Innovative Automation that facilitate Scalable Automation Solutions
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A self-reliant India powered by indigenous technologies. Several startups in the IIoT era provide frugal innovations that facilitate scalable automation in areas of AI/ML, machine vision, autonomous mobile robots, AR/VR, and skills/training. Frugal innovations also promote sustainability.
Automation and artificial intelligence (AI) are transforming businesses and will contribute to economic growth via contributions to productivity. At the same time, these technologies will transform the nature of work and the workplace itself. Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will change.
Innovative Automation Builds Scalable Automation Solutions for the Future
Change is a constant in our world and impacts the technologies we rely on to do business. New upgrades and updates to computer systems and mobile technology are an almost daily occurrence. This change is expected to come as an opportunity to grow and improve. This philosophy can be seen in our high-quality automation systems. Using this type of automation innovation will ensure that the system can grow and change with your business and that it will be compatible with tomorrow’s technology.
If chosen strategically, what you purchase today can integrate with the technology of tomorrow and help you maintain a competitive advantage even when you don’t know what demand will be down the line.
As machines increasingly complement human labor in the workplace, we will all need to adjust to reap the benefits.
Creating a System That’s Scalable
Like a human team consisting of individuals with specialized skills and abilities, and automation system consists of smaller subsystems, with each part providing a specific outcome. Think of them as specialized employee units. These automated components can be small or grow larger, depending on the changing demands. These subsystems should be flexible and easily interchanged or adapted so that your business can respond to market changes. Standard modules should be totally reusable, which makes the investment rewarding technically as well as economically.
The term “scale agility” refers to changing the scale to respond to increasing or decreasing loads. If demand for a product has changed then the design – or architecture – should enable you to add or remove components or resources quickly, without having to modify other components function or process. Related to this is “flexibility”, which refers to a system’s ability to change its behavior without changing its configuration. This reduces downtime and minimizes production stops and losses.
Challenges remain before these technologies can live up to their potential for the good of the economy and society everywhere
AI and automation still face challenges. The limitations are partly technical, such as the need for massive training data and difficulties “generalizing” algorithms across use cases. Recent innovations are just starting to address these issues. Other challenges are in the use of AI techniques. For example, explaining decisions made by machine learning algorithms is technically challenging, which particularly matters for use cases involving financial lending or legal applications. Potential bias in the training data and algorithms, as well as data privacy, malicious use, and security, are all issues that must be addressed. Europe is leading with the new General Data Protection Regulation, which codifies more rights for users over data collection and usage.
A different sort of challenge concerns the ability of organizations to adopt these technologies, where people, data availability, technology, and process readiness often make it difficult. Adoption is already uneven across sectors and countries. The finance, automotive, and telecommunications sectors lead AI adoption. Among countries, the US investment in AI ranked first at USD 18 billion to USD 25 billion in 2016, followed by Asia’s investments of USD 11 billion to USD 15 billion, with Europe lagging behind at USD 5 billion to USD 7 billion.
Progress in AI and automation is creating opportunities for the economy Companies, and Culture
AI and automation aren't new, but recent advancement is currently pushing at the frontier of what machines can do. Our study suggests that society requires these improvements also make once progress on some of our hardest societal challenges, bring about economic development, and to provide value for companies. In summary:
Rapid technological progress
Beyond conventional industrial automation and robots, new generations of more systems are emerging in environments that range from autonomous vehicles on streets to automatic check-outs in grocery stores. Much of this progress was driven by improvements in components and systems, such as sensors, mechanics, and software. Strides that were massive have been made by AI in the past several decades, as machine-learning calculations made and have become more complex use of huge increases in computing power and also the exponential growth in data available to train them. Spectacular breakthroughs are currently making headlines, many affecting abilities in natural language processing, computer vision, and complex games like Go.
Possible to transform companies and bring about economic development
These technologies are generating value in various services and products, and businesses across sectors use them in a range of methods to personalize product recommendations, find anomalies identify fraudulent transactions, and more. The latest creation such as techniques that address estimation, classification, and problems, guarantees significantly more significance. An investigation we ran of many hundred AI use cases discovered that the most advanced deep learning techniques deploying artificial neural networks may account for as much as USD 3.5 trillion to USD 5.8 trillion in yearly price, or 40 % of the value made by all analytics techniques.
Increase prosperity and deployment of both automation and AI technology can do much to raise the market, at a time when aging and decreasing birth rates are currently acting as a drag on development. Labor productivity growth, an integral driver of economic growth, has slowed in many economies, dropping to an average of 0.7% in 2015-2019 by 2.8% a decade earlier from the USA and major European economies, in the aftermath of the 2008 financial catastrophe following a preceding productivity boom had waned. AI and automation have the capacity to reverse this decline: productivity growth could reach 2% each year over the next decade, with 60% of this growth from opportunities.
Possible to help handle moonshot challenges that are several
AI is being utilized in areas that range from material science to climate science and medical research. Application of that technology in these and other disciplines could help tackle societal moonshot challenges. For instance, researchers at Geisinger have developed an algorithm that may reduce diagnostic times for hemorrhaging by up to 96%. Researchers at George Washington University are currently using machine learning to accurately weight the climate models used by the Intergovernmental Panel on Climate Change.
Challenges before these technologies can live up to their possible for the good of society and the economy, remain everywhere
AI and automation face challenges. The limitations are partly technical, like the demand for massive training data and difficulties "generalizing" algorithms across use cases. Inventions are beginning to address these difficulties. Other challenges are from using AI techniques. For instance, decisions are challenging, which especially matters to be used in cases involving lending or software that are legal. Possible bias in data privacy and calculations, as well as the training data, malicious usage, and safety, are issues that have to be addressed. Europe is currently top with all the General Data Protection Legislation, which codifies rights for consumers over data collection and usage.
A different sort of challenge concerns the ability of organizations to adopt these technologies, where people, data availability, technology, and process readiness often make it difficult. Adoption is already uneven across sectors and countries. The finance, automotive, and telecommunications sectors lead AI adoption. Among countries, US investment in AI ranked first at USD 18 billion to USD 26 billion in 2016, followed by Asia’s investments of USD 11 billion to USD 15 billion, with Europe lagging behind at USD 5 billion to USD 7 billion.
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