Tuesday, March 2, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Artificial Intelligence

How AI Can Help Solve Some of Humanity’s Greatest Challenges

February 19, 2019
in Artificial Intelligence
How AI Can Help Solve Some of Humanity’s Greatest Challenges
586
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Credit: AI Trends


You might also like

Asimov’s Three Laws Of Robotics And AI Autonomous Cars 

Tesla Working on Full Self-Driving Mode, Extending AI Lead 

RAND Corp. Finds DoD “Significantly Challenged” in AI Posture 

By Marshall Lincoln and Keyur Patel, cofounders of the Lucid Analytics Project

In 2015, all 193 member countries of the United Nations ratified the 2030 “Sustainable Development Goals” (SDG): a call to action to “end poverty, protect the planet and ensure that all people enjoy peace and prosperity.” The 17 goals – shown in the chart below – are measured against 169 targets, set on a purposefully aggressive timeline. The first of these targets, for example, is: “by 2030, [to] eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day”.

The UN emphasizes that Science, Technology and Innovation (STI) will be critical in the pursuit of these ambitious targets. Rapid advances in technologies which have only really emerged in the past decade – such as the internet of things (IoT), blockchain, and advanced network connectivity – have exciting SDG applications.

No innovation is expected to be more pervasive and transformative, however, than artificial intelligence (AI) and machine learning (ML). A recent study by the McKinsey Global Institute found that AI could add around 16 per cent to global output by 2030 – or about $13 trillion. McKinsey calculates that the annual increase in productivity growth it engenders could substantially surpass the impact of earlier technologies that have fundamentally transformed our world – including the steam engine, computers, and broadband internet.

AI/ML is not only revolutionary in its own right, but also increasingly central to the foundation upon which the next generation of technologies are being built. But the pace and scale of the change it will bring about also creates risks that humanity must take very seriously.

Our research has led us to conclude that AI/ML will directly contribute to at least 12 of the 17 SDGs – likely more than any other emerging technology. In this piece, we explore potential use cases in three areas which are central to the Global Goals: financial inclusion, healthcare and disaster relief, and transportation.

FINANCIAL INCLUSION

Access to basic financial services – including tools to store savings, make and receive payments, and obtain credit and insurance – are often a prerequisite to alleviating poverty. Around 2 billion people around the world have limited or no access to these services.

AI/ML is increasingly helping financial institutions create business models to serve the unbanked. For example, one of the biggest barriers to issuing loans is that many individuals and micro businesses have no formal credit history. Start-ups are increasingly running ML algorithms on non-traditional sources of data to establish their creditworthiness – from shopkeepers’ orders and payments history to psychometric testing. Analysis of data on crop yields and climate patterns can be used to help farmers use their land more effectively – reducing risks for lenders and insurance providers.

AI/ML is also being used to help service providers keep their costs down in markets where revenue per customer is often very small. These include automated personal finance management, customer service chat-bots, and fraud detection mechanisms.

HEALTHCARE AND DISASTER RELIEF

The inequality between urban and rural healthcare services is an urgent problem in many developing countries.

Rural areas with poor infrastructure often suffer from severe shortages of qualified medical professionals and facilities. Smart phones and portable health devices with biometric sensors bring the tools of a doctor’s office to patients’ homes – or a communal location in a village center for shared use. AI then automates much of the diagnostic and prescriptive work traditionally performed by doctors. This can reduce costs, enable faster and more accurate diagnoses, and ease the burden on overworked healthcare workers.

AI is also being used to get medical supplies where they are needed. A start-up called Zipline, for example, is using AI to schedule and coordinate drones to deliver blood and equipment to rural areas in Rwanda (and soon other countries in Africa) which are difficult to access by road. Doctors order what they need via a text messaging system, and AI handles delivery. This dramatically reduces the time it takes to obtain blood in an emergency and eliminates wastage.

When it comes to disaster relief, predictive models – based on data from news sources, social media, etc. – can help streamline crisis operations and humanitarian assistance. For example, AI-powered real-time predictions about where earthquakes or floods will cause the most damage can help emergency crews decide where to focus their efforts.

Read the source article at KDNuggets.

 

Credit: AI Trends By: John Desmond

Previous Post

Skills gap, corporate culture holding back AI adoption, O'Reilly survey says

Next Post

Cross-Validation: Concept and Example in R

Related Posts

Asimov’s Three Laws Of Robotics And AI Autonomous Cars 
Artificial Intelligence

Asimov’s Three Laws Of Robotics And AI Autonomous Cars 

February 26, 2021
Tesla Working on Full Self-Driving Mode, Extending AI Lead 
Artificial Intelligence

Tesla Working on Full Self-Driving Mode, Extending AI Lead 

February 25, 2021
RAND Corp. Finds DoD “Significantly Challenged” in AI Posture 
Artificial Intelligence

RAND Corp. Finds DoD “Significantly Challenged” in AI Posture 

February 25, 2021
SolarWinds Hackers Targeted Cloud Services as a Key Objective 
Artificial Intelligence

SolarWinds Hackers Targeted Cloud Services as a Key Objective 

February 25, 2021
IBM Reportedly Retreating from Healthcare with Watson 
Artificial Intelligence

IBM Reportedly Retreating from Healthcare with Watson 

February 25, 2021
Next Post
Cross-Validation: Concept and Example in R

Cross-Validation: Concept and Example in R

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Scientists have built this ultrafast laser-powered random number generator
Internet Security

Scientists have built this ultrafast laser-powered random number generator

March 2, 2021
Companies in the Global Data Science Platforms Resorting to Product Innovation to Stay Ahead in the Game
Data Science

Companies in the Global Data Science Platforms Resorting to Product Innovation to Stay Ahead in the Game

March 2, 2021
Aries becomes next Hyperledger project graduating to active status
Blockchain

Aries becomes next Hyperledger project graduating to active status

March 2, 2021
Government trialling machine learning tech to detect pests at shipping ports
Machine Learning

Government trialling machine learning tech to detect pests at shipping ports

March 2, 2021
Data Annotation Service: a Potential and Problematic Industry Behind AI | by ByteBridge
Neural Networks

Data Annotation Service: a Potential and Problematic Industry Behind AI | by ByteBridge

March 2, 2021
SolarWinds security fiasco may have started with simple password blunders
Internet Security

SolarWinds security fiasco may have started with simple password blunders

March 2, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Scientists have built this ultrafast laser-powered random number generator March 2, 2021
  • Companies in the Global Data Science Platforms Resorting to Product Innovation to Stay Ahead in the Game March 2, 2021
  • Aries becomes next Hyperledger project graduating to active status March 2, 2021
  • Government trialling machine learning tech to detect pests at shipping ports March 2, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates