In 2020, OVHcloud launched a portfolio of PaaS solutions dedicated to Data and AI. As an innovative cloud player for the past 20 years, our up-to-date technology coupled with our close collaboration with data science communities as well as the most cutting-edge players in the sector has enabled us to identify 10 strong AI trends in 2021.
1. Moving towards a limited number of standard AI libraries
With double digit growth, the French-American startup HuggingFace is paving the way towards the convergence of different Artificial Intelligence techniques within a single library.
Neural network techniques have considerably evolved in recent years. Like machine learning libraries such as Scikit-Learn, which had laid the foundations of standardization, the rise of HuggingFace’s Transformers library seems to usher in a new technical era: the convergence of tools that simplify and democratize the use of AI.
2. Advances in NLP spread to other areas of AI application
Natural Language Processing (NLP) grew exponentially in 2019 and 2020 due to the emergence of new technologies including Transformers. The latter was particularly noteworthy because it brought with it high-performance and highly agnostic NLP models. It mainly addresses the difficulties inherent to the temporal and spatial aspects of language. It solves the difficulty of establishing the link between the beginning and the end of a sentence and identifying key elements.
Given the performance of those new techniques in solving this type of problem, it becomes clear that the same techniques can also be used in areas not directly related to language such as video, voice, or even image processing. Even if there were few research paper concerning this topic in 2020, there are bringing out 2021 to be a year of improvements in the state of the art surrounding these subjects.
3. The New Era of Speech Recognition
As we have seen, techniques related to NLP will benefit to numerous learning fields in which data temporality plays a very important role, among which Speech Recognition. Thus, MILA (known for its eminent professor Yoshua Bengio), in collaboration with Nvidia, Samsung and Nuance have announced the Speechbrain Project but has not yet revealed all its secrets but could be a “game changer” in 2021.
4. I annotate, you annotate…
We all will annotate this year! The widespread use of AI by companies will lead to an explosion in data labeling solutions, and should be accompanied by an expansion of open-source tools. A few big startups should stand out this year, like Weights and Biases for experiment management, which was democratized last year.
5. AI to be taught earlier in the IT curriculum
Although the bachelor and master’s degree programs in computer science already deals with the notions of artificial intelligence, in September 2021 the first artificial intelligence teaching programs may arrive in scientific fields upstream of the master’s or bachelor’s degrees.
6. Fake or not fake?
The rollout of Generative Neural Networks (GANs), over about 3 years, will spawn a real revolution in multimedia, especially in video games and video creation. As with great power comes great responsibility, with those technologies comes risk of malicious uses of generated images, as for example deep fake. Stay vigilant GAFAM, you are under the magnifying glass!
7. A major open source player?
All indications are that an open-source player, centralizing several areas of artificial intelligence applications such as image, sound, text, video – might emerge in the course of 2021 or 2022.
8. Indicators to help reduce energy consumption
The associated power consumption remains important, especially for the operation and cooling of GPUs. The ecological impact of Artificial Intelligence is a growing topic. We can expect new indicators related to ecological impact in the research papers as an indicator… and why not in some cloud providers communications 😉.
OVHcloud already started working on this topic through the Green Cloud Task Force.
9. Responsible and ethical AI certifications
Everyone is talking about ethics and responsibility; it is certain that the subject will be a priority for the major certification bodies.
New ISO certifications, dedicated to AI, are expected to be launched this year to address critical topics such as: reversibility transparency of algorithms, multi-locality context application avoiding biases (skin color, age, gender, language, culture, accent, …).
10. Collaborative solutions and container to secure reproducibility and to put in production
As the processes for implementing AI projects within companies are becoming more widely accessible and structured, we are seeing the trend of entire ecosystem looking forward to use/implement several collaborative data science tools, based on Project Jupyter’s logic. Real-time collaborative code editing seems like a promising path! Reproducibility and production proof AI implementations seems to converge toward the container technology which should arrive in force for the data scientist community.
And a last one, my personal conclusion
And here is an 11th prediction in the form of a more personal conclusion: the trend towards simplifying usage for developers/data scientist will grow… It is for this reason that we have worked to simplify as much as possible the user experience of our AI services such as AI Training and ML Serving 😉
And what a bonus if these tools are on a trusted cloud 💖
Happy cloud year 2021!