When to solve math problems, most language models take a sampling strategy to predict next word according conditional probabilities. In the math reasoning step, it may generate wrong answer. Considering math problems are deterministic, we propose a mixed policy exploration approach to solve math problems with reinforcement learning. In peculiar, we propose a two level token exploration policy: the abstract level explores next token with probability and the second level is deterministic. Specifically, the abstract level policy will decide whether the token is operator or operand with probability sampling, while the second level is deterministic to select next token with the highest score in a greedy way. We test our method on GSM8K dataset with GPT-2 model, and demonstrate more than $2\%$ performance gain. Our implementation is available at https://github.com/vividitytech/math_lm_rl.

In the first year of the pandemic, science happened at light speed. More than 100,000 papers were published on COVID in those first 12 months -- an unprecedented human effort that produced an unprecedented deluge of new information. It would have been impossible to read and comprehend every one of those studies. No human being could (and, perhaps, none would want to). Galactica is an artificial intelligence developed by Meta AI (formerly known as Facebook Artificial Intelligence Research) with the intention of using machine learning to "organize science."

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone …

In her fascinating book, Atlas of AI, Kate Crawford relates how, at the end of the 19th century, Europe was captivated by a horse called Hans that apparently could solve maths problems, tell the time, identify days on a calendar, differentiate musical tones and spell out words and sentences by tapping his hooves. But, as Crawford says, the story is compelling: "the relationship between desire, illusion and action; the business of spectacles, how we anthropomorphise the non-human, how biases emerge and the politics of intelligence". Eliza was the first chatbot, but she can be seen as the beginning of a line of inquiry that has led to current generations of huge natural language processing (NLP) models created by machine learning. Last year, the Guardian assigned it the task of writing a comment column to convince readers that robots come in peace and pose no dangers to humans. Having typed that last sentence, I had the idea of asking GPT-3 to compose an answer to the question: "Why did Google fire Timnit Gebru?" But then I checked out the process for getting access to the machine and concluded that life was too short and human conjecture is quicker – and possibly more accurate.

A robotics firm has built an Artificial Intelligence minibot that talks, walks and even looks like famed theoretical physicist Albert Einstein. The firm, Hanson Robotics, specializes in designing human-like robots that are capable of displaying facial expressions and understanding speech. The Professor Einstein robot can solve math problems, recognize your voice and even hold a conversation. The robotics firm launched a crowdfunding campaign on Kickstarter this month to fund the production of the Einstein bot. The firm is aiming to begin production of the bot in March this year, with rewards sent to backers in April.