Interview with Spiros Margaris

My interview 

with @joefields_ from @Onalytica 

 #fintech #insurtech #AI

  • He is ranked No. 1 FinTech and No. 2 InsurTech global influencer by Onalytica, and he regularly appears in the top three positions in several industry rankings.
  • We must support all the great FinTech, InsurTech and AI minds with their great ideas that make this fantastic ecosystem possible and strong.
  • Cybersecurity – For me, this is the big elephant in the room that affects incumbents as well as FinTech startups.
  • So many people influence me that I will avoid naming a particular FinTech, InsurTech or AI influencer.
  • I get my inspirations not only from the FinTech, InsurTech and AI industries but also from other great sources unrelated to it, such as art or literature.

An interview with top FinTech influencer Spiros Margaris – VC and Founder of MARGARIS ADVISORY on his expertise, background and network.
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The 5 best programming languages for AI development

The 5 best #programming languages for #AI development  #ArtificialIntelligence

  • Are you an AI (artificial intelligence) aspirant who’s confused on which programming language to pick for your next project?
  • And there’s no authoritative answer as to which programming language you should use for AI project.
  • Python is one of the most widely used programming languages in the AI field of Artificial Intelligence thanks to its simplicity.
  • It is an object-oriented programming language that focuses on providing all the high-level features needed to work on AI projects, it’s portable, and it offers in-built garbage collection.
  • Peter Norvig, the famous computer scientist who works extensively in the AI field, and also the writer of the famous AI book, “Artificial Intelligence: A modern approach,” explains why Lisp is one of the top programming languages for AI development in a Quora answer.

Are you one of the AI aspirants who is confused on which programming language to pick for your next project? Then, you have come to the right place as here we are going to look at the best 4 programming languages for AI development.
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Demystifying machine learning part 2: Supervised, unsupervised, and reinforcement learning

#machinelearning use cases:

1. Supervised
2. Unsupervised
3. Reinforcement

  • It is a type of machine learning, where one guides the system by tagging the output.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the  ‘spam’ email and distinguish it from those of ‘not spam’ emails.
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • Now instead of telling the child which toy to put in which box, you reward the child with a ‘big hug’ when it makes the right choice and make a ‘sad face’ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).
  • Based on your problem domain and the availability of data do you know which type of machine learning system you want to build?

Where business and experience meet emerging technology.
Continue reading “Demystifying machine learning part 2: Supervised, unsupervised, and reinforcement learning”

Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning

Machine learning use cases #AI:

1. Supervised
2. Unsupervised
3. Reinforcement #DF16

  • You start by showing a block and then placing the block in the block box; similarly you pick up a soft toy and then place it in the toy box.
  • Unsupervised learning is a somewhat harder form of machine learning.
  • Very quickly after a few iterations the child learns which toys need to go into which box – this is called Reinforcement Learning .
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails.

Where business and experience meet emerging technology.
Continue reading “Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning”

Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning

Machine learning use cases #AI:

1. Supervised
2. Unsupervised
3. Reinforcement

  • You start by showing a block and then placing the block in the block box; similarly you pick up a soft toy and then place it in the toy box.
  • Unsupervised learning is a somewhat harder form of machine learning.
  • Very quickly after a few iterations the child learns which toys need to go into which box – this is called Reinforcement Learning .
  • Just as the three year old learns the difference between a ‘block’ and a ‘soft toy’, the supervised machine learning system learns which email is ‘spam’ and which is ‘not spam’.
  • For example, a supervised machine learning system that can learn which emails are ‘spam’ and which are ‘not spam’ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails.

Where business and experience meet emerging technology.
Continue reading “Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning”

Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning

Machine learning use cases #AI

  • You start by showing a block and then placing the block in the block box; similarly you pick up a soft toy and then place it in the toy box.
  • Unsupervised learning is a somewhat harder form of machine learning.
  • For example, a supervised machine learning system that can learn which emails are â spamâ and which are â not spamâ will have its input data tagged with this classification to help the machine learning system learn the characteristics or parameters of the â spamâ email and distinguish it from those of â not spamâ emails.
  • Control theoretic techniques and Markov decision processes are types of reinforcement learning.
  • Now instead of telling the child which toy to put in which box, you reward the child with a â big hugâ when it makes the right choice and make a â sad faceâ when it makes the wrong action (e.g., block in a soft toy box or soft toy in the block box).

In this blog we explore different types of machine learning.
Continue reading “Demystifying Machine Learning Part 2: Supervised, Unsupervised, and Reinforcement Learning”