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Many eagerly await the day when genuinely advanced artificial intelligence (AI) becomes a reality, and we're seeing more and more discussion of AI and another related technology, Bots. Understanding key distinctions between the two can help legal professionals make the best use of each. Both are important and useful in different ways, and this article is an introduction to their capabilities and applications.

AI and Bots both rely on computer programming to carry out tasks and interact with users. They also work toward similar goals, such as automating processes or improving efficiency. In some cases, Bots and AI work together. For example, a chatbot might utilize machine learning algorithms to understand user queries and improve its responses over time. 

While they are different technologies, there are many ways Bots and AI complement one another. AI is designed to learn, evolve, and improve using algorithms and neural networks to analyze data and make decisions. Neural networks are systems where weighted connections between data nodes are refined to produce increasingly accurate results, such as pattern recognition or problem-solving. Because of its ability to make sense of vast quantities of data, AI can discover patterns and uncover connections in data that would be impossible for unassisted humans. Conversely, bots are typically rule-based and task-oriented, often relying on human programming rather than self-learning. In legal tech, this can play out in several ways. For example, a law firm might use AI to analyze massive quantities of legal documents to extract and classify key information. On the other hand, a Bot might be used to automate routine tasks like data entry, scheduling appointments, or managing client inquiries. 

When evaluating legal tech products touting AI capabilities, there are some key questions you can ask vendors to determine if a product is leveraging AI or utilizing Bots. 

1. How does AI drive specific functionalities or capabilities of your product?

A vendor should be able to give a detailed answer that outlines how AI is integral to their tool. They might, for example, explain how machine learning algorithms extract and classify information automatically from large volumes of documents or how AI enables the tool to continuously improve its interactions with users over time.

2. What type of machine learning algorithms and neural networks are used in your product?

AI developers in the legal space are experimenting with various architectures, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). The specific type of neural network used often depends on the task at hand and the availability and type of data available to train the AI model. There is also common usage of natural language processing (NLP) in conjunction with the previously mentioned neural network architectures. NLP is all about enabling computers to understand and interact with language, whether written, spoken, or otherwise. This is a crucial element of AI tools in the legal space as it allows for extracting information from documents or understanding the intent behind a question or request.

3. Can you describe how your AI tool can analyze and make sense of large volumes of data?

Typically an AI tool will break down the data into smaller chunks to analyze it more effectively. Applying machine learning techniques and neural network architectures
enables AI to 'learn' from the data, gradually becoming better at identifying patterns and extracting insights.

4. How has your AI tool evolved or improved over time, and how do you plan to continue refining it? 

Most AI tools are constantly being trained and are evolving. Vendors can fine-tune the tool to become more accurate, efficient, and insightful on various tasks as new data sets become available. 

5. How can you prove that your AI is real and will work on our data?

Some vendors are more careful about how they describe the capabilities of their AI than others. This discrepancy has created concerns about some AI not being "real" or easily applied to an organization's data set. Kevin Cohn, Chief Customer Officer at Brightflag, strongly recommends requiring potential vendors to conduct proofs of concept to validate their AI's efficacy. "Because machines do the work of AI, rather than humans," Cohn says, "it should be self-evident through the timeline and output quality whether the AI is real and effective."

It is always beneficial to ask for specific case studies or success stories that demonstrate a product's AI capabilities in action. By asking these questions, legal professionals can better understand a given product's capabilities and limitations and evaluate its use. 

Pablo Arredondo, Co-Founder and Chief Innovation Officer at Casetext, believes these technologies will be a game changer, particularly for legal. 

Arredondo says, "In my opinion, the most important thing happening in legal tech right now is the accelerating progress of large language models generated by neural networks. This technology is poised to have a substantial impact across every aspect of legal practice."

Understanding the key differences between AI and Bots is crucial for making informed decisions about which technology matches your specific business goals and how to leverage their cumulative advantages in the future. This knowledge helps you leverage these powerful tools with higher confidence and lower risk, allowing you to reap their benefits while avoiding potential pitfalls. 

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The information in any resource collected in this virtual library should not be construed as legal advice or legal opinion on specific facts and should not be considered representative of the views of its authors, its sponsors, and/or ACC. These resources are not intended as a definitive statement on the subject addressed. Rather, they are intended to serve as a tool providing practical advice and references for the busy in-house practitioner and other readers.