Machine learning systems take input data to make useful predictions and decisions about previously unseen data without being explicitly programmed. Machine learning involves finding patterns in examples to make predictions and can be used to label or classify data, predict values, cluster similar data, infer patterns, and create complex outputs. The document provides examples of using machine learning for early dementia diagnosis, drone-based wildlife surveys, and recommending videos, and discusses supervised and unsupervised learning models.
While machine learning is an exciting subject, it is wrong to assume that it will solve all your problems. Scroll down to take a look at some myths in the machine learning field and how to overcome them.
Machine learning is a term thrown around in technology circles with an ever-increasing intensity. Major
technology companies have attached themselves to
this buzzword to receive capital investments, and every
major technology company is pushing its even shinier
parentartificial intelligence (AI).
Intro to Machine Learning by Google Product ManagerProduct School
Ground breaking technologies like neural-net algorithms along with the ability to run much more powerful computation started a new era in Machine Learning, ML. We're now able to use Machine Learning for products in ways we could only dream about and companies from all around the world are starting to seize the opportunity.
11 Insane Machine Learning Myths Debunked for You!Kavika Roy
https://www.datatobiz.com/blog/machine-learning-myths/
The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality.
Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it.
With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures.
This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other.
The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality.
It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives.
In this presentation, you will discover how you can begin to leverage on the power and potential of Machine Learning as a technology tool and as a framework for growth.
While machine learning is an exciting subject, it is wrong to assume that it will solve all your problems. Scroll down to take a look at some myths in the machine learning field and how to overcome them.
Machine learning is a term thrown around in technology circles with an ever-increasing intensity. Major
technology companies have attached themselves to
this buzzword to receive capital investments, and every
major technology company is pushing its even shinier
parentartificial intelligence (AI).
Intro to Machine Learning by Google Product ManagerProduct School
Ground breaking technologies like neural-net algorithms along with the ability to run much more powerful computation started a new era in Machine Learning, ML. We're now able to use Machine Learning for products in ways we could only dream about and companies from all around the world are starting to seize the opportunity.
11 Insane Machine Learning Myths Debunked for You!Kavika Roy
https://www.datatobiz.com/blog/machine-learning-myths/
The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality.
Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it.
With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures.
This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other.
The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality.
It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives.
In this presentation, you will discover how you can begin to leverage on the power and potential of Machine Learning as a technology tool and as a framework for growth.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Machine Learning The Powerhouse of AI Explained.pdfCIO Look Magazine
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.
Machine Learning in Business What It Is and How to Use ItKashish Trivedi
Machine learning revolutionizes business by offering effective suggestions, accurate predictions, and advanced analytics, streamlining operations without extensive human effort. It's a process where AI learns autonomously, akin to human cognition, as demonstrated by DeepMind, learning from images and sounds without explicit labeling. This article delves into the essence of machine learning, showcasing its benefits, diverse business applications, various types, and real-world examples. Understanding these facets is key to harnessing its power in optimizing businesses and enhancing customer experiences.
The Key Differences Between Rule-Based AI And Machine LearningRobert Smith
While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
Machine Learning: Need of Machine Learning, Its Challenges and its ApplicationsArpana Awasthi
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.
Machine Learning is a powerful statistical tool to help predict the probability that a prospect is a good candidate for your product or service. This guide is meant as an introduction to help you and your team know how machine learning may be useful to your organization.
These are some general ideas to get one started with "Machine Learning".Machine learning is a vast subject in the field of computer science & needs intense research to master.
Ethical AI: Establish an AI/ML Governance framework addressing Reproducibility, Explainability, Bias & Accountability for Enterprise AI use-cases.
Presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (https://events.linuxfoundation.org/open-compliance-summit/)
Full article: https://towardsdatascience.com/ethical-ai-its-implications-for-enterprise-ai-use-cases-and-governance-81602078f5db
Artificial Intelligence vs Machine Learning.pptxChetnaGoyal16
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that often come up when discussing the future of technology.
Learning Artificial Intelligence can be highly beneficial because there is increasing demand for artificial intelligence professionals so taking an artificial intelligence course in Delhi will help you to gain a new skill.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Machine Learning The Powerhouse of AI Explained.pdfCIO Look Magazine
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.
Machine Learning in Business What It Is and How to Use ItKashish Trivedi
Machine learning revolutionizes business by offering effective suggestions, accurate predictions, and advanced analytics, streamlining operations without extensive human effort. It's a process where AI learns autonomously, akin to human cognition, as demonstrated by DeepMind, learning from images and sounds without explicit labeling. This article delves into the essence of machine learning, showcasing its benefits, diverse business applications, various types, and real-world examples. Understanding these facets is key to harnessing its power in optimizing businesses and enhancing customer experiences.
The Key Differences Between Rule-Based AI And Machine LearningRobert Smith
While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
Machine Learning: Need of Machine Learning, Its Challenges and its ApplicationsArpana Awasthi
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.
Machine Learning is a powerful statistical tool to help predict the probability that a prospect is a good candidate for your product or service. This guide is meant as an introduction to help you and your team know how machine learning may be useful to your organization.
These are some general ideas to get one started with "Machine Learning".Machine learning is a vast subject in the field of computer science & needs intense research to master.
Ethical AI: Establish an AI/ML Governance framework addressing Reproducibility, Explainability, Bias & Accountability for Enterprise AI use-cases.
Presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (https://events.linuxfoundation.org/open-compliance-summit/)
Full article: https://towardsdatascience.com/ethical-ai-its-implications-for-enterprise-ai-use-cases-and-governance-81602078f5db
Artificial Intelligence vs Machine Learning.pptxChetnaGoyal16
Artificial Intelligence (AI) and Machine Learning (ML) are two terms that often come up when discussing the future of technology.
Learning Artificial Intelligence can be highly beneficial because there is increasing demand for artificial intelligence professionals so taking an artificial intelligence course in Delhi will help you to gain a new skill.
This session will cover IaaS (Compute Engine), PaaS (App Engine), FaaS (Cloud Functions), CaaS (GKE), compute offerings on GCP and IAM, and Storage in General.
Introductory Session for the Registered Participants in order to discuss the further steps.
In this session, we discussed the further steps for our GCCP Camp i.e. the way we will complete our labs together and the upcoming events.
It’s time to start a new journey filled with suprises and quests. We at Google Developer Student Clubs, IIT Patna congratulates Freshers Batch’22 for making this far.
We had our Introductory session on 15/11/22 from 8:30 PM to 9:30 PM in room number 107, Block 9, IIT Patna.
Regardless of one’s technical knowledge, they can participate in our club activities and learn various domains like Blockchain, Web Development, Android Development, and more. Remember, there are no branch barriers and no prerequisites required so all can learn together.
DSC IIT Patna concluded its last two sessions of Compose Camp held on 2nd October'22.
The 3rd session began with the clarification of Jetpack Compose.
The subject for the primary session was to set up a Cheerful Birthday Wishing App utilizing Kotlin language with a few challenges and a few practice designs.
The steps were :-
-Adding a text element in proper font size
-Arranging text in row and column
-Adding an image element and box layout
-Position and Scaling of image
The subject for the 4th session was to set up a Dice Roller App utilizing Kotlin language. It moreover clarified Android Debugger for the errors which developers get while building an app.
The steps were:-
-Layout infrastructure
-Adding a button
-Importing images
-Building Dice Logic
DSC IIT Patna concluded its first two sessions of Compose Camp held on 1st October'22.
The first session started with the Android studio installation, introduced some of its basic features, and covered how to create a new project.
The second session guided the students about AVD setup and USB debugging. The session further covered Kotlin playground. The topics covered under Kotlin are variable and data types, functions in Kotlin, conditional statements, nullability, classes and objects.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
3. Machine Learning systems take inputs
(data) to make useful predictions and
decisions about previously unseen pieces
of data.
ML Extended
PROPRIETARY + CONFIDENTIAL
4. Machine learning is a specific field of AI
where a system learns to find patterns in
examples in order to make predictions.
ML Extended
PROPRIETARY + CONFIDENTIAL
5. Computers learning how to do a task
without being explicitly programmed to
do so.
ML Extended
PROPRIETARY + CONFIDENTIAL
6. Machine Learning systems might:
● Label or classify data
● Predict numerical values
● Cluster similar pieces of data together
● Infer association patterns in data
● Create complex outputs
7. Machine Learning could be
used for early dementia
diagnosis
Automating drone-based wildlife
surveys saves time and money
"Machine Learning: Why or Why not?"
Read a couple of news articles
involving applications of ML.
1. Would a traditional programming
solution be more efficient?
1. Could a human perform the same
task in less time?
1. What are the benefits of a
Machine Learning model in these
instances?
8. Model learns patterns
from unlabelled data.
Machine Learning
Supervised Unsupervised
Model is trained on
labeled data
stop_sign_4
stop_sign_1 stop_sign_2
stop_sign_3
9. See it in action!
Image label verification
Supervised learning
Visit https://crowdsource.app to try these tasks
Semantic Similarity
Unsupervised learning
11. Predicting the Price of a House
Features
● Location
● Number of bedrooms
● Size of property
● Number of light switches?
● Color of house?
12. Recommending which video a user should watch next
Features
● Topic
● Popularity of a video/Number of views
● Creator of video
● Length of video?
● Age of video?
13. A baker would like to optimize pricing of cakes in their bakery depending
on previous pricing, cost to make, and time of year.
Checkpoint on Learning
1. What about the scenario make it suitable for ML?
2. What is the benefit to the business?
3. Would a human perform the job better?
4. What are the inputs to the system?
5. How could the ML model go wrong?
14. An ocean conservationist would like to track fish populations over time.
Checkpoint on Learning
1. What about the scenario make it suitable for ML?
2. What is the benefit for the organization?
3. Would a human perform the job better?
4. What are the inputs to the system?
5. How could the ML model go wrong?
15. An online brand influencer wants a model that can predict the number of
‘likes’ that a particular post may get.
Checkpoint on Learning
1. What about the scenario make it suitable for ML?
2. What is the benefit for the business or individual?
3. Would a human perform the job better?
4. What are the inputs to the system?
5. How could the ML model go wrong?
16. Create your own scenario
Checkpoint on Learning
1. What about the scenario make it suitable for ML?
2. What is the benefit for the business or individual?
3. Would a human perform the job better?
4. What are the inputs to the system?
5. How could the ML model go wrong?
Editor's Notes
This session will go into a little more detail about machine learning. It is helpful if you have already completed some introduction to machine learning but if you have any questions please ask.
Let's review. How would you explain machine learning?
Answers could include (shown on the following slides):
Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions.
Computers learning how to do a task without being explicitly programmed to do so.
Here's one definition.
Here's another definition.
And another definition.
Here is a list representing some of the things a machine learning system can be trained to do.
Question: What are some examples of features and products that you assume use machine learning? How could you tell it was ML?
Use your computer and go to news.google.com or some other search engine to find and read a couple of online news articles involving applications of ML. Note: Sometimes news stories refer to ML as artificial intelligence (AI).
While you are reading, think about these questions.
Give learners 20 minutes to complete this task.
Machine learning looks for patterns in data.
The majority of ML applications today are supervised learning where labeled data is used to teach the model. This includes tasks like classification and regression.
Supervised machine learning is analogous to a student learning a tricky maths concept by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, the student can then provide answers to new (never-before-seen) questions on the same topic.
The signs on the left are labeled data so a supervised machine learning system could learn from these labels what stop signs around the world look like.
The signs on the right are unlabeled data. Even the sign with the word stop because labeled data requires a human's rating to indicate what it is.
Images:
https://wikipedia.org/wiki/Stop_sign
https://wikipedia.org/wiki/Comparison_of_European_road_signs
In the previous slide, I said supervised machine learning is analogous to a student taking a test. Let's say I created the 4 machine learning regression models above. Which one is the best?
It depends on your goal and what variable you are trying to optimize for. To grade how well a model is doing on its test, machine learning practitioners measure the distance between the model's prediction (indicated in these graphs by the blue line) and the example data. This is known as loss.
[Click to animate slide]
With the loss displayed. Which model is best at achieving the goal?
[Click to animate slide]
Model #3 has the lowest loss which indicates this model is best at achieving the goal.
Features are the variables which distinguish one example from another. They tell the machine learning model what parts of the data to look for patterns for achieving the goal.
The first three variables would probably help the model determine a home price the other two probably would not. So lots of data is crucial to a machine learning system but it needs to be helpful and relevant data. Though you never know until you experiment to see what variables truly make an impact.
Ask learners to come up with a list of features which might be helpful in recommending the next video to watch.
The last two might turn out to be very important variables, there's no way to know unless you experiment.
What about the scenario make it suitable for ML?
multiple variables, potentially lots of data examples, a goal to optimize
What is the benefit to the business?
the business will benefit by finding the ideal price for their cakes accounting for multiple variables.
Would a human perform the job better?
Data analysis could find the optimal value but would need to be continuously adjusted as new data comes in.
The more variables that play a role, the more effort would be needed.
What are the inputs to the system?
Previous prices
Cost to make
Time of year
How could the ML model go wrong?
If there was not enough data
It was biased in some way (e.g. only trained for certain times of year)
It was mislabeled
Stretch question: Why might time of year be a factor in the price of cake?
A: Wedding season! Because demand is high during the summer a baker can charge more for cakes.
(Other seasonal answers, such as a holiday time, also work)
What about the scenario make it suitable for ML?
Using prior examples to train a system to recognize new examples.
Lots of examples over time
What is the benefit for the individual?
Reduce time labeling photos of fish
Could identify issues more quickly
invasive species
reduction in population
Would a human perform the job better?
It would be a very slow process and even experts make mistakes and have to refresh their knowledge
What are the inputs to the system?
Type of fish
Region found
Time of year
Number of fish
How could the ML model go wrong?
If the data is not diverse enough, a fish could be misclassified
Fish are not homogeneously distributed, the count could be off depending on the sampling.
Stretch question for 2: What would happen if the conservationist found a fish the model had never seen before?
A: ML models will always provide an answer- even if it is totally wrong!! This is why (at least for the time being) ML models still need humans to check up on their work.
What about the scenario make it suitable for ML?
Lots of examples of data
Can run experiments to see which variables have an effect
What is the benefit for the business or individual?
The model can get the most visibility for their customer
Would a human perform the job better?
With so many variables, a human could get a sense for optimal times for a specific situation but it would become complex if the product, influencer, audience, etc were changed
What are the inputs to the system?
Potential variables could include: Time of day, person posting, brand, product, social network
How could the ML model go wrong?
If trained only for a specific type of post, product, etc it might not generalize to another scenario
If the training data is for the wrong demographic (gender, age, marital status, etc) the patterns might not be relevant.
Give learners 2 minutes to create their own scenario.
Have them turn to the person next to them and present the scenario.
The other person has 2 minutes to answer the questions on the slide.
Learners swap roles and repeat steps 2 and 3.