Monthly Archives: August 2021

August 26, 2021, Mountain View, Calif., Frontier Development Lab (FDL), in partnership with the SETI Institute, NASA and private sector partners including Google Cloud, are transforming space and Earth science through the application of industry-leading artificial intelligence (AI) and machine learning (ML) tools.

FDL tackles knowledge gaps in space science by pairing ML experts with researchers in physics, astronomy, astrobiology, planetary science, space medicine and Earth science.These researchers have utilized Google Cloud compute resources and expertise since 2018, specifically AI / ML technology, to address research challenges in areas like astronaut health, lunar exploration, exoplanets, heliophysics, climate change and disaster response.

With access to compute resources provided by Google Cloud, FDL has been able to increase the typical ML pipeline by more than 700 times in the last five years, facilitating new discoveries and improved understanding of our planet, solar system and the universe. Throughout this period, Google Clouds Office of the CTO (OCTO) has provided ongoing strategic guidance to FDL researchers on how to optimize AI / ML , and how to use compute resources most efficiently.

With Google Clouds investment, recent FDL achievements include:

"Unfettered on-demand access to massive super-compute resources has transformed the FDL program, enabling researchers to address highly complex challenges across a wide range of science domains, advancing new knowledge, new discoveries and improved understandings in previously unimaginable timeframes, said Bill Diamond, president and CEO, SETI Institute.This program, and the extraordinary results it achieves, would not be possible without the resources generously provided by Google Cloud.

When I first met Bill Diamond and James Parr in 2017, they asked me a simple question: What could happen if we marry the best of Silicon Valley and the minds of NASA? said Scott Penberthy, director of Applied AI at Google Cloud. That was an irresistible challenge. We at Google Cloud simply shared some of our AI tricks and tools, one engineer to another, and they ran with it. Im delighted to see what weve been able to accomplish together - and I am inspired for what we can achieve in the future. The possibilities are endless.

FDL leverages AI technologies to push the frontiers of science research and develop new tools to help solve some of humanity's biggest challenges. FDL teams are comprised of doctoral and post-doctoral researchers who use AI / ML to tackle ground-breaking challenges. Cloud-based super-computer resources mean that FDL teams achieve results in eight-week research sprints that would not be possible in even year-long programs with conventional compute capabilities.

High-performance computing is normally constrained due to the large amount of time, limited availability and cost of running AI experiments, said James Parr, director of FDL. Youre always in a queue. Having a common platform to integrate unstructured data and train neural networks in the cloud allows our FDL researchers from different backgrounds to work together on hugely complex problems with enormous data requirements - no matter where they are located.

Better integrating science and ML is the founding rationale and future north star of FDLs partnership with Google Cloud. ML is particularly powerful for space science when paired with a physical understanding of a problem space. The gap between what we know so far and what we collect as data is an exciting frontier for discovery and something AI / ML and cloud technology is poised to transform.

You can learn more about FDLs 2021 program here.

The FDL 2021 showcase presentations can be watched as follows:

In addition to Google Cloud, FDL is supported by partners including Lockheed Martin, Intel, Luxembourg Space Agency, MIT Portugal, Lawrence Berkeley National Lab, USGS, Microsoft, NVIDIA, Mayo Clinic, Planet and IBM.

About the SETI InstituteFounded in 1984, the SETI Institute is a non-profit, multidisciplinary research and education organization whose mission is to lead humanity's quest to understand the origins and prevalence of life and intelligence in the universe and share that knowledge with the world. Our research encompasses the physical and biological sciences and leverages expertise in data analytics, machine learning and advanced signal detection technologies. The SETI Institute is a distinguished research partner for industry, academia and government agencies, including NASA and NSF.

Contact Information:Rebecca McDonaldDirector of CommunicationsSETI Institutermcdonald@SETI.org

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Frontier Development Lab Transforms Space and Earth Science for NASA with Google Cloud Artificial Intelligence and Machine Learning Technology - SETI...

During armed conflict, unequal power relations and structural disadvantages derived from gender dynamics are exacerbated. There has been increased recognition of these dynamics during the last several decades, particularly in the context of sexual and gender-based violence in conflict, as exemplified for example in United Nations Security Council Resolution 1325 on Women, Peace, and Security. Though initiatives like this resolution are a positive advancement towards the recognition of discrimination against women and structural disadvantages that they suffer from during armed conflict, other aspects of armed conflict, including, notably, the use of artificial intelligence (AI) for targeting purposes, have remained resistant to insights related to gender. This is particularly problematic in the operational aspect of international humanitarian law (IHL), which contains rules on targeting in armed conflict.

The Gender Dimensions of Distinction and Proportionality

Some gendered dimensions of the application of IHL have long been recognized, especially in the context of rape and other categories of sexual violence against women occurring during armed conflict. Therefore, a great deal of attention has been paid in relation to ensuring accountability for crimes of sexual violence during times of armed conflict, while other aspects of conflict, such as the operational aspect of IHL, have remained overlooked.

In applying the principle of distinction, which requires distinguishing civilians from combatants (only the latter of which may be the target of a lawful attack), gendered assumptions of who is a threat have often played an important role. In modern warfare, often characterized by asymmetry and urban conflict and where combatants can blend in with the civilian population, some militaries and armed groups have struggled to reliably distinguish civilians. Due to gendered stereotypes of expected behavior of women and men, gender has operated as a de facto qualified identity that supplements the category of civilian. In practice this can mean that, for women to be targeted, IHL requirements are rigorously applied. Yet, in the case of young civilian males, the bar seems to be lower gender considerations, coupled with other factors such as geographical location, expose them to a greater risk of being targeted.

An illustrative example of this application of the principle of distinction is in so-called signature strikes, a subset of drone strikes adopted by the United States outside what it considers to be areas of active hostilities. Signature strikes target persons who are not on traditional battlefields without individually identifying them, but rather based only on patterns of life. According to reports on these strikes, it is sufficient that the persons targeted fit into the category military-aged males, who live in regions where terrorists operate, and whose behavior is assessed to be similar enough to those of terrorists to mark them for death. However, as the organization Article 36 notes, due to the lack of transparency around the use of armed drones in signature strikes, it is difficult to determine in more detail what standards are used by the U.S. government to classify certain individuals as legal targets. According to a New York Times report from May 2012, in counting casualties from armed drone strikes, the U.S. government reportedly recorded all military-age males in a strike zone as combatants [] unless there is explicit intelligence posthumously proving them innocent.

However, once a target is assessed as a valid military objective, the impact of gender is reversed in conducting a proportionality assessment. The principle of proportionality requires ensuring the anticipated harm to civilians and civilian objects is not excessive compared to the anticipated military advantage of an attack. But in assessing the anticipated advantage and anticipated civilian harms, the calculated military advantage can include the expected reduction of the commanders own combatant casualties as an advantage in other words, the actual loss of civilian lives can be offset by the avoidance of prospective military casualties. This creates the de facto result that the lives of combatants, the vast majority of whom are men, are weighed as more important than those of civilians who in a battlefield context, are often disproportionately women. Taking these applications of IHL into account, we can conclude that a gendered dimension is present in the operational aspect of this branch of law.

AI Application of IHL Principles

New technologies, particularly AI, have been increasingly deployed to assist commanders in their targeting decisions. Specifically, machine-learning algorithms are being used to process massive amounts of data to identify rules or patterns, drawing conclusions about individual pieces of information based on these patterns. In warfare, AI already supports targeting decisions in various forms. For instance, AI algorithms can estimate collateral damage, thereby helping commanders undertake the proportionality analysis. Likewise, some drones have been outfitted with AI to conduct image-recognition and are currently being trained to scan urban environments to find hidden attackers in other words, to distinguish between civilians and combatants as required by the principle of distinction.

Indeed, in modern warfare, the use of AI is expanding. For example, in March 2021 the National Security Commission on AI, a U.S. congressionally-mandated commission, released a report highlighting how, in the future, AI-enabled technologies are going to permeate every facet of warfighting. It also urged the Department of Defense to integrate AI into critical functions and existing systems in order to become an AI-ready force by 2025. As Neil Davison and Jonathan Horowitz note, as the use of AI grows, it is crucial to ensure that its development and deployment (especially when coupled with the use of autonomous weapons) complies with civilian protection.

Yet even if IHL principles can be translated faithfully into the programming of AI-assisted military technologies (a big and doubtful if), such translation will reproduce or even magnify the disparate, gendered impacts of IHL application identified previously. As the case of drones used to undertake signature strikes demonstrates, the integration of new technologies in warfare risks importing, and in the case of AI tech, potentially magnifying and cementing, the gendered injustices already embodied in the application of existing law.

Gendering Artificial Intelligence-Assisted Warfare

There are several reasons that AI may end up reifying and magnifying gender inequities. First, the algorithms are only as good as their inputs and those underlying data are problematic. To properly work, AI needs massive amounts of data. However, neither the collection nor selection of these data are neutral. In less deadly application domains, such as in mortgage loan decisions or predictive policing, there have been demonstrated instances of gender (and other) biases of both the programmers and the individuals tasked with classifying data samples, or even the data sets themselves (which often contain more data on white, male subjects).

Perhaps even more difficult to identify and correct than individuals biases are instances of machine learning that replicate and reinforce historical patterns of injustice merely because those patterns appear, to the AI, to provide useful information rather than undesirable noise. As Noel Sharkey notes, the societal push towards greater fairness and justice is being held back by historical values about poverty, gender and ethnicity that are ossified in big data. There is no reason to believe that bias in targeting data would be any different or any easier to find.

This means that historical human biases can and do lead to incomplete or unrepresentative training data. For example, a predictive algorithm used to apply the principle of distinction on the basis of target profiles, together with other intelligence, surveillance, and reconnaissance tools, will be gender biased if the data inserted equate military-aged men with combatants and disregard other factors. As the practice of signature drone strikes has demonstrated, automatically classifying men as combatants and women as vulnerable has led to mistakes in targeting. As the use of machine learning in targeting expands, these biases will be amplified if not corrected for with each strike providing increasingly biased data.

To mitigate this result, it is critical to ensure that the data collected are diverse, accurate, and disaggregated, and that algorithm designers reflect on how the principles of distinction and proportionality can be applied in gender-biased ways. High quality data collection means, among other things, ensuring that the data are disaggregated by gender otherwise it will be impossible to learn what biases are operating behind the assumptions used, what works to counter those biases, and what does not.

Ensuring high quality data also requires collecting more and different types of data, including data on women. In addition, because AI tools reflect the biases of those who build them, ensuring that female employees hold technical roles and that male employees are fully trained to understand gender and other biases is also crucial to mitigate data biases. Incorporating gender advisors would also be a positive step to ensure that the design of the algorithm, and the interpretation of what the algorithm recommends or suggests, considers gender biases and dynamics.

However, issues of data quality are subsidiary to larger questions about the possibility of translating IHL into code and, even if this translation is possible, the further difficulty of incorporating gender considerations into IHL code. Encoding gender considerations into AI is challenging to say the least, because gender is both a societal and individual construction. Likewise, the process of developing AI is not neutral, as it has both politics and ethics embedded, as demonstrated by documented incidents of AI encoding biases. Finally, the very rules and principles of modern IHL were drafted when structural discrimination against women was not acknowledged or was viewed as natural or beneficial. As a result, when considering how to translate IHL into code, it is essential to incorporate critical gender perspectives into the interpretation of the norms and laws related to armed conflict.

Gendering IHL: An Early Attempt and Work to be Done

An example of the kind of critical engagement with IHL that will be required is provided by the updated International Committee of the Red Cross (ICRC) Commentary on the Third Geneva Convention. Through the incorporation of particular considerations of gender-specific risks and needs (para. 1747), the updated commentary has reconsidered outdated baseline gender assumptions, such as the idea that women have non-combatant status by default, or that women must receive special consideration because they have less resilience, agency or capacity (para. 1682). This shift has demonstrated that it is not only desirable, but also possible to include a gender perspective in the interpretation of the rules of warfare. This shift also underscores the urgent need to revisit IHL targeting principles of distinction and proportionality to assess how their application impacts genders differently, so that any algorithms developed to execute IHL principles incorporate these insights from the start.

As a first cut at this reexamination, it is essential to reassert that principles of non-discrimination also apply to IHL, and must be incorporated into any algorithmic version of these rules. In particular, the principle of distinction allows commanders to lawfully target only those identified as combatants or those who directly participate in hostilities. Article 50 of Additional Protocol I to the Geneva Conventions defines civilians in a negative way, meaning that civilians are those who do not belong to the category of combatants and IHL makes no reference to gender as a signifier of identity for the purpose of assessing whether a given individual is a combatant. In this regard, being a military-aged male cannot be a shortcut to the identification of combatants. Men make up the category of civilians as well. As Maya Brehm notes, there is scope for categorical targeting within a conduct of hostilities framework, but the principle of non-discrimination continues to apply in armed conflict. Adverse distinction based on race, sex, religion, national origin or similar criteria is prohibited.

Likewise, in any attempt to translate the principle of proportionality into code, there must be recognition of and correction for the gendered impacts of current proportionality calculations. For example, across Syria between 2011 and 2016, 75 percent of the civilian women killed in conflict-related violence were killed by shelling or aerial bombardment. In contrast, 49 percent of civilian men killed in war-related violence were killed by shelling or aerial bombardment; men were killed more often by shooting. This suggests that particular tactics and weapons have disparate impacts on civilian populations that break down along gendered lines. The studys authors note that the evolving tactics used by Syrian, opposition, and international forces in the conflict contributed to a decrease in the proportion of casualties who were combatants, as the use of shelling and bombardment two weapons that were shown to have high rates of civilian casualties, especially women and children civilian casualties increased over time. Study authors also note, however, that changing patterns of civilian and combatant behavior may partially explain the increasing rates of women compared to men in civilian casualties: A possible contributor to increasing proportions of women and children among civilian deaths could be that numbers of civilian men in the population decreased over time as some took up arms to become combatants.

As currently understood, IHL does not require an analysis of the gendered impacts of, for example, the choice of aerial bombardment versus shooting. Yet this research suggests that selecting aerial bombardment as a tactic will result in more civilian women than men being killed (nearly 37 percent of women killed in the conflict versus 23 percent of men). Selecting shooting as a tactic produces opposite results, with 23 percent of civilian men killed by shooting compared to 13 percent of women. There is no right proportion of civilian men and women killed by a given tactic, but these disparities have profound, real-world consequences for civilian populations during and after conflict that are simply not considered under current rules of proportionality and distinction.

In this regard, although using force protection to limit ones own forces casualties is not forbidden, such strategy ought to consider the effect that this policy will have on the civilian population of the opposing side including gendered impacts. The compilation of data on how a certain means or method of warfare may impact the civilian population would enable commanders to take a more informed decision. Acknowledging that the effects of weapons in warfare are gendered is the first key step to be taken. In some cases, there has been progress in incorporating a gendered lens into positive IHL, as in the case of cluster munitions, where Article 5 of the convention banning these weapons notes that States shall provide gender-sensitive assistance to victims. But most of this analysis remains rudimentary and not clearly required. In the context of developing AI-assisted technologies, reflecting on the gendered impact of the algorithm is essential during AI development, acquisition, and application.

The process of encoding IHL principles of distinction and proportionality into AI systems provides a useful opportunity to revisit application of these principles with an eye toward interpretations that take into account modern gender perspectives both in terms of how such IHL principles are interpreted and how their application impacts men and women differently. As the recent update of the ICRC Commentary on the Third Geneva Convention illustrates, acknowledging and incorporating gender-specific needs in the interpretation and suggested application of the existing rules of warfare is not only possible, but also desirable.

Disclaimer:This post has been prepared as part of a research internship at theErasmus University Rotterdam, funded by the European Union (EU) Non-Proliferation and Disarmament Consortium as part of a larger EU educationalinitiative aimed at building capacity in the next generation of scholars and practitioners innon-proliferation policy and programming. The views expressed in this post are those of theauthor and do not necessarily reflect those of the Erasmus University Rotterdam, the EU Non-Proliferation andDisarmament Consortium or other members of the network.

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Embedding Gender in International Humanitarian Law: Is Artificial Intelligence Up to the Task? - Just Security

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Valued to be $4.9 Billion by 2026, Artificial Intelligence (AI) in Oil & Gas Slated for Robust Growth Worldwide - thepress.net

ADA is the assistant that operates as Artificial Intelligence on the SimpliRoute platform. It helps solve about 25 tasks and is based on machine learning.

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For more technology and data that one integrates into a software, in the end always experience and learning are the fundamental pillars. The important thing is to understand how to extract them intelligently . With that phrase, lvaro Echeverra, co-founder and CEO of SimpliRoute, recalls the need that shaped the idea of creating an AI virtual assistant to optimize its logistics platform.

The startup is dedicated to optimizing routes for dispatch vehicles. The problem, according to Echeverra, was that despite the fact that logarithms and data science effectively optimize logistics a lot, there are things that no default software can evaluate, such as whether a street is in poor condition, whether it is too narrow for a truck. or if it is unsafe at a certain time. This valuable information is held by the drivers .

This premise led us to think of intelligence as the core of the operation, capable of learning from the behavior of the drivers who use the platform. Today, after more than a year of development, this has resulted in ADA, the first AI Virtual Assistant developed 100% in-house and integrated into a logistics platform, such as the popular Siri on Apple devices.

Photo: SimpleRoute

ADA has been fully integrated into SimpliRoute for a few months, and its mission is to send alerts and suggestions to drivers of companies that use the platform, in addition to collecting learning to reschedule future actions and thus further optimize routes. For example, based on learning, the AI recommends which driver should use which vehicle based on the performance of each one on historic routes; whether the company should change its fleet size based on historical utilization; o suggest optimized time windows when dispatching; among other tasks.

For us it is a big step to implement our own AI that works as a nuclear intelligence that collects the real experience in the street. Our focus as a Chilean scaleup is to be at the technological forefront in the world, and we will only achieve this by constantly improving our integration with artificial intelligence and machine learning , says the CEO of Simpliroute. .

Currently, the AI is already working together with the drivers on the new version of the app. And while for now it issues alerts and works in the background, it is expected that users will soon be able to interact directly with the AI to request information or advice.

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Artificial Intelligence as the core of logistics operation - Entrepreneur

New York, USA, Aug. 25, 2021 (GLOBE NEWSWIRE) -- According to a report published by Research Dive, artificial intelligence in construction market is expected to generate a revenue of $2,642.4 million, growing at a CAGR of 26.3% during the forecast period (2019-2026). The inclusive report provides a brief overview of the current scenario of the market including significant aspects of the market such as growth factors, challenges, restraints and various opportunities during the forecast period. The report also provides all the market figures making it easier and helpful for the new participants to understand the market.

Download FREE Sample Report of the Global Artificial Intelligence in Construction Market: https://www.researchdive.com/download-sample/46

Dynamics of the Market

Drivers: The application of artificial intelligence does not only provide a great deal of efficacy and productivity in various construction processes, but it also reduces the overall time required to complete any given task. Moreover, companies can save a lot of money by adopting AI in their construction processes. These factors are expected to drive the growth of the market during the forecast period.

Restraints: Lack in availability of skilled and knowledgeable professionals is expected to impede the growth of the market during the forecast period.

Opportunities: Persistent technological advancements in AI and IOTs are expected to create vital opportunities for the growth of the market during the forecast period.

Check out How COVID-19 impacts the Global Artificial Intelligence in Construction Market: https://www.researchdive.com/connect-to-analyst/46

Segments of the Market

The report has divided the market into different segments based on application and region.

Application: Planning and Design Sub-segment to be Most Profitable

The planning and design sub-segment are expected to grow exponentially with a CAGR of 28.9% during the forecast period. Massive amount of money is being invested in the planning, designing, research, architecture and so on for the construction of buildings, especially with the help of artificial intelligence. This factor is expected to bolster the growth of the sub-segment during the forecast period.

Check out all Information and communication technology & media Industry Reports: https://www.researchdive.com/information-and-communication-technology-and-media

Region: Europe Anticipated to have the Highest Growth Rate

European AI in construction market is expected to grow exponentially in the coming years with a CAGR of 26.7% during the forecast period. The adoption of Industry 4.0, eased governmental regulations and advancements in internet of things (IOT) are expected to fuel the growth of the market during the forecast period.

Access Varied Market Reports Bearing Extensive Analysis of the Market Situation, Updated With The Impact of COVID-19: https://www.researchdive.com/covid-19-insights

Key Players of the Market

Autodesk, Inc., Building System Planning, Inc. Smartvid.io, Inc. Komatsu Ltd NVIDIA Corporation Doxel Inc. Volvo AB Dassault Systemes SE

For instance, in May 2021, Procore Technologies Inc., a leading provider of construction management software, acquired INDUS.AI, an advanced AI construction platform, to add computer vision abilities to the Procore platform in order to maximize its efficiency and future profitability.

The report also summarizes many important aspects including financial performance of the key players, SWOT analysis, product portfolio, and latest strategic developments.Click Here to Get Absolute Top Companies Development Strategies Summary Report.

TRENDING REPORTS WITH COVID-19 IMPACT ANALYSIS

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Quantum Computing Market: https://www.researchdive.com/8332/quantum-computing-market

Payment Processing Solutions Market: https://www.researchdive.com/416/payment-processing-solutions-market

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Artificial Intelligence in Construction Market Estimated to Generate a Revenue of $2642.4 Million by 2026, Growing - GlobeNewswire

NVIDIAs CEO Jensen Huang had been addressing a virtual audience during his keynote at the GTC21 event like he had done beforefrom his kitchen.

But this time around, the kitchen keynote (as it had been dubbed) got a bit sci-fi. For 14 seconds of the 1-hour-and-48-minute presentation, Huang wasnt quite himself. Instead, a photorealistic digital clone of the CEO (and his kitchen) popped up on screenand no one knew.

The GPU Technology Conference (GTC), which took place online this spring from April 12-16, showcases the latest in artificial intelligence, accelerated computing, intelligent networking, game development and more. So it makes sense that the company would show off its tech prowess there. Based in Santa Clara, Calif., NVIDIA designs graphics processing units for various industries, including gaming and automotive.

To create the virtual version of Huang, a full face and body scan was done to create a 3D model, then AI was trained to mimic his gestures and expressions, all via the companys Omniverse technology, a multidisciplinary collaboration tool for creating 3D virtual spaces. Unlike the common approach of creating a 3D digital replica of a real person where you scan and capture as much data of that person as possible, we set a very difficult goal of replicating Jensen's behavior and performance without much data of him, explained David Wright, VP and executive creative director of NVIDIA.

Wright explained that the concept began around February, with the final version of Huang being built, using a voice recording of his keynote, roughly a week before the event.

The use of virtual stand-ins at digital events isnt necessarily new, and this past year has seen a sharp increase in the development and implementation of avatar-based platforms. But those characters dont really look like you or me.

But what if they could?

Founded in 2017, U.K.-based Synthesia set out to make it easier to create synthetic video content. Its now the world's largest platform for AI video generation, boasting the creation of six million videos to date.

Just like Photoshop completely changed how we work with photos, keyboards and computers completely changed how we work with text from pen and paper, of course, and in music, synthesizers and software have also completely changed how we create songs today, explained Victor Riparbelli, CEO and co-founder of Synthesia, about the technologys impact on video production.

Interestingly, in order to create a video in Synthesia, you need to check the captcha box that states Im not a robot. Victor Riparbelli, the company's CEO and co-founder, said that the company continues to work on avatar realism, making our avatars come to life more. You can add emotions to them, make them smile, make them sad, make them happy, make them nod their heads. Watch the video we made.Screenshot: Courtesy of SynthesiaTo create an AI-generated video on Synthesia, users either select an existing avatar image or design a custom one by submitting three to four minutes of video footage and a script thats used to build talking head-style videos.

Primarily used by companies for training, learning and marketing and sales purposes, Synthesias API can be used to create personalized event invites, video chat bots or virtual facilitators, interactive videos and interstitial videos during conferences.

We're working on making experiences that today are text-driven and making them video-driven, Riparbelli said. For example, a warehouse worker in a big tech company consuming their training as a two-minute video versus a five-page PDF is a much better experience.

One of Synthesia's clients, EY (formerly known as Ernst & Young) uses AI avatars, not as a replacement for taking real meetings, but after they've had a call, instead of sending an email, they can now send the video, he said. While Volkswagen trains teams at its car dealerships around the world. The software is able to translate text into 55 languages, which is key since the company works with many global companies that need to communicate to remote team members across borders.

The company is also currently working with a conference producer to create AI-generated content for upcoming in-person events, using interactive videos at kiosks to help navigate attendees throughout the space. Riparbelli also explained that the technology could be used to easily insert different data points such as location or industry into sponsored messages, similar to auto-generated email formats.

I think there's never been a bigger need among people to consume information by video, Riparbelli said. I think businesses very much realized that if they communicate by text it's just not as effective. They want to communicate by video because they want to increase engagement. They want to increase conversion rates. They want to increase information retention. And video is just the natural way to do that. But he noted that the costs and lengthy production process of shooting IRL videos make it prohibitive and unfeasible for most companies.

According to company research, Riparbelli said that nine out of 10 people dont realize they're watching a synthetic videoprobably because theyre not looking for it.

This brings up the question of the ethical use of such content. Several years ago, AI-generated imagery, commonly known as deepfakes, of Hollywood actors presented in compromising positions made headlines, which raised concerns over the potential dangers of this type of content. Riparbelli explained that Synthesia has safeguards in place to prevent users from abusing the platform. That includes requesting consent when creating custom avatars.

Despite the possible pitfalls, both Wright and Riparbelli emphasized the desire to make the technology easier to use.

Regarding the future, we do not pause. We are always pushing the boundaries of what is possible today and creating something new, Wright said. We want to make it easier and faster for anyone to create digital characters. We will always be working on virtual humans, virtual avatars and the like, and we will continue to bridge the experience between the physical and virtual worlds closer together.

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Is This CEO Real or Fake? How Artificial Intelligence Is Taking Over the Event Industry - BizBash

The US and its allies have found themselves in the middle of an AI arms race, with the prize of decision dominance on the battlefield for whoever gets there first.

Brought to you in partnership with Systel

Artificial intelligence (AI) is widely recognised as a vital military capability that will only grow in importance in the era of multi-domain operations (MDO).

But what does this mean in practical terms, and how will the technology change the modern battlefield?

In the MDO concept also known as Joint All-Domain Command and Control (JADC2) platforms and systems across land, sea, air, space and cyber will interact and reinforce one another.

To make this possible, militaries will process and exploit vast reams of data, meaning that information processing and human-machine teaming will be essential. AI can provide vital advantages in all these areas, sifting through data at a rate far beyond any human operator.

When asked what brings the urgency to this space today, defence sources stress that there is little choice. Commercial technological innovations in AI have led to rapid, transformative changes across all service branches for all major powers.

Aneesh Kothari, vice president of marketing at Systel, a manufacturer of rugged computers, highlights that the US Department of Defenses Third Offset Strategy, for instance, holds that rapid advances in AI along with robotics, autonomy, big data and increased collaboration with industry will define the next generation of warfare.

We are in the middle of an AI arms race, and the end goal is decision dominance on the battlefield, Kothari said, noting that the same impulses are driving US allies and their adversaries.

AI enables operators to move past the limits of human capacity for mission-critical data-processing workloads. It reduces a significant degree of risk to personnel on the battlefield, such as the increasing ability to deploy uncrewed vehicles.

There is a wide range of programmes aiming to exploit such advances. One example is the UK Royal Air Forces Nexus Combat Cloud, which allows data from any sensor on any platform in a given operating space to be processed in real-time. The service has also advanced a swarming drone capability through the Alvina programme.

The area has also naturally become a growing focus for industry. BAE Systems, for instance, has worked on AI in a range of areas, with some of this coming through Defense Advanced Research Project Agency (DARPA) programmes.

Such work includes MindfuL, software that can independently audit Machine Learning-based systems, helping build trust in the technology, which will be crucial as militaries boost their focus on human-machine teaming.

BAE Systems is also developing the Multi-domain Adaptive Request Service (MARS) for DARPA, which will enable semi-autonomous multi-domain mission planning.

Michael Miller, technical area director for BAE Systems FAST Labs, said that MARS significantly increases available resources, enabling battle managers to solve unforeseen requirements in a dynamic tactical environment rapidly. Crucially, the system empowers human operators, an essential element of AIs practical utility on the battlefield.

The beauty of it is that it actually allows the human to make that final decision; it helps them find important capabilities and lets them decide which is the one they prefer, Miller explained.

AI and machine learning will help not just with data processing but also managing that data.

Fundamental to MDO or JADC2 is that in great power competition, communications will not be as assured as they once were in fact, they will be under attack.

Data must be moved judiciously, while forward forces will be dispersed, disaggregated and sometimes disconnected, said Jim Wright, technical director for intelligence, surveillance and reconnaissance systems at Raytheon Intelligence & Space.

Against this backdrop, Raytheon is working on architectures in which cognitive agents manage the data flow, he said, considering the commanders intent, how the battlefield is evolving, and the threats to communications, then using this information to determine how data should be placed.

Wright noted that AI/ML would support not just data processing, but works itself into the management of data around the network.

Nevertheless, the US and its allies dont operate in isolation. As they develop their capabilities, so do potential rivals, most obviously China.

John Parachini, a senior international defence researcher at the RAND Corporation, pointed to several ways the country is applying the technology, including domestic security.

China is also making significant progress in applying AI to uncrewed vehicles, he noted. Likewise, Russia has made significant advances, particularly in the ground domain. However, the robotics must fit in with what a military force is trying to do and the environment in which it operates.

Other countries have also made substantial progress, including Israel and Turkey. Its when the systems are used in the field that you see the successes and failures which is the real way that leapfrog advances are made, Parachini said, pointing to the use of Turkish drones in Syria and other regions.

Its those experiences that will provide the lessons learned that will allow them to improve their capabilities, he argued.

AI and humans have complementary strengths and weaknesses. While AI provides unrivalled data processing and management capabilities, humans can introduce a different perspective and intuition.

When these are combined, the result is an increasingly resilient capability. For example, Miller points to tools like Google Maps, which develop a new solution if a human deviates from a route.

Similarly, in defence applications, human intuition still matters, he said: human understanding of intangibles, things that the algorithm itself cant contemplate.

Machines and humans have complementary strengths and weaknesses, Kothari noted. We must align these in the most productive way. While machines have exponentially faster abilities to crunch data, human intuition will remain critical for tactical decision making.

The human ability to see all the shades of grey, complemented by the machines ability to see black and white incredibly quickly and accurately, is a very powerful combination and a winning combination for the nation that gets it right.

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Artificial intelligence becomes the critical enabler of future operations (Studio) - Shephard News

Edge computing is witnessing a significant interest with new use cases, especially after the introduction of 5G. The 2021 State of the Edge report by the Linux Foundation predicts that the global market capitalization of edge computing infrastructure would be worth more than $800 billion by 2028. At the same time, enterprises are also heavily investing in artificial intelligence (AI). McKinseys survey from last year shows that 50% of the respondents have implemented AI in at least one business function.

While most companies are making these tech investments as a part of their digital transformation journey, forward-looking organizations and cloud companies see new opportunities by fusing edge computing and AI, or Edge AI. Lets take a closer look at the developments around Edge AI and the impact this technology is bringing on modern digital enterprises.

AI relies heavily on data transmission and computation of complex machine learning algorithms. Edge computing sets up a new age computing paradigm that moves AI and machine learning to where the data generation and computation actually take place: the networks edge. The amalgamation of both edge computing and AI gave birth to a new frontier: Edge AI.

Edge AI allows faster computing and insights, better data security, and efficient control over continuous operation. As a result, it can enhance the performance of AI-enabled applications and keep the operating costs down. Edge AI can also assist AI in overcoming the technological challenges associated with it.

Edge AI facilitates machine learning, autonomous application of deep learning models, and advanced algorithms on the Internet of Things (IoT) devices itself, away from cloud services.

Also read: Data Management with AI: Making Big Data Manageable

An efficient Edge AI model has an optimized infrastructure for edge computing that can handle bulkier AI workloads on the edge and near the edge. Edge AI paired with storage solutions can provide industry-leading performance and limitless scalability that enables businesses to use their data efficiently.

Many global businesses are already reaping the benefits of Edge AI. From improving production monitoring of an assembly line to driving autonomous vehicles, Edge AI can benefit various industries. Moreover, the recent rolling out of 5G technology in many countries gives an extra boost for Edge AI as more industrial applications for the technology continue to emerge.

A few benefits of edge computing powered by AI on enterprises include:

Implementation of Edge AI is a wise business decision as Insight estimates an average 5.7% return on Investment (ROI) from industrial Edge AI deployments over the next three years.

Machine learning is the artificial simulation of the human learning process with the use of data and algorithms. Machine learning with the aid of Edge AI can lend a helping hand, particularly to businesses that rely heavily on IoT devices.

Some of the advantages of Machine Learning on edge are mentioned below.

Privacy: Today, information and data being the most valuable assets, consumers are cautious of the location of their data. The companies that can deliver AI-enabled personalized features in their applications can make their users understand how their data is being collected and stored. It enhances the brand loyalty of the customers.

Reduced Latency: Most of the data processes are carried out both on network and device levels. Edge AI eliminates the requirement to send huge amounts of data across networks and devices; thus, improve the user experience.

Minimal Bandwidth: Every single day, an enterprise with thousands of IoT devices has to transmit huge amounts of data to the cloud. Then carry out the analytics in the cloud, and retransmit the analytics results back to the device. Without a wider network bandwidth and cloud storage, this complex process would turn it into an impossible task. Not to mention the possibility of exposing sensitive information during the process.

However, Edge AI implements cloudlet technology, which is small-scale cloud storage located at the networks edge. Cloudlet technology enhances mobility and reduces the load of data transmission. Consequently, it can bring down the cost of data services and enhance data flow speed and reliability.

Low-Cost Digital Infrastructure: According to Amazon, 90% of digital infrastructure costs come from Inference a vital data generation process in machine learning. Sixty percent of organizations surveyed in a recent study conducted by RightScale agree that the holy grail of cost-saving hides in cloud computing initiatives. Edge AI, in contrast, eliminates the exorbitant expenses incurred on the AI or machine learning processes carried out on cloud-based data centers.

Also read: Best Machine Learning Software in 2021

Developments in knowledge such as data science, machine learning, and IoT development have a more significant role in the sphere of Edge AI. However, the real challenge lies in strictly following the trajectory of the developments in computer science. In particular, next-generation AI-enabled applications and devices that can fit perfectly within the AI and machine learning ecosystem.

Fortunately, the arena of edge computing is witnessing promising hardware development that will alleviate the present constraints of Edge AI. Start-ups like Sima.ai, Esperanto Technologies, and AIStorm are among the few organizations developing microchips that can handle heavy AI workloads.

In August 2017, Intel acquired Mobileye, a Tel Aviv-based vision-safety technology company, for $15.3 billion. Recently, Baidu, a Chinese multinational technology behemoth, initiated the mass-production of second-generation Kunlun AI chips, an ultrafast microchip for edge computing.

In addition to microchips, Googles Edge TPU, Nvidias Jetson Nano, along with Amazon, Microsoft, Intel, and Asus, embarked on the motherboard development bandwagon to enhance edge computings prowess. Amazons AWS DeepLens, the worlds first deep learning enabled video camera, is a major development in this direction.

Also read: Edge Computing Set to Explode Alongside Rise of 5G

Poor Data Quality: Poor quality of data of major internet service providers worldwide stands as a major hindrance for the research and development in Edge AI. A recent Alation report reveals that 87% of the respondents mostly employees of Information Technology (IT) firms confirm poor data quality as the reason their organizations fail to implement Edge AI infrastructure.

Vulnerable Security Feature: Some digital experts claim that the decentralized nature of edge computing increases its security features. But, in reality, locally pooled data demands security for more locations. These increased physical data points make an Edge AI infrastructure vulnerable to various cyberattacks.

Limited Machine Learning Power: Machine learning requires greater computational power on edge computing hardware platforms. In Edge AI infrastructure, the computation performance is limited to the performance of the edge or the IoT device. In most cases, large complex Edge AI models have to be simplified prior to the deployment to the Edge AI hardware to increase its accuracy and efficiency.

Virtual assistants like Amazons Alexa or Apples Siri are great benefactors of developments in Edge AI, which enables their machine learning algorithms to deep learn at rapid speed from the data stored on the device rather than depending on the data stored in the cloud.

Automated optical inspection plays a major role in manufacturing lines. It enables the detection of faulty parts of assembled components of a production line with the help of an automated Edge AI visual analysis. Automated optical inspection allows highly accurate ultrafast data analysis without relying on huge amounts of cloud-based data transmission.

The quicker and accurate decision-making capability of Edge AI-enabled autonomous vehicles results in better identification of road traffic elements and easier navigation of travel routes than humans. It results in faster and safer transportation without manual interference.

Apart from all of the use cases discussed above, Edge AI can also play a crucial role in facial recognition technologies, enhancement of industrial IoT security, and emergency medical care. The list of use cases for Edge AI keeps growing every passing day. In the near future, by catering to everyones personal and business needs, Edge AI will turn out to be a traditional day-to-day technology.

Read next: Detecting Vulnerabilities in Cloud-Native Architectures

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Edge AI: The Future of Artificial Intelligence and Edge Computing | ITBE - IT Business Edge

NEW YORK, Aug. 24, 2021 /PRNewswire/ -- TheDeloitte AI Institutetoday unveiled a new report that examines the most compelling business use cases for artificial intelligence (AI) across six major industries. The report, "The AI Dossier," helps business leaders understand the value AI can deliver today and in the future so that they can make smarter decisions about when, where and how to deploy AI within their organizations.

"The AI Dossier" illustrates use cases across six industries, including consumer; energy, resources and industrial; financial services; government and public services; life sciences and health care; and technology, media and telecommunications. For each industry, the report highlights the most valuable, business-ready use cases for AI-related technologies examining the key business issues and opportunities, how AI can help, and the benefits that are likely to be achieved. The report also highlights the top emerging AI use cases that are expected to have a major impact on the industry's future.

"Artificial intelligence has made the leap to practical reality and is quickly becoming a competitive necessity. Yet, amidst the current frenzy of AI advancement and adoption, many leaders are questioning what AI can actually do for their businesses," said Nitin Mittal, U.S. AI co-leader and principal, Deloitte Consulting LLP. "The AI Dossier can help these leaders understand the value AI can deliver and how to prioritize their investment in AI, today and in the future."

Deloitte's "State of AI in the Enterprise, 3rd Edition"study found that 74% of businesses are still in the AI experimentation stage with a focus on modernizing their data for AI and building AI expertise through an assortment of siloed pilot programs and proofs-of-concept, but without a clear vision of how all the pieces fit together. By contrast, only 26% of businesses are focused on deploying high impact AI use cases at scale, which is where AI can create real value.

"While AI adoption rates and maturity vary widely across industries, AI is driving new levels of efficiency and performance for businesses of all sizes," said Irfan Saif, U.S. AI co-leader, Deloitte Risk & Financial Advisory, and principal, Deloitte & Touche LLP. "Organizations have the opportunity to unlock the full potential of AI when they embrace it and deploy it at scale throughout their enterprise."

Six ways AI creates value for business

The report looks across all the industry-specific use cases to identify six major ways AI can create value for business:

The Deloitte AI Institute supports the positive growth and development of AI through engaged conversations and innovative research. It also focuses on building ecosystem relationships that help advance human-machine collaboration in the Age of With, a world where humans work side-by-side with machines.

About Deloitte

Deloitte provides industry-leading audit, consulting, tax and advisory services to many of the world's most admired brands, including nearly 90% of the Fortune 500 and more than 7,000 private companies.Our people come togetherfor the greater good and work across the industry sectors that drive and shape today's marketplace delivering measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to see challenges as opportunities to transform and thrive, and help lead the way toward a stronger economy and a healthier society. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them.Building on more than 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte's more than 330,000 people worldwide connect for impact at http://www.deloitte.com.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see http://www.deloitte.com/aboutto learn more about our global network of member firms.

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Deloitte AI Institute Unveils the Artificial Intelligence Dossier, a Compendium of the Top Business Use Cases for AI - KPVI News 6

Free Guy bills itself as a comedy, but it exists in a world in which Ryan Reynolds doesnt exist. Which, of course, makes it a tragedy. The movie solves this problem by building its own Ryan Reynolds out of code, inside a video game. A reasonable response to such a terrible lack. But,Guy (Reynolds) is more than just a simple NPC. He's self-aware and, in a way, alive.

In truth, the emergence of Guy as a fully-fledged awareness inside the game wasnt wholly directed. Instead, he blossomed from a set of prior conditions, much like the IOs in Tron Legacy, having learned from his experiences inside the game environment.

If anyone has yet built living entities out of zeroes and ones, theyre keeping that information to themselves (a terrifying prospect) but artificial intelligences have long been a staple of video games. And theyre getting smarter.

WOULD YOU LIKE TO PLAY A GAME?

For decades, video games have been a ready benchmark for testing the latest artificial intelligences. First, we need a quick primer in defining terms. While the term "artificial intelligence"conjures images of replicants and Skynet, it can refer to any number of systems designed to help a computer or machine complete a task.

The simplest of these systems is reactive; a machine takes in a set of conditions and, based on its programming, determines an action. These sorts of AI are common in video games. An enemy may attack once youve entered into a pre-determined perimeter then, depending on conditions set by the game designers, its behavior plays out. Maybe it continues to attack until you or it are defeated. Maybe it attacks until its health bar reaches a critical low and then retreats.

To the player, the game character appears to be making decisions, even while its essentially navigating a flowchart. And this sort of AI will behave in the more-or-less the same wayeach time you encounter it. It isnt thinking about what happened in the past or what might happen in the future, its simply taking existing conditions, bumping them up against potential actions, and selecting from those available.

It could be argued that these types of AI have existed since the dawn of video games. Even the computer opponent in Pong took a measure of the playing field and altered position in order to better defend the ball. Were that not the case, the opposing cursor would simply move randomly along the field, and the game would be no fun.

More advanced AI have limited memory, they store at least some of their past interactions and use that knowledge to modify future behavior. This is closer to what we think of when we think of AI, a machine that not only thinks, but learns and then thinks differently.

Much was made of the Nemesis system in Warner Bros. Middle-earth: Shadow of Mordor, when the game dropped. Instead of seemingly brainless adversaries which could be defeated through sheer force of will, Shadow of Mordor offered something closer to life. Enemies remember you, hold grudges, and alter their tactics based on yours. They have limited memory, and they learn from you. It made for a different sort of game-play experience by making the other characters a little more real.

In 2019 Googles AlphaStar AI, built by their DeepMind division, set about rising the ranks of StarCraft II. The folks at DeepMind chose StarCraft because of its complexity when compared to games like chess.

Chess, with its comparatively limited tokens and move-types, still boasts a truly staggering number of possibilities. StarCraft ratches up the complexity, making it a reasonable next step for game AI. The team started by feeding AlphaStar roughly a million games played by human players. Next, they created an artificial league, pitting versions of AlphaStar against one another. The system learned.

Eventually, AlphaStar was let loose on some of the best StarCraft players in the world and quickly rose in the ranks. While it didnt beat everyone, it did place in the top half-a-percent, and thats even with its speed capped to match what humans are capable of. All of this was possible due to AlphaStars ability to take in information and learn from it, refining its process as it goes.

Most modern AI are built on this model. They take in a data set, either provided in advance or learned through interaction, and use it to build a model of the world. Or, at leasta model of the thin slice of the world with which they are concerned. Image detection programs are trained on previously viewed images. They look for patterns and, over time, get better at recognizing novel images for what they are. Chatbots do something similar, cataloging the various conversations they have with people or with other bots, to improve their responses. Each of these programs can become skilled at limited tasks, matching or even exceeding human ability.

Those limits, the boundaries within which AI operate, are precisely what make them good at their jobs. Its also what prevents them from awareness. AlphaStar might be good at StarCraft, but it doesnt enjoy the thrill of victory. For that, wed need

THEORY OF MIND AND FULL AWARENESS

We cant get a robot uprising, a mechanical Haley Joel Osment, or a digital Ryan Reynolds without stepping up our game. Building truly intelligent machines requires that they have a theory of mind, meaning they understand there are other thinking entities with feelings and intentions.

This understanding is critical to cooperation and requires a machine to not only understand a specific task, but to more fully understand the world around them. Today, even the best AI are operating solely from the information theyve been given or have gleaned from interaction.

The goal is to have machines capable of taking in the complex and seemingly random information in the real world and making decisions similar to the ones we would make. Instead of delivering commands, they would pick up on social queues, unspoken human behavior, and unpredictable chance occurrences. We, likewise, should be able to have some understanding of their experience if it can be called experience if we hope to collaborate meaningfully.

Alan Winfield, a professor of robot ethics at the University of West England, suggests one way of accomplishing an artificial theory of mind. By allowing machines to run internal simulations of themselves and other actors (machines and humans), they might be able to play out potential futures and their consequences. In this way, they might gain an understanding that other entities exist, have motives and intentions, and how to parse them.

One major difficulty is the stark reality that we dont really understand theory of mind even in ourselves. So much of what our brains dohappens behind the scenes, and its the amalgamation of those processes which likely result in awareness.

This, too, is the major hurdle in designing machines with full awareness. But pushing toward a machine theory of mind might help us get closer. If human awareness is an emergent property of countless simpler processes all working together, that might also be the path for achieving awareness in machines or programs. Not with a flash, but slowly and by degrees.

True artificial intelligence of the kind seen in novels, movies, and television shows is probablya long way off but the seeds may already be planted, maybe even in a video game you've been playing.

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How close are we to Free Guy's digital awareness? The Science Behind the Ficiton - SYFY WIRE