Artificial Intelligence is undoubtedly the buzzword of our time. Its popularity, particularly with the emergence of generative AI applications like ChatGPT, has brought it to the forefront of technological debates.

Everyone is talking about the impact of AI generative apps like ChatGPT and whether it is fair to take advantage of their capabilities. However, amid all this perfect storm, there has been a sudden surge of numerous myths and misconceptions around the term Artificial Intelligence or AI.

I bet you might have heard many of these already! Let’s dive deep into these myths, shatter them, and understand the true nature of AI.

 1. AI is Intelligent

Contrary to popular belief, AI isn’t intelligent at all. Most people nowadays do think that AI-powered models are intelligent indeed. This might be led by the inclusion of the term “intelligence” within the name “artificial intelligence”

But what does intelligence mean?

Intelligence is a trait unique to living organisms defined as the ability to acquire and apply knowledge and skills. This means that intelligence allows living organisms to interact with their surroundings, and thus, learn how to survive.

AI, on the other hand, is a machine simulation designed to mimic certain aspects of this natural intelligence. Most AI applications we interact with, especially in business and online platforms, rely on machine learning.

These are specialized AI systems trained on specific tasks using vast amounts of data. They excel in their designated tasks, whether it’s playing a game, translating languages, or recognizing images.

However, out of their scope, they are usually quite useless…  The concept of an AI possessing human-like intelligence across a spectrum of tasks is termed general AI, and we are far from achieving this milestone.

2. Bigger is Always Better

The race among tech giants often revolves around boasting the sheer size of their AI models. Llama’s 2 open-source LLM launch surprised us with a mighty 70 billion features version, while Google’s Palma stands at 540 billion features and OpenAI’s latest launch ChatGPT4 shines with 1.8 trillion features. However, the LLM’s amount of billion features doesn’t necessarily translate to better performance.

The quality of the data and the training methodology are often more critical determinants of a model’s performance and accuracy. This has already been proved with the Alpaca experiment by Stanford where a simple 7 billion features powered Llama-based LLM could tie the astonishing 176 billion features powered ChatGPT 3.5.

So this is a clear NO!

Bigger is not always better. Optimizing both the size of LLMs and their corresponding performance will democratize the usage of these models locally and allow us to integrate them into our daily devices.

3. Transparency and Accountability in AI

A common misconception is that AI is a mysterious black box, devoid of any transparency. In reality, while AI systems can be complex and are still quite opaque, significant efforts are being made to enhance their transparency and accountability. Regulatory bodies are pushing for ethical and responsible AI utilization. Important movements like the Stanford AI Transparency Report and the European AI Act are aimed to prompt companies to enhance their AI transparency and provide a basis for governments to formulate regulations in this emerging domain?.

Transparent AI has emerged as a focal discussion point in the AI community, encompassing a myriad of issues such as the processes allowing individuals to ascertain the thorough testing of AI models and understanding the rationale behind AI decisions. This is why data professionals all over the world are already working on methods to make AI models more transparent.

So while this might be partially true, it is not as severe as common though!

4. Infallibility of AI

Many believe that AI systems are perfect and incapable of errors. This is far from the truth. Like any system, AI’s performance is contingent on the quality of its training data. And this data is often, not to say always,  created or curated by humans.

If this data contains biases, the AI system will inadvertently perpetuate them.

An MIT team’s analysis of widely-used pretrained language models revealed pronounced biases in associating gender with certain professions and emotions. For example, roles such as flight attendant, or secretary were mainly tied to feminine qualities, while lawyer and  judge were connected to masculine traits. The same behavior has been observed emotion-wise. Other detected biases are regarding race. As LLMs find their way into healthcare systems, fears arise that they might perpetuate detrimental race-based medical practices, mirroring the biases inherent in the training data.

It’s essential for human intervention to oversee and correct these shortcomings, ensuring AI’s reliability. The key lies in using representative and unbiased data and conducting algorithmic audits to counteract these biases.

5. AI and the Job Market

One of the most widespread fears is that AI will lead to mass unemployment.

History, however, suggests that while technology might render certain jobs obsolete, it simultaneously births new industries and opportunities. For instance, the World Economic Forum projected that while AI might replace 85 million jobs by 2025, it will create 97 million new ones.

 6. The AI Takeover

The final and most dystopian one. Popular culture, with movies like The Matrix and Terminator, paints a grim picture of AI’s potential to enslave humanity.

While influential voices like Elon Musk and Stephen Hawking have expressed concerns, the current state of AI is far from this dystopian image.

Today’s AI models, such as ChatGPT, are designed to assist with specific tasks and don’t possess the capabilities or motivations depicted in sci-fi tales.

So for now… we are still safe!

Main Conclusions

In conclusion, as AI continues to evolve and integrate into our daily lives, it’s crucial to separate fact from fiction.

Only with a clear understanding can we harness its full potential and address its challenges responsibly. Myths can cloud judgment and impede progress.

Armed with knowledge and a clear understanding of AI’s actual scope, we can move forward, ensuring that the technology serves humanity’s best interests.

Source: 6 Artificial Intelligence Myths Debunked: Separating Fact from Fiction – KDnuggets

Cybersecurity firms are increasingly looking at integrated platforms to provide holistic online security for both for their B2B and end users as disconnected point tools struggle to support enterprise security requirements

The main reason that cybersecurity companies give for focusing on development of integrated platforms is that they make it easier for companies to provide a comprehensive solution to a problem that is “categorised by its complexity and constant evolution” says Kartik Shahani, Country Manager, Tenable India.

What are integrated cybersecurity platforms?

Integrated platforms provide solutions to this problem. A 2020 IBM survey found that organisations deploy approximately 45 solutions on average, with most requiring coordination across 19 security tools.

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Integrated cybersecurity platforms “equip security teams with a holistic view of the entire attack surface and amalgamates diverse elements, including vulnerability management, cloud security, web app security, identity security, and attack surface management,” Shahani says.

Essentially, integrated platforms are better equipped at identifying threats, filling gaps in security, and mitigating threats due to the ability to collate and use information gathered from different areas of a network.

These platforms offer consolidated risk insights, exposure analytics, prioritises risk, recommends remediation strategies, and quantifies the reduction of cyber risk, he added.

Use of cloud-based computing has further allowed cybersecurity firms to provide access to their integrated platforms without requiring users to upgrade their systems. Integrated platforms from most firms have been designed to be easily integrated into organisation’s existing tech stacks, while also allowing customisations based on the user’s needs.

In some cases, “customers are beginning to pay on consumption basis rather than a subscription,” Binod Singh, CEO and Chairman, Cross Identity said.

Why are enterprises increasingly shifting to integrated platforms?

Integrated platforms are also increasingly finding takers in businesses due to their economic advantage, the lack of skilled personnel to manage security apparatus, and improved visibility of gaps in security for enterprises.

Businesses looking to cut costs may pivot to integrated platforms as there is minimal overlap in the implementation and identification of security products. Further, the use of integrated platforms also allows enterprises to sidestep the problem of hiring cybersecurity experts, which can be a problem due to the lack of skilled personnel in the domain.

Are platforms the future of cybersecurity?

With the benefits of integrated platforms, B2B organisations with complex IT environments are increasingly adopting unified platforms for monitoring and responding to security threats.

“B2B organizations are embracing integrated cybersecurity systems because they provide robust protection, simplify management, offer cost savings, adapt to changing needs, support regulatory compliance, and enhance the ability to respond to threats quickly and effectively,” Ritesh Chopra, Director Sales & Field Marketing, India and SAARC Countries, Norton, said.

Small and medium-sized enterprises in India with a view to restrict expenditure, which earlier involved adopting multiple points solutions on individual licenses or subscriptions, are now increasingly turning to integrated platforms.  The adoption of these platforms is aimed at bolstering security posture in accordance with the laws of the land without the overwhelming costs and complexities associated with managing multiple standalone security tools.

An of integrated cybersecurity platforms has its benefits for enterprises, it poses a challenge for startups in the cybersecurity space which may struggle to finding the right constellation of cybersecurity experts to develop all the features needed be truly comprehensive. “Most often, the best experts are specialized in topics such as threat intelligence, malware analysis, application security, data loss prevention, network security, vulnerability management, AI, privacy, etc. Thers is also a shortage of these experts on the market, which indeed presents a challenge to startups,” Chopra said.

For the end users, integrated platforms allow cybersecurity companies to present users with an all-round security solution, which while may not be a silver bullet, ensures protection across devices which may not be possible with point security solutions.

Source: How are cybersecurity firms adapting to gen AI – The Hindu