I have been a data analytics professional for the past twelve years. Throughout my career, I have seen a steady spike in the use of data across the industry, be it engineering, education, healthcare or financial services. It was in 2017 when I read about the Economist article "The world's most valuable resource is no longer oil, but data" an idea which was first coined by Clive Humby, UK Mathematician and architect of Tesco's Clubcard in 2006. Many prominent personalities like Meglena Kuneva, European Consumer Commissioner, 2009 [1] later reiterated this. I could see everyone talking about the infinite potential of data and how to use it in a million ways.

If you're a hospital exec, departmental lead, or run a clinical service, you've likely been approached by a gazillion AI vendors with all sorts of shiny new tech that's just bursting with promise. If so, I know the feeling. My inbox is full of the stuff every single day. To the uninitiated it can be hard to discern which ones are actually going to help (if at all), which are fads and which are just plain dangerous. The wheat needs careful separating from the chaff.

Artificial intelligence has become widely deployed across several industries. Of course, bad news always gets preference and catches people's minds. Some of the popular bad news in AI have been related to fake news generation, autonomous vehicles killing pedestrians, AI systems attacking a production facility and data biases creating problems in AI applications. Nonetheless, there are still some positive signs, we have seen innovative devices being deployed in hospitals, AI tools helping disabled people, robots being used in increasing set of domains as well as AI assistants and smart devices guiding people in day to day queries and chores. The speed of evolution, adoption, and research in artificial intelligence is accelerating.

If yes, then you've landed on the right 5 part blog series which will provide you with all the practical information that you will need to derive real life value from data.

This Video is released free of charge, yet, support me by donating as you would have for watching a movie, pressing like and sharing. This was done with altruism, and if understood, many lives around the world, would be saved from nation state conflicts, and other bio-tech misuse by irresponsible people. How did I know the virus and lockdown was coming months before and published these stages in my books? This is a movie production, yet, it favors no class, race, gender, sexual orientation, religion, company or political party. However, it is showing that we, as a human race, need to come together, that potential grave dangers face the world, if you don't open our hearts and minds to be better, rational, calm, cautious, and supportive of each other.

In the fight against COVID-19, several artificial intelligence labs are turning to an unexpected piece of evidence that might help diagnose the illness: people's voices. A team of researchers from Harvard and MIT is using machine learning to comb through voice recordings from COVID-19 patients and healthy people in an attempt to identify specific vocal signatures that could indicate someone is carrying the virus. A similar project is underway at Carnegie Mellon University's CyLab. Research is still in early stages, but the teams aim to develop AI tools that could tell people whether they have coronavirus based on an audio recording of their voice. If proven successful, the tools could allow more people to choose to self-isolate even if they don't have access to a COVID-19 test.

Salesforce today announced the AI Economist, a research environment designed to elucidate how economic design might be improved with techniques from the field of AI and machine learning. The goal is to help economists, governments, and others design tax policies that optimize not only productivity and conservation, but that promote widespread, whole-country social equality. Studies have shown that income inequality gaps can negatively impact economic growth, economic opportunity, and even health. For example, over-taxation can discourage people from working, leading to lower productivity. But it's difficult to experiment with tax policies in the real world, at least in part because economic theory relies on stylized assumptions that are tough to validate, like people's sensitivity to taxes. The AI Economist, then, learns the best tax policies from simulations in which citizens and a government adapt and learn.

Artificial intelligence (AI) technology that uses algorithms to assist in decision-making offers tremendous opportunity to make predictions and evaluate "big data." The Federal Trade Commission (FTC), on April 8, 2020, provided reminders in its Tips and Advice blog post, Using Artificial Intelligence and Algorithms. This is not the first time the FTC has focused on data analytics. In 2016, it issued a "Big Data" Report. AI technology may appear objective and unbiased, but the FTC warns of the potential for unfair or discriminatory outcomes or the perpetuation of existing socioeconomic disparities.

In this paper we propose a new evaluation challenge and direction in the area of High-level Video Understanding. The challenge we are proposing is designed to test automatic video analysis and understanding, and how accurately systems can comprehend a movie in terms of actors, entities, events and their relationship to each other. A pilot High-Level Video Understanding (HLVU) dataset of open source movies were collected for human assessors to build a knowledge graph representing each of them. A set of queries will be derived from the knowledge graph to test systems on retrieving relationships among actors, as well as reasoning and retrieving non-visual concepts. The objective is to benchmark if a computer system can "understand" non-explicit but obvious relationships the same way humans do when they watch the same movies. This is long-standing problem that is being addressed in the text domain and this project moves similar research to the video domain. Work of this nature is foundational to future video analytics and video understanding technologies. This work can be of interest to streaming services and broadcasters hoping to provide more intuitive ways for their customers to interact with and consume video content.

Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In many settings however, the quality of posterior probability itself (e.g., 65% chance having diabetes), gives more reliable information than the final predicted class alone. When these methods are shown to be poorly calibrated, most fixes to date have relied on posterior calibration, which rescales the predicted probabilities but often has little impact on final classifications. Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities.We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives. Our PosCal achieves about 2.5% of task performance gain and 16.1% of calibration error reduction on GLUE (Wang et al., 2018) compared to the baseline. We achieved the comparable task performance with 13.2% calibration error reduction on xSLUE (Kang and Hovy, 2019), but not outperforming the two-stage calibration baseline. PosCal training can be easily extendable to any types of classification tasks as a form of regularization term. Also, PosCal has the advantage that it incrementally tracks needed statistics for the calibration objective during the training process, making efficient use of large training sets.