Ten Everyday Machine Learning Use Cases

Summary

This video script delves into the practical applications of machine learning (ML) in daily life, highlighting its impact across various sectors. It covers ML's role in enhancing customer service through chatbots, powering voice assistants like Siri and Alexa, and personalizing mobile app experiences. The script also underscores ML's significance in areas such as financial fraud detection, stock market trading, cybersecurity, transportation with Google Maps and ridesharing apps, email filtering, and healthcare, particularly in improving diagnostic accuracy. The video concludes by noting the extensive use of ML in marketing and sales, emphasizing its current and tangible presence in our lives rather than the theoretical concept of artificial general intelligence.

Takeaways

• 💡 Machine Learning (ML) is a subset of AI that enables machines to learn from data and past experiences to recognize patterns and make predictions.
• 🗣️ Natural Language Processing (NLP) is a significant aspect of ML, allowing machines to understand and process human language.
• 💬 Chatbots in customer service use ML to handle text-based queries, providing immediate responses and routing to human assistance when needed.
• 🔊 Voice assistants like Siri and Alexa utilize ML for speech recognition and understanding, making interactions with devices more natural.
• 🎵 ML is integral to mobile apps, powering personalized recommendations in services like Spotify and LinkedIn.
• 📱 Modern smartphones use ML for features such as computational photography, facial recognition, and on-device image classification.
• 💳 ML and deep learning are widely used in fraud detection within financial transactions, identifying and flagging suspicious activities.
• 📈 A significant portion of stock market trading is conducted by ML algorithms, which are becoming increasingly prevalent.
• 🛡️ ML plays a crucial role in cybersecurity, using reinforcement learning to identify and respond to cyber attacks and intrusions.
• 🚗 ML algorithms are used by services like Google Maps for real-time traffic analysis and by ridesharing apps to match riders with drivers.
• 🏥 In healthcare, ML assists in areas such as tumor classification in mammograms, increasing the accuracy and efficiency of medical diagnostics.
• 🎯 Marketing and sales departments leverage ML for lead generation, data analytics, and personalized marketing campaigns based on consumer interests.

Q & A

What is the projected value of the machine learning industry by 2029?

The machine learning industry is projected to become a $200 billion industry by 2029.

How does natural language processing (NLP) enable machines to understand human language?

NLP allows machines to make sense of the unstructured nature of human language by recognizing patterns and generating predictions.

What is the role of chatbots in customer service?

Chatbots act as virtual agents on e-commerce sites, handling text-based queries and routing customers to human representatives when necessary.

How do voice assistants utilize machine learning?

Voice assistants use speech-to-text and NLP machine learning models to understand spoken commands.

In what ways are machine learning models integrated into mobile apps?

Machine learning models are used in mobile apps for personalized services like song recommendations in Spotify and employment suggestions on LinkedIn.

What is an example of machine learning in smartphones that enhances user experience?

Smartphones use machine learning for computational photography, facial recognition for unlocking, and image classification to help users search their photo libraries.

How does machine learning assist in detecting fraudulent credit card transactions?

ML and deep learning are used by financial institutions to train models that recognize suspicious transactions and flag them for investigation.

What percentage of stock market trading is conducted by machine learning algorithms?

Between 60 and 73% of all stock market trading is conducted by machine learning algorithms, with this percentage increasing annually.

How does machine learning contribute to cybersecurity?

Reinforcement learning within ML trains models to identify and respond to cyber attacks, as well as detect intrusions.

What role does machine learning play in transportation and logistics?

ML is used by services like Google Maps for traffic analysis and route optimization, and by ridesharing apps like Uber and Lyft to match riders with drivers.

How does machine learning help in healthcare, specifically in radiology?

ML assists in healthcare by training pattern recognition models to classify tumors in radiology images, increasing the accuracy of diagnoses and the efficiency of radiologists.

Which department in an organization is reported to use AI and machine learning the most?

According to Forbes, the marketing and sales department uses AI and machine learning the most, for lead generation, data analytics, and personalized marketing campaigns.

Outlines

00:00

🤖 Introduction to Machine Learning Applications

The paragraph introduces the concept of machine learning (ML) as a subset of artificial intelligence, emphasizing its practical applications in daily life. It mentions the projected growth of the ML industry to $200 billion by 2029. The speaker highlights the importance of natural language processing (NLP), which enables machines to understand human language. Examples of ML in action include chatbots in customer service, voice assistants like Siri and Alexa, mobile apps with personalized recommendations, and on-device functionalities in smartphones such as computational photography and facial recognition. The paragraph also touches on the use of ML in fraud detection in financial transactions, where it helps identify suspicious activities among millions of daily transactions.

05:00

💡 Advanced Machine Learning Use Cases

This paragraph delves into more advanced applications of machine learning. It discusses the role of ML in stock market trading, with 60 to 73% of trades being conducted by ML algorithms. The paragraph also covers ML's impact on cybersecurity, where it is used to identify and respond to cyber attacks. In transportation, ML algorithms are used by Google Maps for traffic analysis and route optimization, and by ridesharing services to match riders with drivers. Email filtering is another area where ML is utilized, helping to classify messages and provide auto-complete responses. The paragraph then shifts to healthcare, where ML is improving the accuracy of medical imaging interpretation, such as in mammogram analysis, and aiding in early cancer screening and bone fracture detection. The speaker concludes with a question about the most common departmental use of AI and ML in organizations, which, according to Forbes, is marketing and sales. The use of ML in marketing for lead generation, data analytics, and personalized campaigns is also mentioned.

Keywords

💡Generative AI

Generative AI refers to a subset of artificial intelligence where algorithms generate new content, such as text, images, or music, based on patterns learned from existing data. In the video, generative AI is mentioned as a popular topic, but it is emphasized that it is only a part of the broader field of machine learning.

💡Machine Learning (ML)

Machine Learning is a type of artificial intelligence that enables machines to learn from data, identify patterns, and make decisions with minimal human intervention. The video discusses ML as a foundational technology that powers various applications in everyday life, such as chatbots and voice assistants.

💡Natural Language Processing (NLP)

Natural Language Processing is a subfield of AI that focuses on the interaction between computers and human languages. It enables machines to understand, interpret, and generate human language in a useful way. The video highlights NLP's role in customer service chatbots and voice assistants like Siri and Alexa.

💡Chatbots

Chatbots are computer programs designed to simulate conversation with human users, providing automated responses to queries. In the context of the video, chatbots are used by businesses to handle customer service inquiries, either resolving them directly or directing customers to human assistance.

💡Voice Assistants

Voice assistants are AI-powered tools that respond to voice commands, typically converting speech to text and then processing the request. Examples given in the video include Siri and Alexa, which use ML to understand and execute spoken commands.

💡Smartphones and ML

The video discusses how machine learning is integrated into smartphones for various functionalities, such as computational photography for background blur in photos, facial recognition for unlocking devices, and image classification for searching photo libraries.

💡Fraud Detection

Fraud detection involves the use of ML algorithms to identify and flag suspicious activities, such as fraudulent credit card transactions. The video mentions that ML and deep learning are widely used by financial institutions to process the vast number of transactions and detect anomalies.

💡Cybersecurity

Cybersecurity refers to the practice of protecting systems, networks, and data from digital attacks. The video explains that reinforcement learning, a subset of ML, is used to train models that can identify and respond to cyber threats, enhancing security measures.

💡Google Maps

Google Maps is a web mapping service that uses ML algorithms to analyze traffic conditions and suggest the fastest routes for travel. The video uses Google Maps as an example of how ML informs transportation and navigation services.

💡Healthcare and ML

Healthcare is increasingly utilizing machine learning to augment human capabilities, such as in the interpretation of medical images. The video points out that ML models can help identify tumors in mammograms more accurately and efficiently than humans alone, improving both accuracy and radiologists' productivity.

💡Marketing and Sales

The video concludes by noting that marketing and sales departments in organizations often use AI and ML the most, particularly for lead generation, data analytics, and personalized marketing campaigns. It suggests that the same ML models used in recommendation algorithms for entertainment platforms can be adapted for targeted marketing.

Highlights

Generative AI is a subset of machine learning, which is projected to become a $200 billion industry by 2029.

Machine learning involves machines learning from datasets and past experiences by recognizing patterns and generating predictions.

Natural language processing (NLP) is a significant aspect of machine learning, enabling machines to understand human language.

Chatbots in customer service use ML to handle text-based queries and route customers to human assistance when needed.

Voice assistants like Siri and Alexa utilize ML for speech-to-text and natural language understanding.

ML powers auto-transcription services on platforms like Slack and YouTube for spoken word content.

ML is integrated into mobile apps for personalized services like Spotify's song recommendations and LinkedIn's job suggestions.

Smartphones use ML for computational photography, facial recognition, and on-device image classification to enhance user experience.

ML is crucial in detecting fraudulent credit card transactions, handling 1,739 transactions per second in the US.

Between 60 and 73% of stock market trading is conducted by ML algorithms, a percentage that is increasing annually.

Reinforcement learning within ML is used to train models for identifying and responding to cyber attacks.

Google Maps uses ML algorithms to assess traffic conditions and suggest the fastest routes.

Ridesharing apps like Uber and Lyft use ML to match riders with drivers efficiently.

ML aids in email filtering by classifying messages and providing auto-complete responses.

In healthcare, ML helps increase the accuracy of interpreting radiology imaging, such as mammograms, and speeds up diagnosis.

ML is also used in early lung cancer screening and detecting bone fractures, augmenting medical diagnostics.

The marketing and sales department in organizations utilizes AI and ML the most, particularly for lead generation and personalized marketing.

Machine learning is already an integral part of everyday life, despite the theoretical nature of artificial general intelligence.

Transcripts

Everybody is talking about generative AI,

but gen AI is a subset of the larger field of machine learning,

and I'm going to give you ten use cases

of how machine learning, or ML, is used today in everyday life.

And by machine learning, I'm talking about these subfields of artificial intelligence

in which machines learn from datasets and past experiences

by recognizing patterns and generating predictions.

Now machine learning is projected to become a $200 billion industry by 2029,

but it's already very much here today.

So let's get into it.

Now, one aspect of machine learning that's seen huge utility is NLP,

or natural language processing.

That's the ability for machines to make sense

of the unstructured mess that we like to call human language.

So use case number one is customer service.

Text based queries can be handled by chatbots,

which act as virtual agents that many businesses provide on their e-commerce sites.

The chatbots can resolve many queries themselves,

and where they can't, they can route customers to where they can find the appropriate help

from a human customer service representative.

ML also powers, voice assistants -

things like Siri and Alexa -

where first speech-to-text and then NLP machine learning models

help understand a spoken command.

That same capability is used by services like Slack and YouTube

to power auto-transcription of spoken words in video content.

Now, number three is ML and mobile apps.

Where would we be without Spotify's ML models for generating song recommendations

or LinkedIn's use of ML to make employment suggestions?

Your phone is likely filled with apps that call out to services running machine learning models.

And actually ML in smartphones really deserves its own category

because with the power of modern smartphones, some of that machine learning is performed directly on the device

such as computational photography to generate background blur in your selfie shots,

or unlocking your phone with facial recognition,

or on-board device image classification models that help you to search your photo library.

Like that time I was trying to find this picture of my cat where he jumped into the dryer.

ML helped me to find that without me spending a ton of time scrolling through my photos app.

Hey, the drive wasn't actually on!

Now, that's an example of a needle-in-a-haystack problem

thousands of images and there's only one I'm looking for,

which in a way is similar to use case number five, that is financial transactions.

Now, in the US alone, there are 150 million credit card transactions every day,

and the vast majority of those are legitimate.

How to detect the fraudulent ones?

Well, ML and deep learning are widely used in fraud detection

where financial institutions train models and classification algorithms

to recognize suspicious online transactions and flag them for further investigation.

150 million credit card transactions every day is 1,739 every second,

so this is a task that would be near impossible to perform manually.

Did you also know that between 60 and 73% of all stock market trading is conducted by ML algorithms?

And that percentage is increasing every year.

All right, let's quickly knock out a couple more.

So ML is used frequently in cybersecurity.

Reinforcement learning uses ML to train models to identify

and respond to cyber attacks and detect intrusions.

ML informs a lot of our transportation these days.

For instance, Google Maps uses ML algorithms to check current traffic conditions

and determine the fastest route.

And ridesharing apps like Uber and Lyft using ML to match riders to the drivers.

And ML plays a large role in filtering email messages as well,

through classification of incoming messages and autocomplete responses.

Now, number nine, that's health care.

This is one example where machine learning can help augment and speed up human capabilities.

Now, it's estimated that doctors evaluating mammograms miss between 30 to 40% of cancers,

and the rate of false positives is even higher.

ML is already helping here, where pattern recognition models are trained to classify tumors

that are hard to see with the human eye.

This is increasing not only the accuracy of interpreting radiology imaging,

but it's also increasing the reading time of radiologists,

allowing them to focus their attention on the more suspicious examinations flagged by the ML models.

There are also ML successes in early lung cancer screening and finding bone fractures.

OK, one last one and a question for you:

In general, which department in an organization uses AI and machine learning the most?

Well, according to Forbes, it is the marketing and sales department.

Marketers use ML for lead generation, data analytics and search engine optimization.

And they often build on top of existing ML models.

So, for example, consider how recommendation algorithms, like those at Netflix,

make TV and movie suggestions as to what to watch next based on your derived tastes and interests.

Well, the marketing and sales department can use those same ML models for targeted, personalized marketing campaigns

tailored to those very same tastes and interests.

Look, we hear so much these days about the future of AI,

and in particular AGI, artificial general intelligence,

that will one day match and surpass the intelligence of humans.

But right now, that level of AI doesn't exist, it's theoretical.

But machine learning?

That's AI that is already here.

And it really is very much part of our everyday lives.

If you have any questions, please drop us a line below.

And if you want to see more videos like this in the future,

please like and subscribe.

Thanks for watching.