Tech On Trial

In the ever-evolving world of technology, ethics play a pivotal role in shaping the future. Recently, an intriguing development caught our attention – Canada's AI Code of Conduct. In this article, we'll dissect the key points discussed in our latest podcast episode, shedding light on the significance of these regulations for the field of artificial intelligence.

In the ever-evolving landscape of technology, artificial intelligence (AI) stands as a beacon of promise, holding the potential to transform industries and redefine our daily lives. In this article, we delve into two thought-provoking pieces that shed light on distinct aspects of the AI realm. The first discusses the challenges startups face in realizing the AI dream, while the second provides an early view of the future of generative AI through compelling charts.

In the ever-evolving landscape of technology, businesses are navigating uncharted territories, seeking innovation and efficiency. A recent McKinsey & Company article sheds light on the transformative potential of Generative AI, emphasizing its role as a catalyst for innovation in the corporate realm. This technological marvel goes beyond automation, providing businesses with a creative force capable of generating ideas, designs, and even human-like text.

In the fast-paced world of technology, one sector that has often been viewed as traditional and resistant to change is the legal industry. However, in recent years, this sector has witnessed a transformation powered by Natural Language Processing (NLP), a branch of artificial intelligence focused on enabling computers to understand and generate human language. This innovation is revolutionizing the legal landscape, streamlining processes, and opening up new possibilities for the legal profession.

Natural Language Processing (NLP) is not just a buzzword but a revolutionary technology. This article serves as a beginner's guide, delving into the world of NLP, its significance, and its transformative potential.

Generative AI has captured the imagination of many, promising to be a technological marvel that can mimic human creativity, generating text, art, and even music. But beneath the surface of this digital wizardry lies a shadow—a pressing ethical conundrum. In this article, we'll delve into the depths of Generative AI and explore the potential dangers, misuse, and the critical importance of ethical guidelines.

In the realm of artificial intelligence, Generative AI has emerged as a transformative force, pushing the boundaries of what machines can create. Over the past decade, this field has witnessed remarkable advancements, thanks to the collective efforts of AI researchers, engineers, and data scientists. To demystify the intricate world of Generative AI and explore its boundless potential, we turn to the insights and perspectives of leading experts in the field.

Generative AI has emerged as a groundbreaking force in the realm of technology, enabling machines to craft content, replicate human cognitive processes, and even produce awe-inspiring works of art. As we find ourselves on the cusp of a new era, the future of generative AI promises nothing short of spectacular. This episode delves into the visions of prominent researchers to provide a glimpse of where Generative AI is heading and the ethical considerations that must accompany its progress.

Welcome to "Tech on Trial," your ultimate source for unraveling the dynamic interplay between technology and the legal realm. Join us as we explore the revolutionary impact of Artificial Intelligence (AI) and Natural Language Processing (NLP) on legal workflows. In this exciting episode, we're diving headfirst into a transformation that's reshaping the way legal professionals work, paving the way for enhanced efficiency, accuracy, and strategic decision-making.

In today's digital age, where technology is reshaping industries at an unprecedented pace, the legal profession stands at the threshold of a transformational journey. At the heart of this evolution lies the realm of Natural Language Processing (NLP), an innovative branch of artificial intelligence that bridges the gap between human language and computer understanding. In this episode, we embark on an exploratory voyage into the world of NLP, uncovering its profound implications for legal professionals and the intricate tapestry of the law.

In the dynamic landscape of technology, the legal sector is experiencing a groundbreaking transformation thanks to AI-powered solutions. In this episode of Tech on Trial, we delve into the revolution taking place in the realm of legal document search and retrieval. Joined by Victor, the visionary CEO of Apogee Suite, we explore how AI is reshaping the way legal professionals navigate the complex world of documents.

In a world where technology continually reshapes industries, the legal field is no exception. Welcome to the riveting realm of "Tech on Trial," a podcast that delves into the convergence of law and technology, unraveling the innovations that are propelling the legal profession into uncharted territories.

Welcome to "Tech on Trial," the podcast where we explore the dynamic intersection of technology and the legal world! I'm your host, Sal, and today's episode promises to be a fascinating deep dive into the realm of legal tech. In this blog article, we'll uncover the transformative impact of Artificial Intelligence (AI) and Natural Language Processing (NLP) on document creation in the legal industry.

In this fast-paced digital age, the legal landscape demands innovation and efficiency, and that's where NLU comes in. NLU is like a wizard of language processing, enabling machines to comprehend human language nuances contextually, going beyond simple word matching. It's a game-changer for legal research, e-discovery, contract analysis, predictive analytics, and beyond!

The legal landscape is undergoing an incredible metamorphosis, catalyzed by the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. These cutting-edge innovations are fundamentally changing how legal professionals conduct research, assess contracts, and draft legal documents.

In the fast-paced world of legal practice, managing vast volumes of documents efficiently can be a daunting challenge. Lawyers and legal professionals often find themselves buried under piles of paperwork, searching for the proverbial needle in a haystack. However, a groundbreaking solution has emerged - AI-driven document classification - poised to transform the legal landscape.

In our latest episode of "Tech on Trial," we explored the fascinating world of Natural Language Processing (NLP) and its revolutionary impact on e-discovery processes for law firms. NLP is not just a buzzword; it's a game-changer that empowers legal professionals to navigate vast volumes of electronic data with unprecedented efficiency and accuracy.

Welcome to the "Tech on Trial" blog, where we explore the latest tech trends and innovations that are reshaping the world around us. In this episode, we delve into the incredible potential of AI-powered natural language generation (NLG) in document creation.

Welcome to Tech on Trial,the podcast where we put cutting-edge technologies to the test. In this episode, your host, Sal, is joined by the brilliant CEO of Apogee Suite, Victor, to explore the fascinating world of natural language understanding (NLU) in contract management systems. The podcast aims to delve into the latest advancements in the tech industry, and today, they're focused on how NLU is revolutionizing the way businesses handle their contracts.

Welcome to another episode of Tech On Trial!

Today we will discover how AI and NLU technologies enhance legal research and analysis, enabling smarter decision-making.

Stay tunned!

Welcome to Tech On Trial! Your new podcast focused on how Technology can be used in the Legal Industry.

In this first episode, we are covering how you can use NLP in the Legal Industry, especially when it comes to document analysis!

In today's episode of All Things Data, we delve into the world of contract management and how it can significantly impact businesses. Contract management, with the help of automation, artificial intelligence (AI), and natural language processing (NLP), streamlines workflows and enhances productivity. By optimizing contract processes, businesses can achieve cost savings, and revenue generation, and minimize legal liability and risk. In this blog article, we explore the key benefits of contract management automation and highlight the role of Zenith, an innovative contract management suite.

Today, we focus on the role of AI and Natural Language Processing (NLP) in fraud detection. Fraud detection involves building a cyclical feedback loop to continuously improve detection methods over time. When it comes to content that can be analyzed using NLP, the first step is understanding the text. By mining the data and developing a fraud dictionary based on patterns and keywords, potential signals for fraud can be identified. This initial understanding is then used to create models and evaluate their performance.

Today, we'll delve into the realm of legal and judicial AI and explore the possibilities of developing an AI program capable of predicting legal decisions and case outcomes. This field presents a ripe opportunity for automation, as it involves a common and repeatable process that legal professionals undertake when handling cases.

In the realm of artificial intelligence, Natural Language Understanding (NLU) has emerged as a crucial component in various applications, transforming the way we interact with machines. One fascinating area where NLU is making significant strides is in Document AI. By enabling machines to comprehend and extract meaningful insights from unstructured text documents, NLU in Document AI is revolutionizing information processing, data analysis, and decision-making. In this episode, we'll explore the transformative potential of NLU in Document AI and delve into its practical applications.

Discover how AI and NLP technologies are revolutionizing supply chain management in our latest blog article, "Streamlining Supply Chain Processes: The Role of AI and NLP." Dive into the world of artificial intelligence and natural language processing and explore their pivotal role in optimizing supply chain operations.

In the realm of legal artificial intelligence (AI), understanding and extracting relevant clauses from legal documents are essential tasks. However, the real challenge lies in comprehending the meaning and context of these clauses.

In the realm of Natural Language Understanding (NLU) and Natural Language Processing (NLP), contextual analysis plays a vital role in deciphering the meaning and intention behind a piece of text. Without context, words and sentences can be subject to multiple interpretations, leading to ambiguity. In this article, we will delve into the significance of context in NLP and explore how it is analyzed to enhance text understanding.

Legal documents can be complicated and time-consuming to process, but with the advancement of Natural Language Processing (NLP) and Artificial Intelligence (AI), companies have been able to work with them more efficiently. In this episode, we will delve into how legal document processing works using NLP and AI.

We'll explore how NLP can revolutionize businesses by improving employee efficiency and enhancing the commercial side. Specifically, we'll focus on the main uses of NLP in large businesses.

RPA, AI, and NLP are three technologies that can work together to automate repetitive tasks and create a more efficient system. In this podcast, we will provide an overview of these technologies and discuss how they can be combined to benefit organizations.

Document AI is a product suite offered by 1000ML that enables businesses to extract valuable insights from their documents using natural language processing (NLP) techniques. Unlike traditional document processing systems, Document AI employs semantics and context to understand the desired output, rather than being limited by pre-existing rules.

Today we will be discussing the lifecycle of AI projects and how they are undertaken, both internally and externally. The focus will be on why certain internal AI projects fail to meet the expected outcomes, and how organizations can avoid falling into these traps.

One of the biggest factors affecting the success of internal AI projects is the quality of data. Gathering complete data, including metadata, is crucial to developing accurate AI models.

In today’s episode, we will talk about NLP and its use in business from an executive's point of view. NLP is explained as the process of extracting data from language to be used by computers and AI programs to create decisions or predictions, and the process involves extracting content from documents, understanding the meaning of the content, analyzing the content, and making a decision.

By integrating AI and NLP with ERP, organizations can achieve significant benefits such as reducing manual processes, improving accuracy, making better decisions, increasing efficiency, productivity, and cost savings, identifying new growth opportunities, minimizing risk, and improving their bottom line. As unstructured data grows, AI and NLP will become increasingly important for organizations that want to remain competitive.

Extracting clauses from legal documents is a complex task that requires an understanding of the legal domain and its technical language. Machine learning and natural language processing (NLP) programs are used to understand the meaning of clauses and classify them. The most commonly used language model in the legal domain is the FILAC model, but it has limitations and does not help with crafting arguments or understanding more about the case. To overcome these limitations, the team at 1000ML developed a better classification system.

Advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) have made it possible for companies to automate the process of analyzing legal documents, which are often lengthy and complex. The process involves document ingestion, where the algorithms can extract entities, understand the structure and formatting of the document, and summarize the document to provide an overview of its contents and classify it into topics such as lawsuits or rental disputes.

Today we will talk about AI in Legal Organizations when it comes to classification and partitioning clauses. By organizing your documents you can interact with them in a way that you have a full knowledge of the context and information that a document has.

Today we will talk about AI, NLP & RPA interests with each other. There are several advantages of combining all these technologies into a system in order to create the perfect AI environment and pipeline.

Today we will talk about the RPA can optimize processes in legal operations. In the legal world, being a document-search based there is room for optimization and automation, and here is where RPA enters. These systems help you reduce cost and time, without substituting other systems you might have. 

Join us as we explore the power of ChatGPT and its impact on document analysis in the Apogee Suite. Our expert guests will delve into the different models of ChatGPT and how they can enhance creativity and language understanding. Discover how ChatGPT can help you ask the right questions for your document analysis process and why context is so critical in this field. Tune in to our podcast for a deep dive into the world of ChatGPT and document analysis.

Welcome to our latest episode of the podcast where we'll be delving into the world of NLU in Document AI.

We'll start by discussing Optical Character Recognition (OCR) and how it differs from Natural Language Understanding (NLU). OCR allows for the transformation of non-digital formats into digital ones, but it does not provide understanding of the content. NLU, on the other hand, goes beyond OCR by providing a readable version for the computer, allowing it to understand any kind of document.

Traditionally, understanding documents was a rule-based process that relied on the word order in a phrase. However, this system lacked semantic understanding and was not portable, requiring the building of new systems for each industry, resulting in longer and less accurate processes.

At 1000ml, we've worked with a variety of industries to adapt Contract AI, a field of Document AI, to their specific needs. For example, in the pharmaceutical industry, we built a pipeline for categorizing invoices and contracts according to the medicine they bought. In the legal industry, we worked on a system that allowed NLU to focus on patent documentation. In the financial and insurance industries, our work centered around form information extraction, resulting in faster and more accurate processes. And in healthcare, we combined different information about a patient in one place.

All paper-driven industries can benefit from Document AI, especially when NLU is involved. Join us next week as we continue to explore the exciting world of NLU in Document AI.

We started the year talking about NLP, and to continue it’s mandatory to talk about context, the contextual analysis of a text. Contextual analysis is understanding where a sentence is coming from.

When we think about how computers understand human language, we have to understand that it's a process and not an easy one also each language has a sense and syntax model that defines each one. So in order to process and understand that information, computers need signals to guide the process and to associate the language to action themselves.

 We're at the tail end of the year now, and we're just talking about what 2023 may hold for 1000ml and generally in the world of NLP.

So 1000ml has a suite of NLP and AI products that we called Apogee. The suite is comprised of just general text ingestion that can be documents, which is usually what people go for but also allows you to ingest any website or webpage basically whatever you want that has text in it, including.

The crux of it is that Apogee suite builds up a series of pipelines and APIs including a recommendation engine and relevancy engine API that allows all the tools that we build on top of it to utilize these engines to properly search for content. So you might think of things like, okay, well, I go online and I do a Google search and I'm looking for a flight or something. 

Imagine all kinds of descriptions, text messages, and emails that you send internally, giving you the ability to ask content questions. So imagine a use case like you're in e-commerce and you're looking for the right type of pants. Somebody's like, I need black pants, you can go all the way from like athleisure to like super serious, like tuxedo pants if you're a man.  So are you just gonna surface everything along the lines and how do you know exactly the kind of things that they're looking for?

ADA sort of allows you to ask your database, your text, anything. The curated data searching that we've done prior with just our semantic search is now getting a big boost, allowing us the ability to really ask the document things, so not just metadata things like who's the author, which basically any system could do.

When will 1000ml tackle as ambitious Witness Prep AI, come out of the world of us doing a lot of work in legal and judicial and deeply understanding the paradigms that exist there with the advent of us creating semantic search and then layering on ADAs so you can ask the content anything and you could read any content. So again, Witness Prep AI is all about ingesting a lot of things about a specific case or a specific area of law or kind of context that may occur. 

It's largely going to be an exercise for the enterprise world until we really understand how to maximize its value and use and then can reduce it to a point where it's then manageable for people who want it as an add-on into our main suite, whether that's for our regular B2C clients or for clients and customers. 

So that's a bit of a view into 2023 and what 1000ml is gonna be working on. It's still all about NLP and AI and mostly focused on Apogee Suite. It's our big framework and it's what we sell the most of.

2022 has been a fun year in AI and NLP, and today we thought we'd take a minute and reflect back on all the things that have occurred. Some of the important things that have occurred in the world of NLP and a lot of the generative world of NLP in 2022.

GPT has been a big deal in NLP, Generative, Pre-trained Transformer is a generation technology to help us generate usually text content and that can really be like all kinds of things. If you go look on YouTube and you kind of like dabble around with GPT-3 OpenAI, you'll see examples of all kinds all the way from like, give me a tweet basically, you know, like generate a tweet from you, which is kind of interest.

So for a machine to generate and do it well and in within the context of what you want is actually really cool and really good. So this year, obviously there's been advances in GPT-3 they did allow for the editing and insertion of data, like human data back into the reports. So if you were to ask GPT to specifically generate something for you,  could actually embed new information in there to make it slightly better is a big deal.

Another thing that they've done since they noticed in the past that was happening is they've added a lot of plagiarism detection into their generation. They've done that with slightly better word parsing and quite a bit of synonym generation so that the content will be as unique as possible and avoid plagiarism.

Another really big thing done this year was to allow for the monitoring or ongoing review of data repositories that allow GPT to constantly scan and add new content as it becomes available and then make GPT's engine more relevant to your search, as well.

Tune in next week we're gonna talk a little bit about our roadmap for 2023 and what we see could happen in NLP next year.

Happy 2023 everyone!

We started to wrap up the year a little bit talking about the considerations that you have to think of when you're deciding whether to build or buy AI. That really feeds in well and quite nicely into today's topic where we're gonna talk more about the technical knowledge required for AI projects. So this is assuming you went through the framework of deciding whether to build or buy, and you were like, you know what? We can build this, let's do this ourselves.

So now that you're in the world of let's build this because you've decided that we have the staffing resources, and we're able to get consultants who could do this,  having enough know-how and enough people with the breadth of knowledge required for this.  Also, we've worked with or have the infrastructure available for this.

Generally, the organization is in a good shape, or our change management processes are quite good,  understanding how to make sure that this blends into our organization well having in mind the total cost of ownership of such a project. 

For a very long time anybody who's done a lot of work in NLP has chosen to usually start their journey with the package NLTK is largely like the go-to and it is the basis, it's the foundation that provides quite a bit of functionality, but some of the things that are critical for you to know if you're going to do serious NLP you need to know stemming.

You need to split the content either into phrases because you may want to analyze phrases or sentences, or split it into paragraphs, but usually into words in order to understand and know how to use a part of speech triggers bringing you endless opportunities.

For example, there's the possibility of doing a strictly extractive summary where you're going to pull things out of a document. Imagine an entire thesis,  there is an abstract, and using an NLP on that whole thesis, you'd probably just pull that abstract becoming your summary. 

.So you want to make an abstract, so you're largely looking to take the knowledge and understand it and then abstract the knowledge of that document. So you're really summarizing as opposed to pulling specific parts of the content out.

Again, high risk for it cause we're really good at this stuff. But you could also use machine learning and AI as a sort of input to NLP. You'd find that there are many clusters when you're doing this that can then help lead you to create an AI program based on those clusters.

Lastly, if you wanted to use the NLP as an input to different programs, you could do that for things like the things we do, which we do a lot of work in document intelligence, in contract AI.

Today we're gonna talk about whether to buy or build AI systems. 

As opposed to the shorter write-ups that we usually have, you'll probably get a lot of benefit from rating it and even using it as notes when you are making the decision on whether to build or buy a new system generally, not just AI.

Like building or buying most technology systems or new systems for our company you have to start thinking of the total cost of ownership. That's a big deal for people for companies actually and a lot is wrapped up in that. At the end of the day though the biggest hurdle that most companies have when deciding whether or not to build or buy a system is that there needs to be a stakeholder who is going to really be responsible for it.

So generally in a buying scenario, and you see this a lot for big organizations and government entities the smart play is to understand all the features that your company needs. It could be a big system or a little system, you should come up with a matrix of sorts or a checklist. You want really good and clean user interfaces and user experience, an easy example is Google.

You also want to think of the ability to change the data model or AI model, and whether that's baked into the app and you know, rigid or whether you can actually affect those models that could and should be important to you.

Think that technology does 85% of what you want, and you're okay with that, but you can create a roadmap to get to closer to a hundred percent down the future, not costing anything. Finally, you want to think of ecosystems, like communities,  the number of partners they have all the users, are people writing about that. Are there examples and forms and stack overflow in all these places do they have a large list of clients and do they provide training? Is there self-led training that they provide? Do they come in the house and do some training? So there's all of that for thinking of future proof in your purchase. 

We've been talking a lot about our witness prep AI and what's gone into making all of that and today we thought we'd actually culminate into its intended uses and a bit about how we deliver it. 

It's possible to acquire all the data that you need especially about cases and laws and decisions and whatnot you have to build a language model. There's been an academic language model called FILAC and we've extended that. 

So given the ability to now understand legal texts and to model how to get to an outcome, basically to have all of them, I can render a decision, a law an actual case, whatever into a computer, a usable bit of data so that the computer can actually ingest it properly and not just have the unstructured text.

So now that you have this AI model, how do you deliver that to the best? You have to think about what the most natural thing for people is, when you're doing a user interface exercise you don't want to take people to a place where they have to think about the UI while they're also thinking about the context and problem that you're working on.

Because then your brain kind of fractures itself and you're lost of it. So a very common use case for most people just searches for a really good job of search and curation, which is something that we do.

It's all in the end powered by the AI model. But the way to deliver that is a very curated search especially if you're able to do it in a hierarchical view of what you're searching for so that you have, like a meta topic and you get yourself all the way down to specifics that get people the idea.

Most law offices & legal professionals go through common or similar cases,  when they work on a case they look for prior arguments, prior cases, and precedents, allowing an automatization process to be implemented since it makes it searchable and usable information.

They use a sort of search tool and it's generally only a keyword search tool,  since someone with a lot of experience, will know that in each case what to look at, the specific set of keywords that is pretty unique and doesn't usually happen in the rest of them.  Of course, that other hand has some trouble since limiting to only those keywords you could lose relevant other information. 

So when you need to know the exact keywords in order to search for precedent and prior cases. Imagine that there is the need to understand all of these details and information to really build a case up so that it's possible to understand what people have said in the past, what's worked and what hasn't, these being the material facts of the case.

The other thing that you really can't do without actual live debates and trials is to judge the strength of the argument, so this type of judging will obviously going to rely on the entirety of that package. In order to really understand and get to a point where there is confidence in an argument it really benefits everybody to, have sort of a score about it,  so when there is an argument with a high degree of confidence, that there is the conviction that will or power up that case over the other side.

There's an opportunity here to make it more scientific and that's where we shine quite a lot in getting into the AI for legal decisions of courts and obviously also pushing the envelope and innovation in the world of witness readiness and witness prep. 

In order to get to a world where you can build an AI program you're going to have to build up your internal capabilities and knowledge because you're dealing largely with unstructured texts, for example, images and video, those are definitely unstructured, but you can structure them by pre-processing in text. You can do the same, you can turn extreme, extremely large pieces of content, whether they are legal decisions.