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Artificial Intelligence (AI)
Development & Protection of AI Technologies
Our Intellectual Property (IP) and Privacy teams work closely together to counsel clients in building, protecting, and commercializing proprietary AI technologies and data; the use of third party or open source AI technologies and data; and the implications that these activities may have under privacy laws.
Experience 26 results
Experience
|October 21, 2024
Investcorp AI Acquisition Corp. Announces Entry into Business Combination
Experience
|July 26, 2024
Winston represented DRONE VOLT in its rights issue on Euronext Growth
Insights & News 581 results
Press Release
|November 18, 2024
|3 Min Read
Director of United States Patent and Trademark Office Kathi Vidal to Rejoin Winston & Strawn
Washington, D.C. & Silicon Valley, CA – November 18, 2024 – Winston & Strawn LLP announced today that Kathi Vidal will rejoin the firm’s Litigation Department as a partner in the firm’s Silicon Valley and Washington, D.C. offices and as a member of the Executive Committee after stepping down from her role as Undersecretary of Commerce for Intellectual Property and Director of the United States Patent and Trademark Office (USPTO). Kathi will rejoin Winston on December 16, 2024.
Webinar
|November 15, 2024
How Will the U.S. Election Results Impact Your Financial Services Businesses and Clients?
On November 15, 2024, join Winston & Strawn’s Financial Services Industry Group for a webinar discussing the impact of the U.S. election results on the financial services sector for 2025 and beyond. This virtual roundtable discussion—featuring more than a dozen Winston partners including moderators Amanda Groves and George Mastoris—will provide insights and potential implications to various financial services areas and regions, including:
Client Alert
|November 14, 2024
|7 Min Read
SEC Division of Examinations 2025 Priorities
On October 21, 2024, the U.S. Securities and Exchange Commission’s (the SEC) Division of Examinations (the Division) announced its annual list of examination priorities for 2025 (the Priorities), which are developed in consultation with various internal SEC divisions and offices. The priorities reflect practices, products, and services that the Division believes present heightened risks to investors or the integrity of the U.S. capital markets. The Priorities are not an exhaustive list of issues the Division intends to target in examinations. The Division’s examinations are also likely to address emerging risks, products, market events, and other investor concerns as they arise. In this alert, you will find a summary of the priorities for the SEC in examining registered investment advisers, registered investment companies, broker-dealers, and other market participants in 2025.
Other Results 26 results
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What Is Artificial Intelligence (AI)?
The definition of artificial intelligence, also known as AI, is the capability of computers or robots to execute tasks that humans normally do. The meaning of AI can also include the development of computer systems that perform intellectual processes. In other words, machines perform tasks intelligently, such as reasoning and generalizing. Narrow AI is a type of artificial intelligence where the focus is placed on specific tasks. An example of this would be a virtual assistant who has targeted abilities, such as the ability to respond to questions. Strong AI is machine intelligence featuring human cognitive capabilities, such as the ability to make judgments, find solutions, or communicate. Today, it’s important to understand what artificial intelligence systems are commonly used for, including visual perception, speech recognition, and decision-making.
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Generative AI tools can create new content, such as text, computer code, images, audio, sound, and video, in response to a user’s prompt, often in the form of a short written description of the desired output. Generative AI tools are based on machine learning, trained using enormous amounts of data.[1] Generative AI tools are built on a system of inputs and outputs. First, the tool goes through a machine learning period whereby it is trained to generate predictive models and creative outputs through a large data set, often varied and diverse but tailored to the goal of the tool (i.e., customer service, generating scientific or marketing models, etc.). For in-house tools, this can be done with the company’s own data; for larger tools such as ChatGPT, this is done with the creator’s data set.[2] Once the tool has been trained, the individual user “inputs” a short prompt for the tool to synthesize and produce an “output.” Inputs are often retained on the servers controlled by the company that supports the tool, for monitoring of the tool’s performance and, in some cases, continued learning. The “outputs” are created by combining the machine learning during the training period with the inputs to produce an output.[3]