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From retail to transport: how AI is changing every corner of the economy

The high profile race to enhance their search products has underscored the importance of artificial intelligence to Google and Microsoft – and the rest of the economy, too. Two of the world’s largest tech companies announced plans for AI-enhanced search this month, ratcheting up a tussle for supremacy in the artificial intelligence space. However, the debut of Google’s new chatbot, Bard, was scuppered when an error appeared, knocking $163bn (£137bn) off the parent company Alphabet’s share price. The stock’s plunge showed how crucial investors think AI could be to Google’s future.

However, the increasing prominence of AI has implications for every corner of the economy. From retail to transport, here’s how AI promises to usher in a wave of change across industries.

Neuromorphic camera and machine learning aid nanoscopic imaging

In a new study, researchers at the Indian Institute of Science (IISc) show how a brain-inspired image sensor can go beyond the diffraction limit of light to detect miniscule objects such as cellular components or nanoparticles invisible to current microscopes. Their novel technique, which combines optical microscopy with a neuromorphic camera and machine learning algorithms, presents a major step forward in pinpointing objects smaller than 50 nanometers in size. The results are published in Nature Nanotechnology.

Since the invention of optical microscopes, scientists have strived to surpass a barrier called the , which means that the microscope cannot distinguish between two objects if they are smaller than a certain size (typically 200–300 nanometers).

Their efforts have largely focused on either modifying the molecules being imaged, or developing better illumination strategies—some of which led to the 2014 Nobel Prize in Chemistry. “But very few have actually tried to use the detector itself to try and surpass this detection limit,” says Deepak Nair, Associate Professor at the Center for Neuroscience (CNS), IISc, and corresponding author of the study.

Replika’s chatbot dilemma shows why people shouldn’t trust companies with their feelings

Users of the Replika chatbot system can no longer engage in erotic or sexual dialogue with their digital counterparts, after years of being able to do so. This highlights a dilemma when people become emotionally attached to chatbots.

Under the name “AI Companion”, Replika is marketing a chatbot system that, like ChatGPT and the like, converses with users in natural language and is also embodied as a visual avatar. Replika will be “there to listen and talk” and “always on your side”, the company promises. With augmented reality, you can project the avatar chatbots life-size into your room.

It is a paid-for chatbot service that in the past used a fine-tuned variant of GPT-3 for language output. Luka, the company behind Replika, was an early OpenAI partner, using the language model via an API.

A German AI startup just might have a GPT-4 competitor this year

Benchmarks from German AI startup Aleph Alpha show that the startup’s latest AI models can keep up with OpenAI’s GPT-3. A success that should not lull Europe into a false sense of security.

ChatGPT has catapulted artificial intelligence into the public discussion like no other product before it. Behind the chatbot is the U.S. company OpenAI, which made headlines with the large-scale language model GPT-3 and later with the text-to-picture model DALL-E 2. The impact of systems like ChatGPT or Midjourney on education and work, which can be felt today, was foreseeable even then.

The underlying language models are often referred to in research as foundation models: a large AI model that, due to its generalist training with large datasets, can later take on many tasks for which it was not explicitly trained.

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https://youtu.be/ELiyvT6Fq3g

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Developing fabrics that change shape when they heat up

New textiles developed at Aalto University change shape when they heat up, giving designers a wide range of new options. In addition to offering adjustable esthetics, responsive smart fabrics could also help monitor people’s health, improve thermal insulation, and provide new tools for managing room acoustics and interior design.

The new fabrics weave together old technology and a new approach. Liquid crystalline elastomers (LCEs) were developed in the 1980s. LCEs are a smart material that can respond to heat, light, or other stimuli, and they’ve been used as thin films in soft robotics. Although LCEs have been made into fibers, so far they haven’t been made into textiles.

In collaboration with researchers at the University of Cambridge, a team from the Multifunctional Materials Design research group at Aalto, led by Prof. Jaana Vapaavuori, has now used LCE yarns to make woven fabric using conventional crafting techniques and tested how the fabric behaved. The findings were published in Advanced Materials.

A Deep-learning Search For Technosignatures Of 820 Nearby Stars

The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their “technosignatures”. One theorized technosignature is narrowband Doppler drifting radio signals.

The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radio frequency interference (RFI). Here, we present the most comprehensive deep-learning based technosignature search to date, returning 8 promising ETI signals of interest for re-observation as part of the Breakthrough Listen initiative.

The search comprises 820 unique targets observed with the Robert C. Byrd Green Bank Telescope, totaling over 480, hr of on-sky data. We implement a novel beta-Convolutional Variational Autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false positive rate manageably low. This new approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.

Microsoft Researchers Are Using ChatGPT to Control Robots, Drones

ChatGPT is best known as an AI program capable of writing essays and answering questions, but now Microsoft is using the chatbot to control robots.

On Monday, the company’s researchers published (Opens in a new window) a paper on how ChatGPT can streamline the process of programming software commands to control various robots, such as mechanical arms and drones.

“We still rely heavily on hand-written code to control robots,” the researchers wrote. Microsoft’s approach, on the other hand, taps ChatGPT to write some of the computer code.

AI loses to human being at Go after seven years of victories

The man beat the machine by using a flaw uncovered by another computer system.

A human beat a top-ranked AI system in the board game Go, proving that the rise of machines may not be as imminent as previously believed.

This is according to a report by the Financial Times published on Sunday.


Zerbor/iStock.

The player was Kellin Pelrine, an American one level below the top amateur ranking. He achieved this victory by taking advantage of a previously unknown weakness that another computer had identified.

New AI system to help save lives of earthquake survivors in Turkey

An AI system called “xView2” is helping ground rescue efforts in regions of Turkey devastated by this month’s earthquakes.

The U.S. Department of Defense is using a visual computing artificial intelligence system to aid ongoing disaster response efforts in Turkey and Syria following the devastating earthquake on February 6 that has claimed tens of thousands of lives.

AI system helps disaster response teams in Turkey.


Getty Images.

The AI system, called xView2, is still in the early development phase, but it has already been deployed to help ground rescue missions in Turkey.

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