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The capabilities of artificial intelligence are transforming industries by strengthening data, new personnel, and financial power by pushing the technological revolution to heights. As we say, data has become wealth today the potential of AI is not ignorable under any circumstance. Lets look at the top AI cities in the world that are doing pretty well to attain advancement.

It is not a surprise to see Chinas capital city Beijing topping the list of AI cities in the world. China has come up to secure the position of World Leader in AI by 2030 earlier in 2017, which itself is a big step towards taking artificial intelligence to the next level. The country is also aiming to exceed US$150 billion for its AI industry in the coming next decade. Beijing also has the first Googles AI research lab and leading institutions such as Tsinghua and Peking Universities and has more than 1,00 AI companies, the capital city becoming important for developments.

Austin has many tech companies and so it is called Silicon Hills. This is one of the AI cities in the world thriving for AI advancement in the currency artificial intelligence space. Austin is home to several big tech companies such as Spark Cognition, Hypergiant, and many more. While coming to giants like Apple and Facebook has also been playing a major part in contributing to the growth. Apple has confirmed plans to house the next US$1 billion campuses and recently Facebook has also announced Austin as their third-largest hub in the US.

When we talk about Silicon Valley, AI comes into the picture too. San Francisco is also referred to as the center of innovation. Even though it has a small geographical area, it accommodates more than 2,000 companies in approximately 50,000 square miles. Many well-known universities such as Stanford and UC Berkeley also lend their support to contribute to the citys artificial intelligence development. San Francisco is one of the AI cities in the world.

Earlier in 2019, the UK government outlined the AI sector deal highlighting the plans to lead the AI revolution from their end. The main idea of the city is to raise the total research and development investment to 2.4% of GDP by 2027. London is the capital city, is a home for many artificial intelligence companies becoming one of the top AI cities in the world. The companies such as AlphaGo creators DeepMind, Mindtrace, Kwiziq, Cleo, Swiftkey, and Babylon Health. London is also soon to be home for Alphabets which accommodates about 7,000 staff too. London Datastore had over 800 datasets that were used by over 50,000 researchers and companies per month.

New York has about 7,000 high-tech companies, the city has a diverse economy and proximity to the European market is attracting talent towards the Big Apple. It is one of the AI cities in the world making space for tech companies such as Apple, Facebook, and Amazon.

It is one of the AI cities in the world that has many tech companies such as Nvidia, Thomson Reuters, Samsung, General Motors, and Amazon in the space of cloud computing and engineering. Recently, Google, Accenture, and Nvidia have also partnered to start the Vector Institute which is completely dedicated to the development of AI.

AI Singapore is a national scheme across industries to create an artificial intelligence ecosystem for the nation. To this development, the National University of Singapore has also been driving the change through research, innovation, and technology. The government of the country is one of the government frameworks to address ethical dilemmas. Singapore is one of the top AI cities in the world.

Read more here:
Top AI Cities to Know Across the Globe in Race Towards Advancement - Analytics Insight

DeepMind has proved once again the outstanding prowess of neural networks. Working with the UK Met Office ithas developed a deep-learning tool that can accurately predict the likelihood of rain in the next 90 minutes, one of weather forecastings toughest challenges.

Climate change is bringing an ever-increasing number of catastrophic weather events such as the devastating floods in Germany, Belgium and the Netherlands in July 2021 that claimed almost 200 lives with more than 700 injured. DeepMind's new tool, DMGR standing for Deep Generative Model of Rain, which can accurately predict where, when and how much rain will fall in the next 1-2 hours, could provide vital information to assist emergency services in this type of scenario.

DMGR is used for Nowcasting, the term for forecasting rain and other precipitation with the next 1-2 hours based on the most recent past high-resolution radar data.

In a paper published by Nature and on open access the 20-person Nowcasting team claimed:

"Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods".

This illustration compares DGMR to the two alternatives, PySTEPS and UNet.

A heavy precipitation event in April 2019 over the eastern US (Target is the observed radar). The generative approach DGMR balances intensity and extent of precipitation compared to an advection approach (PySTEPS), the intensities of which are often too high, and does not blur like deterministic deep learning methods (UNet).

The practical applicability of DeepMind's DGMR shows that it is making good on its undertaking to build on its experience of using deep learning to play games, recall the triumph of AlphaGo, and tackle real world problems. We have already reported on its contributions to quantum chemistry and to protein folding and now it has added meteorology to its growing list of skills.

Nowcasting the Next Hour of Rain (DeepMind blog)

Skilful precipitation nowcasting using deep generative models of radar (Nature)

Why AlphaGo Changes Everything

David Silver Awarded 2019 ACM Prize In Computing

AlphaFold Reads The DNA

AlphaFold Solves Fundamental Biology Problem

AlphaFold DeepMind's Protein Structure Breakthrough

DeepMind Solves Quantum Chemistry

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DeepMind Takes On The Rain - iProgrammer

Q. How is AI changing the way research is done? What does that mean for Columbia?

A. In traditional computing, people write programs. In machine learning, people feed the computer data, and the computer itself writes the program; itlearnsthe program from data. The termmachine learningis germane here. The machine learns the rules on its own. Because the machine, not the human, is writing the program, the program is not easily interpretable to us. In the case of deep learning, the most successful machine-learning technique to date, we dont really understand the science of how it works or why its so successful. Its an example of applications coming ahead of theory.

These tools are already in our daily lives. AI systems recommend movies and books, respond to our voice commands, and translate web pages from one language to another. AI also adds to our repertoire of scientific methods. In medicine, deep-learning models are processing medical scans faster than humans and catching warning signs that even the experts sometimes miss. And they dont get tired! In astronomy, theyre analyzing images from telescopes and space probes to make new discoveries about our universe. In climate modeling, theyre helping to reduce the uncertainty around climate change and its impacts.

These tools are accelerating science, and I expect the trend to continue. AI holds great promise for the social sciences, too. At Microsoft, I saw how bringing economists together with machine learning experts helped the company better forecast sales of some products.

Q. What are you most proud of accomplishing at the Data Science Institute?

Creating bridges. Everything I did was about building collaboration across schools and disciplines. The Data Science Institute connected a lot of dots across campuses and beyond Columbias gates. When people from different perspectives and areas of expertise come together, sparks fly. Through data science, researchers and educators asked questions they never would have thought to ask, let alone answer.

I also feel good about creating theTrustworthy AIinitiative to investigate some of machine learnings unintended consequences. Our goal is to find out whether the AI systems making decisions about peoples lives can be trusted: Do I really have cancer? Is the moving object in front of my car a ball or a child? Will the bank approve my loan? It turns out that its hard to formally define the properties of trustworthiness, let alone prove and guarantee that an AI system has any of them.

A. Columbia Engineering and the Data Science Institute built the IBM Center on Blockchain and Data Transparency under your tenure. And Columbia continues to court corporate funders. Why is industry collaboration so vital?

In certain areas of research, AI especially, industry is ahead. They have the data, which is mostly proprietary consumer data. They also have vast amounts of computing power. Amazon, Microsoft, Google have nearly limitless computing power through their cloud infrastructure. They have GPU clusters academia could never afford. I see enormous potential for collaboration. If faculty could gain access to data and compute, they could validate their algorithms at scale and identify new research directions.

Its a mutually beneficial relationship. Industry looks to academia for new ideas and talent.Academia looks to industry for real-world problems to solve, and opportunities to scale solutions. Its an important way to broaden our impact.

Q. Youve held leadership roles in academia, industry, and the federal government. What skills allowed you to succeed in such different cultures?

A. To be able to listen and learn. To know what you dont know, and to surround yourself with superb talent.

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Meet the Computer Scientist Overseeing Columbia's $1 Billion Research Portfolio - Columbia University

BOSTON The wild-card game is not fair. Long live the wild-card game.

Those might be contradictory opinions, but it all depends on your perspective. If youre a player, you hate it. If youre a viewer, you love it.

Is it fair? No, its not fair, Curtis Granderson, the former major league outfielder, said on Tuesday. Is it fun? Absolutely.

The Yankees fell to the Boston Red Sox, 6-2, in the American League wild-card game at Fenway Park on Tuesday, a day before the National League game between the Los Angeles Dodgers and the St. Louis Cardinals at Dodger Stadium. The winner of each advances to a division series, meaning that the Dodgers are already facing elimination despite tying their franchise record for victories with 106.

The reason is a quirk: As great as the Dodgers were, they played in the same division as the San Francisco Giants, who were one game better. The Cardinals needed only 90 victories to share the stage.

Theres no crying in baseball. Were in second place. Were in the Wild Card game, said Dodgers pitcher Max Scherzer, who also said he liked that the playoff format encouraged competition to the very end of the season.

If Im playing on a team that just won 100 games, I want to have the right to be out there and kind of stretch my chances for at least three games not just one and done, said the Hall of Famer Pedro Martinez, who will analyze the playoffs for TBS with Granderson and Jimmy Rollins. The efforts of my entire team, my entire organization, going down the drain by losing one game? One little mistake?

Martinez won his only championship with a wild-card team, the 2004 Red Sox. Back then, baseball gave a wild-card berth to just one team in each league, and the playoffs started with the best-of-five division series. The format changed in 2012, with each league staging a knockout game between two wild cards to open the postseason.

The games have often brought high drama: rollicking comebacks in Kansas City and Washington, a walk-off homer by Edwin Encarnacion in Toronto, two road shutouts by Madison Bumgarner for the Giants. Whatever you think of the format, its pull is irresistible.

Im going to watch both games, and Im going to enjoy them as a spectator, said Bud Black, who has won a wild-card game and lost another as the Colorado Rockies manager. But when its over that quickly bam, youre done. Youd like to have a chance to show why you got into the playoffs with at least three games. I think thats the viewpoint of any player, manager, general manager, coach. But from a fans standpoint, theyre great.

Should baseball keep the wild-card game because its so much fun? Or kill it because its a gimmicky way for a team to risk its season? Ultimately, it seems, neither question is relevant.

As Major League Baseball and the players union negotiate a new collective bargaining agreement, they will strongly consider expanding the playoffs. Creating more content is an easy way to raise revenue, and last years 16-team field a cash grab after a 60-game regular season offers a template.

I thought last year was pretty cool, Black said. I know it was a different year with the pandemic, but if somehow through negotiations we could shorten the season a bit not much, 152, 154 games, whatever the number is add another team or two and play two out of three, I think that works. And maybe with that time frame, not as many days off between series. Make it like the regular season: You play consecutive games, you get on a plane, you play the next day. Those things could be worked out, so you dont drag it out a great deal.

For most of baseball history, the regular-season champions of each league advanced directly to the World Series. The 1942 Brooklyn Dodgers (104-50) had an even better winning percentage than this years Dodger team, but lost the N.L. pennant to the Cardinals.

I was with the Cubs when we were nosed out on three or four championships, but this is the hardest thing Ive ever gone through, Dodgers second baseman Billy Herman said in the next days Brooklyn Eagle. When you win 104 games and finish second, there isnt anything to say.

The format finally changed in 1969, when baseball split into four divisions and added the League Championship Series. That was the only playoff round through 1993, when the Giants won 103 games but lost the N.L. West by a game to Atlanta.

You just accepted it, said Black, who pitched for the Giants then. We were conditioned to that: You either win your division or not. But it was such a good year and we played so well, we did feel a little shorted.

While Black said he felt for the 2021 Dodgers, others from his era do not. Ken Singleton, who retired last weekend as a Yankees broadcaster, once played for six second-place teams in an eight-year span with Baltimore. In 1980, the Orioles won 100 games but lost the A.L. East to the Yankees; they would have welcomed even a one-game playoff ticket.

Who knows, we might have gone on to win the World Series but there was no wild card in those days, Singleton said. The Dodgers are in the playoffs. We werent. So I dont have much sympathy for them.

Four teams have used the wild-card game to launch a run to the World Series, with the 2014 Giants and the 2019 Nationals winning the championship. Singleton said he hoped the playoff format would stay as it is, because the postseason can stretch to November already. He does not mind that the unbalanced schedule puts an emphasis on divisional play.

When the Red Sox won the division in 2018, they went 16-3 against the Orioles, Singleton said. When the Yankees won in 2019, they went 17-2 against the Orioles. And this year, the Rays went 18-1 against them. So in a way, the Orioles hold the key to the division. If you play great against them, youre going to win.

The Orioles lost 110 games this season, tying with the Arizona Diamondbacks for the most in the majors. And how did the Giants and the Dodgers do against the Diamondbacks in the N.L. West?

The Giants beat them 17 times, and the Dodgers beat them 16 times. That one game was the difference in the division.

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MLB's Wild-Card Game Is Loved and Loathed - The New York Times

Rajasthan Royals might bring back Kartik Tyagi in place of Akash Singh, as they take on a star-studded but out-of-form Mumbai Indians at Sharjah on Tuesday. Its a win-or-bust contest for both teams, especially Mumbai Indians, for a loss will mean curtains for them. MIs net run-rate, -0.45, keeps them at the foot of the table.

Rajasthan Royals

Batting: The way their top order chased a big total against Chennai Super Kings, Royals are unlikely to disturb their batting combination. With the two openers, Yashasvi Jaiswal and Evin Lewis, in good touch, the team once again looks forward to a top-order blast.

Shivam Dubes electric hitting in the last game gives Royals the freedom to retain the same batting order, while David Miller, too, is expected to be a part to complement Dube. Skipper Sanju Samson plays more like an anchor, trying to bat through the innings and pacing his knock at the right moment.

Bowling: Left-arm seamer Akash Singh is likely to make way for right-arm quick Kartik Tyagi, who didnt play the last game against CSK. Chetan Sakariya and Mustafizur Rahman will be the other two pacers. Rahul Tewatia and Mayank Markande, the two leg-spinners, are expected to take care of the spin department. Royals have a habit of tinkering with their playing combinations and young Tyagi has shown his ability to rise up to tough situations. Him defending four runs in the final over against Punjab Kings was a case in point.

Mumbai Indians

Batting: MIs batting has been off-colour this term, a reason for their slide. But despite their captain Rohit Sharma voicing his concerns over the teams batting form, the defending champions are expected to keep faith in the tried and tested. A lot will depend on Suryakumar Yadav.

Bowling: The MI bowling has performed decently and in a virtual knockout game, they will bank on their proven performers as far as fast bowling is concerned. MI are too successful a franchise to press the panic button while going through a lean patch, but bringing back leg-spinner Rahul Chahar could be an option. Then again, Royals have four left-handers in their batting line-up and MI might play safe by retaining off-spinner Jayant Yadav.

Royals likely XI: Yashasvi Jaiswal, Evin Levis, Sanju Samson, Shivam Dube, Glenn Phillips, David Millar, Rahul Tewatia, Mayank Markande, Mustafizur Rahman, Kartik Tyagi, Chetan Sakariya

MI likely XI: Rohit Sharma, Quinton de Kock, Suryakumar Yadav, Saurabh Tiwary, Hardik Pandya, Kieron Pollard, Krunal Pandya, Nathan-Coulter Nile, Jayant Yadav, Jaspreet Bumrah, Trent Boult

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Kartik Tyagi set to be back for Rajasthan Royals, Mumbai Indians likely to be unchanged - The Indian Express