The San Francisco company has unveiled a safer version of its language technology GPT.
Category: robotics/AI – Page 1,514
The Industrial Internet of Things (IIoT) connects IoT with Industry. The IIoT allows companies to reap information from their machines and environments to create intelligent, self-learning machines. This interconnected ecosystem provides numerous benefits for enterprises, such as reduced downtime, increased throughput and safety, and predictive maintenance — leading to greater efficiency. The 4th Industrial Revolution is fueled by exponential advancements in digital technology and brings us closer to a sustainable future of intelligent manufacturing environments that operate with zero emissions. With the advent of Industry 4.0, there has been a massive increase in the levels of data being produced by intelligent machines. This enormous increase in information can be hard to manage and analyze effectively without converting into usable insights. These insights are gained through the use of various technologies, including intelligent digital twins that allow for real-time monitoring of a machine’s condition, AI that can analyze large amounts of data to produce actionable insights, and connected devices that provide live status updates.
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Tesla has launched an in-car karaoke microphone called TeslaMic. But it’s only available in China for now.
The American Farm Bureau Federation has recognized Chad and Ben Johnson of Nebraska for designing a robot called “Grain Weevil” to lower maintenance and make grain bins safer.
This is a talk by Ray Kurzweil for course 6.S099: Artificial General Intelligence. For this entire recording, Ray did not use slides, so the video does not show any slides. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world.
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The new foundation of the artificial intelligence (AI) economy is flexible, remote work. Thanks to advances in technology that enable remote work at an unimaginable scale, organizations developing AI can now collaborate with people from almost anywhere, including previously inaccessible areas. People across the globe can now contribute to building AI in meaningful ways, particularly through data preparation and annotation work. This has led to the emergence of a new and growing freelance category — focused on AI training data annotation and collection.
While many AI economy participants join searching for additional income, a good portion of data annotators join the AI economy because they are seeking challenging opportunities. Whatever their reason, contributors benefit positively from the new opportunities flexible work affords. Geography is no longer an impediment to skill development or participation in projects that they’re enthusiastic about.
Organizations building AI are embracing remote contracting arrangements in order to access the contributions of people around the world. These contributors may not necessarily live in technology hubs, nor have had the opportunity to participate in AI before the arrival of these remote options. In fact, professional options in their locale may be limited as a whole. Appen recently released their Impact Pulse survey of the crowd and found that 40% of contributors rely on the work from home model due to barriers of accessing traditional work. Thirty-two percent were living below the global poverty line before starting with Appen, and of those, 53% have been lifted above due to their work in the AI Economy.
Sunflower Labs announces a flurry of client acquisitions of its security drone-in-dock Beehive System in both the US and Europe.
San Carlos-based Sunflower Labs has announced a spate of new clients for its automated Beehive System security drone-and-dock, in deals ranging from Switzerland to the US South.
Sunflower said the recent series of new drone-and-dock deals include partners like US security group ADT Inc, stowage company10 Federal Self Storage, Swiss Federal Railways, and the Gerald R. Ford International Airport in Grand Rapids, Michigan. It also involves a deepening of its previous relationship with German company Security Robotics Development & Solutions.
A newly created nano-architected material exhibits a property that previously was just theoretically possible: it can refract light backward, regardless of the angle at which the light strikes the material.
Researchers from KTH Royal Institute of Technology and Stanford University have fabricated a material for computer components that enables the commercial viability of computers that mimic the human brain.
Electrochemical random access (ECRAM) memory components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology, and contributing toward commercialization of powerful computers that are modeled after the brain’s neural network. Such neuromorphic computers can be thousands times more energy efficient than today’s computers.
These advances in computing are possible because of some fundamental differences from the classic computing architecture in use today, and the ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, says KTH Associate Professor Max Hamedi.
Artificial intelligence will soon become one of the most important, and likely most dangerous, aspects of the metaverse. I’m talking about agenda-driven artificial agents that look and act like any other users but are virtual simulations that will engage us in “conversational manipulation,” targeting us on behalf of paying advertisers.
This is especially dangerous when the AI algorithms have access to data about our personal interests, beliefs, habits and temperament, while also reading our facial expressions and vocal inflections. Such agents will be able to pitch us more skillfully than any salesman. And it won’t just be to sell us products and services – they could easily push political propaganda and targeted misinformation on behalf of the highest bidder.
And because these AI agents will look and sound like anyone else in the metaverse, our natural skepticism to advertising will not protect us. For these reasons, we need to regulate some aspects of the coming metaverse, especially AI-driven agents. If we don’t, promotional AI-avatars will fill our lives, sensing our emotions in real time and quickly adjusting their tactics for a level of micro-targeting never before experienced.
Second, we need to be aware of the manifest biases and fallacies that magnify the weight humans put on potential losses compared to potential future gains. As a result of these biases, humans often seek to preserve the status quo over pursuing activities that lead to future changes, even when the expected (but risky) gains from the latter may outweigh those of maintaining the status quo. The preference for the status quo, and neat narratives that oversimplify complex scenarios, can lead to overlooking (or ignoring) important information that is not consistent with the current generally accepted meme — illustrated, perhaps, in Musk’s continued optimism for autonomous vehicles despite the evidence leading to others downscaling their forecasts.
The first and second points together lead to the third important consideration: the importance of independently verified data over forecasts and opinion in determining the need for and appropriateness of policy interventions. And data is historical by nature. Pausing to collect it rather than rushing to respond is recommended.
To that end, we can use available data to analyze whether increasing use of AI is demonstrably affecting key labor market performance indicators: labor productivity and multifactor productivity growth. If, as Keynes suggests, AI-driven technological change is increasing the potential for new means of economizing the use of labor to outrun the pace of finding new ways to use it, we would expect to see both statistics rising in the era dominated by AI. Yet as Figures 1 and 2 show, the exact opposite appears true for a wide range of OECD countries. Neither does the data suggest that other key labor market indicators have changed negatively with the advent of AI. As with the computer industry, we see the effects of AI everywhere but in the productivity statistics.