Empower

Welcome to the March 2025 issue of Empower, the periodic newsletter of the IsDB-BISEW IT Scholarship Programme. This edition features the following topics of note:

• What is Artificial Intelligence (AI)?

• First Quarter Placement Performance – 2025

What is Artificial Intelligence (AI)?

Artificial intelligence (AI) is the ability of a constructed machine, such as a computer, to simulate or duplicate human cognitive tasks. A machine with AI can make calculations, analyse data in order to create predictions, identify various types of signs and symbols, converse with humans, and help execute tasks without manual input.

For example, a traditional automobile responds only to inputs from its human driver: it accelerates when the driver sees the stoplight has turned green and presses the accelerator, and it stops at a stop sign when the driver sees the sign and presses the brake pedal. An automobile with AI may be able to identify stop signs and stoplights on its own, and stop or accelerate without inputs from the driver.

AI has its roots in the very beginnings of computers, and mathematician Alan Turing was one of the first to describe how an artificially intelligent machine could function. All computers built since then are artificially intelligent on some level, as they are able to perform computations that could previously only be done by humans. However, in recent decades computers' abilities, speed, and storage capacity have expanded rapidly. Today the term "AI" refers to the more advanced cognitive tasks that computers can do.

How does AI work?

Most AI is built on the analysis of big data sets that contain too much information for any human to analyse on their own in a reasonable time. An AI model is built to identify patterns in those data sets, and then use those patterns to predict future or additional patterns. AI models use probability and statistical analysis in order to do so. Some AI models are good enough at this to mimic human behaviours.

What is artificial general intelligence (AGI)?

Artificial general intelligence (AGI) is the term for a computer's ability to think original thoughts, reason, plan, and otherwise duplicate human cognitive abilities. In simpler terms, an AGI program would be, essentially, "human" or "conscious" in its intelligence.

An AGI entity would have moved beyond statistical computation. It would be able to think and make decisions on its own, and it would be able to generalize from much smaller samples than is currently necessary for training AI programs. As an example of the latter, a human child may only need to see a few dozen examples of stop signs to be able to identify stop signs in a variety of contexts, whereas current AI programs need to see thousands or millions of images of stop signs before they can consistently do so. But an AGI would be able to learn about as quickly as a human, if not faster.

To some extent, determining if AGI has been achieved is a philosophical question rather than a technical one. But regardless of how AGI is defined, most researchers and experts agree that it is at least several years away from being practical.

What is machine learning?

Machine learning is a branch of AI, and it refers to the practice of feeding a program structured or labelled data in order to train the program how to identify that data without human intervention. For example, a machine learning model for finding bottles of ketchup in photos of open refrigerators may start out unable to identify any condiments, let alone ketchup. It is then fed millions of images of ketchup bottles in various refrigerators and is told that each one represents a ketchup bottle. Eventually, it is able to automatically identify ketchup bottles even in photos it has never seen before.

Machine learning relies on the use of predefined processes called algorithms. A machine learning program will "learn" slightly differently depending on how the algorithm is set up.

Machine learning also relies on access to large data sets. A machine learning program shown only three or four photos of ketchup will almost certainly fail to accurately identify ketchup bottles on a consistent basis, or will identify ketchup in photos where it is not present. The more data the model gets, the more accurate it is.

A wide range of software and technological solutions use machine learning today. From security solutions that use machine learning to detect fraud and identify bots, to social media platforms that use machine learning to recommend content or accounts to follow, machine learning has proved to be a hugely useful development tool.

What is deep learning?

Just as machine learning is a type of AI, deep learning is a type of machine learning. Deep learning models are able to use probabilistic analysis to identify differences in raw data. A deep learning model could potentially learn what a bottle of ketchup is and how to distinguish it from other condiments from photos of open refrigerators alone, without being told what a bottle of ketchup is.

Like other types of machine learning, deep learning requires access to large data sets. Even an advanced deep learning model would probably need to analyse millions of photos of open refrigerators to be able to identify ketchup.

What is generative AI?

Generative AI is a type of AI model that can create content, including text, images, audio, and video. A generative AI model could, for example, receive a photo of an empty refrigerator and populate it with probable contents based on photos it has been shown in the past. While the content generated by such a model may be considered "new," it is based on content that the model has been previously fed.

Generative AI tools are increasingly popular. In particular, the large language model (LLM) ChatGPT and image generators DALL-E and Midjourney have captured the public's imagination and the business world's attention. Other popular generative AI tools include Bard, Bing Chat, and Llama.

How is AI used?

The use cases for AI are still expanding. These are some of the real-world applications already being explored:

• Chatbots: AI-based programs can produce human-sounding answers, and can often reply realistically to unpredictable inputs from human users. In other words, some AI models can converse naturally, enhancing the capabilities of chatbots.
• Self-driving automobiles: AI's ability to make predictions enables it to respond to real-world road conditions even if they have never been previously encountered.
• Recommendation algorithms: Such as those used by streaming platforms and social media apps.
• Healthcare: AI can be used to help diagnose health conditions, along with other repetitive tasks in the healthcare world.
• Finance: Many finance companies have used AI to try to identify market trends or predict which stocks will perform well.
• Coding: LLMs offer the ability to quickly generate code for new functions, create documentation, and scan existing code for vulnerabilities.
• Content creation: Generative AI models can generate text, images, video, and so on.
• Report creation: Parsing and summarizing data is a repetitive task that can often be automated using machine learning.
• Experimental uses: More uses for AI are still being discovered, and use cases will continue to expand as AI capabilities keep growing.

What are the risks of AI for businesses?

Security risks

Data leaks: AI services use inputs to further train their models; they are not designed to be secure vaults for data. But many people use LLMs in ways that enhance risk of data exposure, including processing confidential information or closed-source code. Such data may be reproduced or imitated in further responses from these LLMs.

Loss of control over data: Data passes outside one's control once it is uploaded to an LLM, and users may not have visibility into what happens to provided inputs. For instance, if a baker puts their new secret recipe for focaccia into an LLM and asks it to write a compelling description for their bakery's website, they may get back such a description — but the baker's recipe is no longer secret, as it may be exposed to other users of the LLM, or the operators of the LLM.

Regulatory violations: Using external AI tools often introduces some degree of data risk. As a result, AI may put an organization out of compliance with regulatory frameworks like the GDPR.

Other risks

Hallucinations: Generative AI tools may invent information in order to generate responses — the technical term for this phenomenon is "hallucinations." If businesses incorporate such information uncritically into their content, they may damage their brands.

Over-reliance on AI for decision-making: Because the information provided by AI models is not always reliable, over-use of AI in the decision-making process can lead to decisions that negatively impact a business.

What are the risks of AI for consumers?

• Loss of privacy and personal data leaks: People who enter revealing or confidential data into publicly available LLMs may find that their data gets repeated to other users of the same tools.
• Security flaws in AI applications: Like any app, AI tools can have security vulnerabilities that lead to the exposure of personal data.
• Hallucinations: As described above, generative AI tools often invent information in order to create plausible-sounding responses to user prompts. This can result in the spread of misinformation.
• Deepfakes in phishing or social engineering attacks: AI tools can generate convincing imitations of a person's image, voice, or writing style. This can be used in social engineering attacks to impersonate a known individual and trick the victim into giving up their money or data.

First Quarter’s Placement Performance – 2025

Placement in the job market fuels the success of the IsDB-BISEW IT Scholarship Programme. The programme has been running successfully since 2003 and has so far produced 13,589 IT professionals who are working in 2,821 organisations at home and abroad.

The curriculums and pedagogy used in all the IT courses taught in the IT Scholarship Programme are geared towards the employment requirements of the IT industry. This ensures a steady placement of the programme in the industry.

A summary of placements occurring in the first quarter of this year is shown below along with the underlying data:

Placement During the First Quarter of 2025