About a decade ago, Žiga Avsec, a PhD physics student at the time, unexpectedly delved into the world of genomics through a university machine learning module. His academic journey soon led him to a laboratory focused on rare diseases, where the mission was to decipher the intricate genetic mutations responsible for a peculiar mitochondrial disorder. It was akin to searching for a “needle in a haystack” as millions of potential genetic suspects resided within the complex DNA code. Particularly intriguing were the “missense variants” – single-letter genetic alterations that resulted in the creation of different amino acids within proteins. Since proteins constitute the fundamental building blocks of the human body, even subtle changes could trigger significant consequences.

The human genome harbors a staggering 71 million possible missense variants, with the average individual carrying over 9,000 of them. While most are benign, some are linked to genetic disorders like sickle cell anemia and cystic fibrosis, and even complex conditions like type 2 diabetes, potentially arising from a combination of subtle genetic alterations. Avsec was faced with a pressing question: “How can we pinpoint the genuinely hazardous ones?” Unfortunately, the response he encountered was disheartening: “In most cases, we simply can’t.”

In a groundbreaking endeavor, the Toyota Research Institute (TRI) has harnessed the power of generative AI to educate robots on the art of preparing breakfast, breaking away from the traditional arduous coding and debugging process. The key to their success lies in imparting robots with a sense of touch and connecting them to an AI model, allowing them to learn much like humans do.

This tactile sense is identified as a pivotal element in the robots’ learning process. By equipping them with a soft, pliable “thumb,” as seen in the accompanying video, the AI model gains the ability to “feel” its actions, providing invaluable sensory input that surpasses the limitations of sight alone. Consequently, intricate tasks become more manageable for the robots.

Virtual influencers have taken the digital world by storm, revolutionizing content creation, consumption, and marketing. These digital avatars, a fusion of cutting-edge technology and our innate desire for connection, have captured millions of adoring fans. However, beneath the surface lies a world of risks and blurred realities.

What Are Virtual Influencers?

Virtual influencers, also known as digital influencers or AI influencers, are not an entirely new concept, with virtual Japanese popstar Kyoko Date dating back to 1996. Recent technological advances have brought them into the limelight. These digital personalities maintain a presence on social media, engaging with the world from a first-person perspective. Crafted by 3D artists using CGI, motion-capture technology, and AI tools, they can be customized in appearance and behavior to align with specific target audiences. There are three main types of virtual influencers: non-humans, animated humans, and lifelike CGI humans, each offering innovative ways to connect with audiences.

Artificial intelligence (AI) has become a prominent tool in the media industry, generating content and assisting with tasks such as writing travel guides and explanatory articles. However, some tech outlets are taking a different approach, using generative AI to provide readers with quick and accurate answers to tech-related questions. On August 1st, Macworld, PCWorld, Tech Advisor, and TechHive introduced an AI chatbot called Smart Answers, aimed at helping readers find specific information within their vast archives of tech content.

Developed by media and marketing company Foundry, Smart Answers is not designed to replace human authors but to enhance the reader experience. This AI chatbot, trained exclusively on English-language articles from the four tech websites, offers responses based on the content published by expert writers. Readers can enter questions or select from an FAQ list generated by AI, with responses including relevant article links.

Bellevue-based startup Irreverent Labs, a player in the surging field of generative artificial intelligence, has successfully secured a new round of funding led by Samsung Next, with undisclosed financial details due to Samsung’s corporate policy. This latest investment will support Irreverent Labs in expanding its AI-driven video creation platform, enabling users to effortlessly produce engaging short-form videos.

Irreverent Labs, established in 2021, previously attracted $45 million in funding, primarily from Andreessen Horowitz. Initially, the company garnered attention as a blockchain-based startup with a robot cockfighting game that utilized non-fungible tokens (NFTs) called MechaFightClub. However, the company clarifies that the game served as a demonstration of its substantial machine learning model. This model will soon empower users to craft videos using diverse inputs, including images, text, and audio.

Dereck Paul, a former medical student at UC San Francisco, recognized the need for innovation in medical software to match advancements in other industries. His vision prioritized patients and doctors over hospital administrators or insurance companies. Teaming up with Graham Ramsey, an engineer at Modern Fertility, they co-founded Glass Health in 2021. Glass Health offers a unique notebook for physicians, allowing them to store, organize, and share their approaches to diagnosing and treating medical conditions throughout their careers. Ramsey describes it as a “personal knowledge management system” tailored to the medical field.

The inspiration behind Glass Health stemmed from the overwhelming challenges faced by healthcare providers during the pandemic, along with the escalating issue of provider burnout. Paul, who experienced burnout as a medical student and internal medicine resident, aimed to leverage technology to enhance medical practice.

Artificial intelligence’s impressive performance comes at the cost of significant energy consumption, with complex tasks demanding even more power. In response, researchers Víctor López-Pastor and Florian Marquardt from the Max Planck Institute for the Science of Light in Erlangen, Germany, have introduced an innovative approach to training AI that offers remarkable energy efficiency. Their method diverges from the conventional use of digital artificial neural networks and relies on physical processes instead. The results of their work are published in the journal Physical Review X.

Training models like GPT-3, the foundation of eloquent chatbots like ChatGPT, demands an astounding amount of energy. While the exact energy consumption for GPT-3 remains undisclosed by Open AI, estimates suggest it could equate to the annual consumption of 200 German households. Despite these energy expenditures, such AI models have yet to grasp the underlying meaning of phrases they analyze.