Applying AI Towards A Better World: GDP, Jobs Growth & Less Pollution

Applying AI Towards A Better World: GDP, Jobs Growth & Less Pollution

Applying AI Towards A Better World: GDP, Jobs Growth & Less Pollution

The economic recession that follows as a consequence of the Covid-19 crisis and in particular the demise of certain sectors of the economy (physical retail, hospitality sector, etc) means that there will be greater pressure on politicians around the world to consider how to stimulate GPD growth in the post-pandemic world.

However, there are also increasing pressures on politicians to combat the threat posed by climate change. Are the desired objectives of GDP and employment growth as well as reducing pollution at odds with each other?

What if there is a pathway to GDP growth with the creation of new jobs and yet at the same time we are able to reduce emissions of Green House Gasses (GHGs)?

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A report entitled "How AI can enable a sustainable future" by PWC and commissioned by Microsoft (lead authors Celine Herweijer of PWC and Lucas Joppa of Microsoft) estimates that using AI for environmental applications across four sectors – agriculture, water, energy and transport. The report estimated that such applications could contribute up to $5.2 trillion USD to the global economy in 2030, a 4.4% increase relative to business as usual.

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The projections for the four sectors of the economy are shown in the figure below from the report:

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The report states that "Productivity benefits of AI applications across the four key sectors can generate an overall global economic uplift, yielding a potential gain of US$3.6 – 5.2 trillion driven by optimized use of inputs, higher output productivity and automation of manual and routine tasks. In parallel, these applications can accelerate the move to a low-carbon world with a reduction in worldwide greenhouse gas emissions by 0.9 – 2.4 gigatons of CO2e, equivalent to the 2030 annual emissions of Australia, Canada and Japan combined,13 and an overall reduction in carbon intensity of 4.4 – 8.0% relative to BAU.

Jobs Growth 

The report forecasts that AI applications modelled will also create 18.4 – 38.2 million net jobs globally (broadly equivalent to the number of people currently employed in the UK), offering more skilled occupations as part of this transition."

Leading AI Researchers on Machine Learning and Climate

Karen Hao authored "Here are 10 ways AI could help fight climate change" in the MIT Tech review noted that "Some of the biggest names in AI research have laid out a road map suggesting how Machine Learning can help save our planet and humanity from imminent peril." The paper was led by David Rolnick of the University of Pennsylvania and the list of authors include Andrew Y. Ng, Demis Hassabis, and Yoshua Bengio.

Source Image below: MIT Technology Review, MS. Tech | Thumbnnail:Chuttersnap/Unsplash

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The 10 areas that they identified in the paper Tackling Climate Change with Machine Learning:

1. Improve predictions of how much electricity we need

2. Discover new materials

3. Optimize how freight is routed

4. Lower barriers to electric-vehicle adoption

5. Help make buildings more efficient

6. Create better estimates of how much energy we are consuming

7. Optimize supply chains

8. Make precision agriculture possible at scale

9. Improve deforestation tracking

10. Nudge consumers to change how we shop

Artificial Intelligence & Reducing Wastage in Retail Inventory

Larry Claasen observed in "Taking stock of fashion’s $210bn inventory problem" that "about 30% of the clothes made around the world are never sold. For 2018, the cost of this inventory distortion was estimated to be $210bn"

"What does a clothing retailer do with lines that don’t sell? If it’s international fast-fashion house H&M, some of that stock gets incinerated. Having found itself saddled with $4.4bn in unsold inventory, the Sweden-based retailer has few options when it comes to disposing of it."

"A similar story may be told about luxury goods maker Burberry, which had a policy of burning its unsold merchandise."

"The company reversed its position in September, after a public backlash to the news that it had destroyed £28.6m worth of stock in 2017 to protect its brand."

Lydia Mageean observed that "Managing inventory – A huge problem that retailers face is over-ordering and losing out on profit due to the unsold product. It has always been quite tricky to keep business moving, yet not hold too much that you are left with wasted items. Today, companies like Chain of Demand use Machine Learning algorithms to make more accurate predictions and choices. While demand forecasting techniques have been around for years, traditional methods only used historical sales data. The majority of calculative work needed to be done by humans and with limitations when the data sets go over 10,000. Yet, with Machine Learning, you can reduce forecasting errors by up to 50%, and are not restricted to how many data points and sources are used, thereby making more precise predictions."

Rolnick et al. observed state that "...the clothing industry sells an average of only 60% of its wares at full price, but some brands can sell up to 85% due to just-in-time manufacturing and clever intelligence networks. As online shopping and just-in-time manufacturing become more prevalent and websites offer more product types than physical storefronts, better demand forecasts will be needed on a regional level to efficiently distribute inventory without letting unwanted goods travel long distances only to languish in warehouses." Ronnick et al. also argue that recommender systems could also direct customers towards the more climate friendly products.

Artificial Intelligence and Economic Growth

PWC forecast that by 2030 AI will contribute $15.7 Trillion or 14% to global GDP.

Source for Infographic below PWC

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Source Gif below from PWC showing the potential contribution from AI to the economy.

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Artificial Intelligence To Create 58 Million New Jobs By 2022, Says Report" where it was noted that there will be a major shift in quality, location and permanency for the new roles and that "54% of employees of large companies would need to up-skill in order to fully harness these growth opportunities."

The Role of 5G

Furthermore, it is essential that business leaders, politicians and regulators understand the potential of technologies such as AI and 5G to actually generate jobs and drive economic growth. For example Accenture reported that deploying the next generation of high-speed 5G wireless networks could create up to three million jobs and add approximately $500 billion to US GDP through direct and indirect potential benefits. This shows that if we adopt policies and approaches to incentivise the adoption of the next generation of technology then we can also deliver benefits across communities as well as at the corporate level.

Source Infographic below Accenture

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Accenture estimated the the impact of 5G on employment based upon research relating to economic impacts that resulted from previous shifts with particular focus on 3G and the figures relating to end of the 7 year buildout. The forecast for GDP growth is determined by estimates relating to the elasticity of employment relating to growth in the U.S. and jobs created by 5G.

Chloe Taylor noted in CNBC Barclays prediction that 5G could add $21 billion per year to the UK economy by 2025. IHS Markit predicted that 5G would add $12 Trillion of global economic activity in 2035.

Industry 4.0

The Fourth Industrial Revolution was first introduced by Klaus Schwab, executive chairman of the World Economic Forum, in a 2015 article published by Foreign Affairs...Schwab expects this era to be marked by breakthroughs in emerging technologies in fields such as robotics, Artificial Intelligence, nanotechnology, quantum computing, biotechnology, the Internet of Things, the industrial internet of things, decentralized consensus, fifth-generation wireless technologies, 3D printing, and fully autonomous vehicles."

Erik Josefssonin Solving the climate crisis with Industry 4.0 observes that "...the Fourth Industrial Revolution has arrived at a critical time for climate action. The emergence of this new revolution connects physical assets to digital ones and fosters a more collaborative approach across organizations and society. In particular, technologies like 5G and cellular IoT are laying the foundation for Industry 4.0 and will change how businesses are wired and global challenges are addressed. "

"By 2025, 5G is expected to provide network coverage for up to 65 percent of the world’s population, while LTE is forecasted to cover up to 90 percent. Not only will the technology pave the way for the development of the Internet of Things (IoT), but 5G is also expected to have more energy efficient options than previous generations, thereby allowing us to better control consumption levels and contribute to breaking the energy curve."

"Additionally, IoT will drive an increase in efficiency that will help us to better measure our climate impact. With billions of connections and sensors being incorporating into our daily life, industries will be able to access real-time data and remote operating capabilities necessary in making positive change. With 1.5 billion cellular IoT connections expected by 2025, the possibility of modifying our energy usage in scenarios that were previously overlooked becomes a reality."

"In our new Industry 4.0 world, digital technologies can also allow for autonomous operations. Industries utilizing IoT with 5G networks will be able to minimize the amount of power used during periods of low traffic by having devices power off when not in use and power up in times of high traffic – all without human input and to conserve energy. This giant leap in technological capability will move us towards a more connected society that ultimately supports a sustainable future for everyone."

Source for image below Rolnick et al. Tackling Climate Change with Machine Learning

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Rolnick et al. also observe in relation to the overproduction issue that "Global excess inventory in 2011 amounted to about $8 trillion worth of goods, according to the Council of Supply Chain Management Professionals. This excess may be in part due to mis-estimation of demand, as the same organisation noted that corporate sales estimates diverged from actual sales by an average of 40%. Machine Learning may be able to mitigate these issues of overproducing and/or overstocking goods by improving demand forecasting."

One also hopes that Fintech solutions could result in an end to paper receipts.

A survey of firms across the EU revealed that at least 50% used thermal paper. Christian Lavingia observed that "Environmentally, we’re speaking an entirely different language. The Huffington Post observed that such thermal paper receipts require:

  • 250 million gallons of oil
  • 10 million trees
  • 1 billion gallons of water

The figures above represent a year for just the US.

There are also healthcare issues that result. Sanjana Varghese authored "The UK's 11 billion yearly receipts are an environmental nightmare" and notes how annually retailers within the UK process 11.2 billion till receipts, which cost at least £32 million to make. "The problem: thermal paper is coated with a substance called bisphenol A (BPA), or its lesser known but also harmful substitute BPS; both react to the heat from a printer head to produce the numbers and letters on the paper. If you scratch a receipt and leave a dark mark, it contains BPA and BPS."

"BPA and BPS have both been banned from other plastic products, such as sippy cups and water bottles, because they are harmful when ingested in large amounts. Research suggests that both substances linger in the body for months, which means that mere traces can accumulate and do plenty of harm. They can be absorbed through the skin, so to avoid harmful health effects, you should use gloves when handling BPA and BPS products or at least scrub your hands vigorously. And, of course, thermal paper can’t be recycled."

The Way Forward

The convergence of AI with the Internet of Things (IoT) as 5G scales with more Edge Computing will result in devices and sensors that may interact with their environment and result in a fundamentally transformed economy.

This will result in transformations for healthcare with remote medicine and the emergence of personalised medicine, smart cities, the rise of the invisible bank with frictionless payments maybe using biometric features and digital receipts rather than paper receipts. Smart grids and more efficient industry may also play a key part in our daily lives later this decade.

New businesses and opportunities for employment will be created. Policy makers need to be bold and to see the opportunity for the AIIoT and the potential to stimulate growth focussed on the emerging technologies that result in cleaner living and GDP growth.

China, in Pointed Message to U.S., Tightens Its Climate Targets

Somini Sengupta in the New York Times authored an article entitled "China, in Pointed Message to U.S., Tightens Its Climate Targets" that President Xi Jinping pledged, among other goals, to achieve “carbon neutrality by 2060.” It was China’s boldest promise yet on climate change. In the past when I presented on Clean Technology opportunities inCapitol Hill to cross party senators the feedback from some was "We won't act until China will act." It will be fascinating to see what the response will be from the US. Governments around the world including across the EU, UK, and the emerging markets have an opportunity to stimulate their economies to create jobs and GDP growth that will be needed after the impact of the Covid-19 pandemic and also to adopt a pathway with next generation technologies that is cleaner.

A vision and summary of the cleaner and more efficient future is provided in the infographic images below.

AIoT: When Artificial Intelligence Meets the Internet of Things

Source for infographic images below: Iman Ghosh VisualCapitalist.com

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Imtiaz Adam 

Artificial Intelligence Expert

Imtiaz Adam is focussed on the latest developments in artificial intelligence and machine learning techniques with a particular focus on deep learning.

   

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