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As core segments of the global economy continue to embrace sustainable and carbon-neutral energy resources as complementary and integral to fossil-based systems of energy production and consumption, we stand ready to guide our clients through the energy transition. Our team is at the forefront of the flourishing energy transition market, and we bring practical experience, perspective, and innovative thinking to the sector. By combining our more than 40 years of experience in renewable and conventional energy with an integrated service approach, we help clients to plan for, invest in, and thrive in today’s shifting environment. Not only does our team include experienced energy and corporate lawyers, but also practitioners with deep experience in tax credits and tax equity, project finance, infrastructure, and power and renewables to serve the varied needs of our client base.
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Winston has a well-established presence in Europe representing clients for over three decades. We demonstrate excellence advising clients on transactional, disputes and regulatory matters in the financial services, private equity, industrial, health, and technology sectors. Our European offices bring together a unique blend of deep local knowledge and global expertise, allowing us to effectively navigate complex cross-border legal matters.
Experience 54 results
Experience
|January 10, 2025
Winston Advises on Pioneering Bitcoin Crypto Lending Programs
Experience
|November 12, 2024
Experience
|September 19, 2024
Insights & News 2,151 results
Sponsorship
|May 28, 2025
Winston & Strawn Sponsors SBIA’s 2025 Midwest Deal Summit
Winston & Strawn is proud to be an elite sponsor for the Small Business Investor Alliance’s (SBIA) 2025 Midwest Deal Summit on May 28-29, 2025, in Chicago.
Speaking Engagement
|May 9, 2025
Join Winston & Strawn partners M. Imad Khan and Keerthika M. Subramanian for the upcoming online program “Hot Topics in International Arbitration and Strategic Transactions: Recent Developments and Key Trends in India” hosted by the Practising Law Insititute.
Product Liability & Mass Torts Digest
|April 23, 2025
|5 Min Read
Supreme Court Opens Door to Civil RICO Claims Arising from Personal Injury
The Supreme Court just opened the doors to civil RICO suits against defendants alleged to have caused personal injury. This ruling could have major implications for product manufacturers and sellers, which may now face RICO claims—and treble damages—alongside traditional state-law claims in product-liability cases.
Other Results 81 results
Site Content
What Is the Electronic Data Gathering Analysis and Retrieval (EDGAR) System Used For?
EDGAR is the SEC’s electronic database system for filing forms with the SEC, including registration statements and periodic reports under the 1933 and 1934 Acts.
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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]