US Army Awards General Dynamics Contract for Next-Generation M1E3 Abrams Tank Design

General Dynamics (GD) concluded the recent trading session at $296.08, signifying a +0.89% move from its prior day&#x27;s close.

Why the Market Dipped But General Dynamics (GD) Gained Today

The plant in an industrial park in Mesquite is expected to employ about 150 people, increasing to 350 once it reaches full production.

General Dynamics opens new factory in Mesquite, Texas | wfaa.com

General Electric GE released its third-quarter earnings report on Oct. 24. Here’s Morningstar’s take on General Electric’s earnings and stock.

After Earnings, Is General Electric Stock a Buy, a Sell, or Fairly Valued?

Buying $100 In GD: If an investor had bought $100 of GD stock 20 years ago, it would be worth $571.06 today based on a price of $239.96 for GD at the time of writing.

$100 Invested In General Dynamics 20 Years Ago Would Be Worth This Much Today

On Oct. 7, militant group Hamas struck Israeli towns in a surprise attack and took more than 200 hostages. More than 7,000 people have been killed in Gaza, per Palestinian health officials, while the Israeli Defense Forces said more than 1,400 have been killed in the country.

The Israel-Hamas war is affecting the financial outlooks of these large companies

Companies in the Industrial Goods sector have received a lot of coverage today as analysts weigh in on General Dynamics (GD – Research Report) and Cleanaway Waste Management Ltd. (TSPCF – Research Report). General Dynamics (GD) Goldman Sachs analyst ...

Analysts’ Opinions Are Mixed on These Industrial Goods Stocks: General Dynamics (GD) and Cleanaway Waste Management Ltd. (OtherTSPCF)

As a result, the eruption of war, whether in Ukraine or Israel and the Gaza strip, generally sends the price of at least some defense stocks higher.Among the world's current conflicts, Russia's assault on Ukraine and Israel's response to the Oct.

The Israel-Hamas Conflict: Impact On And Outlook For Defense Stocks

General Dynamics stock performed well, with 15.8% share price appreciation and 17.2% total return despite supply chain challenges. Find out why GD stock is a Buy.

General Dynamics Still Soars As An Investment Opportunity Despite Supply Chain Challenges

General Dynamics stock has performed well, with a 15.8% appreciation in share price and a total return of 17.2% despite supply chain challenges. The company's third quarter results were impacted by supply chain issues and the absence of G700 deliveries, but there's potential for improvement in the future.

General Dynamics

generaldynamics

Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Abstract From fake social media accounts and generative-AI chatbots to financial trading algorithms and self-driving vehicles, robots, bots, and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions, and transportation arteries. Networks of multiple interdependent and interacting humans and autonomous machines constitute complex social systems where the collective outcomes cannot be deduced from either human or machine behavior alone. Under this paradigm, we review recent research from across a range of disciplines and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open-collaboration community, and a discussion forum. To ensure more robust and resilient human-machine communities, researchers should study them using complex-system methods, engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines. Introduction Robotic trains and cars drive us around, auction bots outbid us for purchases, ChatGPT answers our questions, while social media bots feed us with dubious facts and news. Modern society is a complex human-machine social system in which machines are becoming more numerous, human interactions with machines – more frequent, and machine-machine interactions – more consequential. With recent advances in generative AI models, the existential threat of unexplainable and uncontrollable general AI is looming large again[1,2,3]. However, when they are numerous and interdependent, even simple unintelligent artificial agents can produce unintended and potentially undesirable outcomes. If we want to prevent financial crashes, improve road safety, preserve market competition, increase auction market efficiency, and reduce misinformation, it is no longer sufficient to understand humans – we need to consider machines, understand how humans and machines interact, and how the collective behavior of systems of humans and machines can be predicted. We require a “new sociology of humans and machines.” This narrative review synthesizes research and ideas related to social systems composed of multiple autonomous yet interacting and interdependent humans and machines such as algorithms, bots, and robots (Fig. 1). Similar to the conceptualizations of socio-technical systems [4], actor-network theory 5,6, cyber-physical social systems [7,8] social machines [9,10,11] and human-machine networks12,13, our principal assumption is that humans and machines form a single social system. In contrast, we do not approach machines as a single medium or entity – “technology” – but emphasize their multiplicity, independence, and heterogeneity and their interactions [14]. Thus, we target machines capable of social behavior, namely, a behavior that influences and is influenced by the behavior of other actors (human or machine) in the system. Further, we approach the aggregates as complex social systems where network effects and nonlinear dynamic processes produce collective outcomes that cannot be necessarily deduced from individual preferences and behavior alone [15]. Our conceptualization extends and complements the ecological approach to studying machine behavior[16], the “hybrid collective intelligence perspective” [17], and the budding field of Social AI [18]. We aim to offer a conceptual overview of the topic. Hence, we do not utilize a systematic literature search strategy and instead present selected examples to showcase the new conceptualization[19,20,21]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Abstract From fake social media accounts and generative-AI chatbots to financial trading algorithms and self-driving vehicles, robots, bots, and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions, and transportation arteries. Networks of multiple interdependent and interacting humans and autonomous machines constitute complex social systems where the collective outcomes cannot be deduced from either human or machine behavior alone. Under this paradigm, we review recent research from across a range of disciplines and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open-collaboration community, and a discussion forum. To ensure more robust and resilient human-machine communities, researchers should study them using complex-system methods, engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines. Introduction Robotic trains and cars drive us around, auction bots outbid us for purchases, ChatGPT answers our questions, while social media bots feed us with dubious facts and news. Modern society is a complex human-machine social system in which machines are becoming more numerous, human interactions with machines – more frequent, and machine-machine interactions – more consequential. With recent advances in generative AI models, the existential threat of unexplainable and uncontrollable general AI is looming large again[1,2,3]. However, when they are numerous and interdependent, even simple unintelligent artificial agents can produce unintended and potentially undesirable outcomes. If we want to prevent financial crashes, improve road safety, preserve market competition, increase auction market efficiency, and reduce misinformation, it is no longer sufficient to understand humans – we need to consider machines, understand how humans and machines interact, and how the collective behavior of systems of humans and machines can be predicted. We require a “new sociology of humans and machines.” This narrative review synthesizes research and ideas related to social systems composed of multiple autonomous yet interacting and interdependent humans and machines such as algorithms, bots, and robots (Fig. 1). Similar to the conceptualizations of socio-technical systems [4], actor-network theory 5,6, cyber-physical social systems [7,8] social machines [9,10,11] and human-machine networks12,13, our principal assumption is that humans and machines form a single social system. In contrast, we do not approach machines as a single medium or entity – “technology” – but emphasize their multiplicity, independence, and heterogeneity and their interactions [14]. Thus, we target machines capable of social behavior, namely, a behavior that influences and is influenced by the behavior of other actors (human or machine) in the system. Further, we approach the aggregates as complex social systems where network effects and nonlinear dynamic processes produce collective outcomes that cannot be necessarily deduced from individual preferences and behavior alone [15]. Our conceptualization extends and complements the ecological approach to studying machine behavior[16], the “hybrid collective intelligence perspective” [17], and the budding field of Social AI [18]. We aim to offer a conceptual overview of the topic. Hence, we do not utilize a systematic literature search strategy and instead present selected examples to showcase the new conceptualization[19,20,21]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How Human-Machine Networks Shape Collective Behavior in a Hybrid Society 

Authors:
(1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
(2) Andrey Rass, Den Haag, Netherlands. Table of Links Abstract and 1 Introduction
2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture
2.1 Data Augmentation Robustness Scouting
2.2 The Specifics Of Data Affect Augmentation-Induced Bias
2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias
2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias
3 Conclusion and Limitations, and References
Appendices A-L 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias Having carried out a data-centric analysis of DA-induced class-specific bias, we dedicated the last series of experiments (see Figure 4) to a more model-centric approach to the phenomenon. Balestriero, Bottou, and LeCun (2022) illustrates that different architectures tend to agree on the label-preserving regimes for DA(α) - in other words, that swapping ResNet50 for a different model in the above experiments would not yield a notable difference in resulting class-specific and overall performance dynamics. To validate and expand on this claim, we recreated the experiment from Section 2.2 on the Fashion-MNIST dataset using another residual CNN, EfficientNetV2S, as well as a Vision Transformer in SWIN-Transformer. The ”small” EfficientNetV2S (Tan and Le 2021) architecture was selected due to being a modern and time-efficient implementation of the EfficientNet family of models. The model was downloaded, tuned and optimized using a procedure matching previous sections. The EfficientNetV2S runs of this section’s experiments seemed to further validate the original assumption that the phenomenon appears to be modelagnostic (though data-specific), at least for the residual family of CNNs, as the different complexity and size of the architecture did have an effect on the speed of class-specific bias occurring as the random crop α was increased, but the general dynamics of how performance evolved were preserved. For example, as can be seen in Figures 4, deterioration in per-class accuracy begins to occur rapidly for ”Dress” at an α value of 30% for EfficientNetV2S, in contrast to 40% for ResNet50. For a full summary of the diverse α at which each class and mean test set performance reach their peak, see Appendix I. With the previous section confirming the model-agnostic nature of the phenomenon as it pertains to residual CNNs, the final trial followed up by evaluating the performance of a SWIN Transformer, as the architecture is composed in a fundamentally different way, and can be adjusted to perceive images with a finer level of granularity via the patch size parameter. The model was prepared similarly to previous sections. As visible in Figure 4 and Appendix L, while the use of a Vision Transformer model did not entirely avert the overall class-specific bias trend as random cropping was applied more aggressively, it severely delayed its heavier impacts, and even noticeably changed the behavior of certain classes (for example, maintaining the stability of the top performers in ”Trouser” and ”Sandal” even for very high values of α, while delaying ”Dress” and ”Coat”’s rapid deterioration in accuracy to 47% and 30% from 40% and 23%, respectively) in effect making the model-dataset combination more robust against the potential negative trade-offs data augmentation can bring. Despite this, and being similar in terms of computational requirements, its best performance achieved in practice was marginally lower than that of ResNet50 . While it does not outright disagree with Balestriero, Bottou, and LeCun (2022)’s model-agnostic proposition, the implications of this result are such that architectures for computer vision tasks can and should be chosen not just by best overall test set performance, but also based on other merits, such as robustness to bias and label loss resulting from aggressive data augmentations, especially in cases where such augmentations are expected to be applied en masse or without intensive monitoring, such as in MLOps systems with regular retraining. While the above experiment does serve to illustrate the potential merit of applying alternative architectures in the context of regulating DA-induced class-specific bias, broader research is likely warranted. A natural direction of such an extension may be to continue with applying different families and instances of image-classifying neural network architectures to the task. One option is to expand the research utilizing SWIN Transformer by testing it on a further plurality of datasets with a wider variety of patch sizes, as the concepts of regulating the granularity with which the model learns to see details in images and the random cropping DA are conceptually adjacent. Following this, exploring the same problem with different Vision Transformers, such as the more lightweight MobileViT (Mehta and Rastegari 2021) or Google’s family of large ViT models (Zhai et al. 2022) is another step in that direction. Another possible direction to explore the model-specific degree of the phenomenon is to investigate how it pertains to Capsule Networks, first described in Sabour, Frosst, and Hinton (2017), as the architecture, once again, vastly differs from both Residual CNNs and Vision Transformers, and is generally considered to be less susceptible to variance from image transformations. This family of models is also very demanding to train, and would require a greater investment of resources or smaller scope than what our work had. As CapsNets are, at the time of writing, a very active area of research (Kwabena Patrick et al. 2022), we recommend this as a very valuable line of inquiry. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands (2) Andrey Rass, Den Haag, Netherlands. Authors: Authors: (1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands (2) Andrey Rass, Den Haag, Netherlands. Table of Links Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References Appendices A-L Abstract and 1 Introduction Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References 3 Conclusion and Limitations, and References Appendices A-L Appendices A-L 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias Having carried out a data-centric analysis of DA-induced class-specific bias, we dedicated the last series of experiments (see Figure 4) to a more model-centric approach to the phenomenon. Balestriero, Bottou, and LeCun (2022) illustrates that different architectures tend to agree on the label-preserving regimes for DA(α) - in other words, that swapping ResNet50 for a different model in the above experiments would not yield a notable difference in resulting class-specific and overall performance dynamics. To validate and expand on this claim, we recreated the experiment from Section 2.2 on the Fashion-MNIST dataset using another residual CNN, EfficientNetV2S, as well as a Vision Transformer in SWIN-Transformer. The ”small” EfficientNetV2S (Tan and Le 2021) architecture was selected due to being a modern and time-efficient implementation of the EfficientNet family of models. The model was downloaded, tuned and optimized using a procedure matching previous sections. The EfficientNetV2S runs of this section’s experiments seemed to further validate the original assumption that the phenomenon appears to be modelagnostic (though data-specific), at least for the residual family of CNNs, as the different complexity and size of the architecture did have an effect on the speed of class-specific bias occurring as the random crop α was increased, but the general dynamics of how performance evolved were preserved. For example, as can be seen in Figures 4, deterioration in per-class accuracy begins to occur rapidly for ”Dress” at an α value of 30% for EfficientNetV2S, in contrast to 40% for ResNet50. For a full summary of the diverse α at which each class and mean test set performance reach their peak, see Appendix I. With the previous section confirming the model-agnostic nature of the phenomenon as it pertains to residual CNNs, the final trial followed up by evaluating the performance of a SWIN Transformer, as the architecture is composed in a fundamentally different way, and can be adjusted to perceive images with a finer level of granularity via the patch size parameter. The model was prepared similarly to previous sections. As visible in Figure 4 and Appendix L, while the use of a Vision Transformer model did not entirely avert the overall class-specific bias trend as random cropping was applied more aggressively, it severely delayed its heavier impacts, and even noticeably changed the behavior of certain classes (for example, maintaining the stability of the top performers in ”Trouser” and ”Sandal” even for very high values of α, while delaying ”Dress” and ”Coat”’s rapid deterioration in accuracy to 47% and 30% from 40% and 23%, respectively) in effect making the model-dataset combination more robust against the potential negative trade-offs data augmentation can bring. Despite this, and being similar in terms of computational requirements, its best performance achieved in practice was marginally lower than that of ResNet50 . While it does not outright disagree with Balestriero, Bottou, and LeCun (2022)’s model-agnostic proposition, the implications of this result are such that architectures for computer vision tasks can and should be chosen not just by best overall test set performance, but also based on other merits, such as robustness to bias and label loss resulting from aggressive data augmentations, especially in cases where such augmentations are expected to be applied en masse or without intensive monitoring, such as in MLOps systems with regular retraining. While the above experiment does serve to illustrate the potential merit of applying alternative architectures in the context of regulating DA-induced class-specific bias, broader research is likely warranted. A natural direction of such an extension may be to continue with applying different families and instances of image-classifying neural network architectures to the task. One option is to expand the research utilizing SWIN Transformer by testing it on a further plurality of datasets with a wider variety of patch sizes, as the concepts of regulating the granularity with which the model learns to see details in images and the random cropping DA are conceptually adjacent. Following this, exploring the same problem with different Vision Transformers, such as the more lightweight MobileViT (Mehta and Rastegari 2021) or Google’s family of large ViT models (Zhai et al. 2022) is another step in that direction. Another possible direction to explore the model-specific degree of the phenomenon is to investigate how it pertains to Capsule Networks, first described in Sabour, Frosst, and Hinton (2017), as the architecture, once again, vastly differs from both Residual CNNs and Vision Transformers, and is generally considered to be less susceptible to variance from image transformations. This family of models is also very demanding to train, and would require a greater investment of resources or smaller scope than what our work had. As CapsNets are, at the time of writing, a very active area of research (Kwabena Patrick et al. 2022), we recommend this as a very valuable line of inquiry. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 

General Dynamics (GD) is the sixth-largest defense corporation in the world. Obviously, it's an American company. To realise how important GD is, we have to know that they were the original developers of F-16 Falcon, arguably the most lethal fighter jet engineered by mankind, and then sold that division to Lockheed Martin. However, GD is more involved and more prolific in land and marine weapons, especially guided missile systems on battleships. In Navy terms, a Destroyer is a fast and maneuvrable battleship used to escort larger battleships and defend them from attackers. In order to defend, they have a variety of guided missile systems to launch against enemy aircraft, submarines, even land targets. We go back to 1999, on one such Destroyer in the US Navy, where GD is trying to solve a seemingly intractable computer problem. Their man responsible for managing computer systems on that battleship is a 38-year-old experienced programmer named Dwayne Richard Hipp. The problem was deceptively simple. Every system having a computer - be it a laptop, a car, a refrigerator, a factory machine, or a guided missile - needs to store data. This data is almost always stored in the form of tables linked with each other, called a database. We can talk to a database using a special language called SQL. So, if you want to read or write data, you submit SQL commands to the database through your program. For example, if I deposit money in a bank, the cashier makes entries into the bank program, which sends the account number and amount to the bank's database using SQL and the balance is updated. The US Navy at that time was using Informix database developed by the mighty IBM. Although Informix was a great database for normal usage, it had two problems. One, it was too big in size to fit on small computers like the ones on guided missiles. It was impossible to do so. Two, even if you did the impossible and somehow fit it on a missile, it needed to be 'installed' and 'administered' by a person because it was a separate process running on the missile's computer. Think how much time it takes to install software on your laptop. Now think how a missile - which after being fired is going to be destroyed anyways - might need tens of such databases for each task. One might be a GPS trying to calculate trajectory, another might be a fuel control mechanism, and another monitoring explosive material. If each database required a separate administrator, a few hundred missiles on a Destroyer would require thousands of non-military personnel only to manage databases and soon the battleship would be turned into the floor of an IT company. And what if a war starts? Would you take them into battle? I am sure some of my veteran IT friends would love to go! We can imagine Richard Hipp sipping coffee aboard, thinking continuously why the hell did he take up this project from General Dynamic. Although a PhD in computer science, Richard had believed the doctorate market was too saturated with far more talented people than him. So he started his own small software consulting firm, got married, lived a happy and settled life by 35. Now he was in the middle of a very real problem and the only way out was to solve it. And solve it he did. Within a year, in the spring of 2000, Richard came up with an answer. His solution was a single file written in the fastest programming language invented by humanity, the C language. This file could be simply copy-pasted in your program and it allowed the program to use any normal file (think notepad .txt) like a 100% SQL database. No need to install anything, administer anything or hire anyone! Once a program to calculate GPS is written, the special C file creates a complete little database inside which lives symbiotically with that program. The program would just do its job and manage its data somehow, making no fanfare about how it went about doing so. But inside, there is a complete, self-contained database engine at work. This was how SQLite was born and used first to enable destroyer-guided missiles of the US Navy, without filling their battleships with nerdy techies. Can you guess the size of that special file? It's 900 KB. Means you can fit five such files in the space required to store a .mp3 song! The story doesn't end here. In a great act of selflessness, Richard made his creation free for anybody to use for whatever purpose. Within 5 years, SQLite was being used on 8 out of 10 machines in the world from your music player, your smart TV, your car, and your consumer durable. And the funniest place: there's a little SQLite that lives in your browser, like Firefox and Chrome. Isn't that amazing! A tiny database that lives inside a program. Because it's rarely advertised, very few know about SQLite. It's not taught in any engineering class across India. However, I like to think it a proof of its greatness that none of us know about something that is present everywhere. Whether anyone understood the importance of SQLite or not, Google did and in 2005 Dwayne Richard Hipp was awarded Google-O'Reilly Open Source Award. Google also secretly roped him in to consult on the development of a new mobile operating system it was building, called Android. And 15 years later, every app on your smartphone whether it is Android or Apple, has a tiny SQLite living inside it. D. Richard Hipp is one of the most underrated inventors of our generation. He went on and invented many other things, but SQLite will remain his greatest gift to humankind, because if ever spaceships embark in search for habitable planets, they are sure to have millions of little SQLites ticking away inside them. A respected salute to the selfless inventor!

The Story of Dwayne Richard Hipp and the Development of SQLite in 1999

There’s an ever-increasing reliance on technology in today’s workplace. That’s good news for IT professionals, whose skills are in great demand and whose work can often be done remotely. According to the Bureau of Labor Statistics, . This is a much faster rate than the average for all occupations. About 531,200 new jobs are projected to be added during this time frame. computer and information technology jobs are projected to grow 11% between 2019 and 2029 IT jobs are typically remote-friendly, too. And, if is any indication, remote work isn’t going anywhere. When asked, 65% of respondents said they want to keep working remotely full-time when the pandemic is over. our recent survey We’ve compiled a list of 20 companies that commonly hire for work-from-home IT jobs, along with some recent job postings. Check out these remote IT job opportunities, and if you’re a FlexJobs member, log in to your account and to see the full postings. Note: FlexJobs is a subscription service for job seekers that features flexible and remote jobs. With an A+ rating from the Better Business Bureau , the monthly subscription costs allow us to fully vet and verify all of the jobs on our site —ensuring that customers have a safe and positive job searching experience. Companies That Hire for the Most Remote IT Jobs 1. Cisco A global technology solutions firm, Cisco has become a powerhouse of internet and communications products, services, and solutions that have revolutionized the way business is conducted. Recent remote IT jobs: Senior Systems Engineer Senior Infrastructure Operations Engineer – Compute Senior IP Network Technical Lead 2. Cognizant Softvision A division of Cognizant, Softvision’s goal is to continuously improve its clients’ digital maturity, combining skill, scale, and cost in each stage of the product development lifecycle to create unforgettable digital experiences. Recent remote IT jobs: Solution Architect Mobile Device Management Engineer 3. ConsenSys A blockchain venture production studio based in Brooklyn, New York, ConsenSys builds and scales consumer-centric enterprise software products and tools powered by Ethereum, a public, blockchain-based, open-source distributed computing platform and operating system featuring smart contract functionality. Recent remote IT jobs: Technical Support Engineer, Multi-Lingual Technical Support Engineer Help Desk Agent 4. CrowdStrike CrowdStrike provides cloud-delivered security technology, threat intelligence, and endpoint protection for blue-chip companies in multiple industries. Recent remote IT jobs: Escalation Engineer – Services Engineering Red Team Tool Developer Security Analyst 5. Fastly Internet company Fastly provides the Fastly Edge Cloud platform, a content delivery network that provides clients with improved web and application performance, quality video and streaming, and cloud security. Recent remote IT jobs: Senior Director, Hardware Systems Engineering Senior Provisioning Engineer Senior IT Systems Engineer 6. Gartner Gartner, an information technology research and advisory company, delivers technology-based insights to help companies and organizations around the world make efficient, high-quality business decisions. Recent remote IT jobs: Consulting Associate Director, IT Strategy, Data and Analytics IT Services and Software Analyst Security Programs Director 7. General Dynamics General Dynamics is a global aerospace and defense company. The company has businesses in aerospace, combat systems, information technology, marine systems, and mission systems. Recent remote IT jobs: Dynamics 365 Operation and Maintenance Engineer Senior Appian Developer – DevSecOps 8. GitHub GitHub is an open-source code-hosting website, publishing service, and social networking site for programmers. It has hosted more than 66 million projects and supports a community of over 24 million people. Recent remote IT jobs: Staff Product Security Engineer Staff Security Engineer – Operations and Telemetry Business Systems Analyst, People Systems 9. Kelly Kelly has provided workforce and staffing solutions since 1946. They place workers in diverse industries, including information technology, life sciences, call centers, and more. The company also provides outsourcing and consulting services. Recent remote IT jobs: IT Project Manager – Healthcare Database Architect IT Technical Writer 10. Kforce Kforce is a publicly-traded staffing and recruiting firm that provides staffing solutions in the technology, financial services, healthcare, communications, and government sectors. Recent remote IT jobs: Cloud Operations Engineer Desktop Support Technician Technology Manager – Database 11. Motion Recruitment Partners The parent company of three recruitment agencies, Jobspring Partners, Sevenstep, and Workbridge Associates, Motion Recruitment Partners places people in computer & IT, software development, writing and editing, and graphic design jobs. Recent remote IT jobs: Senior Security Engineer Lead Systems Administrator 12. NTT Group NTT Group consists of regional, long-distance, international, mobile, and data communications businesses. Their mission is to provide high-quality, value-added services that enrich their clients in the areas of artificial intelligence, big data, and the internet-of-things. Recent remote IT jobs: Incident Manager COBOL Developer Data Backup-System Integration Specialist 13. Rackspace Rackspace Technology is a leading managed cloud company dedicated to helping businesses harness the power of cloud computing without the cost and complexity of having to manage it on their own. Recent remote IT jobs: Cloud Architect – Rackspace Government Solutions ServiceNow Business Analyst ServiceNow Architect 14. Randstad One of the biggest staffing firms in the U.S., Randstad provides HR solutions to clients in the accounting, engineering, IT, human resources, and logistics sectors. Recent remote IT jobs: IT Project Manager Informatica Developer Systems Analyst 15. Red Hat Red Hat is an award-winning technology firm. The company develops a broad range of solutions and provides services that are not only open and secure but trusted by over 90% of the Fortune Global 500. Recent remote IT jobs: Software Maintenance Engineer Senior Business Systems Analyst Senior Solution Manager – Senior Product Manager 16. Robert Half International Robert Half is a staffing and consulting firm that places job seekers in a variety of career fields, including computer & IT, administrative, and accounting and finance. The company consists of eight divisions and has over 400 global locations. Recent remote IT jobs: Sharepoint Developer Database Administrator Developer Network Manager 17. Tanium Tanium products help companies with IT security and systems management. Their platform offers 15-second visibility and control of all endpoints on some of the most complex and largest networks in the world. Recent remote IT jobs: Senior Manager, Web Operations Senior Director, Product Management – Security Operations Senior QA Engineer 18. Twilio Twilio is a Platform-as-a-Service (PaaS) company operating as a cloud communications company. Users can make and receive calls using texts and web service APIs. The company also provides clients with privacy and security for messaging. Recent remote IT jobs: Senior Information Security Compliance Analyst Technical Onboarding Manager 19. Unisys Using secure, reliable, advanced technology, Unisys provides clients in the commercial, government, and financial services industries with solutions to enhance the lives of their customers and citizens. Recent remote IT jobs: Enterprise Architect Storage Administrator Cloud Security Engineer 20. UnitedHealth Group UnitedHealth Group consists of two businesses: UnitedHealthcare (a benefits and insurance provider) and Optum (a provider of technology and information-enabled health services). The company provides benefit services and healthcare coverage in all 50 states and in 125 countries worldwide. Recent remote IT jobs: Server Administrator Senior IT Project Manager Senior Director, Quality Operations and Analytics Landing a Work-from-Home IT Job With FlexJobs FlexJobs has a whole category dedicated to , with six subcategories: flexible computer and IT jobs Computer Security Database Administration Networking SQL System Administration Technical Support All of our job listings are fully vetted and verified, giving you a safer, faster, and better job search experience. Previously published at https://www.flexjobs.com/blog/post/top-companies-hiring-for-remote-it-jobs-v2/

20 Companies With Remote Tech Jobs

FlexJobs just completed it's and companies dominated this year’s list! Through an analysis of the remote job posting histories of approximately 57,000 companies in the FlexJobs database, we’ve pinpointed the companies that offered the most remote jobs from January through December 2020. 8th annual list of the Top 100 Companies to Watch for Remote Jobs Computer & IT Below are the 22 tech companies that are known for hiring remote workers, as well as their respective rank on FlexJobs’ list. Software Engineer, Web Developer, Project Manager, and IT Consultant are also among the most common job titles for remote jobs! 13 	Citrix 23 	Oracle 25 	Salesforce 36 	VIQ Solutions 38 	Twilio 41 	Dell Technologies 42 	CrowdStrike 45 	Veeva 46 	SAP 47 	Red Hat 49 	Shopify 50 	Atlassian 54 	NTT Group: NTT DATA 57 	GitHub 59 	Keywords Studios 60 	Elastic 64 	General Dynamics; General Dynamics Information Technology 69 	Motorola Solutions 70 	Tanium 73 	Pega - Pegasystems, Inc. 81 	ServiceNow 83 	Cisco 92 	Leidos To view the entire 2021 Top 100 Companies for Remote Jobs list, please visit . https://www.flexjobs.com/blog/post/100-top-companies-with-remote-jobs-2021/

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