Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings. Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. One limitation of this setting is that it does not allow for the emergent protocols to generalize beyond the training partners. Furthermore, so far emergent communication has primarily focused on the use of symbolic channels. In this work, we extend this line of work to a new modality, by studying agents that learn to communicate via actuating their joints in a 3D environment. We show that under realistic assumptions, a non-uniform distribution of intents and a common-knowledge energy cost, these agents can find protocols that generalize to novel partners.

OpenAI has come up with a language generator GPT-3, which is a successor of GPT-2. This newly developed AI was put forward to a few selected outside software developers for testing. GPT-2 released a year prior, and it let out convincing streams in regards to message in the extent of different styles when induced with an underlying sentence. The differentiating factor of GPT-3 is having 175 billion parameters(the qualities that a neural system attempts to upgrade during preparing), whereas GPT-2 had only 1.5 billion. GPT-3 is the most significant language model ever.

The Hoover Institution's Condoleezza Rice and HAI Co-Director Fei-Fei Li discuss AI ethics, technology advances, and the...

Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.

In June 2020 an Artificial Intelligence system called GPT-3 went live. This AI model is focused on Natural Language Programming and was trained by reading trillions of words and sentences online. The net result is that it can generate impressive text that humans can barely tell was created by a computer. A growing number of developers are being given access to GPT-3 to create real-world applications. In the coming months, you are going to start to see a plethora of AI applications that create content such as blogs, articles, reports, emails, advertising copy, and sales scripts.

We are near the end of the hype cycle for artificial intelligence (AI). The human champion of the game of Go decided to retire, saying AI cannot be beaten after AlphaGo defeated him. Domain-specific chatbots are engaging with customers and providing them with the answers they need. AI is about to revolutionize our broken health-care system. Is your company ready for AI? Anyone with deep data claims to be using AI.

We are near the end of the hype cycle for artificial intelligence (AI). The human champion of the game of Go decided to retire, saying AI cannot be beaten after AlphaGo defeated him. Domain-specific chatbots are engaging with customers and providing them with the answers they need. AI is about to revolutionize our broken health-care system. Is your company ready for AI? Anyone with deep data claims to be using AI. Credible pilots and use cases have succeeded in many different sectors.

GPT-3, an advanced language-processing artificial intelligence algorithm developed by OpenAI, is really good at what it does -- churning out humanlike text. But Yann LeCun, the Chief AI Scientist at Facebook who's been called a "godfather of AI," trashed the algorithm in a Tuesday Facebook post, writing that "people have completely unrealistic expectations about what large-scale language models such as GPT-3 can do." LeCun cites a recent experiment by the medical AI firm NABLA, which found that GPT-3 is woefully inadequate for use in a healthcare setting because writing coherent sentences isn't the same as being able to reason or understand what it's saying. "It's entertaining, and perhaps mildly useful as a creative help," LeCun wrote. "But trying to build intelligent machines by scaling up language models is like [using] high-altitude airplanes to go to the Moon. You might beat altitude records, but going to the Moon will require a completely different approach."

In October 2020 the US House Antitrust Subcommittee, chaired by Congressman David Cicilline, published its report on competition in digital markets. It conducted a full review of the market from top to bottom, focusing on the dominance of the giants in the industry: Facebook, Amazon, Apple and Google. The report zeroes in on their business practices, and how these could potentially amount to monopolies. They found that each platform had become, in one way or another, in direct and singular control of channels of mass distribution. They are no longer disruptive and innovative start-ups, but now resemble business monoliths akin to the oil barons and railroad tycoons of the past, controlling their respective industries, absorbing or removing competitors with ease.

The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts. However, NLP models have historically been developed using dominant English varieties, such as Standard American English (SAE), due to text corpora availability. We investigate the performance of GPT-2 on AAVE text by creating a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating syntactic structure and AAVE- or SAE-specific language for each pair. We evaluate each sample and its GPT-2 generated text with pretrained sentiment classifiers and find that while AAVE text results in more classifications of negative sentiment than SAE, the use of GPT-2 generally increases occurrences of positive sentiment for both. Additionally, we conduct human evaluation of AAVE and SAE text generated with GPT-2 to compare contextual rigor and overall quality.