ChatGPT Maker OpenAI Releases Tool To Check If Text Was Written By A Human“It should not be used as a primary decision-making tool, but instead as a complement to other methods of determining the source of a piece of text...”ZeroHedgeTyler Durden

As evidenced by the example of ChatGPT, artificial intelligence is advancing in unprecedented directions to solve exciting new problems. But, as AI is being pointed toward critical cybersecurity operations, do the gains outweigh the potential risks and concerns? "You should be worried," said Andy Thurai (pictured), vice president and principal analyst at Constellation Research Inc. "The problem people don't realize is that ChatGPT, being a new, shiny object, it's all the craze that's about. But the problem is that most of the content that's produced either by ChatGPT or others are assets with no warranties, accountability or whatsoever. If it is content, it's OK. But if it is something like code that you use, then it's mostly not."

For five years or so, I have been running around as a pale imitation of Paul Revere, yelling, "The robots are coming! At schools, social settings, with family and friends, or even to complete strangers with whom I fell into conversations, I have uttered the same warning: "It's critical that you or your children identify a career -- now -- that won't be taken over by robots and artificial intelligence." My particular midnight ride started well before the pandemic reared its ugly head. But the pandemic may have planted a seed in the minds of certain CEOs that human beings are the weakest link on their chain to profit and prosperity. When the first "Terminator" movie was released -- eerily enough, in 1984 -- the world was introduced to Cyberdyne Systems and its "Skynet" artificial superintelligence system, which not only gained self-awareness but realized it could do everything infinitely faster and better than its human creators. Well, ever since that movie got people asking, "What if," the fictional theme -- and warnings about AI -- have been morphing into reality. The latest example of a technology poised to replace a human workforce is ChatGPT, the chatbot auto-generative system created by Open AI for online customer care. It is a pre-trained generative chat, which makes use of natural language processing, or NLP. The source of its data is textbooks, websites and various articles, which it uses to model its own language for responding to human interaction. It's certainly not a stretch to believe that any number of CEOs might think, "Interesting… A self-teaching artificial intelligence system that won't call in sick, doesn't need to be fed or to take bathroom breaks, does not require health care, but can and will work 24/7/365." Not shockingly, it has been reported that Microsoft, which is laying off 10,000 people, announced a "multiyear, multibillion-dollar investment" in this revolutionary technology, which apparently is growing smarter by the day. Pengcheng Shi, an associate dean in the Department of Computing and Information Sciences at Rochester Institute of Technology, warned in an interview with the New York Post: "AI is replacing the white-collar workers.

Chief national security correspondent Jennifer Griffin reports that the U.S. will only shoot down the Chinese spy balloon if officials can assure zero civilian casualties. U.S. investors were involved in at least 37% of all investment transactions in China's artificial intelligence, or AI, sector between 2015 and 2021, according to a new report. Georgetown University's Center for Security and Emerging Technology found that $40.2 billion of the total money raised by all Chinese AI companies over this time period had U.S. backing. However, the center couldn't determine what percentage of that amount came from U.S. investors or investors abroad. The money went to 251 Chinese AI companies, and 91% of the U.S. investment came as venture capital to earlier-stage businesses.

Potatoes, Yorkshire puds, gravy, meat and veggies - the Sunday Roast is a meal integral to British identity. It is believed to have its origins in medieval times, around the 12th century, when village serfs were rewarded with roasted ox by their masters after church. At first glance, these plates show golden roast potatoes, billowing Yorkshire puddings, a perfectly juicy joint and lashings of gorgeous gravy. But take a closer look and the traditional dish appears more and more suspicious. Artificial intelligence has generated several photos of the traditional dinner, including all the trimmings. A user on Reddit shared the images, however it was unclear how the AI created the pictures or what software was used.

Offering no criteria by which to define unacceptable risk for AI systems and no method to add new high-risk applications to the Act if such applications are discovered to pose a substantial danger of harm. This is particularly problematic because AI systems are becoming broader in their utility. Only requiring that companies take into account harm to individuals, excluding considerations of indirect and aggregate harms to society. An AI system that has a very small effect on, e.g., each person's voting patterns might in the aggregate have a huge social impact. Permitting virtually no public oversight over the assessment of whether AI meets the Act's requirements.

Sorry for the long email first, but I have been paying attention to ChatGPT since last December. It can generate near-perfect Chinese based on the questions that users ask, including literature reviews, movie critiques, student essays, recommendation letters, etc. "The foreign policy of the Chinese government is aimed at safeguarding national security, development interests and international status, while advocating peace, development, cooperation and justice. China has always advocated multilateralism and promoted international relations based on the principles of equality and mutual respect. At the same time, China is also actively participating in global governance and playing an active role in international affairs." How to use Zhao Lijian's method to answer Sino-US relations?

Originally published on Towards AI. Artificial Intelligence (AI) is a subject that has been discussed almost everywhere. It continuously gets so popular that it permeates practically every field, from the business world to the entertainment industry. This technology, however, is more than simply a fad; it's a serious means through which businesses may boost their productivity. As a result of the proliferation of use cases demonstrating how AI improved various operations, an increasing number of businesses have realized that AI and other forms of cutting-edge technology are the new arenas in which to compete.

Capitalism aims to convert ambition to success. Its alchemy of incentives fosters a relentless pursuit of economic opportunity from things we crave: food, shelter, bigger iPhones, desirable mates. Our hunger for wealth and love drives us to work and create -- capitalism's genius is finding new avenues for that drive. The philosopher's stone of capitalism is technological innovation. It's no accident capitalism flourished and spread across the globe contemporaneously with the adoption of new technologies in production, transport, and information.

Natural language processing (NLP) is a promising approach for analyzing large volumes of climate-change and infrastructure-related scientific literature. However, best-in-practice NLP techniques require large collections of relevant documents (corpus). Furthermore, NLP techniques using machine learning and deep learning techniques require labels grouping the articles based on user-defined criteria for a significant subset of a corpus in order to train the supervised model. Even labeling a few hundred documents with human subject-matter experts is a time-consuming process. To expedite this process, we developed a weak supervision-based NLP approach that leverages semantic similarity between categories and documents to (i) establish a topic-specific corpus by subsetting a large-scale open-access corpus and (ii) generate category labels for the topic-specific corpus. In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in about 13 hours. The labeled climate and NCF corpus enable targeted, efficient identification of documents discussing a topic (or combination of topics) of interest and identification of various effects of climate change on critical infrastructure, improving the usability of scientific literature and ultimately supporting enhanced policy and decision making. To demonstrate this capability, we conduct topic modeling on pairs of climate hazards and NCFs to discover trending topics at the intersection of these categories. This method is useful for analysts and decision-makers to quickly grasp the relevant topics and most important documents linked to the topic.