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5 Global Machine Learning and Data Science Future Trends.

January 5th 2023

Ontology Of Value 5-Global-Machine-Learning-and-Data-Science-Future-Trends-1024x683 5 Global Machine Learning and Data Science Future Trends IT Job Market Analysis and Predictions Uncategorized

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SUMMARY / KEY TAKEAWAYS

  • Data Science and Machine Learning have become fundaments for development in the IT industry. They lie at the core of almost all innovation in the today’s IT market — like a solid, concrete foundation to any new building crafted by software engineers worldwide. 

  • The incoming years will be game-changing in Data Science, as new, cutting-edge technologies make data processing and mining much easier and faster, allowing for the technological revolution to reach a whole new level. 

  • In this article, we will review five seminal trends in Data Science and Machine Learning that are likely to dominate the IT industry in the incoming years.

We Live in Times of Specialization. What Are the Data Science Future Trends?

Data Science and Machine Learning have become fundaments for development in the IT industry. They lie at the core of almost all innovation in the today’s IT market — like a solid, concrete foundation to any new building crafted by software engineers worldwide.

The incoming years will be game-changing in Data Science, as new, cutting-edge technologies make data processing and mining much easier and faster, allowing for the technological revolution to reach a whole new level.

If you are pondering career options and you are interested in starting a career as a Data Scientist, please take a look at our article “5 Steps To Build a Career in Data Science and Land Top Data Science Jobs.”

As explained in this article, we live in times of specialization. Today, “Data Science” is too broad to become a generic “Data Scientist” or “Data Analyst” — to stay competitive, you need to merge your skills with some extra expertise such as your background.

Here, we will review five seminal trends in Data Science and Machine Learning that are likely to dominate the IT industry in the incoming years. And, text-to-image algorithms or chatbots such as ChatGPT are not one of them!

Data Science Future Trends #1: Generative Adversarial Networks.

Generative Adversarial Networks (GANs) are a relatively new concept. Conceptualized by Ian Goodfellow in 2014, these layered neural networks have the power to generate brand new objects by learning on the features of the training set. For instance, given a training set of thousands of existing faces, they are able to generate a new face of a non-existent person.

The current prospects of GANs are fascinating, with the new applications and improvements under development. One area where GANs are expected to make a significant impact is in the area of data augmentation.

Data Augmentation.

Data augmentation is the process of artificially increasing the size of a dataset by creating new data points. This is often done by adding noise to existing data points or synthesizing new data points from scratch.

GANs are well-suited for data augmentation because they can generate realistic data points without trivial interpolation. Therefore, data augmentation using GANs should be able to improve the performance of multiple machine learning models. Additionally, GANs can create new, original data for unsupervised learning tasks, for instance, the data for training autoencoders.

Image Synthesis.

Another area where GANs are expected to make an impact in the near future is in the area of Image Synthesis: the process of generating new images from existing images.

This process can be used to create unique photos of things that do not exist in the real world or to generate realistic new ideas for things that live in the real world (for instance, creating a new concept for interior design based on thousands of pictures of existing interiors).

GANs are particularly well-suited for image synthesis because they can generate high-quality images. Additionally, GANs can be used to generate ideas from scratch, which helps to create synthetic data for training machine learning models.

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Data Science Future Trends #2: New Methods in Reinforcement Learning for Processing Sequential Data.

In general, reinforcement learning is a training technique in which agents are trained to complete tasks by providing positive feedback (reinforcement) when the agent performs the desired behavior. It is a powerful family of techniques that can be used to train neural networks to complete thousands of different tasks.

However, processing sequential data such as text is challenging when it comes to training neural networks. Currently, recurrent neural networks (RNNs) are a type of neural network that is well-suited to natural language processing and often used in this context. However, RNNs can be challenging to train and often require large amounts of data to achieve good results.

Transformers is a new type of neural network that is more effective at learning from sequential data than RNNs. Instead of RNNs, transformers can be trained using reinforcement learning for natural language processing, which speeds up the training process and improves results.

Google and other companies are using transformers to scale so-called attention algorithms and process text more efficiently. This is because transformers can learn to attend to the most essential parts of an input sequence, which can be helpful for complex tasks on text such as machine translation.

Data Science Future Trends #3: Deep Fake Audio and Video.

The new exciting — but also a bit thrilling — trend is the rise of deep fake video and audio content. Deep fakes employ AI to modify or generate information to impersonate another person. This is often a photograph or video of someone altered to look like somebody else. However, it may also be audio.

This technology leads to increasingly realistic outputs, and it’s only going to get better with time. The potential applications for deep fake content are endless. They could be used to create realistic CGI characters in movies or games or to create realistic training simulations for military training or medical procedures.

There are lots of potential positive applications for deep fakes. For example, they could be used to create more realistic and emotive avatars for virtual reality experiences. Or they could be used to create personalized messages from loved ones who have passed away.

However, deep fakes could also have a lot of implications online and lead to misinformation on social media and the spread of fake news. Given the ability to create original videos of people saying and doing things they never actually said or did, the potential for misuse is quite broad. You can already find dozens of deep fake video apps and websites online.

The online world has become an indispensable part of our lives. We use it for hundreds of daily activities, including communication, entertainment, work, and shopping. Because we rely so heavily on the Internet, it must be a safe and secure place. Unfortunately, as we reviewed in the article “Online Mental Hygiene, Part 1: How To Stay Safe Online And Why Is It Important For Your Professional Development?,” there are always people looking to take advantage of others online.

One way we can better protect ourselves online is by being aware of the latest cybersecurity threats. One way to build safety online is by being aware of deep fake technology and knowing how to detect fake audiovisual materials.

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Data Science Future Trends #4: Further Commercialization of the Data.

There has never been a more exciting time to enter the field of data analytics than today. In the past decade, we have seen a rapid increase in the amount of data being produced and a corresponding increase in the demand for professionals who can make sense of this data and help organizations make better data-driven decisions.

The field of data analytics is still relatively new, and as such, there is a lot of potential for growth and development. One of the critical open field areas is the room for platforms that can help commercialize data.

There are several reasons why commercializing data is worth attention. First, it can help to increase the visibility of data and make it more accessible to the people who need it. Second, it can help create a revenue stream for organizations that can be reinvested into further data analytic efforts. Finally, it can help create a feedback loop that can improve data quality over time.

Many different approaches to commercializing data exist. One option is to create a data marketplace where organizations can buy and sell data. Another option is to create a data-as-a-service platform where organizations can subscribe to a service that delivers data regularly.

Whichever approach is taken, the key is to create a user-friendly platform that offers value to the organizations that use it. With the right platform in place, commercializing data can be a great way to help organizations maximize the value of their data assets.

Data Science Future Trends #5: Gamification in Machine Learning.

The procedure of programming machines to adapt to data is known as machine learning. It’s a subset of artificial intelligence, and it’s been around since the 1950s.

Machine learning is utilized in several applications. It can be used to develop self-driving cars, improve search engine results, or automatically group photos on your phone.

Gamification is the process of adding game-like elements to non-game applications. It’s used to make tasks more fun, motivate users to keep using a product and teach new concepts.

One way is to use game-like elements to teach machine learning algorithms. For example, in the Molecule.one app, users are given a set of atoms and molecules and asked to predict the properties of new molecules. They teach the machine learning algorithm to predict molecule properties as they play the game.

Another way to gamify machine learning is to use it to create AI in a game. In StarCraft, players control one of three races: Terran, Zerg, or Protoss. Each race has unique units and abilities. The AI controlling the Zerg race was created using machine learning.

Machine learning can also create game features such as matchmaking (pairing players of similar skill levels) or in-game recommendations (suggesting new items or quests to players).

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Other Probable Directions for Development.

Big Data Storage and Management Solutions.

The sheer volume of data generated daily is at an alarming rate, and it will only get worse in time, especially in the times of text-to-image software and chatbots such as ChatGPT.

This leads to a need for better and more efficient data storage and processing solutions. In the next few years, we can expect to see newer and better big data technologies being developed to deal with this issue.

Rise of the Edge Computing.

With the proliferation of devices connected to the Internet, the need for edge computing is also increasing. Edge computing refers to processing data closer to the source instead of in a centralized location. This would help reduce the latency and increase the data processing speed.

IoT Gaining in Importance.

The Internet of Things is yet another area where Data Science is extensively used. The data that physical objects generate is also increasing with the increasing number of devices connected to the Internet. This data is being used to improve these devices’ efficiency and get valuable insights from them.

More Emphasis On Data Security.

With the increasing number of data breaches, data security has become a significant concern for organizations. In the next few years, we can expect to see more emphasis on data security with the development of new technologies to protect data.

Summary: Data Science Future Trends — What is the Future of Data Science as an Industry?

These are just some of the Data Science trends that will significantly impact the IT industry in the next few years. Exciting new applications for Data Science constantly emerge in the market.

But what’s the future of Data Science as a branch of the IT industry in general? In the past, global IT firms primarily focused on providing services to other businesses and consumers. However, in recent years they have increasingly become involved in funding and conducting their own research.

American and Chinese IT giants are slowly turning into republics of their own, with their own private funding of fundamental research in mathematics, medicine, neuroscience, and materials science.

This major shift in focus has been triggered by the enormous scale and profits of these firms and by the increasing importance of data and technology in all aspects of society. Now it could have profound implications for the future of science and education.

Firstly, it could lead to the further privatization of science and education. Global IT firms are already pouring billions of dollars into private education services, such as online courses and MOOCs. They are also increasing funding for scientific research, often intending to turn it into new products and services. This trend could accelerate if these firms continue to grow and expand their research capabilities.

Secondly, this shift could increase competition between universities and private education providers. Universities are already struggling to compete with Coursera and Udacity in online education. If global IT firms continue to expand their investment in scientific research, they could soon become significant competitors to universities in fundamental analysis.

Thirdly, this shift could have profound implications for the future of science and innovation. Giant IT firms have the resources and incentive to invest in long-term, high-risk research projects that may not pay off for many years.

This investment is essential for fundamental breakthroughs in science and, in particular, Data Science. It remains unclear how these trends will play out in the coming years but we can be sure about one thing: we entering the era of wonders in technology!

Please find more information about the recent developments in AI and machine learning in our article “Galloping Progress in AI and Machine Learning: How Can It Influence Our Jobs?

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