Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neural Agent Assistants (NAA) with three high-level components for: (1) Intent Identification, (2) Context Retrieval, and (3) Response Generation. In this paper, we outline the pipeline of the NAA's core system and also present three case studies in which three industry partners successfully adapt the framework to find solutions to their unique challenges. Our findings suggest that a collaborative process is instrumental in spurring the development of emerging NLP models for Conversational AI tasks in industry. The full reference implementation code and results are available at \url{https://github.com/VectorInstitute/NAA}

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

Well, every year, for the first week of this module, we engage in what I like to call our Data Science Bootcamp. This is an intensive preparatory week in which I present the students with a sample data science project, from start to finish, to give them a sense of what is expected from their group efforts. Every year I try to create a new project on a contemporary topic. In the past, I have covered topics such as marathon running (linked to Elide Kipchoge's efforts to break the 2-hour barrier), Hollywood movies (is it true that movies are rarely as good as the books on which they are based?), and the COVID pandemic, among others. During the bootcamp week, I describe how to take a topic from a vague project idea to a concrete set of suitable research questions, how to assemble an appropriate dataset, how to clean and analyze the data, and how to use the results of the analysis to carefully answer the research questions in a clear and compelling way.

Data scientist Oisín Boydell is working on a project that seeks to democratise access to an ever-increasing volume of Earth observation data. Dr Oisín Boydell is principal data scientist and head of the applied research group at CeADAR, the SFI-funded centre for applied AI at University College Dublin (UCD). His primary research interests include trustworthy AI, deep learning, natural language processing and applications of AI to Earth observation data. After working as a software developer in the UK, Boydell returned to UCD to undertake a PhD in computer science, researching novel approaches for personalised information retrieval. Prior to joining CeADAR he worked with SMEs and multinationals on big data analytics and machine learning solutions for the telecommunications industry.

The New Brunswick Innovation Foundation (NBIF) is an independent funding corporation. In January, Jeff White, its CEO, Ginette Petitpas Taylor, Minister of Official Languages and Minister responsible for ACOA, and Jenica Atwin, Member of Parliament for Fredericton, officially launched the new "NBIF Artificial Intelligence (AI) Fund". The objective of the Fund is to bring together academic researchers and companies to collaborate. New Brunswick is a Canadian province of about 750,000 inhabitants, located south of Quebec, on the Atlantic coast. Artificial intelligence is, as in Canada (and all developed countries), considered a major strategic asset for economic growth, employment and innovation.

Advancing trustworthy AI and machine learning to mitigate agency risk is a priority for the US Department of Energy (DOE), and identifying best practices for implementing AI at scale is a priority for the US General Services Administration (GSA). That's what attendees learned in two sessions at the AI World Government live and virtual event held in Alexandria, Va. last week. Pamela Isom, Director of the AI and Technology Office at the DOE, who spoke on Advancing Trustworthy AI and ML Techniques for Mitigating Agency Risks, has been involved in proliferating the use of AI across the agency for several years. With an emphasis on applied AI and data science, she oversees risk mitigation policies and standards and has been involved with applying AI to save lives, fight fraud, and strengthen the cybersecurity infrastructure. She emphasized the need for the AI project effort to be part of a strategic portfolio.

More than $124 million in new funding for artificial intelligence research and industry development support allocated in the federal budget in May is still locked up inside the Industry department, with no clear signal on how and when it will be rolled out. The Australian Information Industry Association says Australia can't afford to sit on its hands in relation to the AI research and commercialisation – the industry is moving too fast, and the nation can't afford to fall behind. AIIA chief executive Ron Gauci says national capability in artificial intelligence is critical, because of the transformational impact that AI-based products and services are having across all industries. The AIIA has been pressing government for a funding allocation to drive commercialisation outcomes in the sector. The industry association had been told its "modest" proposal to bring together industry partners and state governments in a dollar-for-dollar funding arrangement with the Commonwealth had been agreed to.

The Joint Artificial Intelligence Center is out with a $241 million contract vehicle to help the Defense Department become ready for AI development by preparing data for the emerging technology. The Data Readiness for Artificial Intelligence Development services solicitation covers a five-year performance period and will result in multiple basic ordering agreements, according to the solicitation documents published Wednesday to beta.sam.gov. "The purpose of this Performance Work Statement (PWS) is to help the DoD and Government users prepare data for use in AI applications by providing an easily accessible path to access the cutting-edge commercial services needed to meet the complex technical challenges involved in preparing data for AI," the documents read. "Through access to AI data preparation tools, capabilities, and services, the DoD will be positioned to effectively prepare AI data to support the full range of AI activities across the DoD." The PWS indicates the services the Defense Department is looking for under this contract include curating, preparing, securing, and encrypting data for AI, securing, packaging and delivering AI tools, and making sure those tools can be integrated into cloud platforms.

The Joint Artificial Intelligence Center is looking for AI test and evaluation services to support the Defense Department and "the entire U.S. government," according to a new request for proposal posted Feb. 11. The JAIC, through Army Contracting Command-Rock Island, intends to award multiple blanket purchase agreements for AI testing and evaluation services. The contract has a ceiling of more than $249 million, according to a question and answer document posted along with the RFP. Offers on the solicitation are due March 5. Jane Pinelis, the JAIC's testing and evaluation chief, said the contract and another forthcoming multi-award contract for data readiness assessments will help connect DOD components to industry partners with readily available services facilitating AI adoption. Data readiness has been one of the biggest impediments thwarting fielding of AI in DOD, she said.

Even before countries began rolling out their vaccination campaigns, Pfizer, Moderna and AstraZeneca's announcements had already proved fortifying shots. Stocks rallied and healthcare workers celebrated in the wake of the vaccine news late last year. But months on, that early euphoria has evaporated, replaced by uncertainty and debate over vaccine safety, possible side effects and varying degrees of citizen reluctance. Artificial intelligence (AI) researchers and health experts modeling COVID-19's spread have warned that for vaccines to be useful in curbing the pandemic, a significant percentage of the population must be vaccinated to reach herd immunity. But, as SMU's Vice Provost of Research Professor Archan Misra pointed out at an AI-centered panel discussion, held in conjunction with the SMU- Global Young Scientists Summit (GYSS) on 15 January 2021, from a purely self-interested point of view, each person would be best served if all the others got vaccinated and they themselves did not have to vaccinate--because that would stop the spread of the virus without their having to take on the possible risks of side effects. To account for these considerations, Professor Misra explained, the most powerful AI-based epidemiology models actually need to incorporate concepts from the behavioral sciences and game theory.