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In today’s episode, we’ll be diving into the fascinating world of one of the most advanced machinelearning tools out there: ChatGPT. We’ll explore the potential impacts of ChatGPT not only on everyday life, but also on the legal industry, education, intellectualproperty law, geopolitics, and more.
Through machinelearning algorithms, e-discovery platforms can quickly identify patterns and connections in data. Additionally, digital forensics has become essential in cases involving digital evidence, such as cybercrimes and intellectualproperty disputes. This assists legal teams in building stronger cases.
This protects the researcher from the AI “creating” the answer from all the non-relevant information it has collected in its large language model of machinelearning. They do AI machinelearning proofs of concepts for governments and large companies and so on. We would love to hear from you. I mean, this is yes.
Next, we plan to expand the product’s scope to cover more aspects of the litigation process, to improve the machinelearning summarization model, and to develop visualizations of evidence based on the data present in the chronology. Finally, we plan to build integrations with e-discovery and practice management products.
And that’s one thing, providing it a change control clause, which is proprietary, valuable intellectualproperty for that organization. We’d love to hear from you. And as always, the music you hear is from Jerry David DeCicca Thank you, Jerry. And in asking it to provide some analysis on that.
And that’s one thing, providing it a change control clause, which is proprietary, valuable intellectualproperty for that organization. We’d love to hear from you. And as always, the music you hear is from Jerry David DeCicca Thank you, Jerry. And in asking it to provide some analysis on that.
Next, we plan to expand the product’s scope to cover more aspects of the litigation process, to improve the machinelearning summarization model, and to develop visualizations of evidence based on the data present in the chronology. Finally, we plan to build integrations with e-discovery and practice management products.
And let alone that there’s a lot of risk in this these datasets, others HR risks, there’s intellectualproperty risks. So you’re probably familiar with that yourself when you’re using this machinelearning. We’d love to hear from you. And then you get the feeling that’s you can control things.
And then after that, I made my way into the world of legal tech, working in data science for a company called RPI x, which was involved in serving the General Counsel’s Office of large tech firms on intellectualproperty matters. Because I can hear my, my IT security guy screaming right now. How was that marketing working?
And then after that, I made my way into the world of legal tech, working in data science for a company called RPI x, which was involved in serving the General Counsel’s Office of large tech firms on intellectualproperty matters. Because I can hear my, my IT security guy screaming right now. How was that marketing working?
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