Perhaps no tech topic is more ubiquitous — or more hyped — than Artificial Intelligence (AI). When OpenAI brought AI to the masses with its release of ChatGPT in November 2022, the world was forever changed. AI startups began to attract massive funds, giant tech companies raced to implement AI in their own products, and public awareness of AI as a tangible tool peaked.
Now, terms like generative AI, large language models, machine learning, and neural networks are appearing in virtually every commercial sphere, from professional services to consumer electronics. But what is AI exactly? And how do you know if it’s just being used as a buzzword to sell a product, or performing a higher level of intelligence?
Unpacking the AI hype: What can AI do?
Artificial intelligence is an area of computer science devoted to creating systems capable of performing tasks that are normally associated with humans. These systems can be built to carry out tasks like making decisions, solving complex problems, and thinking creatively. AI systems use sophisticated algorithms and data to achieve these feats.
But this general definition just scratches the surface. To better understand what AI is and isn’t, let’s start by defining the two broad categories of AI: inference AI and generative AI.
Interface AI is a type of technology that focuses on inferring information from content like text, images, audio, and video. Although this type of AI can make inferences based on information, it cannot generate its own content.
Generative AI is a technology that can generate new content — text, images, audio, and video — from instructions. GenAI development involves a combination of machine learning and neural network training.
Is it AI or AI washing?
AI washing occurs when companies make misleading or exaggerated claims about the amount of AI used in their products to increase profitability by capitalizing on AI hype.
To identify AI washing, consider the following questions:
Does this product require significant human involvement to generate an acceptable output? True AI is marked by a high degree of autonomy, delivering an acceptable result with minimal human input.
Is the company behind this product transparent about the types of data and the algorithms that it uses to power its AI? The black box approach, focusing solely on the inputs and outputs of a product rather than revealing its internal workings and processes, is often a red flag that the technology may not be all it’s cracked up to be.
Unfortunately, there are too many instances of companies using the term AI to promote a product or service that can’t pass the quiz above. That’s why it’s important to look beyond companies’ initial claims and probe a bit deeper to determine whether a product is truly AI-powered.
Biases and hallucinations
“Hallucinations” are a key concept to keep in mind. AI hallucinations are results that are false, misleading, or nonsensical. These incorrect results are caused by a variety of factors, including training with data that is insufficient or biased, which can lead to assumptions by the AI implementation.
It is important to remember that AI models are designed to predict outcomes based on the data they train on; therefore, incorrect data ingestion will simply result in incorrect results.
Understanding the AI hype cycle
One way to understand the fast-changing tides, false promises, and overhyped expectations that seem to come along with each new AI breakthrough is to look at a chart developed by Gartner.
In June, Gartner released its 2024 Hype Cycle for Artificial Intelligence, which tracks how emerging technology evolves, matures, and is adopted by the public.
In its first stage, a new technology moves through the innovation trigger, gaining traction and hype as it heads toward the peak of innovation.
After reaching this peak, the technology plummets to the trough of disillusionment, where the hard work begins. No longer influenced by inflated expectations, people can begin to understand the technology’s true capabilities and devise practical applications for it, moving it further along the cycle to the plateau of productivity.
According to Gartner, AI moves through the hype cycle quickly, and generative AI has already reached the trough of disillusionment. But despite its melancholy name, this stage of the cycle presents an opportunity for genuine innovation — spurring people to better understand AI’s limitations and put its promise into action. One industry in which this is currently happening is cybersecurity.
AI in cybersecurity: The future of threat detection
There’s no question that the AI revolution has transformed cybersecurity, for better and for worse.
As AI models become more sophisticated, so do the tactics employed by cybercriminals. Threat actors are increasingly using generative AI to automate, enhance, and scale their attacks, resulting in threats that are harder to detect and mitigate.
However, the integration of automation in cybersecurity presents significant opportunities, especially in the realm of threat detection.
AI can also be used to streamline incident response processes. By automatically carrying out predefined actions in response to detected threats — such as isolating affected systems, blocking malicious traffic, or initiating alerts — AI can respond to potential threats in real time.
(Authored by Berk Veral, Senior Director of Product Marketing, Akamai )
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