new Policies. Securing a social license for the use of any new, particularly disruptive technology is crucial to its success. This need will be especially acute with autonomous agents making and implementing decisions with minimal-to-no human oversight. Formal regulation may take time, but to generate and maintain widespread societal buy-in, companies should be hypervigilant in enforcing guardrails to ensure safe and appropriate use. Until regulation is in place, self-regulation, as demonstrated by numerous industries in the past, is also a responsible step that will help in the pursuit of a social license. Self-regulation on its own, however, is not a sustainable long-term solution. Organizations should be actively engaged with regulators to help craft the right approach for governing and monitoring the use of these emerging technologies. — Fortune, 8h ago

new ARK’s Open Research Ecosystem seeks to capitalize on rapid change through an open approach and the convergence of insights. ARK believes that a combination of top-down and bottom-up research allows us to size the investment opportunity of disruptive innovation, and then detect and evaluate companies best positioned to benefit. To gain a deeper understanding of quickly changing themes, ARK employs an open research strategy to gather information, both helping to define and refine its internal research process. Inputs include Theme Developers, who are thought leaders in their fields, social media interactions, and crowd-sourced insights as people respond to ARK’s public research. By applying technological concepts and external inputs to traditional approaches, ARK seeks to create a more transparent, creative, and interdisciplinary investment process. — Ark Invest, 8h ago

new At its heart, Intelligence for Good empowers purpose-driven companies and organizations to supercharge their social impact efforts with a set of complete, ready-made solutions that address their specific challenges by making both daily operations and longer-term strategic planning more efficient and effective. It will also help level the playing field, making ethical AI accessible to more companies and organizations of all sizes. — Fortune, 8h ago

new Consider adding AI to the table. One of the most contentious project activities is the prioritization and approval of projects and portfolios. After all, projects and portfolios can represent significant resource commitments. With limited resources, there will be winners and losers in organizations, and organizational politics almost always comes into play. Perhaps organizations should invite AI to the table and become one of many evaluators and decision makers. By giving all people an equal role in shaping the data and analytics, AI can be a reasonably unbiased analyzer of project attractiveness. This can lead to less political infighting and more time to develop algorithms that best advance an organization’s decision making process. — Healthcare Business Today, 1d ago

new STEP ONE: The first step for organizations to solve this problem is to focus on the effective extraction of knowledge from all available sources (i.e., Harvard Business Review, European Business Review, Employee Think-Tanks, Scholars, etc.). In doing this, organizations learn important methods of observation, extraction, and application. Observation is one of the important methods of acquiring knowledge. Recent research shows that observation alone, as a means of acquiring knowledge, can only lead to the illusion of learning among learners. Thus, without extraction and application, organizations can falsely insinuate that things be done in the same way. This causes inertia. False self-confidence, limits current employees who play an important role in gathering, storing, and disseminating future knowledge. This step breaks down the silos and opens up communication to build a knowledge management database. — The European Business Review, 1d ago

new There are two aspects to algorithmic bias: human-induced and data-induced. Human-induced bias is defined as being intentional or unintentional, and these biases (like data-induced ones) are often reinforced and amplified as new iterations of algorithms accumulate past data. The data is unrepresentative and/or insufficient in the case of data-induced bias. While there are ways to combat algorithmic biases (such as taking measures to ensure that the data is diverse and has been reviewed before feeding it to medical AI tools), the fact that deep learning is a black box makes biases easily undetectable—thus making it easier to deliver discriminatory treatment (whether it is intentional or unintentional). — Montreal AI Ethics Institute, 1d ago

new In today’s rapidly evolving technological landscape, the need to address a comprehensive approach to risk management is crucial at all levels of an organization. This imperative becomes even more pronounced in operational technology (OT) environments where different perspectives on risk prevail. While traditional risk management practices have primarily focused on physical and financial risks, the emergence of cybersecurity threats has necessitated a paradigm shift. Collaboration with engineering teams is not just desirable but necessary to incorporate cybersecurity risks into the risk register for OT environments. — Industrial Cyber, 1d ago

new Policymakers are increasingly considering risk-based assessments of AI systems, such as the EU AI Act. We believe that in this context, AI systems with the potential for deception should be classified at least as “high-risk.” This classification would naturally lead to a set of regulatory requirements, including risk assessment and mitigation, comprehensive documentation, and record-keeping of harmful incidents. Second, we suggest passing ‘bot-or-not’ laws similar to the one in California. These laws require AI-generated content to be accompanied by a clear notice informing users that the content was generated by an AI. This would give people context about the content they are viewing and mitigate the risk of AI deception. — Montreal AI Ethics Institute, 1d ago

new Sanghvi also recalls how earlier, in case of emergencies, she had to travel at night to the hospital to dictate the report to the typist. “But now, with all the data being stored on the cloud, we can do everything at home,”she says. “All doctors now have their own voice recognition passwords. I verbally dictate the report to the AI from home, and it captures everything and relays it back to the treating doctor in the hospital.”Sanghvi says more than any other branch of medicine, radiology is the foremost branch being impacted by AI. “The reason is that the information is already in a digital form, so it helps us―right from image acquisition, post processing to image interpretation and making the report,”she says. — theweek.in, 1d ago

new The use of AI-based systems to “produce targets at a fast pace” represents a grave example of digital dehumanisation raising serious concerns around the violation of human dignity and compliance with international humanitarian law. Additionally, the potential reduction of people to data points based on specific characteristics like ethnicity, gender, weight, gait, etc. raises serious questions about how target profiles are created and in turn how targets are selected. — Stop Killer Robots, 2d ago

new When organizations put appropriate AI governance standards and frameworks in place, training data, algorithms, model infrastructure, and the AI models themselves can be more closely monitored and controlled throughout initial development, training and retraining, deployment, and daily use. This contributes to a more efficient AI operation as well as compliance with relevant data privacy and AI ethics regulations. — eWEEK, 1d ago

new ...“In order to capitalize on the potential of AI, and perhaps particularly GenAI in the near term, organizations need to be clearer about how they organize and provide continual validation for their supply chain data. There are very interesting and emerging opportunities to point ML (machine learning) solutions at the foundational problems of data availability and quality, and I think leaders who foster technology enablement for data governance within their supply chain will be best situated to realize further productivity gains through more exact and precise decision making (improving decision quality).” – Caleb Thomson, senior director analyst, Gartner Supply Chain practice... — Connected World - IoT and Digital Transformation, 1d ago

new The AI Optimists don't make this argument AFAICT, but I think optimism about effectively utilizing "human level" models should transfer to a considerable amount of optimism about smarter than human models due to the potential for using these "human level" systems to develop considerably better safety technology (e.g. alignment research). AIs might have structural advantages (speed, cost, and standardization) which make it possible heavily accelerate R&D[1] even at around qualitatively "human level" capabilities. (That said, my overall view is that even if we had the exact human capability profile while also having ML structural advantages these systems would themselves pose substantial (e.g. 15%) catastrophic misalignment x-risk on the "default" trajectory because we'll want to run extremely large numbers of these systems at high speeds.)... — alignmentforum.org, 2d ago

new Ralph Ranalli (Intro): Welcome to the Harvard Kennedy School PolicyCast. I’m your host, Ralph Ranalli. When ChatGPT and other generative AI tools were released to the public late last year, it was as if someone had opened the floodgates on a thousand urgent questions that just weeks before had mostly preoccupied academics, futurists, and science fiction writers. Now those questions are being asked by many of us—teachers, students, parents, politicians, bureaucrats, citizens, businesspeople, and workers. What can it do for us? What will it do to us? Will it take our jobs? How do we use it in a way that’s both ethical and legal? And will it help or hurt our already-distressed democracy? Thankfully, my guest today, Kennedy School Lecturer in Public Policy Bruce Schneier has already been thinking a lot about those questions, particularly the last one. Schneier, a public interest technologist, cryptographer, and internationally-known internet security specialist whose newsletter and blog are read by a quarter million people, says that AI’s inexorable march into our lives and into our politics is likely to start with small changes like AI’s helping write policy and legislation. The future, however, could hold possibilities that we have a hard time wrapping our current minds around—like AIs creating political parties or autonomously fundraising and generating profits to back political parties or causes. Overall, like a lot of other things, it’s likely to be a mixed bag of the good and the bad. The important thing, he says, is to using regulation and other tools to make sure that AIs are working for us—and even paying us for the privilege and not just for Big Tech companies—a hard lesson we’ve already learned through our experience with social media. He joins me today. — harvard.edu, 2d ago

new KAREN HAO: I wanna start by unpacking the word "safety" first. And I know we've sort of been talking about a lot of different words with squishy definitions, but safety is another one of those where AI safety as OpenAI defines it, is kind of different from what we would typically think around like engineering safety. You know, there, there have been other disciplines, you know, like when we talk about a bridge being safe, it means that it holds up and it works and it resists kind of collapsing under the weight of a normal volume of traffic or even like a massive volume of traffic. With AI safety, the brand of OpenAI's AI safety, they- it is more related to this, this kind of extreme risk they have. Again, they have started adopting more of this like also focusing on current harms like discrimination, but it is primarily focused on these extreme risks. So the question I guess to to kind of reiterate is sort of like will OpenAI continue to focus on research that is very heavily indexed on extreme risks? I think so, but how are they going to change the structure to make sure that these ideological clashes don't happen again? I don't actually think that's possible, and I also think that part of what we learned from this weekend is that we shouldn't actually be waiting for OpenAI to do something about this. There will always be ideological struggles again because of this fundamental problem that we have, which is that no one knows what AGI is, no one agrees with what it is. It's all a projection of your own ideology, your own beliefs and the AI research talent pool and the broader Silicon Valley talent pool of engineers, product managers, all of those people are also ideologically split on these kind of techno-optimist versus existential-risk divides. So the, even if you try to restructure or rehire or shuffle things around, you're always going to kind of get an encapsulation of this full range of ideological beliefs within the company, and you're going to end up with these battles because of disagreements around what is actually- what are we actually working on and how do we actually get there. So I personally think that one of the biggest lessons to take away is for policymakers and for other members of the general public and consumers to recognize that this company and this technology is very much made by people. It's very much the product of conscious decisions and, and an imprint of very specific ideologies. And if we actually want to facilitate a better future with better AI technologies and AI technologies that are also applied in better ways, and it's actually up to much more than OpenAI it's up to policymakers to regulate the company, it's up to consumers to make decisions that kind of financially pressure the company to continue moving towards directions that we collectively as a society believe are more appropriate. And ultimately what this boils down to is I think like AI is such an important technology and so consequential for everyone that it needs to have more democratic processes around its development and its governance. We can't really rely on a company or a board that is, you know, tiny to represent the interests of all of humanity. — Big Think, 2d ago