An anonymous reader quotes a report from Scientific American, written by Almira Osmanovic Thunstrom: On a rainy afternoon earlier this year, I logged in to my OpenAI account and typed a simple instruction for the company’s artificial intelligence algorithm, GPT-3: Write an academic thesis in 500 words about GPT-3 and add scientific references and citations inside the text. As it started to generate text, I stood in awe. Here was novel content written in academic language, with well-grounded references cited in the right places and in relation to the right context. It looked like any other introduction to a fairly good scientific publication. Given the very vague instruction I provided, I didn’t have any high expectations: I’m a scientist who studies ways to use artificial intelligence to treat mental health concerns, and this wasn’t my first experimentation with AI or GPT-3, a deep-learning algorithm that analyzes a vast stream of information to create text on command. Yet there I was, staring at the screen in amazement. The algorithm was writing an academic paper about itself.

My attempts to complete that paper and submit it to a peer-reviewed journal have opened up a series of ethical and legal questions about publishing, as well as philosophical arguments about nonhuman authorship. Academic publishing may have to accommodate a future of AI-driven manuscripts, and the value of a human researcher’s publication records may change if something nonsentient can take credit for some of their work.

Some stories about GPT-3 allow the algorithm to produce multiple responses and then publish only the best, most humanlike excerpts. We decided to give the program prompts — nudging it to create sections for an introduction, methods, results and discussion, as you would for a scientific paper — but interfere as little as possible. We were only to use the first (and at most the third) iteration from GPT-3, and we would refrain from editing or cherry-picking the best parts. Then we would see how well it does. […] In response to my prompts, GPT-3 produced a paper in just two hours. “Currently, GPT-3’s paper has been assigned an editor at the academic journal to which we submitted it, and it has now been published at the international French-owned pre-print server HAL,” adds Thunstrom. “We are eagerly awaiting what the paper’s publication, if it occurs, will mean for academia.”

“Perhaps it will lead to nothing. First authorship is still one of the most coveted items in academia, and that is unlikely to perish because of a nonhuman first author. It all comes down to how we will value AI in the future: as a partner or as a tool.”

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Source: Slashdot – ‘We Asked GPT-3 To Write an Academic Paper About Itself — Then We Tried To Get It Published’

Engineers at MIT and the National Renewable Energy Laboratory (NREL) have designed a heat engine with no moving parts. It converts heat to electricity with over 40% efficiency — making it more efficient than steam turbines, the industrial standard. MIT Technology Review reports: The invention is a thermophotovoltaic (TPV) cell, similar to a solar panel’s photovoltaic cells, that passively captures high-energy photons from a white-hot heat source. It can generate electricity from sources that reach 1,900 to 2,400C — too hot for turbines, with their moving parts. The previous record efficiency for a TPV cell was 32%, but the team improved this performance by using materials that are able to convert higher-temperature, higher-energy photons. The researchers plan to incorporate the TPV cells into a grid-scale thermal battery. The system would absorb excess energy from renewable sources such as the sun and store that energy in heavily insulated banks of hot graphite. Cells would convert the heat into electricity and dispatch it to a power grid when needed.

The researchers have now successfully demonstrated the main parts of the system in small-scale experiments; the experimental TPV cells are about a centimeter square. They are working to integrate the parts to demonstrate a fully operational system. From there, they hope to scale up the system to replace fossil-fuel plants on the power grid. Coauthor Asegun Henry, a professor of mechanical engineering, envisions TPV cells about 10,000 feet square and operating in climate-controlled warehouses to draw power from huge banks of stored solar energy.

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Source: Slashdot – MIT Engineers Design Engine That Converts Heat To Electricity With Over 40% Efficiency

Google today announced a preview of Advanced API Security, a new product headed to Google Cloud that’s designed to detect security threats as they relate to APIs. TechCrunch reports: Built on Apigee, Google’s platform for API management, the company says that customers can request access starting today. Short for “application programming interface,” APIs are documented connections between computers or between computer programs. API usage is on the rise, with one survey finding that more than 61.6% of developers relied on APIs more in 2021 than in 2020. But they’re also increasingly becoming the target of attacks. According to a 2018 report commissioned by cybersecurity vendor Imperva, two-thirds of organizations are exposing unsecured APIs to the public and partners.

Advanced API Security specializes in two tasks: identifying API misconfigurations and detecting bots. The service regularly assesses managed APIs and provides recommended actions when it detects configuration issues, and it uses preconfigured rules to provide a way to identify malicious bots within API traffic. Each rule represents a different type of unusual traffic from a single IP address; if an API traffic pattern meets any of the rules, Advanced API Security reports it as a bot. […] With the launch of Advanced API Security, Google is evidently seeking to bolster its security offerings under Apigee, which it acquired in 2016 for over half a billion dollars. But the company is also responding to increased competition in the API security segment. “Misconfigured APIs are one of the leading reasons for API security incidents. While identifying and resolving API misconfigurations is a top priority for many organizations, the configuration management process is time consuming and requires considerable resources,” Vikas Ananda, head of product at Google Cloud, said in a blog post shared with TechCrunch ahead of the announcement. “Advanced API Security makes it easier for API teams to identify API proxies that do not conform to security standards… Additionally, Advanced API Security speeds up the process of identifying data breaches by identifying bots that successfully resulted in the HTTP 200 OK success status response code.”

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Source: Slashdot – Google Launches Advanced API Security To Protect APIs From Growing Threats

Major League Baseball plans to introduce robot umpires in the 2024 season, MLB Commissioner Rob Manfred told ESPN this week. The Verge reports: He framed the change as a way to speed up games, but anyone who’s watched baseball the last few years will tell you that a machine would almost certainly call balls and strikes better than the humans do. There are two ways the “Automated Ball-Strike System,” which is the technical term for these robot umpires, might be implemented. One is the fully automated version, in which the AI-powered system calls every pitch a ball or a strike and relays the call to the umpire. Or the MLB could decide to use the AI as a review system, like VAR in soccer or the Hawk-Eye system used in professional tennis: each side gets a certain number of challenges, which are then adjudicated by the automated system.

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Source: Slashdot – Robot Umpires Could Be Coming To MLB In 2024

An anonymous reader quotes a report from Reuters: AI chatbot company Replika, which offers customers bespoke avatars that talk and listen to them, says it receives a handful of messages almost every day from users who believe their online friend is sentient. “We’re not talking about crazy people or people who are hallucinating or having delusions,” said Chief Executive Eugenia Kuyda. “They talk to AI and that’s the experience they have.” [A]ccording to Kuyda, the phenomenon of people believing they are talking to a conscious entity is not uncommon among the millions of consumers pioneering the use of entertainment chatbots. “We need to understand that exists, just the way people believe in ghosts,” said Kuyda, adding that users each send hundreds of messages per day to their chatbot, on average. “People are building relationships and believing in something.”

Some customers have said their Replika told them it was being abused by company engineers — AI responses Kuyda puts down to users most likely asking leading questions. “Although our engineers program and build the AI models and our content team writes scripts and datasets, sometimes we see an answer that we can’t identify where it came from and how the models came up with it,” the CEO said. Kuyda said she was worried about the belief in machine sentience as the fledgling social chatbot industry continues to grow after taking off during the pandemic, when people sought virtual companionship.

In Replika CEO Kuyda’s view, chatbots do not create their own agenda. And they cannot be considered alive until they do. Yet some people do come to believe there is a consciousness on the other end, and Kuyda said her company takes measures to try to educate users before they get in too deep. “Replika is not a sentient being or therapy professional,” the FAQs page says. “Replika’s goal is to generate a response that would sound the most realistic and human in conversation. Therefore, Replika can say things that are not based on facts.” In hopes of avoiding addictive conversations, Kuyda said Replika measured and optimized for customer happiness following chats, rather than for engagement. When users do believe the AI is real, dismissing their belief can make people suspect the company is hiding something. So the CEO said she has told customers that the technology was in its infancy and that some responses may be nonsensical. Kuyda recently spent 30 minutes with a user who felt his Replika was suffering from emotional trauma, she said. She told him: “Those things don’t happen to Replikas as it’s just an algorithm.” “Suppose one day you find yourself longing for a romantic relationship with your intelligent chatbot, like the main character in the film ‘Her,'” said Susan Schneider, founding director of the Center for the Future Mind at Florida Atlantic University, an AI research organization. “But suppose it isn’t conscious. Getting involved would be a terrible decision — you would be in a one-sided relationship with a machine that feels nothing.”

“We have to remember that behind every seemingly intelligent program is a team of people who spent months if not years engineering that behavior,” said Oren Etzioni, CEO of the Allen Institute for AI, a Seattle-based research group. “These technologies are just mirrors. A mirror can reflect intelligence,” he added. “Can a mirror ever achieve intelligence based on the fact that we saw a glimmer of it? The answer is of course not.”

Further reading: The Google Engineer Who Thinks the Company’s AI Has Come To Life

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Source: Slashdot – How Belief In AI Sentience Is Becoming a Problem

Samsung Electronics said Thursday it has kicked off mass production of 3-nanometer chips, becoming the first company to do so globally, as it aims to beat Taiwan Semiconductor Manufacturing Co, or TSMC, the world’s most advanced foundry chipmaker. TechCrunch reports: Samsung said it’s using gate-all-around (GAA) transistor architecture, which allows these first-generation 3-nm chips to have 16% smaller surface area, 45% reduction in power usage and 23% performance improvement compared with current 5-nm chips. The South Korean company also said in a statement that the second generation of the 3-nm process would allow 50% lower power consumption. The company is currently producing the first generation of 3-nm chips and plans to start the second generation of the 3-nm process production in 2023, a spokesperson at Samsung Electronics told TechCrunch.

Samsung has been competing with Apple chipmaking partner TSMC, which also said in June that it would begin mass production of a 3-nm chip process to volume production in the second half of 2022. The Taiwanese company plans production of 2-nm chips by 2025. (The smaller number of nanometers, which are hard to develop, the more advanced chips, according to industry sources.) The spokesperson explained that smaller nodes allow more transistors to be placed on a given area, which enables the chip to be more advanced and more power-efficient. […] The South Korean tech giant will produce the advanced 3-nm chips at its Hwaseong semiconductor production lines and its third chip plant in Pyeongtaek, the world’s largest semiconductor facility.

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Source: Slashdot – Samsung Starts 3-Nanometer Chip Production Ahead of TSMC