Diane Wilson, founding partner of My Social Security Analyst. If his benefit at Full Retirement Age is $2,000 and your FRA benefit is $800, youd get half of his ($1,000). Youd technically receive a spousal benefit of $200, so that youre getting your benefit plus an additional amount of money to bring you to half of his. The rules are complicated and not easy to understand, she said.

But wait, there are more rules! If you claim Social Security earlier than your Full Retirement Age (in your case, 66 and a few months), you will get less than your full retirement benefit this applies even with the spousal benefit, Gregory said. And if you take the spousal benefit at your FRA and your husband took his benefit after his FRA, which would increase his benefits, youll still only get 50% of what hed get at 66, not whatever hes getting every month now. A beneficiary gets roughly 8% more in her retirement benefit checks for each year she delays claiming Social Security after her FRA, but that figure would not be factored into a spousal benefit. Comparatively, for each year before FRA, the benefit is reduced.

There are caveats, of course, such as if you havent earned enough credits to qualify for the Social Security retirement benefit, in which case, youd only qualify for the spousal. People born before 1954 have the option to file for their spousal benefits and then switch to their own benefit later to take advantage of the 8% delayed credit, but that wouldnt apply to you that could apply to your husband, though.

Also see: You can still claim Social Security spousal benefits even if your spouse is gone

And even after making these decisions, double check that your benefits are correct, said Avani Ramnani, director of wealth management and financial planning at Francis Financial. She once had new clients where the wife was receiving only 30% of her husbands benefit, because she was a few years older than him and had elected her benefit before he had elected his.

You mentioned claiming benefits and working part time. Thats definitely doable, but be aware you may be subjected to the earnings test, said Mike Miller, managing director of Integra Shield Financial Group. For every $2 you earn over $18,240 in 2020, your benefit is reduced by $1. The earnings test is inflation-adjusted every year, and applies for the years before the one in which you reach your FRA. Your benefit will also be adjusted to account for those lost benefits at full retirement age.

Does it make sense to work part-time and collect Social Security early? I would say no unless you need the income due to all the potential reductions in benefits, Gregory said. If you arent going to work, it makes more sense to collect while her husband is alive, especially if her own benefit is less than her spousal.

You also asked about where the best place is to retire and honestly, that depends on a variety of personal factors, including proximity to family and health facilities, taxes, cost of living, weather and entertainment. MarketWatch created a tool that helps readers pick desired qualities in a dream retirement spot maybe it will help you too! Also check out our Where Should I Retire? column that helps people answer this question.

Have a question about your own retirement savings? Email us at HelpMeRetire@marketwatch.com

See original here:
Im 63, my husband is 70, well have $90,000 a year in retirement how can we claim our Social Security benefits? - MarketWatch

Game-playing artificial intelligence

AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go. This algorithm uses an approach similar to AlphaGo Zero.

On December 5, 2017, the DeepMind team released a preprint introducing AlphaZero, which within 24 hours of training achieved a superhuman level of play in these three games by defeating world-champion programs Stockfish, elmo, and the 3-day version of AlphaGo Zero. In each case it made use of custom tensor processing units (TPUs) that the Google programs were optimized to use.[1] AlphaZero was trained solely via "self-play" using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel, with no access to opening books or endgame tables. After four hours of training, DeepMind estimated AlphaZero was playing at a higher Elo rating than Stockfish 8; after 9 hours of training, the algorithm defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws).[1][2][3] The trained algorithm played on a single machine with four TPUs.

DeepMind's paper on AlphaZero was published in the journal Science on 7 December 2018.[4] In 2019 DeepMind published a new paper detailing MuZero, a new algorithm able to generalise on AlphaZero work playing both Atari and board games without knowledge of the rules or representations of the game.[5]

AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include:[1]

Comparing Monte Carlo tree search searches, AlphaZero searches just 80,000 positions per second in chess and 40,000 in shogi, compared to 70 million for Stockfish and 35 million for elmo. AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectively on the most promising variation.[1]

AlphaZero was trained solely via self-play, using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks. In parallel, the in-training AlphaZero was periodically matched against its benchmark (Stockfish, elmo, or AlphaGo Zero) in brief one-second-per-move games to determine how well the training was progressing. DeepMind judged that AlphaZero's performance exceeded the benchmark after around four hours of training for Stockfish, two hours for elmo, and eight hours for AlphaGo Zero.[1]

In AlphaZero's chess match against Stockfish 8 (2016 TCEC world champion), each program was given one minute per move. Stockfish was allocated 64 threads and a hash size of 1 GB,[1] a setting that Stockfish's Tord Romstad later criticized as suboptimal.[6][note 1] AlphaZero was trained on chess for a total of nine hours before the match. During the match, AlphaZero ran on a single machine with four application-specific TPUs. In 100 games from the normal starting position, AlphaZero won 25 games as White, won 3 as Black, and drew the remaining 72.[8] In a series of twelve, 100-game matches (of unspecified time or resource constraints) against Stockfish starting from the 12 most popular human openings, AlphaZero won 290, drew 886 and lost 24.[1]

AlphaZero was trained on shogi for a total of two hours before the tournament. In 100 shogi games against elmo (World Computer Shogi Championship 27 summer 2017 tournament version with YaneuraOu 4.73 search), AlphaZero won 90 times, lost 8 times and drew twice.[8] As in the chess games, each program got one minute per move, and elmo was given 64 threads and a hash size of 1GB.[1]

After 34 hours of self-learning of Go and against AlphaGo Zero, AlphaZero won 60 games and lost 40.[1][8]

DeepMind stated in its preprint, "The game of chess represented the pinnacle of AI research over several decades. State-of-the-art programs are based on powerful engines that search many millions of positions, leveraging handcrafted domain expertise and sophisticated domain adaptations. AlphaZero is a generic reinforcement learning algorithm originally devised for the game of go that achieved superior results within a few hours, searching a thousand times fewer positions, given no domain knowledge except the rules."[1] DeepMind's Demis Hassabis, a chess player himself, called AlphaZero's play style "alien": It sometimes wins by offering counterintuitive sacrifices, like offering up a queen and bishop to exploit a positional advantage. "It's like chess from another dimension."[9]

Given the difficulty in chess of forcing a win against a strong opponent, the +28 0 =72 result is a significant margin of victory. However, some grandmasters, such as Hikaru Nakamura and Komodo developer Larry Kaufman, downplayed AlphaZero's victory, arguing that the match would have been closer if the programs had access to an opening database (since Stockfish was optimized for that scenario).[10] Romstad additionally pointed out that Stockfish is not optimized for rigidly fixed-time moves and the version used is a year old.[6][11]

Similarly, some shogi observers argued that the elmo hash size was too low, that the resignation settings and the "EnteringKingRule" settings (cf. shogi Entering King) may have been inappropriate, and that elmo is already obsolete compared with newer programs.[12][13]

Papers headlined that the chess training took only four hours: "It was managed in little more than the time between breakfast and lunch."[2][14]Wired hyped AlphaZero as "the first multi-skilled AI board-game champ".[15] AI expert Joanna Bryson noted that Google's "knack for good publicity" was putting it in a strong position against challengers. "It's not only about hiring the best programmers. It's also very political, as it helps make Google as strong as possible when negotiating with governments and regulators looking at the AI sector."[8]

Human chess grandmasters generally expressed excitement about AlphaZero. Danish grandmaster Peter Heine Nielsen likened AlphaZero's play to that of a superior alien species.[8] Norwegian grandmaster Jon Ludvig Hammer characterized AlphaZero's play as "insane attacking chess" with profound positional understanding.[2] Former champion Garry Kasparov said "It's a remarkable achievement, even if we should have expected it after AlphaGo."[10][16]

Grandmaster Hikaru Nakamura was less impressed, and stated "I don't necessarily put a lot of credibility in the results simply because my understanding is that AlphaZero is basically using the Google supercomputer and Stockfish doesn't run on that hardware; Stockfish was basically running on what would be my laptop. If you wanna have a match that's comparable you have to have Stockfish running on a supercomputer as well."[7]

Top US correspondence chess player Wolff Morrow was also unimpressed, claiming that AlphaZero would probably not make the semifinals of a fair competition such as TCEC where all engines play on equal hardware. Morrow further stated that although he might not be able to beat AlphaZero if AlphaZero played drawish openings such as the Petroff Defence, AlphaZero would not be able to beat him in a correspondence chess game either.[17]

Motohiro Isozaki, the author of YaneuraOu, noted that although AlphaZero did comprehensively beat elmo, the rating of AlphaZero in shogi stopped growing at a point which is at most 100~200 higher than elmo. This gap is not that high, and elmo and other shogi software should be able to catch up in 12 years.[18]

DeepMind addressed many of the criticisms in their final version of the paper, published in December 2018 in Science.[4] They further clarified that AlphaZero was not running on a supercomputer; it was trained using 5,000 tensor processing units (TPUs), but only ran on four TPUs and a 44-core CPU in its matches.[19]

In the final results, Stockfish version 8 ran under the same conditions as in the TCEC superfinal: 44 CPU cores, Syzygy endgame tablebases, and a 32GB hash size. Instead of a fixed time control of one move per minute, both engines were given 3 hours plus 15 seconds per move to finish the game. In a 1000-game match, AlphaZero won with a score of 155 wins, 6 losses, and 839 draws. DeepMind also played a series of games using the TCEC opening positions; AlphaZero also won convincingly.

Similar to Stockfish, Elmo ran under the same conditions as in the 2017 CSA championship. The version of Elmo used was WCSC27 in combination with YaneuraOu 2017 Early KPPT 4.79 64AVX2 TOURNAMENT. Elmo operated on the same hardware as Stockfish: 44 CPU cores and a 32GB hash size. AlphaZero won 98.2% of games when playing black (which plays first in shogi) and 91.2% overall.

Human grandmasters were generally impressed with AlphaZero's games against Stockfish.[20] Former world champion Garry Kasparov said it was a pleasure to watch AlphaZero play, especially since its style was open and dynamic like his own.[21][22]

In the chess community, Komodo developer Mark Lefler called it a "pretty amazing achievement", but also pointed out that the data was old, since Stockfish had gained a lot of strength since January 2018 (when Stockfish 8 was released). Fellow developer Larry Kaufman said AlphaZero would probably lose a match against the latest version of Stockfish, Stockfish 10, under Top Chess Engine Championship (TCEC) conditions. Kaufman argued that the only advantage of neural networkbased engines was that they used a GPU, so if there was no regard for power consumption (e.g. in an equal-hardware contest where both engines had access to the same CPU and GPU) then anything the GPU achieved was "free". Based on this, he stated that the strongest engine was likely to be a hybrid with neural networks and standard alphabeta search.[23]

AlphaZero inspired the computer chess community to develop Leela Chess Zero, using the same techniques as AlphaZero. Leela contested several championships against Stockfish, where it showed similar strength.[24]

In 2019 DeepMind published MuZero, a unified system that played excellent chess, shogi, and go, as well as games in the Atari Learning Environment, without being pre-programmed with their rules.[25][26]

The match results by themselves are not particularly meaningful because of the rather strange choice of time controls and Stockfish parameter settings: The games were played at a fixed time of 1 minute/move, which means that Stockfish has no use of its time management heuristics (lot of effort has been put into making Stockfish identify critical points in the game and decide when to spend some extra time on a move; at a fixed time per move, the strength will suffer significantly). The version of Stockfish used is one year old, was playing with far more search threads than has ever received any significant amount of testing, and had way too small hash tables for the number of threads. I believe the percentage of draws would have been much higher in a match with more normal conditions.[7]

Link:

AlphaZero - Wikipedia

As with Go, we are excited about AlphaZeros creative response to chess, which has been a grand challenge for artificial intelligence since the dawn of the computing age with early pioneers including Babbage, Turing, Shannon, and von Neumann all trying their hand at designing chess programs. But AlphaZero is about more than chess, shogi or Go. To create intelligent systems capable of solving a wide range of real-world problems we need them to be flexible and generalise to new situations. While there has been some progress towards this goal, it remains a major challenge in AI research with systems capable of mastering specific skills to a very high standard, but often failing when presented with even slightly modified tasks.

AlphaZeros ability to master three different complex games and potentially any perfect information game is an important step towards overcoming this problem. It demonstrates that a single algorithm can learn how to discover new knowledge in a range of settings. And, while it is still early days, AlphaZeros creative insights coupled with the encouraging results we see in other projects such as AlphaFold, give us confidence in our mission to create general purpose learning systems that will one day help us find novel solutions to some of the most important and complex scientific problems.

This work was done by David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis.

Follow this link:

AlphaZero: Shedding new light on chess, shogi, and Go ...

Artificial intelligence that plays Go

AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in the journal Nature on 19 October 2017, introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version.[1] 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.[2]

Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills because expert data is "often expensive, unreliable or simply unavailable."[3]Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was "no longer constrained by the limits of human knowledge".[4]David Silver, one of the first authors of DeepMind's papers published in Nature on AlphaGo, said that it is possible to have generalised AI algorithms by removing the need to learn from humans.[5]

Google later developed AlphaZero, a generalized version of AlphaGo Zero that could play chess and Shgi in addition to Go. In December 2017, AlphaZero beat the 3-day version of AlphaGo Zero by winning 60 games to 40, and with 8 hours of training it outperformed AlphaGo Lee on an Elo scale. AlphaZero also defeated a top chess program (Stockfish) and a top Shgi program (Elmo).[6][7]

AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four TPUs were used for inference. The neural network initially knew nothing about Go beyond the rules. Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome.[8] In the first three days AlphaGo Zero played 4.9 million games against itself in quick succession.[9] It appeared to develop the skills required to beat top humans within just a few days, whereas the earlier AlphaGo took months of training to achieve the same level.[10]

For comparison, the researchers also trained a version of AlphaGo Zero using human games, AlphaGo Master, and found that it learned more quickly, but actually performed more poorly in the long run.[11] DeepMind submitted its initial findings in a paper to Nature in April 2017, which was then published in October 2017.[1]

The hardware cost for a single AlphaGo Zero system in 2017, including the four TPUs, has been quoted as around $25 million.[12]

According to Hassabis, AlphaGo's algorithms are likely to be of the most benefit to domains that require an intelligent search through an enormous space of possibilities, such as protein folding or accurately simulating chemical reactions.[13] AlphaGo's techniques are probably less useful in domains that are difficult to simulate, such as learning how to drive a car.[14] DeepMind stated in October 2017 that it had already started active work on attempting to use AlphaGo Zero technology for protein folding, and stated it would soon publish new findings.[15][16]

AlphaGo Zero was widely regarded as a significant advance, even when compared with its groundbreaking predecessor, AlphaGo. Oren Etzioni of the Allen Institute for Artificial Intelligence called AlphaGo Zero "a very impressive technical result" in "both their ability to do itand their ability to train the system in 40 days, on four TPUs".[8]The Guardian called it a "major breakthrough for artificial intelligence", citing Eleni Vasilaki of Sheffield University and Tom Mitchell of Carnegie Mellon University, who called it an impressive feat and an outstanding engineering accomplishment" respectively.[14]Mark Pesce of the University of Sydney called AlphaGo Zero "a big technological advance" taking us into "undiscovered territory".[17]

Gary Marcus, a psychologist at New York University, has cautioned that for all we know, AlphaGo may contain "implicit knowledge that the programmers have about how to construct machines to play problems like Go" and will need to be tested in other domains before being sure that its base architecture is effective at much more than playing Go. In contrast, DeepMind is "confident that this approach is generalisable to a large number of domains".[9]

In response to the reports, South Korean Go professional Lee Sedol said, "The previous version of AlphaGo wasnt perfect, and I believe thats why AlphaGo Zero was made." On the potential for AlphaGo's development, Lee said he will have to wait and see but also said it will affect young Go players. Mok Jin-seok, who directs the South Korean national Go team, said the Go world has already been imitating the playing styles of previous versions of AlphaGo and creating new ideas from them, and he is hopeful that new ideas will come out from AlphaGo Zero. Mok also added that general trends in the Go world are now being influenced by AlphaGos playing style. "At first, it was hard to understand and I almost felt like I was playing against an alien. However, having had a great amount of experience, Ive become used to it," Mok said. "We are now past the point where we debate the gap between the capability of AlphaGo and humans. Its now between computers." Mok has reportedly already begun analyzing the playing style of AlphaGo Zero along with players from the national team. "Though having watched only a few matches, we received the impression that AlphaGo Zero plays more like a human than its predecessors," Mok said.[18] Chinese Go professional, Ke Jie commented on the remarkable accomplishments of the new program: "A pure self-learning AlphaGo is the strongest. Humans seem redundant in front of its self-improvement."[19]

Future of Go Summit

89:11 against AlphaGo Master

On 5 December 2017, DeepMind team released a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of AlphaGo Zero in each case.[6]

AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include:[6]

An open source program, Leela Zero, based on the ideas from the AlphaGo papers is available. It uses a GPU instead of the TPUs recent versions of AlphaGo rely on.

Link:

AlphaGo Zero - Wikipedia

In news reminiscent of the initial AlphaZero shockwave last December, the artificial intelligence company DeepMind released astounding results from an updated version of the machine-learning chess project today.

The results leave no question, once again, that AlphaZero plays some of the strongest chess in the world.

The updated AlphaZero crushed Stockfish 8 in a new 1,000-game match, scoring +155 -6 =839. (See below for three sample games from this match with analysis by Stockfish 10 and video analysis by GM Robert Hess.)

AlphaZero also bested Stockfish in a series of time-odds matches, soundly beating the traditional engine even at time odds of 10 to one.

In additional matches, the new AlphaZero beat the"latest development version" of Stockfish, with virtually identical results as the match vs Stockfish 8, according to DeepMind. The pre-release copy of journal article, which is dated Dec. 7, 2018, does not specify the exact development version used.

[Update: Today's release of the full journal article specifies that the match was against the latest development version of Stockfish as of Jan. 13, 2018, which was Stockfish 9.]

The machine-learning engine also won all matches against "a variant of Stockfish that uses a strong opening book," according to DeepMind. Adding the opening book did seem to help Stockfish, which finally won a substantial number of games when AlphaZero was Blackbut not enough to win the match.

AlphaZero's results (wins green, losses red) vs the latest Stockfish and vs Stockfish with a strong opening book. Image by DeepMind via Science.

The results will be published in an upcoming article by DeepMind researchers in the journal Scienceand were provided to selected chess media by DeepMind, which is based in London and owned by Alphabet, the parent company of Google.

The 1,000-game match was played in early 2018. In the match, both AlphaZero and Stockfish were given three hours each game plus a 15-second increment per move. This time control would seem to make obsolete one of the biggest arguments against the impact of last year's match, namely that the 2017 time control of one minute per move played to Stockfish's disadvantage.

With three hours plus the 15-second increment, no such argument can be made, as that is an enormous amount of playing time for any computer engine. In the time odds games, AlphaZero was dominant up to 10-to-1 odds. Stockfish only began to outscore AlphaZero when the odds reached 30-to-1.

AlphaZero's results (wins green, losses red) vs Stockfish 8 in time odds matches. Image by DeepMind via Science.

AlphaZero's results in the time odds matches suggest it is not only much stronger than any traditional chess engine, but that it also uses a much more efficient search for moves. According to DeepMind, AlphaZero uses a Monte Carlo tree search, and examines about 60,000 positions per second, compared to 60 million for Stockfish.

An illustration of how AlphaZero searches for chess moves. Image by DeepMind via Science.

What can computer chess fans conclude after reading these results? AlphaZero has solidified its status as one of the elite chess players in the world. But the results are even more intriguing if you're following the ability of artificial intelligence to master general gameplay.

According to the journal article, the updated AlphaZero algorithm is identical in three challenging games: chess, shogi, and go. This version of AlphaZero was able to beat the top computer players of all three games after just a few hours of self-training, starting from just the basic rules of the games.

The updated AlphaZero results come exactly one year to the day since DeepMind unveiled the first, historic AlphaZero results in a surprise match vs Stockfish that changed chess forever.

Since then, an open-source project called Lc0 has attempted to replicate the success of AlphaZero, and the project has fascinated chess fans. Lc0 now competes along with the champion Stockfish and the rest of the world's top engines in the ongoing Chess.com Computer Chess Championship.

CCC fans will be pleased to see that some of the new AlphaZero games include "fawn pawns," the CCC-chat nickname for lone advanced pawns that cramp an opponent's position. Perhaps the establishment of these pawns is a critical winning strategy, as it seems AlphaZero and Lc0 have independently learned it.

DeepMind released 20 sample games chosen by GM Matthew Sadler from the 1,000 game match. Chess.com has selected three of these games with deep analysis by Stockfish 10 and video analysis by GM Robert Hess. You can download the 20 sample games at the bottom of this article, analyzed by Stockfish 10, and four sample games analyzed by Lc0.

Update: After this article was published, DeepMind released 210 sample games that you can download here.

Selected game 1 with analysis by Stockfish 10:

Game 1 video analysis by GM Robert Hess:

Selected game 2with analysis by Stockfish 10:

Game 2 video analysis by GM Robert Hess:

Selected game 3 with analysis by Stockfish 10:

Game 3 video analysis by GM Robert Hess:

IM Anna Rudolf also made a video analysis of one of the sample games, calling it "AlphaZero's brilliancy."

The new version of AlphaZero trained itself to play chess starting just from the rules of the game, using machine-learning techniques to continually update its neural networks. According to DeepMind, 5,000 TPUs (Google's tensor processing unit, an application-specific integrated circuit for article intelligence) were used to generate the first set of self-play games, and then 16 TPUs were used to train the neural networks.

The total training time in chess was nine hours from scratch. According to DeepMind, it took the new AlphaZero just four hours of training to surpass Stockfish; by nine hours it was far ahead of the world-champion engine.

For the games themselves, Stockfish used 44 CPU (central processing unit) cores and AlphaZero used a single machine with four TPUs and 44 CPU cores. Stockfish had a hash size of 32GB and used syzygy endgame tablebases.

AlphaZero's results vs. Stockfish in the most popular human openings. In the left bar, AlphaZero plays White; in the right bar, AlphaZero is Black. Image by DeepMind via Science. Click on the image for a larger version.

The sample games released were deemed impressive by chess professionals who were given preview access to them. GM Robert Hess categorized the games as "immensely complicated."

DeepMind itself noted the unique style of its creation in the journal article:

"In several games, AlphaZero sacrificed pieces for long-term strategic advantage, suggesting that it has a more fluid, context-dependent positional evaluation than the rule-based evaluations used by previous chess programs," the DeepMind researchers said.

The AI company also emphasized the importance of using the same AlphaZero version in three different games, touting it as a breakthrough in overall game-playing intelligence:

"These results bring us a step closer to fulfilling a longstanding ambition of artificial intelligence: a general game-playing system that can learn to master any game," the DeepMind researchers said.

You can download the 20 sample games provided by DeepMind and analyzed by Chess.com using Stockfish 10 on a powerful computer. The first set of games contains 10 games with no opening book, and the second set contains games with openings from the 2016 TCEC (Top Chess Engine Championship).

PGN downloads:

20 games with analysis by Stockfish 10:

4 selected games with analysis by Lc0:

Love AlphaZero? You can watch the machine-learning chess project it inspired, Lc0, in the ongoing Computer Chess Championship now.

Read the rest here:

AlphaZero Crushes Stockfish In New 1,000-Game Match ...

Lifestyle / Leisure / Who was Carlo Acutis? Get to know the tech whiz on his way to becoming a saint Who was Carlo Acutis? Get to know the tech whiz on his way to becoming a saint Five things you should know about the first millennial saint-to-be

Carlo Acutis, a Catholic Italian teenager who passed away in 2006, was beatified recently in Assisi. A gamer and computer programmer who loved soccer and the Eucharist, he has been the subject of interest around the world. So who was Carlo Acutis? Heres what you need to know:

Carlo, dubbed the cyber apostle of the Eucharist, was born in London to Italian parents, and moved to Milan with them as a young boy. From a tender age, Carlo seemed to have a special love for God, even though his parents werent especially devout. He loved to pray the rosary.

While his peers spent their spare time hanging out with friends, Carlo utilized his time by drawing closer to God. He attended daily Mass and asked his parents to take him on pilgrimages.

Skilled in film editing and computer programming, Carlo set up a website where he researched and documented miracles attributed to the Eucharist. The millennial, whose body lies in state in Assisi, dressed in a tracksuit and trainers, also warned his contemporaries that the internet could be a curse as well as a blessing.

An inspiration to so many, including young children and teens, Carlo died a young boy at the age of 15 after a brief battle with leukemia. He offered his sufferings forPope Benedict XVIand the Church, and was buried in Assisi, at his request, because of his love for St.Francis of Assisi.

His console of choice was a Playstation, or a PS2, which was released in 2000, when Carlo was nine. Reports say he only allowed himself to play games for an hour a week, as a penance and a spiritual discipline.

He was designated Venerable after the Pope approved a miracle involving the healing of a Brazilian boy suffering from a rare pancreatic disease. The boy came into contact with an Acutis relic, a piece of one of his T-shirts.

SIGN UP TO DAILY NEWSLETTER

2020-10-15 10:21:20

["leisure","lifestyle","moms-kids","youth","lifestyle"]

[2485297,2487216,2487118,2487055,2486944,2486122,2486692]

Originally posted here:
Who was Carlo Acutis? Get to know the tech whiz on his way to becoming a saint - Manila Bulletin

Kaeng Khiao Wan Nuea (Green Curry With Beef and Thai Eggplant) MUST (The Washington Post photo/Laura Chase de Formigny)

I really appreciate a recipe where the sauce is the best part of the dish. Butter chicken? Yes, please, Ill take a bowl of sauce and a piece of naan for dipping. And the wine-infused gravy from my familys chicken and mushrooms is basically liquid gold, perfect for dunking chunks of crusty bread.

Id put coconut-based Thai curries in the same category. Give me a generous pour of curry liquidred, yellow or greenand a plate of jasmine rice, and Im a happy camper.

Youll agree once you take a slurp of this green curry from cookbook author Leela Punyaratabandhu. Of course, what makes the sauce is the curry paste, and youll need a good one, preferably from Thailand.

I can appreciate the impulse to make your own paste, and initially thats what I wanted to do with Punyaratabandhus recipe. But after a couple of fruitless calls to my local Asian markets and largely striking out online (makrut limes are especially hard to come by), I decided to take Punyaratabandhu up on her suggestion that commercial pastes are the best way to get the traditional flavors if you cant acquire the right ingredients to make your own. Plus, at only a few bucks for a whole container, its a cheaper upfront investment than buying everything individually, particularly if youre not sure youd use up all the components. Asian markets and some well-stocked grocery stores carry curry pastes from Thailand. Brands to look for include Mae Ploy, Maesri, Pantai, Nittaya and Lobo. Maesri, for example, is available at some Wegmans, and some Targets carry Mae Ploy.

One other ingredient note: If you cant find round green Thai eggplants, which should be at most Asian markets and even some farmers markets, I tested the recipe with halved and sliced long Asian eggplants. They cooked up softer than the Thai variety, which wasnt necessarily bad, but you may want to knock back the cooking time a bit to account for the difference.

Once you have your ingredients in hand, its pretty much smooth sailing with this one-pot meal that comes together in just about an hour. The beef (boneless, skinless chicken thighs work well, too) essentially poaches in the aromatic, rich liquid. That leaves the meat beautifully tender and also means it infuses the broth as much as the broth infuses it.

As to that broth, it includes coconut three waysoil, milk and cream (the solids that rise to the top of a can of coconut milk, dont mistake it for cream of coconut). Low-fat this is not, but you can certainly control how much liquid you include in your portion, even though its pretty hard to resist. If you do manage to end up with extra, youll be left with a next-day treat that I can confirm is fantastic on its own poured over rice, or any other grain or stir-fry your heart desires.

Recipe note: Youll need two 14-ounce cans of coconut milk for this dish. For the cream, skim off the thick top without stirring. For the milk, stir the contents of the can together to incorporate.

Kaeng Khiao Wan Nuea (Green Curry With Beef and Thai Eggplant)

1 hour

4 to 6 servings

Thai curry just as good or better than your typical takeout is within reach, thanks to this one-pot recipe. The key here is to buy a good Thai curry paste. Cookbook author Leela Punyaratabandhu says commercial pastes are the best way to get the traditional flavors if you cant acquire the right ingredients to make your own. We decided to take her up on that suggestion.

We also tested the recipe with chicken (boneless, skinless thighs), which worked well. Round, green Thai eggplants can be trickier to find, but we found that long Asian eggplant was a fine substitute, though they do tend to cook up softer.

Youll need two 14-ounce cans of coconut milk for this dish. For the cream, skim off the thick top without stirring. For the milk, stir the contents of the can together to incorporate.

Serve with plenty of rice, preferably jasmine, to soak up all the flavorful cooking liquid

Storage Notes: Leftovers keep well in the refrigerator for up to 3 days.

Where to Buy: Most Asian markets and some well-stocked grocery stores will carry curry pastes from Thailand. Brands to look for include Mae Ploy, Maesri, Pantai, Nittaya and Lobo. Maesri, for example, is available at some Wegmans.

INGREDIENTS

8 ounces round Thai eggplants, stemmed and quartered through the stem end (see headnote; may substitute Asian eggplant, halved vertically and cut into 1/2-inch slices)

1/2 lemon

1/2 cup coconut cream (skimmed from the top of a 14-ounce can of coconut milk)

2 tablespoons coconut oil or vegetable oil

2 tablespoons green curry paste (see headnote)

1 1/2 cups canned coconut milk (from one 14-ounce can)

12 ounces chuck roast, cut against the grain and on the diagonal into bite-size pieces

2 phrik chi fa (fresh Thai long chiles; may substitute 1/2 green bell pepper), cut lengthwise on the diagonal into 1/4-inch-wide strips

2 tablespoons fish sauce

2 teaspoons packed grated palm sugar (may substitute 1/2 teaspoon granulated sugar)

1/2 cup packed Thai basil leaves (may substitute Italian sweet basil)

DIRECTIONS

Place the eggplant pieces in a bowl of water with a squeeze of lemon juice.

In a 2-quart saucepan, combine the coconut cream, coconut oil and curry paste over medium-high heat and stir until the paste is fragrant and the coconut fat separates (youll see shiny droplets of fat on the surface), about 2 minutes. Add the coconut milk and beef and bring to a gentle boil, stirring occasionally. Cover, lower the heat to a simmer, and cook for about 15 minutes, stirring occasionally.

Drain the eggplants and add them to the pan along with the chiles, fish sauce, sugar and, if necessary, just enough water to keep everything submerged. Turn up the heat just enough to return the curry to a simmer, cover and continue to cook until the eggplants are tender, 5 to 7 minutes.

Taste for seasoning. No more sugar should be added, but more fish sauce may be needed. When the flavor is to your liking, remove the pan from the heat, stir in the basil leaves and serve.

Nutrition (based on 6 servings) | Calories: 380; Total Fat: 35 g; Saturated Fat: 25 g; Cholesterol: 40 mg; Sodium: 770 mg; Carbohydrates: 9 g; Dietary Fiber: 3 g; Sugar: 3 g; Protein: 12 g.

Adapted from Simple Thai Food, by Leela Punyaratabandhu (Ten Speed Press, 2014).

Becky Krystalwrites for The Washington Post.

Visit link:

Recipe: This Thai green curry with beef is all about the sauce - Indianapolis Business Journal

Charoen Pokphand Foods PLC (CPF) together with Charoen Pokphand Group organised a number of events on 13 October to mark the 4th anniversary of His Majesty King Bhumibol Adulyadej The Great's passing.

Overwhelmed by the gratitude to King Rama IX who had devoted himself for Thais' well-being throughout his long reigning period, CPF's executives and employees stationed in Bangkok, provinces and abroad gave alms to monks and joined commemorative events in tribute to the late His Majesty. In Bangkok, an alms-giving ceremony was organised at CP Tower Silom and CPF was one of private organisations participating in a public event to give food to 89 monks and novices, lay garlands and pay respect to the late His Majestys portrait at Sanam Luang. In provinces, for example, the feed mill at Bangna KM 21 and the water animal feed mill in Ban Bueng, executives and employees were invited to a ceremony to give monks dried food and pay respect to the late His Majestys portrait as a way of remembering the unfathomable depths of his benevolence.

The executives and employees of CPF abroad, Thai and foreign, were invited to commemorative events. CP Vietnam together with the Thailand-Vietnam Business Council hosted an event to present necessities to 8 monks at Buu Long Temple in Ho Chi Minh City. Joining the event were executives, employees and Thais residing in Vietnam.

In Cambodia, Thai and Cambodian executives and employees of CP Cambodia joined Thais in Cambodia in a merit-making event at the Royal Thai Embassy in Phnom Penh. CPF Philippines livestock business organised a ceremony to pay respect to the late His Majestys portrait at the headquarters in Manila, while the aquaculture business hosted a commemorative social event whereby executives and employees handed out lunch, consumer products, face masks and alcohol to an orphanage in Bataan Province.

At the head offices of CPF abroad such as India, Laos, Malaysia and Turkey, food was presented to monks and organisation members paid respect to the late His Majestys portrait. On the day, all executives and employees in Thailand and abroad wore yellow shirts, showing the world their unwavering loyalty and gratitude to King Rama IX who will forever stay in Thais hearts.

More:

CPF and CP Group host activities in commemoration of King Rama IX - Bangkok Post

The event was held in Ho Chi Minh City by the Investment and Trade Promotion Centre (ITPC) of Ho Chi Minh City in partnership with Central Retail Vietnam.

ITPC Deputy Director Tran Phu Lu said that Thailand is the Vietnams largest trade partners in ASEAN, noting that import and export revenue between Vietnam and Thailand in the 2016-2019 period accounted for 30% of Vietnams total import and export revenue from all countries in ASEAN.

Vietnams export revenue to Thailand makes up one fifth of Vietnams total export revenue from ASEAN and Thailand always maintains its position as the largest export market of Vietnam in ASEAN. However, Vietnam always posts a trade deficit with Thailand.

Through Central Retail, one of the leading retail groups in Thailand, Vietnamese enterprises are expected to bring their products to the Big C supermarket chain and export to Thailand in a bid to ease negative impacts from the COVID-19 pandemic, restore business activities, and expand markets.

Enterprises were also informed of the needs and tastes of Thai consumers as well as the opportunities and challenges in selling goods in this market. At the event, Vietnamese enterprises were also given the chance to introduce their products to Thai enterprises through virtual platforms.

Link:

Conference seeks to promote exports to Thailand - Nhan Dan Online - Nhan Dan Online