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Generative AI, large language models (LLMs), and chatbots are standalone technologies and a network of interconnected tools reshaping the AI landscape. Their collective impact on the modern-day Tech Industry is significant and rapidly evolving. This article delves deep into these technologies’ technical underpinnings, applications, and limitations.
Generative AI is a subset of artificial intelligence algorithms that can generate new, previously unseen data based on the patterns learned from training data. The primary techniques used in generative AI include:
GANs consist of two simultaneously trained neural networks, a Generator and a Discriminator. The discriminator evaluates the authenticity of the synthetic data generated by the generator. This adversarial training helps the generator produce increasingly realistic data and makes our model more robust.
VAEs are probabilistic models that rebuild the input by first encoding the input data into a latent space and then decoding it again. By sampling from the latent space, VAEs can produce new data similar to the training data.
These models, such as the GPT (Generative Pre-trained Transformer), produce data consecutively. They predict the next element in a sequence using past elements, making them very apt for text production.
Below are some applications of Generative AI:
Generative AI can be beneficial in various situations, as illustrated in the picture below:
Image Credit – Please refer to the Reference section.
Despite its potential, generative AI has several limitations:
Deep learning models that can comprehend and produce human language are known as large language models or LLMs. Transformer architectures, which use self-attention processes to analyze and produce text, are often used in their construction. Essential traits of LLMs consist of:
LLMs, such as GPT-3 and BERT, are characterized by their large number of parameters (often billions) and extensive training datasets (spanning terabytes of text data). This scale enables them to capture various linguistic patterns and knowledge.
LLMs are usually pre-trained on massive text corpora in an unsupervised manner and then fine-tuned on specific tasks using smaller, task-specific datasets, resulting in a broader range of use cases.
LLMs can leverage self-attention mechanisms to capture long-range dependencies and contextual information, generating coherent and contextually relevant text.
LLMs have numerous applications, including:
LLMs also face several challenges:
Chatbots are AI systems designed to engage in authentic, humanized conversations with users. There are two main types:
Critical components of AI-driven chatbots include:
Chatbots are widely used across various industries, such as:
Here is an overall statistic, with the sources listed by the side, on the jaw-dropping impact that chatbots have had on the market.
Despite their widespread adoption, chatbots have several limitations:
A simple statistic (check the snapshot below) from 2018 is enough to describe this condition.
Significant advances in artificial intelligence may be seen in generative AI, large language models, and chatbots, each having distinct technical underpinnings and a broad range of applications. Chatbots offer interactive conversational experiences, generative AI is great at producing fresh content, and LLMs are better at generating and interpreting natural language.
However, these technologies do have certain drawbacks. To fully realize their potential, problems with bias, interpretability, resource consumption, ethical issues, and quality control must be resolved. We must take a balanced approach that considers responsibility and creativity as we create and improve these technologies. By doing this, we can ensure that AI upholds ethical principles and serves the greater good, all while unlocking its transformative potential.
