AI News, 7 Ways An Artificial Intelligence Future Will Change The World

Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning

It holds the promise of solving some of society’s most pressing issues, but also presents challenges such as inscrutable “black box” algorithms, unethical use of data and potential job displacement.

As rapid advances in machine learning (ML) increase the scope and scale of AI’s deployment across all aspects of daily life, and as the technology can learn and change on its own, multistakeholder collaboration is required to optimize accountability, transparency, privacy and impartiality to create trust.

Harnessing artificial intelligence

Artificial intelligence (AI) is changing the economy: it is impacting on the way we shop, on the way we communicate, on the way we do research.

US investment bank Goldman Sachs argues that AI: “is a needle-moving technology for the global economy […] impacting every corporation, industry, and segment of the economy in time”.[1] AI is an enabler that some have likened to the invention of the combustion engine or electricity –

AI generally refers to efforts to build computers able to perform actions that would otherwise require human intelligence, such as reasoning and decision-making.

Recently, though, computers have improved in performance and more data have become available: in fact, a 2017 report estimated that 90 percent of the world’s data had been created within the preceding five years.

However, the bank also warns that: “Management teams that fail to invest in and leverage these technologies risk being passed by competitors that benefit from the strategic intelligence, productivity gains, and capital efficiencies they create.”[5] Given that companies are warning of the risk of being overtaken by competitors that adopt AI, states should take a hard look at whether they do enough with regard to AI applications to guarantee their economies’

This competition can even touch on matters important to the culture and history of each country, such as when a reported 280 million people in China watched a machine owned by Google parent company Alphabet win at Go against one of the world’s best human Go players.

Kennedy’s landmark speech calling for America to land a man on the moon”.[6] If AI is indeed like the combustion engine or electricity in its transformative potential, failing to adopt this technology will have both economic repercussions and could lead to massive geopolitical gaps between countries.

takes a clearly geopolitical approach, and emphasises that: “Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States and to shaping the global evolution of AI in a manner consistent with our Nation’s values, policies, and priorities”.

This is worrying, given the distinct risk that states around the world may adopt techno-nationalist agendas, including increased protectionism to support national champions.

In a noteworthy essay, Ian Hogarth, a machine learning engineer and AI investor, warns that: “machine learning will be such a dramatic cause of instability that nation states will be forced to put their citizens ahead of broader goals around internationalism”.

In light of this, it is crucial that the EU, its member states, and European countries outside the EU more broadly avoid falling behind in AI research and use, and that they remain aware of the impact AI may have on their economies and societies.

Ian Hogarth puts this nicely: “There are perhaps 700 people in the world who can contribute to the leading edge of AI research, perhaps 70,000 who can understand their work and participate actively in commercialising it and 7 billion people who will be impacted by it.”

The scarcity of AI researchers has made them a precious commodity, with Microsoft Research chief Peter Lee comparing the cost of hiring a leading AI researcher to hiring a National Football League quarterback.[8] This scarcity has even led to the practice of “acquihires”, whereby larger firms take over smaller firms with the primary aim of hiring their employees.[9] 2.

As an example, Tesla’s fleet of vehicles has accumulated more than 1.2 billion miles of driving data, and in 2011 alone US Air Force drones amassed about 37 years’

This is leading to increasing interest in and development of GPUs, which are a more specialised electronic circuit fast emerging as the pillar of AI.[10] Cloud companies (such as Google, Microsoft, Tencent, and others, which are primarily American and Chinese) are investing in such hardware.

The value of the AI-related hardware market (computing, memory, storage) is predicted to reach over $100bn by 2025, with US and Chinese first-movers capturing most of it.[11] How do the two leading AI markets –

Goldman Sachs believes that: “talent of the highest calibre has and will continue to drive the innovative nature of the industry in China”.[13] While the Chinese BAT companies (Baidu, Alibaba, and Tencent) underspend Google and Microsoft slightly on Research and Development (R&D), they have higher percentages of R&D employees.[14] China’s internet users are more numerous than those of any other country.

Day shopping festival in 2016, Alibaba recorded 175,000 transactions per second.[16] In addition, Chinese data privacy and data collection rules are lax, and Chinese users tend not to be as concerned about data privacy as the inhabitants of many Western countries are.

Article 7 of China’s National Intelligence Law gives the government legal authority to compel such assistance, though the government also has powerful non-coercive tools to incentivize cooperation.”US companies, on the other hand, are much less national –

comparatively small size and their strong data security rules mean that, in comparison to their colleagues elsewhere, European AI researchers and developers have relatively limited access to data pools.

There are, however, areas in which European companies show strength, such as in natural language processing, where almost half of the 12 key companies are European.[21] The Economist has observed that: “Germany has as many international patents for autonomous vehicles as America and China combined”.[22] With DeepMind based in London, Europe does have one global champion in AI –

It is also relevant that European populations tend to see AI, as with technological advances more broadly, not as an opportunity but as a threat: survey after survey has found higher levels of scepticism, if not outright rejection, of AI in Europe than the US and, even more so, China.

Key findings of this study include: “73 per cent of people in China [believe that] the future impact of digital technology will be positive overall, as well as in terms of its ability to create jobs and address societal challenges.”

Although multi-country surveys rarely capture cultural nuances and should be used with caution, the results nevertheless point to generally higher levels of scepticism in Europe, and the impact of scandals such as that surrounding Cambridge Analytica.

[23] In 2016, venture capital investment in the EU totalled about €6.5 billion, while the comparable US figure was €39.4 billion.[24] And, as noted above, the EU’s regulatory framework and free-market policies forbid a Chinese-style government approach to sheltering and nurturing its tech industry.[25] For Europe, the risks associated with missing the boat on AI are potentially enormous.

France’s AI strategy is already heading in the right direction on this when it argues that: “The public authorities must introduce new ways of producing, sharing and governing data by making data a common good”.

It plans to achieve this by opening up data gathered as part of government and publicly funded projects, and by incentivising private players to make their data public and transparent.

Europe’s AI industry has made clear its concerns about falling further behind its international competitors: more than 2,000 experts from CLAIRE (the Confederation of Laboratories for Artificial Intelligence Research in Europe) recently called for large-scale funding from the EU to counter China’s and America’s rapid progress.

$25 billion more in a time period that was ten times shorter.[26] Projects that explicitly aim to fund “moon-shot projects”, such as the Franco-German JEDI (Joint European Disruptive Initiative), are therefore a step in the right direction.

In the digital realm, it already has a headstart: “Europe seems to be in the lead when it comes to setting standards for regulation and privacy protection in the digital age”, comments Deutsche Bank, specifically citing the General Data Protection Regulation as evidence of this strength.[27] Emmanuel Macron has been outspoken on this front too, declaring that: “My goal is to recreate a European sovereignty in AI …

focus on data privacy, as Kai-Fu Lee notes, “will cause the American giants some amount of trouble and may give local European entrepreneurs the chance to build something that is more consumer and individual-centric …

In this respect, Europe should also look for other likeminded partners among its liberal democratic allies, such as Canada or Australia, to further increase the area in which such rules are applied and thereby increase their impact.

Indeed, there may even be opportunities for European countries that they have not yet acknowledged: the new competitive landscape could, in fact, benefit middle powers, as they will have greater capacity to compete than they did in the creation of the complex –

Political scientist Michael Horowitz argues: “As long as the standard for air warfare is a fifth-generation fighter jet, and as long as aircraft carriers remain critical to projecting naval power, there will be a relatively small number of countries able to manufacture cutting-edge weapons platforms.

Horowitz even goes as far as to say that it is “possible, though unlikely, that AI will propel emerging powers and smaller countries to the forefront of defense innovation while leaving old superpowers behind”.

Beyond lethal autonomous systems, whose possible development and use have become a hotly debated issue and given rise to public protests (for good reason), there are many AI applications in the military realm that are attractive for armed forces, as they can help to lower costs, reduce need for human operatives, and improve planning and foresight.

a fact that became known to the wider public in June 2018, when, following protests from its employees, Google ended ‘Project Maven’, a joint initiative with the US Department of Defense that aimed to use AI to analyse data collected by drones.

An educated and informed population may also be more resistant to handing over too much of its data to US (or Chinese) firms and insist on better privacy laws, thereby strengthening Europe’s regulatory power.

Indeed, it is in this element that Europe has a chance to go beyond mere sovereignty to become a norm-setter, embedding its ethics and values into AI governance and development, and serving as an example to fight back against AI nationalism.

In doing so, it will need to take significant steps itself, such as rapidly educating its own citizens and policymakers, as well as substantially increasing investment in AI and carefully choosing which subfields of AI to fund.

it is liable to find itself surrounded by more powerful rivals that have set the ground rules for AI, leaving it unable to compete or to provide citizens with the protection that they expect and deserve.

Here are 10 ways AI could help fight climate change

Some of the biggest names in AI research have laid out a road map suggesting how machine learning can help save our planet and humanity from imminent peril.

Within each domain, it breaks out the contributions for various subdisciplines within machine learning, including computer vision, natural-language processing, and reinforcement learning.

If we’re going to rely on more renewable energy sources, utilities will need better ways of predicting how much energy is needed, in real time and over the long term.

This could, for example, help create solar fuels, which can store energy from sunlight, or identify more efficient carbon dioxide absorbents or structural materials that take a lot less carbon to create.

Shipping goods around the world is a complex and often highly inefficient process that involves the interplay of different shipment sizes, different types of transportation, and a changing web of origins and destinations.

Intelligent control systems can dramatically reduce a building’s energy consumption by taking weather forecasts, building occupancy, and other environmental conditions into account to adjust the heating, cooling, ventilation, and lighting needs in an indoor space.

In the same way that machine learning can optimize shipping routes, it can also minimize inefficiencies and carbon emissions in the supply chains of the food, fashion, and consumer goods industries.

As a result, many farmers rely heavily on nitrogen-based fertilizers, which can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide.

Robots run on machine-learning software could help farmers manage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.

Satellite imagery and computer vision can automatically analyze the loss of tree cover at a much greater scale, and sensors on the ground, combined with algorithms for detecting chainsaw sounds, can help local law enforcement stop illegal activity.

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