We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Some say, "data is the new oil," with an air of seriousness. And while the phrase may capture a certain truth about the modern digital economy, it fails to model the way that bits can be copied again and again. Sometimes the ease of sharing creates a distinct absence of scarcity and that changes the economics of the entire game. One of the best ways to visualize this is to tap into some open source datasets that are proliferating on the Internet.

This article is going to be a snapshot of some things going on in Artificial Intelligence at the intersection of healthcare. Are you an iOS User and like Online surveys? Jasmine Sun asked me to share this opportunity with you guys: Participate in a Substack reader interview. They are looking for Substack users who aren't power users. I most recently covered in the A.I. intersection of Healthcare the following topics: Artificial Intelligence is Taking on Parkinson's Disease.

Getting a census count wrong can cost communities big. A March 10 report from the US Census Bureau showed an overcount of white and Asian people and an undercount of people who identify as Black, Hispanic or Latino, or multiracial in 2020, a failure that has led to renewed calls to modernize the census. Progress reaching historically undercounted groups has been slow, and the stakes are high. The once-a-decade endeavor informs the distribution of federal tax dollars and apportions members of the House of Representatives for each state, potentially redrawing the political map. According to emails obtained through a records request, Trump administration officials interfered in the population count to produce outcomes beneficial to Republicans, but problems with the census go back much further.

STR develops advanced analytics to find connections, patterns, and threats in both structured and unstructured data. Our customers are overwhelmed with data and need effective methods to index and search for the information they need in extremely large datasets. Recent advances in graph analytics offer a compelling approach to addressing this need. How can AI be used to align entities from different data sources? What algorithms can be used to efficiently search very large graphs?

Australians want more digital government services and a majority are comfortable with Artificial Intelligence-generated services that personalise assistance based on circumstances, according to the inaugural Publicis Sapient Digital Citizen Survey on digital government in Australia. The survey of more than 5000 respondents across Australia looked at peoples usage, experience, and perspectives on engaging with Governments through digital services. The report highlights an openness to leveraging AI technologies with 83% of Australians open to digital services that remember details of their past interactions, and 78% are comfortable with a government website that personalised services based on their employment status and income or previous interactions with Government. The survey found that millennials are the most likely of any age group to use government digital services (94%) compared to boomers (79%) and builders (61%), driven by their higher use of employment and family-related services. Most citizens are open to many services being made as available digitally as possible - healthcare, ATO, and Centrelink were the most common areas where citizens have suggested extending digital service offers.

BEIJING – Dozens of Chinese firms have built software that uses artificial intelligence to sort data collected on residents, amid high demand from authorities seeking to upgrade their surveillance tools, a Reuters review of government documents shows. According to more than 50 publicly available documents examined by Reuters, dozens of entities in China have over the past four years bought such software, known as "one person, one file." The technology improves on existing software, which simply collects data but leaves it to people to organize. "The system has the ability to learn independently and can optimize the accuracy of file creation as the amount of data increases. Henan's department of public security did not respond to requests for comment about the system and its uses.

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to "spin" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a "meta-backdoor" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call "pseudo-words," and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models.

Toby: My role here at Darktrace is the Global Head of Threat Analysis. My day-to-day job involves looking at the 100 or so cybersecurity analysts we have spread from New Zealand to Singapore, the UK, and most major time zones in the US. My main role is to evaluate how we can use the Darktrace platform to work with our customers. How can we ensure that our customers get the most out of our cybersecurity expertise and support when using AI to secure their network? The other half of my role at Darktrace is subject matter expertise. This role involves talking to reporters like yourself or our customers who want to hear more about what Darktrace can do to help them from a cybersecurity perspective, discussing the context of current events. That part of my role was born out of a nearly 20-year career in cybersecurity. I first started in government and was one of the founding members of the National Cybersecurity Center here in the UK.

Text mining is a popular topic for exploring what text you have in documents etc. Text mining and NLP can help you discover different patterns in the text like uncovering certain words or phases which are commonly used, to identifying certain patterns and linkages between different texts/documents. Combining this work on Text mining you can use Word Clouds, time-series analysis, etc to discover other aspects and patterns in the text. Check out my previous blog posts (post 1, post 2) on performing Text Mining on documents (manifestos from some of the political parties from the last two national government elections in Ireland). These two posts gives you a simple indication of what is possible.

A dozen QA contractors laid off at Activision Blizzard-owned Raven Software in December will not be among those converted to full-time employees. The layoffs sparked an intense internal backlash and led 200 workers from across the company to walk out in protest. In response, remaining employees at Raven Software, which produces games like "Call of Duty: Black Ops Cold War" and "Call of Duty: Warzone," have spearheaded unionization efforts, with several dozen members of its quality assurance department requesting recognition from the National Labor Relations Board, which is expected to deliver a ruling in the next few months.