]]> The FLY AI report sets out key steps towards a stronger adoption of AI. Credit: EUROCONTROL.

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British mathematician and scientist Alan Turing first looked into computing intelligence in 1950. In a paper called Computing Machinery and Intelligence, he suggested using a now-famous Imitation Game to test a machines sentient capabilities, which eventually laid the groundwork for the development and discovery of artificial intelligence (AI).

Decades later, AI and its subsets machine learning and deep learning are set to influence the future of many sectors, including aviation. Over the last few years, AI has found a wide array of applications in the industry from ground handling services to airport security and air traffic management (ATM) and there is now scope for more.

This is the argument of the recently-published FLY AI report, which sets out key steps towards a stronger adoption of AI, machine learning and other digital tools in several areas of aviation. Released by the newly formed European Aviation High Level Group on AI, the paper draws upon expertise from key players in the sector. These include leader EUROCONTROL, the Single European Sky ATM Research (SESAR) Joint Undertaking, the International Air Transport Association, Airbus and Airport Council Internationals European division.

Alongside debunking some myths around AI, the paper identifies the most promising areas for its uptake. It also features a FLY AI Action Plan that outlines future measures to better integrate the technology in ATM and other segments of aviation.

AI has been around for more than 60 years but has gained ground more recently, thanks to advances in computing and access to data, comments SESAR JU executive director Florian Guillermet. Machine learning and deep learning are helping to create applications that can learn autonomously and advise on complex problems. Aviation is no stranger to the virtues of AI.

The aviation industry has started to exploit the potential of machine learning algorithms on non-safety critical applications.

In recent times the technology has gained traction in segments such as intelligent maintenance, engineering and prognostics tools, supply chains and customer services. The sector is now eager to find more applications for AI, with some European countries particularly Ireland, Finland, Cyprus, Luxembourg, Sweden and the Netherlands leading the way.

The aviation industry has started to exploit the potential of machine learning algorithms on non-safety critical applications, adds EUROCONTROL head of infrastructure division Paul Bosman. Recently, significant effort has been put on adapting the current certification framework to the specific characteristics of AI applications. With AI, the industrys focus on cybersecurity has also increased.

Based on this assumption, the report aims to demystify and accelerate the use of AI in aviation and ATM in particular, says Guillermet. This field directly involves SESAR, which has been coordinating all EU research and development activities in ATM since 2007 and actively contributed to the production of the report. We also looked at the future evolution in the use of AI, he continues, in particular with the development of joint human-machine cognitive systems.

On the other hand, EUROCONTROL has acted as leader of the project under the European Aviation High Level Group on AI. The group was formed during EUROCONTROLs inaugural conference on AI that took place in May last year.

Guillermet explains that Europe has a strong basis of expertise and knowledge to further develop AI for ATM. Here, automation can help improve operational efficiency in different segments of aviation.

For instance, he says, machine learning digital assistants can mine huge amounts of historical data to support human operators on the ground or in the cockpit to make the best possible decisions.

Within this framework, the FLY AI report identified four areas where AI can help tackle current and future challenges. These are airspace capacity, which is rapidly running out in Europe, the climate change crisis, digital transformation and new levels of complexity in the integration of unmanned aircraft in an already overcrowded airspace.

Machine learning digital assistants can mine huge amounts of historical data to support human operators on the ground or in the cockpit.

AIs ability to identify patterns in complex real-world data that human and conventional computer-assisted analyses struggle to identify makes it extremely well-suited to the aviation sector, says Bosman. AI has the potential to transform aspects of the aviation sector, enabling ATM functions to be performed in entirely different ways in the future.

Bosman also believes that automation can play a pivotal role in improving the industrys environmental credentials. By accelerating the digital transformation in terms of optimising trajectories, creating green routes and increasing prediction accuracy, he says, AI could make a real difference to mitigating the environmental impacts of aviation, in addition to providing decision-makers and experts with new features that could transform the ATM paradigm in terms of new techniques and operating procedures.

In addition, better use of data will help increase and improve predictions with more sophisticated tools, while also boosting the scalability, efficiency and resilience of the current ATM system. Lastly, the technology can enhance safety in segments such as cybersecurity, conflict detection, traffic advisory and resolution tools.

A key takeaway from the report is that stronger cooperation is needed to integrate AI in the existing aviation architecture. The aim is to create an ecosystem involving industry, research institutes, start-ups, policymakers and all relevant stakeholders, in which all conditions are met to progress collectively on this, says Guillermet. No one entity can address it alone.

In response to this need, the reports FLY AI Action Plan looks at the whole value chain from research to implementation and provides stakeholders with a call for action.

According to Bosman, the plan identifies six accelerators that will help achieve this purpose. It is now critical that the community gets together, he adds. A way forward would be to set up a community of practice.

The aim is to create an ecosystem involving industry, research institutes, start-ups, policymakers and all relevant stakeholders.

The first step will be developing a federated data foundation and AI-infrastructure that will grant access to data and enable the creation of an AI aviation partnership. In addition, the European Aviation High Level Group on AI suggests launching specific aviation/ATM training, reskilling/upskilling programmes, change management, a knowledge-based toolbox, and European AI aviation/ATM master classes to share best practices. Awareness and demystification campaigns should also be included.

Meanwhile, the industry will also have to build an AI aviation/ATM community to attract future experts to the sector. This, Bosman concludes, will help consolidate community expertise.

Both EUROCONTROL and SESAR believe that the ongoing coronavirus crisis could help foster automation in the industry despite the challenges that it is causing. The crisis has shown the limits and significant efforts required when using the current manual approach or analytical tools to try to understand the impact of the crisis, predict and help business recovery, says Bosman.

The crisis has shown the limits and significant efforts required when using the current manual approach.

Specifically, AIs reliance on historical data sets on which to train neural networks means that in the event of a second wave of the pandemic, using these data sets will help improve crisis response. As a precaution in the face of such a risk, it makes sense to gather and store all possible data related to the virus and its impact. This is so that they can be used to develop new AI applications that could support the aviation industry in dealing with any future waves of Covid-19 or other pandemics.

In addition, Guillermet says that the pandemic is shining light on the importance of new technologies to help businesses through a crisis. Mastering these technologies and accelerating our plans for a digital Europe sky will deliver an aviation operating environment, which is more resilient, scalable and economically and environmentally sustainable in the long run, he says.

Because aviation is one of the industrial sectors most impacted by the coronavirus pandemic, it should further accelerate the adoption of AI, Bosman concludes. AI can really help transform the industry, provide better decision-making tools, and improve industrial and operational efficiency.

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FLY AI: the role of artificial intelligence in aviation - Airport Technology

John Allen and Darrell West discuss artificial intelligence on The Lawfare Podcast Skip to main content Editor's Note:

This podcast episode originally appeared on the Lawfare blog.

Darrell West and John Allen recently spoke about their new Brookings book Turning Point: Policymaking in the Era of Artificial Intelligence with Benjamin Wittes on The Lawfare Podcast. Darrell West is a senior fellow in the Center for Technology Innovation and the vice president and director of Governance Studies at the Brookings Institution. John Allen is the president of Brookings and a retired U.S. Marine Corps four-star general. In this podcast episode, West and Allen describes what AI is, how it is being deployed, why people are anxious about it, and what we can do to move forward. You can download or listen to the episode in the podcast player below.

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John Allen and Darrell West discuss artificial intelligence on The Lawfare Podcast - Brookings Institution

SAN FRANCISCO, July 21, 2020 (GLOBE NEWSWIRE) -- Sisu, the fastest and most comprehensive diagnostic analytics platform, has been named a 2020 Hot Vendor in Artificial Intelligence by Aragon Research, Inc.

Aragon Researchs Hot Vendor Report identifies noteworthy and innovative vendors transforming their markets and driving change for their customers. Sisu was founded by Peter Bailis, associate professor at Stanford and co-leader of the DAWN lab, whose research is focused on making it dramatically easier to build and deploy machine learning-enabled applications.

Theres a disconnect between a companys ability to collect incredibly rich data and their ability to extract real value from it, said Peter Bailis, CEO and Founder of Sisu. We built Sisu to help data-driven businesses do one thing incredibly well - understand why their metrics are changing, fast enough to inform daily operational decisions.

Sisu is built on a novel machine-learning engine, purpose-built to handle the kind of high-volume, high-dimensional data enterprises are collecting in cloud data warehouses. Sisus cloud-native platform enables unmatched analytics speed and result quality, processing hundreds of columns and millions of records in seconds. Unlike conventional BI tools, Sisu automates the manual, rote work of data exploration and surfaces useful explanations in even the most complex data sets.

Read more about the Aragon Research Hot Vendor Report here: https://aragonresearch.com/special-report-aragon-research-hot-vendors-for-2020-part-iii/

About SisuSisu is the fastest and most comprehensive diagnostic analytics platform, helping analysts rapidly diagnose why critical business metrics are changing. Based on years of research at Stanford University and proven at scale at Microsoft, Facebook and Google, Sisus diagnostic analytics platform combines machine learning and powerful statistical analysis to help anyone get answers to their toughest business questions. To learn more about Sisu, visit https://sisudata.com/.

Required Disclaimer:Aragon Research does not endorse vendors, or their products or services that are referenced in its research publications, and does not advise users to select those vendors that are rated the highest. Aragon Research publications consist of the opinions of Aragon Research and Advisory Services organization and should not be construed as statements of fact. Aragon Research provides its research publications and the information contained in them "AS IS," without warranty of any kind.

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Sisu Identified as a 2020 Hot Vendor in Artificial Intelligence by Aragon Research - GlobeNewswire

(Bloomberg Opinion) -- This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters 10 times more than previous competitors is surprisingly powerful.

The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense a typical failing for many automated response systems. You can even ask it questions about God.

Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.

GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.

It is not difficult to imagine a wide variety of GPT-3 spinoffs, or companies built around auxiliary services, or industry task forces to improve the less accurate aspects of GPT-3. Unlike some innovations, it could conceivably generate an entire ecosystem.

There is a notable buzz about GPT-3 in the tech community. One user in the U.K. tweeted: I just got access to gpt-3 and I can't stop smiling, i am so excited. Venture capitalist Paul Graham noted coyly: Hackers are fascinated by GPT-3. To everyone else it seems a toy. Pattern seem familiar to anyone? Venture capitalist and AI expert Daniel Gross referred to GPT-3 as a landmark moment in the field of AI.

I am not a tech person, so there is plenty about GPT-3 I do not understand. Still, reading even a bit about it fills me with thoughts of the many possible uses.

It is noteworthy that GPT-3 came from OpenAI rather than from one of the more dominant tech companies, such as Alphabet/Google, Facebook or Amazon. It is sometimes suggested that the very largest companies have too much market power but in this case, a relatively young and less capitalized upstart is leading the way. (OpenAI was founded only in late 2015 and is run by Sam Altman).

GPT-3 is also a sign of the underlying health and dynamism of the Bay Area tech world, and thus of the U.S. economy. The innovation came to the U.S. before China and reflects the power of decentralized institutions.

Like all innovations, GPT-3 involves some dangers. For instance, if prompted by descriptive ethnic or racial words, it can come up with unappetizing responses. One can also imagine that a more advanced version of GPT-3 would be a powerful surveillance engine for written text and transcribed conversations. Furthermore, it is not an obvious plus if you can train your software to impersonate you over email. Imagine a world where you never know who you are really talking to Is this a verified email conversation? Still, the hope is that protective mechanisms can at least limit some of these problems.

We have not quite entered the era where Skynet goes live, to cite the famous movie phrase about an AI taking over (and destroying) the world. But artificial intelligence does seem to have taken a major leap forward. In an otherwise grim year, this is a welcome and hopeful development. Oh, and if you would like to read more, here is an article about GPT-3 written by GPT-3.

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.

Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the blog Marginal Revolution. His books include "Big Business: A Love Letter to an American Anti-Hero."

For more articles like this, please visit us at bloomberg.com/opinion

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Artificial Intelligence Is the Hope 2020 Needs - Yahoo Finance

CHICAGO, July 21, 2020 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Manufacturing Marketby Offering (Hardware, Software, and Services), Technology (Machine Learning, Computer Vision, Context-Aware Computing, and NLP), Application, End-user Industry and Region - Global Forecast to 2026", published by MarketsandMarkets, the Artificial Intelligence in Manufacturing Marketis expected to be valued at USD 1.1 billion in 2020 and is likely to reach USD 16.7 billion by 2026; it is expected to grow at a CAGR of 57.2% during the forecast period. The major drivers for the market are the increasing number of large and complex datasets (often known as big data), evolving Industrial IoT and automation, improving computing power, and increasing venture capital investments.

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The hardware segment in the AI in manufacturing market projected to grow at highest CAGR during forecast period

The hardware segment is projected to grow at the highest CAGR from 2020 to 2026. Most of the AI hardware manufacturers have been in the business of providing the same hardware components for other technologies such as connected cars, machine vision cameras, and IoT for a long time. This will enable the companies to transfer the technology easily and accordingly develop the AI hardware. Moreover, the increasing participation of startups in AI hardware is complementing the growth of the hardware segment.

Quality control application of AI in manufacturing market to grow at highest CAGR during forecast period

The quality control application is expected to register the highest CAGR during the forecast period. Governments impose regulations on maintaining the quality according to certain benchmarks; for instance, the USD Food and Drug Administration (FDA) imposes stringent guidelines to regulate the quality of pharmaceutical products in accordance with the Current Good Manufacturing Practices (CGMPs). The growing use of robotics and deep learning technology in the manufacturing industry is expected to drive the growth of the AI in manufacturing market for the quality control application

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AI in manufacturing market in APAC projected to grow at highest CAGR from 2020 to 2026

The AI in manufacturing market in APAC is expected to grow at the highest CAGR during 20202026. The presence of a large number of industries, especially in countries such as China, India, and Taiwan is resulting in the adoption of AI in the manufacturing sector in APAC. The increasing adoption of AI-based robots is also fueling the market's growth in this region.

NVIDIA Corporation (US), IBM Corporation (US), Alphabet Inc. (Google) (US), Microsoft Corporation (US), Intel Corporation (US), Siemens AG (Germany), General Electric Company (US), General Vision Inc. (US), Progress Software Corporation (US), Micron Technology Inc., (US), Mitsubishi Electric Corporation (Japan), Sight Machine (US), Cisco Systems Inc., (US), and SAP SE (Germany) are the prominent players of AI in manufacturing market.

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Artificial Intelligence in Supply Chain Marketby Offering, Technology, Application (Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, Freight Brokerage), End-User Industry, and Geography - Global Forecast to 2025

Artificial Intelligence Marketby Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025

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Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve.

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Artificial Intelligence in Manufacturing Market Worth $16.7 Billion by 2026 - Exclusive Report by MarketsandMarkets - PRNewswire

(VasilyevD/iStock/Getty Images Plus)A new study casts doubts on the notion that automation is fundamentally altering American life.

NRPLUS MEMBER ARTICLEEarly in the 2010s, academics and entrepreneurs began to raise concerns about the economic consequences of artificial intelligence. Technological advances, the thinking went, would soon render vast swathes of the labor force obsolete, deepening income inequality and destabilizing society. Proponents of the automation revolution thesis called on policymakers to cushion workers from the effects of technological displacement through fiscal transfers and increased job training for technical fields.

MIT economist Erik Brynjolfsson, a pioneer in the economics of AI, said in 2014 that job loss due to automation would be the biggest challenge of our society for the next decade. Six years into the decade in question, it is time to take stock of his prediction. Is it true, as former Secretary of the Treasury Lawrence Summers said, that rapid automation isnt some hypothetical future possibility but something thats emerging before us right now?

Not quite, say economists Keller Scholl and Robin Hanson. In a paper published last month, they found that over the past 20 years, both the level and growth rate of job automation have been more or less flat. According to their analysis of 1,505 expert reports published by the Occupational Information Network (O*NET), while many workers are losing their jobs to machines, they are doing so at roughly the same rate as in the past.

Among the 261 occupational characteristics reported by O*NET such as the degrees to which jobs require creativity, physical strength, or numeracy two stand out in predicting automation: the importance of machinery and the importance of routine tasks. Unsurprisingly, assembly-line workers and data-entry clerks are particularly vulnerable to automation.

But factory work has seen a trend of automation going back several decades. Those sounding the alarms on AI have warned that not only factory workers but also skilled knowledge workers would face competition from machines. Indeed, algorithms are said to be capable of customer service, medical diagnostics, and news writing, among numerous other tasks. Yet the analysis of Scholl and Hanson indicates that workers are far more likely to be displaced by relatively dated technologies: manufacturing machinery, word processors, and spreadsheets. In other words, the types of jobs being automated havent changed much, despite technological advances.

The study also considers the vulnerability of jobs to automation by computers and machine-learning algorithms in light of two metrics devised by academics, called computerizability and machine-learning suitability. While the potential of digital technologies and AI to replace a given occupation appears to be a strong predictor of automation, its significance disappears when other factors, such as routineness, are taken into consideration. Which is to say that the threat posed by artificial intelligence is more or less the same as that posed by older technologies.

The fundamental nature of automation hasnt changed over the past 20 years, Hanson tells National Review. Theres this AI media story thats been played over and over again for the last decade, and people are so familiar with it that they dont bother to research it.

These finding calls into question the need for policies to address automation, such as former Democratic presidential candidate Andrew Yangs flagship universal-basic-income (UBI) proposal. Endorsed by a growing number of politicians and technologists, UBI is premised on the belief that automation will eliminate millions of jobs. The evidence doesnt suggest the need for such large-scale structural changes to the U.S. economy, but the threat of automation serves as an easy talking point for politicians. People pitch what they want to pitch, and frame it in terms of automation, Hanson argues. You can be pretty confident that those recommending a certain policy response to AI wont change their minds in light of his studys findings.

Because it relies on subjective reporting, the paper does not definitively disprove the automation-revolution hypothesis. In general, it is hard to get an objective picture of the magnitude of automation, and it is possible that labor experts have underestimated the rate at which it is happening. Scholl and Hanson do find that the average job is significantly more susceptible to automation today than 20 years ago, even as the level of automation remains somewhat constant. And the papers central finding that jobs dependent on technology are more likely to be automated raises the possibility of a feedback loop in which automation begets automation, potentially spurring exponential growth in the number of jobs replaced by machines.

But that possibility remains remote, and the burden of proof lies with those arguing that technology is fundamentally transforming American life, and that we must fundamentally transform public policy in response. Until they can marshal convincing evidence, we should be skeptical of proposals that would remake the economy to fit their vision.

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The Automation Revolution That Wasnt? - National Review

A point of tension that is emerging with CGI models is that their creators arent just designing them as avatars, but also giving them entire backstories, personalities, and causes to champion. Take Blawko, a digital male model and self-proclaimed sex-symbol, with tattoos and a sarcastic sense of humor. He referenced being hungover in an interview with Dazed Digital. Or consider right-wing, pro-Trump Bermuda, whose bio describes her as unapologetic, and representing a breakthrough in modern political thought. Then there is Shudu Gram, who, hopes to champion diversity in the fashion world, collaborate with creators from emerging economies and under-represented communities, and get together with up-and-coming designers.

There are major issues of transparency and authenticity here because the beliefs and opinions dont actually belong to the digital models, they belong to the models creators. And if the creators cant actually identify with the experiences and groups that these models claim to belong to (i.e., person of color, LGBTQ, etc.), then do they have the right to actually speak on those issues? Or is this a new form of robot cultural appropriation, one in which digital creators are dressing up in experiences that arent theirs?

I connected with Cameron-James Wilson, the creator behind Shudu Gram, to talk more about this and ask whether he sees the ethical implications of it all. Wilson is white and male. Shudu is Black and identifies as female. I absolutely do, [see the ethical implications], which is why I work alongside writer, Ama Badu, who is a woman of color. Its important to have that voice. He went on to say that being an ex-fashion photographer allows him to create beautiful imagery, but when it comes to developing her story and her background, authenticity was needed. I want Shudus story and her background to be just as authentic as the way she looks.

But we human models have worked really hard to have our stories heard and our authentic experiences considered, and weve fought to change the perception that we are just a sample size or a prop for clothes. Weve mobilized in groups, such as the Model Mafia network that I am a part of, to advocate for social issues and push back on exclusivity in the fashion industry. In some cases our activism has even cost us jobs. But now that we are finally starting to see changes in the industry, digital models can just land the jobs that we took risks for. Or worse, brands can just create CGIs that champion causes instead of actually having to invest in those causes themselves.

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I Am a Model And I Know That Artificial Intelligence Will Eventually Take My Job - Vogue.com