The 5 biggest trends in data science in 2022
The emergence of data science as a field of study and practical application over the past century has led to the development of technologies such as deep learning, natural language processing, and computer vision. Generally speaking, this has enabled the emergence of machine learning (ML) as a way to work towards what we call artificial intelligence (AI), a technological field that is rapidly transforming the way we work and live.
Data science encompasses the theoretical and practical application of ideas, including Big data, predictive analytics and artificial intelligence. If data is the oil of the Information Age and ML is the engine, then data science is the digital equivalent of the laws of physics that cause pistons to burn and move. .
A key point to remember is that as the importance of understanding how to work with data increases, the science behind it becomes more accessible. Ten years ago, it was considered a niche cross-subject straddling statistics, math and computer science, taught at a handful of universities. Today, its importance to business and commerce is well established, and there are many avenues, including online courses and on-the-job training, that can equip us to apply these principles. This led to the much discussed “Democratization” of data science, which we will no doubt see impacting many of the trends mentioned below, in 2022 and beyond.
Small data and TinyML
The rapid growth in the amount of digital data we generate, collect and analyze is often referred to as Big Data. However, it’s not just the data that’s big – the ML algorithms we use to process it can be pretty big as well. GPT-3, the largest and most complicated system capable of modeling human language, is made up of approximately 175 billion parameters.
This is fine if you are working on cloud based systems with unlimited bandwidth, but by no means covers all use cases where ML is able to add value. This is why the concept of “small data” has emerged as a paradigm to facilitate rapid cognitive analysis of the most vital data in situations where time, bandwidth or energy expenditure are of the essence. It is closely related to the concept of advanced computing. Self-driving cars, for example, cannot rely on the ability to send and receive data from a centralized cloud server when trying to avoid a road collision in an emergency situation. TinyML refers to machine learning algorithms designed to take up as little space as possible so that they can run on low-power hardware, close to where the action is taking place. In 2022, we’ll see it appear in a growing number of in-vehicle systems, from wearables and home appliances, to cars, industrial equipment and farm machinery, making them all smarter and more useful.
Data-driven customer experience
It’s about how businesses take our data and use it to provide us with increasingly interesting, valuable, or enjoyable experiences. This could mean reducing friction and hassle in e-commerce, more user-friendly interfaces and front-ends in the software we use, or spending less time on standby and being transferred between different departments when we establish contact with customer service. .
Our interactions with businesses are becoming increasingly digital – from AI chatbots to Amazon chatbots convenience stores without cashier – which means that often every aspect of our engagement can be measured and analyzed to better understand how processes can be smoothed out or made more enjoyable. It has also led to a desire to create higher levels of personalization of the goods and services offered to us by businesses. The pandemic sparked a wave of investment and innovation in online retail technology, for example, as companies sought to replace the hands-on, tactile experiences of brick-and-mortar shopping trips. Finding new methods and strategies to leverage this customer data into better customer service and new customer experiences will be a priority for many people working in data science in 2022.
Deepfakes, generative AI and synthetic data
This year, many of us have been led to believe that Tom Cruise started posting on TikTok when scary and realistic “deepfake” the videos have gone viral. The technology behind this is known as generative AI because it aims to generate or create something – in this case, Tom Cruise regaling us with stories of meeting Mikhail Gorbachev – that does not exist in reality. . Generative AI quickly integrated into the arts and entertainment industry, where we saw Martin Scorsese aging Robert DeNiro in The Irishman and (spoiler alert) a young Mark Hamill appear in The Mandalorian.
In 2022, I think we’ll see it burst into a lot of other industries and use cases. For example, it is considered to have enormous potential when it comes to creating synthetic data for training other machine learning algorithms. Synthetic faces of people who never existed can be created to train facial recognition algorithms while avoiding the privacy concerns of using real faces. It can be created to train image recognition systems to spot signs of very rare and rarely photographed cancer in medical images. It can also be used to create language-to-image conversion capabilities, allowing, for example, an architect to produce conceptual images of a building simply by describing what it will look like in words.
AI, the Internet of Things (IoT), cloud computing, and super-fast networks like 5G are the cornerstones of digital transformation, and data is the fuel they all burn to create results. All these technologies exist separately, but combined; they allow each other to do a lot more. Artificial intelligence enables IoT devices to act intelligently, to interact with each other with as little human interference as possible – leading to a wave of automation and the creation of smart homes and smart factories, up to To Smart cities. 5G and other super-fast networks don’t just allow data to be transmitted at higher speeds; they will allow new types of data transfer to become mainstream (just as super high speed and 3G have made mobile video streaming a daily reality) and AI algorithms created by data scientists play a key role in this regard, from routing traffic to ensure optimal transfer speeds to automating environmental controls in cloud data centers. In 2022, an increasing amount of exciting data science work will take place at the intersection of these transformative technologies, ensuring that they complement and work well together.
Short for “automated machine learning,” AutoML is an exciting trend leading to the “democratization” of data science mentioned in the introduction to this article. The developers of autoML solutions aim to create tools and platforms that can be used by anyone to build their own ML applications. In particular, it is aimed at subject matter experts whose expertise and specialist knowledge make them ideally placed to develop solutions to the most pressing problems in their particular fields, but who often lack the coding knowledge necessary to apply the system. ‘IA to these problems.
Quite often, a large portion of a data scientist’s time will be spent cleaning and preparing data – tasks that require data skills and are often repetitive and mundane. AutoML in its most basic form involves automating these tasks, but it also increasingly means building models and creating algorithms and neural networks. The goal is that very soon, anyone with a problem to solve, or an idea they want to test, can apply machine learning through simple, user-friendly interfaces that keep the inner workings of ML out of sight, leaving free space to focus on their solutions. 2022 is likely to see us take a big step towards this daily reality.
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