AI News, BOOK REVIEW: Standards for Artificial Intelligence Can Shape a More Secure and ... artificial intelligence

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.

Artificial intelligence in telecom - from hype to reality

It's true, the recent advancements of narrow AI are mind-blowing: algorithms are beating humans in applications ranging from gaming to healthcare.

But however 'magical' these accomplishments may seem – especially when we retrospectively look back at what we thought AI would be able to do just a couple of years ago – this is far from the reality of the everyday work we do at Ericsson.

A further 19 percent are looking at an adoption timescale of within three to five years.  At Ericsson, we aim high with our ambitions for AI while also operating in the here and now.

Today, a misbehaving network may drop your call or cancel your Instagram upload, and while this is of course annoying (especially if it's an important call or upload), the consequences can be far-reaching.

But tomorrow, networks will be an integral part of any mission-critical use case relying on connectivity, be it remote control of heavy machinery, autonomous drones or self-organizing logistics.

Automation means replacing human effort with machines, opening up the possibility of reassigning human effort to different types of activities that are not as easy to automate, giving us the ability to scale up the business.

To build a network serving billions of connections and automating countless handovers, making them work flawlessly is a daunting task.

Design of products and services, life-cycle management, operations, both remote and in the field, all benefit from AI algorithms given the massive amounts of data and knowledge that is constantly being produced by telecom networks.

This implies that industries using networks should be able to communicate their high-level intent of use of a service and expect a quality of service from the network to comply to this intent.

However, it's a double-edged sword because consumers also fear the lack of human emotion as the largest weakness – they say another human can sympathize and go out of protocol in order to make a wrong a right – whereas a machine must stick to protocol.

One could go as far to say that the dream for consumers would be a human-centric AI – incorporate all the benefits of eliminating human error while still leaving the human in control of the decisions taken by AI.

Even when it comes to equipment deployed in the field, preventive maintenance visits should be held with the help of robotics and drones to eliminate the need for humans to perform challenging and dangerous tasks such as climbing radio towers.

Industry Lab, we have studied consumers' relationship to privacy in several studies over a number of years, and we see that there are three conditions which are important in order for consumers to be willing to share their data: Permissibility, Value and Control.

This implies that in order to be willing to share data, consumers want to feel they get a substantial value in return, most usually presented in the form of improved products and services.

They want to be able to delete their data if they would feel that would be appropriate and do not want companies to sell their data on to third parties beyond their control.

Fifty-five percent of advanced internet users in one of our studiesbelieve influential groups use social networks to broadcast their messages, and a similar number think politicians use social networks to spread propaganda.

On the other hand, half of the studied consumers in this study say AI would be useful to help check whether facts stated on social networks are true or false.

AI is a new technology which emphasizes the responsibility to build truly human-centric systems – automating networks for sure – but with the human in control and networks which are secure, ensuring privacy and responsible by design.

AI IN BANKING: Artificial intelligence could be a near $450 billion opportunity for banks — here are the strategies the winners are using

Discussions, articles, and reports about the AI opportunity across the financial services industry continue to proliferate amid considerable hype around the technology, and for good reason: The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.

In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence.

Winning trust in Artificial Intelligence

AI is reshaping our world as we know it, transforming every sector, from education to transportation.

These principles don’t require complexity, but they should, as a minimum, be championed by a company’s board/senior management, be available for public input and be translatable into tangible actions.

With those responsible for gathering the data, creating the algorithms, and applying it to businesses often working separately, it’s important to consider the different stages involved in creating AI solutions and monitor each stage closely.

IBM, through our AI Ethics Global Leader, Francesca Rossi, helped create the guidelines which identify seven fundamental requirements to help businesses shape their approach to building trustworthy AI.

It covers a broad range of areas, but businesses can start with two of the most crucial challenges of AI: mitigating bias and ensuring people understand the rationale behind AI decisions.

There are tools in development and already in the market to detect and mitigate bias like this, and companies have a responsibility to ensure they’re using these and/or only working with AI providers deploying them.

To this end, at IBM we have already been putting principles of trusted AI into practice in tools such as Watson Open Scale to increase control of AI for the organisations using it by detecting bias and demonstrating explainability of how an outcome was reached.

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