AI general applications
Machine Learning vs Prediction Machines
Despite having similar aims and processes, these are two entirely different concepts, let’s see below why.
Application of machine learning
When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves. A data analysis method, machine learning automates analytical model building. In simpler words, it allows computers to find insightful information without being programmed into where to look for a particular piece of information. To do this, ML uses algorithms that iteratively learn from data.
Machine learning is behind language translation apps,
the shows Netflix suggests to you, and
how your social media feeds are presented.
It powers autonomous vehicles and machines that can diagnose medical conditions based on images.
From manufacturing to retail and banking to bakeries, even legacy companies are using machine learning to unlock new value or boost efficiency.
Machine learning starts with data - numbers, photos, or text, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports. The data is gathered and prepared to be used as training data, or the information the machine learning model will be trained on. The more data, the better the program. From there, programmers choose a machine learning model to use, supply the data, and let the computer model train itself to find patterns or make predictions. Over time the human programmer can also tweak the model, including changing its parameters, to help push it toward more accurate results.Examples of machine learning problems include,
“Is this cancer?”,
“Which of these people are good friends with each other?”,
“Will this person like this movie?”
such questions to be answered are excellent targets for Machine Learning, and in fact, machine learning has been applied to such problems with great success.
“Which of these people are good friends with each other?”,
“Will this person like this movie?”
such questions to be answered are excellent targets for Machine Learning, and in fact, machine learning has been applied to such problems with great success.
Application of prediction machines (predictive machines)
The ability to predict future events and trends is crucial across industries.
Predictive analytics appears more often than you might assume—from your weekly weather forecast to algorithm-enabled medical advancements. Here’s an overview of predictive analytics to get you started on the path to data-informed strategy formulation and decision-making.
Predictive analytics appears more often than you might assume—from your weekly weather forecast to algorithm-enabled medical advancements. Here’s an overview of predictive analytics to get you started on the path to data-informed strategy formulation and decision-making.
Predictive analytics is the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.
The predictions could be for
the near future - for instance, predicting the malfunction of a piece of machinery later that day
or the more distant future, such as predicting your company’s cash flows for the upcoming year.
or the more distant future, such as predicting your company’s cash flows for the upcoming year.
Predictive analysis can be conducted manually or using machine-learning algorithms. Either way, historical data is used to make assumptions about the future.
Forecasting can enable you to make better decisions and formulate data-informed strategies.
Here are some examples of predictive analytics in action to inspire you.
1. Finance: Forecasting Future Cash Flow
Every business needs to keep periodic financial records, and predictive analytics can play a big role in forecasting your organization’s future health. Using historical data from previous financial statements, as well as data from the broader industry, you can project sales, revenue, and expenses to craft a picture of the future and make decisions.
2. Entertainment & Hospitality: Determining Staffing Needs
In entertainment and hospitality, customer influx and outflux depend on various factors, all of which play into how many staff members a venue or hotel needs at a given time. Overstaffing costs money, and understaffing could result in a bad customer experience, overworked employees, and costly mistakes.
To predict the number of hotel check-ins on a given day, a multiple regression model that considers several factors can help avoid overstaffing to the best of its ability.
3. Health Care: Early Detection of Allergic Reactions
Another example of using algorithms for rapid, predictive analytics for prevention comes from the health care industry. The Wyss Institute at Harvard University partnered with the KeepSmilin4Abbie Foundation to develop a wearable piece of technology that predicts an anaphylactic allergic reaction and
automatically administers life-saving epinephrine. The sensor, called AbbieSense, detects early physiological signs of anaphylaxis as predictors of an ensuing reaction—and it does so far quicker than a human can. When a reaction is predicted to occur, an algorithmic response is triggered. The algorithm can predict the reaction’s severity, alert the individual and caregivers, and automatically inject epinephrine when necessary. The technology’s ability to predict the reaction at a faster speed than manual detection could save lives.
Here are some examples of predictive analytics in action to inspire you.
1. Finance: Forecasting Future Cash Flow
Every business needs to keep periodic financial records, and predictive analytics can play a big role in forecasting your organization’s future health. Using historical data from previous financial statements, as well as data from the broader industry, you can project sales, revenue, and expenses to craft a picture of the future and make decisions.
2. Entertainment & Hospitality: Determining Staffing Needs
In entertainment and hospitality, customer influx and outflux depend on various factors, all of which play into how many staff members a venue or hotel needs at a given time. Overstaffing costs money, and understaffing could result in a bad customer experience, overworked employees, and costly mistakes.To predict the number of hotel check-ins on a given day, a multiple regression model that considers several factors can help avoid overstaffing to the best of its ability.
3. Health Care: Early Detection of Allergic Reactions
Another example of using algorithms for rapid, predictive analytics for prevention comes from the health care industry. The Wyss Institute at Harvard University partnered with the KeepSmilin4Abbie Foundation to develop a wearable piece of technology that predicts an anaphylactic allergic reaction and
automatically administers life-saving epinephrine. The sensor, called AbbieSense, detects early physiological signs of anaphylaxis as predictors of an ensuing reaction—and it does so far quicker than a human can. When a reaction is predicted to occur, an algorithmic response is triggered. The algorithm can predict the reaction’s severity, alert the individual and caregivers, and automatically inject epinephrine when necessary. The technology’s ability to predict the reaction at a faster speed than manual detection could save lives.The Difference Between Machine Learning and Predictive Analytics
In the table below you can see what the differences are between the two.
| Machine Learning | Predictive Modelling |
| To solve complex problems it uses various ML models | To predict future outcomes, it uses past data. |
| They have the tendency to adapt themselves and learn from experiences. | They do not have the tendency to adapt to the data. |
| No need to explicitly programmed. | To process data, they need to be programmed the system manually. |
| To deal with a particular problem, their models are smart enough to adapt and update. | They don’t have smart models which can take decision by themselves. |
| It is a data- driven approach | It is a use case driven approach. |
| It does not require a huge amount of historical data to process task. | It requires a high amount of historical data to process a particular task, i.e. to predict future outcomes. |
| To solve a problem, it requires a detailed description of the problem. | To solve a problem, it does not requires a detailed description of the problem. |
Train a machine learning system easily
Train a machine learning system easily and playfully online - max. 10 minutes.
Open this link in your browser: https://machinelearningforkids.co.uk/#!/welcome - and follow the simple and short instructions. As a result, you will be able to understand how to train a machine learning system in the easiest way.Further resources: Videos and/or useful links
● What is ML, introduction in 7 minutes: https://www.youtube.com/watch?v=ukzFI9rgwfU
● Learn how to use ML by Google https://ai.google/education/
● Learn more about ML: https://elitedatascience.com/machine-learning-interview-questions-answers
References
● https://www.europarl.europa.eu/news/en/headlines/society/20200827STO85804/what-is-artificial-intelligence-and-how-is-it-used
● Ennustuskoneet AI, https://www.predictionmachines.ai/ ja https://store.hbr.org/product/prediction-machines-the-simple-economics-of-artificial-intelligence/10195?sku=10195-HBK-ENG
● https://medium.datadriveninvestor.com/prediction-machines-a-summary-278d3fdf94f1
● Koneoppiminen (ML) vs. tekoäly (AI) - ratkaisevat erot, https://towardsai.net/p/machine-learning/differences-between-ai-and-machine-learning-1255b182fc6.
● Miten koodittomat alustat voivat tuoda tekoälyn pienille ja keskisuurille yrityksille, Jonathon Reilly, 2021, https://hbr.org/2021/11/how-no-code-platforms-can-bring-ai-to-small-and-midsize-businesses.
● Lisäarvosta https://static1.squarespace.com/static/59d6456137c581acfcef3422/t/5cadd76015fcc0f9e6594106/1554896736783/IM_Joshua+Gans+and+authors.pdf
● Koneoppiminen, MIT. https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
● Syväoppiminen https://searchenterpriseai.techtarget.com/definition/deep-learning-deep-neural-network
https://online.hbs.edu/blog/post/predictive-analytics
Self-reflection
Watch the video - Google Talk about Prediction Machines and Economics of Ai and reflect on the below questions:
What is the real job of artificial intelligence?
How does AI contribute by making knowing what is coming in the future cheaper and more certain?
How do managers separate tasks into components of prediction and judgment for making prediction cheaper?
How does AI contribute by making knowing what is coming in the future cheaper and more certain?
How do managers separate tasks into components of prediction and judgment for making prediction cheaper?
