What’s new in Artificial Intelligence?
A breakdown of the latest AI breakthroughs, ethical debates, and how AI is reshaping job industries
In recent years, there has been no shortage of headlines centring around breakthroughs in Artificial Intelligence (AI) and its sub-fields, such as machine learning. With buzzwords like deep learning, neural networks, computer vision, natural language processing, and more being thrown around, it’s difficult to keep up. In this article, we explore some of the latest breakthroughs in AI and machine learning, the ethical considerations around their applications, and dive into how AI is reshaping job industries.
In simple terms, AI is a set of technologies that enable machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. AI continues to evolve exponentially — revolutionising industries, improving daily tasks, and pushing the boundaries of what machines can accomplish.
Machine learning (ML) is a branch of AI that enables a machine or system to learn and improve from experience. Deep learning, in turn, is a branch of machine learning that uses artificial neural networks to process and analyse information, in a way that is inspired by the human brain (hence the term “neural”). Instead of explicit programming, ML uses deep learning to analyse large amounts of data and learn from the insights to make informed recommendations. This can be useful for problems involving complex information, such as those in the medical domain.
While AI and ML are used interchangeably, these trending technologies differ in several ways, including their scope and applications. AI has a broader scope, comprising anything that allows computers to mimic human intelligence, including robotics, problem-solving, and language recognition. Meanwhile, ML is specifically focused on the development of algorithms that can learn from data.
Here are some of the latest AI and ML breakthroughs across different fields:
Science
One of the most remarkable recent breakthroughs is AlphaFold 3 by Google DeepMind. The model earned its two leading researchers a Nobel Prize in Chemistry this year. AlphaFold, first launched in 2018, is an artificial intelligence (AI) system that uses machine learning (ML) to predict a protein’s 3D structure based on its primary amino acid sequence. Amino acids, of which there are 20 types, are molecules that combine to form proteins. Their linkage in sequences of various arrangements and lengths is what gives rise to an impeccably versatile array of proteins. Proteins underpin every biological process, in every living thing — they are vital for life as we know it.
Evaluating the unique complex 3D structure of just one protein is a laborious process, and can take several years and hundreds of thousands of pounds. AlphaFold has helped to solve this problem, with the ability to predict protein structures in minutes, to an astounding degree of accuracy, and in 2021, Forbes described it as “the most important achievement in AI — ever”. With over two million users in 190 countries and the prediction of 200 million protein structures so far, this innovative discovery has transformed protein structure research and is being applied in biotechnology, medicine, agriculture, food science, and bioengineering.
Healthcare
The applications of AI in Medicine and Health are truly revolutionary, with an ever-growing influence on healthcare delivery. AI is everywhere, from biomedical applications focusing on diagnosis and drug discovery to basic life sciences research. Take the example of diagnosing eye disease — machine learning technology has been used to analyse thousands of eye scans to identify the early warning signs of eye disease and recommend how patients should be referred for care. This AI system developed, yet again, by DeepMind, can match the accuracy of expert ophthalmologists and optometrists when identifying a range of eye conditions (such as age-related macular degeneration and diabetic eye disease) and generating the correct referral recommendation. The deployment of this technology across health services can allow for earlier intervention, help to reduce preventable sight loss, and tremendously improve the quality of life of those affected.
Education
With such an abundance of technology available, there is no doubt an effort to utilise this in education to make it more accessible and adaptive to individual learning needs. For example, Khan Academy’s Khanmigo uses GPT-4 to provide a personalised learning experience for students, offering explanations, generating practice problems, and tutoring across subjects.
Economy
AI-driven economic forecasting tools are now used to analyse and predict market trends, particularly in real-time financial trading and risk management. Machine learning models are increasingly utilised for customer behaviour analysis, impacting e-commerce strategies and enabling more effective economic planning. Examples include predictive analytics tools for financial markets, such as Aladdin Copilot by BlackRock (a prominent asset management firm), and IBM’s Watsonx platform.
Climate
AI plays a significant role in climatology, from predictive models in weather forecasting (such as Google’s MetNet-3) to real-time climate risk assessment tools for flood and wildfire predictions. These tools support emission reduction goals and aid in climate adaptation strategies by identifying risks associated with climate change.
What are the ethical considerations around AI & ML?
Numerous ethical considerations need to be accounted for when it comes to the development and deployment of AI and ML technologies. These range from bias, fairness, privacy, and transparency to accountability. For example, facial recognition systems have been shown to have higher error rates for individuals of certain racial or ethnic groups, reinforcing concerns about fairness and inclusivity. Furthermore, misuse or breach of personal data can lead to identity theft, surveillance, and loss of user trust. Regulations like the GDPR in Europe aim to safeguard data privacy, however, ensuring compliance and transparency is a challenge for organisations globally.
Many AI models, especially deep learning systems, operate as “black boxes”, meaning the inputs and operations taking place within the system to produce the desired output aren’t visible, making it difficult for users to understand how decisions are made. This lack of transparency is especially problematic in high-stakes areas like healthcare, finance, and criminal justice. In the absence of clear explanations, users cannot fully trust AI-driven decisions, which limits accountability.
How is AI reshaping job industries?
AI is causing a massive shift in job industries by pushing them into digital transformation. Every field is utilising AI in one form or another, whether that is through the automation of repetitive tasks or predictive analytics to improve performance or customer experience. While there is a lot of uncertainty and some (understandable) apprehension around what AI could mean for professions in the short and long run, there are clear advantages to embracing these new tools that can increase efficiency and productivity in just about any discipline. Adding on to this, there is a shifting demand towards high-skill roles in data analysis, AI management, and technical maintenance across many disciplines such as health, engineering, economics, etc., and developing skills in these areas is hugely beneficial. In summary, professionals and prospective professionals should welcome the use of AI, albeit with a hint of caution.
