The US Federal Trade Commission has sent a request for information to startup OpenAI Inc. as part of a probe into its ChatGPT conversational AI bot, according to a person familiar with the request. The document request was sent recently to the Microsoft Corp.-backed AI company seeking information on whether ChatGPT harms consumers, according to the person, who asked not to be named discussing a non-public investigation. FTC Chair Lina Khan, who is set to appear before Congress Thursday, has raised concerns about AI, saying enforcers "need to be vigilant early" with transformative tools like artificial intelligence. The Washington Post earlier reported on the FTC's probe.

"Google does not own the internet, it does not own our creative works, it does not own our expressions of our personhood, pictures of our families and children, or anything else simply because we share it online," the plaintiffs' attorney Ryan Clarkson said in a statement.

Vice President Kamala Harris on Wednesday explained artificial intelligence as she convened a roundtable with labor and civil rights leaders to talk about the technology. "AI Czar" and Vice President Kamala Harris was ridiculed on social media for her "stunning" description of artificial intelligence on Wednesday. In the latest example of Harris' "word salad" moments, the vice president spoke at the Eisenhower Executive Office Building in Washington, D.C., and gave what people saw as a condescending and long-winded description of AI. "I think the first part of this issue that should be articulated is AI is kind of a fancy thing," Harris said. It means artificial intelligence, but ultimately what it is, is it's about machine learning." She added, "And so, the machine is taught -- and part of the issue here is what information is going into the machine that will then determine -- and we can predict then, if we think about what information is going in, what then will be produced in terms of decisions and opinions that may be made through that process." Vice President Kamala Harris speaks during a meeting with civil rights leaders and consumer protection experts to discuss the societal impact of artificial intelligence, in the Eisenhower Executive Office building in Washington, DC, on July 12, 2023. "So to reduce it down to its most simple point, this is part of the issue that we have here is thinking about what is going into a decision, and then whether that decision is actually legitimate and reflective of the needs and the life experiences of all the people," Kamala concluded. "Kamala Harris talks to Americans like we are all in kindergarten.

The agency's focus on such fabrications comes after numerous high-profile reports of the chatbot producing incorrect information that could damage people's reputations. Mark Walters, a radio talk show host in Georgia sued OpenAI for defamation, alleging the chabot made up legal claims against him. The lawsuit alleges that ChatGPT falsely claimed that Walters, the host of "Armed American Radio," was accused of defrauding and embezzling funds from the Second Amendment Foundation. The response was provided in response to a question about a lawsuit about the foundation that Walters is not a party to, according to the complaint.

Larger training sets don't reduce bias in artificial intelligence Many tech companies have operated under the assumption that training artificial intelligence on more data can help fix the ongoing problem of AIs replicating human prejudices. But a study has found that AIs trained on increasingly larger data sets can produce even more racist results. Abeba Birhane at the Mozilla Foundation and her colleagues compared two data sets provided by the Large-scale Artificial Intelligence Open Network (LAION), a non-profit that offers open-source data sets for AI training.

Large AIs can have a significant environmental impact because they rely on thousands of power-hungry computing servers housed within huge data centres. But the environmental damage could be reduced by better distributing the demands to different locations. Such scheduling algorithms might lighten the AI workload on data centres in Arizona during summer droughts to reduce water-based cooling.

Heritage Foundation tech policy research associate Jake Denton joined'Fox & Friends First' to discuss growing concerns surrounding the political implications of artificial intelligence. AI's breakthrough into popular culture, marked by chatbot tools like ChatGPT, has turned this technology into a battleground for culture warriors. However, equating artificial intelligence or AI with social media platforms could cost us significant advances in healthcare, transportation and global leadership in technology. Over the past decade, politicians have developed a playbook for scoring political points by criticizing social media. Democrats have focused on the spread of misinformation and disinformation, while Republicans have raised concerns about perceived bias against conservative views.

Recent developments in natural language processing have demonstrated the potential of large language models (LLMs) to improve a range of educational and learning outcomes. Of recent chatbots based on LLMs, ChatGPT and Bard have made it clear that artificial intelligence (AI) technology will have significant implications on the way we obtain and search for information. However, these tools sometimes produce text that is convincing, but often incorrect, known as hallucinations. As such, their use can distort scientific facts and spread misinformation. To counter polarizing responses on these tools, it is critical to provide an overview of such responses so stakeholders can determine which topics tend to produce more contentious responses -- key to developing targeted regulatory policy and interventions. In addition, there currently exists no annotated dataset of ChatGPT and Bard responses around possibly polarizing topics, central to the above aims. We address the indicated issues through the following contribution: Focusing on highly polarizing topics in the US, we created and described a dataset of ChatGPT and Bard responses. Broadly, our results indicated a left-leaning bias for both ChatGPT and Bard, with Bard more likely to provide responses around polarizing topics. Bard seemed to have fewer guardrails around controversial topics, and appeared more willing to provide comprehensive, and somewhat human-like responses. Bard may thus be more likely abused by malicious actors. Stakeholders may utilize our findings to mitigate misinformative and/or polarizing responses from LLMs

Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train on large datasets, avoiding the recurrence of expensive data collection. To advance the field, it is crucial to generate large-scale datasets. Compositional RL is particularly appealing for generating such large datasets, since 1) it permits creating many tasks from few components, 2) the task structure may enable trained agents to solve new tasks by combining relevant learned components, and 3) the compositional dimensions provide a notion of task relatedness. This paper provides four offline RL datasets for simulated robotic manipulation created using the 256 tasks from CompoSuite [Mendez et al., 2022a]. Each dataset is collected from an agent with a different degree of performance, and consists of 256 million transitions. We provide training and evaluation settings for assessing an agent's ability to learn compositional task policies. Our benchmarking experiments on each setting show that current offline RL methods can learn the training tasks to some extent and that compositional methods significantly outperform non-compositional methods. However, current methods are still unable to extract the tasks' compositional structure to generalize to unseen tasks, showing a need for further research in offline compositional RL.

In the midst of the emerging trend of integrating artificial intelligence (AI) with crypto mining, we identify three major challenges that create a gap between these two fields. To bridge this gap, we introduce the proof-of-training (PoT) protocol, an approach that combines the strengths of both AI and blockchain technology. The PoT protocol utilizes the practical Byzantine fault tolerance (PBFT) consensus mechanism to synchronize global states. To evaluate the performance of the protocol design, we present an implementation of a decentralized training network (DTN) that adopts the PoT protocol. Our results indicate that the protocol exhibits considerable potential in terms of task throughput, system robustness, and network security.