The excitement of using Artificial Intelligence has not dwindled from the time it has been unfolded. In KPMG study on “living in the AI world 2020: achievements and challenges of AI across 5 industries (retail, financial service, healthcare, transportation, and technology), revealed that 92% of respondents agreed that leveraging the spectrum of AI technologies will make their companies run more efficiently. Amidst the admiration towards AI, IBM created the Women Leaders in AI program in 2019. This was a way to acknowledge the women leading in AI and encourage females to lend a hand in the field of AI. Through this IBM, planned to make the efforts of the honourees more visible to the world. 2020 IBM women leaders were honoured for outstanding leadership in the AI space. Here is the list of women leaders in AI 2020 honorees:- Aarthi Fernandez Who is a Global head of Trade Operations and SEA Trade COO at Standard Chartered Bank? She is a C-suite executive with deep insight on how digitalization can positively disrupt US$17 trillion global trade. She is into deploying AI/Machine learning to make trade financing simple, faster, and better for corporate clients and mitigate compliance risk. Piera Valeria Cordaro She is a commercial Operations Innovation Manager, Wing Tre S.p.A., Italy. She is a speaker, advocating the use of AI in customer operations. Along with her team and with support by IBM Watson, implemented two chatbots, to improve customer experience. Both bots have made it possible to handle a million queries efficiently. Amala Duggirala Who is the enterprise Chief operation and Technology officer, Regions Bank, United States. To handle customers’ inquiries she deployed IBM Watson’s assistant- virtual banker persona, ”Reggie”. From the time of its implementation 4.3 million customer calls have been answered, with 22% of them being handled by AI. Mara Reiff Vice President, Strategy and Business Intelligence, Beli Canada, Canada. She used AI to improve operations, loyalty, and brand. She worked with IBM to install Watson studio Local using Red Hat open shift. This resulted in smarter, fast decision-making with improved customer experience leading to increased sales. Mara suggests everybody to “Make sure to stop and smell the roses. Take each opportunity to learn something new and embrace change”. Amy Shreve- McDonald She is lead Product Marketing Manager for Business Digital experience, AI&T, USA. EVA (Enterprise Virtual Agent) was launched in February 2019, to improve customer chat experience, it uses Watson assistant. This system has been able to handle 45% chats on its own, resulting in reduced costs and expanding 24/7 support. She also received AT&T’s 2019 Visionary Award for her work advocating EVA. Ryoko Miyashita Manager, customer service department, customer service section JACCS CO., LTD Japan. She launched a Watson-enabled operator onboarding tool, that resulted in reduced new operator training period by 30%. The tool has increase customer satisfaction. Her advice to the younger self is “It is important to believe in yourself, but it is equally or more important to believe in people around you. I would encourage myself to have many experiences and garner knowledge to objectively evaluate things, not blindly accept or exclude others’ opinions”. Carol Chen She is Vice President for Global Marketing, Global Commercial, Royal Dutch Shell, United Kingdom. Along with her team, Carol is partnering is planning for digital transformation with the creation of “Oren”- a Smart Minning Platform, by partnering with IBM. This platform will offer an innovative and creative experience for users in the sector to deliver connectivity and integration across the ecosystem. To use AI, she advice commencing with analyzing the business outcome that one wants and customer pain points that one can cater to. The next step would be to determine how to leverage AI and data to solve the problem. Rosa Martinez Cognitive Project Manager, CiaxaBank, Spain. For those who consider using AI, her advice to them is ‘first to understand the business case as it may take time more than expected. This phase can result in a non-AI project example a ‘software as usual’. But moving further with the project there can be more AI application for sure to work on’. Lee- Lim Sok Know Deputy Principal, Temasek Polytechnic, Singapore. Under the leadership of Sok Keow, The higher education institution in Singapore ‘Temasek Polytechnic’ launched the “Ask TP” chatbot in January 2018. The chatbot helped current as well as prospective students to get answers to the questions asked about Temasek and also gave personalized course advice. In the 1st two weeks of 2020, ‘Ask’ TP’ responded to more than4,351 questions. She suggests everybody “deeply appreciate ‘people’ as they are the most critical asset in an organization, and a leader must develop a team”. Itumeleng Monale Executive Head of Enterprise Information Management Personal and Business Banking, Standard Bank of South Africa, South Africa. By deploying many analytical tools in her organization, she can uplift the revenue of the company. Through models of analytics relationships, bankers are experiencing a 40% revenue uplift when comparing to their peers. She sees AI as a tool through which business delivery can be accelerated, value could be added to human capital and relationships can build further. With this AI era, Research has postulated that corporate giants still have less percentage of women in the technical department. Facebook’s diversity report suggests that there are 22 % of women in the technical department and 15 per cent of women work in the AI research group. Similarly, Google’s diversity report suggests that only 10% women are working on  “machine intelligence”. There is a need to encourage women participation as there are many more women around the world, stepping out of the pre-existed sheathe and going beyond the walls to shape the future. Opening up the AI platform for all will fetch us more talented beings which can help us celebrate the use of AI in different fields and different ways. Reference:- https://www.ibm.com/watson/women-leaders-in-ai/2020-list https://advisory.kpmg.us/content/dam/advisory/en/pdfs/2020/technology-living-in-an-ai-world.pdf   About the author:- Kirti Kumar is a budding HR professional currently pursuing PGDM in HR and Marketing at New Delhi Institue of Management. She looks forward to opportunities that can hone her skills. She is agile in her attitude with versatility in her action

The graph represents a network of 2,121 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 22 September 2020 at 21:13 UTC. The requested start date was Tuesday, 22 September 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 16-hour, 57-minute period from Saturday, 19 September 2020 at 07:03 UTC to Tuesday, 22 September 2020 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Recently, novel multi-hop models and datasets have been introduced to achieve more complex natural language reasoning with neural networks. One notable task that requires multi-hop reasoning is fact checking, where a chain of connected evidence pieces leads to the final verdict of a claim. However, existing datasets do not provide annotations for the gold evidence pieces, which is a critical aspect for improving the explainability of fact checking systems. The only exception is the FEVER dataset, which is artificially constructed based on Wikipedia and does not use naturally occurring political claims and evidence pages, which is more challenging. Most claims in FEVER only have one evidence sentence associated with them and require no reasoning to make label predictions -- the small number of instances with two evidence sentences only require simple reasoning. In this paper, we study how to perform more complex claim verification on naturally occurring claims with multiple hops over evidence chunks. We first construct a small annotated dataset, PolitiHop, of reasoning chains for claim verification. We then compare the dataset to other existing multi-hop datasets and study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex, and achieve the best performance using an architecture that specifically models reasoning over evidence chains in combination with in-domain transfer learning.

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models' predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field.

Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e.g, FLAIR MRI from T1 MRI). However, such frameworks are primarily designed to operate on images, limiting their generalizability to non-Euclidean geometric data such as brain graphs. While a growing number of connectomic studies has demonstrated the promise of including brain graphs for diagnosing neurological disorders, no geometric deep learning work was designed for multiple target brain graphs prediction from a source brain graph. Despite the momentum the field of graph generation has gained in the last two years, existing works have two critical drawbacks. First, the bulk of such works aims to learn one model for each target domain to generate from a source domain. Thus, they have a limited scalability in jointly predicting multiple target domains. Second, they merely consider the global topological scale of a graph (i.e., graph connectivity structure) and overlook the local topology at the node scale of a graph (e.g., how central a node is in the graph). To meet these challenges, we introduce MultiGraphGAN architecture, which not only predicts multiple brain graphs from a single brain graph but also preserves the topological structure of each target graph to predict. Its three core contributions lie in: (i) designing a graph adversarial auto-encoder for jointly predicting brain graphs from a single one, (ii) handling the mode collapse problem of GAN by clustering the encoded source graphs and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the reconstruction of topologically sound target brain graphs. Our MultiGraphGAN significantly outperformed its variants thereby showing its great potential in multi-view brain graph generation from a single graph.

Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease. Although limited, existing BNA estimation methods have several limitations. First, they primarily rely on a similarity network diffusion and fusion technique, which only considers node degree as a topological measure in the cross-network diffusion process, thereby overlooking rich topological measures of the brain network (e.g., centrality). Second, both diffusion and fusion techniques are implemented in fully unsupervised manner, which might decrease the discriminative power of the estimated BNAs. To fill these gaps, we propose a supervised multi-topology network cross-diffusion (SM-netFusion) framework for estimating a BNA satisfying : (i) well-representativeness (captures shared traits across subjects), (ii) well-centeredness (optimally close to all subjects), and (iii) high discriminativeness (can easily and efficiently identify discriminative brain connections that distinguish between two populations). For a specific class, given the cluster labels of the training data, we learn a weighted combination of the topological diffusion kernels derived from degree, closeness and eigenvector centrality measures in a supervised manner. Specifically, we learn the cross-diffusion process by normalizing the training brain networks using the learned diffusion kernels. Our SM-netFusion produces the most centered and representative template in comparison with its variants and state-of-the-art methods and further boosted the classification of autistic subjects by 5-15%. SM-netFusion presents the first work for supervised network cross-diffusion based on graph topological measures, which can be further leveraged to design an efficient graph feature selection method for training predictive learners in network neuroscience.

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.

The Cairo-based startup Elves has raised $2 million in seed funding from Egyptian VC fund Sawari Ventures. According to Menabytes, part of the investment was received in February and the other in July. Sawari Ventures was the investor in both cases. If we are to factor in this recent investment, the startup will have thus far raised $5 million. In 2017, Elves raised a record sum of investment in what was MENA region's largest seed round.

International organizations and corporations are racing to develop global guidelines for the ethical use of artificial intelligence. Declarations, manifestos, and recommendations are flooding the internet. But these efforts will be futile if they fail to account for the cultural and regional contexts in which AI operates. AI systems have repeatedly been shown to cause problems that disproportionately affect marginalized groups while benefiting a privileged few. The global AI ethics efforts under way today--of which there are dozens--aim to help everyone benefit from this technology, and to prevent it from causing harm. Generally speaking, they do this by creating guidelines and principles for developers, funders, and regulators to follow.

Verdict lists ten of the most popular tweets on artificial intelligence (AI) in August 2020 based on data from GlobalData's Influencer Platform. The top tweets were chosen from influencers as tracked by GlobalData's Influencer Platform, which is based on a scientific process that works on pre-defined parameters. Influencers are selected after a deep analysis of the influencer's relevance, network strength, engagement, and leading discussions on new and emerging trends. Ronald van Loon, principal analyst and CEO of Intelligent World, shared a video from the World Economic Forum on a neural machine translation technology developed by Google to provide natural translation between different languages using artificial intelligence and deep learning. The system was also used to translate two languages without using English as a bridge.