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U.S. Government Blacklists Top AI Startups in China

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U.S. Government Blacklists Top AI Startups in China

The United States government has blacklisted several top artificial intelligence startups in China. This action follows the already existing trade blacklist that has been present against China since the beginning of the ongoing trade war. The new developments are a response to the current actions being taken against Muslim minorities in the country. The decision will undoubtedly increase the current tensions between the U.S. and China. 

The new policy will require U.S. government approval for firms who want to buy components from U.S. companies. It was the same tactic used against China in the Huawei Technologies Co Ltd conflict. 

According to the U.S. Government and Department of Commerce, “entities have been implicated in human rights violations and abuses in the implementation of China’s campaign of repression, mass arbitrary detention, and high-technology surveillance against Uighurs, Kazakhs, and other members of Muslim minority groups.” 

Secretary of Commerce Wilbur Ross has said that the U.S. government will not tolerate the actions that are taking place in the Xinjiang region of China.

Blacklist Comes Days Before Trade Talks Resume  

The new developments come just as trade talks are set to resume between Washington and Beijing in the coming days. 

Companies that are being targeted include some of China’s most important AI startups. Included in the list are Hikvision, a video surveillance gear company with a market value of $42 billion, the $7.5 billion valued ScienceTime, the Alibaba connected Megvii valued at $4 billion, speech recognition specialist iFlytek Co, data recovery company Xiamen Meiya Pico Information Co, and facial recognition company Yitu Technology. 

In total, there are 28 entities that the U.S. Commerce Department has added to the blacklist; Eight of them are companies and the other 20 are organizations including local public security bureaus, which have been targeted for their direct role in the ongoing human rights abuses taking place in Xinjiang. 

The Massachusetts Institute of Technology has announced that they will be reviewing their relationship with SenseTime Group Ltd. According to the university, their relationship with the company is to “confront some of the world’s greatest challenges.” The co-founder of SenseTime is MIT graduate Xiao’ou Tang. 

Damage to AI Startups in China

Many of the companies should be able to change over to backup supply chains, but there is still the strong possibility of heavy damage. Research will likely slow down as many of the companies rely on the chips created in the United States, and partnerships with U.S. companies can start to deteriorate or even come to a complete stop. 

Beijing has been largely quiet on the issue, and they will still attend the trade meetings in Washington. However, the companies involved have not been quite like the government. 

According to Hikvision, “Punishing Hikvision, despite these engagements, will deter global companies from communicating with the U.S. government, hurt Hikvision’s U.S. businesses partners and negatively impact the U.S. economy.” 

In a statement released by SenseTime, the company expressed their views on the issue while also claiming they follow all relevant laws in the jurisdictions they operate within. The company also reiterated their commitment to ethics within the AI industry. 

After the announcement of the blacklist, iFlytek fell by 2.7% and Xiamen Meiya by 1.8%. 

With artificial intelligence becoming such a huge part of the global technology market with its enormous potential, it will likely keep becoming a target. It can be expected that the AI industry becomes a tool used against nations and companies, and it will be included in actions such as blacklisting.

 

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Alex McFarland is a historian and journalist covering the newest developments in artificial intelligence.

Big Data

Risks And Rewards For AI Fighting Climate Change

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Risks And Rewards For AI Fighting Climate Change

As artificial intelligence is being used to solve problems in healthcare, agriculture, weather prediction and more, scientists and engineers are investigating how AI could be used to fight climate change. AI algorithms could indeed be used to build better climate models and determine more efficient methods of reducing CO2 emissions, but AI itself often requires substantial computing power and therefore consumes a lot of energy. Is it possible to reduce the amount of energy consumed by AI and improve its effectiveness when it comes to fighting climate change?

Virginia Dignum, an ethical artificial intelligence professor at the Umeå University in Sweden, was recently interviewed by Horizon Magazine. Dignum explained that AI can have a large environmental footprint that can go unexamined. Dignum points to Netflix and the algorithms used to recommend movies to Netflix users.  In order for these algorithms to run and suggest movies to hundreds of thousands of users, Netflix needs to run large data centers. These data centers store and process the data used to train algorithms.

Dignum belongs to a group of experts advising the European Commission on how to make human-centric, ethical AI. Dignum explained to Horizon Magazine that the environmental impact of AI often goes unappreciated, but under the right circumstances data centres can be responsible for the release of large amounts of C02.

‘It’s a use of energy that we don’t really think about,’ explained Prof. Dignum to Horizon Magazine. ‘We have data farms, especially in the northern countries of Europe and in Canada, which are huge. Some of those things use as much energy as a small city.’

Dingum noted that one study, done by the University of Massachusetts, found that creating a  sophisticated AI to interpret human language lead to the emissions of around 300,000 kilograms of the equivalent of C02. This is approximately five times the impact of the average car in the US. These emissions could potentially grow, as estimates done by a Swedish researcher, Anders Andrae, projects that by the year 2025 data centers could account for apporixmately 10% of all electricity usage. The growth of big data and the computational power needed to handle it has brought the environmental impact of AI to the attention of many scientists and environmentalists.

Despite these concerns, AI can play a role in helping us combat climate change and limit emissions. Scientists and engineers around the world are advocating for the use of AI in designing solutions to climate change. For example, Professor Felix Creutzig is affiliated with the Mercator Research Institute on Global Commons and Climate Change in Berlin and Crutzig hopes to use AI to improve the use of spaces in urban environments. More efficient space usage could help tackle issues like urban heat islands. Machine learning algorithms could be used to determine the optimal position for green spaces as well, or to determine airflow patterns when designing ventilation architecture to fight extreme heat. Urban green spaces can play the role of a carbon sink.

Currently, Creutzig is working with stacked architecture, a method that uses both mechanical modeling and machine learning, aiming to determine how buildings will respond to temperature and energy demands. Creutzig hopes that his work can lead to new building designs that use less energy while maintaining quality of life.

Beyond this, AI could help fight climate change in several ways. For one, AI could be leveraged to construct better electricity systems that could better integrate renewable resources. AI has already been used to monitor deforestation, and its continued use for this task can help preserve forests that act as carbon sinks. Machine learning algorithms could also be used to calculate an individual’s carbon footprint and suggest ways to reduce it.

Tactics to reduce the amount of energy consumed by AI include deleting data that is no longer in use, reducing the need for massive data storage operations. Designing more efficient algorithms and methods of training is also important, including pursuing AI alternatives to machine learning which tends to be data hungry.

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