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Nayda Alvarez's family has lived at least five generations on land on the Texas-Mexico border where her house is but 200 feet from the Rio Grande river.

Not only is there no need for a border wall near her home in Starr County, she said, but if one were to be erected, it would be just feet from the back of her home. The high school teacher fought the Trump administration in court over an attempt to build on her property and if Texas Gov. Greg Abbott moves forward with his announced plan to try to accomplish what President Donald Trump did not, Alvarez will fight him too.

Hes trying to make his portfolio look real good because he wants to run for president, Alvarez surmised.

Abbott is likely to face logistical challenges because most of the borderland in Texas is privately owned and some of it is federally owned, which would require the Biden administration to approve any barriers built on federal land.

The Republican governor said Wednesday he would use $250 million in state money and crowdsourced financing to start building a wall on Texas' 1,200-mile border with Mexico. He did not specify how much the project would cost, where it will go and how long it would be.

Abbott claimed that a combination of state land and land volunteered by property owners would yield 100s of miles of wall. He said he is asking the federal government to return land obtained for the U.S. government's wall and return it to private citizens who can allow Texas to finish the job.

In response to the federal governments neglect of all of the people who live along the border, the people who are facing the consequences of the spread of drugs like fentanyl, Texas is stepping up and doing more than any other state ever has done to respond to these challenges along the border, Abbott said. Texas taxpayers are having to step up so we as a state can protect our citizens."

The United States currently has 771 miles of barriers along its border with Mexico, according to U.S. Customs and Border Protection. During the Trump administration, 373 miles of old or outdated barriers were replaced and 80 miles of new "primary and secondary" wall were erected where no barriers previously existed. Wall construction mainly focused on federally owned land in Arizona, California and New Mexico.

Trumps signature campaign promise faced consistent legal and environmental obstacles in Texas, which has the largest section of the U.S.-Mexico border, most of it without fencing. And much of the land along the Rio Grande, the river that forms the border in Texas, is privately held and environmentally sensitive.

The federal government can seize private property for public use through eminent domain, a process that could take years.

David Donatti of the ACLU of Texas said there are 100 court cases pending that involve the government trying to seize land through eminent domain. The Biden administration has not formally dropped them though it has said it is re-evaluating them.

So these cases remain in a case of limbo where the Biden administration could continue to press these cases, take property, build border wall, but they have not given that sort of concrete commitment one way or the other, Donatti said.

He called Abbotts announcement all hot air.

I think its a preposterous idea, Donatti said.

He said that although he hoped it would come to nothing, he thought it likely that Abbott was determined to do something. If the governor tried to use the power of eminent domain to take land, the ACLU would contest his authority to do that, Donatti said.

Any wall would have to be far enough away from the Rio Grandes flood plain to honor a treaty between the United States and Mexico and so it could end up being some sort of freestanding wall somewhere in the interior of Texas, he said.

So, whatever the governor builds we imagine would be at least a mile inland, if not more, thereby walling off part of the state to an area south of the border, he said.

The issue with migrants aside for a moment, the border wall is also reeking untold damage on the environment

The Fifth Amendment of the U.S. Constitution requires the government pay just compensation to anyone whose land is taken for public use. But the government can deposit an amount it deems fair with the court, then seek to take the land immediately on the basis that a border wall is urgently needed.

Domingo Garcia, national president of the League of United Latin American Citizens, said that he thought few landowners along the Rio Grande supported walls or barriers on their property and would likely fight as long as they can. He also questioned whether a court would consider a government request an emergency or legitimate public use of the land.

Its highly doubtful that any court would grant eminent domain to build a 13th century wall to deal with a 21st century problem, Garcia said.

Donatti noted that the Trump administration had been spending $20 billion a mile on the border, far in excess of what Abbott could spend. The state of Texas has set up a webpage and post office box so anyone can donate money for Abbott's wall. The ACLU said it would scrutinize the project for transparency and public accountability.

These projects are extremely cost intensive and allow ample opportunity for fraud and grift, he said.

An online fundraising campaign called We Build the Wall, ended with four indictments, including that of Trumps former adviser, Steve Bannon, accused with the others of defrauding hundreds of thousands of donors. Trump pardoned Bannon before he left office.

Large numbers of migrants have been seeking asylum at the U.S.-Mexico border by turning themselves over to U.S. Border Patrol agents. At the same time the number of families and children crossing into the U.S. without their parents has dropped sharply since March and April.

Abbott has taken increased action over immigration since Biden took office,including announcing last week that state troopers will now begin arresting migrants crossing the southern border and charging them with trespassing.

His plan has drawn skepticism and ridicule. Critics note that the U.S. Supreme Court has ruled that the power to enforce immigration laws, including prosecuting illegal entry, is that of the federal government's. Whether or not there is a presidential run in Abbotts future, he is up for re-election as governor next year and is being accused of using the issue for political benefit.

This is just political grandstanding by the governor who is running for re-election, said Garcia. He knows he has no authority, he knows he has no ability to build a wall much less arrest people for trespassing and putting them in jail.

The chairwoman for the Native American tribe Hia C-ed Oodham, which means Sand People, shares her story about how the Trump administrations border wall has hurt her community. Chairwoman Christina Andrews said construction has already destroyed a childrens shrine and sacred trails.

Donatti said that the ACLU of Texas would scrutinize trespassing and other arrests and Abbotts efforts to ratchet up penalties. It is well established that the federal government has authority over the countrys immigration laws and if a state interferes by arresting non Americans, there is the possibility of diplomatic problems, he said.

Meanwhile, Alvarez is hoping the Biden administration drops hers and other eminent domain cases.

We cant celebrate until we get a real dismissal, she said.

However, she's still worried that going forward the security of her property will depend on the political affiliation of the administration in office.

Alvarez also questioned claims by Abbott and others who have said those crossing illegally are armed and dangerous. The immigrants crossing the river are mostly trying to claim asylum but would be turned away on the bridges, she said.

The violence is coming in? Where? Because I sure dont see it, she said.

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Gov. Abbott Vowed to Build a Wall With Mexico. Texas Borderland Owners Say Not in My Backyard - NBC 5 Dallas-Fort Worth

David Silver, leader of the reinforcement learning research group at DeepMind, being awarded an honorary "ninth dan" professional ranking for AlphaGo.

JUNG YEON-JE | AFP | Getty Images

Computer scientists are questioning whether DeepMind, the Alphabet-owned U.K. firm that's widely regarded as one of the world's premier AI labs, will ever be able to make machines with the kind of "general" intelligence seen in humans and animals.

In its quest for artificial general intelligence, which is sometimes called human-level AI, DeepMind is focusing a chunk of its efforts on an approach called "reinforcement learning."

This involves programming an AI to take certain actions in order to maximize its chance of earning a reward in a certain situation. In other words, the algorithm "learns" to complete a task by seeking out these preprogrammed rewards. The technique has been successfully used to train AI models how to play (and excel at) games like Go and chess. But they remain relatively dumb, or "narrow." DeepMind's famous AlphaGo AI can't draw a stickman or tell the difference between a cat and a rabbit, for example, while a seven-year-old can.

Despite this, DeepMind, which was acquired by Google in 2014 for around $600 million, believes that AI systems underpinned by reinforcement learning could theoretically grow and learn so much that they break the theoretical barrier to AGI without any new technological developments.

Researchers at the company, which has grown to around 1,000 people under Alphabet's ownership, argued in a paper submitted to the peer-reviewed Artificial Intelligence journal last month that "Reward is enough" to reach general AI. The paper was first reported by VentureBeat last week.

In the paper, the researchers claim that if you keep "rewarding" an algorithm each time it does something you want it to, which is the essence of reinforcement learning, then it will eventually start to show signs of general intelligence.

"Reward is enough to drive behavior that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalization and imitation," the authors write.

"We suggest that agents that learn through trial and error experience to maximize reward could learn behavior that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence."

Not everyone is convinced, however.

Samim Winiger, an AI researcher in Berlin, told CNBC that DeepMind's "reward is enough" view is a "somewhat fringe philosophical position, misleadingly presented as hard science."

He said the path to general AI is complex and that the scientific community is aware that there are countless challenges and known unknowns that "rightfully instill a sense of humility" in most researchers in the field and prevent them from making "grandiose, totalitarian statements" such as "RL is the final answer, all you need is reward."

DeepMind told CNBC that while reinforcement learning has been behind some of its most well-known research breakthroughs, the AI technique accounts for only a fraction of the overall research it carries out. The company said it thinks it's important to understand things at a more fundamental level, which is why it pursues other areas such as "symbolic AI" and "population-based training."

"In somewhat typical DeepMind fashion, they chose to make bold statements that grabs attention at all costs, over a more nuanced approach," said Winiger. "This is more akin to politics than science."

Stephen Merity, an independent AI researcher, told CNBC that there's "a difference between theory and practice." He also noted that "a stack of dynamite is likely enough to get one to the moon, but it's not really practical."

Ultimately, there's no proof either way to say whether reinforcement learning will ever lead to AGI.

Rodolfo Rosini, a tech investor and entrepreneur with a focus on AI, told CNBC: "The truth is nobody knows and that DeepMind's main product continues to be PR and not technical innovation or products."

Entrepreneur William Tunstall-Pedoe, who sold his Siri-like app Evi to Amazon, told CNBC that even if the researchers are correct "that doesn't mean we will get there soon, nor does it mean that there isn't a better, faster way to get there."

DeepMind's "Reward is enough" paper was co-authored by DeepMind heavyweights Richard Sutton and David Silver, who met DeepMind CEO Demis Hassabis at the University of Cambridge in the 1990s.

"The key problem with the thesis put forth by 'Reward is enough' is not that it is wrong, but rather that it cannot be wrong, and thus fails to satisfy Karl Popper's famous criterion that all scientific hypotheses be falsifiable," said a senior AI researcher at a large U.S. tech firm, who wished to remain anonymous due to the sensitive nature of the discussion.

"Because Silver et al. are speaking in generalities, and the notion of reward is suitably underspecified, you can always either cherry pick cases where the hypothesis is satisfied, or the notion of reward can be shifted such that it is satisfied," the source added.

"As such, the unfortunate verdict here is not that these prominent members of our research community have erred in any way, but rather that what is written is trivial. What is learned from this paper, in the end? In the absence of practical, actionable consequences from recognizing the unalienable truth of this hypothesis, was this paper enough?"

While AGI is often referred to as the holy grail of the AI community, there's no consensus on what AGI actually is. One definition is it's the ability of an intelligent agent to understand or learn any intellectual task that a human being can.

But not everyone agrees with that and some question whether AGI will ever exist. Others are terrified about its potential impacts and whether AGI would build its own, even more powerful, forms of AI, or so-called superintelligences.

Ian Hogarth, an entrepreneur turned angel investor, told CNBC that he hopes reinforcement learning isn't enough to reach AGI. "The more that existing techniques can scale up to reach AGI, the less time we have to prepare AI safety efforts and the lower the chance that things go well for our species," he said.

Winiger argues that we're no closer to AGI today than we were several decades ago. "The only thing that has fundamentally changed since the 1950/60s, is that science-fiction is now a valid tool for giant corporations to confuse and mislead the public, journalists and shareholders," he said.

Fueled with hundreds of millions of dollars from Alphabet every year, DeepMind is competing with the likes of Facebook and OpenAI to hire the brightest people in the field as it looks to develop AGI. "This invention could help society find answers to some of the world's most pressing and fundamental scientific challenges," DeepMind writes on its website.

DeepMind COO Lila Ibrahim said on Monday that trying to "figure out how to operationalize the vision" has been the biggest challenge since she joined the company in April 2018.

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Computer scientists are questioning whether Alphabets DeepMind will ever make A.I. more human-like - CNBC

A Chinese AI system has defeated a top human pilot in a simulated dogfight, according to Chinese media. The AI was pitted against Fang Guoyu, a Group Leader in a PLA aviation brigade and a previous champion in such contests.

"At first, it was not difficult to win against the AI," said Fang in a report in Global Times, a Chinese state newspaper. But as the exercise continued the AI learned from each encounter and steadily improved. By the end it was able to defeat Fang using tactics it had learned from him, coupled with inhuman speed and precision.

"The AI has shown adept flight control skills and errorless tactical decisions, said brigade commander Du Jianfeng.

The Chinese exercise of setting human pilots against AI aims to improve both. The AI gives the pilots a new and challenging opponent which thinks out of the box and can come up with unexpected tactics, while each dogfight adds to the AIs experience and helps it improve.

The AI was developed by a number of unspecified research institutes working with the aviation brigade, according to the report.

In the culmination of DARPA's AlphaDogfight exercise, the Falco AI decisively beat a skilled human ... [+] pilot in simulated combat between F-16s.

The event echoes DARPAs AlphaDogfight competition last year which featured human and AI pilots fighting it out in simulated F-16s. In the initial rounds, different AIs competed to find the best. In the final round, the winning AI, Falco from Heron Systems, took on the human champion, an unnamed U.S. Air Force pilot. The AI triumphed, scoring a perfect 5-0 win in a series of encounters.

AIs have significant advantages in this situation. One is that they are fearless and highly aggressive compared to human pilots; another term might be reckless. They can react faster than any human, and can track multiple aircraft in all directions, identifying the greatest threats and the best targets in a rapidly changing situation. They also have faster and more precise control: Falco was notably skilled at taking aim and unleashing a stream of simulated cannon fire at opponents who were still lining up their shot. Whether these advantages would carry over into a messy real-world environment is open to question further planned exercises by DARPA, the USAF and others may help settle the matter.

DARPAs ACES program, of which AlphaDogfight was part, plans to port dogfighting algorithms onto small drones and test various scenarios of one-on-one, one-versus-two, and two-versus-two encounters in the next year. At the same time they are also preparing for combat autonomy on a full-scale aircraft. This may utilize existing dumb QF-16 target aircraft, the drone versions of F-16s used for air-to-air combat practice.

The QF-16, an unmanned version of the F-16 used as an aerial target, could be upgraded to a ... [+] dogfighter with smart software

The contest for AI supremacy between the U.S. and China is attracting increasing attention, with the National Security Commission on AI (NSCAI) concluding in March that, for the first time since World War II, Americas technological predominance is under threat. China has created hundreds of new AI professorships and developed an efficient ecosystem for AI start-ups with tax breaks and lucrative government contracts on offer.

AI fighter pilots are just a tiny piece in the military balance, and not a meaningful indicator on their own. However, the fact that China chooses to publicize the latest development sends a message that they are hard on Americas heels, if not drawing ahead, in direct military applications of AI. If their AI can really learn skills that rapidly from contests with human pilots, then, like DeepMind's AlphaGo, it may now be competing with versions of itself and developing tactics and levels of skill impossible for humans.

Meanwhile, in the larger evolutionary contest between humans and AIs, the machines have just taken another tiny step forward in chipping away our superiority. The new Top Gun movie out later this year may be nostalgic on more ways than one.

Continued here:
Chinese AI Learns To Beat Top Fighter Pilot In Simulated Combat - Forbes

Robots are by far the most efficient use of modern science. Robots not only reduce human labor but also execute error-free activities. Many businesses are expressing an interest in robotics. Automated machines have gained popularity in recent years. Keeping the situation in mind, we shall discuss robotic computer languages.

So, in order for robots to do tasks, they must be programmed. Robot programming is the process through which robots acquire instructions from computers. A robotic programmer must be fluent in several programming languages. So lets get started.

There are about 1500 robotic programming languages accessible worldwide. They are all involved in robotic training. In this section, we will go through the top programming languages accessible today.

The easiest way to get started with robotics is to learn C and C++. Both of these are general-purpose programming languages with almost identical features. C++ is a modified version of C that adds a few features. You should now see why C++ is the most popular robotic programming language. It enables a low-level hardware interface and delivers real-time performance.

C++ is the most mature programming language for getting the greatest results from a robot. C++ allows you to code in three different ways. The Constructor, Autonomous, and OperatorControl methods are among these. In this constructor mode, the initializing code runs to build a class. It will execute at the start of the program in this scenario.

It aids in the initialization of sensors and the creation of other WPILib objects. The autonomous approach guarantees that the code is executed. It only works for a set amount of time. The robot then moves on to the teleoperation section. The OperatorControl technique is used in this case.

Python is a powerful programming language that may be used to create and test robots. In terms of automation and post-process robotic programming, it outperforms other platforms. You may use this to build a script that will compute, record, and activate a robot code.

It is not necessary to teach anything by hand. This enables rapid testing and visualization of the simulations, programs, and logic solutions. Python uses fewer lines of code than other programming languages. It also includes a large number of libraries for fundamental functions. Pythons primary goal is to make programming easier and faster.

Any item can be created, modified, or deleted. In addition, we may code the robots motions in the same script. All of this is accomplished with very little code. Python is among the finest robotic programming languages as a result of this.

Java is a programming language that enables robots to do activities that are similar to those performed by humans. It also provides a variety of APIs to meet the demands of robots. Java has artificial language characteristics to a high degree.

It enables you to construct high-level algorithms, searching, and neural algorithmic algorithms. Java also allows you to run the same code on many computers.

Java is not built into machine code since it is an interpretative language. Rather, in execution, the Java virtual computer interprets the commands. Java has become quite popular in the field of robotics as a result of this. As a result, Java is preferable to alternative robotic programming languages. Java is used by modern AIs such as IBM Watson and AlphaGo.

Microsofts .NET programming language is used to create apps with Visual Studio. It provides a good basis for anyone interested in pursuing a career in robotics. .NET is primarily used by programmers for port and socket development.

It supports various languages while allowing for horizontal scaling. It also offers a uniform environment and makes programming in C++ or Java easier. All of the tools and IDEs have been thoroughly tested and are accessible on the Microsoft Developer Network.

In addition, the merging of languages is smooth. As a result, we can confidently rank this among the best robotic programming languages.

In robotic engineering, MATLAB and its open-source cousins like Octave are extremely popular. In terms of data analysis, it is considerably ahead of many other robotic computer languages. MATLAB is not really a programming language in the traditional sense. Yet, engineering solutions based on complex mathematics can be found here.

Robotic developers will learn how to create sophisticated graphs using MATLAB data. It is quite helpful in the development of the complete robotic system. It also aids the development of deeply established robotic foundations in the robot business. Its a tool that lets you apply your methods to simulate the outcome. Engineers may use this simulation to fine-tune the system design and eliminate mistakes.

There have been cases when MATLAB has been used to build a complete robot. As a result, it must be included among the top ten languages. Kuka kr6 is one of the greatest instances of MATLAB application. MATLAB was also used to create and simulate this robot by the developers.

One of the first robotic computer languages was Lisp. It was introduced to the market to allow computer applications to use mathematical terminology. Lisp is an AI domain that is mostly used for creating Robot Operating Systems.

Tree data structures, automated storage management, syntax highlighting, and elevated-order characteristics are among the features available. As a result, it is simple to use and aids in the elimination of implementation mistakes after an issue have been identified.

This problem-solving procedure takes place at the prototype stage, not the manufacturing stage. It also includes capabilities like the read-eval-print loop and self-hosting compilation.

One of the earliest programming languages to hit the market was Pascal. Its still quite useful, especially for newcomers. It is based on the Fundamental programming language and teaches excellent programming skills. Pascal is being used by manufacturers to create robotic programming languages.

ABBs RAPID and Kukas KRL are two examples. Nevertheless, most developers consider Pascal to be obsolete for everyday use. Theyve also highlighted its significance for newcomers.

It will assist you in learning other robot programming languages more quickly. This is only recommended for complete novices. When youve gained some expertise in robotics programming, you can transition to another language.

And its a wrap. We hope that you found this article helpful regarding robotic programming languages. Weve covered all of the pros and cons of the top robotic programming languages. You can choose the most appropriate language for your needs. Even now, robotics has a promising future. So now is the ideal moment to get started.

Read more from the original source:
Different Types of Robot Programming Languages - Analytics Insight

What do these gambling firms have in common?

They are all based in Asia and access the UK market via the white-label system.

A white-label is a site that is operated by someone other than the brand owner.

The companies set up UK-facing websites via firms in offshore jurisdictions such as Malta or the Isle of Man, and then apply to access the UK market via a white-label. This gives them the right to advertise to TV-watching consumers in Asia via Premier League football shirts.

Many of these firms advertise on UK shirts and stadium billboards in Chinese Mandarin, despite the fact that gambling is largely prohibited in China.

The UK Gambling Commission has admitted to concern that some of the companies running white-label websites may not have effective anti-money-laundering controls or carry out sufficient due diligence to ensure there are no links to criminal activity.

When The Athletic approached multiple football clubs earlier this year about their arrangements with white-label firms, virtually none could answer basic questions about the companies, such as what countries they are based in.

Dr James Noyes, senior fellow at the Social Market Foundation, said: We do not know enough about where these companies are based, who owns them or where their wealth comes from.

SportQuake is a sports marketing agency with offices in London, Madrid, Beijing and Hong Kong. It works with football clubs, national teams, competitions, leagues, players and media outlets to help brands secure sponsorship deals.

On its website, it mentions recent high-profile deals including between Cristiano Ronaldo and Garena, a mobile game, and FC Barcelona and FBS, the clubs official trading partner.

Last May The Athletic revealed how Bournemouth ended their deal early with M88 after the Gambling Commission opened an investigation into the firm. M88 is the Asian-facing arm of Mansion Group and previously sponsored Tottenham Hotspur. The north London club now have a deal with Fun88, which also sponsors Newcastle.

LoveBet sponsored Burnleys shirts for the last two seasons as well as having a deal with PSG. Earlier this year The Athletic revealed how the firm was ditched by Burnley mid-season.

Footballs relationship with gambling firms has come under increasing scrutiny in recent weeks following Norwich Citys deal with BK8 imploding after it emerged the firm was using sexualised content in some of their marketing. Norwich used Singapore-based Outlast Sports and Entertainment to broker their 5 million shirt sponsorship agreement.

BK8 said in a statement after the deal was terminated: We apologise for the social media content published by affiliates of BK8 which has caused offense, and have taken immediate steps to change the way we market the brand.

The UK government is undertaking a review into gambling laws that promises to look closely at white labels.

The Athletic approached SportQuake for comment and has repeatedly contacted the white-label gambling firms, via the relevant offshore intermediary firms, but has never received a response.

A spokesperson from the Gambling Commission said: Before we licence an operator we investigate the suitability of a company to hold a licence... We also require operators to tell us websites they will be using under their licence and this includes white label websites.

"We do not currently investigate marketing partners as part of an application, but we would if an issue was brought to our attention.

We expect licensees to ensure that when agreeing commercial deals with sports clubs, that all parties are aware of, and compliant with, the relevant advertising and sponsorship rules and regulations.

(Photo: Visionhaus)

Continued here:
Premier League: The London-based firm behind controversial gambling sponsors - The Athletic