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A recent study revealed that when individuals are given two solutions to a moral dilemma, the majority tend to prefer the answer provided by artificial intelligence (AI) over that given by another human.

The recent study, which was conducted by Eyal Aharoni, an associate professor in Georgia State’s Psychology Department, was inspired by the explosion of ChatGPT and similar AI large language models (LLMs) which came onto the scene last March.

“I was already interested in moral decision-making in the legal system, but I wondered if ChatGPT and other LLMs could have something to say about that,” Aharoni said. “People will interact with these tools in ways that have moral implications, like the environmental implications of asking for a list of recommendations for a new car. Some lawyers have already begun consulting these technologies for their cases, for better or for worse. So, if we want to use these tools, we should understand how they operate, their limitations, and that they’re not necessarily operating in the way we think when we’re interacting with them.”

The founder of the dating app Bumble Whitney Wolfe Herd believes the future of dating will involve having your personal AI “dating concierge” talk to hundreds of other AIs to find a match.

That unabashed vision may sound familiar: it’s literally the plot of a 2017 episode of “Black Mirror,” as countless people on social have pointed out.

“You could, in the near future, be talking to your AI dating concierge,” Wolfe Herd, who stepped down as Bumble CEO in 2023 but remains involved in the company, told an audience at the Bloomberg Technology Summit on Thursday. “You could share your insecurities. There is a world where your dating concierge could go and date for you with other dating concierges.”

New Atlas robot from Boston Dynamics and Figure 1 from OpenAI, leaked $100b OpenAI plan and a new project to avoid our extinction.
Sam Altman, Elon Musk, Geoffrey Hinton, Sora.

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Human Brain as Supercomputer

Brain-emulating computers hold the promise of vastly lower energy computation and better performance on certain tasks. “The human brain is the most advanced supercomputer in the universe, and it consumes only 20 watts to achieve things that artificial intelligence systems today only dream of,” says Hector Gonzalez, cofounder and co-CEO of SpiNNcloud Systems. “We’re basically trying to bridge the gap between brain inspiration and artificial systems.”

Aside from sheer size, a distinguishing feature of the SpiNNaker2 system is its flexibility. Traditionally, most neuromorphic computers emulate the brain’s spiking nature: Neurons fire off electrical spikes to communicate with the neurons around them. The actual mechanism of these spikes in the brain is quite complex, and neuromorphic hardware often implements a specific simplified model. The SpiNNaker2 can implement a broad range of such models however, as they are not hardwired into its architecture.

In 2024, security teams face new opportunities and obstacles, such as escalating geopolitical tensions, stricter compliance mandates, and the rise of generative AI — which will transform the industry in new and unexpected ways.

In the State of Security 2024: The Race to Harness AI, we identify organizations that are pulling ahead of their peers and share key characteristics and findings.

ChemCrow, an AI developed by researchers at EPFL, integrates multiple expert tools to perform chemical research tasks with unprecedented efficiency.

Chemistry, with its intricate processes and vast potential for innovation, has always been a challenge for automation. Traditional computational tools, despite their advanced capabilities, often remain underutilized due to their complexity and the specialized knowledge required to operate them.

AI Revolution in Chemistry.

A team led by Prof Frank Glorius from the Institute of Organic Chemistry at the University of Münster has developed an evolutionary algorithm that identifies the structures in a molecule that are particularly relevant for a respective question and uses them to encode the properties of the molecules for various machine-learning models.