Ready or not, the AI revolution is upon us and one of its most immediate impacts is the emergence of chatbots like ChatGPT. "It will be a boon to the societies that pick this up," said junior student leader and president of the Metropolitan State University of Denver Chess Club Paul Nelson. Nelson is talking about ChatGPT and its rapid emergence on college campuses throughout the U.S. One educated at MSU Denver said the first time he heard of the chatbot was in November and now, four months later, it's a part of almost every conversation he has. "My first reaction when I first saw ChatGPT was, 'Oh my God. We are in trouble,'" said Dr. David Merriam, assistant professor of biology.

Government shutdown or not, plaintiffs' lawyers haven't stopped filing new crypto lawsuits. This week we look at three new complaints, one involving lost crypto and a demand for a fork (the software kind), another that says that pre-sold mining hardware contracts were actually securities, and last but not least artificial intelligence on the blockchain (but not so much, it turns out). Disclaimer: These summaries are provided for educational purposes only by Nelson Rosario [twitter: @nelsonmrosario] and Stephen Palley [twitter: @stephendpalley]. They are not legal advice. These are our opinions only, aren't authorized by any past, present or future client or employer.

An unprecedented voice-transcription technology can tell you not only what's being said, but who is saying it. The web app, named Trint, can listen to an audio recording or a video of two or more speakers (or just one) engaged in natural speech, then provide a written transcript of what was said. Unlike Siri or Google Talk, Trint is designed to transcribe long blocks of text. While news organizations have invested heavily in video content, the ability to optimize those clips for search engines remains elusive. Trint's technology is still nascent, but it could eventually give new life to vast swaths of non-text-based media on the internet, like videos and podcasts, by making them readable to both humans and search engines.

A new voice-transcription technology can tell you not only what's being said, but who is saying it. The software, named Trint, can listen to an audio recording or a video of two or more speakers engaged in a natural conversation, then provide a written transcript of what each person said. While news organizations have invested heavily in video content, the ability to optimize those clips for search engines remains elusive. Trint's technology is still nascent, but it could eventually give new life to vast swaths of non-text-based media on the internet, like videos and podcasts, by making them readable to both humans and search engines. People could read podcasts they lack the time or ability to listen to.

In an age when technology companies routinely introduce new forms of everyday magic, one problem that remains seemingly unsolved is that of long-form transcription. Sure, voice dictation for documents has been conquered by Nuance's Dragon software. Our phones and smart home devices can understand fairly complex commands, thanks to self-teaching recurrent neural nets and other 21st century wonders. However, the task of providing accurate transcriptions of long blocks of actual human conversation remains beyond the abilities of even today's most advanced software. When solved on a broad scale, it is a problem that might unlock vast archives of oral histories, make podcasts easier to consume for speed-readers (tl;dl), and be a world-changing boon for journalists everywhere, liberating precious hours of sweet life.

In an age when technology companies routinely introduce new forms of everyday magic, one problem that remains seemingly unsolved is that of long-form transcription. Sure, voice dictation for documents has been conquered by Nuance's Dragon software. Our phones and smart home devices can understand fairly complex commands, thanks to self-teaching recurrent neural nets and other 21st century wonders. However, the task of providing accurate transcriptions of long blocks of actual human conversation remains beyond the abilities of even today's most advanced software. When solved on a broad scale, it is a problem that might unlock vast archives of oral histories, make podcasts easier to consume for speed-readers (tl;dl), and be a world-changing boon for journalists everywhere, liberating precious hours of sweet life.

In an age when technology companies routinely introduce new forms of everyday magic, one problem that remains seemingly unsolved is that of long-form transcription. Sure, voice dictation for documents has been conquered by Nuance's Dragon software. Our phones and smart home devices can understand fairly complex commands, thanks to self-teaching recurrent neural nets and other 21st century wonders. However, the task of providing accurate transcriptions of long blocks of actual human conversation remains beyond the abilities of even today's most advanced software. When solved on a broad scale, it is a problem that might unlock vast archives of oral histories, make podcasts easier to consume for speed-readers (tl;dl), and be a world-changing boon for journalists everywhere, liberating precious hours of sweet life.

We have de,Jeloped an automated system for the design of laboratory experiments in molecular biology. The system uses a planning method known as skeletal plan refinement that attempts to emulate the human cognitive task of experiment design. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments.

A new method for automated planning, progressive refinement of skeletal plans, has been developed for the problem of experiment design in the domain of molecular biology. The method resulted from a study of the problem-solving behavior of scientists which showed that design usually consisted of lookup of abstracted plans followe6 by hierarchical plan-step refinement. The skeletal plan method has been implemented through two generations of problem-solving systems: the second generation involving a synthesis with the metaplanning approach of Stefik.

We have developed an automated system for the design of laboratory experiments in molecular biology. The system uses a planning method known as skeletal plan refinement that attempts to emulate the human cognitive task of experiment design. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments.