Daily Archives: August 28, 2021

Country

United States of AmericaUS Virgin IslandsUnited States Minor Outlying IslandsCanadaMexico, United Mexican StatesBahamas, Commonwealth of theCuba, Republic ofDominican RepublicHaiti, Republic ofJamaicaAfghanistanAlbania, People's Socialist Republic ofAlgeria, People's Democratic Republic ofAmerican SamoaAndorra, Principality ofAngola, Republic ofAnguillaAntarctica (the territory South of 60 deg S)Antigua and BarbudaArgentina, Argentine RepublicArmeniaArubaAustralia, Commonwealth ofAustria, Republic ofAzerbaijan, Republic ofBahrain, Kingdom ofBangladesh, People's Republic ofBarbadosBelarusBelgium, Kingdom ofBelizeBenin, People's Republic ofBermudaBhutan, Kingdom ofBolivia, Republic ofBosnia and HerzegovinaBotswana, Republic ofBouvet Island (Bouvetoya)Brazil, Federative Republic ofBritish Indian Ocean Territory (Chagos Archipelago)British Virgin IslandsBrunei DarussalamBulgaria, People's Republic ofBurkina FasoBurundi, Republic ofCambodia, Kingdom ofCameroon, United Republic ofCape Verde, Republic ofCayman IslandsCentral African RepublicChad, Republic ofChile, Republic ofChina, People's Republic ofChristmas IslandCocos (Keeling) IslandsColombia, Republic ofComoros, Union of theCongo, Democratic Republic ofCongo, People's Republic ofCook IslandsCosta Rica, Republic ofCote D'Ivoire, Ivory Coast, Republic of theCyprus, Republic ofCzech RepublicDenmark, Kingdom ofDjibouti, Republic ofDominica, Commonwealth ofEcuador, Republic ofEgypt, Arab Republic ofEl Salvador, Republic ofEquatorial Guinea, Republic ofEritreaEstoniaEthiopiaFaeroe IslandsFalkland Islands (Malvinas)Fiji, Republic of the Fiji IslandsFinland, Republic ofFrance, French RepublicFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabon, Gabonese RepublicGambia, Republic of theGeorgiaGermanyGhana, Republic ofGibraltarGreece, Hellenic RepublicGreenlandGrenadaGuadaloupeGuamGuatemala, Republic ofGuinea, RevolutionaryPeople's Rep'c ofGuinea-Bissau, Republic ofGuyana, Republic ofHeard and McDonald IslandsHoly See (Vatican City State)Honduras, Republic ofHong Kong, Special Administrative Region of ChinaHrvatska (Croatia)Hungary, Hungarian People's RepublicIceland, Republic ofIndia, Republic ofIndonesia, Republic ofIran, Islamic Republic ofIraq, Republic ofIrelandIsrael, State ofItaly, Italian RepublicJapanJordan, Hashemite Kingdom ofKazakhstan, Republic ofKenya, Republic ofKiribati, Republic ofKorea, Democratic People's Republic ofKorea, Republic ofKuwait, State ofKyrgyz RepublicLao People's Democratic RepublicLatviaLebanon, Lebanese RepublicLesotho, Kingdom ofLiberia, Republic ofLibyan Arab JamahiriyaLiechtenstein, Principality ofLithuaniaLuxembourg, Grand Duchy ofMacao, Special Administrative Region of ChinaMacedonia, the former Yugoslav Republic ofMadagascar, Republic ofMalawi, Republic ofMalaysiaMaldives, Republic ofMali, Republic ofMalta, Republic ofMarshall IslandsMartiniqueMauritania, Islamic Republic ofMauritiusMayotteMicronesia, Federated States ofMoldova, Republic ofMonaco, Principality ofMongolia, Mongolian People's RepublicMontserratMorocco, Kingdom ofMozambique, People's Republic ofMyanmarNamibiaNauru, Republic ofNepal, Kingdom ofNetherlands AntillesNetherlands, Kingdom of theNew CaledoniaNew ZealandNicaragua, Republic ofNiger, Republic of theNigeria, Federal Republic ofNiue, Republic ofNorfolk IslandNorthern Mariana IslandsNorway, Kingdom ofOman, Sultanate ofPakistan, Islamic Republic ofPalauPalestinian Territory, OccupiedPanama, Republic ofPapua New GuineaParaguay, Republic ofPeru, Republic ofPhilippines, Republic of thePitcairn IslandPoland, Polish People's RepublicPortugal, Portuguese RepublicPuerto RicoQatar, State ofReunionRomania, Socialist Republic ofRussian FederationRwanda, Rwandese RepublicSamoa, Independent State ofSan Marino, Republic ofSao Tome and Principe, Democratic Republic ofSaudi Arabia, Kingdom ofSenegal, Republic ofSerbia and MontenegroSeychelles, Republic ofSierra Leone, Republic ofSingapore, Republic ofSlovakia (Slovak Republic)SloveniaSolomon IslandsSomalia, Somali RepublicSouth Africa, Republic ofSouth Georgia and the South Sandwich IslandsSpain, Spanish StateSri Lanka, Democratic Socialist Republic ofSt. HelenaSt. Kitts and NevisSt. LuciaSt. Pierre and MiquelonSt. Vincent and the GrenadinesSudan, Democratic Republic of theSuriname, Republic ofSvalbard & Jan Mayen IslandsSwaziland, Kingdom ofSweden, Kingdom ofSwitzerland, Swiss ConfederationSyrian Arab RepublicTaiwan, Province of ChinaTajikistanTanzania, United Republic ofThailand, Kingdom ofTimor-Leste, Democratic Republic ofTogo, Togolese RepublicTokelau (Tokelau Islands)Tonga, Kingdom ofTrinidad and Tobago, Republic ofTunisia, Republic ofTurkey, Republic ofTurkmenistanTurks and Caicos IslandsTuvaluUganda, Republic ofUkraineUnited Arab EmiratesUnited Kingdom of Great Britain & N. IrelandUruguay, Eastern Republic ofUzbekistanVanuatuVenezuela, Bolivarian Republic ofViet Nam, Socialist Republic ofWallis and Futuna IslandsWestern SaharaYemenZambia, Republic ofZimbabwe

Read more here:

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.

Submit your email below to get an exclusive glimpse of Chapter 3: Good Idea! How Do I Know If I Have a Great Idea for a Business?

August25, 20213 min read

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.

Go here to read the rest:

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

Point of Sale Software Market: https://www.researchdive.com/8423/point-of-sale-software-market

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

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

Here is the original post:

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.

Read more from the original source:

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.

Read the original:

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

Continue reading here:

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.

View original content to download multimedia:https://www.prnewswire.com/news-releases/deloitte-ai-institute-unveils-the-artificial-intelligence-dossier-a-compendium-of-the-top-business-use-cases-for-ai-301361300.html

SOURCE Deloitte AI Institute

Read the original post:

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.

Visit link:

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

Artificial Intelligence has catapulted in recent years, and the advancements being made in this field make me feel as if it wont be long before we have robots walking amongst us all the time! There was a point in time when the only forms of robots that we could see were toys or vacuum cleaners, or if we were lucky an AI-enabled lawnmower in some tech-trendy individuals backyard! But we have come a long long way since then. From a basketball-playing Japanese robot at the Tokyo 2021 Olympics to a Microsoft-powered robot that cleans up littered cigarette butts on the beach the potential and scope of robots grow exponentially day by day. The world at large is slowly moving away from the perception of robots as evil beings who want to take over the Earth, and accepting that they may have vast and undeniable utility in even our day-to-day lives. Whether programmed for fun or functionality, robots are always intriguing to watch and examine! And, weve curated some really innovative ones that completely blew our minds away!

At the Tokyo 2021 Olympics, world-class athletes were showcasing their talent, but a robotstole all the limelight during a basketball game between the U.S. and France. Demonstrating the early stages of the machine-dominated dystopian future, the seven-foot robot developed byToyota engineers scored a perfect three-pointer and half-court shot. The eerily designed robot took to the center stage at halftime break during a showdown game. The Toyota engineers created this free throw shooting robot in their free time over the last couple of years and at the game the smart machine beat human players shot for shot. It perfectly landed an easy free throw, a three-pointer, and a flawless half-court shot (just like Stephen Curry) in tandem to wow the crowd!

In collaboration with OTTOBO Robotics, product and car designer Berk Kaplan developed a concept design for a task robot that integrates smart technology to streamline ergonomics and package-carrying flexibility. During the beginning stage of the concept design phase, Kaplan first conducted his own research to settle on the overall mood and personality of the robot. Following the research period, Kaplan sat down to sketch outlines of his robot in development, toying around with practical elements and aesthetic touches. The first proposal envisioned the robot with both a hard outer shell and inner core, giving it a tough, hardworking personality and weighty body. Where the first proposal found durability in a tough exterior and interior, the third proposal from Kaplan wrapped the robot in a soft outer shell to cover the robots soft interior core. The second proposal, which Kaplan and OTTOBO Robotics ultimately chose as the concept designs final form, conceived the robot with a soft outer shell and hard inner core for a cushioned tactile experience, outfitting the robot with a friendly and approachable disposition.

Xiaomi, a Chinese tech company, recently unveiled more 3D renders of their own Quadruped robotic creation, CyberDog. Currently, the bio-inspired, four-legged robot has been engineered as a robotic companion whose future technical capabilities are still in development. In a recent press release from Xiaomi, its said that CyberDog comes complete with AI interactive cameras [and sensors], binocular ultra-wide-angle fisheye cameras, and Intel RealSense D450 Depth module, and can be trained with its computer vision algorithm.

Oliver is a collaborative robot that can operate both automated and manual delivery services. Smart technology equips Oliver with the know-how to handle autonomous delivery outings most likely contained within indoor spaces like warehouses and office buildings. Goods can be placed inside of Oliver the same way items are carried by utility carts and additional packages can be attached to Olivers rear trailer. Once the goods are packed away, a touchscreen display allows users to orient Oliver and schedule their deliveries. The vertical carrying space automatically rises at each delivery destination to make the unloading process more manageable. Besides automated delivery services, Oliver can operate as a conventional utility cart if users would prefer to deliver their goods on foot.

This robot may look like theMarsrover, but its a unique cigarette bud collecting bot designed to clean up the litter on beaches. Called the BeachBot (BB), this cute little four-wheeled machine was developed by Edwin Bos and Martijn Lukaart of TechTics. The duo got livid with the amount of trash (cigarette butts in particular) on the Scheveningen Beach in Holland and wanted to design arobotthat could help clean up the mess. Thats how the 2.5-feet wide BeachBot came into existence, looking to navigate the beaches on its bloated wheels that dont create any marks on the sand. The battery-powered bot has an AI brain that uses image-detection software to identify the butts and then pick them up with its gripper arms. The collected trash is then stored in the onboard compartment to dispose of later.

The KODA Robot Dog holds the title for being the first high-end domestic robot-dog running on a decentralized blockchain network, with its own brain an 11 teraflop processor capable of A.I. machine-learning. The dog-type quadruped robot relied on a decentralized network to share data and optimize behavior, making all KODA dogs smarter by relying on a hive-mind of sorts. For example, a KODA dog in Phoenix can use the knowledge it automatically receives from other KODAs that are based in colder climates, like Anchorage, Alaska or Toronto, Canada, Harden mentions to Yanko Design. Without ever having set foot on ice, the KODA in Phoenix will learn how to avoid slipping. This includes warning its owner as well. Armed with that incredibly powerful software, Whipsaws design took an interesting-yet-logical decision of ensuring the KODA robot dog (as intelligent and capable as it was) still retained a friendly, cute demeanor.

Keunwook Kim designed Post-Plant, a collection of non-humanoidrobots that respond to and move through non-verbal, physical interaction. Following a period of researching how humans can read emotion from non-verbal cues, Kim gathered that arousal (dynamic energy), valence (intrinsic attractiveness), and stance (visual disposition) can each be interpreted as signs for emotional analysis. Applying this information to Post-Plant, Kims non-humanoid robots do not express emotion through facial expression, but through movement and changing forms. Built into each one of his Post-Plant robots, Kim incorporated a motor interface that combines an input and output system, registering when the robot is touched and responding with movement.

Imagine if R2-D2 got a 2021 makeover? Well, BEBOP Design did something like thatthey took the concept and gave it a sleek makeover to give us all Information Robot! This is an autonomous robot designed specifically for the Korean startup Zetabank that aims to make human lives safer and healthier with the help of robots. Zetabank has a range of robots and this is their second collaboration with BEBOP. The companys mission is to improve our lives using artificial intelligence. Their Disinfectant Robot, Hospitality Robot, and Untact Robot are all designed keeping in mind how they can maximize utility and bring practicality to make our day-to-day more efficient. Continuing that legacy is Information Robot which is created as a service platform for digital interactions building upon the Hospitality Robots intelligence. These digital interactions are enhanced by the robots autonomous movement in various commercial and residential spaces.

Eggos mission is simple to give you a robot pet that is always by your side and provides a positive experience to you. This egg-shaped companion lets you raise a pet online or offline without taking away from the experience. It has a simple design, minimal interface, and an organic shape that invites interaction. Eggo moves autonomously by grasping the terrain through a camera. The smart pet also automatically goes to charge itself when the battery is low and I honestly wish my phone did the same thing. Even though it is a robot, designer Hyunjae Tak made sure to include an emotional side so Eggo can express how it is feeling through the LED colors which are extremely important when interacting with children. It uses the inner wheel to move on its own and actually forms a unique personality according to how you take care of it just as you would with a real-life pet!

This gadget can be fixed to the wearers forehead, who is too busy looking down at the smartphone. You know where we are heading, dont you? Yes, the 3rd Eye keeps a lookout on obstacles as you walk on the street, with the phone screen keeping you preoccupied. The inbuilt ultrasonic sensor automatically detects whenever your head is tilted down to check the phone and beeps a warning buzz when a hazard is detected up to a distance of one meter. This niche creation is a part of Minwooks Innovation Design Engineering degree at Londons Royal Imperial College of Art and Imperial College. The designer sees this evolution of human beings as a sarcastic imagination for him to do something creative. He labels the evolution as phono sapiens, and understandably so, seeing how we are so deeply lost in the world of the internet. How do you identify a phono sapien? With their forward-leaning neck vertebrae resulting in the dreaded turtle neck syndrome!

Read the original post:

Intriguing and Inventive Robot Designs that prove artificial intelligence is here to stay + make the world a better place! - Yanko Design

As recent events have shown, military decision-making is one of the highest-stakes challenges in the world: Diplomatic relations are at stake; billions of dollars of tax-funded budgets are in the balance; the safety and well-being of thousands of military and civilian personnel around the globe are on the line; and above all, the freedom and liberty of the United States and its more than 330 million citizens must be protected. But with such immense stakes comes an almost unfathomably large amount of related data that must be taken into account. Whether it is managing population health in an increasingly complex and connected world, or managing decisions on the network-centric battlefield, standalone humans are proving insufficient to harness the data, analyze it, and make timely and correct decisions.

Spanning six branches and upward of 1.3 million active duty military personnel on all seven continents, how can all of the data points from dictates from the commander-in-chief to handwritten notes on the deck of an aircraft carrier be taken into account? In matters of national security, speed and reliability in decision-making and avoiding technological surprises or being caught off guard by the nations political rivals require massive real-time analysis and first and second order thinking that includes the complexities of human behavior.

Consider all of the stakes and moving parts facing the leadership at a large domestic military base during the recent COVID-19 pandemic. Concerns of COVID-19 did not just need to consider the base personnel, but also the behavior of the civilians in the surrounding counties, as people from throughout the region, military and civilian contractors alike, were coming and going daily. The information necessary to consider starts with infection and hospitalization rates, but also includes behavior monitoring (and influencing) as well as staying up to date with steps being taken by local, regional and state officials to monitor the virus and limit its spread. With so many moving parts, it is very difficult to stay up to the minute on everything and to determine the right decision with any degree of certainty.

The answer to this guesswork and analysis paralysis lies in the capabilities of artificial intelligence and machine learning. If the military continues to waste too much time with human hours of effort and analysis that could be handled by machines, that could lead to danger and even death of military personnel or civilians. At the heart of complex systems, such as the U.S. military, there is a critical tipping point where the systems are so complex that humans can no longer track them. But AI solutions are capable of delivering up-to-the-minute data modeling, considering all factors at play and second and third order consequences, that can present tangible, data-driven intelligence that takes actions far beyond the limitations of linear human minds. Perhaps the biggest benefit is the confidence to avoid the negative publicity from the podium moment, when asked to justify decisions. Decision-makers can confidently move beyond relying on hunches and instead identify data based on sub-indexes, models from experts, and simulations specific to that day and the circumstances specific to each facility.

When President Biden was recently called onto the carpet to explain the rapid fall of Afghanistan in nine days, he should have had an AI that could at least explain the data, the models and weights that fed the analysis, conclusions and decisions based on the belief that the 300,000 strong Afghan army would be able to hold off the 60,000 Taliban fighters long enough for an orderly withdrawal. Journalists would then be free to question the data sources, the models or the weightings, but not the president, who would be relying on these systems for his judgment. But more importantly, such a system would have certainly predicted this rapid fall in its Monte Carlo distribution of potential outcomes, and would have generated counter measures and cautions.

Without a deeper commitment to AI, the military risks missing out on intelligence that transcends classified, siloed and otherwise restricted information without compromising security. One of the biggest challenges to high-stakes decision-making in the military is silos of classified information, making it difficult or impossible for every party to know every factor that is shaping the situation.

Using AI and machine learning solves this challenge safely. Rather than dumping disparate data from various branches of the military and clearance level into one gigantic data lake, it is possible to leave all the data safely and securely where it is, and train a machine to know and inform the human decision-makers that the data exists. AI is capable of processing not only all of the information in the corpus, but it is also able to know which parties do and do not have clearance to each individual piece of data. In matters of classified information, it can tell different personnel that the information exists, and direct these individuals to the authority qualified to disclose it.

Capabilities like these can be readily applied to large, complex military undertakings, featuring processes, decisions and volumes of information. For instance, when a new aircraft carrier is being built, management requires information in hand-written reports. It is difficult for the naked eye to tell if the project is on time or on budget because of the heavy reliance on human judgment. If any human assessment is just a fraction off, it can massively impact the whole project.

Recent challenges that factor in the vagaries of human behavior illustrated starkly by COVID-19 and the withdrawal from Afghanistan, beg for the rapid analysis and creative input of machine learning systems. From digestion and quantification of countless data points to absorbing and cataloging knowledge of experts who will not always be around to help with predictive modeling of circumstances with dozens of variables, this amplified intelligence is the key to better outcomes.

Richard Boyd is CEO at Tanjo, a machine learning company.

See the article here:

COVID-19 showed why the military must do more to accelerate machine learning for its toughest challenges - C4ISRNet