We are currently witnessing an AI revolution, with an unprecedented AI arms race among big techs to embed AI in search engine and chatbot functions. Most notably, ChatGPT is dominating the news headlines. ChatGPT is a chatbot developed by OpenAI that can understand human language prompts and continuously generate human-like interactions and content. Generative Artificial Intelligence (“Generative AI”) is a type of artificial intelligence (“AI”) technology that can generate new content of various types, such as text, images, and audio. This means that you can generate new content instead of just spitting existing relevant information out of your database. Generative AI starts with verbal prompts, text sent as part of a chat, or prompts containing images. The AI algorithm then returns new content in response to the prompt.
ChatGPT in general is powered by generative AI, but more specifically ChatGPT is powered by two key technologies.
First, large-scale language models (“LLMs”) that use deep learning to process natural language. Deep learning is based on algorithms that use neural networks to recognize patterns and relationships between datasets. Therefore, LLM can decipher natural human language.[1] and
Then use reinforcement learning from human feedback. This means that the model is trained and fine-tuned based on human feedback.
Aside from ChatGPT, various tools use generative AI, such as DALL-E, Bard, and Harvey. Bard is Google’s response to Microsoft’s incorporation of ChatGPT into its search engine Bing. DALL-E automatically creates images from text descriptions and generates text captions from images. Harvey is another generative AI-powered tool, but tailored specifically as a legal research tool for lawyers. Harvey provides lawyers with a natural language interface where they can verbally describe the task they want to accomplish with simple instructions, and the legal chatbot will generate a response. [2]
potential pitfalls
Generative AI is still in its infancy and, like any new technology, there is a lot of room for improvement. There are concerns that need to be addressed on many fronts. The fact that generative AI applications may provide answers that sound correct and coherent, but are actually wrong. plagiarism; bias propagation; intellectual property infringement issues; and data protection and privacy issues in generative AI applications. To name a few. If we can address these concerns head-on, the full potential of generative AI could be realized.
One of the major risks associated with AI is the potential for data privacy and security breaches. AI systems rely on vast amounts of data to learn and make decisions, and this data often contains sensitive information about individuals and organizations. If this data is not properly protected through robust security protocols, it can be accessed and misused by unauthorized third parties, leading to data breaches. To mitigate this risk, businesses should implement robust data security and privacy protocols, including measures such as conversation encryption, access control implementations, and regular security audits.
Another risk associated with AI is the potential for intellectual property infringement. AI tools, such as generative AI models such as ChatGPT, are developed based on large datasets, including publicly available text data, social media posts, and web pages, to generate new content. This may lead to unintentional infringement of patents, trademarks or copyrights. Organizations should conduct thorough IP clearance research before developing and launching AI-powered products and services. You should also consider consulting an intellectual property professional to ensure that your product does not infringe existing intellectual property rights. Additionally, AI-based tools can make it difficult to determine who owns the rights to content produced. AI-based tools can create content without direct human input, so it may not be clear who is responsible for the output. The use of generative AI systems to create infringing content can result in legal liability for the developers, users, or owners of the systems. The legal landscape for intellectual property infringement by generative AI is still evolving, and it is unclear how courts will handle these types of cases.[3]
Overall, there are many risks and concerns on several fronts related to AI, especially generative AI, that developers, users, and policy makers should consider and work to mitigate. Essential. This includes establishing a government agency dedicated to overseeing and regulating technology, developing legal frameworks to address these concerns, and providing technological protections to prevent these risks from materializing. may include implementing means.
Possibilities of Generative AI
This technology has the potential to disrupt many industries. Generative AI has the ability to write code, design new drugs, develop products, redesign business processes and innovate the supply chain. Investors are enthusiastic about the potential of AI, as evidenced by the exponential growth in AI investment. AI investment will grow 71% year-over-year in 2022, from $1.5 billion to $2.6 billion (BofA research article). Gartner research shows that AI-powered drug discovery and AI software coding are the most funded.[4] While many industries have already incorporated AI into some aspects of their business processes and operations, the use of generative AI and its capabilities is still in its infancy and not fully realized.
legal industry
One industry that can be transformed by generative AI is the legal industry. Innovative tools powered by generative AI raise many questions. Will the legal industry ban or embrace this technology? What would the adaptation look like in practice if it did? Do they have the potential to become obsolete? Today, some companies are outright banning the use of generative AI tools in the workplace due to the aforementioned concerns, while others are fully embracing it and incorporating it into their practices. I have.
AI is already being used in the legal industry by automating standard legal documents and extracting targeted contract clauses to assist attorneys in the M&A due diligence process. But generative AI has the potential to take these uses one step further. Generative AI tools that can create different types of content can be used in many ways to increase efficiency and reduce legal fees. Uses of generative AI in the legal industry range from providing basic overviews of the legal field, assisting in legal research, and creating nuanced agreements from knowledge banks of templates. Harvey is a prime example of a company using the technology behind ChatGPT for legal tasks. It’s currently in beta stage, but we see the potential. For transactional practices that rely heavily on templates, the tool allows transactional personnel to ask a legal chatbot in a conversational manner to verbally state the main business and legal terms and specific terms that practitioners make. It may evolve to be able to create a purchase contract by providing it. I wanna see. Additionally, generative AI may evolve to mark up counter-lawyer agreements based on training tools to identify off-market provisions.
The next question is whether this eliminates the need for an actual lawyer. The answer is no. We still need legally trained individuals to review AI output and make educated decisions. AI can help lawyers become more efficient and automate time-consuming administrative and standard tasks. Of course, the great potential of generative AI will only be realized if the pitfalls and concerns are addressed first, and responsible development of generative AI tools is at the heart of it.
