10 USEFUL AI & ML SLIDES

10 Useful AI & ML Slides

1. Evolution of Analytics

2. Future of Data Science

3. Machine Learning Workflow

4. Deep Learning Workflow

5. Deep Learning Continuous Integration and Delivery

6. Anatomy of a Chatbot

7. Five ethical challenges of AI

8. NLP / NLU Technology Stack

9. Condition Monitoring / Predictive Maintenance Solution Architecture

10. Artificial Intelligence in Marketing

Blockchain can keep flawed data from machine learning systems

Blockchain can keep flawed data from machine learning systems

Blockchain would feed AI with authenticated data. It would also act as a powerful tool against breaches, write Terence Tse, Zoran Đorđević and Mark Esposito

With the dramatic fall in popularity of cryptocurrencies and the wave of unpredictable volatility in the value of these immaterial currencies, comes a seeming drop in the interest in blockchain. Yet, the development of this technology and its application are still charging ahead in full force and blockchain’s full potential is yet to be seen, understood and implemented.

Examples from healthcare

All things considered, there are areas where a combination of blockchain and AI could demonstrate their synergy. For example, healthcare could utilise all the benefits from blockchain technology in terms of providing validated, secured and GDPR-aligned data to enhance cancer diagnosis. By cross-examining the private data collected, processed and validated through blockchain, AI would be much more able to identify early signs of cancer in patients. Better yet, the benefits are also reciprocated, AI can assist blockchain in smart contract testing by providing for example, automated troubleshooting or debugging and root cause analysis and identification.

 

How to explain machine learning in plain English

https://enterprisersproject.com/article/2019/7/machine-learning-explained-plain-english

What is machine learning? What is ML vs. AI? What data problems could ML solve for your organization? Here’s how to discuss the key issues in plain terms

If you’re not using AI or ML yet, you soon will be evaluating its potential. “AI as a workload is going to become the primary driver for IT strategy,” Daniel Riek, senior director, AI, Office of the CTO, Red Hat, recently told us. “Artificial intelligence represents a transformational development for the IT industry: Customers across all verticals are increasingly focusing on intelligent applications to enable their business with AI. This applies to any workflow implemented in software – not only across the traditional business side of enterprises, but also in research, production processes, and increasingly, the products themselves.”

Machine learning definitions

Machine learning makes computers more intelligent without explicitly teaching them how to behave.

“At its heart, machine learning is the task of making computers more intelligent without explicitly teaching them how to behave. It does so by identifying patterns in data – especially useful for diverse, high-dimensional data such as images and patient health records.” –Bill Brock, VP of engineering at Very

Machine learning vs. AI vs. deep learning

These are good big-picture definitions of machine learning that don’t require much technical expertise to grasp. Things get more detailed – and more complex – from there. Brock notes, for example, that ML is an umbrella term that includes three subcategories: supervised learning, unsupervised learning, and reinforcement learning.