Despite all the havoc, 2020 has been a good year for tech and a good year for AI.

We already see the green shoots of recovery at the end of 2020 and 2021 holds much promise for growth and technology

 Here…

Much has been said about GPT-3 already.

With its 175 billion parameters and a massive corpus of data on which it is trained – GPT-3 is already enabling some innovative applications

But GPT-3 could help pave the way for a new way of developing AI models

The GPT-3 paper is called…

I am reading a very interesting paper called Principles and Practice of Explainable Machine Learning by

Vaishak Belle (University of Edinburgh & Alan Turing Institute) and Ioannis Papantonis (University of Edinburgh) which presents a taxonomy of explainable AI (XAI).

XAI is a complex subject…

For the last few years, I have read the free state of AI report

Here are the list of insights which I found interesting

The full report and the download link is at the end of this article

AI research is less open than you think: Only 15% of papers publish their…

 Nvidia CEO Jensen Huang proposed an idea which the media has labelled ‘Huang’s law’ along the lines of Moore’s law.

Moore’s predicts that the number of transistors in an integrated circuit doubles every two years.

As per Huang’s law, GPU performance will double every two years.

Whether or not you…

I always seek good resources for my teaching at #universityofoxford and this is a great free book on Cloud native – a topic that is very much in the focus

The book needs a free registration and is provided as a full ebook by d2iq.

D2iq was formerly mesosphere – the commercial arm of mesos – but…

Free book - Artificial Intelligence: Foundations of Computational Agents

There are many excellent free books on Python – but Artificial Intelligence: Foundations of Computational Agents is about  a subject not commonly covered

I found the book useful as a introduction to Reinforcement Learning

As the title…

This blog continues from GPT3 and AGI: Beyond the Dichotomy – Part One

GPT3 and AGI

Let’s first clarify what AGI should look like

Consider the movie ‘Terminator’

When the…

Background

Earlier this week, I spoke at an interesting online event organized by Khaleej times in the UAE (UAE’s longest running daily English newspaper).

This two-part blog is based on the talk. I addressed a hard topic – and one which I hope sparks some…

I was asked this question: What is the connection between AI, Cloud-Native and Edge devices?

On first impressions, it sounds like an amalgamation of every conceivable buzzword around - but I think there is a coherent answer which points to a business need. 

Let us…

I like to approach algorithms from the perspective of problem solving. I created this list from a Mc Kinsey document (link below). It’s a good indicative approach. In practice,

Background

Last week, the UK witnessed chaos over exam results (GCSE and A-levels).

The fiasco also provided a textbook case on how to build a biased algorithm.

Sadly, this will be the shape of things to come.

These decisions were overturned because of the backlash – but many more…

In my previous blog what is driving the innovation in nlp and gpt3 , I talked about how GPT3 has evolved from the basic transformer architecture.

Based on that blog, a start-up approached me saying that they had an idea which they felt could only be implemented by GPT3.

They were eagerly waiting to be approved (isn’t everybody - he…

2019 and 2020 have seen rapid strides in NLP

What’s driving the rapid strides in NLP and will this trend continue?

Here is a simple way to explain the rise and rise of NLP

Today, GPT-3 is displaying some amazing results. Some call it more like AGI (Artificial General Intelligence). Created by OpenAI with a large investment from Microsoft, GPT stands for Generative Pretrained Transformer

The three words offer a clue to the success and future…

In this post, we explore a new python package for feature engineering

Feature engineering is the process of using domain knowledge of the data to transform existing features or to create new variables from existing ones, for use in machine learning. Using feature engineering, we can pre-process raw data and make it suitable…

Introduction

I had this conversation some time ago with an Engineer who came from a traditional background.

By that I mean, he had been in the same industry (heavy engineering) for 30 years.

Of these, he had been in the same company for 25 years (and this was his second job).

After understanding from me about data science, he said that as an engineer, he did…

Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (due to COVID 19)

A few months ago, this headline would have been laughable

In London, central London and the London underground are a key fabric of daily…

I have been reading a book recently about algorithms in the wider sense

(40 algorithms every programmer should know -book and github link below)

I spend a lot of time with algorithms considering my teaching (AI at University of Oxford).

For Machine Learning and Deep Learning, we need to study a…

In the paper A survey on bias and fairness in machine learning.- the authors outline 23 types of bias in data for machinelearning. The source is good – so below is an actual representation because I found it useful as it is

full paper link below

1) Historical Bias. Historical bias is the already…