The 5 biggest trends in data science in 2022
The emergence of data science as a subject and practical application over the past century has led to the development of technologies such as deep learning, natural language processing, and computer vision. Overall, the advent of machine learning (ML) has made it possible to work towards what we call artificial intelligence (AI), a field of technology that is rapidly changing the way we work and live.
The 5 biggest trends in data science in 2022
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 equivalent of the laws of physics that make pistons burn and move in the digital realm.
An important point to remember is that the science behind it is becoming more and more accessible as it becomes more and more important to understand how to work with data. Ten years ago, it was considered a niche bridging subject between statistics, math and computer science that was taught at a handful of universities. Today, its importance to business and commerce is well established and there are many ways, including online courses and on-the-job training, that will enable us to apply these principles. This has led to the much-discussed ‘democratization’ of data science that will undoubtedly impact many of the trends below in 2022 and beyond.
Small data and TinyML
The rapid growth in the amount of digital data that we generate, collect and analyze is often referred to as big data. But 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 complex system capable of modeling human speech, comprises approximately 175 billion parameters.
This is fine if you are working on cloud-based systems with unlimited bandwidth, but by no means covers all of the use cases where ML can add value. For this reason, the concept of “small data” has evolved as a paradigm allowing rapid cognitive analysis of the most important data in situations where time, bandwidth or power consumption is of critical importance. It is closely related to the concept of edge computing. For example, self-driving cars cannot rely on the ability to send and receive data from a central cloud server in an emergency when trying to avoid a road collision. TinyML refers to machine learning algorithms designed to take up as little space as possible so that they can be run on underperforming hardware close to where it is going. In 2022, it will begin to appear in more and more in-vehicle systems – from wearables and home appliances, to automobiles, industrial equipment and farm equipment – to make them all smarter and more useful.
Data-driven customer experience
It’s about how companies use our data and use it to provide us with ever more enriching, more valuable or more enjoyable experiences. This could mean reducing the friction and hassle in e-commerce, using more user-friendly interfaces and front-ends in the software we use, or spending less time on standby and switching between departments when we initiate contact with customer service.
Our interactions with businesses are becoming increasingly digital – from AI chatbots to convenience stores to Amazon cashiers – which means that often every aspect of our engagement can be measured and analyzed to better understand how processes are smoothed out or rendered. more enjoyable. It has also led to an effort to further personalize the goods and services that companies offer us. For example, the pandemic sparked a wave of investment and innovation in online retail technology as businesses sought to replace the hands-on, tactile experiences of brick-and-mortar shopping trips. Finding new methods and strategies to use this customer data for better customer service and new customer experiences will be a priority for many people in data science in 2022.
Deepfakes, generative AI and synthetic data
This year, many of us were led to believe that Tom Cruise started posting on TikTok when terrifying and realistic âdeepfakeâ videos went viral. The technology behind it is called Generative AI because it aims to generate or create something – in this case Tom Cruise, who delights us with stories about meeting Mikhail Gorbachev – that doesn’t actually exist. Generative AI quickly caught on in the arts and entertainment industries, where we saw Martin Scorsese Robert DeNiro in The Irishman and (spoiler alert) a young Mark Hamill in The Mandalorian.
In 2022, I expect it to enter many other industries and use cases. This is said to have great potential in creating synthetic data for training other machine learning algorithms, for example. Synthetic faces of people who never existed can be created to train facial recognition algorithms while avoiding the privacy concerns associated with using real faces. It can be created to train image recognition systems to detect signs of very rare and rarely photographed cancer in medical images. It can also be used to create text-to-speech functions that would allow, for example, an architect to create conceptual images of a building by simply describing in words what it will look like.
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 get results. All these technologies exist separately but in combination; they allow each other to do a lot more. Artificial intelligence enables IoT devices to act intelligently and interact with each other without human intervention, leading to a wave of automation and the creation of smart homes and smart factories all the way to smart cities. 5G and other super-fast networks not only allow data transmission at higher speeds; They will allow new types of data transmission to become mainstream (just as super-fast broadband and 3G have made mobile video streaming a daily reality) and AI algorithms created by data scientists will play a role. key role in everything from traffic routing for optimal transmission speeds to automation. Environmental controls in cloud data centers. In 2022, more and more exciting data science work will take place at the intersection of these transformative technologies to ensure they complement and work well together.
AutoML stands for “Automated Machine Learning” and is an exciting trend driving the “democratization” of data science mentioned in the introduction to this article. AutoML Solution Developers aim to develop tools and platforms that can be used by anyone to build their own ML applications. It is particularly aimed at subject matter experts who, due to their technical expertise and insight, are ideally placed to develop solutions to the most pressing problems in their respective fields, but who often lack the programming knowledge to apply. AI to these problems.
Quite often, a large portion of a data scientist’s time is spent cleaning and preparing data – tasks that require knowledge of the data and are often repetitive and mundane. AutoML basically involves automating these tasks, but increasingly it also means building models and building algorithms and neural networks. The goal is that anyone with a problem to solve or an idea they want to test will soon be able to apply machine learning through simple, easy-to-use interfaces that leave the inner workings of ML out of sight. You can focus on your solutions. In 2022, we should get closer to this daily reality.
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