Archive for the ‘Artificial Intelligence’ Category

Artificial Intelligence In 2021: Five Trends You May (or May Not) Expect – Forbes

5 Trends in AI 2021

Artificial Intelligence innovation continues apace - with explosive growth in virtually all industries. So what did the last year bring, and what can we expect from AI in 2021?

In this article, I list five trends that I saw developing in 2020 that I expect will be even more dominant in 2021.

MLOps

MLOps (Machine Learning Operations, the practice of production Machine Learning) has been around for some time. During 2020, however, COVID-19 brought a new appreciation for the need to monitor and manage production Machine Learning instances. The massive change to operational workflows, inventory management, traffic patterns, etc. caused many AIs to behave unexpectedly. This is known in the MLOps world as Drift - when incoming data does not match what the AI was trained to expect. While drift and other challenges of production ML were known to companies that have deployed ML in production before, the changes caused by COVID caused a much broader appreciation for the need for MLOps. Similarly, as privacy regulations such as the CCPA take hold, companies that operate on customer data have an increased need for governance and risk management. Finally, the first MLOps community gathering - the Operational ML Conference - which started in 2019, also saw a significant growth of ideas, experiences, and breadth of participation in 2020.

Low Code/No Code

AutoML (automated machine learning) has been around for some time. AutoML has traditionally focused on algorithmic selection and finding the best Machine Learning or Deep Learning solution for a particular dataset. Last year saw growth in the Low-Code/No-Code movement across the board, from applications to targeted vertical AI solutions for businesses. While AutoML enabled building high-quality AI models without in-depth Data Science knowledge, modern Low-Code/No-Code platforms enable building entire production-grade AI-powered applications without deep programming knowledge.

Advanced Pre-trained Language Models

The last few years have brought substantial advances to the Natural Language Processing space, the greatest of which may be Transformers and Attention, a common application of which is BERT (Bidirectional Encoder Representations with Transformers). These models are extremely powerful and have revolutionized language translation, comprehension, summarization, and more. However, these models are extremely expensive and time-consuming to train. The good news is that pre-trained models (and sometimes APIs that allow direct access to them) can spawn a new generation of effective and extremely easy-to-build AI services. One of the largest examples of an advanced model accessible via API is GPT-3 - which has been demonstrated for use cases ranging from writing code to writing poetry.

Synthetic Content Generation (and its cousin, the Deep Fake)

NLP is not the only AI area to see substantial algorithmic innovation. Generative Adversarial Networks (GANs) have also seen innovation, demonstrating remarkable feats in creating art and fake images. Similar to transformers, GANs have also been complex to train and tune as they require large training sets. However, innovations have dramatically reduced the data sizes of creating a GAN. For example, Nvidia has demonstrated a new augmented method for GAN training that requires much less data than its predecessors. This innovation can spawn the use of GANs in everything from medical applications such as synthetic cancer histology images, to even more deep fakes.

AI for Kids

As low-code tools become prevalent, the age at which young people can build AIs is decreasing. It is now possible for an elementary or middle school student to build their own AI to do anything from classifying text to images. High Schools in the United States are starting to teach AI, with Middle Schools looking to follow. As an example - in Silicon Valleys Synopsys Science Fair 2020, 31% of the winning software projects used AI in their innovation. Even more impressively, 27% of these AIs were built by students in grades 6-8. An example winner, who went on to the national Broadcom MASTERS, was an eighth-grader who created a Convolutional Neural Network to detect Diabetic Retinopathy from eye scans.

What does all this mean?

These are not the only trends in AI. However, they are noteworthy because they point in three significant and critical directions

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Artificial Intelligence In 2021: Five Trends You May (or May Not) Expect - Forbes

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

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.

Related Reports:

Military Embedded Systems Marketby Component (Hardware, Software), Server Architecture (Blade Server, Rack-Mount Server), Platform (Land, Airborne, Naval, Space), Installation (New Installation, Upgradation), Application, Services, and Region - Global Forecast to 2025.

Network Centric Warfare (NCW) Marketby Platform (Land, Air, Naval, Unmanned), Application (ISR, Communication, Computer, Cyber, Combat, Control & Command), Mission Type, Communication Network, Architecture, and Region - Global Forecast to 2021

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

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

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 and its Complexity: Breaking the Ice – Analytics Insight

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.

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

Artificial Intelligence and the Art of Culinary Presentation – Columbia University

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