Generative AI: The Future of Data Creation
Generative AI Tools Ruling the Market
The launch of the infamous ChatGPT in November 2022 took the world by storm. The AI- chatbot was built by OpenAI using GPT-3.5 implementation. It is widely used to fine-tune text responses via a chat interface with interactive feedback. Earlier versions of GPT were only accessible via an API. GPT-4 was released on March 14, 2023. ChatGPT works by incorporating the history of its conversation with a user into its results, simulating a real conversation. Attributing to the incredible popularity of the new GPT interface, Microsoft invested in OpenAI and integrated a version of GPT into its Bing search engine.
Another product launched by OpenAI named Dall-E is trained on a large data set of images and their associated text descriptions to facilitate text image generation. It connects the meaning of words to visual elements. It provides multimodal AI application by identifying connections across multiple media, such as vision, text, and audio. Dall-E 2 an advanced version of Dall-E is capable of generating imagery in multiple styles driven by user prompts.
Likewise, Google entered the generative AI market with its model named Bard. However, it never released a public interface for this model, until Microsoft implemented GPT into Bing. Google made this lightweight version of its LaMDA family of large language models available in March 2023. Where it can code, answer math problems, and write content like other AI chatbots, Google aims to enhance its capabilities to understand YouTube videos.
LeewayHertz is yet another prominent generative AI company that develops AI products focusing on computer vision and natural language processing. It owns a variety of platforms including Zbrain.ai and ChatGPT for various industries such as finance, manufacturing, automotive, hospitality, healthcare, IT, and logistics. These models can generate realistic images, understand and respond to human language, and assist in data analysis. Other major players in the industry include Markovete, NVIDIA, DeepMind, IBM Watson, Adobe, and Salesforce.
Applications of Generative AI
Generative AI models have a wide range of applications and the most fascinating one is the capability to build new models. Models like GPT-3 can be used to generate computer program code. For instance, Microsoft's Github uses a version of GPT-3 for code generation called CoPilot. The new versions of these models can also identify bugs, fix mistakes in their code, and even explain what the code does. These models help programmers to increase their speed effectively. However, they can yet not replace humans completely as the integration of LLM-based code generation into a larger program and the integration of the program into a particular technical environment still require human programming capabilities.
Another major application is seen in marketing. For instance, Jasper a marketing-focused version of GPT-3, can produce blogs, social media posts, web copy, sales emails, ads, and other types of customer-facing content. The outputs are generated using A/B testing and the content is optimized for search engine placement. Jasper's customer base largely comprises individuals and small businesses, however, it is also used by some groups within larger companies. Image generation platforms such as DALL-E 2 and other image generation tools are already being used for advertising by organizations including Heinz, Nestle, Stitch Fix, and Mettel.
When talking about effective content the most important is effective conversations that earn you fruitful results. Thus, LLMs are increasingly being used at the core of conversational AI or chatbots. For instance, Facebook's BlenderBot can carry on long conversations with humans while maintaining context. Other such platforms include Google's BERT and LaMBA. These work by predicting words used in conversation based on past conversations. The major drawback of such systems is that they tend to replicate any racist, sexist, or biased language to which they were exposed in training. Although the companies are working on filtering out hate speech, they have not yet been fully successful.
Another major application of generative AI is seen in the management of information in the form of text, images, or videos to use as a source of knowledge. The labor intensiveness involved in creating structured knowledge bases has made large-scale knowledge management difficult for many large companies. Thus, LLMs are being used by such organizations to effectively manage such information. Additionally, the knowledge within such LLMs could be accessed by questions issued as prompts. For instance, Morgan Stanley is working with OpenAI's GPT-3 to fine-tune training on wealth management content, so that financial advisors can search for existing knowledge within the firm and create tailored content for clients easily.
Concerns Surrounding the Use of Generative AI
Although the newfound capabilities of Generative AI have opened up diverse opportunities such as better movie dubbing and rich educational content, it has also created several legal and ethical issues. Deepfakes that is images and videos that are created by AI and pretend to be realistic but are not, have already arisen in media, entertainment, and politics. OpenAI has attempted to control fake images by watermarking each DALL-E 2 image with a distinctive symbol, however, more control is required for the same. Also, it can lead to concerns about cybersecurity.
Another major concern is data credibility. At times these platforms tend to gather random and irrelevant data from irrelevant or even copyrighted sources resulting in erroneous output. A typical case of the same was witnessed when Google suffered a significant loss in stock price following Bard's debut as the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system. Consequently, Microsoft and ChatGPT implementations also lost face due to inaccurate results and erratic behavior. Google has since unveiled a new version of Bard built on its most advanced LLM, PaLM 2, which allows Bard to be more efficient and visual in its response to user queries. This is also resulting in the fall of the users of ChatGPT.
Generative AI also raises numerous questions about what constitutes original and proprietary content. Since the created text and images are not exactly like any previous content, the providers of these systems argue that they belong to their prompt creators. However, they are derivative of the previous text and images used to train the models. Needless to say, these technologies will provide substantial work for intellectual property attorneys in the coming years.
Is there any Future for Generative AI?
The horizon of Generative AI is continuously expanding, promising a future across various domains. The present advancements in the technology are only the topmost layer of its capability, however, the development of such capabilities would have dramatic and unforeseen implications across industries. Although one of the major concerns of the technology is cybersecurity, however, if used efficiently it can contribute significantly to the strengthening of cybersecurity. This can be done by generating scenarios to test and bolster security systems against a myriad of threats. By simulating cyber-attacks, it helps in identifying vulnerabilities and fortifying security infrastructures.
Generative AI is poised to play a pivotal role in drug discovery, by generating molecular structures and novel compounds with desired properties that could potentially be new drug candidates. It can accelerate the process significantly reducing the time and resources required to bring new treatments to market.
Additionally, climate modeling is another area where generative AI can have a profound impact. By generating simulations of climate scenarios, it can aid in predicting climate change dynamics thus, guiding policy-making and planning, contributing to a more sustainable future.
Moreover, generative AI holds promise in automating and enhancing design processes in fields including architecture and engineering. By generating design proposals based on specified criteria it can foster creativity and efficiency, facilitating the realization of innovative and optimized designs. Furthermore, generative AI can also contribute to the customization of the learning materials to cater to individual needs and preferences, thus personalizing education. It could generate practice problems, essays, or interactive lessons, enhancing the learning experience.
Encapsulating the use of responsibly generative AI can substantially increase labor productivity across the economy. However, as generative AI has been known to produce content that's biased, factually wrong, or illegally scraped from a copyrighted source organizations should reckon with the reputational and legal risks to which they may become exposed. A potential method is keeping the human in the loop to ensure effective surveillance of the outputs generated by generative AI.
As we stand on the cusp of a future intertwined with generative AI, fostering a culture of ethical awareness, continuous learning, and responsible innovation is imperative. It is through such a balanced approach that the promise of Generative AI can be fully realized, ushering in a new epoch of technological advancement and societal betterment.
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