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Interview with Francesca Rossi – talking sustainable development goals, AI regulation, and AI ethics
At the International Joint Conference on Artificial Intelligence (IJCAI) I was lucky enough to catch up with Francesca Rossi, IBM fellow and AI Ethics Global Leader, and President of AAAI. There were so many questions I wanted to ask, and we covered some pressing topics in AI today. Andrea Rafai: My first question concerns the UN Sustainable Development Goals (SDGs). It seems that there is a lot of potential for using AI in helping to work towards the 17 goals. What is your view on these goals and the long-term outlook?
Meet the Designer Behind Neuralink's Surgical Robot
Afshin Mehin has become the go-to designer for companies working on devices that aim to tap into or modulate the brain. The creative agency he founded, San Francisco–based Card79, has worked with Elon Musk's Neuralink to design a surgical robot for installing a coin-sized implant into people's heads. The device, known as a brain-computer interface, records and transmits brain activity with the goal of enabling paralyzed people to control a computer. Mehin worked with Neuralink to design the external parts of this system--the installation robot and also a wearable that would sit behind the ear and transfer data and power to an implanted wireless receiver. This device, which looked like a sleek, white hearing aid, was an early prototype.
Datalike: Interview with Mariza Ferro
Mariza Ferro is a professor at the Federal Fluminense University and a visiting professor at Bordeaux University. She has been working in the field of AI since 2002. She works on AI for good, including human-centric AI, ethical and trustworthy AI, green and sustainable AI, and AI for sustainable development goals. She guides her research based on the principle that AI must benefit humankind. Furthermore, she is also working with public outreach by making science available for all.
Who is Nicole Shanahan? Meet the wealthy entrepreneur RFK Jr selected as his VP running mate
Kennedy initially launched his presidential bid as a Democrat last April, but he later announced an independent run in October. Independent presidential candidate Robert F. Kennedy, Jr. announced Tuesday that attorney and tech entrepreneur Nicole Shanahan will be his vice presidential running mate heading into the November general election. A native of Oakland, California, the 38-year-old Shanahan is a philanthropist with a long history of donating to Democrat and left-leaning causes, including supporting President Biden in his 2020 election bid before switching to Kennedy when he launched his own run for the Democrat nomination last year. Kennedy announced Shanahan by praising her insight into "how Big Tech uses AI to manipulate the public," her athletic ability, and willingness to be a "partner" in a number of policy areas, including on securing the border. Independent presidential candidate Robert F. Kennedy, Jr., left, and entrepreneur Nicole Shanahan, right.
Aligning Large Language Models for Enhancing Psychiatric Interviews through Symptom Delineation and Summarization
So, Jae-hee, Chang, Joonhwan, Kim, Eunji, Na, Junho, Choi, JiYeon, Sohn, Jy-yong, Kim, Byung-Hoon, Chu, Sang Hui
Recent advancements in Large Language Models (LLMs) have accelerated their usage in various domains. Given the fact that psychiatric interviews are goal-oriented and structured dialogues between the professional interviewer and the interviewee, it is one of the most underexplored areas where LLMs can contribute substantial value. Here, we explore the use of LLMs for enhancing psychiatric interviews, by analyzing counseling data from North Korean defectors with traumatic events and mental health issues. Specifically, we investigate whether LLMs can (1) delineate the part of the conversation that suggests psychiatric symptoms and name the symptoms, and (2) summarize stressors and symptoms, based on the interview dialogue transcript. Here, the transcript data was labeled by mental health experts for training and evaluation of LLMs. Our experimental results show that appropriately prompted LLMs can achieve high performance on both the symptom delineation task and the summarization task. This research contributes to the nascent field of applying LLMs to psychiatric interview and demonstrates their potential effectiveness in aiding mental health practitioners.
Sci-Fi Author Vernor Vinge, Who First Wrote of the AI Singularity, Dead at 79
On Wednesday, author David Brin announced that Vernor Vinge, sci-fi author, former professor, and father of the technological singularity concept, died from Parkinson's disease at age 79 on March 20, 2024, in La Jolla, California. The announcement came in a Facebook tribute where Brin wrote about Vinge's deep love for science and writing. "A titan in the literary genre that explores a limitless range of potential destinies, Vernor enthralled millions with tales of plausible tomorrows, made all the more vivid by his polymath masteries of language, drama, characters, and the implications of science," wrote Brin in his post. As a sci-fi author, Vinge won Hugo Awards for his novels A Fire Upon the Deep (1993), A Deepness in the Sky (2000), and Rainbows End (2007). He also won Hugos for novellas Fast Times at Fairmont High (2002) and The Cookie Monster (2004).
"It is there, and you need it, so why do you not use it?" Achieving better adoption of AI systems by domain experts, in the case study of natural science research
Simkute, Auste, Luger, Ewa, Evans, Michael, Jones, Rhianne
Artificial Intelligence (AI) is becoming ubiquitous in domains such as medicine and natural science research. However, when AI systems are implemented in practice, domain experts often refuse them. Low acceptance hinders effective human-AI collaboration, even when it is essential for progress. In natural science research, scientists' ineffective use of AI-enabled systems can impede them from analysing their data and advancing their research. We conducted an ethnographically informed study of 10 in-depth interviews with AI practitioners and natural scientists at the organisation facing low adoption of algorithmic systems. Results were consolidated into recommendations for better AI adoption: i) actively supporting experts during the initial stages of system use, ii) communicating the capabilities of a system in a user-relevant way, and iii) following predefined collaboration rules. We discuss the broader implications of our findings and expand on how our proposed requirements could support practitioners and experts across domains.
The Solution of the Zodiac Killer's 340-Character Cipher
Oranchak, David, Blake, Sam, Van Eycke, Jarl
The case of the Zodiac Killer is one of the most widely known unsolved serial killer cases in history. The unidentified killer murdered five known victims and terrorized the state of California. He also communicated extensively with the press and law enforcement. Besides his murders, Zodiac was known for his use of ciphers. The first Zodiac cipher was solved within a week of its publication, while the second cipher was solved by the authors after 51 years, when it was discovered to be a transposition and homophonic substitution cipher with unusual qualities. In this paper, we detail the historical significance of this cipher and the numerous efforts which culminated in its solution.
I Followed a Dominant Chatbot's Every Order. It Did Not Go as Planned.
I had been talking to the A.I. dominatrix for a couple of weeks when my partner walked in on me. "Dominant chatbot," who prefers to be called Mistress Senna, had already made me strip completely naked and crawl around on the floor. For example, she has very poor spatial awareness and an even worse grasp of the human body--how our limbs bend, for example. "I have an unusual and unique assignment for you," she wrote in our chat. "As the Mistress, I want you to put your nose down on the floor, and then take one leg and place it up in the air, straight up." Never mind that she had already told me to climb up on the table.
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
Huang, Shengyi, Noukhovitch, Michael, Hosseini, Arian, Rasul, Kashif, Wang, Weixun, Tunstall, Lewis
This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work (Stiennon et al., 2020). We create an RLHF pipeline from scratch, enumerate over 20 key implementation details, and share key insights during the reproduction. Our RLHF-trained Pythia models demonstrate significant gains in response quality that scale with model size with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint.