Lifeboat Foundation

Ex-NASA astronaut says we must fix Earth’s big problems before we colonize other planets.

He tells Inverse that humans should seek to colonize distant planets. But before that happens, he acknowledges the tremendous amount of work that needs to be done on Earth first.

“We need to spread human presence throughout the Solar System and beyond, but we need to do it as ambassadors of a thriving planet,” Garan says. “We can’t do it as refugees escaping environmental disaster.”

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Providing a new means for storing excess renewable energy.

Reimagining A Healthier Future for All — Dr. Pat Verduin PhD, Chief Technology Officer, Colgate, discussing the microbiome, skin and oral care, and healthy aging from a CPG perspective.

Dr. Patricia Verduin, PhD, (https://www.colgatepalmolive.com/en-us/snippet/2021/circle-c…ia-verduin) is Chief Technology Officer for the Colgate-Palmolive Company where she provides leadership for product innovation, clinical science and long-term research and development across their Global Technology Centers’ Research & Development pipeline.

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From search engines to voice assistants, computers are getting better at understanding what we mean. That’s thanks to language-processing programs that make sense of a staggering number of words, without ever being told explicitly what those words mean. Such programs infer meaning instead through statistics—and a new study reveals that this computational approach can assign many kinds of information to a single word, just like the human brain.

The study, published April 14 in the journal Nature Human Behavior, was co-led by Gabriel Grand, a graduate student in electrical engineering and computer science who is affiliated with MIT’s Computer Science and Artificial Intelligence Laboratory, and Idan Blank Ph.D. ‘16, an assistant professor at the University of California at Los Angeles. The work was supervised by McGovern Institute for Brain Research investigator Ev Fedorenko, a cognitive neuroscientist who studies how the human brain uses and understands language, and Francisco Pereira at the National Institute of Mental Health. Fedorenko says the rich knowledge her team was able to find within computational language models demonstrates just how much can be learned about the world through language alone.

The research team began its analysis of statistics-based language processing models in 2015, when the approach was new. Such models derive meaning by analyzing how often pairs of words co-occur in texts and using those relationships to assess the similarities of words’ meanings. For example, such a program might conclude that “bread” and “apple” are more similar to one another than they are to “notebook,” because “bread” and “apple” are often found in proximity to words like “eat” or “snack,” whereas “notebook” is not.

Large language models — natural language processing (NLP) systems with more than 100 billion parameters — have transformed NLP and AI research over the last few years. Trained on a massive and varied volume of text, they show surprising new capabilities to generate creative text, solve basic math problems, answer reading comprehension questions, and more. While in some cases the public can interact with these models through paid APIs, full research access is still limited to only a few highly resourced labs. This restricted access has limited researchers’ ability to understand how and why these large language models work, hindering progress on efforts to improve their robustness and mitigate known issues such as bias and toxicity.

In line with Meta AI’s commitment to open science, we are sharing Open Pretrained Transformer (OPT-175B), a language model with 175 billion parameters trained on publicly available data sets, to allow for more community engagement in understanding this foundational new technology. For the first time for a language technology system of this size, the release includes both the pretrained models and the code needed to train and use them. To maintain integrity and prevent misuse, we are releasing our model under a noncommercial license to focus on research use cases. Access to the model will be granted to academic researchers; those affiliated with organizations in government, civil society, and academia; along with industry research laboratories around the world.

We believe the entire AI community — academic researchers, civil society, policymakers, and industry — must work together to develop clear guidelines around responsible AI in general and responsible large language models in particular, given their centrality in many downstream language applications. A much broader segment of the AI community needs access to these models in order to conduct reproducible research and collectively drive the field forward. With the release of OPT-175B and smaller-scale baselines, we hope to increase the diversity of voices defining the ethical considerations of such technologies.