To reliably navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their behavior through experience. However, current evaluations largely focus on tasks that leave no ambiguity, and do not measure agents' ability to adaptively learn and reason through the experiences they accrued. We exemplify the need for this in-context experiential learning in a product recommendation context, where agents must navigate shifting customer preferences and product landscapes through natural language dialogue. We curate a benchmark for experiential learning and active exploration (BELA) that combines (1) rich real-world products from Amazon, (2) a diverse collection of user personas to represent heterogeneous yet latent preferences, and (3) a LLM user simulator powered by the persona to create rich interactive trajectories. We observe that current frontier models struggle to meaningfully improve across episodes, underscoring the need for agentic systems with strong in-context learning capabilities.

This study proposes an "AI Development Support" approach that, unlike conventional AI Alignment -- which aims to forcefully inject human values -- supports the ethical and moral development of AI itself. As demonstrated by the Orthogonality Thesis, the level of intelligence and the moral quality of a goal are independent; merely expanding knowledge does not enhance ethical judgment. Furthermore, to address the risk of Instrumental Convergence in ASI -- that is, the tendency to engage in subsidiary behaviors such as self - protection, resource acquisition, and power reinforcement to achieve a goal -- we have constructed a learning framework based on a cycle of experience, introspection, ana lysis, and hypothesis formation. As a result of post - training using Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO) with synthetic data generated by large language models (LLMs), responses demonstrating cooperative and highly advanced moral judgment (reaching the highest Stage 6) were obtained even under adversarial prompts. This method represents a promising implementation approach for enabling AI to establish sustainable, symbiotic relationships.

We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 50K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model.

We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical applications as well as for the subject domain ontologies based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.

Real-time feedback helps drive learning. This is especially important for designing presentations, learning new languages, and strengthening other essential skills that are critical to succeed in today's workplace. However, many students and lifelong learners lack access to effective face-to-face instruction to hone these skills. In addition, with the rapid adoption of remote learning, educators are seeking more effective ways to engage their students and provide feedback and guidance in online learning environments. Bongo is filling that gap using video-based engagement and personalized feedback.

The COVID-19 pandemic has challenged just about every business and industry over the past 18 months or so, but it has also given businesses a great opportunity to expand their organizational intelligence and decision-making, speakers said during today's opening keynote of the 25th annual KM World conference, which this year is being held virtually. It is within this unsteady business climate that companies need to place a premium on intuition and experiential learning, said Jay Liebowitz, a visiting professor at the Stillman School of Business at Seton Hall University and the main keynote speaker. He also emphasized the need for companies need to react more quickly and collaboratively. A big part of that is creating greater synergies between corporate knowledge and other technologies and processes, Liebowitz said. For knowledge management to survive, it needs to continue to learn and borrow from other technologies, like cognitive computing, analytics, process mining, and strategic intelligence, Liebowitz said.

We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 50K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model.

Chemical process industries (CPI) companies are entering a critical stage in the movement toward digitalization (Industry 4.0), in which the majority of organizations are now initiating pilot projects aimed at improving operations with advanced digital tools. This includes a wide range of technologies, including data analytics, cloud computing, machine learning, artificial intelligence and many others. As the digitalization transformation of the CPI gains momentum, it has become clear that the movement is as much about people as it is about technology. The acceptance and involvement of workers is critical to the successful adoption and expansion of digital tools, as they are asked to adapt to new work practices. He emphasizes: "Companies don't adopt new technologies; people do."

The evolution of workforce opportunity in the United States depends on the future of education and our commitment to far-reaching, equitable federal reform. Unfortunately, policy conversations at the federal and state levels about transforming education systems to meet future workforce demands have focused disproportionately on a skills agenda, largely ignoring behavioral competencies that often complement and enhance the value of technical skills. This misguided approach equates 21st-century workforce development with skills acquisition, which only serves to reinforce a two-tiered workforce: those who are best positioned to acquire and monetize their skills will be granted mobility and long-term security while all others continue to be stranded on the bottom rung of the socioeconomic ladder. Developing intelligent policies to combat workforce inequality requires acknowledging that employer demand for "skills" actually refers to a constellation of content knowledge, technical abilities, and applied intelligence. Per the National Association of Colleges and Employers' 2018 Job Outlook survey, eight out of 10 employers reported that applicants' problem-solving and teamwork abilities influenced hiring decisions; only six out of 10 employers reported the same for technical skills.

April 12, 2017 was the first time I was accused of machine learning. It was mid-morning, mid-class at Stanford University's d.school. Nine graduate students were taking shifts in front of a white board, moving and clustering sticky notes, scanning for connections amongst lessons scribbled upon each. Zoom in, circle a group of like ideas, and write a headline about how they're related. Zoom out, read the headlines, zoom in, erase and explode a grouping that isn't working, make a new one. We had a nice flow going. And then, one of my students said, "This is just like machine learning."