DISTRIBUTION MAPPING OF GLYPHOSATE-RESISTANT Eleusine indica IN SERDANG BEDAGAI REGENCY

The presence of Eleusine indica from oil palm plantations in Serdang Bedagai Regency has never been overall reported glyphosate-resistant. This study aims to distribution mapping and resistance classification of E. indica population to glyphosate herbicide of oil palm plantations in Serdang Bedagai Regency. This research was conducted on Weed Research Center Land in Faculty of Agriculture, Universitas Sumatera Utara in October 2016 to August 2017. This research used glyphosate herbicide with the recommended dose at 720 g ai ha-1 and three replications. Population ESU0 (from Politeknik Negeri Medan Ball Field) as a comparison. Data analysis using IBM SPSS Statistics 20 software. The results showed there E. indica population classified as glyphosate-resistant amount 89.36% (42 population), classified as glyphosate-resistant moderate amount 10,64% (5 population) and there is no population glyphosate-susceptible on oil palm plantations in Serdang Bedagai Regency of recommended dose at 720 g ai ha-1

Research Agenda into Human-Intelligence/Machine-Intelligence Governance

Since the birth of modern artificial intelligence (AI) at the 1956 Dartmouth Conference, the AI community has pursued modeling and coding of human intelligence into AI reasoning processes (HI Þ MI). The Dartmouth Conference\u27s fundamental assertion was that every aspect of human learning and intelligence could be so precisely described that it could be simulated in AI. With the exception of knowledge specific areas (such as IBM\u27s Big Blue and a few others), sixty years later the AI community is not close to coding global human intelligence into AI. In parallel, the knowledge management (KM) community has pursued understanding of organizational knowledge creation, transfer, and management (HI Þ HI) over the last 40 years. Knowledge management evolved into an organized discipline in the early 1990\u27s through formal university courses and creation of the first chief knowledge officer organizational positions. Correspondingly, over the last 25 years there has been growing research into the transfer of intelligence and cooperation among computing systems and automated machines (MI Þ MI). In stark contrast to the AI community effort, there has been little research into transferring AI knowledge and machine intelligence into human intelligence (MI Þ HI) with a goal of improving human decision making. Most important, there has been no research into human-intelligence/machine-intelligence decision governance; that is, the policies and processes governing human-machine decision making toward systemic mission accomplishment. To address this gap, this paper reports on a research initiative and framework toward developing an HI-MI decision governance body of knowledge and discipline

Elementary, My Dear Watson: An Undergraduate Comic Books Course Using Enterprise AI and TEI

Two librarians taught an Honors course at James Madison University titled “Comic Books, Analysis, and Digital Scholarship.” This non-coding-requirement course introduced students to the critical study of comic books by way of DH and online tools like IBM Watson. JMU Libraries has a growing collection of comic books (more than 10,000 single issues) and a commitment to foster DH research, hence rationale for the course. Students were introduced to online annotation platforms and comic-book-extended TEI (Text Encoding Initiative), using spreadsheet entry to code a Golden Age comic book in the public domain. In addition, the students used enterprise AI (IBM-Watson) and search engine reverse image lookups to spark engagement and to promote digital literacy, most notably a hermeneutics of suspicion in relation to the corporate interests vested in these powerful tools. The blend of comic books and these technologies proved an excellent entryway into DH projects at the undergraduate level

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Using Chatbots as AI Conversational Partners in Language Learning

Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (n = 115) and Poland (n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction.This study is part of a larger research project, [The application of AI and chatbots to language learning], financed by the Instituto de Ciencias de la Educacion at the Univesity of Alicante (Reference number: 5498)

IBM Cloud Strategic Audit

An examination of IBM Cloud\u27s strategy and history and a recommendation for what to do moving forward