First half of 2023: Data science and artificial intelligence development

Photo by Tara Winstead

A lot happened in the first half of 2023. There have been significant advances in data science and artificial intelligence. So much so that it was difficult for us to climb them. We can definitely say that the first half of 2023 showed a rapid progress that we did not expect.

So instead of talking too much about how we’re going to take care of all these innovations, let’s talk about them.

I’ll start with the most obvious. Natural Language Processing (NLP). Something that was being built in the dark and in 2023 came to light.

This advancement has been proven in OpenAI’s ChatGPT, which has taken the world by storm. Since their official release at the beginning of the year, ChatGPT has moved from GPT-4 and now we are waiting for GPT-5. They have released plug-ins to improve people’s daily lives and workflows for data scientists and machine learning engineers.

We all know after ChatGPT came out, Google released Bard AI which was successful with people, business and more. Bard AI competes with ChatGPT for the top chatbot position, providing similar services such as improving the tasks of machine learning engineers.

In the midst of these chatbot launches, we’ve seen large language models (LLMs) pop out of thin air. The Large Model Systems Organization (LMSYS Org), an open research organization founded by students and faculty at UC Berkeley, created the ChatBot Arena – LLM benchmark to make models more accessible to everyone using a co-development approach using open datasets, models, using systems. and assessment tools.

So now people are getting used to chatbots answering their questions and making their work and personal lives a lot easier – what about data analysts and machine learning specialists?

Well, they used AutoML, a powerful tool for data professionals like data scientists and machine learning engineers to automate data preprocessing, hyperparameter tuning, and complex tasks like feature engineering. With advances in data science and artificial intelligence, naturally we’ve seen a high demand for data and AI specialists. However, as progress continues at a rapid pace, we are seeing a shortage of these AI professionals. Therefore, finding ways to explore, analyze and predict data in an automated process will improve the success of many companies.

Not only will it be able to free up time for data specialists, but organizations will have more time for expansion and other more innovative tasks.

If you were up for the chatbot buzz, you’d see the words “generative AI.” Generative AI can generate text, images, or other forms of media based on user requests. As well as the above advances, generative AI is helping various industries perform tasks to make their lives easier.

It has the ability to create new content, modify repetitive tasks, work on custom data, and generate pretty much anything you want. If you’re new to generative AI, you’ll want to learn about stable diffusion—the foundation of generative AI. If you’re a data scientist or data analyst, you might have heard of PandasAI – a generative AI Python library, if not, it’s an open source toolkit that integrates generative AI capabilities into Pandas for easier data analysis.

But with the release of these generative AI tools and software, are data scientists needed in the era of generative AI?

Deep learning continues to evolve. With recent advances in data science and artificial intelligence, more time and energy is being spent on industry research. As a subset of machine learning related to algorithms and artificial neural networks, it is widely used in tasks such as image classification, object recognition, and face recognition.

As we experience the 4th Industrial Revolution, deep learning algorithms allow us to learn from data just as humans do. We’re seeing more self-driving cars on the road, fraud detection tools, virtual assistants, predictive healthcare modeling, and more.

2023 has proven to show deep learning working through automated processes, robotics, blockchain and various other technologies.

With all this going on, you must think these computers are pretty tired, right? To meet the advances in artificial intelligence and data science, companies need computers and systems to help support them. Edge computing brings computing and data storage closer to data sources. Working with these advanced models, edge computing provides real-time data processing and enables seamless communication between all devices.

For example, with LLMs being released every two seconds, it was clear that organizations would need efficient systems like edge computing to succeed. Google released TPU v4 this year – computing resources that can handle the high computing needs of machine learning and artificial intelligence.

Because of these advances, we’re seeing more organizations move from the cloud to the edge to meet their current and future needs.

A lot was happening and it was happening in a short time. It becomes very difficult for organizations like the government. Governments around the world are asking the question, “How and what are these AI applications impacting the economy and society?”.

People are concerned about bias and discrimination, privacy, transparency and security of these AI and data science applications. What are the ethical aspects of artificial intelligence and data science and what can we expect in the future?

We already have the European AI Act, which establishes a framework that groups AI systems into 4 risk areas. OpenAI CEO Sam Altman testified to a US Senate committee on Tuesday the 16th about the concerns and potential pitfalls of the new technology. Although a lot of progress has been made in a short time, many people are concerned. Over the next 6 months, we can expect several more laws to be passed and regulations and frameworks to come into force.

If you haven’t been following AI and data science in the last 6 months, I hope this article gives you a quick overview of what’s going on. It will be interesting to see over the next 6 months how these advances are taken and ensure the responsible and ethical use of these technologies.

There is a niche is a data scientist, freelance technical writer and community manager at KDnuggets. He is particularly interested in data science career advice or tutorials and theory-based knowledge about data science. He also wants to explore different ways in which artificial intelligence is/can benefit from human longevity. Eager learner looking to expand their technical knowledge and writing skills while helping others.