Anthropic Walks Back Policy That Could Have'Sabotaged' AI Researchers Using Claude The company changed course after researchers spoke out against the policy, which would have covertly limited Claude's ability to develop competing AI models. Anthropic is backtracking on a policy that would have covertly limited competitors from using its new AI model, Claude Fable 5, to develop other AI models. The company changed course after the move received significant backlash from the AI research community . "We're changing Fable 5's safeguards for frontier LLM development to make them visible," Anthropic said in a statement to WIRED. "We made the wrong tradeoff and we apologize for not getting the balance right."

With the goal of uncovering the challenges faced by European AI students during their research endeavors, we surveyed 28 AI doctoral candidates from 13 European countries. The outcomes underscore challenges in three key areas: (1) the findability and quality of AI resources such as datasets, models, and experiments; (2) the difficulties in replicating the experiments in AI papers; (3) and the lack of trustworthiness and interdisciplinarity. From our findings, it appears that although early stage AI researchers generally tend to share their AI resources, they lack motivation or knowledge to engage more in dataset and code preparation and curation, and ethical assessments, and are not used to cooperate with well-versed experts in application domains. Furthermore, we examine existing practices in data governance and reproducibility both in computer science and in artificial intelligence. For instance, only a minority of venues actively promote reproducibility initiatives such as reproducibility evaluations. Critically, there is need for immediate adoption of responsible and reproducible AI research practices, crucial for society at large, and essential for the AI research community in particular. This paper proposes a combination of social and technical recommendations to overcome the identified challenges. Socially, we propose the general adoption of reproducibility initiatives in AI conferences and journals, as well as improved interdisciplinary collaboration, especially in data governance practices. On the technical front, we call for enhanced tools to better support versioning control of datasets and code, and a computing infrastructure that facilitates the sharing and discovery of AI resources, as well as the sharing, execution, and verification of experiments.

In recent news, the research team at Facebook has introduced a general AI bot, ReBeL that can play both perfect information, such as chess and imperfect information games like poker with equal ease, using reinforcement learning. As the company says, it is a big step towards creating a general AI algorithm that could perform well over a range of games. The researchers believe that this algorithm will have real-world applications, including dealing with negotiations, fraud detection, and even cybersecurity. AlphaZero from DeepMind rapidly caught the fancy of the AI research community when it was released back in 2017. An AI-based program that could play games like chess, shogi, and Go is not unheard of, but AlphaZero is different as it uses reinforcement learning with search (RL Search) to'learn on its own' by mimicking the world-class players.

We've built and are now sharing Dynabench, a first-of-its-kind platform for dynamic data collection and benchmarking in artificial intelligence. It uses both humans and models together "in the loop" to create challenging new data sets that will lead to better, more flexible AI. Dynabench radically rethinks AI benchmarking, using a novel procedure called dynamic adversarial data collection to evaluate AI models. It measures how easily AI systems are fooled by humans, which is a better indicator of a model's quality than current static benchmarks provide. Ultimately, this metric will better reflect the performance of AI models in the circumstances that matter most: when interacting with people, who behave and react in complex, changing ways that can't be reflected in a fixed set of data points.

At the Tackling Climate Change workshop at this year's NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight. The panel included Yoshua Bengio, MILA director and University of Montreal professor; Jeff Dean, Google's AI chief; Andrew Ng, cofounder of Google Brain and founder of Landing.ai; and Cornell University professor and Institute for Computational Sustainability director Carla Gomes. The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms. The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. One essential step in better addressing the world's pressing challenges, says Bengio, is changing the way AI research is valued.

It's been an exciting, but complex year in the data world. Just as last year, the data tech ecosystem has continued to "fire on all cylinders". If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.

It's been an exciting, but complex year in the data world. Just as last year, the data tech ecosystem has continued to "fire on all cylinders". If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.

Artificial Intelligence (AI) is already fundamentally changing information technology and stands poised to permeate and transform technology both online and off ranging from manufacturing and transportation to medicine and military applications. The US, Russia and China have all noted that dominance in this field of technology will be an essential ingredient to holding global primacy in the near future. What resembles a sort of arms race has emerged between prominent nations around the globe. Perhaps in an effort to provide the US with an edge, or perhaps in an effort to mitigate the impact of such an arms race, Google has opened an AI center in China. CNN in its article, "Google is opening an artificial intelligence center in China," would announce: Despite many of its services being blocked in China, Google has chosen Beijing as the location for its first artificial intelligence research center in Asia.

Ever since Google pulled some of its core businesses out of the Chinese soil, the internet ecosystem in China has expanded. Having produced some of the biggest tech companies including Baidu, Tencent and Alibaba, China has consolidated its position as one of the leading technology centers in the world with a strong R&D community and vibrant talent pool. China has been a front-runner in AI technologies, largely due to the lavish support from the government. Couple with a growing AI talent pool makes it an irresistible prospect to some of the biggest names in the tech industry including Microsoft, IBM and other Western giants who are busy hiring Chinese staff members in a field with a wide variety of potential applications. Quite understandably, Google wants a part of it too.

Humans don't get to do much with Google in China, where the world's most popular search engine has been unavailable for more than five years. But artificial intelligence is another matter entirely, as Google announced Wednesday plans to open an AI research center in Beijing. While the various Google sites have been unavailable in mainland China ever since the company declined to continue censoring search results, the country's emergence as a global power means Google can't stay out of China entirely. In this case, the focus is on harnessing China's growing dominance of machine learning research, hence the new Google AI China Center. "Chinese authors contributed 43 percent of all content in the top 100 AI journals in 2015--and when the Association for the Advancement of AI discovered that their annual meeting overlapped with Chinese New Year this year, they rescheduled," Fei-Fei Li, Google's chief scientist for AI and machine learning, wrote in a blog post Wednesday explaining the move.