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CHICAGO, March 15, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Military Marketby Offering (Software, Hardware, Services), Technology (Machine Learning, Computer vision), Application, Installation Type, Platform, Region - Global Forecast to 2025",published by MarketsandMarkets,the Artificial Intelligence in Military Marketis estimated at USD 6.3 billion in 2020 and is projected to reach USD 11.6 billion by 2025, at a CAGR of 13.1% during the forecast period. An increase in funding from military research agencies and a rise in R&D activities to develop advanced AI systems are projected to drive the increased adoption of AI systems in the military sector.

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Artificial Intelligence (AI) is becoming a critical part of modern warfare as it can handle massive amounts of military data in a more efficient manner as compared to conventional systems. It improves the self-control, self-regulation, and self-actuation abilities of combat systems using inherent computing and decision-making capabilities. Some industry experts have noted that the COVID-19 pandemic has not affected the demand for Ai in Military, especially for military end use. Companies such as Lockheed Martin Corporation (US), Northrop Grumman Corporation (US), BAE Systems (UK), Rafael Advanced Defense Systems (Israel) and Thales Group (France) received contracts for the supply of AI systems to the armed forces of various nations in the first half of 2020, showcasing continuous demand during the COVID-19 crisis.

Even though the COVID-19 pandemic has caused a large-scale impact on economies across the world, leading to many challenges, the AI in military market has continued to expand. This can be seen from both, the demand and supply sides, as leading manufacturers like Lockheed Martin (US), IBM (US), Northrop Grumman (US), and others continue to invest heavily in developing AI capabilities, and governments continue to invest significantly in securing these systems. This can be attributed to governments realizing the potential of improved capabilities that these AI systems offer in terms of defense arsenal as the global AI arms race tightens.

However, even though the development of AI technology witnessed expansion, the overall building of the AI systems saw a hit. This was a result of the shortage of raw materials due to disruptions in the supply chain. Resuming manufacturing and demand depends on the level of COVID-19 exposure a country is facing, the level at which manufacturing operations are running, and import-export regulations, among other factors. Although companies may still be taking in orders, delivery schedules might not be fixed.

Increasing Threats of Cyber Attacks is driving the growth of the defense applications that leverages AI

The defense industry across countries is constantly under threat of cyberattacks. For instance, in September 2019, SolarWinds, a US technology company, was hacked, revealing sensitive data of many hospitals, universities, and US government agencies. Another notable incident was in October 2020, when the FBI and the US Cyber Command announced that a North Korean group had hacked think tanks, individual experts, and government entities of the US, Japan, and South Korea to illegally obtain intelligence, including that on nuclear policies.

Current cybersecurity technology falls short in terms of tackling advanced ransomware and spyware threats. The above mentioned SolarWinds hack was revealed when FireEye, a cybersecurity provider, was probing one of its own hacks. Such incidents indicate the increasing importance of having advanced cybersecurity capabilities. Artificial intelligence-based cybersecurity solutions that can be trained to independently gather data from various sources, analyze the data, correlate it to the signals indicating cyberattacks, and take relevant actions, can be deployed.

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Based on platform, the space segment of theArtificial Intelligencein military market is projected to grow at the highest CAGR during the forecast period

Based on platform, the space segment of the Artificial Intelligence in military market is projected to grow at the highest CAGR during the forecast period. The space AI segment comprises CubeSat and satellites. Artificial intelligence systems for space platforms include various satellite subsystems that form the backbone of different communication systems. The integration of AI with space platforms facilitates effective communication between spacecraft and ground stations.

Software segment of theArtificial Intelligencein Military market by offering is projected to witness the highest CAGR during the forecast period

Based on offering, the Software segment is projected to witness the highest CAGR during the forecast period. Technological advances in the field of AI have resulted in the development of advanced AI software and related software development kits. AI software incorporated in computer systems is responsible for carrying out complex operations. It synthesizes the data received from hardware systems and processes it in an AI system to generate an intelligent response. Software segment is projected to witness the highest CAGR owing to the significance of AI software in strengthening the IT framework to prevent incidents of a security breach.

The North America market is projected to contribute the largest share from 2020 to 2025 in theArtificial Intelligencein Military market

The US and Canada are key countries considered for market analysis in the North American region. This region is expected to lead the market from 2020 to 2025, owing to increased investments in AI technologies by countries in this region. This market is led by the US, which is increasingly investing in AI systems to maintain its combat superiority and overcome the risk of potential threats on computer networks. The US plans to increase its spending on AI in military to gain a competitive edge over other countries.

The North America US is recognized as one of the key manufacturers, exporters, and users of AI systems worldwide and is known to have the strongest AI capabilities. Key manufacturers of Ai systems in the US include Lockheed Martin, Northrop Grumman, L3Harris Technologies, Inc., and Raytheon. The new defense strategy of the US indicates an increase in Ai spending to include advanced capabilities in existing defense systems of the US Army to counter incoming threats.

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Artificial Intelligence in Military Market worth $11.6 billion by 2025 - Exclusive Report by MarketsandMarkets - PRNewswire

Since 2019 the FDAs crosscutting work has implemented artificial intelligence (AI) as part of the its New Era of Smarter Food Safety initiative. This new application of available data sources can strengthen the agencys public health mission with the goal using AI to improve capabilities to quickly and efficiently identify products that may pose a threat to public health by impeding their entry into the U.S. market.

On February 8 the FDA reported the initiation of their succeeding phase for AI activity with the Imported Seafood Pilot program. Running from February 1 through July 31, 2021, the pilot will allow FDA to study and evaluate the utility of AI in support of import targeting, ultimately assisting with the implementation of an AI model to target high-risk seafood productsa critical strategy, as the United States imports nearly 94% of its seafood, according to the FDA.

Where in the past, reliance on human intervention and/or trend analysis drove scrutiny of seafood shipments such as field exams, label exams or laboratory analysis of samples, with the use of AI technologies, FDA surveillance and regulatory efforts might be improved. The use of Artificial intelligence will allow for processing large amount of data at a faster rate and accuracy giving the capability for revamping FDA regulatory compliance and facilitate importers knowledge of compliance carrying through correct activity. FDA compliance officers would also get actionable insights faster, ensuring that operations can keep up with emerging compliance requirements.

Predictive Risk-based Evaluation for Dynamic Imports Compliance (PREDICT) is the current electronic tracking system that FDA uses to evaluate risk using a database screening system. It combs through every distribution line of imported food and ranks risk based on human inputs of historical data classifying foods as higher or lower risk. Higher-risk foods get more scrutiny at ports of entry. It is worth noting that AI is not intended to replace those noticeable PREDICT trends, but rather augment them. AI will be part of a wider toolset for regulators who want to figure out how and why certain trends happen so that they can make informed decisions.

AIs focus in this regard is to strengthen food safety through the use of machine learning and identification of complex patterns in large data sets to order to detect and predict risk. AI combined with PREDICT has the potential to be the tool that expedites the clearance of lower risk seafood shipments, and identifies those that are higher risk.

The unleashing of data through this sophisticated mechanism can expedite sample collection, review and analysis with a focus on prevention and action-oriented information.

American consumers want safe food, whether it is domestically produced or imported from abroad. FDA needs to transform its computing and technology infrastructure to close the gap between rapid advances in product and process technology solutions to ensure that advances translate into meaningful results for these consumers.

There is a lot we humans can learn from data generated by machine learning and because of that learning curve, FDA is not expecting to see a reduction of FDA import enforcement action during the pilot program. Inputs will need to be adjusted, as well as performance and targets for violative seafood shipments, and the building of smart machines capable of performing tasks that typically require human interaction, optimizing workplans, planning and logistics will be prioritized.

In the future, AI will assist FDA in making regulatory decisions about which facilities must be inspected, what foods are most likely to make people sick, and other risk prioritization factors. As times and technologies change, FDA is changing with them, but its objective remains in protecting public health. There is much promise in AI, but developing a food safety algorithm takes time. FDAs pilot program focusing on AIs capabilities to strengthen the safety of U.S. seafood imports is a strong next step in predictive analytics in support of FDAs New Era of Smarter Food Safety.

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Regulatory Cross Cutting with Artificial Intelligence and Imported Seafood | FoodSafetyTech - FoodSafetyTech

Artificial Intelligence has changed our lives for better. Be it in the form of robots, automated cars, or voice based applications like Alexa and Siri, we have seen it all. Without a doubt, AI is that one technology that makes the best use of human intelligence to take up tasks that earlier could only be performed by humans. Machines now stand the potential to learn and put the knowledge gained in the best possible use. All the human-like tasks are now performed using AI.

There are several aspects to Artificial Intelligence and so are the fields within this splendid technology. Some of them that have successfully garnered attention and appreciation equally from every corner of the world are natural language processing (NLP), computer vision, and deep learning. Machine learning is that sub field of deep learning that mainly revolves around analysing data and making predictions out of the analysed data. Needless to say, all this relies heavily on human supervision.

SMU Assistant Professor of Information Systems, Sun Qianru, talks about how training an Artificial Intelligence model has so much in similarity to that of how parents teach their child to identify objects.

Considering the complexity that Artificial I is associated with, Professor Suns research mainly talks about

Meta learning

Semi-supervised learning

Deep convolutional neural networks

Incremental learning

Well, not just that. The research also revolves around the application of all of these in recognizing images and videos.

The research, Fast-Adapted Neural Networks (FANN) for Advanced AI Systems is currently in its early stage. The research revolves around computer vision. This aspect of computer vision employs algorithms that rely on CNNs (Convolutional neural networks). The areas under scrutiny are image recognition, image processing, etc. All of this work is funded by the Agency for Science, Technology and Research (A*STAR).

Building the reasoning level ofmodeladaptation based on statistical-level knowledge learning is the hypothesis of FANN. Heres everything that the research talks about

Knowing the fact as to how complex AI is, Suns research talks about how critical it is to train AI model that is in line with the current trends in the field.

When a model is trained to yield accurate recognition results, the amount of data that goes in is immense. Sun cites an example of face recognition to support this. She argues that if theres just one face available for the system to recognize, then how will it be possible for it to differentiate that one face from the rest? Only when adequate amount of data comes into play, only when other faces too are employed for face recognition should the model be successful in distinguishing. To learn the differences, the model should have huge data that it can rely on.

All said and noted, the fact that machine learning models stand the potential to identify the global features cannot be overlooked. These models encode the data available that help in producing desired identification results. The models are successful in recognizing from images, text or sound. All of these employ deep neural network architectures that contain many layers.

Suns research takes into account two main aspects. One is where some machine learning models train themselves on a labelled data set. The other being how the best performing AI models are all based on deep learning. The research addresses the point how models are built to determine the data followed by classifying it.

The professor talks about how some models get updated when the prediction made turns out to be wrong.

Theres yet another project that Sun is working on. It is a food related application for the Health Promotion Board based out of Singapore. The main idea behind this app is to enable the users have a fair knowledge about the nutritional values of the food that they consume. The users can make use of this information to lead a healthy lifestyle. All that the users have to do is take pictures of the food theyre consuming and thats it. All the relevant information is out there on their smartphones.

However, this is where the complexity began. While training a model, her team had introduced a limited set of categories into it. But, with different photos being clicked, the need of expanding the categories came into play. Not just this, the category list was required to be updated and modified in the Application Programming Interface (API) on a regular basis.

The rich diversity that the place brings in posed a challenge for the team. With a different place, comes in a different culture. Hence, the team needs to pay extra attention to train their models by employing effective learning algorithms.

All this calls for not only diverse data collection but also on developing different adaptation learning algorithms. The complexity is for sure in existence and the team plans to deal with this by making use of a small data set.

This research by Sun and her team aims to achieve high robustness and computational efficiency, especially in the image recognition aspect. The research team is confident the outcomes of the research will have tons of benefits to offer. The key ones being great improvement in the yield rate and reduction in the manufacturing costs. All this would play a pivotal role when the fast-adapted inspection devices undergoes the process of installation, fabrication, and testing.

Read this article:
Artificial Intelligence and its Complexity: Breaking the Ice - Analytics Insight

How can culinary traditions be preserved, Spratt asked, when food is ultimately meant to be consumed? UNESCO recognizes French cuisine as an intangible heritage, which it defines as not the cultural manifestation itself, but rather the wealth of knowledge and skills that is transmitted through it from one generation to the next.

The gastronomic algorithms project, in contrast, emphasizes the cultural manifestation itself. Specifically, the project focuses on the artistic dimension of plating through Passards use of collages to visually conceive of actual plates of food. Taking this one step further, the project also explores how fruit-and-vegetable-embellished paintings by the Italian Renaissance artist Giuseppe Arcimboldo (1526-1593) could be reproduced through the use of artificial intelligence tools.

Spratt then asked the leading question of her research: How could GANs, a generative form of AI, emulate the culinary images, and would doing so visually reveal anything about the creative process between the chefs abstracted notions of the plates and collages, and their actual visual execution as dishes?

Experimenting With Datasets

Although Passards collages are a source of inspiration for his platings, a one-to-one visual correlation between the appearance of both does not exist. The dataset initially comprised photos posted by Passard on Instagram, images provided by the restaurants employees, and photos captured by Spratt at L'Arpge during each of the different seasons. This was later supplemented by images of vegetables and fruits on plates, as well as sliced variations procured from the internet using web scraping tools.

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Artificial Intelligence and the Art of Culinary Presentation - Columbia University

Artificial intelligence is set to be the future of humanity and the next step of our evolution. Scientists and analysts believe that with this type of technology, we would be able to become multi-planetary species in the future and continue to conquer the Milky Way.

Even though the concept of AI is still in its early stages of development, some industries have managed to integrate it and use it as a key tool in their everyday operations. With that being said, we wanted to take a look at some industries and sectors that have managed to incorporate this type of technology and make the best use of it. Lets check them out.

Gaming Sites

Online gaming has become extremely popular in the past 5-6 years. Online casinos are now accessed by millions of players who are in search of unique gaming experience, excitement and fun. These sites are expected to rule the gambling industry in the next couple of years due to the great features that they have.

There are two types of AI that they use, and to explain them in the best way possible, well use NetBet, a reputable platform that offers players the chance to both play casino games and bet on sports. When it comes to playing casino games, this platform uses AI software called RNG. Random Number Generators create random outcomes of the games, thus giving each player an equal chance of winning.

The second type of AI covers both the sports betting online section and the gambling section. This AI software is called SSL-encryption technology and it protects the players by taking all of their data and turning it into an unbreakable code. The SSL-encryption security system makes it impossible for third parties to gain access to sensitive data. In doing so, players can bet on the games and play casino games without having to worry about anything.

Automotive Industry

The automotive industry is progressing at a really fast pace. Futuristic concept cars are already in their trial versions and every manufacturer is on a quest to bring the future of cars right now. If you are asking how AI is used in this sector, well take two companies as an example, as they both have implemented the same AI for their products.

The first example is Tesla. Ever since this car company entered the market, it has been creating futuristic electric vehicles that are believed to be the future of cars. They have great endurance, fantastic performance, and most recently they use AI in the form of self-driving mode. Autopilot mode in cars is set to be the future of the automotive industry. The second example that we wanted to mention is Waymo. This company is already exploring advanced technologies on self-drive mode on the streets of Phoenix.

Medical Sector

The medical sector is one of the sectors in which artificial intelligence would literally revolutionize. With advanced AI in this industry, it would be much easier to develop drugs that can cure some of the worlds current unbeatable diseases. STDs are a good example of that. Advanced AI in the medical sector would help in patient care and monitoring, developing specific drugs, diagnosis, treatment protocol development, and personalized medicine.

The good thing about AI in medicine/healthcare is that some companies already use basic forms of AI to power their networks. The Mayo Clinic and the British National Health Service are the two most popular and most well-known examples.

Conclusion

While many people oppose AI and believe that the technology will ruin our lives by replacing the human workforce and increasing the unemployment rate, it is a fact that there are some tasks that these robots would complete in a much more efficient and effective way. As time goes by, it is becoming clearer that this technology will represent our future.

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Artificial Intelligence in the World Today - Prescott eNews