We introduce a new linear-algebraic tool based on group representation theory, and use it to address three key problems in machine learning.1. Past researchers have proposed fast attention algorithms for LLMs by approximating or replace softmax attention with other functions, such as low-degree polynomials. The key property of these functions is that, when applied entry-wise to the matrix $QK^{\top}$, the result is a low rank matrix when $Q$ and $K$ are $n \times d$ matrices and $n \gg d$. This suggests a natural question: what are all functions $f$ with this property? If other $f$ exist and are quickly computable, they can be used in place of softmax for fast subquadratic attention algorithms.

We introduce a new linear-algebraic tool based on group representation theory, and use it to address three key problems in machine learning.1. Past researchers have proposed fast attention algorithms for LLMs by approximating or replace softmax attention with other functions, such as low-degree polynomials. The key property of these functions is that, when applied entry-wise to the matrix QK {\top}, the result is a low rank matrix when Q and K are n \times d matrices and n \gg d . This suggests a natural question: what are all functions f with this property? If other f exist and are quickly computable, they can be used in place of softmax for fast subquadratic attention algorithms.

On Wednesday, OpenAI announced ChatGPT, a dialogue-based AI chat interface for its GPT-3 family of large language models. It's currently free to use with an OpenAI account during a testing phase. Unlike the GPT-3 model found in OpenAI's Playground and API, ChatGPT provides a user-friendly conversational interface and is designed to strongly limit potentially harmful output. "The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests," writes OpenAI on its announcement blog page. So far, people have been putting ChatGPT through its paces, finding a wide variety of potential uses while also exploring its vulnerabilities.

Dynam.AI, an artificial intelligence (AI) software development firm best known for full stack AI innovation, today announced the early commercial release of Vizlab, an AI/Machine Learning (ML) platform designed to address the complex needs of enterprise data scientists and solve the key problems with AI/ML applications in the market today. This customizable, intuitive, end-to-end AI/ML development solution enables ML data scientists to design, build, improve and deploy AI engines at scale. Vizlab empowers data scientists with necessary, in-demand tools to deploy explainable AI solutions with highly accurate analytic insights. Vizlab was initially built as an internal tool by the Dynam.AI team as a platform to support Dynam's consultative services, to automate and standardize AI pro-workflow and advance Dynam's core AI capabilities for customers with complex business use cases. Customers without internal data science teams seek out Dynam's end-to-end AI development services to learn more from their proprietary business and customer data to automate and improve processes and target more of their best customers.

We present 27 problems encountered in automating the translation of movie/TV show subtitles. We categorize each problem in one of the three categories viz. problems directly related to textual translation, problems related to subtitle creation guidelines, and problems due to adaptability of machine translation (MT) engines. We also present the findings of a translation quality evaluation experiment where we share the frequency of 16 key problems. We show that the systems working at the frontiers of Natural Language Processing do not perform well for subtitles and require some post-processing solutions for redressal of these problems

Last year, when Kate Brodock, CEO and founder of Women 2.0, pointed out that one of the key problems faced in the new tech industry the world over was that it was dominated by'white men'. Lack of racial, cultural diversity and most importantly, lack of gender equality was shaping the internet in a skewed manner. The scant number of women in new tech, especially in the areas of data science, analytics, and artificial intelligence has been a worrying trend in India as well. The resultant sexism is increasingly becoming one of the side-effects, making the global protests for gender equality so much more necessary. Anuradha Sharma, chief operating officer at Scienaptic says, "There is a very prominent male culture in programming and that kind of makes women less visible in data science and analytics field… Women in analytics are hidden, they are doing all the good work, but they are not asserting themselves. I know enough number of women in analytics, who are doing great work but they are not visible."

In 2017, artificial intelligence will play a big role in HR. It's a complete evolution that will help HR face challenges facing leadership and talent management now. Like the cloud, it's a technological sea-change when we really need it. But in HR we're not sure what to do with it yet -- so I'm here to help. The world of work is marked by profound disruptions right now.

In 2017, artificial intelligence will play a big role in HR. It's a complete evolution that will help HR face challenges facing leadership and talent management now. Like the cloud, it's a technological sea-change when we really need it. But in HR we're not sure what to do with it yet -- so I'm here to help. The world of work is marked by profound disruptions right now.

Google is worried about artificial intelligence. No, not that it will become sentient and take over the world, but that, say, a helpful house robot might accidentally skewer its owner with a knife. The company's latest AI research paper delves into this issue under the title "Concrete Problems In AI Safety." Really, though, that's just a fancy way of saying "How Are We Going To Stop These Terror-Bots Killing Us All In Our Sleep." To answer this brain-tickler, Google's computer scientists have landed on five "practical research problems" -- key issues that programmers will need to consider before they start creating the next Johnny Five.

Google is worried about artificial intelligence. No, not that it will become sentient and take over the world, but that, say, a helpful house robot might accidentally skewer its owner with a knife. The company's latest AI research paper delves into this issue under the title "Concrete Problems In AI Safety." Really, though, that's just a fancy way of saying "How Are We Going To Stop These Terror-Bots Killing Us All In Our Sleep." To answer this brain-tickler, Google's researchers have landed on five "practical research problems" -- key issues that programmers will need to consider before they start creating the next Johnny Five.