Lifeboat Foundation

After six years of peace, the two tech giants are on course to butt heads again over the future of artificial intelligence.

Microsoft is about to go head-to-head with Google in a battle for the future of search. At a press event later today, Microsoft is widely expected to detail plans to bring OpenAI’s ChatGPT chatbot to its Bing search engine. Google has already tried to preempt the news, making a rushed announcement yesterday to introduce Bard, its rival to ChatGPT, and promising more details on its AI future in a press event on Wednesday.

The two tech giants are on course to butt heads again.

Setting boundaries for these tools, then, could be crucial, some researchers say. Edwards suggests that existing laws on discrimination and bias (as well as planned regulation of dangerous uses of AI) will help to keep the use of LLMs honest, transparent and fair. “There’s loads of law out there,” she says, “and it’s just a matter of applying it or tweaking it very slightly.”

At the same time, there is a push for LLM use to be transparently disclosed. Scholarly publishers (including the publisher of Nature) have said that scientists should disclose the use of LLMs in research papers (see also Nature 613, 612; 2023); and teachers have said they expect similar behaviour from their students. The journal Science has gone further, saying that no text generated by ChatGPT or any other AI tool can be used in a paper⁵.

One key technical question is whether AI-generated content can be spotted easily. Many researchers are working on this, with the central idea to use LLMs themselves to spot the output of AI-created text.

Diffusion models have recently produced outstanding results on various generating tasks, including the creation of images, 3D point clouds, and molecular conformers. Ito stochastic differential equations (SDE) are a unified framework that can incorporate these models. The models acquire knowledge of time-dependent score fields through score-matching, which later directs the reverse SDE during generative sampling. Variance-exploding (VE) and variance-preserving (VP) SDE are common diffusion models. EDM offers the finest performance to date by expanding on these compositions. The existing training method for diffusion models can still be enhanced, despite achieving outstanding empirical results.

The Stable Target Field (STF) objective is a generalized variation of the denoising score-matching objective. Particularly, the high volatility of the denoising score matching (DSM) objective’s training targets can result in subpar performance. They divide the score field into three regimes to comprehend the cause of this volatility better. According to their investigation, the phenomenon mostly occurs in the intermediate regime, defined by various modes or data points having a similar impact on the scores. In other words, under this regime, it is still being determined where the noisy samples produced throughout the forward process originated. Figure 1(a) illustrates the differences between the DSM and their proposed STF objectives.

Figure 1: Examples of the DSM objective’s and our suggested STF objective’s contrasts.

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“I’ll teach you how to jump on the wind’s back, and then away we go,” Peter Pan says to Wendy.

In J.M. Barrie’s book, fairies can be brought back to life if enough people believe in them.

Continue reading “Clap if you believe in robot fairies” »

Scientists and roboticists have long looked at nature for inspiration to develop new features for machines. In this case, researchers from Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland were inspired by bats and other animals that rely on echolocation to design a method that would give small robots that ability to navigate themselves — one that doesn’t need expensive hardware or components too large or too heavy for tiny machines. In fact, according to PopSci, the team only used the integrated audio hardware of an interactive puck robot and built an audio extension deck using cheap mic and speakers for a tiny flying drone that can fit in the palm of your hand.

The system works just like bat echolocation. It was designed to emit sounds across frequencies, which a robot’s microphone then picks up as they bounce off walls. An algorithm the team created then goes to work to analyze sound waves and create a map with the room’s dimensions.

In a paper published in IEEE Robotics and Automation Letters, the researchers said existing “algorithms for active echolocation are less developed and often rely on hardware requirements that are out of reach for small robots.” They also said their “method is model-based, runs in real time and requires no prior calibration or training.” Their solution could give small machines the capability to be sent on search-and-rescue missions or to previously uncharted locations that bigger robots wouldn’t be able to reach. And since the system only needs onboard audio equipment or cheap additional hardware, it has a wide range of potential applications.

Google said Monday it will release a conversational chatbot named Bard, setting up an artificial intelligence showdown with Microsoft which has invested billions in the creators of ChatGPT, the hugely popular language app that convincingly mimics human writing.

ChatGPT, created by San Francisco company OpenAI, has caused a sensation for its ability to write essays, poems or programming code on demand within seconds, sparking widespread fears of cheating or of entire professions becoming obsolete.

Continue reading “Google to release ChatGPT rival named Bard” »

Engineers at the University of Waterloo have developed artificial intelligence (AI) technology to predict if women with breast cancer would benefit from chemotherapy prior to surgery.

The new AI algorithm, part of the open-source Cancer-Net initiative led by Dr. Alexander Wong, could help unsuitable candidates avoid the serious side effects of chemotherapy and pave the way for better surgical outcomes for those who are suitable.

“Determining the right treatment for a given breast cancer patient is very difficult right now, and it is crucial to avoid unnecessary side effects from using treatments that are unlikely to have real benefit for that patient,” said Wong, a professor of systems design engineering.

Architectures based on artificial neural networks (ANNs) have proved to be very helpful in research settings, as they can quickly analyze vast amounts of data and make accurate predictions. In 2020, Google’s British AI subsidiary DeepMind used a new ANN architecture dubbed the Fermionic neural network (FermiNet) to solve the Schrodinger equation for electrons in molecules, a central problem in the field of chemistry.

The Schroedinger equation is a partial differential equation based on well-established theory of energy conservation, which can be used to derive information about the behavior of electrons and solve problems related to the properties of matter. Using FermiNet, which is a conceptually simple method, DeepMind could solve this equation in the context of chemistry, attaining very accurate results that were comparable to those obtained using highly sophisticated quantum chemistry techniques.

Researchers at Imperial College London, DeepMind, Lancaster University, and University of Oxford recently adapted the FermiNet architecture to tackle a quantum physics problem. In their paper, published in Physical Review Letters, they specifically used FermiNet to calculate the ground states of periodic Hamiltonians and study the homogenous electron gas (HEG), a simplified quantum mechanical model of electrons interacting in solids.