This document discusses machine learning and artificial intelligence. It provides definitions of both from various experts, with machine learning described as the algorithms that enable AI by making machines smarter. The document also lists some common machine learning techniques like computer vision and natural language processing. It discusses the promise of automation through exponential technologies and lists several popular machine learning tools and frameworks like TensorFlow, IBM Watson, and Scikit Learn. Quotes are provided on the relationship between tools and humanity.

1. Machine Learning in Startup
@masasdani

2. –Pablo Picasso
“computer are useless, they can only answer”

4. Machine learning vs AI:
what's the difference?

5. –Reza Zadah -Intel
“For a long time we were trying to replicate our
thought process by putting in a lot of different rules
into the computer, by programming them… a lot of
logical rules that went one by one — and the
computer could follow them, and eventually we
thought if we had enough of these rules, we could
come up with AI.”

6. “AI is a branch of computer science attempting to
build machines capable of intelligent behaviour”
–Stanford University

7. –Stanford University
“the science of getting computers to act without
being explicitly programmed”

8. –Nidhi Chappell - Intel
“AI is basically the intelligence – how we make
machines intelligent, while machine learning is the
implementation of the compute methods that
support it. The way I think of it is: AI is the science
and machine learning is the algorithms that make
the machines smarter.
So the enabler for AI is machine learning,”

9. Data mining?
Computer Vision?
Natural Language Processing?

10. –Gerd Leonhard
“The great promise of many exponential technology
is that we can digitalize everything, make it
intelligent, then automate and virtualize it.
Automation is the key of hyper-efficiency because it
makes it possible to substitute human with
machine”

11. “Automation is an inevitable destination”

12. https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/

13. http://en.proft.me/2015/12/24/types-machine-learning-algorithms/
http://www.apress.com/us/book/9781484223338

14. Segmentation
Identification / Recognition
Better Decision
Prediction
Automation

15. Tools

16. Azure Machine Learning
https://azure.microsoft.com/en-us/overview/machine-learning/
If you don't have advanced programming skills but are looking to get into machine learning, you should check
out Azure Machine Learning. (Note that you should have some knowledge of machine learning and data
science to truly benefit from using this platform.) This cloud-based service provides tooling for deploying
predictive models as analytic solutions. It can also be used to test machine learning models, run algorithms,
and create recommender systems, to name a few. However, it's been criticized for its poor performance and
unintuitive UI, particularly when it comes to writing code. Learn more about Azure Machine Learning here!
Tensorflow
https://www.tensorflow.org/
Originally developed by members of Google's Machine Intelligence research division to conduct deep learning
neural networks and machine learning research, TensorFlow is now a semi-open-source library that allows
developers to perform numerical computations. AI developers can use the TensorFlow library to build and
train neural networks in pattern recognition. It is written in Python and C++, two powerful and popular
programming languages, and allows for distributed training. Some cons are that it doesn't contain many pre-
trained models and there's no support for external datasets, like Caffe. Learn more about TensorFlow here!
IBM Watson
https://www.ibm.com/watson/
IBM Watson is called a "question answering machine." It uses analytical powers and artificial intelligence to
replicate human-like abilities to respond to questions optimally. It can help you make great business insights
and make informed decisions based on thoroughly informed decisions. IBM also ensures that your data is
protected with world-class security and encryption capabilities and that they will not share your data unless
you say that they can. On the other hand, its disadvantages include that it is only available in English, it
doesn't process structured data directly, and switching and integrating come with high costs. Learn more
about IBM Watson here!

17. DiffBlue
http://www.diffblue.com/
Protege
https://protege.stanford.edu/
Nervana Neon
https://www.intelnervana.com/neon/
Amazon AI
https://aws.amazon.com/amazon-ai/
OpenNN
http://www.opennn.net/
Apache Spark
https://spark.apache.org/
Apache Mahout
http://mahout.apache.org/
Caffe
http://caffe.berkeleyvision.org/
Veles
https://velesnet.ml/
Scikit Learn
http://scikit-learn.org/

18. –Marshall McLuhan
“First we build the tools, then the tools build us”

19. –Albert Einstein
“The human spirit must prevail over the technology”

20. –Gerd Leonhard
“Our toughest challenge are often the most
incredible opportunity, and vice versa”

21. –masasdani
“Thank you, let’s discus”