Tuesday, November 29

Artificial Intelligence: Future of growth

The data reveals a disheartening truth about current growth. There has been a marked decline in the ability of the traditional levers of production – capital and labor – to drive economic growth.

However, this is simply the view that the figures give us. Artificial intelligence (AI) is a new factor of production and has the potential to introduce new sources of growth, transform how work gets done, and strengthen the role of people in driving economic growth.

Accenture’s study on the impact of Artificial Intelligence in 12 developed economies reveals that AI could double annual economic growth rates by 2035, changing the nature of work and establishing a new relationship between man and machine. The impact of AI on business is predicted to increase work productivity by up to 40% and enable people to make more efficient use of their time.

Three paths of growth based on AI

As a new factor of production, AI opens up at least three important avenues for growth.

Contrary to traditional automation solutions, AI-based innovation automates complex physical tasks that require adaptability and agility, as well as enabling self-learning.

Diffusion of innovations

One of the less talked about advantages of artificial intelligence is its ability to drive innovation as it penetrates the economy.

Contrary to traditional automation solutions, AI-based innovation automates complex physical tasks that require adaptability and agility, as well as enabling self-learning.

Capital and labor enrichment

Existing capital and workforce can be used much more effectively as AI allows workers to focus on what they do best: create and innovate.

“AI presents great growth potential for the economy and for human beings.”
— MARCOS PURDY, Managing Director of Economic Research, Accenture Institute for High Performance

double growth rates
By acting as a hybrid between capital and labor, AI offers the possibility of expanding and transcending the current capacity of both factors to propel economic growth. Our research reveals unprecedented opportunities for value on video creation.

Annual growth rates in 2035 of gross value added (an approximation of GDP), comparison of baseline growth in 2035 with a scenario where artificial intelligence has been embedded in the economy

Paves the way to a future of Artificial Intelligence

Empower the next generation for the future of AI: Integrate human intelligence with machine intelligence so they can successfully co-exist and reinforce the role of people in driving growth.

Foster AI Regulation: Update and create adaptive and self-improving laws on an ongoing basis to bridge the gap between the pace of technological change and the pace of regulatory response.

Advocates for a code of ethics for Artificial Intelligence: Ethical debates must be complemented by tangible standards and best practices in the development and use of intelligent machines.

Address redistribution effects: Policymakers need to highlight how AI can lead to tangible benefits and address any perceived disadvantages of AI in advance.

Getting “smart”

Artificial Intelligence (AI) can be defined as a part of computer science that seeks to design “intelligent” computer systems. These are systems with characteristics that are normally associated with intelligence in human behavior, such as understanding language, learning, reaching own conclusions, etc. Sometimes the term is used more loosely, in relation to many other disciplines such as neuroscience or philosophy, since it refers to “human intelligence” and we lack a complete understanding of the neurological mechanism of intelligence.

In computer science, AI includes a broad set of disciplines (machine learning, learning (deep learning, networks) and has multiple applications (language processing natural, voice recognition, robotics). The term was coined in the 1950s, although the idea of machines acting like humans is much older, and has since gone through a
period of great expectations and disappointments. Looks like he’s finally catching up hopes thanks to deep learning, the latest development in AI, which goes one step beyond the machine learning.

Machine learning was all the rage in the 1990s and 2000s, focusing on solving specific problems with the ease of collecting labeled data. Deep learning is more ambitious, since its objective is to build “intelligent” machines that work like the human brain. This is achieved through the development of huge distributed neural networks (method of AI processing of a computer that allows self-learning from experience) that They analyze huge amounts of data at high speed. Unlike machine learning, Deep learning does not follow a logical set of instructions, but rather makes decisions through pattern analysis.