Please note, I often use #TidyTuesday to develop my technical abilities in data visualisation at the expense of not always producing something particularly meaningful or useful. Finally found a reason to use trigonometric functions in a visualisation. I ran out of ideas this week so brushed up on some simple text analyses. First time using a map as a legend - I should probably do this more often. There was huge variation in total votes received for each personality type, but extroverts were generally more popular.

In a country such as India that has a low doctor-patient ratio, Artificial Intelligence (AI) can enable greater access to expert care from anywhere, with telehealth and robotics applied across inpatient and outpatient environments. Experts says AI will bolster the role of healthcare by assisting in screening, diagnosis, and treatment of diseases thereby improving quality of life and reducing the cost burden for patients. "India has a tremendous opportunity to lead human-centric applications and democratise AI for the world backed by high skilled talent, technology, vast data availability, and the potential for population-scale AI adoption," says Vice-president and managing director of Sales, Marketing and Communications Group, Intel India. Intel has been focusing its efforts towards accelerating AI innovation to deliver transformative healthcare solutions and democratise healthcare access and delivery in India. The company's portfolio of compute, memory, storage, and networking technologies powers some of the most exciting healthcare and life sciences applications.

If artificial intelligence is the future, then the future is now. This pandemic has shown us just how fast artificial intelligence, or AI, works and what it can do in so many different ways. Right from the start, AI has helped us learn about SARS-CoV-2, the virus that causes COVID-19 infections. It's helped scientists analyse the virus' genetic information -- its DNA -- at speed. DNA is the stuff that makes the virus, indeed any living thing, what it is.

Decentralized autonomous organizations as a new form of online governance arecollections of smart contracts deployed on a blockchain platform that intercede groupsof people. A growing number of Decentralized Autonomous Organization Platforms,such as Aragon and Colony, have been introduced in the market to facilitate thedevelopment process of such organizations. Selecting the best fitting platform ischallenging for the organizations, as a significant number of decision criteria, such aspopularity, developer availability, governance issues, and consistent documentation ofsuch platforms, should be considered. Additionally, decision-makers at theorganizations are not experts in every domain, so they must continuously acquirevolatile knowledge regarding such platforms and keep themselves updated.Accordingly, a decision model is required to analyze the decision criteria usingsystematic identification and evaluation of potential alternative solutions for adevelopment project. We have developed a theoretical framework to assist softwareengineers with a set of Multi-Criteria Decision-Making problems in software production.This study presents a decision model as a Multi-Criteria Decision-Making problem forthe decentralized autonomous organization platform selection problem. Weconducted three industry case studies in the context of three decentralizedautonomous organizations to evaluate the effectiveness and efficiency of the decisionmodel in assisting decision-makers.

Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for this growth include recent legislative changes and increased investments by industry and governments, along with increased concern from the general public. People are affected by autonomous decisions every day and the public need to understand the decision-making process to accept the outcomes. However, the vast majority of the applications of XAI/IML are focused on providing low-level `narrow' explanations of how an individual decision was reached based on a particular datum. While important, these explanations rarely provide insights into an agent's: beliefs and motivations; hypotheses of other (human, animal or AI) agents' intentions; interpretation of external cultural expectations; or, processes used to generate its own explanation. Yet all of these factors, we propose, are essential to providing the explanatory depth that people require to accept and trust the AI's decision-making. This paper aims to define levels of explanation and describe how they can be integrated to create a human-aligned conversational explanation system. In so doing, this paper will survey current approaches and discuss the integration of different technologies to achieve these levels with Broad eXplainable Artificial Intelligence (Broad-XAI), and thereby move towards high-level `strong' explanations.

I recently conducted a 3-day, remote "Data Monetization: Thinking Like a Data Scientist" workshop for a transportation agency in the Middle East. Doing this training remotely is a personal challenge as I miss the face-to-face interaction in ideating, validating, and prioritizing the business areas that can benefit from data and analytics. However, conducting the workshop remotely did provide some valuable learnings for me. One learning was my "Thinking Like a Data Scientist" visual was outdated (Figure 1). Figure 1 portrayed the "Thinking Like a Data Scientist" (TLADS) process as a linear process, where you would complete one step and then cleanly move onto the next step. But in reality, the process is highly iterative where it is common for learnings from one step to impact an earlier step such as refining the KPIs against which the targeted business initiative's progress and success will be measured.

Global Machine Learning Market Report 2021 is latest research study released by HTF MI evaluating the market risk side analysis, highlighting opportunities and leveraged with strategic and tactical decision-making support. The report provides information on market trends and development, growth drivers, technologies, and the changing investment structure of the Global Machine Learning Market. Some of the key players profiled in the study are Microsoft Corporation, IBM Corporation, SAP SE, SAS Institute, Google, Amazon Web Services, Baidu, BigML, Fair Isaac Corporation (FICO), Hewlett Packard Enterprise Development LP (HPE), Intel Corporation, KNIME.com AG, RapidMiner, Angoss Software Corporation, H2O.ai, Alpine Data, Domino Data Lab, Dataiku, Luminoso Technologies, TrademarkVision, Fractal Analytics, TIBCO Software, Teradata, Dell, Oracle Corporation. The study provides comprehensive outlook vital to keep market knowledge up to date segmented by SMEs & Large Enterprises,, Cloud Deployment & On-premise Deployment and 18 countries across the globe along with insights on emerging & major players.

In a country such as India that has a low doctor-patient ratio, Artificial Intelligence (AI) can enable greater access to expert care from anywhere, with telehealth and robotics applied across inpatient and outpatient environments. Experts says AI will bolster the role of healthcare by assisting in screening, diagnosis, and treatment of diseases thereby improving quality of life and reducing the cost burden for patients. "India has a tremendous opportunity to lead human-centric applications and democratise AI for the world backed by high skilled talent, technology, vast data availability, and the potential for population-scale AI adoption," says Prakash Mallya, vice-president and managing director of Sales, Marketing and Communications Group, Intel India. Intel has been focusing its efforts towards accelerating AI innovation to deliver transformative healthcare solutions and democratise healthcare access and delivery in India. The company's portfolio of compute, memory, storage, and networking technologies powers some of the most exciting healthcare and life sciences applications. The cloud-based AI solution Netra.AI is the latest example of the impact and innovation that can be made possible with Intel technology.

The report offers a complete understanding of the improvement approaches, procedures, cost structures, and future growth. Due to the effects of COVID-19, the implementation of AI in Fintech Marketis expected to witness a rapid advance, thereby resulting in the fast growth of the AI in Fintech Market. This is mainly due to the rapid adoption of the technology for mapping the spread of the disease and implementing preventive measures. Hence, various government organizations are utilizing the AI in Fintech Market technology for varied applications during the pandemic. Artificial intelligence enables FinTech to occur in real time.

Majority-SAT is the problem of determining whether an input $n$-variable formula in conjunctive normal form (CNF) has at least $2^{n-1}$ satisfying assignments. Majority-SAT and related problems have been studied extensively in various AI communities interested in the complexity of probabilistic planning and inference. Although Majority-SAT has been known to be PP-complete for over 40 years, the complexity of a natural variant has remained open: Majority-$k$SAT, where the input CNF formula is restricted to have clause width at most $k$. We prove that for every $k$, Majority-$k$SAT is in P. In fact, for any positive integer $k$ and rational $\rho \in (0,1)$ with bounded denominator, we give an algorithm that can determine whether a given $k$-CNF has at least $\rho \cdot 2^n$ satisfying assignments, in deterministic linear time (whereas the previous best-known algorithm ran in exponential time). Our algorithms have interesting positive implications for counting complexity and the complexity of inference, significantly reducing the known complexities of related problems such as E-MAJ-$k$SAT and MAJ-MAJ-$k$SAT. At the heart of our approach is an efficient method for solving threshold counting problems by extracting sunflowers found in the corresponding set system of a $k$-CNF. We also show that the tractability of Majority-$k$SAT is somewhat fragile. For the closely related GtMajority-SAT problem (where we ask whether a given formula has greater than $2^{n-1}$ satisfying assignments) which is known to be PP-complete, we show that GtMajority-$k$SAT is in P for $k\le 3$, but becomes NP-complete for $k\geq 4$. These results are counterintuitive, because the ``natural'' classifications of these problems would have been PP-completeness, and because there is a stark difference in the complexity of GtMajority-$k$SAT and Majority-$k$SAT for all $k\ge 4$.