Machine learning or how to raise self-taught machines
#TechWatchbySeb — Weekly series of Tech sectors decrypted — Issue #28 — June 23rd, 2021
Hello my friends 🖐,
and welcome back to the #TechWatchbySeb ☕️ - The weekly series of Tech sectors decrypted.
If you want to receive the weekly newsletter #TechWatchbySeb feel free to register, I do my best to only share qualitative content 🤓.
This week, I will share insights into a new area of Artificial Intelligence which is Machine Learning while traveling to the U.K. 🇬🇧, Germany 🇩🇪, Switzerland 🇨🇭and Spain 🇪🇸.
This new article is part of a series where I decrypt the different clusters of Artificial Intelligence. In the past weeks, I covered:
Natural Language Processing to build machine chat with humans
Speech recognition or how to make machines understand humans
This week we will have a deeper look at Machine Learning.
The global machine learning market is expected to grow from $1.41Bn in 2017 to $ 8.81Bn by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The main driving factors for the market are the proliferation of data generation and technological progress.
Definition of Machine Learning and examples of Applications
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve themselves from experience, without needing to be reprogrammed. Machine learning focuses on the development of computer programs that can access data and use it to learn by themselves.
As of today, Machine learning methodologies fall into three primary categories:
Supervised learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately. This occurs as part of the cross-validation process to ensure that the model avoids overfitting or underfitting.
Unsupervised learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information makes it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, image and pattern recognition.
Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data to train a supervised learning algorithm.
Thanks to those methods, we can leverage this technology in many different areas.
Customer Service: Online chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms.
Recommendation Engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
Machine Learning in the European Tech Ecosystem
There has definitely been growing interest from investors in machine learning companies in the past few years. It is estimated that there are about 4000 machine learning companies worldwide — and probably more, as it is difficult to count all the Chinese players. The combined funding received by these companies has already reached above $60bn. It’s therefore one of the hottest technologies out there at the moment.
In the graph below, we can see that we are only at the beginning of the edge. In 2016, the level of cumulative investments stood at around $6Bn, so it has seen a 10x increase between then and today. This strong acceleration is partly due to the number of companies that have accelerated, but also the average ticket which has increased a lot in 5 years time from $600K in 2016 to $3M in 2021.
Now if we look at the geographical repartition, we can see a strong dominance of North American and Chinese companies. They represent around 90% of the funded companies in this tech segment. The European ecosystem represents around 10% of the investments ($6Bn) but almost a quarter of the number of companies (1000 Machine Learning companies).
What are the most funded European companies?
The Neurosphere 🇬🇧is a software company that aims to make AI more accessible to everyone. The company has raised a total of $1.1Bn.
Graphcore 🇬🇧 is the inventor of the Intelligence Processing Unit (IPU), a microprocessor designed for AI and machine learning applications. The company has raised a total of $682M.
Monedo 🇩🇪 uses machine-learning technologies to provide access to better credit for the underbanked.
As we can see the level of investment in those companies is encouraging for the rest of the European ecosystem. However, it is important to say that most of the investors backing those 3 companies are not Europeans.
What are the most active funds in machine learning in Europe?
As shared above, and detailed in the graph below, the level of maturity in the market is already quite good. With a good portion of Seed, Series A, B and C. It is normal to see less deals. However, we can expect in the next years to see more Series E and IPO deals.
EASME 🇪🇺is the European Union executive agency for SMEs in charge of Enterprise Europe Network, COSME, and other programs. The fund has backed 79 Machine learnings deals.
Venture Kick 🇨🇭 is a private, philanthropic initiative that provides pre-seed funding to entrepreneurs from Swiss universities. They have invested in 53 Machine Learning deals.
Wayra 🇪🇸 is a globally connected and technologically open innovation hub. They have invested in 37 Machine Learning deals.
Conclusion
Machine learning is definitely a trend that will see massive development in the coming years. An acceleration that will be linked with more powerful calculation technologies, more datasets, and more advanced algorithms. But we can also expect that this technology will be a key challenge for politics to ensure the development of the local ecosystem including skilled regulators working to facilitate the adoption of this technology as it will offer massive competitive advantages for the most developed regions.
That’s it for this week, wishing you a great week ahead🖐
Stay safe ❤️