WDIS AI-ML Series: Module 1 Lesson 1: Understanding Artificial Intelligence - What is to what could be
Written by
Vinay Roy
Published on
04th Mar 2024
Artificial intelligence is all about giving machines an ability to think (and act) like a human
Level of Intelligence
Artificial Intelligence is typically seen on a spectrum of intelligence starting from Narrow intelligence (ANI), where the Machine can perform one task extremely well such as language translation, image recognition, or playing chess but it fails to generalize to other tasks. This is where we are today - ANI or Artificial Narrow Intelligence.
Then comes General Intelligence (AGI), where machines are performing on almost all tasks as well as humans can. To anybody’s guess but based on my class survey, business executives felt it was 20 - 30 years away.
Post-AGI will lead to Super Intelligence (ASI), when machines will exceed human excellence. ASI is considered to be potentially transformative and could have profound impacts on society, although the exact implications are highly speculative.
In essence, ANI represents the AI we have today, AGI is a level of AI that we aspire to achieve in the future, and ASI is a theoretical concept of AI that far surpasses human intelligence. Most fear around AI is centered around us achieving AGI or ASI.
A Brief History of AI Evolution
The history of artificial intelligence (AI) or building a thinking machine is a fascinating journey that spans several decades. The journey encompasses various breakthroughs, setbacks, and milestones.
What is Being Done Today
AI is impacting every industry, every company, and whether we know it or not everyone. With the rise of computing power and exponential growth in data, we have seen AI getting wider adoption in industries ranging from Agriculture to Healthcare to Finance & Banking. Let us see some popular use cases in these fields. We will discuss what AI models lie underneath these use cases in the next few modules.
Healthcare and Medicine:
AI facilitates medical diagnosis, treatment planning, and patient care through image analysis, predictive modeling, and personalized medicine.
Machine learning algorithms analyze medical images, electronic health records (EHRs), and genomic data for disease detection and treatment recommendations.
Finance and Banking:
AI automates financial tasks such as fraud detection, risk assessment, and credit scoring.
Natural language processing (NLP) algorithms analyze news, reports, and social media sentiment for market insights and investment decisions.
Manufacturing and Operations:
AI improves manufacturing efficiency, quality control, and predictive maintenance through sensor data analysis and predictive modeling.
Machine learning algorithms optimize production schedules, identify anomalies, and prevent equipment failures.
E-commerce and Retail:
AI enhances the shopping experience with personalized product recommendations, visual search, and virtual try-on.
Natural language processing (NLP) algorithms analyze customer reviews, social media mentions, and product descriptions for sentiment analysis and market insights.
Marketing and Sales:
AI enhances marketing strategies through predictive analytics, personalized recommendations, and targeted advertising.
Machine learning algorithms analyze customer behavior, segment audiences, and optimize marketing campaigns for better engagement and conversion rates.
AI tools improve sales forecasting, lead scoring, and customer relationship management (CRM) processes.
Predictive analytics and data-driven insights help sales teams identify high-value prospects, prioritize leads, and optimize sales strategies.
Supply Chain Management:
AI optimizes supply chain operations by predicting demand, optimizing inventory levels, and streamlining logistics and distribution processes.
Machine learning algorithms analyze historical data, market trends, and external factors to improve forecasting accuracy and reduce supply chain risks.
Where are we going - The Challenges Ahead
So looks like AI can do possibly everything that we can think of so does it mean we are already there at AGI? or is it just about stitching ANIs together, called "narrow AI aggregation”, to create a huge AGI machine? Well not so easy. There are still limitations of AI that researchers are struggling to solve. Some of them are:
Addressing these limitations remains an active area of research in AI, and overcoming them will be crucial for the development of more robust and trustworthy AI systems in the future.
As a photographer, it’s important to get the visuals right while establishing your online presence. Having a unique and professional portfolio will make you stand out to potential clients. The only problem? Most website builders out there offer cookie-cutter options — making lots of portfolios look the same.
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12 photography portfolio websites to showcase your work
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WDIS AI-ML Series: Module 1 Lesson 1: Understanding Artificial Intelligence - What is to what could be
Vinay Roy
04th Mar 2024
Introduction
Artificial intelligence is all about giving machines an ability to think (and act) like a human
Table of Contents
Key Takeaways
A Framework for Framing ML Problems
Clarify the Business Objective
Frame the ML Problem
Design the ML Pipeline
Define Success Metrics
Plan for Deployment & Beyond
Continually Measure Business Impact
Conclusion
Artificial intelligence is all about giving machines an ability to think (and act) like a human
Level of Intelligence
Artificial Intelligence is typically seen on a spectrum of intelligence starting from Narrow intelligence (ANI), where the Machine can perform one task extremely well such as language translation, image recognition, or playing chess but it fails to generalize to other tasks. This is where we are today - ANI or Artificial Narrow Intelligence.
Then comes General Intelligence (AGI), where machines are performing on almost all tasks as well as humans can. To anybody’s guess but based on my class survey, business executives felt it was 20 - 30 years away.
Post-AGI will lead to Super Intelligence (ASI), when machines will exceed human excellence. ASI is considered to be potentially transformative and could have profound impacts on society, although the exact implications are highly speculative.
In essence, ANI represents the AI we have today, AGI is a level of AI that we aspire to achieve in the future, and ASI is a theoretical concept of AI that far surpasses human intelligence. Most fear around AI is centered around us achieving AGI or ASI.
A Brief History of AI Evolution
The history of artificial intelligence (AI) or building a thinking machine is a fascinating journey that spans several decades. The journey encompasses various breakthroughs, setbacks, and milestones.
What is Being Done Today
AI is impacting every industry, every company, and whether we know it or not everyone. With the rise of computing power and exponential growth in data, we have seen AI getting wider adoption in industries ranging from Agriculture to Healthcare to Finance & Banking. Let us see some popular use cases in these fields. We will discuss what AI models lie underneath these use cases in the next few modules.
Healthcare and Medicine:
AI facilitates medical diagnosis, treatment planning, and patient care through image analysis, predictive modeling, and personalized medicine.
Machine learning algorithms analyze medical images, electronic health records (EHRs), and genomic data for disease detection and treatment recommendations.
Finance and Banking:
AI automates financial tasks such as fraud detection, risk assessment, and credit scoring.
Natural language processing (NLP) algorithms analyze news, reports, and social media sentiment for market insights and investment decisions.
Manufacturing and Operations:
AI improves manufacturing efficiency, quality control, and predictive maintenance through sensor data analysis and predictive modeling.
Machine learning algorithms optimize production schedules, identify anomalies, and prevent equipment failures.
E-commerce and Retail:
AI enhances the shopping experience with personalized product recommendations, visual search, and virtual try-on.
Natural language processing (NLP) algorithms analyze customer reviews, social media mentions, and product descriptions for sentiment analysis and market insights.
Marketing and Sales:
AI enhances marketing strategies through predictive analytics, personalized recommendations, and targeted advertising.
Machine learning algorithms analyze customer behavior, segment audiences, and optimize marketing campaigns for better engagement and conversion rates.
AI tools improve sales forecasting, lead scoring, and customer relationship management (CRM) processes.
Predictive analytics and data-driven insights help sales teams identify high-value prospects, prioritize leads, and optimize sales strategies.
Supply Chain Management:
AI optimizes supply chain operations by predicting demand, optimizing inventory levels, and streamlining logistics and distribution processes.
Machine learning algorithms analyze historical data, market trends, and external factors to improve forecasting accuracy and reduce supply chain risks.
Where are we going - The Challenges Ahead
So looks like AI can do possibly everything that we can think of so does it mean we are already there at AGI? or is it just about stitching ANIs together, called "narrow AI aggregation”, to create a huge AGI machine? Well not so easy. There are still limitations of AI that researchers are struggling to solve. Some of them are:
Addressing these limitations remains an active area of research in AI, and overcoming them will be crucial for the development of more robust and trustworthy AI systems in the future.
About the author:
Vinay Roy
Fractional AI / ML Strategist | ex-CPO | ex-Nvidia | ex-Apple | UC Berkeley
How do we use this data that exists in the Data warehouse and/or Data Lake to build Machine Learning (ML) models? However, before we build models, we need to gain a deeper understanding of our data.
Not many companies invest enough in data as much as they do in Data Science. Albeit the realization is growing that to be seen as an ‘AI-first’ company, one needs to establish itself as a ‘Data-first’ company. The biggest challenge In this section we will give an overview of what end-to-end data processing looks like from the viewpoint of a data science project:
In this lesson, we will learn to do it the right way and we will also introduce the concept of PRD - Product Requirement Document, a wildly misused or unused tool that is needed to align people on a common mission.
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