Technology – Mental Modal https://dev08.mm-sb.com/web05 The Generation for Machine Learning Wed, 04 Nov 2020 10:09:06 +0000 en-US hourly 1 https://wordpress.org/?v=6.2.6 AI technology for a better tomorrow https://dev08.mm-sb.com/web05/ai-technology-for-a-better-tomorrow/ https://dev08.mm-sb.com/web05/ai-technology-for-a-better-tomorrow/#comments Fri, 21 Feb 2020 01:03:01 +0000 https://dev08.mm-sb.com/web05/?p=2864

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. NLP – Natural Language Processing

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

He goal of this task is to predict a class (label) of a document, or rank documents within in a list based on their relevance. It could be used in spam filtering (predicting whether an e-mail is spam or not) or content classification (selecting articles from the web about what is happening to your competitors).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!

Aron Larsson

– CEO, Strategy Director

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]]> https://dev08.mm-sb.com/web05/ai-technology-for-a-better-tomorrow/feed/ 3 How Artificial Intelligence is changing the industry https://dev08.mm-sb.com/web05/how-artificial-intelligence-is-changing-the-industry/ https://dev08.mm-sb.com/web05/how-artificial-intelligence-is-changing-the-industry/#respond Wed, 08 Jan 2020 03:34:31 +0000 https://dev08.mm-sb.com/web05/?p=578

Artificial intelligence in retail is being applied in new ways across the entire product and service cycle—from assembly to post-sale customer service interactions, but retail players need answers to important questions:

1. Sales and CRM Applications

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

Conversica “sales assistant” software is designed to automate and enhance sales operations processes by identifying and conversing with internet leads. The sales lead and management company claims the authentic-sounding messages result in an average engagement rate of 35%.

2. Brilliant Manufacturing

General Electric’s (GE) Brilliant Manufacturing software, in part inspired by GE’s relationships with client manufacturing companies over the past two decades, was designed to make the entire manufacturing process—from design to distribution and services—more efficient and hence save big costs over time. The software includes a suite of analytics and operational intelligence tools appropriate for a range of manufacturers.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

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]]> https://dev08.mm-sb.com/web05/how-artificial-intelligence-is-changing-the-industry/feed/ 0 AI simplified: What computers are good at https://dev08.mm-sb.com/web05/deep-learning-chatbot-how-companies-are-making-money/ https://dev08.mm-sb.com/web05/deep-learning-chatbot-how-companies-are-making-money/#comments Wed, 08 Jan 2020 01:36:06 +0000 https://dev08.mm-sb.com/web05/?p=540

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. Investment banks can use AI in six critical ways

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

Sentiment analysis aims to determine the attitude or emotional reaction of a person with respect to some topic – e.g. positive or negative attitude, anger, sarcasm. It is broadly used in customer satisfaction studies (e.g. analyzing product reviews).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.

]]> https://dev08.mm-sb.com/web05/deep-learning-chatbot-how-companies-are-making-money/feed/ 2 Machine Learning Terms every manager should know https://dev08.mm-sb.com/web05/machine-learning-terms-every-manager-should-know/ https://dev08.mm-sb.com/web05/machine-learning-terms-every-manager-should-know/#comments Tue, 07 Jan 2020 08:38:51 +0000 https://dev08.mm-sb.com/web05/?p=518

Getting started with AI? Perhaps you’ve already got your feet wet in the world of Machine Learning, but still looking to expand your knowledge and cover the subjects you’ve heard of but didn’t quite have time to cover?

1. NLP – Natural Language Processing

Natural Language Processing (NLP) is a common notion for a variety of Machine Learning methods that make it possible for the computer to understand and perform operations using human (i.e. natural) language as it is spoken or written.

The most important use cases of Natural Language Processing are:

Document Summarization is a set of methods for creating short, meaningful descriptions of long texts (i.e. documents, research papers).

2. Reinforcement learning

Reinforcement Learning differs in its approach from the approaches we’ve described earlier. In RL the algorithm plays a “game”, in which it aims to maximize the reward. The algorithm tries different approaches “moves” using trial-and-error and sees which one boost the most profit.

3. Dataset

All the data that is used for either building or testing the ML model is called a dataset. Basically, data scientists divide their datasets into three separate groups:

- Training data is used to train a model. It means that ML model sees that data and learns to detect patterns or determine which features are most important during prediction.

- Validation data is used for tuning model parameters and comparing different models in order to determine the best ones. The validation data should be different from the training data, and should not be used in the training phase. Otherwise, the model would overfit, and poorly generalize to the new (production) data.

- It may seem tedious, but there is always a third, final test set (also often called a hold-out). It is used once the final model is chosen to simulate the model’s behaviour on a completely unseen data, i.e. data points that weren’t used in building models or even in deciding which model to choose.

It’s not by any means exhaustive, but a good, light read prep before a meeting with an AI director or vendor – or a quick revisit before a job interview!

Aron Larsson

– CEO, Strategy Director

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.

]]> https://dev08.mm-sb.com/web05/machine-learning-terms-every-manager-should-know/feed/ 2