NITI Aayog, back in June 2018, released the National Strategy on Artificial Intelligence, and now PM Narendra Modi launched the'AI for All' initiative by CBSE. An approach towards fulfilling NEP 2020 objectives, the government seems keen to have youth ready for the future. "I personally view'AI for All' representing both sides of the coin: the equitable access to AI skills, as well as the equitable deployment of AI technologies. In a diverse country like ours, with citizens across different literacy levels and economic strata, both of these are extremely important," said Vineeth N Balasubramanian, Head of AI Dept., IIT-Hyderabad. Being the third-largest economy in terms of Purchasing Power Parity (PPP), the second-highest number of engineering graduates, and the world's highest data usage per smartphone -- India's low number of academic, corporate peer-reviewed AI publications do not justify its potential.

Cybercriminals are using artificial intelligence (AI) to evolve the sophistication of attacks at a rapid pace. In response, an increasing number of organisations are also adopting the technology as part of their cybersecurity strategies. According to research conducted in Mimecast's State of Email Security Report 2021, 39 per cent of organisations are utilising AI to bolster their email defences. Although we're still in the early phases of these technologies and their application to cybersecurity, this is a rising trend. Businesses using advanced technologies such as AI and layered email defences, while also regularly training their employees in attack-resistant behaviours, will be in the best possible position to sidestep future attacks and recover quickly.

Over the past decade, technological innovation has advanced at an increasingly fast pace, creating both opportunities and disruptions in virtually every industry. The postal industry is no exception. According to the report, "Step into Tomorrow: The U.S. Postal Service (USPS) and Emerging Technology," the Postal Service collects massive quantities of data on an ongoing basis. A challenge is putting this data to its most valued use to improve the customer experience. Data-driven advanced algorithms and analytics can play a critical role in the design of these new, last-mile solutions.

It looks like mankind won't be going back to the moon … on schedule, at least. According to a recent report by the National Aeronautics and Space Administration's inspector general, astronaut suits have been delayed by two years due to an array of technical, funding and COVID-related challenges. But, the unavoidable conclusion, "a lunar landing in late 2024 as NASA currently plans is not feasible," is hardly surprising given NASA's string of failures in trying to take humanity back to the lunar surface. The failures also speak to a larger strategic mistake that places inordinate importance on planting flags on alien worlds despite the practical and scientific disadvantages of that approach. Humanity can venture to infinity and beyond while avoiding the black hole of wasteful spending.

Eight years after Kennedy's initial so-called moonshot challenge, two American astronauts took the famous "one giant leap for mankind"--walking on the Moon for the first time. However, the bigger impact of that moonshot was the resultant building of a massive military and industrial innovation complex that propelled the US to the top of the industrial economy. The industrial economy, in simple terms, grew by selling excess production from one place to another by connected pathways. By 1975, the industrial economy was at its peak, with more than 1 billion places connected by rail, road and airways dominated by multinational companies born in the US, selling technology developed during the moonshot. The other spinoff of this moonshot was ARPANET, kicked off in 1966, which eventually became the internet. The internet gave rise to the knowledge economy driven by connecting people via the world wide web.

What a ride it has been in the Transportation and Logistics (T&L)sector regarding the B2C eCommerce growth boom world-wide, much of this driven by the global retail sales growth during COVID-19. This accelerated growth and now with global trade rapidly rebounding, the timing is right for the transportation and logistics industry to advance smarter digital transformations. According to McKinsey, this industry must be completely digital to secure its future – but what will it take? The future although seems rosy, is complex and challenging due to rapid industry consolidations, new technology acceleration, ever constant regulatory changes such as GDBR, and of course, Brexit impacting European markets. The World Trade Organization (WTO) has been most vocal reinforcing the importance of the T&L Industry to take heed on the importance of customer experiences – how courier drivers ship, route and deliver parcels and products with agile speed has become the new normal. Service speed from Amazon has shifted customer expectations on timeliness on B2B to be as resilient as their B2C personal experiences demanding instant quotations, real time tracking on orders, and personalize messaging on express services.

Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black". Such bias is commonly reflected in false-positive predictions, i.e. non-toxic comments with identity terms. In this work, we propose a novel approach to tackle such bias in toxic comment classification, leveraging the notion of subjectivity level of a comment and the presence of identity terms. We hypothesize that when a comment is made about a group of people that is characterized by an identity term, the likelihood of that comment being toxic is associated with the subjectivity level of the comment, i.e. the extent to which the comment conveys personal feelings and opinions. Building upon the BERT model, we propose a new structure that is able to leverage these features, and thoroughly evaluate our model on 4 datasets of varying sizes and representing different social media platforms. The results show that our model can consistently outperform BERT and a SOTA model devised to address identity term bias in a different way, with a maximum improvement in F1 of 2.43% and 1.91% respectively.

FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.

Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.

Numerical reasoning skills are essential for complex question answering (CQA) over text. It requires opertaions including counting, comparison, addition and subtraction. A successful approach to CQA on text, Neural Module Networks (NMNs), follows the programmer-interpreter paradigm and leverages specialised modules to perform compositional reasoning. However, the NMNs framework does not consider the relationship between numbers and entities in both questions and paragraphs. We propose effective techniques to improve NMNs' numerical reasoning capabilities by making the interpreter question-aware and capturing the relationship between entities and numbers. On the same subset of the DROP dataset for CQA on text, experimental results show that our additions outperform the original NMNs by 3.0 points for the overall F1 score.