If youve ever traveled to South Africa and tried to use your multi-country adapter to recharge your phone or laptop, you may have been surprised that your adapter could not fit into the countrys unique sockets.

South Africa is now putting the electrical plugs and sockets the nation has relied on for generations on the road to retirement. The plugs, which feature three large pins configured in a triangle, are giving way to a compact hexagonal three-pin design, with sockets following suit.

The new plug and socket, which is based on the latest international standard, accommodates the European-type two-pin plugs on cellphone chargers and small appliances, as well as a two-pin plug based on a German design that comes attached to most power tools imported into the country.

Though South Africa has required buildings built since 2018 to have so-called 164-2 type sockets (the number designates the national standard for the new plug and socket type), the South African Bureau of Standards (SABS) recently updated the standard to introduce warnings on adapters not permitted to be plugged into one another in order to avoid straining the socket.

Image courtesy South African Bureau of Standards

A proliferation of smartphones, tablets, and small appliances that rely on the two-pin European plug, combined with the need by South Africans to plug their devices into outlets designed for the traditional large-pin plugs (a so-called 164-1 type) found in millions of homes and offices nationwide, fuels a reliance on adapters that raises the risk of short circuiting, fire, and damage to devices.

With the array of appliances and devices that have become commonplace in todays world, it is critical to ensure that the plugs and sockets are also changing to accommodate the more compact designs of plugs, Jodi Scholtz, lead administrator of SABS, said in a statement that elaborates on the update.

Image courtesy South African Bureau of Standards

The ubiquity of 164-1 type sockets together with a deluge of two-pin devices results in the use of adapters-on-adapters in sockets across South Africa and poses a danger to consumers, she explained.

With its ability to accommodate the European-style plugs, the new South African socket will cut down on the number of adapters that people need to power their devices. Unlike its European cousin, the new South African plug adds a third pin to satisfy a national mandate that sockets have protection for earth leakage, which reduces the risk of shock by detecting stray voltage.

The new standard will not eliminate the use of adaptors, however it will reduce the need and enable safer use of them, Scholtz told Quartz Africa. Most foreign visitors will still need to use their adaptors to have devices work, and sockets will be able to accommodate the old type of plugs.

Image courtesy South African Bureau of Standards

The 164-2 plug and socket incorporate safety by design. A pocket in the socket prevents consumers from touching a live pin during insertion. To prevent adapter plugs and switches on the surface of sockets from hindering each other, the new standard creates clearance between them and allows adapters to be plugged in fully. The socket contains a safety shutter that requires insertion of at least two pins to open.

The new standard solidifies a move by South Africa away from the British standards on which South Africas 164-1 type plug and socket are based. The countries standards began to diverge in the 1960s, when the U.K. moved to a flat-pin plug and South Africa declined to follow. India, which also modeled its standards on the British, uses a three-pin plug that resembles South Africas 164-1, but the pins are much smaller.

The shift now underway in South Africa notwithstanding, the millions of 164-1 type plugs in use throughout the country will be sticking around. Sockets will continue to accommodate them, particularly in kitchens, where the plugs come attached to most appliances.

Eventually, consumers will get tired of having all these adapters, predicts Gianfranco Campetti, an electrical engineer and member of the SABS working group that developed the new standard. This is not a 90-minute game. Its going to take time.

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South Africas unusual electrical plugs, sockets to be retired - Quartz Africa

Katie Brockman, The Motley Fool Published 7:00 a.m. ET Oct. 13, 2020

Retirement is becoming more expensive than ever, with the average American expecting to need nearly $2 million to enjoy their senior years comfortably, according to a survey from Charles Schwab.

Because it's crucial to save as much as possible for retirement,that also means avoiding costly mistakes. And there's one blunder that may seem harmless on the surface, but it can cost you tens of thousands of dollars or more.

Your 401(k) is a powerful investing tool, but it's important to use it wisely. When you have thousands of dollars socked away in your retirement fund, it may be tempting to use your 401(k) as a savings account and withdraw some cash here and there for emergencies. However, that can cost you big time in the long run.

Generally, when you withdraw money from your 401(k) before age 59 1/2, you're subject to income taxes and a 10% penalty on the amount you take out. The more dangerous consequence, though, is how much you'll lose in potential investment gains over time when you withdraw from your savings.

Not sure how much to contribute to your 401 (k)?: Make sure to get your full employer match

More: The year is almost over, here's what to do with your 401(k)

Your savings rely on compound interest to grow, and every time you take money out of your 401(k), you're limiting your investments' growth potential. Withdrawing a few hundred or thousand dollars here and there may not seem like it would make a significant difference, but even one withdrawal can potentially cost you tens of thousands of dollars over time.

To see just how much a single withdrawal could affect your long-term savings, let's look at an example.

Back in April, when many Americans were raiding their retirement funds due to the coronavirus pandemic, the average withdrawal was $5,500, according to date from Fidelity Investments. The average 401(k) account balance is approximately $104,400.

Say you have $104,400 stashed in your 401(k), and you take a $5,500 withdrawal. Here's how much that one withdrawal could affect your long-term savings, assuming you're earning a 7% annual rate of return on your investments and you're not making any additional contributions.

0 (Today)

$98,900

$104,400

15

$272,868

$288,043

20

$382,712

$403,995

25

$536,773

$566,624

Source: Author's calculations

After 25 years, that $5,500 withdrawal can amount to $29,851 in lost potential gains. And if you were to continue saving for more than 25 years, you could potentially miss out on even more money.

It's also important to keep in mind that this is a result of just one withdrawal. If you were to make repeated withdrawals over the years, you'd see an even greater impact on your total savings.

Although tapping your 401(k) may not seem so bad in the short term, it can wreak havoc on your long-term savings especially if you withdraw a lot of cash or make several withdrawals over time. While in true financial emergencies you may have no choice but to raid your savings, try your best to avoid dipping into your 401(k) if at all possible. By leaving your money alone, it will be easier to build a healthy nest egg by retirement age.

The Motley Fool has a disclosure policy.

The Motley Fool is a USA TODAY content partner offering financial news, analysis and commentary designed to help people take control of their financial lives. Its content is produced independently of USA TODAY.

Offer from the Motley Fool: The $16,728 Social Security bonus most retirees completely overlook

If you're like most Americans, you're a few years (or more) behind on your retirement savings. But a handful of little-known "Social Security secrets" could help ensure a boost in your retirement income. For example: one easy trick could pay you as much as$16,728 more... each year! Once you learn how to maximize your Social Security benefits, we think you could retire confidently with the peace of mind we're all after.Simply click here to discover how to learn more about these strategies.

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A $5,500 withdrawal from your 401(k) could cost you $30,000 - USA TODAY

Oct. 17, 2020, 8:30 AM UTC

For over three decades, Stephanie Byers taught music and band at the largest public high school in Kansas. After seeing how decisions made by state lawmakers affected her students, she decided to trade retirement for politics.

They saw a bottom line, a number that needs to be worked with, and didn't think about what that means when a student is staring at a textbook that is being held together by duct tape because it outlived its usefulness and the district didn't have the money to replace textbooks, said Byers, who is running to be the next representative of Kansas House District 86, which includes much of Wichita.

A Democrat who ran unopposed in the primaries, Byers will face off against Republican Cyndi Howerton, a businesswoman, in the November election. While Kansas is largely a conservative state, Byers is a strong contender in Wichita, a progressive enclave that has historically swung left.

If elected, Byers has vowed to fight for increased funding for education and Medicaid expansion in Kansas, one of at least 12 states that have not expanded the program under the Affordable Care Act. She has also made civil rights protections a pillar of her campaign in a state where, according to advocacy group Freedom for All Americans, "there are currently no explicit, comprehensive statewide non-discrimination protections" for LGBTQ people.

When Byers came out as transgender six years ago, she was largely embraced by her students and colleagues, an experience that pushed her to become a trailblazer for trans educators in her school district.

I realized that when I came out as a teacher that I was blazing the pathway, she said. A lot of public educators that are trans may not necessarily come forward and come out during their careers, because the fact that there's the fear of prejudice is going to be there.

As Republican-backed anti-transgender legislation including much designed to keep trans students out of public restrooms and off sports teams proliferated in statehouses across the country, including in Kansas, Byers met with school officials and spoke at community events to educate the public about gender identity.

Last October, she spoke out on behalf of trans educators and students at an American Civil Liberties Union rally outside of the Supreme Court, which at the time was hearing arguments in cases that would determine whether employers had a right to terminate workers because of their sexual orientation and gender identity. In 2018, a year before she retired, Byers was named both state Educator of the Year by GLSEN Kansas and national Educator of the Year by GLSEN, the national LGBTQ youth advocacy organization with chapters across the country.

If Byers wins her election on Nov. 3, she will be the first out transgender lawmaker from Kansas. She is one in a "rainbow wave" of at least 574 LGBTQ candidates who will be on the ballot next month, according to a new report by Victory Fund, a group that trains, supports and advocates for LGBTQ candidates. Byers said politicians who are transgender are seen as novelties, and thats something she hopes to change.

It's a part of who we are. It's part of our identity, but it's not the only thing. There's so many other things we are passionate about as well, she said. It's just a matter of normalizing that enough that it's no longer a thing, and ... it's just a matter of what can we do to serve the communities that elected us?

The candidate, who grew up in neighboring Oklahoma, is a wife, parent of two adult sons and a grandparent of nine children. Shes a member of the Native American Chickasaw Nation and has deep roots in the working class. She said her father, a longtime U.S. Postal Service worker, and her mother, who served as national vice president to the American Postal Workers Union Auxiliary, showed her the struggles that working-class families face.

I'm a parent, I'm the grandparent, and I know the challenges that families face at this time, Byers said, and that's who I want to be a voice for for those families that need somebody who stands up for them.

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Julie Compton is a freelance journalist in Brooklyn, New York. Follow her@julieallmighty

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'Blazing the pathway': Retired teacher could become Kansas' first transgender lawmaker - NBC News

Getting younger employees to save for retirement is right up there with getting a finicky child to eat their vegetables. Sure, it's good for them, but it's not always what they want.

Participation rates and average deferral rates in voluntary enrollment plans for workers younger than 35 are well below those of other age groups, indicating that HR teams may need to take extra steps to reach this segment of their employee base, according to data from Vanguard, a leading 401(k) provider.

HR professionals are uniquely positioned to best assist younger workers. The best tack for HR experts to take with millennials in regard to retirement saving is to point out some of the mistakes their parents' generation has made in that area.

These five points listed below can help your younger workforce be better retirement savers than those in their parents generation.

Help employees understand the destination When it comes to saving for retirement, a lot of older workers are clearly lost. Younger workers have an opportunity to do a better job of staying on track.

Vanguards data show the average 401(k) participant within 10 years of retirement age (i.e., between ages 55 and 64) has a plan balance of just $69,097.

That may not provide much help over a retirement of 10 or 20 years.

Caution young staff members that one reason older workers are so badly behind in retirement saving is that they haven't checked first to see where they're going.

A MoneyRates retirement plan survey finds that 71% of workers within 20 years of retirement age still have not done a calculation of how well their savings will hold up over their retirement years.

Encourage your workforce to determine what enough savings is. Inform your staff that it only takes a few minutes to use a retirement calculator to see how much to put aside to meet savings goals. That way, your employees will know where their retirement plan is heading.

Educate employees on how to get debt under control Saving for retirement is undermined when employees are also building up debt at the same time.

Stress that debt costs more than retirement investments are likely to earn, a dollar in debt can more than counteract the benefit of a dollar in savings.

According to the Federal Reserve's Survey of Consumer Finances, the typical household still has $69,000 in debt by the time the head of that household is within 10 years of retirement.

Notice that this figure almost exactly matches the previously-mentioned amount that the average 401(k) participant in that age group has. In other words, debt can effectively wipe out a person's 401(k) savings.

So, your teams first step toward educating workers about building a more secure retirement should be to do something many in their parents' generation failed to do: get debt under control.

Teach employees how to spread savings to make the burden lighter Retirement saving is a big job, but younger workers have something very important on their side: time. Emphasize that spreading retirement savings out over 25 to 40 years makes the job much easier.

It gets tougher if young workers do what many of their parents' generation have done--wait and then try to catch up in the last ten years or so until retirement.

The golden rule: Don't leave free money on the table When employers provide a 401(k) match, all staff should understand there's a direct financial incentive to start saving now. Every time employees put money into their 401(k) plan, the employer kicks in some on their behalf.

If employees don't contribute money into the plan, they don't get this money from the employer. There's no going back in future years and reclaiming that extra money the employer would have put in on the workers behalf.

The only way not to miss out on this free money is to contribute each and every year and to contribute enough to get the maximum employer match available.

Show employees the benefits of saving A dollar saved today can equal $10 at retirement age.

Saving money is hard work, but HR professionals can show their employees that it gets easier when they let their investments do the work for them.

The investment returns earned become much more powerful when compounded over a long period of time. Compounding means earning a return not just on the original money invested, but also on the returns earned in other years.

Younger workers must recognize that a dollar invested today could be worth much more than a dollar invested toward the end of their career.

There are many people of older generations who would be a lot better off today if they absorbed each of these five lessons when they were younger.

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5 ways HR can help millennials be smarter than their parents about retirement - Employee Benefit News

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.

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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

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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.

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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.

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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.

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