Reflections On The Fourth Industrial Revolution

by Freya Scammells

Share on LinkedInShare on Instagram

November '21 Edition

The Fourth Industrial Revolution is clearly upon us - a new era of innovation in technology - and we are excited to keep you updated with what's new! In this monthly feature we bring you up to date on the latest resources, news and insights in the AI community. Happy Reading!

Tech space on the rise & enterprise software expected to see the highest growth!

Market analysts Gartner expect worldwide IT spending to total $4.5 trillion in 2022, as the post-pandemic recovery continues and working from home and hybrid work takes an even greater hold among businesses everywhere. 

Spending on IT across the world is set to remain high next year as companies across the world look to get fully up to speed with new hiring needs. Staying ahead of the curve when it comes to the The Fourth Industrial Revolution can be a very time-consuming pursuit - there is always so little time and so much to absorb.

Where to start then?
Let us take all the hard work out of the equation for you and give you the greatest hits in the field for this November!
So, let's get the ball rolling:

Top 5 Influencers To Follow This Month

Nzareen Ebrahim
Nazareen Ebrahim is an AI Ethics Officer, a Media & Communications Specialist and Founder & CEO of Socially Acceptable - South Africa.
Ms Ebrahim has worked in marketing and corporate communications for over 15 years, focusing mainly in the tech, agriculture, retail and education spaces. She is an entrepreneurial mentor and also a brand consultant through her firm Naz Consulting International.

Patrick Bangert

Patrick Bangert is VP of Artificial Intelligence at
Samsung SDS. With previous work helping to optimise areas of the process industry, Mr Bangert now directs Science and Engineering AI teams at Samsung SDSA. Since may 2020, he has also held the role of CEO for algorithmica technologies GmbH. Their goal is to join the industrial world with that of academic mathematics, in part utilising machine learning.

Aleksandra Przegalinska

Aleksandra Przegalinska is an AIER Research Fellow and Kozminski University’s Vice Rector. Ms Przegalinska is a future Harvard Senior Research Fellow and also a former MIT Research Fellow. She is an AI Expert and a company adviser on growth with this technology. Passionate about the field of AI, she wants to educate the public and promote its best uses.

Maaike Groenewege
Maaike Groenewege is a Linguist, Content Architect and Conversation designer. Through Convocat (the conversational interface endeavour), where Ms Groenewege is a freelancer, her work involves ‘all aspects of sustainable chatbot and voice assistant implementation’. Experienced in linguistics and conversational design, she also holds an MA in English language & linguistics, Phonology, experimental phonetics, semantics, general linguistics and translation from Leiden University.

Nigel Willson
Nigel Willson is a public speaker, advisor and the Founding Partner at Awaken AI. Mr Willson is regarded as one of the top influencers in the world on Artificial Intelligence. His previous work included holding another very prestigious position as European Chief Technology Officer at Microsoft. He works now, advocating for responsible implementation of AI through his numerous global speaking engagements.

Top 5 Deep Learning Applications

1. 'Image Translation'

Image translation can be done in a number of different ways, but the most common way is to use a machine learning algorithm. Machine learning algorithms can then be used to make predictions based on the data they have been trained on. 

A great example of this type of algorithm is Google's Image Search. This is used by millions of people around the world to find images of things like dogs, cats, flowers, etc.  There are many different types of image-language translation algorithms, each with their own strengths and weaknesses. However, all of these algorithms have one thing in common: they are all based upon the idea of learning from a large amount of information. 

Example – Google image search

2. 'Pixel Restoration'

AI-Pixel restoration is the process of restoring a pixel to its original state, or as close as AI can get, which in 2021 should be pretty close!. It is a process that can also help restore damaged or missing pixels. This tech (in some instances) can also be applied to images that have been damaged by other means, such as exposure to UV light, or by being exposed to high levels of light.

Examples –  GigapixelAI (regarding enhancement and restoration)  and Ebsynth (regarding potential enhancement and potential restoration) 

3. ‘Visual Recognition’

Visual recognition is the ability to recognize a person's face, voice, or other visual features. It is also known as face-to-face recognition or face/voice recognition. This type of recognition can be used in a variety of situations, such as when you need to identify the location of a lost or stolen vehicle or when someone is trying to locate a missing person. 

Example –
IBM Watson

4. ‘Handwriting Generation’

Handwriting generation is the process by which a person's handwriting is automatically entered into a database. This database is then used by a program to generate new handwriting for the person. 

Example –
My Text in Your Handwriting’ developed by UCL Computer Science

5. ‘Machine translation (MT)’

Automated translation or Machine translation (MT) is the process of translating text from one language to another. It can also be used to translate text between languages that are not mutually intelligible (e.g., between English and Chinese). The goal of machine translation is to produce a translation that is as close to the original text as possible, without the intervention of humans.

Example – Google translate 

Are you struggling to propel your career in tech?

Reflection X's ML&DS Career Accelerator Programme can help you! Click the link below to find out more and check out the amazing feedback we received from helping people like you in tech!


5 Top AI Innovations Still Ascending This Month

1. 'AI Engineering'

The term ‘AI Engineering’ or ‘AI Engineer’ came about as a result of machine learning and artificial intelligence research. It is a subfield of computer science concerned with the design, development, and operation of intelligent systems.

Robotics, natural language processing (NLP), image and video processing, speech recognition, computer vision, machine learning, artificial neural networks (ANNs), and cognitive computing are all areas where AI engineers are in charge of designing and developing systems that can perform tasks in a variety of domains. 

2. 'Unsupervised ML'

A Supervised model is trained on a collection of training examples in supervised learning. Based on the data it has seen thus far, the model learns to anticipate the next example. 

Unsupervised ML learns from prior occurrences seen by other models. It does not gain knowledge from the training data.
This means that it is possible to train a model on data that has not yet been used by any other model.

3. 'GAN Technology'

GAN stands for Graphical Network Architecture. ‘GAN’ tools are meant to be extremely simple to use, so a person doesn't need any prior knowledge of computer science or network theory to get started. 

You have the ability with this tech to construct a graph of nodes and edges in a network and subsequently link those nodes to other nodes in the network. This graph may then be used to construct a representation of the full network, with each node connected to every other node by a route of shortest paths.

In this approach, it's possible to create models of very huge and sophisticated networks, such as the Internet, as well as very tiny networks, like a single cell phone tower.You can also utilize the tools to visualize the node relationships. For instance, if you want to understand the connectivity between two nodes, such as two mobile phones in your home, you might construct a basic graph to demonstrate the relationships between those two phones.

They are also incredibly powerful since they may be used to solve a broad variety of issues, ranging from simple network analysis to intricate modelling of complex networks.

4. 'No-code Machine Learning'

No-code machine learning is a type of artificial intelligence that is based on the principles of reinforcement learning. Reinforcement learning is the process of training a machine to perform a task. 

The goal of no code machine learning is to create a system that can learn from its own mistakes and mistakes made by other systems. In other words, the system learns to avoid making the same mistakes over and over again in order to improve its performance. 

5. 'Quantum ML'

Quantum Machine Learning (QML) is a programming language that is intended to be simple to learn and use. The name derives from the combined use of machine-learning algorithms with quantum computers. 

Quantum mechanics is the science of how, on the atomic and subatomic scale, light and matter behave. 

4 Artificial Intelligence Stories

1.  ‘Beethoven’s 10th  Symphony finally finished

The reports that Beethoven’s 10th symphony has now been realised, thanks to a project that included AI and music experts. Their article details the journey of Ahmed Elgammal who headed up a group of scientists from AI company ‘Playform’ for the endeavour. In the piece, Mr Elgammal explains that ‘we taught a machine Beethoven’s entire body of work and his creative process’.  

Harvard University musicologist Robert Levin, computational music expert Mark Gotham and the composer of intel’s famous jingle: Austrian composer Walter Werzowa were also reportedly involved in the effort.

The groundbreaking 2 year project was initiated by Dr Matthias Roeder, the Karajan Institute’s director in Switzerland.

 ‘AI brings optimism to the fight against DIPG brain cancer’

In another piece from the, this time by Lamiat Sabin, scientists are said to have used AI to discover a drug combination that could be an aid in the fight against DIPG brain cancer in children.  DIPG stands for Diffuse intrinsic pontine glioma and is reported in the piece as ‘a rare and fast-growing type of tumour that forms where the spine is connected to the brain.’

According to the article ‘Children with DIPG are typically expected to live only nine to 12 months after their diagnosis, and survival rates have not improved for 50 years’. The AI research was conducted at the Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London (ICR). 

Professor of paediatric brain tumour biology Chris Jones, from the ICR, is quoted in the article as saying “We still need a full-scale clinical trial to assess whether the treatment can benefit children, but we've moved to this stage much more quickly than would ever have been possible without the help of AI.” 


3. ‘Demand for AI Virtual Visors expected to ‘pick up’ in the automotive sector’

According to a piece recently in the prnewswire, provided by, the ‘virtual sun visor market’ is expected to experience a growing demand for the AI-assisted safety technology.

If you are not aware, a ‘virtual sun visor’ is tech that uses AI sun glare, shadow and facial camera detection, in an effort to help reduce road accidents. The article reports that ‘Adoption of AI virtual visor technology in automotive has reduced the chances of road accidents to a large extent’. It highlights that numerous players are now operating in this space, to name 3: Robert Bosch GmbH, Weetect and Visor-AR.


4. ‘Productivity in the legal industry could be optimised by AI’

Additionally in, reportedly, a new AI-powered ‘software solution’ called ‘Apollo’ has just been released to help the legal industry mitigate against the need to often manually complete certain administrative tasks.

The fascinating solution, from the company ZERO, quoting the article, ‘mimics human cognition by learning from users' activities to produce accurate recordings of projects and billable time to drive higher client value and improve employee morale’. 


About the Author

Freya supports the tech talent toolbox arm of Reflection X where she is coaching technologists to achieve their full potential. With over 10 years of experience within the Tech and AI recruitment space, she has been from working closely with thousands individuals and leaders during hiring processes while partnering with very early stage start-ups all the way to some of the top AI Labs in the world.

Meet The Team