Under the Right Conditions, AI Can Improve Very Quickly
With all of the hype around Artificial Intelligence (AI) – robots, self-driving cars, and so on – it’s easy to imagine that AI has little impact on our daily lives. In reality, most of us come into contact with AI in some way or another on a daily basis. From the minute you wake up to check your smartphone to watching another Netflix recommended movie, AI has quickly penetrated our daily life. According to Statista, the global AI market would develop at a rate of up to 54% every year.
Despite its general unfamiliarity, AI is a technology that is altering every aspect of life. It is a versatile tool that allows people to reconsider how we combine information, evaluate data, and use the ensuing insights to make better decision making. While AI is already changing the world and raising fundamental challenges for society, the economy, and governance, it still needs the right conditions to flourish for human benefit.
Advancements in Artificial Intelligence in today’s world
There is almost no significant business that modern AI hasn’t already influenced. It performs objective functions using data-trained models and frequently falls into the categories of deep learning or machine learning. This has been especially true in recent years, as data gathering and analysis have increased significantly because of robust IoT connectivity, the proliferation of linked devices, and ever-faster computer processing. Some industries are just getting started with AI, while others are seasoned travellers. Regardless, the impact of AI on our daily lives is difficult to deny.
How AI can understand the physical world more accurately
AI gains “intelligence” by analysing and finding patterns in a given dataset. It has no idea of the world outside of this dataset, which poses several risks. A single modified pixel may fool the AI system into thinking a horse is a frog or, much scarier, lead to an incorrect medical diagnostic or machine operation. Its reliance on data sets creates a severe security vulnerability: malicious agents can spoof the AI algorithm by making slight, nearly undetectable changes to the data. Finally, the AI system does not know what it does not know, and it can confidently make inaccurate predictions.
Adding more data does not necessarily solve these issues because practical, economic, and technical restrictions always limit the amount of data available. And processing enormous datasets necessitates ever-larger AI models, which outpace available hardware and exponentially increase AI’s carbon footprint.
So, under what conditions may AI be improved?
1. Combine AI and Scientific Laws
Available data can be combined with pertinent principles of physics, chemistry, and biology to capitalise on each’s strengths while overcoming its flaws. AI is good at running machines, but not so good at understanding their surroundings.
For instance, AI can be employed to control a robotic surgical arm, which, when paired with a physics-based model, can predict how skin and tissue would deform under pressure. Combining data-driven and physics-based models makes the process safer, faster, and more efficient.
2. Add Expert Human Insights to Data
When data is scarce, human intuition can be employed to supplement and increase the intelligence of AI. In the realm of advanced manufacturing, for example, developing the unique process recipes required to manufacture a new product is exceedingly expensive and difficult. Data on novel processes is scarce or non-existent, and generating it would necessitate numerous trial-and-error attempts that may take months and cost millions of dollars. Starting from scratch, extremely experienced engineers typically achieve an essentially accurate formula, whereas AI is always collecting data and learning from those attempts. Once the recipe is in the ballpark, the engineers can use AI to help them fine-tune it. Such strategies can boost efficiency by several orders of magnitude.
3. Use Devices to Demonstrate How AI Makes Decisions
AI is frequently used as a “black box,” making confident recommendations without explaining why. If the process by which AI makes decisions cannot be explained, it is usually not actionable. A doctor should not make a medical diagnosis, and a utility engineer should not turn off a crucial piece of infrastructure based on an AI proposal that they cannot intuitively grasp.
4. Use Other Models to Predict Behaviour
Data-driven AI works effectively within the confines of the dataset it has processed, assessing behaviour between real observations or using interpolation. However, in order to extrapolate—that is, forecast behaviour in operating modes outside of the existing data —domain knowledge must be included. Indeed, many applications that use “digital twins” to simulate the operation of a complicated system, such as a jet engine, utilise this method.
We humans comprehend the world around us by combining our senses. When presented with a steaming cup, we immediately recognise it as tea due to its colour, smell, and taste. AI algorithms are trained —and constrained —by a specific dataset, and they do not have access to all of the “senses” that we possess. An AI machine trained solely on photos of coffee cups may “see” this steaming cup of tea and assume it is coffee. A digital twin, therefore, is a dynamic model that always reflects the precise status of an actual system and employs sensors to keep the model up to date in real time.
Any public dataset will always be incomplete, and processing increasingly massive datasets is frequently neither possible nor environmentally viable. Instead, adding additional types of domain understanding can help make data-driven AI safer and more efficient, allowing it to address difficulties that it could not otherwise.
Amalgamating AI with human expertise
The role of human expertise is critical for AI’s rapid advancement. AI systems require human knowledge to design and install, as well as to oversee their operation and guarantee that they are utilised ethically and responsibly. It is through the right and substantial use of data sets, domain knowledge, and human intervention in desired areas that AI technology can successfully live up to the results. Otherwise, it is just a disaster waiting to happen.