Disclaimer: I would like it to be made very clear that I am absolutely 100% open to the idea that I am wrong about anything in this post. I don't only accept but explicitly request arguments that could convince me I am wrong on any of these issues. If you think I am wrong about anything here, and have an argument that might convince me, please get in touch and present your argument. I am happy to say "oops" and retract any opinions presented here and change my course of action. As the saying goes: "When the facts change, I change my mind. I plan on releasing it on the 1st of July. Before criticizing my decision to do so, please read my arguments below. If you still think I'm wrong, contact me on Twitter @NPCollapse or by email (thecurioushacker@outlook.com) and convince me. For code and technical details, see this post. UPDATE: My mind has been changed, and I plan on not releasing. See my update post here that explains my reasoning. UPDATE 2: This post is now part 1 in a series of ...

Public policy attention is being directed toward various elements directly related to the collection, maintenance, and use of data. These elements include privacy and security, cloud computing (primarily processing, such as data storage, networking, and analytics), and machine learning (ML) and artificial intelligence (AI). Recent public policy developments in each of these areas follow. Regulators are taking actions to outline the parameters and expectations for privacy practices as well as to enhance data governance and strengthen consumer protection. Many organizations are dependent on third-party vendors for the rapid deployment or scalability of technology applications, which gives rise to issues and risks related to governance and accountability.

India will soon become the first country to deploy artificial intelligence (AI) and machine learning (ML) in the tax assessment process, as the Finance Minister Nirmala Sitharaman has promised to adopt faceless assessment system from October this year. According to official sources, the government is very particular about the deadline of the implementation. In this regard, the revenue secretary Ajay Bhushan Panday on last Saturday held a meeting of officials of the Ministry of Corporate Affairs (MCA) and various tax agencies. The government has set the deadline of October 8, for the implementation of AI and ML for the assessment purposes. Now data in the income tax return, statement of financial transactions and from other sources will be analysed as per the pre-defined risk criteria.

On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.

As the world becomes increasingly connected through advancements in technology, ensuring the safety and security of automobiles, drones, electronic devices and our cities is a top priority. Artificial intelligence (AI) is a powerful tool that's being used to improve nearly every industry. From digital farming tools used to help growers optimize and sustain their crops, to driverless shuttles that aim to improve mobility solutions in cities worldwide, AI technology is rapidly transforming business models across the globe. Although the industries may be different, the goal remains the same: to use machine learning to create efficiencies and improve operations to produce safer, more effective products for consumers. If the end game is increased safety, cybersecurity needs to be a large part of the conversation.

Inferring commonsense knowledge is a key challenge in natural language processing, but due to the sparsity of training data, previous work has shown that supervised methods for commonsense knowledge mining underperform when evaluated on novel data. In this work, we develop a method for generating commonsense knowledge using a large, pre-trained bidirectional language model. By transforming relational triples into masked sentences, we can use this model to rank a triple's validity by the estimated pointwise mutual information between the two entities. Since we do not update the weights of the bidirectional model, our approach is not biased by the coverage of any one commonsense knowledge base. Though this method performs worse on a test set than models explicitly trained on a corresponding training set, it outperforms these methods when mining commonsense knowledge from new sources, suggesting that unsupervised techniques may generalize better than current supervised approaches.

Fake news and social media posts are such a threat to U.S. security that the Defense Department is launching a project to repel "large-scale, automated disinformation attacks," as the top Republican in Congress blocks efforts to protect the integrity of elections. The Defense Advanced Research Projects Agency wants custom software that can unearth fakes hidden among more than 500,000 stories, photos, video and audio clips. If successful, the system after four years of trials may expand to detect malicious intent and prevent viral fake news from polarizing society. "A decade ago, today's state-of-the-art would have registered as sci-fi -- that's how fast the improvements have come," said Andrew Grotto at the Center for International Security at Stanford University. "There is no reason to think the pace of innovation will slow any time soon."

To paraphrase 1984's The Terminator, the artificial intelligence developed by residential real estate company HouseCanary will absolutely not stop, ever, until your house is properly valued. The robot appraiser takes advantage of new federal regulations raising the threshold value of homes exempt from human evaluation. In doing so, it's allowed to do a job that once required a flesh-and-blood human to perform. Namely, it will determine the current value of a property by inspecting its exterior condition and amenities. Before you start picturing an inspection robot like the quadruped ANYmal, which carries out inspection tasks on oil rigs, this job is largely carried out remotely. The A.I. bases its decisions and predictions on images, using a neural network that's trained to recognize the different qualities of a home that influence its valuation.

"If we do not find a way to strengthen the bonds between the United States government and industry and academia, then I would say we do have the real risk of not moving as fast as China when it comes to artificial intelligence" -Lt.

It was 1984, and the Air Force was holding a conference in Montgomery to check out the latest advancements in commercial technology. Personal computers and networking devices were in their infancy, but the military was eager to find better ways to use them to help the nation. About 600 people showed up, along with a few PC vendors. Air University said that first event was timed to make use of end-of-fiscal-year funds, and people brought along their unit credit cards to buy the latest tech on the spot. Ken Heitkamp was a big part of that 1984 conference as technical director for the Standard Systems Group at Maxwell Air Force Base's Gunter Annex.