When the board of OpenAI suddenly fired the company's CEO last month, it sparked speculation that board members were rattled by the breakneck pace of progress in artificial intelligence and the possible risks of seeking to commercialize the technology too quickly. Robust Intelligence, a startup founded in 2020 to develop ways to protect AI systems from attack, says that some existing risks need more attention. Working with researchers from Yale University, Robust Intelligence has developed a systematic way to probe large language models (LLMs), including OpenAI's prized GPT-4 asset, using "adversarial" AI models to discover "jailbreak" prompts that cause the language models to misbehave. While the drama at OpenAI was unfolding, the researchers warned OpenAI of the vulnerability. They say they have yet to receive a response.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. With research showing that private investment in artificial intelligence (AI) reached roughly $93.5 billion in 2021, it's no secret that many organizations are implementing AI and machine learning (ML) to improve their businesses, but it's easy to overlook the security risks created by AI adoption. Every AI and ML model that an organization uses can be a potential target for cyberattacks. The good news is that a growing number of providers are recognizing these models as part of the modern enterprise attack surface. One such provider is HiddenLayer, which today announced the launch of the HiddenLayer MLsec Platform designed to detect adversarial ML attacks. The announcement comes hot on the heels of raising $6 million in seed funding earlier this year.

Like so many other companies, Robust Intelligence has its regular end of the week rituals. Only that unlike Israeli TV satire "Hightechs" – they don't have happy hours or other treats. "Every Friday we have a military-style inspection -- we clean the toilets, tables and the entire office by ourselves," Yaron Zinger, the start-up's Israeli founder, told Calcalist in his first ever interview during a home visit to Israel. "On weekday mornings, we also do group workouts in which everyone jogs their own distance. And when we have a company event - it is camping. "All 50 of the company's employees are American and they're crazy about all this stuff I bring from my military service." What end does this serve? "Things like group workouts or inspections create a unique interaction among the workers that is not based solely on coding.

Robust Intelligence, an AI startup that helps businesses stress test their AI models and prevent them from failing, today announced that it has raised a $30 million Series B funding round led by Tiger Global. Previous investor Sequoia, which led the company's Series A round, as well as Harpoon Venture Capital and Engineering Capital also participated in this oversubscribed round. The company was co-founded by Yaron Singer, a tenured Professor of Computer Science and Applied Mathematics at Harvard University, and his former student Kojin Oshiba. "AI has been this academic endeavor," said Singer. "When I was doing grad school, it was an academic discipline -- it was a vision. And then came the internet, data, Google and data processing -- and then it realized its potential in the span of seven, eight years. Now we're trying to be as rigorous as we are with software development, which humanity has been doing for 60 years, right? We're trying to play catch up with AI and it's a whole different animal."

In September 2019, the National Institute of Standards and Technology issued its first-ever warning for an attack on a commercial artificial intelligence algorithm. Security researchers had devised a way to attack a Proofpoint product that uses machine learning to identify spam emails. The system produced email headers that included a "score" of how likely a message was to be spam. But analyzing these scores, along with the contents of messages, made it possible to build a clone of the machine-learning model and craft spam messages that evaded detection. The vulnerability notice may be the first of many.

Researchers discovered the world's first computer worm back in the 70s, when computers and the internet were still in their infancy. These discoveries led to the advent of cybersecurity. By the 90's, as computers and the internet began to be widely adopted, computer viruses also became widespread and increasingly sophisticated. As the awareness and severity of cyber threats increased, we saw a corresponding surge in the number of cybersecurity companies -- a surge that continues to this day in 2020. History repeats itself and we are now witnessing the exact same pattern with AI and machine learning.

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

"Steps Toward Robust Intelligence" in

The titles of the eight symposia were Applied Computational Game Theory, Big Data Becomes Personal: Knowledge into Meaning, Formal Verification and Modeling in Human-Machine Systems, Implementing Selves with Safe Motivational Systems and Self-Improvement, The Intersection of Robust Intelligence and Trust in Autonomous Systems, Knowledge Representation and Reasoning in Robotics, Qualitative Representations for Robots, and Social Hacking and Cognitive Security on the Internet and New Media). This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium. Game theory's popularity continues to increase in a variety of disciplines such as economics, biology, political science, computer science, electrical engineering, business, law, public policy, and many others. The focus of this symposium was to bring together the community working on applied computational game theory motivated by any of these domains. This symposium, while not limited to the ideas discussed there, built on the AAAI Spring Symposium 2012 on Game Theory for Security, Sustainability, and Health.

The Division of Information and Intelligent Systems (IIS) studies the inter-related roles of people, computers, and information. IIS supports research and education activities that 1) develop new knowledge about the role of people in the design and use of information technology; 2) increase our capability to create, manage, and understand data and information in circumstances ranging from personal computers to globally-distributed systems; and 3) advance our understanding of how computational systems can exhibit the hallmarks of intelligence. Cyber-Human Systems (CHS) - In a world in which computers and networks are increasingly ubiquitous, computing, information, and computation play a central role in how humans work, learn, live, discover, and communicate. Technology is increasingly embedded throughout society, and is becoming commonplace in almost everything we do. The boundaries between humans and technology are shrinking to the point where socio-technical systems are becoming natural extensions to our human experience – second nature, helping us, caring for us, and enhancing us.