Artificial Intelligence with a Heart — the Benefits of AI for Good

Artificial Intelligence with a Heart — the Benefits of AI for Good

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Republished by Rainmakrr with permission from Nathaniel Jowitt


For several decades artificial intelligence (AI) has looked set to change the world as we know it. But what societal benefits will this technological revolution bring and will they be evenly spread?

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Photo by Марьян Блан | @marjanblan on Unsplash

Artificial intelligence will have a larger impact on the world than the internet revolution has had so far, according to 62% of global CEOs in PwC’s 22nd Global CEO Survey, with 79% saying that AI is good for society. Google CEO Sundar Pichai has stated that AI is “more profound than fire or electricity.”

Since the 1950s the spectre of a predominant AI has been popularised in science fiction books and films. The idea of all-powerful, intelligent machines is not just confined to books and art. The University of Cambridge’s Centre for the Study of Existential Risk, co-founded by Astrophysicist Martin Rees with high-profile advisers including Elon Musk, studies the very real dangers that AI create for humanity.

“[AI] more profound than fire or electricity”

This much-hyped technology has had a fair share of disillusionment including two so-called ‘AI winters’, periods when funding and interest in AI plummeted dramatically. However, recent advances in big data, data science and cloud computing make it seem that this time is different. Tata Sons Chairman Natarajan Chandrasekaran said, “Suddenly, cloud computing has made possible the real-time collection of infinite amounts of data. This opens up the possibilities for AI.” The recent rise of edge computing, 5G and quantum computing, which rely on or support the growth of machine learning, a subset of AI, back up this claim.

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“We’re past Pong, we’re maybe at PacMan by now”

AI is currently being put to work in three main areas: automating tasks that humans would rather not do or do badly, personalising content suggestions for online consumers and recognising patterns to classify objects and predict future outcomes. This has driven a steep increase in investor interest with annual private investment in AI quadrupling from 2015 to 2019, reaching over $35bn. As Graphcore CEO Nigel Toon stated, “We’re past Pong, we’re maybe at PacMan by now.” Investment in the sector has not been limited to the rich world. Google opened its first Africa Artificial Intelligence Centre in Ghana in 2019 and organisations such as Knowledge 4 All are working to build up AI engineering talent globally.

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While predicting whether someone will default on their car insurance is certainly useful for underwriters, being able to identify a malignant from a benign tumour is life-changing. So what are the most impactful ways to use AI and machine learning? Those who champion AI suggest it could solve international crises, bring an end to poverty and help prevent catastrophe.There are a growing number of organisations seeking to use the power of Artificial Intelligence for good, creating positive social and environmental benefits. Here are seven examples:

  1. Spotting signs of abuse

The rAInbow chatbot from AI for Good helps to spot the signs of abuse, judge what’s healthy and unhealthy behaviour, and provide resources that can help people facing gender-based violence.

2. Fighting the spread of deadly diseases

Using machine learning, Zzapp Malaria’s software app optimises intervention strategies that target mosquitoes, prioritising the use of scarce resources and maximising impact. The map-based mobile app guides field workers to streamline execution and monitor progress.

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Photo by Марьян Блан | @marjanblan on Unsplash

3. Measuring the social impact of development initiatives

The UN uses machine learning in its Radio Content Analysis Tool to accelerate sustainable development solutions in Uganda by using speech recognition to leverage public radio content as a source of information on issues relevant to sustainable development.

4. Boosting education

UNICEF Innovation is applying Deep Learning techniques to map every school in the world. The tool uses high-resolution satellite imagery which is visualized through an online platform to help identify where gaps and information needs are. This helps national governments optimise their education systems, assess vulnerabilities and enhance emergency crisis responses.

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5. Enhancing healthcare assessments and delivery

Össur, which focusses on prosthetic, osteoarthritis and injury support solutions, has developed artificial limbs to provide greater comfort by using machine learning to adjust the mobility equipment according to the user’s unique gait.

6. Improving agriculture production

mCrops embeds machine learning in its diagnostic tools which identify viral crop diseases in cassava crops by taking pest and symptom measurements using a mobile device.

7. Mitigating further climate change

Rainforest Connection detects illegal logging over 2,500 sq km of the rainforest by using acoustic monitoring and AI. By feeding audio signals from the rainforest into Google’s open-source machine learning framework, TensorFlow, Rainforest Connection can locate sounds of illegal activity.

Catalysing growth

These examples are just some of many which have shown that AI can be used as a power for good. As more entrepreneurs seek to solve challenges around the world using AI-enabled approaches, there should also be a concerted effort at an ecosystem level to support these founders and businesses. Below are five calls to action to achieve this growth and maximise the impact of Artificial Intelligence for Good projects.

Connect with the wider Tech for Good movement

With the growth of impact investing, Tech for Good has been able to go by the name of ‘high-growth impact’. However, more broadly, Tech for Good companies lack a rigid definition and set of standards, causing the communication of the ‘sector’ to suffer from a lack of clarity. AI for Good can benefit from the enthusiasm Tech for Good has created while retaining unique metrics for evaluating the social impact and viability of its initiatives. This horizontal network growth can be particularly effective for early-stage businesses for which lessons learned and best practice sharing can be largely sector agnostic.

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Develop platforms which showcase success stories

Competitions and growth programmes like Tech Nation’s Applied AI are fantastic ways to shine a light on successful companies. These platforms typically also support ventures to access investment, grow their customer base and boost hiring through job boards. By showcasing successful businesses and the viability of mission-driven AI companies, the next wave of entrepreneurs will be inspired to create solutions to challenges they face in communities around the world.

Build tight networks of founders and angel investors

Businesses that seek venture capital and go through the funding cycle gain access to the networks that investors offer. For Tech for Good firms, this often comes with the additional challenge of explaining their purpose-and-profit business model. Traditional investors who obtain board seats and voting rights may try to direct the business towards maximising profits at the expense of the mission-driven impact sought by its founders. These problems are felt most acutely at the early stage of the business life cycle and can be mitigated by creating an ecosystem of like-minded angel investors in the AI for Good sphere.

Use existing networks within established sectors

AI is a technology, not a sector. Therefore it is crucial for any companies creating AI-enabled solutions to establish sector-specific networks in traditional sectors such as healthcare and education. This vertical network building is most useful for growth-stage ventures that seek to compete with incumbents, expand internationally and create meaningful partnerships in their industry. Unlike established sectors, it is often difficult for Tech for Good businesses to explain their place in the ecosystem and this would support their narrative and help them to appear more credible.

Unlock data for pilot projects

AI and machine learning require large amounts of good quality and relevant data to build accurate models of the real world. On top of this, data used in AI for Good initiatives tends to be expensive and hard to access because of its value to current owners and sensitivity. Large owners of data, including governments, healthcare providers, schools and telecommunications companies, should unlock small parts of the data they hold for use in pilot projects to allow companies to demonstrate the value AI-enabled projects can bring, both in terms of value-added services and social impact. Doing so would further open up the door for large-scale collaboration.

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Undoubtedly, AI is a powerful technology that can be used to efficiently and cheaply solve some of society’s challenges. Governance of AI systems will play a significant part in ensuring that the technology is used appropriately. Ultimately, the flow of capital and the role of regulation will decide how the field of AI develops. However, researchers and practitioners can play their part in making it an accessible and open-minded pursuit in both academia and business. Noticeable gains have been made where humans and AI algorithms collaborate. Partners Martin Casado and Matt Bornstein at Andreessen Horowitz, a venture capital firm, reckon that “the need for human intervention will likely decline as the performance of AI models improves. It’s unlikely, though, that humans will be cut out of the loop entirely.”

Entrepreneurs will continue to seek underserved areas and create adapted AI solutions. Government authorities should welcome such approaches and seek to understand the benefits that AI can bring. In doing so they might be more willing to open up the data that they hold. Without conscious global efforts across the ecosystem, the real winner will be AI itself as it moves unseen in the back-end eating more data, growing ever smarter and taking a bigger seat at the decision-making table.

Kindly republished with permission and minor formatting changes. Original article by Nathaniel Jowitt is COO of Axle


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