Impact Of Artificial Intelligence On Economic Growth

Impact Of Artificial Intelligence On Economic Growth – A new agency has been formed in the US and there is talk of a big push for green technology and the need to promote next-generation infrastructure including AI and 5G, with David Knight predicting an economic overhaul that 5G will bring. GDP growth is likely to continue. 40% or more by 2030. The Biden administration has said it will boost spending on emerging technologies, including AI and 5G, by $300bn to $300bn a year.

On the other side of the Atlantic Ocean, the EU announced that Greenland also needs to consider European AI policies to create next-generation companies that drive economic growth and employment. It would be good if the EU and the US (along with Canada and other allies) would find ways to work together on issues like 5G policy and infrastructure development. The UK will host COP 26 and has also made noises about AI and 5G development.

Impact Of Artificial Intelligence On Economic Growth

The world needs to find a way to successfully end the Covid-19 pandemic and move the post-pandemic world into a phase of economic growth with job creation. With continued economic growth built around next-generation technologies, there is an opportunity for a new era of high-skilled jobs.

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AI and 5G: potential for GDP and job growth and GHG emission reduction scope (source for numbers PWC / Microsoft, Accenture)

The above picture sets the stage for major reductions in GHG emissions while allowing for economic growth.

GDP and job growth will be high on the post-pandemic agenda of governments around the world. At the same time, the economies that will truly offer and grow the fastest in this decade will be those that adopt Industry 4.0 technologies and, as a result, will move away from the era of fossil fuel consumption towards a digital world that may lead to a new Be empowered by the potential. energy and with transport that is either largely electric or, over time, hydrogen-based.

Companies will increasingly be “analytics driven” (it should be emphasized that analytics rather than data driven is the key term).

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Examples of how machine-to-machine communication at the edge enabled by AI can be illustrated by the image below:

The machine-to-machine communication in the image above allows it to be broadcast across the network that a person has been detected walking on the road so that even cars that are not in the person’s sight are aware of their presence.

It is important to note that AI, together with 5G networks, will be at the heart of this transition to the world of Industry 4.0.

5G will play an important role because 5G networks are not only much faster than 4G networks, but they also enable a significant reduction in latency, which in turn allows for near real-time analytics and responses, and Also creates greater capacity for connectivity thus facilitating larger machines. Machine communication for IoT devices at the edge of the network (near where data is generated on the device).

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However, as mentioned above, 5G has many advantages over 4G in terms of speed only, as shown in the image below:

The growth of edge computing will reduce the amount of data that is sent back and forth between remote cloud servers and thus make the system more efficient.

To date AI has been most widespread and effective in the areas of social media and e-commerce giants where large digital data sets give them an advantage and where case studies are not very important in terms of their results. No death, injury, or material damage may result from incorrect recommendations for video, post, or clothing items, except for poor user experience.

However, when we are looking to measure AI in the real world, case by case and interpretation matter. Issues such as causality and explanation are becoming key in areas such as autonomous vehicles and robots, as well as in healthcare.

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Likewise data privacy and security are also really important. On the one hand, as mentioned above, data is the fuel for machine learning models. However, on the other hand, in areas such as healthcare most data is often siled and decentralized as well as protected by strict privacy regulations such as the United States (HIPAA) and Europe (GDPR). This is also an issue in the fields of finance and insurance where data privacy and regulation are critical to the operations of financial services companies.

This is one area where federated learning with differential privacy can play a major role in scaling machine learning in areas such as healthcare and financial services.

This is also an area where the US and Europe can work together to enable collaborative learning and help scale machine learning that also provides data security and privacy for end users (patients). . The healthcare sector around the world is reeling under the pressures of the Covid-19 pandemic and is empowering our healthcare workers with AI to reduce the pressure on them while ensuring that Keeping patient information secure will be key to transforming our healthcare systems. Stress on them and deliver better results for the patient.

In relation to AI, we will need to move away from the large models and techniques that have dominated the past decade towards neural compression (principling) that will enable models to work more efficiently at the edge and Help preserve tools and battery life. Also reduce your carbon footprint by reducing energy consumption.

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Furthermore, we will not only need deep learning models that can make predictions at the edge, but also models that can continue to learn at the edge, on the fly, from small data sets and their environment. You respond dynamically. This will be key to enable effective autonomous systems such as autonomous vehicles (cars, drones) as well as robots.

Addressing these challenges will be key to enabling AI to scale in sectors of the economy beyond social media and e-commerce.

It is no surprise that the most powerful AI companies today and in the last few years are from the e-commerce and social media sector.

Additionally, the following images from Valuewalk show how ByteDance (the owner of TikTok) is the world’s most valuable unicorn and AI company.

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Venture capitalists and angel investors should also strive to understand whether AI meets their client’s needs in terms of access to and use of usable data to measure investment (which may be some or all (Transparency, causality, explanatory ability, model size included, ethics) are key for many sectors.

The number of connected devices and the volume of data is predicted to grow dramatically as digital technology continues to reach its reach. For example, the image below shows Statista’s forecast for 75 billion Internet-connected devices by 2025, with an average Type more than 9 per person. Planet!

In fact, IDC has predicted that “the global datasphere will grow from 45 zettabytes in 2019 to 175 by 2025. Nearly 30% of the world’s data will require real-time processing. … Many of these interactions will be global.” Due to the billions of IoT devices around, it is expected to generate more than 90 ZB of data by 2025.

Key machine learning tools such as XGBoost, Light Gradient Boosting Machines and CatBoost emerged in the last decade (roughly 2015 to 2017) and these tools will provide powerful insights with structured data using supervised learning. to be popular with data scientists. There is no doubt that we will see continuous improvements in machine learning tools over the next few years.

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In relation to fields such as natural language processing (NLP), computer vision and deep learning in drug discovery efforts will be effective tools. However, it is submitted that increasingly the techniques will move in the following direction:

This will lead to an era of pervasive AIs where AI starts to move beyond narrow AI (doing only one task) and starts working with multitasking but not at the level where AI matches the human brain (AGI).

My own work focuses on the above hybrid approaches to Broad AI as we seek to find ways to scale AI across the economy beyond social media and e-commerce, above and beyond traditional sectors of the economy with AI. It is key to enable true digital transformation. Our move into the Industry 4.0 era.

“Narrow AI is the ability to perform specific tasks at a very human level in a variety of categories, from chess, risk, and walking, to voice assistance, debate, language translation, and image classification.”

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“The future of pervasive AIs. We’re just entering this frontier, but when it’s fully realized, it will offer AI systems that use and integrate multimodal data streams, learn more efficiently and flexibly, and multitask And domains are crossing over. Broad AI will have powerful implications for business and society.

“Ultimately, General AI is basically what science fiction has long envisioned: AI systems capable of complex reasoning and complete autonomy. Some scientists estimate that General AI may be around 2050 At some point it might be possible – which is actually less than expected. Others say it will never be possible. For now, we’re focused on leading the next generation of Broad AI technologies for the betterment of business and society. .

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