Neural networks are increasingly penetrating various spheres of our lives: from big data analysis, speech synthesis, and image creation to controlling autonomous vehicles and aircraft. In 2024, Tesla developers added neural network support for autopilot, AI has long been used in drone shows to form various figures and QR codes in the sky, and marketers and designers apply AI in their work to generate illustrations and text.

After the release of ChatGPT at the end of 2022 and its popularity, various companies have been actively developing their services based on GPT models. With the help of various services and AI-based Telegram bots, neural networks have become accessible to a wide range of users. However, if information security rules are not followed, the use of various services and neural networks involves certain risks. Let's discuss these in more detail.

Risks of using neural networks

The euphoria caused by the discovery of GPT chat for many people has been replaced by caution. With the emergence of numerous services based on language models, both free and paid, users have noticed that chatbots can provide unreliable or harmful information. Particularly dangerous is incorrect information regarding health, nutrition, and finances, as well as data on weapon manufacturing, drug distribution, and more. 

Moreover, the capabilities of neural networks are constantly expanding, and the latest versions can create remarkably believable fakes, synthesizing voice or video. Fraudsters use these features to deceive their victims by forging messages and calls from acquaintances and videos with famous personalities.

The main threat is the emerging trust many users have in neural networks and various chatbots in particular. Surrounded by an aura of accuracy and impartiality, people forget that neural networks can operate on fictional facts, provide inaccurate information, and generally make erroneous conclusions. It has been shown repeatedly that mistakes happen. If you ask frivolous questions, the damage will likely be minimal. But, if you use chatbots to resolve issues in finance or medicine, the consequences can be quite destructive. Moreover, often to get a response from a neural network, you must provide some data.

A big question is how this data will then be processed and stored. No one guarantees that the information about you that you included in the queries will not subsequently appear somewhere on the darknet or become the basis for a sophisticated phishing attack.

In March 2024, bug hunters at Offensive AI Lab, thanks to a data encryption feature in ChatGPT and Microsoft Copilot, found a way to decrypt and read intercepted responses. Regardless of how quickly OpenAI was able to patch this hole, its existence is a prime example of how malicious actors might use vulnerabilities in APIs to steal confidential data, including passwords or corporate information. In addition, vulnerabilities make it possible to conduct DDoS attacks on the system and bypass protection.

There are several types of attacks on AI, and it is important to be able to distinguish them. For example, evasion attacks (modification of input data) are potentially the most frequent. If the model requires input data for operation, it can be modified appropriately to disrupt the AI. On the other hand, data poisoning attacks have a long-term character. A Trojan present in the AI model remains even after it is retrained. All this can be combined into adversarial attacks – a way to deceive a neural network to produce an incorrect result.

Neural networks are still not sufficiently protected from attacks, data falsification, and interference in their operation for malicious purposes, so users should be vigilant and follow certain rules when working with chatbots.

Precautions and recommendations

The technology of large language models is rapidly developing, penetrating deeper into everyday life, and gaining more users. To protect yourself and your data from potential threats, it is important to adhere to some rules when working with neural networks:

• Do not share confidential information with chatbots;
• Download neural network applications and services from reliable sources;
• Verify the information provided by the chatbot.

Moreover, the main recommendation when working with public neural networks is not to assume that your dialogue with it is private. It's better to avoid a situation where the questions asked contain any private information about you or your company. The exception is if you are working with an isolated instance of a neural network, located in your environment and for which your company is responsible.

Also, carefully check the services through which you interact with the neural network. An unknown Telegram channel promising free work with all known LLM models definitely should not be trusted.

Companies, whose employees use neural networks at the workplace, should be especially cautious. The interest of malicious actors in corporate data is higher, and they hunt for sensitive organizational information first and foremost.

The best way to protect against cyber threats is to implement ongoing cybersecurity and AI training for employees. This is an important component of any work process, especially in Russia, where there is a shortage of qualified cybersecurity specialists. Only 3.5% fully meet the current requirements for a worker in this field. Through training, it is possible to improve specialists' skills and, consequently, reduce the number of attacks by more than 70%.

Additional measures should also be taken to enhance the overall IT security of the company. First of all, it is necessary to develop improved AI training algorithms considering its potential vulnerabilities, which will make the model more reliable by 87%. It is also necessary to "train" the neural network: to allow it to cope with artificially created cyber attacks to improve the algorithm's performance. This will help reduce the number of hacks by 84%. Moreover, it is necessary to constantly update the software to reduce the number of vulnerabilities by more than 90%.

Conclusion

Both businesses and ordinary users have already managed to "taste" the benefits of neural networks. In a large number of areas, they help solve everyday tasks and save effort and money. For example, generative neural networks have significantly affected the cost of producing movies, TV series, and other videos where graphics and processing are needed. At the same time, roughly the same neural networks have caused a wave of deep fakes, such as the new variation of the Fake Boss attack. 

Every user must understand that the neural network is vulnerable. Just like a messenger, mailbox, or work task planner – it can be hacked or subject to other failures, so it is important to approach working with it consciously.

In the current digital landscape, where we frequently engage in conversations without visual context, our reliance on audio cues to verify the identity of our conversational partners has intensified. Our brains have developed an astonishing ability to discern and recognize the intricate details in someone’s voice, akin to an auditory signature that is unique to each individual. These vocal signatures, composed of elements such as pitch, pace, timbre, and tone, are so distinctive that we can often identify a familiar voice with just a few spoken words. This remarkable auditory acuity serves us well, but it is under threat by the advent of advanced technologies capable of simulating human voices with high accuracy—voice deep fakes.

What are deep fakes? 

The term 'deepfake' has quickly become synonymous with the darker potential of AI. It signifies a new era where artificial intelligence can manipulate reality with precision. Early deepfakes had their tells, but as the technology has progressed, the fakes have become almost indistinguishable from the real thing. 

The entertainment industry's experimentation with deep fakes, such as the lifelike replicas of celebrities in a TV show, serves as a double-edged sword. It showcases the potential for creative innovation but also hints at the perils of AI in the wrong hands, where the distinction between truth and fiction becomes perilously thin.

The creation of voice deep fakes is rooted in complex AI systems, particularly autoencoders, which can capture and replicate the subtleties of human speech. These systems don't just clone voices; they analyze and reproduce the emotional inflections and specific intonations that make each voice unique.

The implications are vast and varied, from actors giving performances in multiple languages without losing their signature vocal emotion, to hyper-personalized virtual assistants. Yet, the same technology also opens avenues for convincing frauds, making it harder to trust the unseen speaker.

The dangers of convincing voice deep fakes

Crafting a voice deepface is a sophisticated endeavor. It involves a series of complex steps, starting with the collection of voice data to feed into AI models. Open-source platforms have democratized access to this technology, but creating a voice deep fake that can pass for the real thing involves not just the right software but also an expert understanding of sound engineering, language nuances, and the intricate details that make each voice distinctive. This process is not for the faint-hearted; it is a meticulous blend of science and art.

The misuse of deepfake technology has already reared its head in various scams, evidencing its potential for harm. Fraudsters have leveraged these fake voices to imitate CEOs for corporate espionage, mimic government officials to spread disinformation, and even duplicate voices of family members in distress as part of elaborate phishing scams. These incidents are not simply one-off events but indicative of a troubling trend that capitalizes on the inherent trust we place in familiar voices, turning it against us.

The path that deepfake technology is on raises profound questions about the future of trust and authenticity. Currently, the most advanced tools for creating deep fakes are closely held by technology companies and are used under strict conditions. But as the technology becomes more accessible, the ability to create deep fakes could fall into the hands of the masses, leading to widespread implications. This potential democratization of deepfake tools could be a boon for creativity and individual expression but also poses a significant threat in terms of misinformation, privacy, and security.

The defense against deep fakes: a multifaceted approach

To tackle the challenge of deep fakes, a robust and varied approach is essential. Researchers are developing sophisticated detection algorithms that can spot signs of audio manipulation that are imperceptible to the human ear. Legal experts are exploring regulatory measures to prevent misuse. And educational initiatives are aiming to make the general public more aware of deep fakes, teaching them to critically evaluate the media they consume. The effectiveness of these measures will depend on their adaptability and continued evolution alongside deepfake technology.

Awareness is a powerful tool against deception. By educating the public on the existence and methods behind deep fakes, individuals can be more vigilant and less susceptible to manipulation. Understanding how deep fakes are made, recognizing their potential use in media, and knowing the signs to look out for can all contribute to a society that is better equipped to challenge the authenticity of suspicious content. This education is vital in an era where audio and visual content can no longer be taken at face value.

Navigating the ethical landscape of deepfake technology is critical. The potential benefits for creative industries, accessibility, and personalized media are immense. Yet, without a strong ethical framework, the negative implications could be far-reaching. Establishing guidelines and best practices for the responsible use of deepfakes is imperative to prevent harm and to ensure that innovation does not come at the cost of truth and trust.

Conclusion

As voice deep fakes become more advanced, they pose a significant challenge to the trust we place in our auditory perceptions. Ensuring the integrity of our digital communications requires not just caution but a comprehensive strategy to navigate this new terrain. We must foster a society that is equipped to recognize and combat these audio illusions—a society that is as critical and discerning of what it hears as it is of what it sees. It is a complex task, but one that is essential to preserving the fabric of trust that binds our digital and real-world interactions together.

The marvels of modern computing are, in part, thanks to advances in artificial intelligence. Specific breakthroughs in large language models, such as OpenAI's GPT-4 and Google's BERT, have transformed our understanding of data processing and manipulation. These sophisticated models masterfully convert input data—whether it be text, numbers, or more—into a form that machines can understand. This intricate process, known as data encoding, serves as the foundation for these models to comprehend and generate human-like text. Let's delve deeper into the intricacies of data encoding and how it powers the magic of AI language models.

The secret code of machines

The beginning of the journey involves comprehending how GPT-4 or BERT processes sentences typed into them. Contrary to human processing capabilities, these models can't directly interpret words. Instead, they employ something known as word embeddings. This complex yet efficient technique transforms each word into a unique mathematical form—akin to a secret code decipherable only by machines. Each encoding is meticulously performed to ensure that semantically similar words receive comparable codes. The aim is to create a rich, multidimensional landscape where each word's meaning is determined by its location relative to other words.

The role of positional encoding in context understanding

While individual words carry their importance, the structure of language extends beyond isolated entities. The sequence of words, the context, can drastically alter the meaning of a sentence. To illustrate, consider the phrases "Dog bites man" and "Man bites dog." The same words are used, but their arrangement creates entirely different narratives. That's where positional encoding enters the picture. By assigning each word an additional code indicating its position in the sentence, positional encoding provides models with a vital understanding of language structure and syntax.

The attention process: making words context-aware

After word and positional encoding, these mathematical representations, or word embeddings, undergo an 'attention' mechanism. Here, each word embarks on a figurative group discussion with all the other words in the sentence. During this interaction, each word decides the importance it should attribute to the others. For instance, in the sentence "Jane, who just moved here, loves the city," the word "Jane" would assign significant attention to "loves."

These 'attention' weights are then used to compute a new representation for each word that is acutely aware of its context within the sentence. This batch of context-aware embeddings journeys through multiple layers within the model, each designed to refine the model's understanding of the sentence. This systematic processing prepares the model to generate responses or predictions that accurately reflect the intended meaning of the sentence.

GPT-4: writing text one word at a time

GPT-4 has adopted a unique approach when it comes to generating text. It operates on a "one word at a time" principle. Beginning with an input, it predicts the next word based on the preceding context. This predicted word is then included in the context for predicting the following word, and the process repeats. This strategy allows GPT-4 to produce text that is not just grammatically coherent, but also semantically relevant, mirroring the way humans write one sentence after another.

BERT: a 360-degree view of sentence context

BERT, on the other hand, possesses a distinct capability that sets it apart from other models. It can process and understand text in both directions simultaneously. BERT does not limit itself to considering words before or after a given word. Instead, it absorbs the entire context at once, effectively offering a 360-degree view of the sentence. This bidirectional understanding enables BERT to comprehend the meaning of words based on their complete context, significantly enhancing the model's ability to interpret and generate nuanced responses.

The versatility of data encoding

While language forms a significant chunk of these models' use cases, they aren't confined to it. An exciting feature of models like GPT-4 and BERT is their ability to work with any kind of sequential data. This characteristic opens up a universe of possibilities for diverse fields, from composing harmonic music to decoding complex genetic sequences, predicting stock market trends, or even simulating game strategies. By analyzing patterns in the sequential data, these models can unearth hidden insights and produce creative outcomes, making them an invaluable asset in numerous areas beyond language processing.

Expanding horizons: applications and future prospects

The wonders of data encoding do not stop with text generation. In fact, the potential applications of these AI models are continually expanding. They can be used to aid human decision-making in complex scenarios, such as medical diagnosis or legal analysis, by digesting massive amounts of textual data and making informed suggestions. In the field of research, they can help summarize lengthy academic papers or generate new hypotheses based on existing literature. The entertainment industry isn't left out either, as these models can create engaging content, ranging from writing captivating stories to generating dialogues for video games.

Moreover, GPT-4 and BERT's remarkable abilities to understand and manipulate language are catalyzing research into other AI models. Researchers are exploring ways to combine the strengths of various models and reduce their limitations, which promises an even more exciting future for AI.

Conclusion

In conclusion, data encoding in AI models like GPT-4 and BERT can be likened to watching a symphony of processes working in perfect harmony. From word embeddings and positional encoding to attention mechanisms, these models leverage a series of intricate techniques to decode the hidden patterns in data, transforming it into meaningful information. The incredible capability of these models to understand context, generate human-like text, and adapt to diverse data types is revolutionizing the field of artificial intelligence, paving the way for a future brimming with AI innovations.