PMs and Presidents around the world are supporting the use of artificial intelligence and here are the most popular ones among them.

The importance of artificial intelligence is known around the world and every nation is on its way to win the AI race as they realize that acquiring excellence in AI technology would make them the biggest superpower. Tesla king, Elon Musk has recently tweeted that “Competition for AI superiority at national level most likely cause of WW3”. Recently India along with Australia, the United States, the United Kingdom, Canada, France, Germany, New Zealand, and others have come together to establish the Global Partnership on Artificial Intelligence (GPAI) for responsible evolution and use of AI. The PMs and presidents of the nations are supporting artificial intelligence in their speeches as well as their establishment of various policies regarding AI and it is demonstrated in the following.

India

In 2020, Prime Minister Narendra Modi inaugurated a virtual summit on artificial intelligence called ‘RAISE 2020’. It was jointly organized by the Ministry of Electronics and Information Technology and the NITI Aayog. This is a first-of-its-kind summit that focused on India’s vision and the potential roadmap of using AI for what is expected to lead to social change, inclusion, and empowerment. In this summit, PM Modi said that while he wants India to become a global hub for AI. It is also a collective responsibility of the nation to protect the world from the misuse of AI by non-state actors such as cybercriminals, terrorists, among others. He also stated that artificial intelligence (AI) can play a crucial role in developing solutions in agriculture, creating next-generation urban infrastructure, as well as for making disaster management systems in the country stronger.

China

President of China, Xi Jinping stated in 2017 that ‘under a situation of increasingly fierce international military competition, only the innovators win’. This sentiment is shared by Lieutenant General Liu Guozhi, deputy of the 19th National Congress and director of the Science and Technology Committee of the Central Military Commission, who stated in an interview that AI presented a rare opportunity for taking shortcuts to achieve innovation and surpass rivals. According to a July 2017 article in The New York Times, a timeline in the new policy explains how the government expects its companies and research facilities to be at the same level as leading countries like the United States by 2020. Five years later, it calls for breakthroughs in select disciplines within A.I. that will become a key impetus for economic transformation. In the final stage, by 2030, China aims to become the world’s premier artificial intelligence innovation center, which in turn will foster a new national leadership and establish the key fundamentals for great economic power.

Russia

Russian President Vladimir Putin has stated “Artificial intelligence is the future, not only for Russia but for all humankind”, as per a report from RT. He further explained that AI comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world. He also said, “It comes with colossal opportunities, but also threats.” Although it’s thought that artificial intelligence will help boost countries’ economies in several areas, from heavy industry to medical research, AI technology will also be useful in warfare. Artificial intelligence can be used to develop cyber weapons, and control autonomous tools like drone swarms, fleets of low-cost quadcopters with a shared brain that can be used for surveillance as well as attacking opponents.

France

French President Emmanuel Macron has announced a €1.5 billion plan to turn his country into a world leader for AI research and innovation. It calls for a hefty investment, a handful of specialized institutes, a focus on ethics and open data, and a call to recruit foreign researchers and French scientists working abroad to the country. Macron presented his plans in a lengthy speech peppered with erudite references and touches of humor at the end of the “AI for Humanity” conference in Paris. He believes that turning the country into an AI leader would allow France to use AI for the public good and ensure that a “Promethean” promise doesn’t become a “dystopia”.

USA

US President Joe Biden is planning to invest billions in technology research and development, but his approach to a national AI strategy still lacks a cohesive vision. His $1.5 trillion discretionary budget and $2 trillion infrastructure allot billions to bring chip manufacturing back to the U.S. and fund research and development in emerging technologies, which broadly include AI.

Before that, under former president Donald Trump, AI became one of the leaders in AI technology all over the world. Trump stated, “Continued American leadership in AI is of paramount importance to maintain 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.” He launched the American Artificial Intelligence Initiative, the Nation’s strategy for promoting American leadership in AI, by signing Executive Order 13859 in February 2019. Reportedly, the American AI Initiative focuses the resources of the Federal Government to support AI innovation that will increase prosperity, enhance national security, and improve the quality of life for the American people.

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Many central banks have undertaken initiatives to develop big data platforms to facilitate storage and processing of large data sets, but progress has varied.

The BIS (Bank of International Settlements) has published a new report on the application of big data by central banks.

The report is based on a survey organised by the BIS’ Irving Fischer Committee, updating a similar survey conducted five years earlier.

The report highlights the growth of big data sources as reflective of the impact of digitisation, new capabilities to process “traditional” information such as text, and the creation of large databases as a by-product of complex operations taking place in modern societies.

“Additionally, vast amounts of data have emerged in the administrative, commercial and financial areas, an evolution spurred by the important data collection strategies undertaken after the Great Financial Crisis of 2007-09,” the report says.

Central banks are said to now have a “comprehensive view” of big data, with 80 percent of respondents reporting its increased use to support policy efforts, compared to just one third in 2015.

The report notes that big data applications supporting central banks’ operational work had initially been limited, contrasting with the rapid pace of innovation seen in the private sector.

Central banks have reported using big data in monetary policy, financial stability, research and the production of official statistics.

“Newly available databases and techniques are increasingly mobilised to support economic analyses and nowcasting/forecasting exercises, construct real-time market signals and develop sentiment indicators derived from semi-structured data,” the report says.

“This has proved particularly useful in times of heightened uncertainty or economic upheaval, as observed during the Covid-19 pandemic.”

A majority of central banks have also reported using big data for micro-level supervision (suptech) and regulation (regtech), with an increasing focus on consumer protection, such as to assess misconduct, detect fraudulent transactions and combat money laundering.

The report underscores the need for adequate IT infrastructure and human capital, highlighting that many central banks have undertaken initiatives to develop big data platforms to facilitate the storage and processing of large and complex data sets.

“But progress has varied, reflecting the high cost of such investments and the need to trade off various factors when pursuing these initiatives,” it says, noting the difficulties involved with hiring and training staff amid a limited supply of data scientists.

The report also highlights challenges related to legal issues, ethics, privacy, data quality, and the fairness and accuracy of algorithms trained on pre-classified and/or unrepresentative data sets.

“Moreover, a key issue is to ensure that predictions based on big data are not only accurate but also ‘interpretable’ and representative, as to carry out evidence-based policy central banks need to identify specific explanatory causes or factors.”

The report says cooperation could facilitate central banks’ use of big data, and that international financial institutions can help foster such cooperation.

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During a walking tour of Queensland’s Daintree rainforest in Australia, a talented guide regularly pointed out creatures that were well camouflaged into their surroundings. At one point, he directed our attention to a tree trunk, where a large grasshopper was camouflaged. The guide’s observations and stories wove together the connections between biology, geology and colonialism, helping explain how big and small changes could transform life in this ecosystem.

Our society has been altered by the rapid proliferation of new technologies and devices that produce digital data. Nested within and feeding on this data ecosystem, artificial intelligence (AI) executes cognitive tasks with more potency and speed than human beings. The large-scale transformative power of AI remains camouflaged in plain sight.

Through the lens of the responsible innovation in health research program at the Université de Montréal, we critically examine what lies beyond our immediate experiences of AI.

Artificial intelligence in our lives

Much like driving a car, we do not need to understand how AI works in order to use its applications. And similar to ways in which the fossil fuel industry shaped the role of cars in our society, AI is delivered through powerful commercial interests and large digital and physical infrastructures. To better understand their impacts, there is an urgent need to critically appraise how AI delivers its much-touted promises.

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At the onset of the Industrial Revolution, people in Montréal had no clue about the kinds of infrastructures that were going to be developed to extract, exploit, distribute and use fossil fuels. Montréal was ideally located to transport goods, including oil, and refineries were later concentrated along the Saint Lawrence River. Beyond negative impacts on residents’ health, the decisions made at the turn of the 20th century to exploit fossil fuels have had long lasting self-reinforcing effects.

And now, in the 21st century, we are seeing the changes AI brings and we need to consider the wide-ranging ramifications.

Physical support networks

The jewel in the crown of the intangibles economy, AI needs expansive e-infrastructures that have tangible impacts and costs. Estimates suggest “that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent” — five times the lifetime emissions of an average car.

If we choose to exploit the “oil of the 21st century,” we will have to build large powerful computational centres and sizable server farms. AI requires networking and cloud infrastructures to capture, analyze, share and archive vast amounts of data.

When deep learning techniques are involved, training is a key step that consists of feeding the algorithm with large and mostly unstructured datasets. The training of a single AI-based application may be split over dozens of chips and may require months to complete.

Although it only takes a low energy tap on a smartphone to use an application, its development is energy intensive and non-renewable energy sources have a much larger environmental impact.

Energy for training

Thankfully, data scientists are starting to calculate the energy required to develop AI tools before they are made available for use. For instance, a process involved in automating the design of a neural network through trial and error — called the Neural Architecture Search (NAS) — is highly energy intensive. Without NAS, training the AI tool Transformer takes 84 hours, but with NAS it takes more than 270,000 hours, thereby “requiring 3,000 times the amount of energy.”

Reducing the carbon footprint of AI requires a “concerted effort by industry and academia to promote research of more computationally efficient algorithms” and the use of more sustainable hardware and model development strategies.

Future policy

Because data generated through digital interactions are worth their weight in gold, commercial agreements are likely to keep the future of AI into the hands of those with corporate interests. Exploiting data to increase corporate profits are the core business of tech giants like Amazon and Google.

This is one of the reasons why it is important for public policy-makers to create alternative entrepreneurial pathways where data scientists and programmers who aim to design much more meaningful AI can thrive.

Could AI empower those who tackle today’s major societal challenges and seek solutions for the common good? For instance, what would an eco-friendly AI tool to help us meet the United Nations Sustainable Development Goals look like? What alternative business and data governance models should be promoted for benefits to be shared equitably?

Seeing the forest and the trees could turn a more responsible vision for the 21st century into a tangible reality.

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SINGAPORE, July 15, 2019 /PRNewswire/ — Today at RSA Asia Pacific & Japan 2019, API security leader and creator of the industry’s first API Firewall – 42Crunch – announced the latest release of its API security platform with full support for Kubernetes environments. This new solution allows organizations to easily automate API security across Kubernetes environments – enabling the zero-trust architecture needed to protect each microservice, and scale without risk. 

The rapid adoption of microservices architectures and Kubernetes lead to proliferation of APIs exposed by these microservices. Developers employ agile practices to quickly iterate on these microservices. Combined, these trends lead to hundreds if not thousands of rapidly changing APIs that modern enterprises often host and need to secure.

Traditional solutions such as Web Application Firewalls (WAF) and API Management tools rely on static rules and policies, and edge protection. While these solutions provide some security functionality within your environment, they still leave the individual microservices vulnerable to API attacks. 

Through a fully automated platform, 42Crunch extends security beyond the edge of the enterprise to each individual microservice, protecting them with an ultra-low latency micro API firewall that can be deployed at scale. 42Crunch API firewall is merely 20 MB in size and when deployed in sidecar proxy mode in Kubernetes pods enforces API security with sub-millisecond overhead. This eliminates the manual process of writing and maintaining individual API security policies, and enforces a zero-trust security architecture.

“Since the initial launch of the 42Crunch API Security platform our customers have informed us that edge protection is no longer enough,” says Jacques Declas, CEO and founder of 42Crunch. “We are excited to make our Kubernetes-native API protection commercially available. Now the teams working on large numbers of microservices can be sure that each and every one of them automatically stays secure throughout its lifecycle.”

In addition, 42Crunch’s unique approach integrates with companies’ DevSecOps pipeline and delivers automated API security across the whole API lifecycle:

• AUDIT: Run 200+ security audit tests of the OpenAPI specification definition with detailed security scoring to help developers define and strengthen API contract.
• SCAN: Scan live API endpoints to discover potential vulnerabilities and discrepancies of the API implementation against the API contract.
• PROTECT: Launch service to protect APIs and apply policies that can be deployed in our lightweight, low-latency micro API firewall.

42Crunch will be participating in RSA Asia Pacific & Japan 2019 as both an exhibitor and speaker. Join Matthieu Estrade, CTO, at 9:00 on Thursday July 18th for his talk: “API Security: Learning from the 20 Years of AppSec Failures,” located in Orchid 4203. Visit the 42Crunch team at booth 1708 to learn more about how we can help you automate API security in your microservices environment.

About 42Crunch
42Crunch bridges the gap between API development and security teams with a simple, automated platform that provides auditing, live endpoint scanning, and micro API firewall protection. Unlike other solutions on the market, the 42Crunch platform empowers development, security and operations teams with a set of integrated tools to easily build security into the foundation of the API and enforce those policies throughout the API lifecycle. By delivering security as code you enable a seamless DevSecOps experience, allowing innovation at the speed of business without sacrificing integrity. Visit https://42crunch.com to learn more. 

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