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A few years or even months ago, if we were asked about the importance of cloud technology and the ability to remotely access data in a secure manner, there would be few businesses that would show interest. However, in recent times, cloud technologies have proven to be the backbone of running a business. As remote working becomes the norm, the focus has quickly shifted to IT Infracture of companies and Machine Learning & Cloud Computing have finally been recognised for the key role that they play in any business. And so the question of whether you are up to date with the latest changes and revolutions in the industry comes to the forefront.

There are many who have analysed this trend and recognised the power that a key understanding of ML & Cloud can have in their career. Cloud technologies not only empower the IT team to provision new application servers and infrastructure on the go but also gives businesses the power to commission and decommission IT infrastructure at a much faster pace. What would have once taken hours or even days can easily be achieved in just a few minutes, thanks to Cloud Technology. upGrad has understood this fast-paced growth of the industry. IIT Madras, in association with upGrad, has designed an online program that can equip you with the required skill set as well as knowledge to set foot in this industry.

The Importance of ML & Cloud Anyone in the world of Information Technology and management knows that Machine Learning and cloud are the future of every industry. Big Data already plays a key role in every decision-making process and focusing on ML & Cloud today can truly help you revamp your career in an impressive and interesting avenue. The Advanced Certification in Machine Learning and Cloud from IIT Madras in association with upGrad offers just that, with utmost ease and comfort.

What the Advanced Certification in Machine Learning and Cloud Program OffersThe 12-month program which offers Advanced Certification from IIT Madras is a brilliant introduction to Machine Learning and also serves as the perfect tool to gain some practical knowledge in this field. The program has been designed to particularly appease ML enthusiasts who are keen on accelerating in this field by giving them a key understanding of machine learning models using Cloud.

Who is the program designed for? The 12-month program requires 12-15 hours of your undivided attention per work, making it a perfect choice not only for freshers but also senior professionals who are looking to accustom their skills with the new developments in technology. The Advanced Certification in Machine Learning and Cloud is priced at a nominal Rs 2,00,000 and you can also avail the no-cost EMI option that makes this program all the more accessible.

Why upGrad?upGrad has already made a name for itself in the Ed-tech segment. Not only does it provide reliable and articulately designed courses that help amplify your career graph but it also has an array of accolades to the brand name. For the Advanced Certification in Machine Learning and Cloud, upGrad has partnered with more than 300 Hiring Partners as well as industry experts from leading companies like Flipkart, Gramener, among others.

"This program puts you from a beginner level to a person who can understand and provide a Machine Learning solution to any given problem provided one has the passion to learn new techniques in a rigorous manner,said Vignesh Ram, who has benefited from upGrads programs that have steered his career in the right direction.

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Machine Learning & Cloud Technologies can make you a valuable resource today: Heres how you can succeed - Times of India

Analytics have been transforming how we watch hockey. The revolution is just beginning. Statisticians and quantitative experts have led the way. Their impact has changed how we discuss and watch hockey.Analytics have been influential. Deep learning will be disruptive.

Advances in computing and understanding of complex relationships will massively alter the sporting landscape. Hockey will not be immune.

Every decision point is potentially affected. This will lead to impacts on and off the ice. Whoever gets there first will have an enormous competitive advantage. Think Moneyball, but with a team that maybe doesnt lose in the playoffs.

Our technology is getting smarter. Deep Learning (also known as machine learning) is coming to many aspects of life. The basic idea is using a computer to analyze complex interactions to come to conclusions. We have seen the concept applied to medicine with great results. The worlds greatest GO player has left the game after realizing the robots cant be beat. Team sports will be conquered next.

High-end computers can do mathematical calculations we humans can only dream of. This is the basis of how it can work.

Machine learning is an application of Artificial Intelligence (AI.) The focus is providing data to computers, which then learn and improve with experience. These machines arent programmed in the traditional sense, rather they are developed by allowing computers to access data and learn from it themselves.

Like in the outside world, the impacts for sports are numerous. There are many potential applications for deep learning. A look at the call for papers for the 2020 Machine Learning and Data Mining for Sports Analytics conference shows what this world is working on.Expected topics include items such as:

A quick glance at the topics demonstrates the field is getting into increasingly complex issues. This has the potential to reshape coaching, management, and player development.

There is good data and bad data. Like the larger debate about analytics, the availability and value of information is of concern. The sheer number of variables in the chaotic environment on the ice makes the analysis complex. Stop and go sports like baseball and football are easier to analyze as the statistics tend to be more clear cut.

All numbers arent created equal. The issue of inconsistent stat keepers will slow progress down. A shot or a hit in one arena may not be the same in the next. Stats also become less reliable away from professional leagues, and so a close look at the numbers going in are needed to produce accuracy. Quantitative analysis is wonderful, but critical analysis to ensure accuracy is needed. In science speak, you need to operationalize things properly.

The complexity of hockey will make adopting deep learning difficult. It will be one of the last sports to truly be able to take advantage of it. There are many ways it will affect the game for fans, players, and teams. The complexity problem will be overcome.

Whos going to win? Can statistics help us understand the answer? Apparently, yes.

Predicting results has been a primary focus of deep learning applied to sports. The first tests have focused on predicting results. The potential of figuring out whos going to win, and how to efficiently bet would be lucrative for outsiders. Like in other sports, this is the first area where deep learning is likely to come.

It has been a long road, but expert pundits are falling. In the early days of deep learning, the experts at prediction on tv were better. This is changing. Back in 2003, early attempts computers were not able to beat expert pundits at prediction. Recently, a deep learning machine (75% accuracy) was able to beat the ESPN teams 63% accuracy over the same time. This is just the first step.

Football experts were the first to fall. Machine learning will change the game well beyond that. They have the ability to be early adopters in the field. Particularly as the NFL has so much money, they are likely to continue to be the league to watch for the effects of deep learning.

That said, this is spreading. It has been applied to the English Premier League and many other sports. When it arrives in the hockey world, it will change how teams manage their decision making at all levels. From who to sign as a free agent, to who to trade for, and even lineup decisions night to night. The applications are limited only to the availability of the data.

While hockey is chaotic and numbers are inconsistent, this problem can be lessened. Stathletes seem likely to be the people who do it. Hockey is well aware of the name Chayhka already. Meghan is the one to watch in this case. She was one of 3 co-founders of the company along with brother John and Neil Lane.

What they do:

Using proprietary video tracking software, Stathletes pulls together thousands of performance metrics per game and compiles analytics related to each player and team. These analytics can provide baseline benchmarking, player comparisons, line matching, and player and team performance trends. Stathletes currently tracks data in 22 leagues worldwide and sells data to a wide variety of clients, including the National Hockey League (NHL). Via FedDev

If they are using machine learning, it is not clear. If not, it seems inevitable that they will. Meghan Chayka currently works with an expert in machine learning at the TD Management Data and Analytics Lab at Rotman (business school) at University of Toronto. Seems likely they can benefit each other, and would know this. (This may be part of the reason why Arizona seems peeved at Chayka currently. They may have just become a data have not.)

Stathletes and other groups are gaining knowledge and information. They will improve as they go. The NHL is open to this, its coming.

Machine learning has arrived. As the ability to obtain information improves, it will coincide with further developments and whats to come. If you are able to follow, Neil Lane (current Stathletes CEO) is to speak at the University of Waterloo on what sports managers can learn from analytics. This should be enlightening.

Embedded items will be key. Chips and sensors in various hockey items are coming. Jerseys and pucks will be transmitting the information. Learning computers will put it together.

The impacts will be numerous. Coaches, players, agents, and teams will have considerably more knowledge. This changes decision making. Training. Diet. Trades. Penalty Kill lineups. The possibilities are endless.

Deep learning will lead to hockey having more knowledge of all aspects. If people like Pierre McGuire hate analytics now, just wait for whats to come.

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Deep Learning is Coming to Hockey - Last Word on Hockey

Since 2017, more than 1 million Central Americans have made their way to the U.S. southwestern border, triggering a disjointed but brutal crackdown by the administration of President Donald Trump. Although the combination of tighter border controls and the coronavirus has reduced these flows, they will resume when the COVID-19 lockdowns lift.

Only this time, Mexicans are likely to join the exodus. The resulting tensions could destabilize one of the worlds most tightly woven bilateral relationships, jeopardizing cooperation on everything from counternarcotics to water rights and the prosperity that closer ties have underpinned on both sides of the border.

Mexican migration to the U.S. peaked at the turn of the last century. At the end of the 1990s and early 2000s, hundreds of thousands of Mexicans moved north every year, many evading border sentries along the way. They fanned out across the nation, drawn to enclaves in California, Texas, Illinois and Arizona, but also to newer locations: Colorado, Florida, Georgia and Idaho. And many switched from seasonal work in the fields to more permanent year-round jobs in child care, landscaping, hotels and car services.

By the mid-2000s, the exodus slowed. For the past 15 years, more Mexicans have left the U.S. than come each year. This shift reflects economic progress at home, not least an end to the financial booms and busts of the 1980s and 1990s. Beefed-up enforcement at the U.S. border has also discouraged circular migration, with workers now rarely returning home for a few months between planting seasons.

Better schooling also helped. With the number of years of education nearly doubling since 1990, the average Mexican 16-year-old is in class, not the workforce. So have changing demographics: Starting in the 1980s Mexican families have had fewer kids, now averaging just over two per household. Compared with the 1990s, fewer Mexicans are turning 18 every year and searching for work either at home or in the U.S.

But in place of Mexicans came a swelling wave of Central Americans, driven by poverty, violence and devastating droughts due to climate change. The majority have been women and children, pulled, too, by the presence of family, friends and economic ties in the U.S.

The Trump administration has made aggressive efforts to stop them. It changed asylum rules, attempting to disqualify those fleeing gang or domestic violence, to limit the right to apply to those arriving at official border crossings, and to otherwise make it more difficult to seek protection. Those families who did enter the U.S. system were often subjected to inhumane living conditions, with children separated from parents and placed in detention pens resembling cages.

The U.S. leaned hard on Central American governments to stop these would-be migrants from leaving in the first place. Under pressure, Mexico also acquiesced to holding tens of thousands of Central Americans for months or more as they waited to have their claims heard in U.S. immigration courts.

The number of Central American migrants did decline. In the start of 2020, flows fell almost by half compared with the year before. With COVID-19 restrictions, the movement nearly ceased in April and May. Yet the reasons pushing families to leave havent changed. Instead, the pandemic is making them all the worse. And not just in Central America, but also in Mexico.

The biggest factor driving a resurgence of Mexicans north is economic desperation: Mexicos economy is expected to shrink by more than 10% this year. Even before the pandemic, both public and private investment had fallen to historic lows. Since then more than 12 million Mexicans have lost their livelihoods, as the government is doing little to keep companies going or preserve jobs. And in addition to the consequences of President Andres Manuel Lpez Obradors misguided economic policies, his reversal of education reforms has made it less important and likely that students will stay in school. Those who do will be less likely to learn the skills needed in a 21st-century Mexican economy.

Rising violence is also driving hundreds of thousands of Mexicans from their homes and communities. Last year homicides topped 34,000. The first half of 2020 has been even more deadly.

As these factors push Mexicans to leave, economic and familial ties pull them north. Mexicans represent the biggest migrant population in the U.S. (the majority here legally). Even with a soft U.S. economy, these fellow citizens can provide a contact, a first place to stay and a lead on a job for future aspiring migrants.

If the past is any guide, many more Mexicans will head north. Their numbers are already ticking up: Since January, more Mexicans than Central Americans have been apprehended at the border.

The Trump administrations methods to discourage Central Americans wont work with Mexico. Lopez Obrador and his National Guard arent able to stop citizens who have a constitutional right to leave their country. Mexican migrants are less likely to be asylum-seekers (even as many flee incredible violence), so the rule changes wont dissuade their journeys. And Mexicans are also more likely to succeed in making it into the U.S.; the nations proximity means that those who have been deported can easily try their luck again.

A migration surge could be a game changer for U.S. politics and policy. On the foreign policy side, it could rupture the bonhomie between Lopez Obrador and Trump, as migration becomes a defining electoral campaign issue. Mexicos president has so far ignored or endured U.S. slights, but a full frontal attack on his citizens would be harder to take given his long-standing (and popular) defense of Mexican migrants.

For the U.S. presidential race, a surge in Mexican migration would mobilize both sides. It would provide anti-immigrant fodder that Trump could use to feed his base. But his tirades could also motivate more of the tens of millions of Mexican Americans, weary of the ugliness directed at them by association, to turn out to vote. With Latinos representing 13% of the electorate, Democrats could benefit.

The hardest part will come later. Whoever wins in November wont have the policy tools to manage this migration effectively or humanely. Outdated laws and an already strained immigration system provide little recourse, and political polarization makes it all the harder to fix them. Mexican migration could easily become the new administrations first big crisis.

Shannon ONeil is a senior fellow for Latin America Studies at the Council on Foreign Relations in New York.

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OPINION EXCHANGE | Mexico's misery, and a resurgence of illegal immigration, could be any new administration's first crisis. - Minneapolis Star...

Facebook researchers have developed a general AI framework called Recursive Belief-based Learning (ReBeL) that they say achieves better-than-human performance in heads-up, no-limit Texas holdem poker while using less domain knowledge than any prior poker AI. They assert that ReBeL is a step toward developing universal techniques for multi-agent interactions in other words, general algorithms that can be deployed in large-scale, multi-agent settings. Potential applications run the gamut from auctions, negotiations, and cybersecurity to self-driving cars and trucks.

Combining reinforcement learning with search at AI model training and test time has led to a number of advances. Reinforcement learning is where agents learn to achieve goals by maximizing rewards, while search is the process of navigating from a start to a goal state. For example, DeepMinds AlphaZero employed reinforcement learning and search to achieve state-of-the-art performance in the board games chess, shogi, and Go. But the combinatorial approach suffers a performance penalty when applied to imperfect-information games like poker (or even rock-paper-scissors), because it makes a number of assumptions that dont hold in these scenarios. The value of any given action depends on the probability that its chosen, and more generally, on the entire play strategy.

The Facebook researchers propose that ReBeL offers a fix. ReBeL builds on work in which the notion of game state is expanded to include the agents belief about what state they might be in, based on common knowledge and the policies of other agents. ReBeL trains two AI models a value network and a policy network for the states through self-play reinforcement learning. It uses both models for search during self-play. The result is a simple, flexible algorithm the researchers claim is capable of defeating top human players at large-scale, two-player imperfect-information games.

At a high level, ReBeL operates on public belief states rather than world states (i.e., the state of a game). Public belief states (PBSs) generalize the notion of state value to imperfect-information games like poker; a PBS is a common-knowledge probability distribution over a finite sequence of possible actions and states, also called a history. (Probability distributions are specialized functions that give the probabilities of occurrence of different possible outcomes.) In perfect-information games, PBSs can be distilled down to histories, which in two-player zero-sum games effectively distill to world states. A PBS in poker is the array of decisions a player could make and their outcomes given a particular hand, a pot, and chips.

Above: Poker chips.

Image Credit: Flickr: Sean Oliver

ReBeL generates a subgame at the start of each game thats identical to the original game, except its rooted at an initial PBS. The algorithm wins it by running iterations of an equilibrium-finding algorithm and using the trained value network to approximate values on every iteration. Through reinforcement learning, the values are discovered and added as training examples for the value network, and the policies in the subgame are optionally added as examples for the policy network. The process then repeats, with the PBS becoming the new subgame root until accuracy reaches a certain threshold.

In experiments, the researchers benchmarked ReBeL on games of heads-up no-limit Texas holdem poker, Liars Dice, and turn endgame holdem, which is a variant of no-limit holdem in which both players check or call for the first two of four betting rounds. The team used up to 128 PCs with eight graphics cards each to generate simulated game data, and they randomized the bet and stack sizes (from 5,000 to 25,000 chips) during training. ReBeL was trained on the full game and had $20,000 to bet against its opponent in endgame holdem.

The researchers report that against Dong Kim, whos ranked as one of the best heads-up poker players in the world, ReBeL played faster than two seconds per hand across 7,500 hands and never needed more than five seconds for a decision. In aggregate, they said it scored 165 (with a standard deviation of 69) thousandths of a big blind (forced bet) per game against humans it played compared with Facebooks previous poker-playing system, Libratus, which maxed out at 147 thousandths.

For fear of enabling cheating, the Facebook team decided against releasing the ReBeL codebase for poker. Instead, they open-sourced their implementation for Liars Dice, which they say is also easier to understand and can be more easily adjusted. We believe it makes the game more suitable as a domain for research, they wrote in the a preprint paper. While AI algorithms already exist that can achieve superhuman performance in poker, these algorithms generally assume that participants have a certain number of chips or use certain bet sizes. Retraining the algorithms to account for arbitrary chip stacks or unanticipated bet sizes requires more computation than is feasible in real time. However, ReBeL can compute a policy for arbitrary stack sizes and arbitrary bet sizes in seconds.

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Facebook develops AI algorithm that learns to play poker on the fly - VentureBeat

Turkish defense minister Hulusi Akar said on Tuesday that Turkeys goal is still to join the European Union (EU), nearly four years after negotiations were suspended.

Turkey has been in often-contentious negotiations to join the EU for decades. The European Parliament and European Council voted to freeze the talks after mass purges by the Turkish government in 2016.

European-Turkish tensions have risen even more in recent months over Turkish foreign policy and the migration crisis. Akar told the Turkish Heritage Organization by video conference that Turkey is still interested in becoming a European state despite the strained relationship.

Membership in the EU remains our political objective, the defense minister said. Turkeys EU relationships are deep-rooted, multidimensional, and crucial, not only for Turkey and the EU, but also for the whole region.

He said that Turkish membership would be the best investment for the European Union, citing extensive economic relations with the EU.

Akar also defended the purges that led to the end of EU accession negotiations.

The Turkish government rightfully took the necessary and proportionate measures to suppress and defeat [a coup attempt] and bring its perpetrators to justice, he said.

Turkey faced a military mutiny in July 2016, which authorities blamed on Pennsylvania-based Islamic televangelist Fethullah Glen.

The government fired hundreds of thousands and jailed thousands of suspected Glen supporters. Human rights critics have described the purge as a crackdown on political dissent, but Akar said that it was necessary to cleanse the state of terrorists.

Akar also emphasized Turkeys role in defending Europe as part of the North Atlantic Treaty Organization (NATO).

NATO is central to Turkeys security, and Turkey is central to NATO, he said. Our commitment to NATO is solid, and we will continue to shoulder our fair share of the burden.

The defense minister pointed out that Turkey has contributed the fifth-most troops and seventh-most money to NATO operations, calling these contributions essential to Euro-Atlantic security.

Turkey has recently attempted to mend its relationship with the United States and other NATO allies as the Turkish military confronts Russian-backed forces in Syria and Libya.

Nevertheless, Europe might not be so interested in rebuilding the alliance.

European leaders responded forcefully after Turkey attempted to push refugees into Greece amid a round of fighting in Syria, and an EU anti-smuggling naval task force has actually attempted to reduce Turkeys involvement in Libya.

France has led the charge to push back against Turkish foreign policy, supporting Greece and Cyprus in their maritime disputes with Turkey.

It is unacceptable that the maritime space of [EU] member states be violated and threatened, French president Emmanuel Macron told reporters last week. Those who are doing that must be sanctioned.

Matthew Petti is a national security reporter at the National Interest. Follow him on Twitter: @matthew_petti.

Image: Reuters.

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Turkish Defense Minister: Turkey Still Wants To Join European Union - The National Interest