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March 29, 2023

Recent trends in AI applications

Recent trends in AI applications

The term “artificial intelligence (AI)” was coined in 1956, at a conference at Dartmouth College. Since then, the developments of artificial intelligence have experienced several ups and downs, in terms of government funding and public interests in the field. The first breakthrough in artificial intelligence technology came in 1997 as IBM's Deep Blue became the first computer to beat a chess champion when it defeated Russian grandmaster Garry Kasparov. Nowadays, AI can be seen in almost every corner of the world and has become part of our life. This blog will take you through the recent trends of AI and analyse opportunities and risks along the way. 

  1. Natural Language Processing (NLP) 

Natural language processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. This technology has a wide range of applications, from voice-controlled assistants like Siri and Alexa to language translation services and multilingual chatbots like Algomo. Hence, it is likely to be the most relevant AI application area for businesses, no matter which industry you are in. 

At its core, NLP involves the use of algorithms and machine learning techniques to process and analyze large amounts of natural language data which are typically collected from sources such as books, news articles, and social media posts, and then annotated with linguistic information to help the algorithms learn. These algorithms can extract meaning and context from the data, and use this information to perform tasks such as text classification, language translation, and summarization.

The most obvious challenge in NLP is the vast variety of human languages. Unlike programming languages, which have strict rules and syntax, natural languages are full of ambiguity, nuance, and colloquialisms. This makes it difficult for computers to accurately interpret and generate human language. To overcome this challenge, NLP researchers and developers use a variety of techniques, including syntactic parsing, semantic analysis, and discourse analysis, to identify the structure and meaning of natural language sentences.

When it comes to NLP, you can't miss out on the latest model trained by OpenAI, ChatGPT. The model interacts with users in a conversational way during which it can respond to any text-related requests. It can be used as a search engine that helps you to collect information about specific topics or write paragraphs according to detailed descriptions. 

Challenges in NLP also apply to specific products like ChatGPT. This leads to ChatGPT sometimes writing plausible-sounding but incorrect or nonsensical answers. It is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times. For example, given one phrasing of a question, the model can claim to not know the answer, but given a slight rephrase, can answer correctly. These limitations illustrate that there is still much room for improvement. 

Although adversely affected by the pandemic situation, the demand for natural language processing solutions and services is increasing as countries recover and there is a growing need for enhanced customer experiences in industries such as healthcare. In the future, the applications of NLP in automated customer service will continue to thrive, in terms of virtual assistants at call centres as well as chatbots integrated into company websites and social media platforms. 

  1. AI-generated Code

Asking an AI to generate software code means that it will be able to generate machine-readable instructions to achieve a specific goal. Indeed, code is a fusion of language, logic and problem-solving that is both a natural fit for a computer’s capabilities and a tough one to crack. 

AI-generated code can save much time and reduce the need for human intervention. By using machine learning algorithms, AI systems can analyze large amounts of code and identify patterns and trends that can be used to automatically generate new code. The process of identifying common errors and bugs also means that the code is more likely to be free of errors and more efficient in its execution. This can reduce the need for extensive testing and debugging, and result in more robust and reliable software.

In addition to improving efficiency and accuracy, AI-generated code could make software development more accessible to a wider range of people who may have limited programming experience to create complex software without needing to learn a programming language from scratch. This could open up new opportunities for people to develop their own software and accelerate their contribution to the tech industry. 

One of the leading AI coding systems is AlphaCode developed by DeepMind. AlphaCode, similar to humans, required the ability to understand a natural language description of a problem and its desired solution, along with background information, in terms of input and output. 

The pre-training dataset used by AlphaCode included 715 GB of code from GitHub repositories written in various languages, including C++, C#, Go, Java, JavaScript/TypeScript, Lua, Python, PHP, Ruby, Rust, and Scala. DeepMind enhanced AlphaCode by utilising large-scale transformer models, such as OpenAI's GPT-3 and Google's BERT language model.

Of course, AI-generated code is not without its challenges and limitations. One concern is that AI systems may not always generate code that is human-readable or easy to understand. This could make it difficult for human programmers to debug and maintain the code and could result in software that is less flexible and adaptable.

  1. AI-generated images and videos

AI-generated content has evolved from texts to images and now to almost any media channel, you can think of. Recently, there has been a popular trend of sharing AI-generated images on social media. It demonstrated how AI-generated images and videos have developed to be used in daily lives, at the same time, also revealed its limitations as people sharing funny and unrealistic images generated. 

One of the most common ways that AI generates images is through the use of Generative Adversarial Networks (GANs). In a GAN, two neural networks are trained simultaneously: a generator network that creates images, and a discriminator network that tries to determine whether the images are real or fake. Over time, the generator network learns to produce increasingly realistic images, while the discriminator network becomes better at distinguishing real images from fake ones.

Another way that AI can be used to generate images and videos is through the use of Variational Autoencoders (VAEs). A VAE is a type of neural network that is trained to encode and decode images and videos. The network learns to compress an image or video into a lower-dimensional representation, and then decode that representation to produce a new image or video that is similar to the original. This process can be used to generate new images or videos that are similar to a given input, allowing users to explore variations on a particular theme.

There are numerous ongoing attempts to use AI to generate fine arts to test the limits of AI technologies to engage in creative work. One of the most striking developments is that an AI-generated art (which is the image on our cover !) for the first time won a prize at the Colorado State Fair’s annual art competition (Source). It is not surprising that this has brought the debate on ethical issues around AI technologies to another level. Many artists feel the pressure of AI replacing their jobs and object that what AI creates is based on a coping strategy as it was trained with existing artwork on the internet. 

However, the quality of outputs by AI varies a lot. In addition to the issue of creativity and originality, there are still many challenges along the way in making images and videos look more realistic and aesthetically pleasing. 

AI ethics

AI has the potential to greatly benefit society, but it also raises important questions about how it should be used and how it can be made safe and fair for all. 

One of the key issues in AI ethics is the issue of bias. AI systems are only as good as the data they are trained on, and if the data is biased, the AI will be too. This can lead to unfair and discriminatory outcomes, such as when an AI system used for hiring is trained on data that is biased against certain groups of people. It is therefore important to ensure that AI systems are trained on diverse and unbiased data and that they are regularly evaluated for bias.

Another important issue in AI ethics is the issue of transparency and accountability. AI systems can be difficult to understand and explain, which can make it difficult for people to hold them accountable for their decisions. This is particularly problematic when it comes to high-stakes decisions that affect people's lives, such as decisions made by AI systems used in the criminal justice system. It is therefore important to develop AI systems that are transparent and accountable, and that can be easily understood and explained by those who use them.

A third important issue in AI ethics is the issue of privacy. AI systems often rely on data that is sensitive and personal, such as medical records or financial information. This raises concerns about how this data is collected, stored, and used, and whether it is being used in ways that respect people's privacy. It is therefore important to develop AI systems that respect people's privacy and that are designed to protect sensitive data.

Key takeaways 

Although there is a general concern about the threats posed by AI in replacing human jobs, it has great potential in complementing human talents if the technologies are carefully treated. Given the wide range of applications of AI technologies, there is no doubt that they will only become more and more prevalent in our lives and play an important role in fueling the development of society. 

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