- This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published.
- These articles are at least 6 month old but no more than 12 month old.
- The previous digest in this series was posted here a while back.
- 18 Great Blogs Posted in the last 12 Months
This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at l… The post 18 Great Blogs Posted in the last 12 Months appeared first on Artificial Intelligence.
- Facebook Messenger bots will help users to interact with a company or service over Facebook Messenger in natural language (NLP).
- Many companies think bots are the next big thing and Facebook wants to make money off of chatbots.
- American companies in the U.S can implement certain native elements into their Facebook chatbots such as: Facebook payment functionality and get the receipt via Facebook receipt messenger bot.
- Facebook wants you to run Pay Per Click ads on their platform, and these bots will get businesses to run more facebook advertisements.
- This is why if you decide to have a chatbot, you should develop a bot dialogue messenger in order to get users to become interested in your products or services.
Many companies think bots are the next big thing and Facebook wants to make money off of chatbots. Let’s have a closer look to facebook chatbots. The post Blazing the Path Through the Jungles of Facebook Messenger Bots appeared first on Artificial Intelligence.
- Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
- Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing model generalizability.
- Many times a correct feature selection allows you to develop simpler and faster Machine Learning models.
- In a time when ample processing power can tempt us to think that feature selection may not be as relevant as it once was, it’s important to remember that this only accounts for one of the numerous benefits of informed feature selection — decreased training times.
- As Zimbres notes above, with a simple concrete example, feature selection can quite literally mean the difference between valid, generalizable models and a big waste of time.
Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.
The post The Practical Importance of Feature Selection appeared first on Artificial Intelligence.
- Is AI on your software development roadmap?
- With technologies like advanced machine learning, deep learning, natural language processing, and business rules, AI is poised to disrupt both how developers build applications and the nature of those applications.
- The risks—unrealistic expectations, integration with traditional applications, and more—can’t be ignored as your organization strives for the rewards of an accelerated development cycle and a new generation of self-learning applications.
- Uncover this shifting digital landscape and how your business can take advantage of it in the Forrester Research report, “How AI Will Change Software Development And Applications.”
- Fill out the form at right to read the free report.
Learn how AI is changing the application development landscape The post Learn how AI is changing the application development landscape appeared first on Artificial Intelligence.
- Tensorflow is an open source machine learning (ML) library from Google.
- To scratch the surface of this incredible ML library, we will implement Linear Regression to predict prices of houses in Boston area.
- The Boston dataset is available at UCI Machine Learning Repository.
- If you don’t know the maths behind Linear Regression, some great resources are provided at the end of this tutorial.
- Firstly, we will import the required libraries which includes: TensorFlow, Numpy and Matplotlib.
Tensorflow is an open source machine learning (ML) library from Google. It has particularly became popular because of the support for Deep Learning. Apart from… The post Linear Regression in Tensorflow appeared first on Artificial Intelligence.
- — Accenture Research
The above key findings was derived from comprehensive new research on the potential economic impact of artificial intelligence (AI) in GVA, a close approximation of GDP that accounts for the value of goods and services produced, developed by Accenture Research in collaboration with Frontier Economics.
- Here are 10 significant takeaways from the new Accenture Research on AI and its potential economic impact:
1.
- To prepare for a successful future with AI, business leaders should consider the following eight strategies:
To further understand the economic impact of AI, Salesforce commissioned a report from IDC on how AI-powered CRM – the fastest growing and soon-to-be largest category of enterprise software – will impact GDP growth and the job market.
- Here are the key findings:
Here’s the link to Accenture’s full AI research report.
- The report found that AI could double annual economic growth rates by 2035 and boost labor productivity by up to 40 percent by fundamentally changing the way work is done.
“Businesses that successfully apply artificial intelligence (AI) could increase profitability by an average of 38 percent by 2035. The introduction of AI… The post Accenture Research: AI Boosts Industry Profits appeared first on Artificial Intelligence.
- It’s the combination of bots and machine learning that holds the key: Think of an assembly line system pushing out barbecue equipment.
- I’m not sure if there will be a human decision between them, but I think as we become more comfortable with the machines’ decisions, we’ll give them more control of the process.
- As a form of decision support, productivity expert Carl Pullein thinks that “machine learning and artificial intelligence [will move] towards creating productivity tools that can schedule your meetings and tasks for you and to be able to know what needs to be done based on your context, where you are and what needs to be done.”
- If everyone in the local area has a smart fridge, that same supermarket would be able to make a better decision about how much water to keep stocked.
- Companies like Alphabet are already working on it, with projects like DeepMind at the forefront of AI and machine learning technologies.
Industry experts explain how AI will change the way we work. The post The Future of Productivity: AI and Machine Learning appeared first on Artificial Intelligence.
- The DeepMind Alberta team will be led by UAlberta computing science professors Richard Sutton, Michael Bowling, and Patrick Pilarski.
- So when we chose to set up our first international AI research office, the obvious choice was his base in Edmonton, in close collaboration with the University of Alberta, which has become a leader in reinforcement learning research thanks to his pioneering work,” said Demis Hassabis, CEO and co-founder of DeepMind. ”
- Sutton is excited about the opportunity to combine the strength of DeepMind’s work in reinforcement learning with UAlberta’s academic excellence, all without having to leave Edmonton.
- “DeepMind has taken this reinforcement learning approach right from the very beginning, and the University of Alberta is the world’s academic leader in reinforcement learning, so it’s very natural that we should work together,” said Sutton.
- Working with Hassabis and the DeepMind team both in London and Edmonton, Sutton, Bowling, and Pilarski will combine their academic strength in reinforcement learning to focus on basic AI research.
University of Alberta The post UAlberta expertise brings DeepMind lab to Edmonton appeared first on Artificial Intelligence.
- Early in 2015, artificial-intelligence researchers at Google created an obscure piece of software called TensorFlow.
- But just months after TensorFlow was released to Google’s army of coders, the company also began offering it to the world for free.
- S. Somasegar, a managing director at venture fund Madrona who was previously head of Microsoft’s developer division, says TensorFlow’s prominence poses a genuine challenge to Google’s cloud rivals.
- The company has created specialized processors to make TensorFlow faster and reduce the power it consumes inside Google’s data centers.
- Since Google released TensorFlow, its competitors in cloud computing, Microsoft and Amazon, have released or started supporting their own free software tools to help coders build machine-learning systems.
Alphabet thinks it can wrest the cloud computing market away from Amazon by helping companies make use of machine learning with a tool called TensorFlow. The post This machine-learning software has transformed Google, and the rest of the world may be next appeared first on Artificial Intelligence.
- We know that artificial intelligence will soon reshape our world.
- But which companies will lead the way?
- To help answer that question, research firm CB Insights recently selected the “AI 100,” a list of the 100 most promising artificial intelligence startups globally.
- The private companies were chosen (from a pool of over 1,650 candidates) by CB Insights’ Mosaic algorithm, based on factors like financing history, investor quality, business category, and momentum.
- A look at the 50 largest startups on the list, ranked by total funds raised, shows that investment in AI is surging worldwide.
A look at the most promising global startups working with artificial intelligence. The post 50 Companies Leading the Artificial Intelligence Revolution appeared first on Artificial Intelligence.
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