We may think of Artificial Intelligence as some obscure, futuristic concept of the Fourth Industrial Revolution that is associated with scenes from "out-there" sci-fi movies like Star Wars and Terminator. But AI is already here and being used in a wide variety of technological developments that make our daily responsibilities more efficient and convenient. In fact, scientists believe we are already'cyborgs' because we already have a'digital self', which lives into perpetuity. Artificial intelligence (AI) is intelligence demonstrated by machines and contrasts with natural intelligence displayed by humans. Towards Data Science says "Artificial Intelligence is the ability of a computer program to learn and think. Everything can be considered Artificial intelligence if it involves a program doing something that we would normally think would rely on the intelligence of a human."

AI thought leader Aruna Pattam discusses the wide-ranging applications of data science in society and the importance of diversity for the future of the industry. Data science draws on a large array of skills and expertise within the tech sphere. It uses statistics and mathematics, analytical modelling, machine learning and AI to analyse data and extract insights. Accenture's Shane O'Neill said those who pursue a career in data science can potentially work in any industry that uses data, which nowadays is pretty much every industry. But while we know that data science is important, you might not be aware of just how many areas of society it touches and how it can be used to solve a broad range of real-world challenges.

2021 was another bumper year for fines slapped against financial institutions (FIs) for failures in anti-money laundering (AML) compliance. AML shortcomings in transaction monitoring are a global problem. Countries whose banks were hit with fines include the United States, Germany, the Netherlands, Norway, Latvia, France, the UAE, India, Malaysia, and South Africa.  Fines imposed on FIs by regulators could reach as high as $2 billion for a second year running when the final figures come in, according to estimates. The continuous vigilance of regulators should serve as a wake-up call for financial institutions worldwide to take stock in failures and take action to change the trend in 2022. Some guilty parties lacked an AML compliance culture or even engaged in outright fraud and corruption. Others turned a blind eye. For FIs investing in large and costly compliance teams and tools, it’s surely frustrating to be hit with fines of tens

In 2020, IDRC and the Swedish International Development Cooperation Agency (Sida) launched the Artificial Intelligence for Development in Africa (IAPD Africa) program. This program aims to support the AI community and policymakers in developing responsible, ethical, and equitable AI that meets the continent's challenges, under the leadership of Africa. IDRC, the International Development Research Centre, was established in Canada in 1970 with a mission "to initiate, encourage, support and conduct research into the problems of the developing regions of the world and into the application of scientific, technical and other knowledge for the economic and social advancement of those regions . IDRC sees climate change and inequality, combined with the HIV/AIDS pandemic, as major obstacles to achieving the UN's sustainable development goals, and it is these challenges that it helps to address. While the center is headquartered in Ottawa, Canada, its five regional offices are located in India, Jordan, Kenya, Senegal, and Uruguay to be as close as possible to the researchers and projects it funds.

The theory of technological uniqueness is that in a quarter of a century, non-biological intelligence will match the field and accuracy of human intelligence. It will then outperform it due to the continuous acceleration of information technology, as well as the ability of machines to share their knowledge immediately, immediately followed by the ability of the machine to (perceive) or (consciously) and reach a level of intelligence similar to that of humans and superior to that it evolves much faster than it. Now, this was the definition of a theory in general that has a lot of endless branches, but the most associated personality with this concept now is Ray Kurzweil. Ray is a missionary, so to speak, of the concept of technological singularity and always tries to make his predictions based on solid scientific basis (as we will see later). Ray has a collection of books, lectures, films and even conferences and universities that discuss and prepare for this uniqueness.

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient zero-short learning method, ZeroGen. Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset. This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free and efficient, we argue that ZeroGen can also provide useful insights from the perspective of data-free model-agnostic knowledge distillation, and unreferenced text generation evaluation. Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference), show the effectiveness of ZeroGen.

Dr. Seneff is a Senior Research Scientist at MIT's Computer Science and Artificial Intelligence Laboratory in Cambridge, Massachusetts, USA. She has a BS from MIT in biology and MS, EE, and PhD degrees from MIT in electrical engineering and computer science. Her recent interests have focused on the role of toxic chemicals and micronutrient deficiencies in health and disease, with a special emphasis on the pervasive herbicide, glyphosate, and the mineral, sulfur. This is an edited segment from the weekly live General Assembly meeting on January 3, 2022. The full meeting can be viewed here. This clip is also available on Rumble and Odysee. "Thank you so much Dr. Seneff!!! Genius presentation, so many important information brought to us easy to understand." -Dr. "Thank you for your important work. The mitigating treatments are hopeful for those who have been coerced into accepting these injections." "Dr Seneff, I hope you will come back and tell us more about your work and the mechanism for the other types of harms that your work has predicted." "Thank you so much, Dr. Seneff." -Helena K "Thank you Dr Seneff, amazing presentation." "Thank you Dr Seneff, that was amazing!" -Dr Tess Lawrie "Beautiful and substantial presentation – thank you, Dr. Seneff!" – Susan I just want to read this quote at the end of this book "The Real Anthony Fauci" and it's because it's Martin Luther King Jr.

There is a significant amount of both circumspection and excitement surrounding artificial intelligence (AI). However, despite the fact that it already touches almost every aspect of our lives, it is in many ways still in its infancy. A PwC study notes AI can transform the productivity and GDP potential of the global economy, with the proviso that strategic investment in different types of AI technology is needed to make that happen. The report says labour productivity improvements will drive initial GDP gains as firms seek to'augment' the productivity of their labour force with AI technologies and automate certain tasks and roles. PwC research also shows that 45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety, with increased personalisation, attractiveness and affordability over time.

In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks. This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after GLUE for Russian NLP models. The new version includes a number of technical, user experience and methodological improvements, including fixes of the benchmark vulnerabilities unresolved in the previous version: novel and improved tests for understanding the meaning of a word in context (RUSSE) along with reading comprehension and common sense reasoning (DaNetQA, RuCoS, MuSeRC). Together with the release of the updated datasets, we improve the benchmark toolkit based on \texttt{jiant} framework for consistent training and evaluation of NLP-models of various architectures which now supports the most recent models for Russian. Finally, we provide the integration of Russian SuperGLUE with a framework for industrial evaluation of the open-source models, MOROCCO (MOdel ResOurCe COmparison), in which the models are evaluated according to the weighted average metric over all tasks, the inference speed, and the occupied amount of RAM. Russian SuperGLUE is publicly available at https://russiansuperglue.com/.

Conditionals are generally considered the backbone of human (and AI) reasoning: the "if-then" connection between two propositions is the stepping stone of arguments and a lot of the research effort in formal logic has focused on this kind of connection. A conditional connection satisfies different properties according to the kind of arguments it is used for. The classical material implication is appropriate for modelling the "ifthen" connection as it is used in Mathematics, but the equivalence between the material implication A B and A B is not appropriate for many other contexts.