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New York Times has revealed interesting details about the assassination of Iranian nuclear scientist Fakhrizadeh, saying that it was carried out with a new weapon equipped with artificial intelligence and multiple cameras operating via satellite.

The newspaper pointed out that the assassination was carried out by a killer robot capable of firing 600 rounds per minute, without agents on the ground. The newspaper also added that its information in this regard was based on interviews with American, Israeli, and Iranian officials, including two intelligence officials familiar with the planning and implementation of the operation.

According to an intelligence official familiar with the plan, Israel chose an advanced model of the Belgian-made FN MAG machine gun linked to an advanced smart robot. Then it was smuggled to Iran in pieces over different times, and then secretly reassembled in Iran.The robot was built to fit the size of a pickup tank, and cameras were installed in multiple directions on the vehicle to give the wheelhouse a complete picture of not only the target and its security details, but the surrounding environment.

In the end, the car was rigged with explosives, so that it could be detonated remotely and turned into small parts after the end of the killing process, in order to destroy all evidence. The newspaper pointed out that the assassination of Fakhrizadeh took less than a minute, during which only 15 bullets were fired. The satellite camera installed in the car sent images directly to the headquarters of the operation.

What happened is not a science fiction scene in a Hollywood movie, but it is a fact that we must deal with in the future, and we must also deal with the unprecedented risks and challenges that this entails on the overall security scene. The end of the world will be at the hands of smart robots. If you believe some of the AI observers, you will find that we are in a race towards what is known as the technological singularity point, a hypothetical point at which AI devices outperform our human intelligence and continue to evolve themselves amazingly beyond all our expectations. But if that happens, which is a rare assumption of course, what will happen to us?

Over the past few months, a number of high-profile celebrities, such as Elon Musk and Bill Gates, have warned that we should worry more about the potentially dangerous consequences of superintelligent AI systems. However, we find that they have already invested their money in projects that they consider to be important in this context. We find that Musk, like many billionaires, supports the OpenAI Foundation, a non-profit organization dedicated to developing artificial intelligence devices that serve and benefit humanity in general.

A recent study conducted by researchers from Oxford University in Britain and Yale University in the United States revealed that there is a 50% chance that artificial intelligence will outperform human intelligence in all areas within 45 years, and is expected to be able to take over all human jobs within 120 years. The results of the study do not rule out that this will happen before this date.

According to the study, machines will outperform humans at translating languages by 2024, writing academic articles by 2026, driving trucks by 2027, working in retail by 2031, writing a bestselling book by 2049, and performing surgery by 2053.

The study also stressed that artificial intelligence is rapidly improving its capabilities, and is increasingly proving itself in areas historically controlled by humans, for example, the AlphaGo programme, owned by Google, recently defeated the worlds largest player in the ancient Chinese game known as Atmosphere. In the same vein, the study also expects that self-driving technology will replace millions of taxi drivers.

A few days ago, the United Nations High Commissioner for Human Rights, Michelle Bachelet, stressed the urgent need to halt the sale and use of artificial intelligence systems that pose a grave threat to human rights, until appropriate safeguards are in place. It also called for banning artificial intelligence applications that cannot be used in line with international human rights law So the reality is more dangerous than we think, and perhaps the disaster will be closer than we think.

See the rest here:
Opinion| The United States and the assassination of Iranian nuclear scientist - Daily News Egypt

Day 3 wrapped up the Group Stage play at the TeamFight Tactics Reckoning World Championships with the conclusion of Group B. Many heavy hitters were present in Group B but the major stories came from the underdog players and regions. With the finals now set, here are the lessons learned from Day 3.

Your 8 TFT Reckoning Championship Finalists!

@shircaneTFT @DeliciousMilkGG @eschatv Zixingche @sealcune @nukotod qituX Huanmie pic.twitter.com/PtSYCm9jy1

Teamfight Tactics (@TFT) October 3, 2021

China entered the TFT Reckoning World Championship with something to prove. After receiving the most Worlds spots last season, they failed to bring any of their six players into the final. This year they received fewer invites but were still tied for the most with Europe. All eyes were on China once again as many did not think they deserved to bring four players to Worlds this season. After Day 3, the critics have been silenced.

After Zixingche became the first Chinese representative to qualify for the finals, since Juanzi and Alphago did it back in TFT: Galaxies two seasons ago, China came into Day 3 to show they had more than one player capable of winning the championship.

But after a thrilling five-game series, the two Chinese players in Group B managed to grab the final qualifier spots. Chinas first seed, qituX, qualified for the finals fairly easily. He managed to hit a Karma three-star to win the first game and followed it up by winning Game 3 with a VelKoz carry where he defeated a Lucian three-star in the final round. Even though he only managed top four in two of the five games, those wins qualified him in third place.

Huanmie had the reverse results as qituX. In the two rounds qituX won, Huanmie placed bottom four. But in the other three games, Haunmie placed in the top four, including a first place finish in Game 5 which secured the fourth and final spot. China now has three players in the top eight, the most of any region.

The three most talked about regions at the World championships were South Korea, Europe, and North America. All three of them were among the favorites to win Worlds. Korea especially came into Worlds trying to defend their world title. After Day 3, a new World championship region should be crowned.

After a poor performance by Ddudu in Group B, Korea will not have a player in the top eight finals. But Korea isnt the only major region to disappoint. Scipaeus, the EU rep in Group B, had a terrible Day 3. He managed to only grab a total of 19 points. Heading into Game 5, he was already mathematically eliminated from top-four contention.

The hope for NA rested on Robinsongz. After a poor start in the first two games, Robinsongz came roaring back with back-to-back top-three finishes in the third and fourth games. With Robinsongz in control of his destiny, the game had other plans. After unfortunate matchmaking and low-rolls, Robinsongz bowed out in sixth place, missing out on top four.

These three regions had a combined 10 spots out of the 20 available at Worlds. Combined they only have two spots in the top eight finals as EU and NA claimed both.

The lesser-known regions have been a major reason why all the other regions are underperforming outside of China. After Escha became the first player at the TFT Reckoning World Championships to qualify for the finals, Nukomaru followed shortly after marking the first time both of the super minor regions have made the top eight finals. Japan and Oceania were the only two regions to send a single player to Worlds. Now both of them have qualified for the finals. Both Nukomaru and Escha even had to play in the Play-In Stage where both of them finished first and second place showing that they belonged.

But, Nukomaru wasnt the only wildcard region player to impress on Day 3. Latin Americas SMbappe put on a show during Group B. After garnering fame from his first or eighth playstyle in the Play-In Stage, Smbappe didnt play that drastically in Group B. Instead, he managed to play better than anyone else. In a tightly contested lobby, with a single point separating first and fourth, Smbappe came out on top giving LATAM their first-ever competitor in the top eight finals at TFT Worlds.

With SMbappes qualification, all four players that qualified for the main event through the Play-In Stage are now in the top eight finals. This may be the first season where the TFT championship is brought back to a Wildcard region.

Continue reading here:
Lessons learned from Day 3 of the TFT Reckoning World Championship - Upcomer

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