Training week for selected students delivered by the SCtrain project.

Artificial intelligence, or AI, is the branch of Computer Science that derives its inspiration from biological systems to solve highly complex problems. There are many facets of AI, including search algorithms, knowledge representation, reasoning and learning. The field of learning is the best known and can take many forms, from Genetic Algorithms that emulate evolution to Deep Learning neural networks that mimic the neural pathways of the brain. These forms can even include swarm-based algorithms that mimic ant colonies. Currently, deep neural nets have captured the imagination of researchers and have provided incredible breakthroughs in neuroscience and beyond.

John Snow Labs is an award-winning AI and NLP company, accelerating progress in data science by providing state-of-the-art software, data, and models. Founded in 2015, it helps healthcare and life science companies build, deploy, and operate AI products and services. John Snow Labs is the winner of the 2018 AI Solution Provider of the Year Award, the 2019 AI Platform of the Year Award, the 2019 International Data Science Foundation Technology award, and the 2020 AI Excellence Award. John Snow Labs is the developer of Spark NLP - the world's most widely used NLP library in the enterprise - and is the world's leading provider of state-of-the-art clinical NLP software, powering some of the world's largest healthcare & pharma companies. John Snow Labs is a global team of specialists, of which 33% hold a Ph.D. or M.D. and 75% hold at least a Master's degree in disciplines covering data science, medicine, software engineering, pharmacy, DevOps and SecOps.

I have written columns on Artificial Intelligence (AI) in recent weeks. In the first one I asked the question "should we fear the AI that is going to hit us" and in the second I asked "can we build trust and ethics in AI systems." Today, I would like to share "the progress of AI": Let's have a look at the tools of AI progress in action: Applications: From AI-generated art, articles, and even dissertations, to applications across industries like health care and manufacturing, we've come a long way from just teaching computers to play chess. Look at this: college students are sharing (anonymously) that they've started using AI tools to generate essays that can bypass anti-plagiarism software and score an A. The widespread use of the tools could reshape education and force schools to figure out new writing prompts or entirely fresh ways of assessing student performance to avoid being duped by the technology. Projections: AI isn't quite classifiable as an "industry" yet, but with its growth expectations, it could soon arrive there.

Speech EdTech company SoapBox Labs has received an AI product certification. These product certifications serve as rigorous, reliable signals for education companies, school administrators, educators, and families looking to select technology that was designed to intentionally meet learners' authentic and variable needs. Speech technology company SoapBox Labs is the first company to receive the Prioritizing Racial Equity in AI Design Product Certification. SoapBox Labs' voice technology has been designed specifically for kids' unique speech patterns and powers some of the most widely used digital learning tools for literacy and language instruction and assessment across all grades PreK-12. "This award not only recognizes SoapBox Labs' commitment to racial equity but also constitutes a meaningful step forward for the edtech industry," said Nidhi Hebbar, co-founder of the Edtech Equity Project.

The program is designed to promote & support research, mentoring & community. SOARS Protgs can participate for up to four (4) summers conducting research in atmospheric & earth-system sciences. SOARS offers comprehensive financial support for summer research, conference travel, as well as undergraduate & graduate school funding. Over 90% of SOARS Protgs advance to graduate school; & many have entered the workforce with the MS degree, and/or continued onto the PhD degree. SOARS invites students from multiple STEM disciplines -- including chemistry, ecology, engineering, geography, mathematics, meteorology, physics, & the social sciences--to deepen their expertise in understanding the Earth's Atmosphere.

September 2022 was apparently the month artificial intelligence essay angst boiled over in academia, as various media outlets published opinion pieces lamenting the rise of AI writing systems that will ruin student writing and pave the way toward unprecedented levels of academic misconduct. Then, on Sept. 23, academic Twitter exploded into a bit of a panic on this topic. The firestorm was prompted by a post to the OpenAI subreddit where user Urdadgirl69 claimed to be getting straight A's with essays "written" using artificial intelligence. Professors on Reddit and Twitter alike expressed frustration and concern about how best to address the threat of AI essays. One of the most poignant and widely retweeted laments came from Redditor ahumanlikeyou, who wrote, "Grading something an AI wrote is an incredibly depressing waste of my life."

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due to their learning abilities in handling data uncertainties in distributed scenarios. However, it is challenging for DFNNs to handle cases in which the local data are non-independent and identically distributed (non-IID). In this paper, we propose a federated fuzzy neural network (FedFNN) with evolutionary rule learning (ERL) to cope with non-IID issues as well as data uncertainties. The FedFNN maintains a global set of rules in a server and a personalized subset of these rules for each local client. ERL is inspired by the theory of biological evolution; it encourages rule variations while activating superior rules and deactivating inferior rules for local clients with non-IID data. Specifically, ERL consists of two stages in an iterative procedure: a rule cooperation stage that updates global rules by aggregating local rules based on their activation statuses and a rule evolution stage that evolves the global rules and updates the activation statuses of the local rules. This procedure improves both the generalization and personalization of the FedFNN for dealing with non-IID issues and data uncertainties. Extensive experiments conducted on a range of datasets demonstrate the superiority of the FedFNN over state-of-the-art methods.

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales. With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.

In any given machine learning problem, there might be many models that explain the data almost equally well. However, most learning algorithms return only one of these models, leaving practitioners with no practical way to explore alternative models that might have desirable properties beyond what could be expressed by a loss function. The Rashomon set is the set of these all almost-optimal models. Rashomon sets can be large in size and complicated in structure, particularly for highly nonlinear function classes that allow complex interaction terms, such as decision trees. We provide the first technique for completely enumerating the Rashomon set for sparse decision trees; in fact, our work provides the first complete enumeration of any Rashomon set for a non-trivial problem with a highly nonlinear discrete function class. This allows the user an unprecedented level of control over model choice among all models that are approximately equally good. We represent the Rashomon set in a specialized data structure that supports efficient querying and sampling. We show three applications of the Rashomon set: 1) it can be used to study variable importance for the set of almost-optimal trees (as opposed to a single tree), 2) the Rashomon set for accuracy enables enumeration of the Rashomon sets for balanced accuracy and F1-score, and 3) the Rashomon set for a full dataset can be used to produce Rashomon sets constructed with only subsets of the data set. Thus, we are able to examine Rashomon sets across problems with a new lens, enabling users to choose models rather than be at the mercy of an algorithm that produces only a single model.