May 22, 2023
Estimated reading time: 5 minutes
The truth about AI: separating fact from fiction
AI researcher and Data Scientist, Aamir Mirza, has over 10 years of experience in Machine Learning and AI in remarkably diverse environments, IoT, signal processing, NLP, NLU, Computer Vision, Finance, and Deep learning-based control systems.
In an era characterised by rapid technological advancements, understanding the reality of Artificial intelligence (AI) is more crucial than ever. CUBE delves into the truth behind AI, demystifying common misconceptions and examining the fictional narratives that have shaped our perception of this transformative technology. By separating fact from fiction, CUBE aims to provide a clearer understanding of AI’s capabilities and its impact on various aspects of our lives.
No AI apocalypse
Let’s face it when it comes to AI (Artificial Intelligence) most of our perception about a dystopian future comes from either social media or from a movie.
Hollywood has long been captivated by the concept of artificial intelligence, often presenting it in a dramatic and exaggerated manner. From sentient robots plotting to take over the world to advanced AI systems with human-like emotions, the portrayal of AI in movies has fuelled both excitement and fear. These cinematic depictions, while entertaining, tend to exaggerate the capabilities and risks associated with AI, blurring the line between science fiction and reality. It is important to recognise that the Hollywood portrayal of AI is primarily driven by storytelling and entertainment value, rather than an accurate representation of the technology’s current state or potential future outcomes.
Firms such as OpenAI are overhyped by the media. In reality, their product is just a statistical model and quite subtle and sometimes even mundane.
It is just another tool
Throughout human history, one thing that has been defined as a species is tool making. We are essentially tool makers, from the invention of the wheel to modern space crafts, MRIs to nanotechnology we have crafted tools sometimes for monetary benefits and other times to make our lives easier. AI is no different, it is just another tool in our ever-growing toolkit.
Here are three uses cases where AI is helping transform the modern office:
1. Automating tasks
AI can be used to automate a wide range of tasks, from data entry to customer service. This can free up employees to focus on more strategic work.
2. Improving productivity
AI can be used to improve productivity in several ways, such as by providing personalised recommendations, identifying patterns, and making predictions. This can help employees to get more done in less time.
3. Making better decisions
AI can be used to make better decisions by analysing data and identifying trends. This can help employees to make more informed decisions that are more likely to be successful.
Transcription and summary
COVID fundamentally changed the way we work. While attending long online meetings, some may get distracted and lose sight of key ideas that were discussed.
Speech-to-text helps to convert spoken words to text, with “who said what” and then summarises key details as bullet points. These can be shared with the team, so everyone is on the same page. Previously, it was all done manually where someone had to remember and summarise the meeting. Just about all modern online meeting software can support this function either as a core service or a plugin provided by a third party. This way office resources can be directed to more productive pursuits leaving the mundane to AI.
Improving the bottom line
What improving productivity really alludes to is increasing financial output. We wish to be more profitable either by increasing market share or streamlining processes. Welcome to recommender systems.
What is a recommender system?
A recommender system is a type of information filtering system that seeks to predict the rating or preference that a user would give to an item. Recommender systems are widely used in many different domains, including e-commerce, music, movies, and social media.
There are two main types of recommender systems: collaborative filtering and content-based filtering. Collaborative filtering systems recommend items to users based on the ratings or preferences of other users. Content-based filtering systems recommend items to users based on the content of the items themselves.
Recommender systems can be very effective at helping users discover new items that they might not have found otherwise. They can also help to increase user engagement and satisfaction, help us discover what consumers prefer now and identify future trends.
Here is a list of companies that have deployed recommender systems with great success:
Better decisions, superior outcomes
The whole premise behind better decision-making is to streamline processes which leads to a healthier bottom line.
From simple regression-based ML (Machine Learning), to complicated decision trees, AI provides the bed frame which can help management in better decision-making.
- Simple regression-based machine learning models are a foundational tool in AI that enable organisations to improve decision-making by analysing relationships between variables and how a change in one affects the others.
- Complicated decision trees, another AI technique, further enhance the decision-making process by mapping out complex decision paths based on a set of conditions, making the “what-if” analysis much more effective.
AI serves as a tool for management decision-making, providing a solid foundation to leverage advanced algorithms and techniques for problem-solving.
By utilising AI, management can gain insights from large and diverse datasets, uncover hidden patterns, and make data-driven decisions.
AI systems have the potential to automate and streamline decision-making processes, reducing errors and improving efficiency.
AI-driven analytics enable management to forecast trends, identify potential risks, and seize new opportunities, leading to more informed and proactive decision-making.
The integration of AI into decision-making processes empowers management to handle complexity and uncertainty more effectively, leading to improved outcomes.
While a dystopian AI-dominated future would make a compelling Hollywood script, in today’s reality, AI is far from its portrayal in social media or the movies. At the end of the day, AI is simply a tool and it is incumbent on each of us to make good use of it.
Get in touch with CUBE to understand how we use AI and Machine Learning.