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NVIDIA is helping the transportation industry by giving it access to its deep neural networks (DNNs) for autonomous vehicles. NVIDIA is providing access to its AI (artificial intelligence) model and introducing advanced training tools. This helps the company to strengthen its end-to-end platform for autonomous vehicle development and, eventually, deployment. Automakers and other companies that develop autonomous vehicles (AVs) on the NVIDIA GPU Cloud container registry will get access. NVIDIA DRIVE is pretty much the standard for the development of autonomous vehicles. It is used by automakers, truck manufacturers, and robotaxi companies along with related software companies and universities.
Article | March 1, 2020
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Article | March 1, 2020
The more time I spend working with data, and watching how our customers work with data, the more convinced I am of two things: 1) the power to do extraordinary things is embedded within data and 2) all of us working or dealing with data have a role to play in using our knowhow and technology to apply data to benefit humanity and tackle some of the biggest challenges of our lifetime – the environment, equality, education, health and safety.
Article | March 1, 2020
In the area of computer intelligence where we have robotics, machine learning, artificial intelligence, etc. There is a new game-changing concept that is so profound that industries are already finding a use for it and it is paying off. This new concept is called Intelligent Automation (IA).
Discussions with heads of global organizations as well as research, and experience of experts show that IA is establishing itself as a future key driver of competitive relevance and enterprise efficiency. This is why IA experts are convinced the concept can provide solutions to several urgent issues in the world right now such as improving our planet, education, and life-saving measures. The impact of IA is becoming more prevalent, and that saw the concept selected by Gartner as the number one tech trend in 2020.
What is IA?
The concept of Intelligent Automation otherwise known as Hyper automation leverages the new-gen software-based automation, which blends technologies and methodologies to implement business processes on automation for knowledge workers. This is achieved through IA imitating the skills used by knowledge workers to execute their work. All these are done to attain a business outcome via purposeful redesigning of automation carried out with little or no human oversight. The end game is cost reduction, which improves process speed, optimization of decision outcomes, improved process resilience, and improved quality and compliance. In the end, businesses and organizations will see an increase in revenues and enhanced employee and customer satisfaction.
Who are the knowledge workers?
Who are the knowledge workers that IA is purposefully designed for? For starters, knowledge workers' main currency is the knowledge they possess. We have examples like pharmacists, designers, programmers, architects, lawyers, physicians, engineers, public accountants, scientists. Any worker that has to “think for a living” is considered a knowledge worker. This type of worker is mainly domiciled in the service industries. A knowledge worker is information-based compared to manual labor that is material-based and mainly domiciled in the manufacturing industries.
Where does IA feature here? We already know the importance of industrial automation to the manufacturing process. We can consider IA the “white collar” version of industrial automation. IA can be used to supplement the job of a knowledge worker such as call center agents, financial controllers, etc.
Let us break down what IA does specifically for a knowledge worker. Imagine IA as a digital worker created to imitate the activities of a knowledge worker to deliver the same outcome as a human would. It mimics all the human business processes, which is a succession of tasks by reproducing the human capabilities of reading, speaking, learning, hearing, seeing, acting, and reacting to produce the same business processes as a knowledge worker.
The synergy between IA and humans
IA creates a synergy by merging the software-based workforce with the human workforce. On the task spectrum, IA shoulders a load of executing tedious, low value, and monotonous tasks like processing and digitizing paper invoices, reconciling data, etc. IA equips a worker with what we can call superhuman abilities like the ability to generate insights from millions of analysed data done in just a few minutes. That is on a human level is virtually impossible to do.
The uniqueness of IA
How is it that a concept so recent that its name was only created in 2017 by IEEE has witnessed a rapid expansion and is expected to have a lasting impact on us? We believe the answer lies in its unique features, which are listed below:
The IA pools together new technologies, most of which are recently developed in the last decade.
The application of several IA functionalities is universal. They are applicable across several business functions like finance, sales, etc., and industries such as retail, banking, among others.
IA programs are scalable. Once developed, scaling can be carried out immediately and infinitely at no added cost.
Its availability is unmatched; IA can deliver 24/7.
IA is economically viable and reliable. It gives the same results based on settings repeatedly at a reasonable cost. In less than a year, the program will normally generate payback on the initial investment.
AI and IA, two sides of the same coin?
Here comes the inevitable question. Is Intelligent Automation (IA) any different from Artificial Intelligence (AI)? Are they not just two sides of the same coin? Well, in the world of computer intelligence, laying down the differences between robotics, AI, IA, among others is a very complicated process.
The line between is so blurry that they can sometimes overlap due to the continuous evolution, emergence, and convergence of these concepts. However, that is not to say there are no areas where there are clear demarcations.
For the purpose of clarity, a few key anchor points are drawn using the analysis of the survey of the opinions of more than 200 IA experts as well as our experience in IA. These are the main anchor points:
AI and IA –Since IA has to do with the automation of knowledge work that is the area where AI and IA interrelate. That means IA comprises all use cases of AI in all industries excluding industries like fundamental research, arts, gaming, or any other that is not information-based.
For robotics –physical robotics utilized in the manufacturing industries are not classified as part of IA. It only covers software-based robots.
Lastly, under workflow, business process management, and cloud; only programs or platforms that exhibit a form of intelligence fall under the class of IA. Programs that have limited capabilities to process end-to-end tasks and offer little insight into business processes are not included.
The unfolding potential of IA
The adoption rate of this phenomenon is already significant to the extent that a recent survey of world business leaders shows that 86% of them believe they must implement IA in the next five years to stay competitive. According to a survey by Gartner, 42% of CEOs have embarked on the digital transformation process already with 56% reporting gains from the application.
Due to the uniqueness of IA, in the next five year, experts believe that it is very likely to reach a sophistication and adoption level that took more than 200 year for industrial automation to achieve. A Deloitte survey already indicated that the adoption rate for IA is more than 50% and the rate is predicted to jump to over 70% in two years. If it continues at this rate, we could see a near-global adoption level achieved in the next five years. Despite being a new concept, IA is progressing very rapidly in terms of capabilities.