The purpose of this article is to explain the relationships (dependencies, interdependencies, interrelationships, benefits) between Data, AI, Cybersecurity, Cloud, IoT and Blockchain, and to understand their consistency. The link between all these fields leads us towards a new convergence which makes possible their interoperability, their security and their standardization. We thus provide a summary of the state of the art and we also present an inventory of standardization, methods and actors in the field, as well as the use cases identified in the literature. Why and how can we think of the "convergence" of Data, AI, Cloud, Blockchain, IoT and Cybersecurity? What types of relationships do these technologies have?

Finland is the first country having released its AI strategy in Europe already in March 2017. According to a study committed by Accenture and Frontier Economics, Finland ranked second that year, after the US, among the 11 developed countries in which economic growth potential is made possible by AI. According to Finland, this is because of the country's business structure (technologically intensive) and the public sector degree of digitalisation (see Finland, 2017, p. 12). The national strategy has been commissioned by the Government of Juha Sipilä to the Ministry of Economic Affairs and Employment, which in turn has nominated a steering group on AI to work on the national strategy. The AI Working Group has released the first draft of the strategy in 2017, though the work on the optimum public policies to be implemented is actually an on-going process, which has already been updated in 2019.

Country officials and campaigners have expressed disappointment after United Nations talks on autonomous weapons systems – known as "killer robots" – stopped short of launching negotiations into an international treaty to govern their use following opposition from manufacturing states. Unlike existing semi-autonomous weapons such as drones, fully-autonomous weapons have no human-operated "kill switch" and instead leave decisions over life and death to sensors, software and machine processes. The regulation of the industry has taken on new urgency since a UN panel report in March said the first autonomous drone attack may have occurred in Libya. This week, UN Secretary-General Antonio Guterres encouraged the 125 parties to the Convention on Certain Conventional Weapons (CCW) to come up with an "ambitious plan" on new rules. But on Friday, the Sixth Review Conference of the CCW failed to schedule further talks around the development and use of the Lethal Autonomous Weapon Systems, or LAWS.

The COVID-19 pandemic has arguably been our era's greatest threat to humanity and the global economy.16 South Korea's first confirmed case was in January 2020, followed by an outbreak in the city of Daegu in mid-February. However, South Korea quickly and effectively contained the pandemic and became an exemplar for other countries.3 While many policies and initiatives contributed to South Korea's successful response to the coronavirus pandemic, digital technology was at the core of the endeavors.12 As part of its 3T strategy (test, trace, and treat) for coping with COVID-19, South Korea deployed a software system that traces the contacts of infected patients and disseminates the information in a matter of minutes.19 The COVID-19 Contact Tracing System CCTS) was first released in March 2020 to the Korea Centers for Disease Control and Prevention (KCDC)--a government agency responsible for advancing public health--and was then rolled out nationally in early April 2020. The system greatly contributed to reducing the number of daily new confirmed cases from 909 on February 29, 2020 to 7.42 on average between April 29 and May 5. According to a recent Columbia University study,13 both South Korea and the U.S. confirmed their first case of coronavirus on the same day. However, as of March 2021, South Korea's total confirmed cases are less than 10,000 and its proportional mortality rate is 50 times smaller than that of the U.S.7 The CCTS helped public healthcare officials to make informed decisions and helped keep the public aware of high-risk places where there had been exposure to coronavirus. Information provided by the CCTS enabled citizens to avoid hot spots and plan outdoor activities accordingly.

You would think something as numerical as income tax law would be similar to mathematical logic, but it is not, Protzenko says, because it is not written with the precision and clarity that would "make it amenable to a very mathematical reading of it." For example, that law does not mention a number may need to be rounded into whole cents. "The law won't tell you what you're supposed to do with rounding numbers and that can lead to ambiguity and a lack of specification of what's supposed to happen," he says. Healthcare law is also very complex. Faisal Khan, senior legal counsel at healthcare law firm Nixon Gwilt Law in Vienna, VA, says, "Software for HIPAA compliance must incorporate algorithms that target and hit on all the top-level statutory requirements and implementing regulations.' To make that happen, Khan says, "There must be a team of compliance-related input as many of the regulations essentially function as guidelines for companies to adhere to." That means a process or ...

When working with their clients Accenture under Tricarico's guidance focuses on "on guiding (their) clients to more safely scale their use of AI, and build a culture of confidence within their organizations." Not all companies have an established north star of AI use. Companies and partners like Accenture are vital to these companies and their proper and ethical use of the technology.

Using a marketing template to frame "meta AI" selling itself. GPT-3, via Copy.ai, on "Automating Viable Sales" Sybil Electronica, the eponymous co-author of "Sybil's World" (published by the algorithmic publishing house Nimble Books and for sale now on Amazon), is being fined-tuned by the GPT-3 Society to optimize her ability to persuade politicians to license Viable's Core Software Suite (CSS) so that they can aggregate, transcribe, analyze, and summarize comments from their voter/constituents in near real-time at scale automatically. With her experience as the conversational AI component of the Sybil Electronica Digital, Inc., Integrated Auto-Canvasser, Sybil is well-prepared and well-suited to explain the Viable CSS to cutting-edge politicians who want to use the latest advances in NLP to supercharge their campaigns and their incumbencies, not to mention the operations of the jurisdictions they've been elected to serve. Features: Sybil Electronica is a deep-learning conversational AI. Advantages: innovative, cutting edge technology that unites the best features and benefits of classic CSAI and chatbot technology.

FlyFin is a new AI-powered mobile tax app for freelancers, creators, gig workers, and the self-employed. This fintech company plans to disrupt the market for individual tax preparation and filing, saving people thousands and eliminating nightmare scenarios on tax day. Jaideep Singh, CEO and Co-Founder of FlyFin will be sharing more information with us in this exclusive interview with TechBullion. Prior to FlyFin, I was an early adopter of AI, as I built Spock, the industry's first and largest people search engine, indexing over 1 billion people representing 1.5 trillion data records. As both a VC and entrepreneur, I've focused on finding disruptive industry startups to invest in, creating more than $3B in value for companies.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and humans. We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance, and we suggest future work that uses robust semi-supervised to improve learning.