Neural Network or how to build a machine with a human brain

#TechWatchbySeb — Weekly series of Tech sectors decrypted — Issue #26 — June 9th, 2021

Hello my friends 🖐,

and welcome back to the #TechWatchbySeb ☕️ - The weekly series of Tech sectors decrypted.

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For this week we will share insights on a new area of Artificial Intelligence which is Neural Network while traveling to Hungria🇭🇺, the U.K. 🇬🇧, Switzerland 🇨🇭, and Germany 🇩🇪

This new article comes in the series I started to decrypt the different clusters of Artificial Intelligence. In the past weeks, I covered:

This week we will have a deeper look at Neural Network

This is a fascinating technology on the verge of taking off. According to 360 Research Reports, the global neural network market size is projected to reach $ 27Bn by 2026, from $ 11Bn in 2020, at a CAGR of 15.9% during 2021–2026.


Definition of Neural Network and examples of Applications

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

A neural network can also be defined as (from IBM Research):

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

The image below (created by IBM) is a good representation of the complexity of a neural network’s information processing method.

With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

  • Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge.

  • Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.

  • Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.

  • Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.

  • Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks.

Here’s a list of other neural network engineering applications currently in use in various industries:

  • Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations

  • Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers

  • Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis

  • Mechanics: Condition monitoring, systems modeling, and control

  • Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems

As you can see, there is a wide variety of potential applications of this complex technology. Now let’s take a closer look at how neural networks are growing in the European tech ecosystem.


Neural Network in the Tech Ecosystem

As mentioned before, AI technologies can be used in a myriad of ways. However, developing these use cases requires complex expertise. Still, there are already several startups that have built strong solutions based on neural networks.

Neural networks is a division of deep learning, which itself falls under the wider umbrella of artificial intelligence. As it’s a fairly niche area, it is difficult to find specific information about the fund that invested in it.

Out of the 160 companies, it estimates that they raised around $1,5Bn, with a very strong standard deviation as the top 5 of the Index have raised more than $1,1Bn which represents 73%.

Interesting to see that Europe and the USA are the most prolific in terms of number of companies, but the average funding in the USA is two times higher than in Europe.

What are the most funded European companies?

  • AImotive🇭🇺 is one of the largest independent teams in the world working on automated driving technologies. They develop self-driving software, proprietary simulation tools and neural network acceleration hardware IP, AImotive is building an ecosystem to aid the deployment of automated driving. The company has raised a total of $67M.

  • BeMyEye🇬🇧 is a European crowdsourced in-store data as a service (DAAS) provider. They track Perfect Store execution metrics, such as share of shelf, promotional compliance, peak trading out of stock and brand recommendation, by deploying more than 1.5 Million on-demand data gatherers using the BeMyEye App, and leveraging cutting-edge neural-network Image Recognition technologies to analyse the data. The company has raised a total of $17M.

  • Cynny🇮🇹 develops MorphCast, a premium adaptive video format that delivers face recognition in a smartphone without the need for an app or plugin, and is underpinned by deep neural network technology that enables real-time content-engagement triggers, whilst fully maintaining the viewer’s data privacy. The company has raised a total of $12M.

What are the most active funds in Neural Network in Europe?

  • EASME 🇪🇺is the European Union’s executive agency for SMEs in charge of Enterprise Europe Network, COSME, and other programs. The fund has backed 4 Neural Network deals.

Within the list of the 160 companies, only EASME has invested in several companies that are using Neural Network technology. But we can also find some renowned investors that have participated in at least one deal such as Idinvest or 360 Capital Partner.


Conclusion

Neural network technology is a very technical dimension of Artificial Intelligence and Deep Learning. That makes it less accessible for startups, even if we can see that there is a good mix of young ventures and larger companies that have already raised hundreds of millions. It is also interesting to see that all the main regions have a presence in this tech segment(Europe, USA and China) and we can expect it to grow significantly in the next year as it increases a lot AI capabilities.

So, that’s it for this week, wishing you a great week ahead🖐

Stay safe ❤️