Top 5 AI Trends in 2023

Since the year 1956, when John McCarthy first coined the term artificial intelligence, the field has experienced several breakthroughs and setbacks.

From IBM’s Deep Blue defeating a chess champion in 1997  to Google DeepMind’s AlphaGo surpassing a Go champion in 2015, AI achieved milestones that captured the world’s attention.

Amidst all these achievements, there also came a short period of pessimism and stagnation called the AI winter.

However, in 1990, there was a resurgence that renewed interest in AI research.

The last decade witnessed a revolution in deep learning, which gave rise to advancements in speech recognition, natural language processing, and audio recognition.

The release of Open Ai’s ChatGPT in 2022, capable of generating human-like text, marked a culmination of these advancements.

What began as a concept in 1956 has now become a pervasive trend, focusing on responsible, explainable, accessible,  sustainable and ethical AI.

In this article, we will look at the top 5 AI trends in 2023. Let’s start!

Explainable AI

The field of explainable AI has experienced significant growth in recent years and will continue to grow in 2023.

Awareness among people is leading towards increased demand for transparency in AI models.

As the use of AI in critical applications, such as healthcare, finance, and law, increases, people do not want to trust AI outcomes blindly but rather seek the rationale and complete understanding of what exactly led to taking a specific action by AI systems.

Researchers and developers are effortlessly working on shifting paradigms from black-box AI systems to glass-box AI systems.

In black box techniques, the internal workings of AI remained anonymous, and the algorithms were too complex to be understood by a human.

On the other hand, the glass box techniques will bring more transparency by explaining algorithms in a way that is understandable to humans.

Apart from the general public call for Explainable AI, the European Union’s GDPR also mandates the right to explanation through Articles 13-15 to provide meaningful information about the logic involved in automated decisions.

Ethical AI

As AI is proliferating, it is impacting individuals’ lives, society and the environment both apparently and unknowingly.

Undeniably, AI is making modern life easy, but there is another aspect as well.

AI models learn from data, so biases, discrimination (gender, race, ethnicity, etc.)  and privacy or human rights violations are also becoming prevalent.

In this scenario, the role of ethical AI is becoming even more prominent.  AI developers and engineers are working towards making AI responsible so that the process of decision-making by these models remains fair and just.

One such instance includes when Amazon engineers discarded their year-old AI hiring software program because the system discriminated against women in recruitment.

Similarly, Optum was investigated for allegedly using a tool that discriminates against black patients by making them less likely to be recommended for certain treatments.

In addition, various organisations, governments, and companies are actively participating by developing ethical AI guidelines and legal frameworks.  Examples include-

• The Google AI Principles
• The IBM Responsible AI Framework. 
• The Microsoft AI for Good initiative.
• EEE Ethically Aligned Design Principles
• Global Partnership on AI Principles
• European Commission AI Ethics Guidelines for Research

Also, the European Union’s stringent law regulating the use of artificial intelligence was passed by the parliament in June 2023 and is all set to come into effect by mid-2024.

Read Also: Is AI Sentient? What if it becomes so?

Sustainable AI

To maintain the AI infrastructure, say to power data centres, requires huge energy consumption.

According to a study conducted in 2019, training a single deep-learning model emits 626,000 pounds of CO2.

However, efforts to utilise green or renewable energy to power data centres are underway to make AI more sustainable.

Not only for AI infrastructure but for overall energy consumption, policymakers hold companies responsible, putting pressure on them to reduce carbon footprints.

Also, the capabilities of AI in developing technology to make household and industries energy consumption responsible is in recognition.

For example, optimising energy consumption in buildings, predicting renewable energy generation from wind turbines or solar panels, and saving household energy through an energy management app are some notable efforts towards sustainable AI.

Democratisation of AI

The democratisation of AI means making AI available to all by making it affordable and easily accessible.

In 2023, the trend takes an even more rapid pace.  Large organisations and tech giants like Microsoft and Google introduced open-source datasets that are being used to train AI models even by those with a low technical skill level.

No code or low code platforms have made creating, testing, and deploying AI-powered solutions easy.  One such example is Google Cloud Platform (GCP), which allows users to train and build an image classifier without having a coding background.

Users need to upload sufficient images of every class, mention the class names, and click on a button to train the model. GCP will find the most effective artificial intelligence algorithm to classify images

Besides, platforms like Kaggle, Google Colab, or Microsoft Azure also provide opportunities for machine learning and access to large datasets at no or very low cost.

Generative AI

Generative AI has taken over the world, leaving people crazy with its capability to generate almost every kind of content, including text, images, audio and videos, in seconds. Almost in every field, people are leveraging AI tools to get their work done; of course, the user-friendly interface has a huge contribution to making them popular.

Since the launch of ChatGPT last year, the trend of generative AI models has accelerated in 2023. From big tech giants to small startups, all actively participate in developing AI models.

When OpenAI dominates the market with GPT versions like ChatGPT or Dall-E 2, companies like Google and Microsoft are not far behind.

In response to ChatGPT, Google introduced its counterpart “Bard”, which excels in understanding human-like language and answering queries in text-form.  Microsoft’s Bing, which runs on the same technology as ChatGPT, is also a conversational chatbot responding to text-based prompts.

Jasper, Writesonic, and Copy.ai are more examples of text-based generative AI.

OpenAI’s Dall-E 2 (enhanced version of Dall-E) and Stable Diffusion are text-to-image models capable of generating photo-realistic images given any text input.

Pictory AI, Kapwing, or Synthesia are some examples of tools designed to generate video content using AI technology.

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